By Sergei Irisov

Digital transformation frequently fails not because of technology, but because organisations attempt to modernise systems without redesigning how decisions are made and work is governed. Drawing on experience from regulated engineering environments, this article explores why operating models, architecture, and governance must evolve together to translate strategic ambition into sustainable execution.

Digital transformation has become a central ambition for organisations across every industry. Boards approve ambitious technology programmes, leaders announce platform strategies, and executives invest heavily in digital capabilities. Yet the long-term results are often disappointing. Productivity gains remain modest, innovation slows, and competitive advantage proves fragile.

The problem is rarely technology.

It is the operating model.

In regulated engineering industries — aerospace, energy, and advanced manufacturing — this reality becomes visible earlier and more sharply than in most sectors. Certification regimes, safety constraints, and complex product lifecycles expose a fundamental truth: digital transformation succeeds only when operating models are redesigned before systems are deployed.

Strategy without execution is not strategy

Strategic ambition frequently outpaces organisational readiness. Leaders articulate digital visions and innovation roadmaps without redefining how decisions are made, how accountability is distributed, and how work flows through the organisation.

Michael Porter argued that strategy is about making choices and building systems that reinforce those choices. Without an operating model that encodes strategic intent into daily operations, digital initiatives become fragmented investments rather than sources of advantage.

In regulated engineering, this misalignment is unsustainable. Certification processes, safety cases, and audit regimes quickly reveal inconsistencies between declared strategy and operational reality.

The operating model as the missing layer

Most transformation programmes focus on three elements: strategy, technology, and talent. The operating model — the structures, governance mechanisms, incentives, and decision rights that determine how work is executed — is often neglected.

In product-based organisations, the operating model governs how requirements become designs, how changes are approved, how risks are managed, and how value is delivered across decades of product life. When operating models remain unchanged, digital platforms merely automate existing dysfunction.

Architecture as organisational design

Enterprise architecture is frequently treated as a technical discipline. In practice, it functions as a form of organisational design.

System boundaries define decision rights. Data ownership shapes accountability. Integration patterns reflect coordination mechanisms between functions. In high-performing engineering organisations, architecture becomes a strategic instrument that encodes governance, aligns incentives, and enables controlled experimentation.

In this sense, architecture is not infrastructure. It is management by design.

Governance that enables speed

The belief that governance slows execution remains deeply embedded in management culture. Regulated environments demonstrate the opposite.

Clear decision rights, explicit role definitions, and automated controls reduce friction by eliminating ambiguity. Teams move faster when they understand ownership, approval thresholds, and compliance obligations. Effective governance is not bureaucracy; it is a scaling mechanism that enables organisations to grow without losing control.

From project delivery to product operating models

The shift from projects to products is widely discussed but rarely implemented with discipline. In regulated engineering, the product operating model becomes essential.

Platforms evolve continuously under configuration control, ownership remains stable, and funding aligns with lifecycle value rather than short-term milestones. This approach integrates naturally with certification regimes and long-term asset management while creating organisational memory — a prerequisite for sustained advantage in complex systems.

Competitive advantage under constraint

Rita McGrath has argued that competitive advantage is increasingly transient. Regulated engineering offers a different perspective.

Here, advantage emerges not from rapid disruption but from the ability to execute reliably under constraint. Organisations that align strategy, operating models, and architecture build capabilities that competitors struggle to replicate. Certification becomes a barrier to entry, governance becomes an asset, and architecture becomes intellectual capital.

Conclusion

Digital transformation does not fail because technology underperforms. It fails because organisations attempt to digitise without redesigning how they operate.

In regulated engineering, the lesson is clear. Sustainable advantage arises when strategy, operating models, and architecture evolve together. Transformation is not a programme. It is an organisational redesign.

Without it, even the most advanced technology cannot deliver the future leaders promise.

Acknowledgements

The author confirms that this article reflects original analysis and intellectual contribution. AI-assisted tools were used only for language refinement and formatting support and did not influence the conceptual framework, argumentation, or conclusions presented in this work.

About the Author

Sergei Irisov is Head of IT & Digital Transformation at ZeroAvia. He leads enterprise architecture and operating model design for regulated engineering organisations across aerospace and advanced manufacturing, focusing on digital platforms, governance, and long-term product systems.

The post Why Digital Transformation Fails Without an Operating Model appeared first on The European Business Review.

By William Thackray

Hybrid working boosts morale and efficiency, but it also introduces hidden risks for professional services. Data drift, fragmented communication, and shadow IT threaten security, compliance, and accuracy. To manage these challenges, firms must strengthen governance, simplify tools, enforce centralised data practices, and embed safe digital behaviours across the hybrid workforce.

Approximately three-quarters of UK businesses now use a hybrid working model. It’s a move that was instigated by the pandemic, but continued because it seemed to carry so many advantages. It’s cheaper for businesses and popular with staff, boosting productivity as well as employee retention and attraction. But it also carries risks, many of which are only just becoming clear, including the looming problems of shadow IT and data drift.

The lesser-known business risks of hybrid working

There are a whole range of arguments supporting hybrid working, but the problems it brings are rarely discussed. When employees split their time between the office and home, working practices become harder to manage. Staff use a wider variety of devices, networks, tools, and workflows, which means that the carefully formulated security protocols that work beautifully within the office environment begin to falter.  And for professional services businesses, where sensitive client data forms the backbone of daily operations, the problem is magnified even further.

With hybrid work, every transfer of a document, every shared message, and every downloaded file becomes a potential point of leakage. A small misstep – a report saved locally instead of to the cloud, or a spreadsheet shared through a personal app – can introduce risks that leadership teams may not detect until a serious breach, complaint, or audit failure occurs.

Hybrid work isn’t creating poor behaviour; it is amplifying pre-existing habits that were once easier to contain within a controlled environment.

The Problem of information drift

One of the biggest and least acknowledged issues associated with hybrid work is information drift. This happens when data gradually spreads across multiple applications, storage locations, and devices—becoming fragmented, inconsistent, or hard to govern.

Information drift typically emerges in three key ways:

Inconsistent storage habits

When you’re working remotely, it’s easy to save documents in local folders. Even with cloud platforms like SharePoint and Google Drive, if you’re working offline, your own hard drive is more convenient. And that’s how documents and updates get lost or duplicated, causing future confusion.

Multiple communication channels

With accountability being such an important feature of contemporary business, communication also needs to be tracked. And for hybrid teams, conversations tend to be scattered across platforms, leading to lost instructions and poor project audit trails.

Tool sprawl and shadow IT

When workers lack the right tools – or don’t know how to use the approved ones – they start adopting their own. This might be a personal cloud drive, a free file-sharing service, a design app, or a note-taking tool that bypasses corporate controls. Individually, these choices seem harmless. Collectively, they create an unmanageable web of unofficial data locations that no central policy can oversee.

Shadow IT isn’t deliberate or malicious; it’s convenient. People use the tools that work for them. Unfortunately, that tends to create blind spots in data governance, security monitoring, and compliance.

What IT leaders can do to prevent data drift

Technology leaders now face the challenge of enabling hybrid work without letting it erode security or operational consistency. Key steps include:

Provide a single source of truth

IT teams must make it both mandatory and frictionless for staff to store and retrieve documents from approved systems. This involves designing intuitive folder structures, strong search functions, and integrated workflows that reduce the need for offline storage.

Implement modern data loss prevention tools

DLP solutions can detect when users save files to unapproved locations, download sensitive documents, or use untrusted apps. Automated reminders or blocks can stop risky behaviour early.

Reduce friction, not flexibility

Shadow IT thrives when official tools are confusing or inefficient. IT leaders should streamline the tech stack, eliminate redundancy, and ensure employees have user-friendly alternatives that genuinely meet their needs.

Provide clear, human-friendly policies

Many hybrid-work data issues stem from unclear or overly technical policies. When you develop guidance based on realistic scenarios and practical instructions, many of the common problems disappear.

Safe working practices to build into any hybrid model

Because hybrid work is here to stay, businesses must integrate safe data practices into everyday operations. Key behaviours include:

• Use company-managed devices and secure VPNs rather than personal equipment.
• Encrypt laptops, phones, and portable storage.
• Mandate multi-factor authentication across all systems and applications.
• Keep all approved apps updated automatically.
• Centralise communication tools so files and conversations stay within one ecosystem.
• Conduct regular audits of data storage patterns and shadow IT usage.

Encourage staff to report mistakes early, without fear of blame.

Hybrid working isn’t going anywhere, but it does need to change. To remove the risks currently overshadowing many professional services businesses, governance must be a priority. Allowing employees the freedom to work where they work best will always be a positive move. But you must have the operational practices and processes in place to ensure that your business is not compromised in the process.

About the Author

William Thackray, Operations Director of AGT Computer Services, is always looking for the next big thing in technology and business. He’s the go-to guy for anything new and exciting in the world of IT.

The post The Hidden Cost of Hybrid: Data Drift and Shadow IT in Professional Services appeared first on The European Business Review.

By Juliana Queiroga

As U.S. research faces disruption, Europe must move beyond funding to capture global talent. By adopting Brazil’s Porto Digital model, Europe can bridge the “valley of death” through “triple helix” collaboration, high-density urban innovation districts, and integrated market validation, transforming a brain drain into a sustainable, world-class ecosystem.

The current talent exodus from the U.S. creates a strategic opportunity for Europe to build transformative innovation ecosystems with the help of Latin America’s proven playbook

The numbers tell the story: By February 2025, hundreds of U.S. researchers had been dismissed from agencies such as NOAA within 90 minutes’ notice. The European Research Council responded by doubling its relocation budget to €2 million per researcher.

Thirteen European countries, including France, Germany, and Spain, signed an urgent letter to the EU Commission demanding accelerated talent acquisition programs.

This isn’t just about individual career moves—it represents a $940 billion research ecosystem in flux. As Gray McDowell at Capgemini Invent warns, “Regulatory uncertainty, funding cuts, immigration restrictions, and diminished international collaboration create a perfect storm for brain drain.”

High-profile departures like historians Timothy Snyder and Marci Shore from Yale to the University of Toronto signal a broader trend: leading researchers are questioning their future in Trump’s America.

Immigration lawyers report unprecedented inquiries from tenured professors and senior technology executives exploring opportunities in Canada, Europe, and Australia.

For European policymakers, this represents the most significant talent acquisition opportunity in decades. But as Princeton’s Michael Oppenheimer notes, “Europe would likely need a long time to overturn that spending advantage…for several decades.”

The challenge isn’t just money; it’s methodology.

Europe’s innovation challenge runs deeper than funding gaps.

Despite the EU’s €381 billion annual R&D investment, the World Economic Forum identifies persistent “valley of death” problems between breakthrough research and commercial success.

The blueprint already exists in the Americas —not in Silicon Valley or Boston, but in an unexpected location: Recife, Brazil in the heart of Porto Digital, dubbed the “quixotic tech hub that actually worked” by WIRED magazine in 2023.

The Porto Digital Model

Twenty five years ago, Porto Digital transformed a declining historic district in northeastern Brazil into one of Latin America’s most successful innovation ecosystems. Today it hosts 475+ companies, employs more than 21,000 professionals, and generates R$6.2 billion in annual revenue after maintaining double-digit employment growth for more than a decade.

What makes Porto Digital remarkable isn’t just its scale; it’s how it achieved sustainable growth through what innovation experts call “triple helix collaboration” that unites academia, industry, and government to drive innovation and economic development.

Unlike traditional tech parks that rely primarily on tax incentives and infrastructure, Porto Digital integrated three critical elements from inception: academic research excellence, government policy coordination, and private sector engagement.

Unlike traditional tech parks that rely primarily on tax incentives and infrastructure, Porto Digital integrated three critical elements from inception: academic research excellence, government policy coordination, and private-sector engagement.

The Federal University of Pernambuco’s top-ranked computer science program provides a talent pipeline. Strategic fiscal incentives create competitive advantages. Most importantly, deliberate urban planning ensures researchers, entrepreneurs, and business leaders interact through what Endeavor’s Anderson Thees calls “synchronicity”—casual, fortunate encounters that drive innovation.

This model directly addresses Europe’s persistent innovation challenge. European institutions excel at technical innovation but struggle with technology transfer. Porto Digital embeds market validation throughout the research process rather than treating commercialization as an afterthought.

Why Traditional European Approaches Often Fall Short

Current European innovation policy often follows linear models: fund research, transfer IP to industry, hope for commercial success. This yields impressive academic publications but limited market impact.

The fundamental problem lies in organizational culture. European institutions often maintain rigid departmental boundaries that prevent the cross-functional collaboration essential for technology transfer. Legal teams block user testing due to privacy concerns. Operations departments resist real-world trials that might disrupt existing relationships. Innovation teams develop solutions isolated from business units expected to implement them.

These silos become particularly problematic when competing against more integrated approaches. Chinese companies benefit from state-coordinated innovation strategies and deeply connected supply chains. American firms leverage sophisticated VC markets that fund rapid iteration. Many European organizations are often caught between academic timelines and commercial demands.

Capitalizing on the Brain Drain Moment

The current disruption creates unprecedented opportunity, but European success requires learning from proven models. Three key insights from Porto Digital directly apply:

First, geographic concentration accelerates innovation. Porto Digital’s “15-minute walkability” principle ensures researchers, entrepreneurs, and investors interact regularly. European cities pursuing scattered innovation districts miss the serendipitous encounters driving breakthrough collaborations. Portugal’s emerging innovation hub demonstrates this density principle in action.

Second, cultural transformation precedes technological breakthroughs. Porto Digital’s success built on the Manguebeat cultural movement, combining traditional Brazilian rhythms with modern influences. This foundation created openness to experimentation that traditional tech parks lack. European innovation hubs must cultivate similar cultures of creative risk-taking.

Third, sustainable models require policy coordination beyond tax incentives. Porto Digital’s fiscal advantages operate within broader strategies aligning municipal, state, and federal priorities. European innovation policy currently sets member countries as competitors. Coordinated regional approaches would amplify individual efforts.

The European Implementation Path

Attracting America’s departing academic talent requires immediate action on three fronts:

• Accelerate visa processes for academic families. Current procedures assume individual relocations rather than household moves involving spouses and children. Streamlined family reunification would eliminate practical barriers preventing academic relocations.
• Create integrated innovation districts. Academics seek communities, not just jobs. European cities must develop neighborhoods where international researchers can establish roots—coordinating housing, schools, and cultural amenities alongside research infrastructure.
• Leverage Brazil-Europe partnerships. Through programs like Horizon Europe and the All-Atlantic Ocean Research Alliance, EU institutions can offer American researchers access to Brazilian biodiversity data and Amazon research that Canadian or Australian institutions cannot match.

breakthrough innovations. This requires coordinating housing policy, school systems, and cultural amenities alongside research infrastructure.

Leverage Brazil-Europe partnerships to create unique value propositions unavailable elsewhere. Through programs like Horizon Europe and the All-Atlantic Ocean Research Alliance, European institutions can offer American researchers access to Brazilian biodiversity data, Amazon research opportunities, and LatAm advantages that Canadian or Australian institutions cannot match.

Beyond Opportunity Capture

The U.S. brain drain offers a chance to fundamentally restructure global innovation networks toward more distributed, collaborative approaches. Porto Digital’s experience demonstrates that successful ecosystems require decades to mature but achieve transformational impact when properly designed.

European policymakers have months, not years, to capitalize on American instability while competing destinations mobilize. The opportunity is clear: build ecosystems that attract displaced American talent, or watch these researchers settle elsewhere.

Porto Digital’s blueprint provides the roadmap. But implementation requires recognizing that successful technology transfer depends more on organizational culture change than funding increases. The organizations that succeed will embrace Porto Digital’s core insight: innovation happens through sustained collaboration between researchers, entrepreneurs, and communities—not through isolated excellence.

The question isn’t whether Europe has sufficient resources to compete. It’s whether EU leaders will move quickly enough while U.S. researchers are still deciding where to rebuild their careers.

About the Author

Juliana Queiroga is Executive Manager of CESAR Europe, expanding Latin America’s proven innovation methodologies into European markets. With more than 18 years in strategic innovation management, she specializes in technology transfer, organizational culture change, and cross-border ecosystem development. She holds a master’s degree from Queen Mary University in Management and Organizational Innovation.

The post Europe’s Innovation Opportunity: How America’s Brain Drain Could Fuel the Next Porto Digital appeared first on The European Business Review.

By Roman Eloshvili

For decades, Europe’s software industry has been built on services: outsourcing, custom development, and consulting. That model created jobs and export revenue, yes, but it is now reaching its limits. As margins compress and AI reshapes standards, European software firms must rethink their future. Productization is now becoming a strategic necessity. And here is why.

Europe’s software sector always had a clear and reliable engine powering it. Talented engineers, strong technical education, competitive costs — there were many factors that, when put together, made the region a natural hub for outsourcing, custom development, and IT consulting.

For a long time, this model worked well: it created thousands of companies and millions of job positions, ultimately helping position Europe as a critical supplier to global tech ecosystems. But today, that engine is losing momentum.

The ironic thing is that when we look at the overall IT services market, numbers show that it keeps growing. By 2029, it is expected to reach $5.17 trillion. On paper, sound impressive, doesn’t it? And it is. But in practice, it also means a very crowded market where providers constantly compete on just about every front: speed, prices, efficiency of services, etc.

As a result, for individual projects, it becomes harder and harder to find success. Pressure is intense, clients expect the same quality of outputs on lower budgets, and retaining top talent is a constant battle in its own right.

Yet at the same time, product-led companies show a major contrast. Despite often being smaller in headcount, they are scaling faster, raising capital more easily, and building far more resilient businesses. Private equity investment in SaaS saw a 66% jump in 2025, indicating that investor preferences lean increasingly toward scalable product companies rather than traditional service businesses.

The way I see it, this shift brings up a question that we must consider in earnest: can Europe’s service-first software companies remain competitive without evolving into product-first organizations?

Why the Service Model is Hitting a Ceiling

The traditional service model has three structural limitations that are becoming impossible to ignore.

First, services scale linearly, which, in simple words, means that revenue growth depends heavily on headcount growth. That made sense when demand was booming, and talent was abundant, but today, hiring is increasingly expensive and scaling teams across borders introduces a lot of operational complexity. Even well-run service firms eventually hit a growth ceiling under such conditions.

Second, like I already mentioned, margins are under constant pressure. With AI tools increasingly automating many development processes, tasks that used to justify large budgets and teams are now becoming faster and cheaper to deliver with fewer people.

From the client’s point of view, this leads to a question: why should I pay the same price for something that now takes less time and effort? So they start pushing for lower rates and shorter service times. Project revenue drops, and as costs remain largely intact, as companies still need to invest in AI tools and professionals to maintain them.

And finally, ambitious talent is harder to retain when you run a service-model business. Strong engineers are often driven by the desire to build something of their own, instead of executing someone else’s roadmap. And when your workers leave to join product startups or launch their own ventures, retaining success in the long run becomes a much heavier task.

None of this means services are “dead,” but it does mean the model, on its own, is no longer enough to sustain growth and competitiveness.

Why Productization is the Way to Go

Basically, it’s because product-led companies operate under a different set of rules. They decouple revenue from headcount, they create reusable value, and they can serve thousands of customers with the same core technology. Most importantly, they build their value by building assets, not just cultivating relationships.

This is why investors consistently reward product companies with higher valuations. It is also why ecosystems that produce strong product businesses tend to generate more innovation, capital reinvestment, and global influence over time.

Here’s a very recent example that proves this dynamic: Databricks recently raised $4 billion in yet another funding round. It’s a product-driven company that continues to bring in capital at significant valuations (the latest being $134B) and reinvests in its platform. This round is the third major one the company had this year, and it’s clear proof that product-led firms can build resilient revenue models.

Because of this, for Europe, productization is a strategic necessity if the region wants to move up the value chain. IT businesses here need to put greater focus on creating proprietary software, platforms, and data-driven products.

Making the Transition is the Hardest Part — What to Account For?

That said, moving from services to products is not so simple as flipping a switch. A software company can’t do it overnight.

The very first major wall that you need to break through here is the difference in mindsets. Service businesses are built around predictability and optimized for client satisfaction. You sign a contract, and you deliver on a short time-scale. But product companies must tolerate a lot of uncertainty that comes with experimentation and delayed returns.

You build products without really knowing when — or even if — they will pay off. Progress is often uneven, and in order to push through on this change, company leadership must think beyond utilization rates and monthly revenue reports. They need to think in terms of long-term value creation.

The second challenge is how capital allocation works. In a service business, most spending is tied directly to client work: if you have a project, you have a budget. But product development requires you to invest time and money long before any kind of revenue has a chance to exist.

You must be willing to put in the work over time, funding your development teams and accepting that without this upfront dedication, your products will never reach a stage where they can start paying themselves off.

Finally, the third key challenge is keeping focus. Many companies fail to make the transition because they spread themselves too thin; try to do everything at once: continue to run service operations, try to build multiple products at the same time, customize features for early customers. More often than not, this haste results in exhaustion and half-baked ideas that never become full-fledged products.

If you want to succeed, start small and with a much narrower approach. Pick one real, proven, and recurring problem that your company already understands deeply, and build a solution around that. This gives you a realistic chance to find your footing first — scaling can come later.

Image of the Future: Who Will Win

Despite all the hardships, I fully believe that what Europe’s software sector is facing now is not a decline or stagnation. What stands before it is a signal to change, and that signal needs to be heeded.

Companies that treat services not as the end goal, but as a foundation, will find themselves going a step further. Think of it like this: your previous client work can be used to understand what problems they’re forced to deal with. And that knowledge can then be turned into products that have good odds of being accepted since they answer the practical needs of your audience.

“Productization” is the next stage of maturity for Europe’s software industry, and the key to building companies that last in the days to come.

About the Author

Roman Eloshvili is a founder and chief executive officer of XData Group, a B2B software development company. There, he directs the development of AI in banking while navigating investor relations and fostering business scalability. Mr. Eloshvili is a C-level executive with an extensive background in developing fintech solutions for banks and a serial entrepreneur with over 10 years in business administration across Europe.

The post Europe’s Software Crossroads: Why Product Thinking is Now Key to The Future appeared first on The European Business Review.

By Vincent Cuvillier

Connectivity has become a strategic cornerstone of, economic and social development directly linked to business success in Europe. High-quality networks drive digital transformation, enable data-driven decisions, support global operations, and enhance customer experience. As sectors adopt AI, 5G, IoT, and cloud solutions, reliable connectivity underpins innovation, efficiency, sustainability, and competitiveness—making it essential for growth in an increasingly digital economy.

In today’s fast-paced and highly digitalized economy, connectivity is no longer just an utility, it is a strategic necessity for businesses throughout the continent.

From small and medium-sized enterprises (SMEs) to multinational corporations or public administrations, the ability to communicate, share data, and operate seamlessly across borders and across the value chain is critical to maintaining competitiveness, driving innovation, and sustaining growth.

As the region advances its digital transformation, high-quality connectivity is increasingly recognized as the strategic backbone for the future of European business success.

Governments across the continent have made connectivity a cornerstone of their industrial and digital strategies. From EU-wide frameworks such as the Digital Europe Programme (DIGITAL) to national initiatives like France 2030 and the UK’s Modern Industrial Strategy, and even regional plans such as Basque Country’s Industria Euskadi 2030, all emphasize connectivity as a critical enabler. This is not a single-country phenomenon, it is a truly pan-European movement.

Additionally, digital sovereignty has become a top priority as governments seek to maintain the control over the industrial data that flows over the networks and technological ecosystems. Thus, technological dependencies, (cyber)security, autonomy, resilience, citizen and company privacy and economic stability are now at the forefront of the policy agenda worldwide.

Driving Digital Transformation and Innovation

Digital transformation is the cornerstone of economic competitiveness. Across industries, companies are harnessing technologies such as artificial intelligence (AI), cloud computing, the Internet of Things (IoT), and advanced data analytics to streamline operations and unlock new sources of value. These innovations depend on one essential foundation: fast, reliable and secure connectivity.

Consider manufacturing, where IoT sensors monitor production lines in real time, enabling predictive maintenance that minimizes downtime and reduces costs. In retail, connected platforms analyze consumer behavior to deliver personalized experiences and strengthen customer engagement. Without robust networks, these breakthroughs would stall, limiting efficiency, scalability, and the ability to compete in global markets.

Enhancing Operational Efficiency and mobility

Reliable connectivity enables businesses to maintain real-time communication across locations, even during commuting, while supporting cloud-based collaboration that reduces costs and accelerates decision-making. At the same time, connectivity extends beyond offices to Europe’s transport corridors rail networks and highways must be fully connected to make autonomous vehicles viable. Without seamless coverage, safety, efficiency, and sustainability in transport cannot be achieved, as vehicles and infrastructure depend on uninterrupted reliable networks to communicate in real time. In essence, robust connectivity transforms operations from reactive to proactive and underpins the next generation of mobility.

Enhancing Customer Experience

Customer expectations are higher than ever, and connectivity plays a central role in delivering superior experiences. Businesses rely on online channels, apps, and digital platforms to engage with clients, provide services, and maintain loyalty.

High-speed, stable connections ensure that e-commerce platforms can handle peak traffic, video support and live chat operate seamlessly, and digital services function without interruptions. For businesses in finance, healthcare, and entertainment, where speed and reliability are critical, connectivity is a key differentiator. Thus, is specially essential in places with high concentrations of people, either indoor or outdoor, where the use of the ditital network may turn into a bottleneck for accessing digital services: stadium, airports, stations, malls, festivals, etc. Companies that fail to invest in robust networks risk frustrating customers, losing business, and damaging their reputation.

Importantly, this commitment to connectivity must extend beyond urban centers or in areas with large crowds. Rural and less populated areas represent untapped markets and communities that increasingly demand equal access to digital services. Expanding high-quality networks to these regions not only enhances customer experience but also drives inclusion, economic development, and brand trust while closing the digital divide among territories to promote equal opportunities for business and social progress.

Supporting Remote Work and Hybrid Teams

The post-pandemic era has cemented remote and hybrid work models across Europe. Businesses now require connectivity that supports seamless access to cloud applications, video conferencing, and secure document sharing. High-quality networks enable employees to remain productive from virtually anywhere, expanding the talent pool and supporting workforce flexibility. Organizations with inadequate connectivity face challenges in maintaining collaboration, productivity, and employee engagement. In this context, connectivity is directly linked to workforce efficiency and the ability to attract and retain top talent.

Facilitating Industry-Specific Transformation

Different sectors in Europe rely on connectivity in unique ways. Industry 4.0 initiatives in manufacturing, for example, leverage connected machines and real-time monitoring to improve productivity and reduce costs. Logistics companies depend on IoT-enabled fleets and intelligent routing to enhance delivery efficiency. Energy and utilities providers implement smart grids and remote monitoring, while healthcare organizations increasingly use telemedicine and AI-driven diagnostics.

Across all these sectors, connectivity is the foundation that allows digital tools to function effectively. Without reliable networks, the promise of digital transformation remains largely theoretical.

Moreover, by enabling this smart solutions through the improvement and evolutions of the digital networks, it opens the door to innovative new business models, creating new opportunities for growth and competitiveness.

Resilience and Business Continuity

Connectivity also underpins business resilience. Reliable networks ensure continuity in the face of disruptions such as cyberattacks, power outages, or unforeseen crises. Cloud-based infrastructure, remote access, and redundant connections allow operations to continue with minimal interruption, protecting revenue and reputation. This is specially relevant in the case of public safety bodies to guarantee their digital communications in case of accidents, natural disasters of emergencies.

For European businesses, which often operate across multiple countries and regulatory environments, resilient connectivity is essential for maintaining competitive advantage and ensuring compliance with data and operational standards.

Driving Competitive Advantage

The future will be hybrid: advanced mobile networks (5G and beyond) combined with satellite solutions to cover highly remote areas and ensure continuity in critical environments. Connectivity is increasingly a source of competitive advantage. Companies with high-speed, reliable, and secure networks can launch products and services faster, provide superior digital experiences, respond dynamically to customer and market demands, reduce operational inefficiencies and scale rapidly across borders. In many sectors, digital capabilities enabled by connectivity now differentiate market leaders from laggards. Firms that fail to invest in robust networks risk losing ground in an increasingly connected economy.

Supporting Sustainability and ESG Goals

Connectivity also plays a critical role in sustainability and environmental responsibility. Energy transition and smart resource management depend on connected networks. Smart grids, IoT monitoring, and real-time data exchange enable reduced emissions and optimised consumption. Shared infrastructure, such as neutral-host towers and co-located network sites, reduces environmental impact and energy consumption. High-efficiency networks enable smart energy management, IoT-based monitoring of resources, and data-driven strategies for reducing carbon footprints.

European businesses are increasingly judged on environmental, social, and governance (ESG) criteria. Connectivity supports initiatives that improve energy efficiency, resource management, and sustainable operations—making it both a business and societal imperative.

In summary, in Europe, connectivity is far more than a utility—it is a strategic enabler of business success. High-quality, reliable, and secure networks underpin digital transformation, operational efficiency, customer engagement, global expansion, and sustainability. They allow businesses to harness data for informed decision-making, scale across borders, innovate rapidly, and maintain resilience in the face of disruption.

As Europe moves toward widespread 5G adoption, AI-driven applications, IoT proliferation, and further digital integration, connectivity will become even more critical. Companies that invest in robust networks today are better positioned to capture market opportunities, optimize operations, and remain competitive in an increasingly digital European economy.

For businesses seeking growth, innovation, and resilience in Europe, connectivity is not optional—it is the backbone of success.

About the Author

Vincent Cuvillier is the Chief Strategy Officer at Cellnex Telecom. Prior to this, he has been CEO of Cellnex France (2019-2023) and Group Business Development and Country Coordination Director at Cellnex Telecom (2018). Also the Chief Financial Officer at SANEF (2015-2017), Head of M&A activities at Abertis Infraestructuras (2008-2014) and financial auditor at EY Luxembourg for two years. Vincent obtained a Master’s degree from IESEG School of Management. He is currently Vice-chairman at the French Chamber of Commerce in Barcelona and member of the International Advisory Board of IESEG School of Management.

The post Connectivity: The Backbone of Business Success Across the Continent – The Strategic Pillar for Europe’s Future. appeared first on The European Business Review.

By Mike Wright

There has long been a divide between the technology function and other parts of many organisations. This hampers performance – strategic opportunities arising from technology innovation are not pursued; cost reductions are missed; and customer service is not personalised as it could be. Leaders need to urgently take four inter-related actions to prosper from the technology revolution.

The pace of technology innovation continues to accelerate. As Justin Trudeau said at Davos in 2018 – “The pace of change has never been this fast, yet it will never be this slow again…..There’s enormous opportunity, and enormous potential”. Indeed, we can see examples of this innovation everywhere in our day to day lives. From the now ubiquitous smart phones to the ever increasing computing capability within cars that allows them to pretty much drive themselves.

This growing role for technology highlights the need for business leaders to proactively think about how to harness these changes for benefit – to their customers, to their employees and to shareholders. As Satya Nadella said in 2019 “every company is a software company now”. He also said “Computing is a core part of every industry. I don’t think in ten years we will have these demarcations. We won’t have the tech industry and other industries.”

And yet we still do. A survey in 2024 by CIO Development found there are 50 times more financially qualified people on UK public boards than technologists. This knowledge and experience gap hampers business performance in 3 major dimensions – (i) strategic opportunities arising from technology innovation are not identified and pursued; (ii) cost reductions are missed; and (iii) customer service is not personalised as well as it should be to enhance sales and retention. Business leaders have a real opportunity to take steps to reduce these areas of underperformance.

So what should business leaders be doing? Four elements are important:

1. Recruit at least one senior technologist to the Board. Ideally this should be the CTO (or CIO) from the executive team but the reality is that CTOs are culturally misunderstood, undervalued and very largely written off by recruiters. That is not to recognise the fact that many such individuals are not fully able to participate in Board debates. But this lack of appropriate skills points to the need to build the training and career pathways to correct this leadership gap. Hiring the relevant digital skills to the Board in a Non Executive role can help reduce the gap in the meantime. This lack of genuine digital capability at Board level limits senior decision making…. And the problem seems to be getting worse:

1. As Gen AI explodes, traditional thinking needs to be challenged and Board decision making enhanced to avoid organisations becoming increasingly irrelevant (as happened to Kodak with digital cameras)
2. As cyber security risks grow, the scale and relative importance of risk is changing (witness the disruption to Jaguar Land Rover and to Marks and Spencer this year)

2. Hire new capabilities and build human capability across the organisation. Having senior decision making capability is of limited use if there is no ability to execute.  Many people still think of business and technology separately.  Organisations need people who do not think in this binary manner – rather they see business issues and technology solutions as a single holistic problem. Locating and hiring these people is vital – and hard. But, it is not just about building the relevant talent, leaders need to nurture these people and create a culture where they feel valued and involved. There are multiple ways to do this (such as role modelling, reverse mentoring, active training programmes). The actual hiring – while vital – is only the start of the real challenge. 

   These two actions will help address the talent issue.  However, in isolation they are insufficient. Two other actions are needed – to help the organisation learn and to build a culture where digital skills are perceived to be valued at all levels.

3. Ensure the business strategy includes a section on data and technology infrastructure. Leaders recognise the need to rethink operating models in the digital world but such thinking needs to be holistic. This is not easy. Many existing business leaders shy away from “techie” subjects as they are outside their own personal comfort zone. Yet, in today’s digital environment, an effective technology architecture is vital to enable the rapid pivots and innovation that business requires. An ongoing programme to eliminate legacy applications is often missing from business strategy. High quality, consistent corporate data is essential to AI and more general data analytics. These elements need to be inter-connected to more traditional strategic initiatives as part of the overall business strategy to ensure they are funded effectively.
4. Undertake multiple Gen AI and other innovative pilots. Much innovation fails, and indeed, according to MIT, only 5% of AI driven projects reach production and deliver meaningful value. Meanwhile, a study published this summer in the Harvard Business Review found that staff were using AI to generate large amounts of what has been dubbed “work slop” – material that has little actual value. Neither of these factors should deter experimentation but they point to the need for clear scope control and explicit metrics to measure value creation and learning. Establishing a culture that celebrates learning, avoiding allocating blame for “failures” is equally vital but often missing. Seemingly small things can be very impactful – not least ensuring the business problem is framed in the right way; that the team skill set is adequate to address the problem; and that they feel psychologically “safe” to experiment and get things wrong.

The four actions described above do not guarantee corporate success. But they will help to reduce the leadership gap that currently limits many organisations from competing effectively in the digital world that already exists, and from falling further behind the increasing rapid pace of change.

About the Author

Mike Wright is an Associate Non-Executive Director at the UK’s DBS (part of the Home Office), having been the CIO for 4 global organisations over the last 25 years, most recently at McKinsey and Company.  Previously he was a consultant at both Accenture and McKinsey, and founded / ran a software products company.  He graduated from Oxford University with an MA in Biochemistry.  He has recently co-authored “Transform!”: The 14 Behaviors Driving Successful Digital Transformation in the Age of Gen AI, a book summarising his learning about digital transformations.

The post How Leadership Gaps are Slowing the Technology Revolution appeared first on The European Business Review.

By Edward Segal

In today’s hyperconnected world, crises spread rapidly through social media, amplifying misinformation and shaping public perception. This article outlines how leaders can regain control by responding immediately, activating tailored crisis plans, and rigorously testing teams. Effective digital-age crisis management hinges on speed, strategic communication, and preparedness across borders and platforms.

From cyberattacks and disinformation campaigns to tariffs and supply chain disruptions, responding to crisis situations in the digital age is both harder and easier than ever before, thanks to the double-edged sword of social media.

The bad news is that anyone with a smartphone or computer can instantly try to shape, distort or define the public narrative about an unfolding situation—or try to create a fake crisis. These messages and videos can, in turn, create a crisis for companies and organizations. Rumors, false claims and emotionally charged narratives can quickly hijack public conversations and perceptions. Posts that go viral can create or escalate a crisis, put pressure on leaders to react, and force leaders to fight two crises at the same time: the real event, and the public’s perception of it.

The good news is that social media platforms can also be powerful tools that enable business executives to communicate strategically, efficiently, and effectively with the public and stakeholders. Posting messages and videos on these platforms can set the record straight about disinformation and misinformation and reclaim the narrative about a crisis. For European multinational organizations, this ability to communicate directly with diverse multilingual and cross-border audiences is especially important.

The First Step: Control the Narrative

In the digital age, speed is the first form of leadership. Company officials need to treat social media and other digital tools as the first step in managing and communicating about a breaking or developing crisis. This is critical in interconnected European and global markets where news cycles and the expectations of stakeholders can move across borders in an instant.

Which is why executives need to respond at once at the first sign of a crisis and do their best to answer these basic questions:

• What happened?
• Who does it affect?
• When did it happen?
• Where did it take place?
• Why did it happen?
• How did it happen?
• How are we going to respond?
• When and how will we provide updates?

Business leaders should not wait to post their messages or videos until they are able to answer any or all of the questions. Simply telling people that you know that you are aware of the crisis and will provide updates about the situation can be enough to assert authority and control of the narrative. But the longer executives wait, the more likely it is that others will fill the vacuum and take control of the narrative.

Step Two: Activate Crisis Plans and Teams

While the first messages and videos are being prepared and posted, companies should activate their crisis management plans and teams. It is just as important for the plans and teams to be ready on a moment’s notice as it is for organizations to immediately take steps to control the narrative about the crisis. If executives do not have the plans and teams in place, they should remedy the situation now. Otherwise, officials will be spending valuable time trying to get organized to respond and address the crisis.

Frequently testing crisis management plans and teams helps guarantee they will work when needed. For multinational corporations that operate in multiple regulatory settings, their crisis plans should account for differences in jurisdictions, laws, and expectations.

Corporate executives can be lulled into a false sense of security about their readiness to respond to a crisis by assuming their companies need only one crisis management plan. That is a big mistake because, like a suit, one plan will not be a good fit for every crisis. Indeed, an organization’s response to a cyberattack will be different from how it reacts to litigation, strikes, or the death of its CEO. When a disaster, scandal, or other emergency that is not accounted for in a basic plan occurs, organizations will waste valuable time scrambling to figure out how to manage it.

Even companies in the same industry or profession could face different sets of challenges than their competitors. Customized crisis plans should include tailored messaging for those impacted by the different emergency situations. Depending on the nature of the company, different plans should be prepared to account for the potential of various crisis scenarios for different variables.

• The nature and extent of the crisis. Is it confined to one office or multiple offices and manufacturing facilities?
• Who is affected by the crisis, whether they are employees, customers, stakeholders, or the public.
• The age and size of companies. Older and larger businesses often have more layers of management—and red tape—to navigate when responding to a crisis. Younger and smaller companies, on the other hand, may have fewer experienced executives, but could be nimbler when reacting to a crisis.
• The degree to which companies test their crisis management plans—if they have them—to ensure they will work when needed. The less they practice their plans, the less likely it is that their plans will work when a crisis strikes.
• The resources that organizations need in order to respond to a crisis—and what outside resources they need, and how quickly they can access them. The fewer in-house resources companies have, the more likely it is that they will have to obtain those resources outside of the company—which could take more time.
• Who has the authority to activate the plans, and how quickly team members can be reached. The fewer the levels of red tape—and the sooner that crisis team members can be contacted—the better.

Step Three: Test Plans and Teams

The degree to which companies test their crisis management plans—if they have them—can help ensure the plans work when needed. There are many ways to test the plans, including thought and tabletop exercises, computer simulations, and field exercises.

When and how crisis management teams are appointed can determine how well they work together. Waiting until a crisis strikes to appoint a team means they will not have been tested to ensure that they would work well under pressure or that personality and other conflicts could prevent them from working well as a team.

Crisis management in the digital age is more than responding to events. It’s about maintaining trust across borders, culture, and information environments. Because a crisis can be created at lightning speed, executives and their staffs cannot afford to be complacent or take anything for granted about their readiness to respond to the next crisis. The same technology that can create and accelerate a crisis can also establish and reinforce stability—if business leaders are ready to use it strategically and without delay.

About the Author

Edward Segal is a crisis management expert and author of “The Crisis Casebook: Lessons in Crisis Management from the World’s Leading Brands.” He has advised corporate, nonprofit, and government leaders on crisis preparedness and response strategies for more than 30 years and has written extensively about leadership and crisis communication. Segal hosts the “Crisis Management Minute” podcast and provides briefings, workshops, and strategic counsel for executives. Visit his website at CrisisCasebook.com

The post Managing Crises in the Digital Age appeared first on The European Business Review.

By Markus Noga

As the digital landscape evolves amid shifting global politics, small- and medium-sized businesses (SMBs) must take decisive action to safeguard their data. This article explores the growing importance of digital sovereignty and how adopting GDPR-compliant, European-based cloud solutions can shield SMBs from regulatory risk while fostering trust and resilience.

Mounting global tensions and sweeping regulatory changes have placed European SMBs at the forefront of a critical challenge: digital sovereignty. Once a forward-looking concept, it has now become a non-negotiable reality. Yet many remain unaware of the urgent need to act. Despite increasing concerns over foreign access to data and the dominance of non-European cloud providers, a large proportion of SMBs across the region have yet to prioritise the security of their digital independence. 

Digital sovereignty refers to the ability of businesses to control and protect their data and digital infrastructure within the bounds of local laws and regulatory frameworks. For European SMBs, this concept has never been more vital, particularly as major US cloud providers dominate the market. A concerning practice, “sovereignty washing,” has taken root, whereby providers position themselves as compliant with local regulations while still being subject to foreign laws, such as the US CLOUD Act. This situation exposes European SMBs to risks associated with foreign government access to data. 

Data control across Europe 

A recent IONOS study, conducted with YouGov, polled decision-makers across multiple European markets and revealed that many small- and medium-sized businesses across Europe view IT security and data protection as a key area of focus in their companies’ digitalisation efforts. IT security and data protection ranked as a priority for 49% of UK businesses, 46% of German businesses and 53% of French businesses, second only to improving the visibility of their companies on the internet. Despite this emphasis, substantial barriers still hinder progress. Limited time (46%) and high costs (54%) remain the most prominent challenges faced by businesses over the past two years.  

The need for sovereign solutions 

Geopolitical tensions and evolving global legislation further heighten the urgency for European SMBs to act not just on data security, but on digital sovereignty. Legislation such as the US CLOUD Act amplifies concerns about foreign access to sensitive data, creating significant challenges for organisations relying on non-European cloud providers. Cloud services operated entirely within the EU offer not just GDPR compliance, but also protection from extraterritorial laws that could compromise data privacy. These regional safeguards are becoming a crucial criterion for businesses re-evaluating their cloud infrastructure. According to IONOS’ study, 83% of SMBs expect technology providers to proactively protect their information from regulatory risks and foreign interference. These challenges underscore the necessity of adopting GDPR-compliant, European-based cloud solutions to enhance security and reduce exposure to external threats. 

European sovereign cloud solutions offer a critical resource for SMBs, providing robust cloud services that secure data and comply with local privacy laws. Not only do these safeguards mitigate risks stemming from foreign interference, but they also ensure businesses are better equipped to navigate uncertain regulatory landscapes in the future. Protecting valuable business data amidst geopolitical unpredictability is essential for securing long-term success and operational security. 

Simplifying the path to digital sovereignty 

Although achieving digital sovereignty may seem complex, there are clear, actionable steps SMBs across Europe can take to simplify the process. For many SMBs, the road to sovereignty must be both secure and manageable. Cloud providers with strong local expertise can help businesses implement compliance-focused infrastructure without excessive complexity or cost. 

True sovereignty begins with ensuring that the ultimate parent company of the provider is headquartered in Europe, as this guarantees that the provider operates exclusively under European laws and is shielded from foreign interference. Equally important is that data centres are located within European jurisdictions, ensuring compliance with GDPR and protecting sensitive information from extraterritorial laws such as the US CLOUD Act. Furthermore, providers should employ staff based in Europe, enabling businesses to benefit from local expertise and ensuring that data management aligns with regional standards and practices. Finally, the technology must be managed autonomously, avoiding dependencies on external entities that could compromise data security and sovereigntyBy engaging with providers that have a strong European presence and can demonstrate compliance with local security standards, businesses can reduce exposure to foreign interference and safeguard their data. 

Furthermore, integrating European-based cloud systems with open-source platforms empowers SMBs to maintain flexibility and control over their data infrastructure. Open-source platforms minimise dependency on single vendors, enabling businesses to adjust their digital strategies in response to shifting legal or technological developments. For European businesses, this combination of European-based systems and open-source tools offers a balanced approach to ensuring data security without compromising innovation. Guidance from providers that combine secure infrastructure with expert consultation can help businesses navigate the regulatory landscape with greater confidence. 

Sovereignty in business strategy 

Making IT security and data protection central to business strategy is a critical measure for European SMBs aiming to achieve digital sovereignty. This involves implementing best practices for secure data handling, conducting regular risk assessments, and fostering a culture of compliance within organisations. By embedding these principles into their operations, businesses can better align with the evolving digital landscape while protecting themselves against future disruptions. 

Digital sovereignty represents far more than a regulatory requirement. It signals a strong commitment to data privacy and security, values that resonate deeply with stakeholders. SMBs that prioritise sovereignty not only protect their operations but also build trust with their customers and partners, differentiating themselves in a competitive market. 

As the global economy grows increasingly interconnected, trust and transparency in data management are becoming determining factors for business success. Customers, partners, and regulators alike are placing higher expectations on organisations to demonstrate strong data ethics. SMBs that address these expectations can enhance their reputation and future-proof their operations against emerging challenges. 

The digital frontier is expanding rapidly and European SMBs face a critical choice. Those that act decisively and adopt GDPR-compliant, European-based cloud solutions will not only secure their operations but also position themselves as resilient and trustworthy leaders in their industries. Digital sovereignty is no longer just an IT consideration, it is a strategic imperative. By safeguarding their data and aligning practices with local regulations, European SMBs can navigate an uncertain world with confidence, ensuring long-term success and operational security. 

About the Author

As CTO of IONOS, Markus Noga brings a compelling combination of technical expertise and strategic business insight. Previously serving in senior AI and Cloud leadership positions at SAP, he offers a complex understanding of both the technical and geopolitical dimensions of cloud computing.  

The post Safeguarding Europe’s Future – Digital Sovereignty and Why SMBs Need to Act Now appeared first on The European Business Review.

Across Europe, a new kind of leadership is emerging at the intersection of technology, research, and governance. Professor Yu Xiong, Fellow of the Academy of Social Sciences, represents this shift — a generation of scholar–practitioners who translate academic rigour into real-world transformation. From data-driven policymaking to the architecture of fair digital economies, his work has become a touchstone for how innovation can align with public good.

In this conversation, The European Business Review explores his views on the next chapter of the digital revolution — and why the future of technology depends less on disruption and more on trust.

You’ve been described as part of a new class of “scholar–practitioners” who bridge research, entrepreneurship, and governance. What defines this new model of leadership?

The modern economy is increasingly data-led, but decisions still require human judgment. The leaders who can bridge both — who understand the algorithms and the ethics — will define the next decade. Scholar–practitioners are not content with writing theories; they build systems. Their research must be testable in the real world and scalable in policy terms.

The boundary between academia and industry is fading. In today’s world, a strong paper can influence investors and policymakers more than a lobbying campaign ever could — if it is grounded in evidence and communicated effectively.

Your Nature Communications paper on Bitcoin’s carbon footprint gained global attention. What did that moment teach you about the relationship between data and decision-making?

It proved that evidence can accelerate change. Our study compared Bitcoin’s energy impact to that of entire nations. The response was immediate — it informed climate discussions from Beijing to Brussels.

Our study compared Bitcoin’s energy impact to that of entire nations. The response was immediate — it informed climate discussions from Beijing to Brussels.

But the lesson went beyond cryptocurrencies. It showed that the right data, interpreted clearly, can shape regulation, investment, and behaviour. Policymakers are looking for clarity, not lobbying. When research is transparent and verifiable, it becomes a trusted foundation for governance.

You often speak about technology as a moral system as much as an economic one. What do you mean by that?

Every digital system embeds values. When you design an algorithm, you make ethical decisions — about who benefits, who is excluded, and how transparent the process is. Efficiency alone is no longer a valid metric of success.

We need to think in terms of fairness, transparency, and sustainability. Technology should enhance these principles, not erode them. That’s the foundation of digital trust — and without trust, no digital economy can scale sustainably.

Your ventures such as Endless Protocol and Luffa aim to build this trust through new kinds of digital infrastructure. How do they reflect your academic philosophy?

Endless Protocol and Luffa both aim to bridge technological innovation with social accountability. Endless Protocol is about designing systems that are transparent by architecture — where fairness and auditability are not afterthoughts but core functions.

Both projects demonstrate that governance and innovation can coexist — that decentralisation does not mean disorder.

Luffa combines programmable loyalty with AI-driven creator tools. It’s about rebalancing power between platforms and users. Both projects demonstrate that governance and innovation can coexist — that decentralisation does not mean disorder.

Many believe the era of speculative blockchain hype is over. What comes next?

The next phase is the institutionalisation of digital trust. The focus is shifting from speculative assets to governance — how value, identity, and rights are managed in digital ecosystems.

Blockchain, AI, and Web3 are not just technologies; they are governance frameworks. The question is no longer “Can we build it?” but “Should we build it — and under what principles?” Those who answer that question responsibly will shape the next generation of the digital economy.

If you had to summarise your mission in one line, what would it be?

To make technology serve humanity, not the other way around. The real innovation is not faster systems — it’s fairer systems.

EBR Analysis:

Professor Yu Xiong’s perspective embodies a new European pragmatism: one that treats technology as a public trust, not a private experiment. His work bridges academia, enterprise, and governance in pursuit of a more accountable digital future — where innovation is measured not by disruption, but by the integrity it sustains.

Executive Profile

Professor Yu Xiong is a leading academic and international expert in business analytics, blockchain, and AI at the University of Surrey, where he serves as Chair Professor of Business Analytics and Founding Director of both the Surrey Centre for Innovation and Commercialisation and the Surrey Academy for Blockchain and Metaverse Applications; he previously served as the university’s Associate Vice-President for External Engagement. His work spans research, entrepreneurship, and policy advisory, with major contributions to blockchain applications, AI-driven innovation, and sustainable technology development. He also chaired the Advisory Board for the All-Party Parliamentary Group (APPG) on Metaverse and Web 3.0. His globally recognised research on the carbon footprint of bitcoin mining has been featured by the BBC. A Fellow of the Academy of Social Sciences and a 2012 London Olympic Torchbearer, he continues to influence the future of technology, innovation, and sustainability.

The post Yu Xiong: Building the Foundations of Digital Trust appeared first on The European Business Review.

The engineering and construction sector faces a striking paradox: digital tools are proven to boost performance, yet adoption remains low. In this article, Pedro Hidalgo Insua explores how forward-thinking EPCs can turn this gap into an advantage, using AI, integrated data ecosystems, and modern commissioning tools to outperform slow-digitalizing competitors and strengthen long-term project success.

A report published at the end of 2024 by the Royal Institution of Chartered Surveyors highlights a paradox in which the engineering and construction industry finds itself – globally, particularly in Europe.

Here’s the paradox: most professionals agree that digital tools improve project delivery. When asked which areas would benefit most from digitalization, 63% identify cost estimation, prediction, planning and control; 57% cite developing an asset lifecycle or “whole-of-life” perspective and 55% pointed to better progress monitoring. In each case, almost all agree upon the benefits of digitalization.

Yet actual adoption remains stubbornly low. In fact, the share of firms not using any digital technologies on projects increases each year, rising from 40 to 43% between 2021 and 2023. European firms believe in the value of digitalization but are now the least likely to use them in comparison to companies in the Americas, the Middle East or APAC.

Putting Value on a Paradox

So, how much does this contradiction between this belief and the lack of progress in practice cost European firms?

When trying to answer this question, one can focus on project performance. The Boston Consulting Group found that the use of digital technologies by E&C firms tended to reduce construction time by 15%-30% and cut lifetime costs by 20%.

But that’s only part of the equation. With several countries such as the UK, Germany, France and Spain mandating the use of BIM or digital twins in public-sector tenders, low digital adoption also represents loss of revenue. Lack of digital capabilities for project selection or project controls can also lead teams to avoid valuable projects by excess of pessimism or conservatism, or to submit uncompetitive bids because of poor cost or risk estimates and lack of benchmark data.

So how can forward-looking EPCs turn this situation to their advantage? The following are four concrete digital strategies that not only address the common barriers to adoption but also build a clear competitive edge over slow adopters.

#1 Leveraging AI to Strengthen Project Controls and Progress Measurement

When surveying large engineering and construction firms (for example, as part of our research into high-performing projects), one consistent finding is that firms fall into one of three groups. Roughly a third deliver on their time and budget commitments in 80% of their projects or more. Another third does so between 50-80% of the time.

This disparity highlights a profound challenge in project controls. Major projects across multiple sectors suffer massive financial losses due to inefficiencies, often stemming from inadequate control mechanisms. A 2021 study by KPMG revealed that major projects, on average, experienced $100 million USD in losses due to waste, with inadequate controls identified as the leading cause. This waste is often rooted in a reliance on manual data entry, subjective status reports and siloed spreadsheets, which collectively create poor visibility into true progress until it is too late to course-correct.

To address this pain point, leading EPCs are automating data capture and shifting toward approaches like Enterprise Project Performance (EPP) that can now be enriched with Artificial Intelligence. This shift replaces traditional subjective measurements, which often suffer from optimism bias and inconsistency, with objective data. For example, construction schedules are now being directly linked with real-time indicators such as quantities installed (via barcode or RFID scans) or actual work completed. AI and Machine Learning then analyze these patterns to predict potential outcomes, which allows for proactive intervention; for example, by flagging that a project is likely to slip a key milestone or suggesting more efficient resource allocation.

Platforms like Hexagon’s EcoSys exemplify this modern approach. As a leading EPP solution, EcoSys addresses poor progress visibility by integrating all key project functions, including scheduling, cost control and resource management, as well as connecting to diverse data sources like ERP systems and field applications. This integration establishes a single source of truth for project performance, injecting the necessary objectivity and consistency into decision-making.

It should be noted, however, that the shift requires more than just the software. True performance uplift is dependent on foundational organizational changes, including process standardization, robust data governance frameworks and a profound cultural shift toward data-driven decision-making across the organization.

When these systems are properly implemented, the impact on success rates is significant. According to a 2022 survey by Logikal, while only 5% of projects fully automate their project controls data, those projects report a 79% success rate in meeting targets.

#2 Collecting, Structuring and Contextualizing Project Data Within a Single Interface

Slow adopters tend to treat project data in a fragmentary way— each phase and department generates its own trove of emails, documents and spreadsheets that often vanish into archives after project close-out.

Three numbers demonstrate how doomed this strategy is: the World Economic Forum found that, on average, a single large infrastructure project today could produce 130 million emails, 55 million document and 12 million work orders. Keeping this volume of information buried in individual inboxes or disparate systems means thousands of hours wasted searching for information. It also leads workers to rely on memory or intuition rather than data.

Leading EPCs address this through integrated data ecosystems—a shift that requires both technological infrastructure and cultural change. These ecosystems are structured around platforms like digital thread or a project/asset digital twin. They spread information across traditionally separate domains (design, procurement, construction, operations).

For example, engineering models and tags can be linked to procurement statuses, which link to construction progress and then feed into an asset management system for operations. The result is greater speed and efficiency across the chain: for instance, when an engineering change occurs, an integrated system automatically notifies affected procurement orders, updates construction schedules and flags potential impacts on commissioning process that would take days or weeks within fragmented systems.

Contextualizing data like this means at any point in the lifecycle, the right people can access trusted, up-to-date information. This lifecycle approach also enables powerful analytics: AI can be applied to the unified dataset to find patterns (e.g. which contractors consistently perform better) or to forecast outcomes across the project portfolio.

Importantly, data connectivity needs to accompany the mere addition of tools. Without integration, adding more digital tools can ironically create extra work as teams jump between systems. It’s no surprise that 73% of EPC executives say lack of data integration has a “strong or severe” negative impact on their operations.

The high-performing EPCs avoid this trap by investing in platforms that speak to each other, often via standards like CFIHOS (Capital Facilities Information Handover Specification) for handover data and by enforcing data governance practices. The results are tangible: less time spent cobbling together reports and more time using information to make decisions.

#3 Avoiding Project Derailment at Commissioning and Handover

Another high-impact area where conventional practices fall short is completions, commissioning and handover to the owner/operator.

Traditionally, preparing turnover dossiers while ensuring every test, inspection and punch-list item is complete typically involves coordinating hundreds of documents across multiple contractors—a process prone to documentation gaps, version control issues and coordination failures between multiple stakeholders. These practices are also highly inefficient: as the Construction Industry Institute notes, “Commissioning failures are too common in frequency and extremely costly in impact. The business case for action is clear”.

So, what does this action look like? Modern completions management systems like Intergraph Smart® Completions turn this into a digital process. All asset information, checklists and test results are captured in a centralized database, with automated tracking Smart Completions of outstanding tasks. The payoff can be significant: on one Australian LNG megaproject, adoption cut the compilation and delivery time of commissioning dossiers by 98%.

Such tools also benefit the EPC in two important ways. First, commissioning is embedded in the design and construction processes rather than treated as an afterthought, which reduces rework, delays and other unforeseen issues during final testing. Second, they drive standardization, repeatability and continuous improvement rather than handling each project as one of a kind.

This emphasis on standardization is crucial: consistently delivering projects on time and on budget requires repeatable processes, not heroic efforts on each project. This consistency requires the effective use of data, from project selection to handover, to reduce risk and guesswork and increase margins.

The tools and methodologies exist and with digitalization adoption still lagging across much of the industry, early movers can establish a significant competitive advantage before digital maturity becomes a baseline expectation, rather than a differentiator.

Discover how Hexagon’s solutions, including project twins and EcoSys, our Enterprise Project Performance platform, can help you digitalize processes and achieve greater project performance here.

About the Author

Pedro Hidalgo Insua is a Senior Industry Consultant specializing in industry-leading solutions like S3D and SPI, SPEL, SP&ID, Smart Materials and construction tool. With a background in piping design, Pedro has over 20 years of experience in Information Management and Automation for the oil and gas and power industries, primarily in EPCs.

The post Three Ways EPCs Can Build a Competitive Advantage Against Slow-Digitalization Competitors appeared first on The European Business Review.

By Dr Helmut Schuster and Dr David Oxley

AI headlines warn of a job apocalypse, but history tells a different story. We love to catastrophize ahead of every major shift, yet we always adapt…eventually. Some will leap ahead, others will lag, but those outcomes are shaped by our choices…not some all-power AI… at least for now!

“AI will eliminate 300 million jobs by 2030.” “A million London jobs will vanish.” “Seven in ten workers fear replacement.” The headlines are bold, breathless, and brilliant at generating clicks. But behind the sensationalism lies a familiar, and very human emotional response: catastrophizing.

We love to imagine the worst. Remember Y2K, the Armageddon that wasn’t? Governments spent an estimated $300 billion preparing for an impending technological apocalypse; planes grounded, power grids dark, chaos imminent. When the clocks finally struck midnight, nothing happened. Not even a flicker of a fridge light.

Or take the panic over barcode scanners in the 1980s. They were supposedly going to destroy supermarket jobs. Yet the number of cashiers actually increased, according to the US Bureau of Labor Statistics, and the only real change was that customers eventually ended up doing some bagging themselves.

We’ve Been Here Before

Every major technological revolution follows the same arc. First, we inflate expectations beyond reason. Then, we panic. And finally, we adapt so successfully that we forget we were ever afraid.

This moment is no different. The Gartner Hype Cycle offers a useful lens for understanding what is happening. Right now, AI sits proudly at the “Peak of Inflated Expectations,” where predictions are wild, and timescales impossibly short.

History shows us that technology doesn’t destroy employment at a global scale. In 1990, the world’s labour force was approximately 2.28 billion. By 2020, it had grown to about 3.81 billion (World Bank). The internet dramatically reshaped employment in the 1990s and 2000s, but it did not eliminate work. It created new sectors, new skills, and entirely new kinds of economic value.

Above all, humans have a track record of adapting. We complain loudly during transitions, then, often reluctantly, embrace the new normal. From the steam engine to the spreadsheet, progress has always been met with initial resistance. Yet each time, we end up with more prosperity and more opportunity than before.

So, Who Should Actually Be Worried?

The common belief is that automation first replaces manual labour. In the AI era, the opposite may be true. This technology is exceptionally good at cognitive repetition and information processing. That means some of the first jobs to feel pressure will be those that sit in the professional middle.

Middle management, mid/back office, administrative coordinators, customer support roles, sales operations teams, and non-specialist software developers are all facing the first wave of disruption. According to McKinsey, approximately 30 percent of tasks within existing jobs are likely to be automated by 2030. Importantly, it is tasks, not entire professions, that are being automated. But when most of a role’s tasks can be handled by machines, the profession must adapt or die.

When Predictions Go Too Far

Of course, with any emergent technology, there are wild predictions that stretch credibility. Recent reports by Sky News, claim that interpreters, mathematicians, and even historians could soon be obsolete. Historians? Will we genuinely entrust the entire preservation and interpretation of culture to systems trained on the internet? Even the most sophisticated language model cannot yet replace expert judgement, contextual nuance, or ethics.

The lesson here is that predictions are often exaggerated. In 1899, the U.S. Patent Office Commissioner apocryphally declared that “everything that can be invented has been invented.” Edison’s early investors believed the phonograph was commercially useless. We consistently underestimate the resilience of human demand and overestimate the speed of technological takeover.

Professions Will Evolve, Not Evaporate

Journalism will continue to thrive where truth, critique, and investigation matter. Accountants and lawyers will still interpret ambiguous regulation and calm anxious clients. Bankers, traders, and financial advisors will still manage trust. Customer service will still require empathy, escalation handling, and accountability.

Even in software development, one of AI’s strongest use cases, the landscape is changing, not disappearing. GitHub reports that 92 percent of U.S. developers already use AI tools. The most valuable engineers will be those who can guide machines, correct them, and translate business needs into reliable systems. AI may write the first draft of code, but humans still define what matters and what good looks like.

The Real Divide: Adapt or Fall Behind

The disruptive line is not between humans and machines. It is between humans who embrace AI and humans who resist it. Those who integrate AI into their skillset will become faster, more accurate and more effective. Those who choose not to adopt it will be overtaken.

There will be turbulence. The professional middle layers will feel the squeeze first. But as with every technological evolution, new opportunities will emerge. We saw it with Microsoft Office in the 1990s: the early adopters accelerated their careers. Those who refused to learn? They were quietly replaced by those who did.

The winning formula is not machine without human or human without machine. The most successful workers will be those who combine human intuition with computational power. These “cyborgs,” half human, half machine, will outperform both humans alone and AI alone.

Don’t Fear the Change — Shape It

The choice ahead is simple. We can hide and hope disruption passes us by. Or we can embrace our role as adaptable, evolving, inventive contributors to the next phase of work. AI is not arriving one day in the future; it is here today.

The biggest risk is not being replaced by a robot but being replaced by a human who uses one. If history teaches us anything, it is that the workforce does not shrink when technology advances. It shifts. It reinvents. And those who step forward early will have the most to gain.

The AI revolution is not a job apocalypse. It is an invitation to level up.

About the Authors

Dr Helmut Schuster and Dr David Oxley are leading career experts and authors of Artificial Death of a Career: How to stay relevant and thrive in the age of AI (Practical Inspiration Publishing).

The post AI Panic: Middle Workers are Most at Risk – Yet We’ll Adapt appeared first on The European Business Review.

As founder and CEO of ZeroBounce, Liviu Tanase has built one of the most trusted names in email deliverability. Under his leadership, the company has evolved from a single email validation service into a full suite of deliverability tools. Most recently, it launched ZeroBounce ONE, a unified platform that simplifies how businesses improve their email performance. 

Speaking with The European Business Review, Tanase shares how his approach to leadership has evolved, what AI means for the future of communication, and why the human touch will always remain at the core of technology.

You’ve built several successful ventures. What personal qualities or habits have been most critical in helping you sustain momentum as an entrepreneur?

Two traits have really helped me sustain momentum: I’m stubborn, and I have a lot of energy for what I do. I wouldn’t call being a workaholic a quality, but I doubt I would have gone very far without persistence and drive.

When it comes to habits, I make a point of staying in tune with what’s happening in the world, especially in tech. The best products anticipate a need before the market even realizes it. So I stay curious, follow new innovations closely, and think ahead about what challenges people might face next. Of course, I’m not always right, but it’s a great exercise in imagination and entrepreneurship.

How has your perspective on leadership changed from your early days as a founder to leading ZeroBounce today?

My perspective on leadership has changed completely. When I started my first business, all I could think about was the product and how to deliver the best experience to our customers. I didn’t think much about building a team culture or developing people. New people would join us and we’d throw them in the water and let them learn to swim.

Investing in your team is tremendous, because happy employees create happy customers. And happy customers always come back.

Today, I lead two companies, and ZeroBounce alone has more than 100 employees. While I’m still very focused on the quality of our products, I’ve learned that leadership is about much more than that. I ask myself: What kind of culture are we building? Do our employees have a sense of purpose? How can we make their experience better?

Investing in your team is tremendous, because happy employees create happy customers. And happy customers always come back.

Looking back at your career, what has been the most unexpected lesson about building technology companies that you wish you had known earlier?

The most unexpected lesson was to trust my instincts more. You can never be 100% sure your product is going to work, and that uncertainty never goes away completely.

When you’re first starting out, you’re even more unsure. Of course, you rely on market data and initial customer feedback. But mostly, you operate based on gut instinct and a belief that what you’re building will serve its purpose and be of service to people.

When I first ran the concept of ZeroBounce to some of my business partners, they thought it was a terrible idea. A niche B2B software-as-a-service (SaaS) company? They thought the chances of making it successful were slim. But I had another email marketing company and could sense how the market was going to shift.

Email validation is now a must-have for all mass senders – and I’m glad I trusted my instinct and went on to build ZeroBounce. It’s now the go-to email deliverability platform for half a million customers around the world. We’re beyond grateful they choose us every day. 

ZeroBounce has become a recognized name in email deliverability. What leadership strategies have helped you keep the company competitive in such a fast-changing industry?

I could sum it up this way: we never rest on our laurels. That mindset runs through everything we do – from product development to the way we treat every one of our customers.

We started as an email validation company, and that foundation helped us become a recognized name in the industry. But we’ve continued to add new services to the platform because we listened to our customers’ feedback through the years. From email scoring to deliverability tools and an email finder, all these services have shaped ZeroBounce into the company we are today.

Many of the features are actually a direct result of customer feedback. We read and respond to every message, social media post, and review – and share all suggestions with our product team. In a real sense, we’ve built ZeroBounce together with our customers.

Another key strategy has been investing in education from day one. Our marketing and PR team launched with a blog, and since then, our content library has grown tremendously. We now publish original studies, reports, guides, and host webinars that draw large audiences. When you think and act like a publisher, people begin to see you as a trusted source of knowledge. That’s something we’ll always strive for. 

What is the larger mission that drives ZeroBounce, and how do you keep your team aligned with that vision as you grow?

Our mission has always been to help people use email safely and more effectively. That hasn’t changed since day one. It’s the foundation for everything we do, from how we build our tools to how we support our customers.

Every team member learns about this mission from the start, but more importantly, they see it in action every day. It comes through in our decisions, our priorities, and the way we treat our customers. That shared sense of purpose makes it easy for everyone to move in the same direction.

We also talk about our mission often – whether in company-wide meetings or in everyday chats – because it’s not a one-time effort. It’s something you keep alive through constant communication and example.

Your team recently launched ZeroBounce ONE. How does this new platform represent the next chapter for your company and reflect the way you approach innovation?

ZeroBounce ONE is tied to our mission of helping companies succeed with their email communication. We thought of a way we could serve them even better by making our entire platform easier to access and use.

ZeroBounce ONE is a subscription that brings together our email deliverability suite, plus 25,000 monthly email validation credits, at a price lower than what the credits alone used to cost.

Previously, 25,000 validation credits cost $175 per month. Now, for just $99 per month (or $79 per month billed annually), ZeroBounce ONE customers gain not only the same 25,000 credits, but all our other tools – Email Warmup, DMARC Monitor, Blacklist Monitor, Inbox Placement Testing, Email Server Testing, and Email Finder – at no additional cost.

Marketers kept telling us deliverability tools felt expensive and fragmented as they had to use separate vendors. ZeroBounce ONE unifies everything into one subscription so businesses don’t have to choose between validation and deliverability. They can get both.

At a time when many SaaS subscriptions are soaring in price, ZeroBounce ONE defies the trend and sets a new standard for value. We’ve made enterprise-grade deliverability tools affordable and accessible to every marketer. And we’re giving them more than ever – for less than they were already paying. 

AI is changing the way companies think about personalization, automation, and data quality. From your perspective, what role will AI play in shaping the future of email deliverability?

AI can analyze massive data sets, detect patterns almost instantly, and help companies make smarter decisions, like predicting which emails might bounce or when to send for the best results.

Our team sees AI as an incredible support system for humans, not a replacement. The human side – strategy, empathy, understanding what makes communication meaningful – still matters just as much. I think the companies that thrive will be the ones who use AI to amplify human expertise, not replace it. 

Beyond deliverability, how do you see AI transforming the broader communications ecosystem, and what opportunities or risks does it create for companies like yours?

AI is changing how we create, personalize, and deliver messages across every channel, from email to chat to social. Companies can now reach people faster and with more relevance than ever before.

But that also comes with responsibility. The easier it becomes to automate communication, the greater the risk of losing authenticity. If every message starts to sound the same, people will tune out. That’s why the real opportunity is in using AI to enhance human connection, not replace it.

For companies like ours, AI opens incredible possibilities to make communication safer, smarter, and more effective. At the same time, it challenges us to stay ethical, transparent, and focused on the human being at the other end of every message.

Fast-forward to the next decade. What do you hope ZeroBounce will stand for in the industry, and how do you envision email’s place in the future of global communication?

A decade from now, I hope ZeroBounce continues to stand for trust, innovation, and reliability. We’ve built our reputation on accuracy and security, and I want us to keep pushing those standards higher while helping businesses communicate more effectively.

If ZeroBounce can keep that human element alive, and help companies reach people in a genuine way while protecting their data and reputation, then we’ll have done our job well.

Email will keep evolving, but it will always be at the heart of digital communication. What will change is how intelligent it becomes: more automated, more personalized, and better integrated with other channels. AI and automation will make it smarter, but the human intention behind every message will still matter most.

If ZeroBounce can keep that human element alive, and help companies reach people in a genuine way while protecting their data and reputation, then we’ll have done our job well.

Want to get more out of your email marketing?

Even the best campaigns fail when emails don’t reach the inbox. With an average of 28% of email lists going bad each year, data accuracy is everything. ZeroBounce helps you keep your lists clean and your messages deliverable. If your email marketing could use a boost, sign up for ZeroBounce. You can check your first 100 emails for free, with 99.6% accuracy guaranteed.

Executive Profile

Liviu Tanase is a serial entrepreneur and telecommunications executive with more than 17 years of experience. He founded five companies and has participated in three exits creating quadruple-digit returns. In 2015, he founded email validation and deliverability company ZeroBounce. ZeroBounce helps businesses maintain email data quality and offers several deliverability tools to ensure the successful delivery of emails to the inbox. Liviu Tanase is also an email deliverability thought leader. He’s a contributor to Inc., Entrepreneur, and countless other industry publications.

The post ZeroBounce CEO Liviu Tanase on Leadership, AI, and the Future of Email appeared first on The European Business Review.

AI is no longer a technology problem but a leadership test. Here, Deepika Chopra highlights leadership readiness as a measurable capability built on trust, alignment, and decision velocity, arguing that it equips boards to move from compliance to conviction and govern AI transformation with speed, coherence, and accountability.

The Governance Illusion

Across industries, AI has moved from experimentation to expectation. Industry research suggests that while a significant majority of global enterprises report active AI programs, a much smaller percentage indicate they have realized measurable ROI. According to available McKinsey research, this performance gap appears to persist even as governance frameworks mature and ethics boards proliferate. The technology is performing as designed — it’s leadership alignment that isn’t.

The technology is performing as designed – it’s leadership alignment that isn’t.

For decades, executives have managed digital transformation as a series of technical or operational challenges. But AI is different. It requires an organization to do something leaders rarely measure: trust a system they do not fully control.

That leap from control to conviction has become the new frontier of competitive advantage. Governance ensures safety. Readiness determines scale.

The Illusion of Control

Most AI strategies over-index on control mechanisms: oversight boards, model audits, and risk reviews. These systems are essential but incomplete. They mitigate liability without generating momentum.

The deeper issue is what I call the control paradox — the more organizations try to control AI outputs, the less they invest in cultivating trust in its insights. In doing so, they slow adoption and erode value.

Across multiple financial institutions, internal reviews consistently showed that AI models outperformed human analysts by 15-20 per cent in forecasting accuracy. Yet in most cases, fewer than 35 per cent of managers used those forecasts in decision-making. The pattern was clear: lack of confidence, not capability.

AI didn’t fail. Conviction did.

From Culture to Conviction

For years, executives have cited “culture” as the obstacle to transformation. But culture is not the barrier; it’s the outcome of what leaders believe and reward. Conviction — the collective confidence to act on intelligence — is the real driver of readiness.

Leadership readiness measures the speed and strength of that conviction. It is not about technical preparedness, but about how rapidly belief in AI’s value diffuses across the organization.

High-readiness organizations exhibit three traits:

1. They build trust in the system.
2. They create alignment around purpose and boundaries.
3. They maintain decision velocity — the ability to move from insight to action without hesitation.

When these forces move together, AI scales. When they fracture, transformation decays into Execution Theater — visible activity without measurable progress.

“Culture describes what an organization believes. Conviction determines what it does with those beliefs when algorithms challenge human judgment.”

The Conviction Equation: Trust × Alignment × Decision Velocity

1. Trust: Confidence That Compounds

Trust is the foundation of readiness. It determines whether humans act on machine insight or override it out of habit. Industry research indicates that a substantial majority of data leaders report challenges in fully tracing AI decisions, with many organizations experiencing deployment delays due to explainability concerns.

This confidence isn’t static — it compounds through transparency, performance consistency, and shared learning. Research suggests that organizations reporting higher trust levels in AI systems tend to demonstrate significantly better adoption rates compared to those where trust metrics are not systematically tracked.

Building this foundation requires intentional cultivation, not assumption. It begins with how leaders communicate uncertainty — acknowledging what AI can and cannot do builds credibility faster than insisting on perfection.

2. Alignment: Coherence Across the System

Alignment ensures that every part of the organization interprets AI’s role in the same way. Misalignment is rarely malicious; it is systemic.

Executives may view AI as a growth driver. Risk teams often focus on exposure management, HR may emphasize workforce implications, and Operations typically consider implementation challenges. Each view is rational, but together they create drag.

Industry studies suggest that enterprises with clearer alignment on AI objectives tend to achieve faster implementation timelines and improved cross-department collaboration. Alignment, unlike consensus, is not agreement on everything; it is clarity on shared intent.

Leaders build it by articulating both vision and limits — defining what AI will and will not replace, who remains accountable, and how learning loops feed back into governance.

3. Decision Velocity: Turning Insight into Action

Velocity is the difference between insight and impact. It reflects how quickly organizations move from recommendation to responsible execution. Decision velocity is often mistaken for speed, but it’s really about confidence under uncertainty.

Fast organizations aren’t reckless; they are aligned. They know which decisions can be made autonomously, which need oversight, and which require ethical debate.

Research from MIT Sloan and BCG (2024) found that top-performing organizations make AI-driven decisions 2.5 times faster and with half the error rate of their peers — not because they automate more, but because they trust their process for escalation and review.

Fast organizations aren’t reckless; they are aligned. They know which decisions can be made autonomously, which need oversight, and which require ethical debate.

The Readiness Deficit

If trust, alignment, and decision velocity define readiness, most organizations are still in deficit. In a 2025 IAPP Governance Survey, 77 per cent of enterprises reported that they have governance structures in place but no metrics for organizational readiness. Only 14 per cent could quantify the impact of AI decisions on business outcomes.

This gap explains why AI maturity doesn’t translate to business performance. Companies measure what they can control, such as accuracy, compliance, uptime, etc., but not what actually drives adoption: belief, coherence, and confidence.

“Until readiness is tracked as systematically as revenue, the gap between AI ambition and execution will persist”

Governance Without Conviction

Across Europe, the EU AI Act, AI Pact, and ISO 42001 have redefined the global standard for responsible AI. These frameworks are essential — they protect citizens, ensure accountability, and set ethical floors.

But governance without conviction can paralyze progress. Compliance reduces risk; it doesn’t create value. Leadership readiness converts ethical frameworks into execution frameworks — embedding trust metrics, feedback loops, and learning systems into board oversight.

“Regulation creates guardrails. Readiness provides traction”

How Leaders Build Readiness Capital

Boards that move from compliance to readiness take three practical steps:

Add a Readiness Brief to the Board Pack

Alongside financial and risk metrics, include indicators of trust, alignment, and decision velocity.

Track decision cycle time, override rates, and sentiment data from cross-functional teams.

Create Alignment Rituals

Replace ad hoc updates with structured reflection sessions where leaders review AI decisions, errors, and lessons learned.

Consistency builds cultural predictability, and predictability builds trust.

Reward Conviction, Not Caution

Recognize teams that move responsibly but decisively — where ethical agility meets speed.
In readiness-driven cultures, conviction is treated as a measurable performance indicator.

Readiness in Practice: Composite Insights

*The following represents composite insights drawn from multiple organizations across different sectors to protect confidentiality while illustrating common patterns in AI readiness transformation.

Across financial services, healthcare, and manufacturing, a pattern emerges: organizations with strong technical AI performance but weak human confidence. In multiple observed cases, adoption rates among middle managers remained below optimal levels despite AI models showing measurably superior performance compared to human-only analysis.

The breakthrough came when leadership teams shifted focus from training campaigns to readiness measurement. Boards introduced conviction dashboards tracking trust metrics and decision velocity. Cross-sector “conviction reviews” became standard practice — structured sessions where teams examined which AI recommendations they overrode and the reasoning behind those decisions.

The observed results showed consistency across industries: Within 12-18 months, adoption rates demonstrated substantial increases. Trust metrics showed meaningful improvement. Efficiency gains varied significantly based on organization size and sector context.

Technology performance remained constant. Leadership alignment transformed everything.

From Compliance to Confidence

The World Economic Forum’s 2024 “Future of Jobs Report” lists “AI governance and risk management” as one of the fastest-growing skill sets for directors. Yet skill does not equal readiness.

When boards measure readiness as carefully as compliance, they gain a new form of governance capital: confidence.

True readiness is not just about having governance expertise, it’s about cultivating organizational conviction. When boards measure readiness as carefully as compliance, they gain a new form of governance capital: confidence.

“Confidence is contagious. It fuels adoption, accelerates learning, and transforms ethics from a constraint into a catalyst”

The Leadership Imperative

The next decade of digital leadership will not be defined by who adopts AI fastest, but by who aligns it best. The winners will be those who can translate responsible principles into decisive action —  organizations where technology scales at the speed of conviction.

Leadership readiness is no longer a soft capability; it’s a strategic differentiator.

“The technology is ready. The question is: are you?”

About the Author

Deepika Chopra is the founder and CEO of AlphaU AI and author of Move First, Align Fast (Wiley, 2025). Her frameworks equip boards and executives to measure trust, alignment, and decision velocity as predictors of AI performance. She previously held senior leadership roles at Citi, AIG, and Siemens, directing large-scale AI transformations.

References 

• McKinsey & Company (2024). “State of AI: Global Survey and Enterprise AI Adoption Trends”.

• Dataiku Research (2024). “AI Implementation and Trust Studies: Cross-Industry Analysis”.

• OneTrust Governance Research (2024). “AI Readiness and Organizational Alignment Studies”.

• International Association of Privacy Professionals (IAPP) (2024). “AI Governance and Readiness Metrics Survey”.

• World Economic Forum (2024). “Future of Jobs Report: AI Skills and Leadership Capabilities”.

• McKinsey & Company (2024). “Digital Transformation and AI Strategy: Executive Leadership Research”.

• Deloitte (2024). “AI Governance Implementation: Global Enterprise Survey”.

The post Leading Through Alignment: Turning Organizational Readiness into a Boardroom Advantage appeared first on The European Business Review.

By Dr. Nina Mohadjer, LL.M

Even today, more than five years on from the pandemic-induced exodus from the office, companies are still grappling to comprehend the risk to their crucial data that this event continues to present. Nina Mohadjer has some inside advice on this multifaceted issue.

Today’s businesses have numerous organisational priorities. They need to protect sensitive information and manage the liability of keeping data in a complex and dynamic environment. How organisations approach this issue poses challenges and opportunities across their entire digital risk spectrum, mostly as they need to ensure continuous operations and increase finance, while managing their human capital.

Poor data quality costs the US economy approximately $3.1 trillion per year (Petrov, 2022), while one in four employees believes that they can use a personal cloud server to transfer work from home.

Cyber-scams increased during the pandemic by 400 per cent (Petrov, 2022), resulting in software platforms pulling from sales and marketing activities (Harper, 2021), and leading to a recovery cost of $1,000 per backup tape (www.ironmountain.com).

Furthermore, research indicates that the number of data breaches involving remote workers will cost organisations more than $1.007 million more than other breaches (www.vpnmentor.com), while it brings into focus cross-border bandwidth, which grew 148 times between 2005 and 2017 (Botwright & Sen, 2019).

Considering that 60 per cent of the world has now passed data privacy laws while managing 100 TB of data (www.spiceworks.com), 40 per cent of companies do not have a defensible disposal programme in place. Thus, the data journey of organisations becomes a challenge across the entire digital risk spectrum.

Issues of Data Management

Cyberattacks and data breaches have increased tremendously in the globally connected world, leading to legal and financial complications. The stated number of data breaches costs organisations more than $4.24 million (www.ibm.com), while stakes are higher when private data is involved. This number does not take into consideration the disruption to an organisation’s operations and the cost of rebuilding, which includes reputational repair, loss of human capital, and increased marketing costs, which can lead to rebranding (Harper, 2021).

• Governance. Organisations need to consider digital risk management and intelligence services that bridge all phases of their data lifecycle. When they acquire other organisations and merge or upgrade their business applications, focusing on every aspect, they should not forget that all of these actions can have a profound effect on their underlying data and technology. If these two aspects remain unaddressed, they can degrade data quality and expose the organisation to additional risk. Their departments and team members are mostly focused on data governance and challenged by maintaining compliance with the increasing number of global standards and regulations. Thus, organisations need to understand their business processes when developing a strategy to reduce their risk factors while maintaining their operational efficiency (Bennett, 2019).
• Privacy and Security. Global privacy laws are increasing and becoming more complex while, at the same time, the tendency is for simplicity in the working environment, which, due to the pandemic, has become comfortable with many working from home (Saporito, 2019). The question arises of how organisations can balance both demands.
• Information Governance. Organisations are under pressure to effectively protect sensitive data. From intellectual property to credit card numbers, the collection of data automatically brings risks and makes the organisation vulnerable (Bennett, 2019). This vulnerability is not recognised by many organisations and they miss developing accurate data security and governance programmes to protect the data and, ultimately, themselves.
• Legal Department Services. These data breaches and security issues have an impact on legal departments and have demanded some legal transformation. They are asked to drive strategic priorities while helping to evaluate risk and opportunities and, at the same time, function effectively and efficiently (Dawson, 2016). Organisations and, particularly, legal departments are asked to create and subsequently implement operating models that meet corporate objectives and demonstrate legal operations.
• Risk & Compliance. Presently, organisations do not need to “just” pay a fine for data breaches or keeping their data in an insecure manner. Compliance breaches suppose reputational risk and lead to loss of customers and reduced share prices (Botwright & Sen, 2019, www.corporatecompliance.com).

The globally connected world brings people and organisations together on a different level than previously known. Business processes have been transformed and diversified, increasing the number of legal actions for data protection and cross-border issues.

Solutions

Risks remain. It is up to organisations to recognise them and remain resilient after damaging incidents. Organisations need to be prepared to implement an effective incident response, as it is essential to mitigate the financial and reputational setbacks, avoid legal and regulatory repercussions, and restore trust. They need to have multidisciplinary expertise and have a strategic approach to cybersecurity and data privacy challenges, while being able to respond to stakeholders and end clients.

Compliance breaches suppose reputational risk and lead to loss of customers and reduced share prices.

The incident response of an organisation becomes essential in evaluating their readiness. A data breach usually involves personal data and can have an adverse effect in terms of physical material and non-material damage for individuals. Before the message is communicated to the individual, the organisation has to notify the data protection authorities, evaluate the consequences of the data breach, and consider measures to address the data breach and its categories, while considering mitigation measurements and the number of exposed records. Incident response includes properly trained staff using the necessary technical tools while selecting and implementing the appropriate controls. Communication should include a proper cybersecurity incident response procedure to assist in an accurate and timely response before a breach has gained publicity. This includes the coaching of executives as well as media coverage.

Effective cyber-incident response depends on an organisation’s ability to react quickly. An organisation needs to calm customers down, control the narrative, and be visible in a humble manner. Immediately after an incident, organisations need to follow up with the customers and develop tailored communication and outreach strategies. It becomes an organisation’s main task to provide strategic counsel to customers and ensure that legal, financial, regulatory, and reputational implications have not been damaged. The organisation also needs to ensure stakeholder engagement, media relations, media monitoring and, last but not least, data breach notification and call centre services.

Teams need to be prepared to remove malicious code, actor accounts, and unauthorised access, and protect data from leaving the network in order to fix the present issues and prevent further damage.

Understanding the scope of the damage requires a comprehension of personal data in relation to global privacy laws. Data types have to be analysed while personal identifiable information (PII) has to be evaluated based on the risk and sensitivity categories in order to prioritise the notification strategy. Finally, organisations have to be able to recover from the damage by adding tools, technologies, and capabilities to ensure best practices (Harper, 2021).

At this point, I usually recommend organisations to conduct a gap analysis, which points up any shortcomings in a business’s performance. It evaluates whether business requirements and objectives are being met. Research determines the “gap” as that between where a business should be and where it presently is (www.techtarget.com). In the world of cybersecurity, a gap analysis refers to the point and time of IT involvement, as it indicates that the given gap needs to be “fixed” and eliminated to match the present status to the required one. Thus, it is an indicator for performance improvement. Different benchmarks can be used to perform the analysis, whether it is IT performance, customer satisfaction, revenue generation, or productivity.

The first step for a gap analysis is to determine target objectives. The organisation has to determine the goals based on the specific requirements of the project, department, and the mission statement of the organisation (www.pivotpointsecurity.com).

The second step is the present state analysis by collecting relevant data, for example how resources are allocated, what the present performance level is, whether documentation exists, what the key performance indicators (KPI) are, who the stakeholders are, and observation of the present activities (www.techtarget.com).

It should be mentioned that a gap analysis is taken into consideration when an organisation is performing a risk assessment. Additionally, a gap analysis should contemplate newly implemented technologies and data types and consider them part of the exercise.

Once both steps have been conducted, the organisation has an image of “Where are we?” and “Where do we want to be?” and can commence with the gap analysis and the strategic planning.

Furthermore, organisations could rely on numerous tools, such as Zabbix, which is used for numerous monitoring purposes, such as the health and integrity of servers, virtual machines, and applications (www.zabbix.com). As an enterprise-class open source tool, it allows users to receive email alert notifications for all events, which allows quick responses to potential server issues. Organisations can rely on the cooperation of Zabbix for capacity planning, as it demonstrates reporting and data visualisation on their stored data. This ensures the monitoring of the IT infrastructure in any given instance (www.oneibct.be).

Conclusion

Data increases as a byproduct of globalisation. It has become more difficult and challenging to keep an overview of where the data is and where it gets stored. Organisations are in a difficult position of handling everyday challenges and adding an unknown threat coming from the technology sector to their SWOT analysis. They are in need of dedicated technology teams who understand the ever-changing data landscape and know how to approach the safety, preservation, and collection of the new data types. Solutions need to be tailored to the organisation’s needs while teams have to have thorough experience in regulatory issues and reputation management. Furthermore, the times of a single department within an organisation have passed, as more subject matter experts in cross-functional services are needed.

While millions of fragments of personal data might be scattered across multiple data sources, it becomes challenging for organisations to reach and fulfil data subject notification requirements.

While new legislation and regulations may be formed around common principles and requirements, the regulatory burden for organisations is not a light issue they can underestimate. Globalisation also brings challenges such as local laws, culture, data use, organisational structure, and language. Organisations need to understand the global privacy and technical field, the culture of learning styles and IT approach, expectations of data usage, cross-border data transfers, and variations of consent requirements. It is also time that the main stakeholders understand and analyse their technology framework and infrastructure and challenge existing procedures.

An effective programme can reduce the pain points of implementing new procedures and analyse data privacy, while using the appropriate change management approach. Thus, organisations need to identify quickly where personal data resides, the data types and languages, and build the right methodology to avoid future incidents.

About the Author

Dr. Nina Mohadjer, LL.M is a published author and legal consultant with global experience. She is experienced in Document Review and eDiscovery, Human and Project Management. Global experience with Fortune 100 Companies and financial institutions (Bayer, Siemens, UBS, CS) in eDiscovery, Risk Management, Human Resources, and Relocation. Coordinated legal review projects and teams across the globe. Mastered US and global patent and pharmaceutical defense sector.

References:

1. “Activity matters”. www.techtarget.com

2. Bennett, S. (2019). “Data as a strategic national resource: The importance of governance and data protection”. Governance Directions, 71(7), 362-6.

3. Botwright, K. & Sen, N. (17, December 2019). “It’s getting harder to move data abroad. Here’s why it matters and what we can do”. www.weforum.org

4. Data privacy security stats. www.vpnmentor.com

5. Dawson, G.S., Denford, J.S., Williams, C.K., Preston, D., &Desouza, K.C. (2016). “An examination of effective IT governance in the public sector using the legal view of Agency Theory”. Journal of Management Information Systems, 33(4). 1180-208. https://doi.org/10.1080/07421222.2016.1267533.

6. Harper, S. (September 1, 2021) “The customer data problem”. www.cloudkettle.com. “New ways to manage discovery costs for back up tapes”. (2021). www.ironmountain.com, www.oneibct.be

7. Petrov, C. (June 2, 2022). “Big data statistics”. www.techjury.net

8. Saporito, P. (2019). “The data divide: Data ethics and data governance need to be part of every employee’s onboarding, highlighting their responsibilities along the supply chain”. Best’s Review, 120 (4), 23.

9. Storage trends. (2020). www.spiceworks.com/marketing, www.techtarget.com, www.zabbix.com

The post Digital Risk Management and Intelligence appeared first on The European Business Review.

Richard Corbridge, a seasoned CIO, and British Computer Society Fellow, has spent over twenty years leading digital transformation in the NHS and private sector. Focusing on people and innovation, he drives system change that enhance patient care and customer interaction, streamlining operations, and harnessing data intelligently, shaping a future where healthcare is proactive, connected, and sustainable.  

What first inspired your journey into digital leadership, and how has your career shaped the way you think about innovation?

I recall the moment when, early in my career, I walked through hospital wards filled with paper charts, nurses chasing records, doctors waiting for pages to arrive, and fax machines. Endless fax machines! And I thought: there is a better way. It wasn’t a fascination with technology for its own sake, but my frustration with the friction it caused in patient care that drove me and inspired this journey. From that point I realised that digital should not be viewed as a novelty, but as a way of fixing deeply ingrained problems of delay, of duplication, of error. 

In my early career I could see how the healthcare system in the UK needed to come together to offer a more joined up approach not just for the patient who had to be at the centre but every element of the system. Data was collected and never turned into real information for clinicians and therefore insight could hardly ever be gathered without reassessment at every touch point with the clinical system.  

The 2000s in the UK started to change some of that but new technology that is becoming more and more capable now is really going to revolutionise this change.  

Over time, working in hospitals, trusts, public health bodies, clinical research, and outside of the first lien healthcare system, I’ve learnt that innovation doesn’t begin with the “what’s new” but with the “what hurts now?” Fixing the pain points has shaped how I lead: always starting with real problems, listening deeply to those in the industry or those delivering care or receiving it, testing, failing, iterating. And always asking: is this approach sustainable? Will this scale across the industry or the health system? Can we build trust so that change sticks? 

Innovation, for me, has become less about spectacular launches and more about continuous improvement: constant small wins that add up; a culture that allows people to say “this is not working” without fear; and governance that enables speed without sacrificing reputation or safety. The next real advance ein how we use technology will be the integration of its possibilities into what we do not a new monolithic ‘thing’ that everyone must adopt.  

You’re known for putting people at the centre of transformation. How has that focus influenced the way you lead and deliver results?

Putting people at the centre is not a slogan. It’s the lens through which every decision must pass. But what does this mean in practice? Firstly, it means that I want the people who are doing the work, doing the jobs, in the room from the start. I don’t want feedback forms, I want to talk to colleagues and customers who are experiencing multiple ‘pain points’ in their daily jobs, and to co-create systems that deliver solutions that address these problems, digital will either be a catalyst for the conversation of a foundation for a solution in almost all cases I believe. 

This is why I aim to build psychological safety: to make sure people at every level or the organisation are comfortable and keen to speak openly about what we don’t know.

Secondly, it’s about empathy. A trait that is far too often overlooked in healthcare or enterprise IT. It’s about recognising that people are busy, change is tiring, and resistance to technological or digital transformations often reflects past change fatigue. This is why I aim to build psychological safety: to make sure people at every level or the organisation are comfortable and keen to speak openly about what we don’t know. From the board room down to the shop floor, so to speak. To admit mistakes and to reward those who flag problems early. 

Digital transformations succeed when ownership is distributed, because when people see their input reflected, they become ambassadors for change rather than obstacles. I want every person how works in digital to refer to ‘our’ organisation not ‘the’ organisation as if it is some separate almost mythically unconnected thing.  

You need to be crystal clear about what you are trying to achieve, and to communicate “what good looks like” effectively and often. You also need patience, particularly about the pace of change. Change in large healthcare systems, for example, doesn’t happen overnight. There is always a temptation for a CIO to overpromise. Instead, I always advise them to under-promise and over-deliver.  

Finally, it’s about providing visible and accessible leadership. Not hiding behind screen, or dashboards, or app development beta test, or whatever it may be. But instead, being present across the organisation, listening, and gathering information and stories, genuinely building culture. And it’s often slow, and messy, doing things this way. But if you try to sprint without pulling everyone with you, you risk collapse or backlash. Transparency of what you are doing today, tomorrow and next week will enable engagement to be honest and support to be positive.  

Having worked across both healthcare and the private sector, what key lessons about digital transformation stand out to you?

Drawing from both sectors gives distance and insight. Being clear about the incentives from the start is vital. In the private sector, the incentives are often clearer: revenue, profit, customer satisfaction. In healthcare, many of those incentives are implicit – patient safety, public health, social justice: less easily monetised but no less real. Transformation must align to those harder-to-measure incentives, or it won’t drive change. I have believed for some time that transformation in both sectors is achieved in a very similar way, a people first approach building a digital mindset that each orgnaisaiton defines and seeks the outcomes it wants from such a change.  

Private sector businesses often tolerate more risk and are driven by more of that “move fast and break things” mentality. In the public sector, in healthcare, you simply cannot risk breaking things, because failures have higher stakes. So, one learns to build a pathway for experimentation with safety: pilots, simulations, governance, rollback options. 

The private sector also tends to invest more in customer experience, personalisation, and so on. Whereas in the NHS, there’s a legacy of one-size-fits-all systems. I’ve seen how applying the user-centric mindset from retail or finance – personalising care, considering patient portals as “customer journeys” – dramatically improves engagement and satisfaction. By having this level of engagement the transformation is way more supported and the journey is one of supported collaboration rather than demand and control.  

Breaking down data silos is always an ongoing challenge: in whatever industry or sector you work in. In healthcare, a hospital is not independent; you need data from primary care, community, labs, social care. That interconnectedness makes interoperability, standards, and regulation absolutely critical. CIOs also have to think in longer time-horizons, and act far more sustainably than in the past. While private companies might invest in upgrading tech on a regular basis, in healthcare, the choices you make now may live for decades. So, architecture, maintainability, security, and ethics have to be embedded from the start. 

Finally, there is a saying I often use, which is that “we are witnessing the end of the expert and the rise of the collaborator”I’ve seen state-of-the-art technologies fail because culture, leadership, and mindset did not change. Conversely, modest technologies succeed when the people are ready, supported, and motivated and collaboration is at the heart of the goal they are trying to achieve. 

Why has the NHS struggled to adopt new technologies at pace, and what do you see as the biggest barriers to change?

If I were to draw on a much used analogy, the NHS is like a huge ocean liner changing course: there’s immense momentum, and while you can move the rudder, it takes time and plenty of force before the ship responds. So many industries are quoted at the NHS as being successful in making the change, asking why hasn’t it, the NHS is not banking, tourism or leisure, it can not make mistakes and fix them.  

In terms of the barriers to technology change that I see, the primary challenge in the NHS is that of legacy and fragmentation: different trusts, primary care networks, community services, social care all using different systems, different data standards. Integration is hard, costly, and often designed on top of decades of older infrastructure. 

Additionally, there are very specific regulatory and governance challenges. The NHS must satisfy many layers of oversight: data protection, patient safety, clinical governance. Those are essential, although they can often become bottlenecks if not designed with agility in mind. Funding models are also a challenge in the NHS, because contracts and budgets are often annual, which means that investment in infrastructure or change programmes may need multi‐year horizons. But political cycles and short funding windows make that difficult. Also, cost pressures mean risk aversion becomes default. 

There are the well-publicised workforce constraints which you have to content with in the NHS, too. Clinical staff are always under pressure. And digital, informatics, and data-science skills are always in short supply. Many clinicians are not trained for the digital tools we ask them to use. Time is always in short supply: if you’re already overburdened, asking someone to change practice without freeing up time is unlikely to work. 

There is change fatigue combined with a challenging trust deficit in the NHS when it comes to adopting new technology‐enabled initiatives. Many are launched with enthusiasm, but then they are delayed, or they are left disconnected from frontline workflows. That inevitably builds scepticism. Without trust, new technologies are always viewed with suspicion. 

Finally, while NHS procurement rules are rightly cautious, they can end up being far too slow and rigid. Often new hardware, software, apps or other products are selected without sufficient input from users, or with contracts that limit speed, flexibility, or interoperability. And it is that last point, interoperability – sharing data privately and securely across multiple stakeholders, organisations, different systems, and so on – that continues to be such a huge challenge with such varied data quality, and a lack of consistent standards and frameworks 

How can AI, cloud computing, and secure data sharing help address challenges such as faster diagnoses, patient discharge, and easing workforce pressures?

I see these not as technologies in isolation, but as parts of a tapestry; what matters is how they interweave to deliver impact. When done well, the compound effect of these technologies is not just incremental improvement, but a shift in how care is delivered: more proactive, connected, and patient centric. 

In terms of a few concrete ways this can be done, I will always highlight the importance of AI diagnostic support: AI tools in imaging, or pathology, for example, can surface an abnormality earlier, detect patterns humans might miss, and accelerate the triage workload. They can also flag any potential deteriorations in monitored patients or assist radiologists by pre-scanning imaging. But these must always be validated, explainable, and integrated into clinician’s workflows. 

Clear and robust governance, as ever, is key. Because mistakes cost lives, so we must always focus on data quality, AI validation, bias checking, transparency, and human oversight. Building public trust is not optional.

Secure data sharing is the glue that holds it all together. If patient data flows across primary, secondary, community care, with patient consent, privacy protections, and standards, we can smooth transitions (such as discharge planning), prevent duplication of tests, and monitor follow-ups remotely. It allows for a much better coordination between home care, social care and hospital. Data sharing plus predictive analytics can help to identify in advance which patients are likely to need post-hospital support, social care, or community beds, and trigger those arrangements proactively. Cloud platforms can host shared dashboards across organisations. 

Perhaps the biggest benefit of AI is in automating repetitive and administrative tasks such as document reviews, letter summarisations, scheduling, and so on, freeing clinical staff for care. And in AI-assisted decision support, auto-triaging, helping junior staff with guidance, and in providing new tools to upskill and enable staff across the NHS to work more efficiently. Clear and robust governance, as ever, is key. Because mistakes cost lives, so we must always focus on data quality, AI validation, bias checking, transparency, and human oversight. Building public trust is not optional. 

What role should public–private collaboration play in driving sustainable innovation in UK healthcare?

For me, public–private partnership is not just useful, it’s essential. But it must be done thoughtfully, or it risks being extractive rather than additive. When it comes to research and development, and piloting new tech projects, for example, the private sector often has agility, deep technical expertise, and resources to build prototypes. Conversely, the public sector has scale, access to delivery settings, regulation, and legitimacy. Together you can test ideas – such as new AI tools, diagnostics, or apps – in real clinical contexts. 

Then, once something works in a trust or region, the private sector can help with scaling, deployment, and maintenance. And similarly, shared investment in platforms or federated data-infrastructures makes sense. Private firms must align with public good, and help establish transparent standards for data, privacy, ethics, and safety. And the public sector has a role in ensuring fairness, equity, and accountability. 

Talent exchange, secondments and bringing in skills from private tech firms is important, in terms of capacity building. So that the NHS can learn better from sectors where digital transformation has been faster, such as in finance or retail). It also helps to create supply chains of innovative SMEs, and start-ups, so the NHS becomes far less reliant on large incumbents. 

Contracting and funding models need to be more innovative and flexible. Which means outcome-based contracts, risk-sharing, and partnering rather than simply procuring, investing in infrastructure jointly, and balancing short-term costs with long-term public value. And, of course, these collaborations must never compromise access, equality, and data privacy. The NHS must retain ownership of health data, properly anonymised where needed, to ensure benefits are not captured only by private interests. 

When all of this is done well, innovation can be sustainable: not one-off or ad hoc projects but ongoing pipelines of improvement, continuously feeding back into the system. 

Looking ahead to the next decade, what do you envision as the most transformative change AI and data innovation could bring to the NHS and its patients?

There are several future horizons I believe are both plausible and potentially transformative, drawn from patterns I see already emerging. Primarily, the shift from reactive to predictive care: using large‐scale population data, wearables, genomics, environmental and social determinants, to predict risk before disease manifests. And to enable early interventions to prevent hospitalisation rather than waiting for crises. 

Secondly, providing personalised medicine at scale. Not just genomics but integrating phenotypic, behavioural, and social data. Tailoring care pathways and choosing diagnostics and treatments that suit individual patients, for reduced adverse events and better outcomes. 

AI offers the incredible potential for the NHS to have real-time decision support embedded everywhere. AI will become part of the routine in EHRs: prompts, alerts, and suggestions. Not as “bells and whistles” but integrated with how clinicians work day to day, aware of context, history, and risk. We’ll see “AI assistants” that help, not replace. 

AI and data innovation will aim to enable the home as the hub. Where wearables, remote monitoring, and telehealth will all provide rich data that flows back to centralised oversight. This means far more care delivered outside the hospital, which becomes a safety net rather than the default treatment site. And within the hospital itself, operations will become optimised by advances in predictive staffing, optimised scheduling (theatres, clinics, imaging), smarter bed management and other supply chain and logistics efficiencies that will help to wring inefficiencies out, and free up resources for care. 

The wider democratisation of data and health literacy will see patients increasingly owning their data and understanding their own health trajectories; tools that help them be partners in care decisions. Transparency, fairness, and trust are all essential here. 

The NHS needs AI systems that are clear, explainable, and auditable; with oversight that is visible; and with bias mitigated and more mature regulation. It also needs to think more in terms of data interoperability as infrastructure: not an afterthought. Data standards, shared platforms, seamless connectivity across hospitals, primary care, community, social care. In a decade I hope we will see this kind of data flow as the norm. 

If we achieve those shifts, the NHS could move toward being a system that is resilient, anticipatory, and more humane. Patients will feel seen, doctors and nurses will feel supported, and administrators will see inefficiencies removed. And the public trust in the system will grow because these changes will be visible, thoughtful, and safe. 

Executive Profile

Seasoned CIO and British Computer Society Fellow, Richard Corbridge is an experienced digital leader, recognised as a transformative technology leader by the Global CIO Forum Committee for his merits and achievements. Most recently the CIO of SEGRO one of the leading industirial properties companies in the UK and Europe.  

The post Advancing Healthcare Transformation with Digital Leadership Strategy and Sustainable Innovation appeared first on The European Business Review.

Interview with Tom Cox, Founder and CEO of 15gifts

As Telco providers become more reliant on digital tools to engage customers, research by 15gifts found that a quarter of consumers are frustrated by chatbots’ inability to answer specific questions, and 20% have abandoned a purchase entirely due to chatbot failures. Tom Cox, CEO of 15gifts, explores how telcos can bridge the gap between current chatbot capabilities and customer expectations.

What specific chatbot limitations do consumers find most frustrating when shopping for telco services?

According to research by 15gifts, one-in-four (24%) consumers are frustrated by chatbots’ inability to answer specific questions effectively. And their limitations aren’t just slowing down or frustrating online buying experiences, they are often ending them altogether. One-in-five (20%) consumers admit that they’ve completely abandoned a purchase purely because of a chatbot’s limitations. Dissatisfaction is also being driven by an inability to emulate the tone of the user, length of time to respond to users and a tendency to send irrelevant material when responses are given. The issue is many chatbots today don’t have the sophisticated functionality to replicate the sales techniques used by human sales advisors in successful face-to-face interactions, leading to lost sales opportunities.

How often do consumers feel the need to switch to a live agent due to chatbot issues?

Our research found that the inability to have specific questions answered online is also the biggest reason why customers choose to speak to a live agent on the phone. This was reported by 28% of telcos in the UK themselves. Consumers also bypass chatbots altogether due to an inherent mistrust, with their preferred alternative likely to be an interaction with a live agent or a visit to a physical store. Customers know they can receive a better experience from talking to a human sales advisor because they can address concerns, such as price or product capabilities, and offer alternatives where needed.

Are there specific types of questions or issues that consumers feel chatbots consistently fail to address?

Consumers often discover that it’s when they need to find out a specific detail about a product that a chatbot falters, or when they seek support halfway through the buying journey without wanting to restart the process from scratch. Chatbots also often struggle to effectively direct customers to alternative products when the initial recommendation doesn’t fit their needs, leading to dead ends in the sales process. When a customer isn’t interested in a suggested product and they are not offered a personalised alternative, it results in frustration and potential drop-offs.

How are telco providers currently measuring the effectiveness of their chatbot support?

There are a number of ways that telcos can measure the effectiveness of their chatbot deployments, such as handling times, time to resolution and conversion rates. Some providers are looking towards their NPS scores to decipher if chatbots are having a positive impact, but the adverse is often true. Customers may turn from indifferent to active detractors when the chatbot experience is perceived to be falling short of expectations. With chatbots being an expensive outlay, (almost half (47%) are spending over £1million on them annually), telcos can’t afford to integrate solutions that have this type of negative impact.

Are there any particular customer demographics (e.g., age, location) that experience higher levels of frustration with chatbots?

While every age demographic struggles to have specific questions answered by chatbots, our data shows that consumers over the age of 55 (28%) are more likely to say that this is the case. The older demographic have had more experience of completing transactions via the phone or heading into stores to speak to an advisor and so are also more frustrated at the inability to access real-time support from a human (23%).

What are the biggest challenges in implementing advanced AI solutions to support customer interactions?

AI is currently being used by every telco surveyed to enhance the customer experience, but some of the biggest challenges in implementing AI-driven solutions effectively comes from consumer concerns. Our research shows that 33% of consumers believe that AI can’t replicate human expertise, it’s not monitored or corrected by human teams (33%), it’s not clear when they are talking with AI or a human (32%) and fears about unethical use (28%) and bias (19%). It’s clear that despite a commitment to bring in AI, these tools are still failing to incorporate the human element that consumers are looking for.

How do telcos plan to bridge the gap between current chatbot capabilities and customer expectations?

Rather than making potentially disruptive wholesale changes by replacing chatbot technology, sophisticated tools, such as virtual sales agents, can support them to handle the customer experience in the same way that a human salesperson would. So far, many telcos have invested in tools that bring consumers as far as the website, such as bid-based online advertising platforms (40%), SEO strategies (25%) and social media paid promotions (25%). However, they are falling at the last hurdle by not converting that traffic. There needs to be a focus on building a more sophisticated sales journey that helps customers build confidence in their digital purchasing experiences and proactively guides them to a sale.

Executive Profile

Tom Cox is the Founder and CEO of 15gifts. Founded in 2010, 15 gifts transforms the digital sales experience by applying the successful strategies of in-store interactions to online platforms. Today, 15gifts powers thousands of confident choices every day across some of the world’s largest brands.

The post Chatbot Challenges in the Telco Industry appeared first on The European Business Review.

By Gary Waldon

AI is reinventing the kids’ game of Leapfrog into a serious, high-stakes business, with players vying for the Master of the Universe title. We have become players in this multi-dimensional, hyper-paced game, as we try to stay on top of each new release. Here are 5 old-school strategies to take back control and help you win at AI leapfrog.

I have never been a gamer, but I think the pace of AI is making me into one. We are all caught up in a high-paced game of AI Leapfrog, and here is how we can get the upper hand.

The rules of playground leapfrog are simple, you all line up, crouch down, and then the person at the back leapfrogs over the players in front, until they reach the head of the line. Then the next person at the back has their turn. It can go on forever, as there is no real winner, it’s just good fun. However, AI Leapfrog turns this kids’ game into serious high-stakes business, where the winner could be crowned Master of the Universe. Here is how it’s playing out. OpenAI releases GPT-XX. Days later, Google counters with Gemini updates. Not to be outdone, Grok jumps in with a native integration. Anthropic quietly upgrades Claude. Meta makes a move. Apple hints. Microsoft flexes. Then OpenAI leaps again.

While it may seem that, as mere mortals, we are just observers, however in reality we are also players in this multi-dimensional, hyper-paced game. While the trillion-dollar stakes are the prizes for the key AI players, we also have a lot at stake. The costs became obvious the other day when a friend asked how she could create a business plan using AI. As a transformational specialist, I wanted to ensure I gave the best advice, so I asked which AI she was subscribed to. She responded, “none, I don’t know which one is best”. As I rattled off the various benefits of the main players, I realised my overview was probably only current for that exact point in time. Things change in days, sometimes hours, as the next AI release leapfrogs its way to the front, leaving us wishing we hadn’t committed our hard-earned dollars to subscribing to something already outdated.

I pay subscriptions to three of the main players, but am guilty of yearning for an endless budget to allow me to subscribe to all of them. “Imagine what we could achieve,” the creative and business voices inside my head argue. However, there is a larger cost to pay for trying to stay ahead in AI leapfrog. There is the obvious financial hit, but there is an even greater personal price as we become more AI addicted. We can spend endless hours trying to master the latest releases, researching YouTube clips titled “Here are 10 insane AI things you need right now”, or similar. Or create business plans for ideas we don’t have the time, or the money, to bring to reality because we are too busy trying to stay in the leapfrogging race.

Here are 5 old-school playground strategies you should play right now to help you reinvent and win at AI leapfrog.

1. Only leap when it’s your turn

Take the pressure off trying to be over everything. You don’t need to jump every time an AI player makes a move. AI Leapfrog will continue to play out at breakneck pace, with or without you. What would happen if you didn’t play this round? If you miss your turn, you will get another chance to rejoin and play again when you are ready. Take control, and avoid comparisonitis by sticking to your game, not playing someone else’s.

2. Stick the landing before your next jump

The pace AI leapfrog is played, often leaves us feeling like there is no time to get our footing, causing anxiety and a fear of missing out. Voices of self-doubt and not being enough will only get louder if you don’t allow yourself a win and celebrate it before you keep playing. Remember, trying every new tool isn’t mastery, it’s struggling to keep up. Get to know a few tools well before moving on to the next one. Mastering three is better than having tried twenty.

3. Build your personalised AI toolkit

Start building your personal AI toolkit that will help you get your job done. Maybe use GPT-XX for writing, Perplexity for research, Claude for summarising, and Firefly for design. Or, keep it simple, and maybe one AI tool can do your job well enough. Allowing you to compromise on those costly less critical traits . Choose tools that help you achieve your goals by playing your game, not the leaderboard.

4. Ask these questions to avoid AI overload

Before leaping into anything new, run it through this filter:

• Why am I interested in the new functionality?
• Will it help me achieve my goals?
• Can I succeed without it?
• Does it inspire or excite me?

If it doesn’t meet at least three, skip it until the next round.

5. Make it a game, not a grind

These are exciting times, and the AI game should excite you, not create unnecessary anxiety. If you find yourself in survival mode, then you are no longer playing a game, you are working to keep your fear under control. AI tools should expand your thinking, creativity and skills, not drain you. If managing your AI toolbox is more work than the tasks it was meant to simplify, it’s time to reassess.

The game of AI leapfrog will continue to play out with, or without us. And because leapfrog is a game without an end, we can choose to step in and out as it suits us. Any changes in the AI leaders board will become inconsequential history when we choose to rejoin the game, because we will be playing in the most up-to-date AI ecosystem. With a reinvention mindset we will be able to quickly adapt and bring ourselves up to speed, allowing us to succeed in the latest game. So, take back control and remember change is inevitable, however reinvention should be intentional.

About the Author

Gary Waldon is the bestselling author of Mastering the Art of Reinvention ($32.95). He is a transformation specialist who works with people at all levels from CEOs, CIOs, business leaders and professional athletes through to teachers and anyone who needs to reinvent themselves when life changes. Find out more at www.garywaldon.com

The post How to Win at the Game of AI Leapfrog appeared first on The European Business Review.

By David Malan 

Digital transformation has shifted from a corporate luxury to a strategic imperative. Once the domain of multinational giants with expansive budgets and dedicated IT departments, it is now being driven by small and mid-sized businesses (SMBs) that are leveraging AI and automation to reshape how they operate and compete. These businesses are not merely adapting, they are architecting a new digital paradigm.

Rather than pursuing sweeping infrastructure overhauls, SMBs are embracing modular, scalable technologies that align with their operational realities. Cloud-based platforms and AI-powered tools are enabling them to streamline workflows and respond to market shifts in real time. This democratisation of digital capability is levelling the playing field, allowing SMBs to challenge incumbents and disrupt traditional models.

For many SMBs, digital transformation begins with a simple challenge: how to do more with less. AI and automation offer a compelling answer, not only through complex deployments, but through accessible platforms that simplify repetitive tasks and unlock strategic bandwidth. Intelligent systems now handle invoice processing, HR documentation and logistics workflows with speed and precision, freeing teams to focus on creative problem-solving and long-term planning.

One of the most impactful applications is intelligent document processing (IDP), which automates data extraction, validation and routing. In sectors like logistics and healthcare, this translates into faster approvals, reduced errors and improved customer service. For example, a regional logistics firm that automates bill-of-lading workflows can cut administrative overhead, accelerate delivery timelines and reduce disputes – all while enhancing client satisfaction.

These gains are not theoretical. They are quantifiable and repeatable. Processes that once took hours now happen in seconds. Accuracy improves, compliance strengthens and employees spend less time chasing paperwork and more time delivering value. This shift reframes digital transformation from a technical upgrade to a strategic enabler, one that empowers SMBs to operate with greater precision and purpose.

Bridging the physical and digital divide

While cloud-native platforms and AI tools dominate the conversation, many industries still rely on physical documentation. In sectors such as healthcare, legal services and shipping, paper remains a critical part of daily operations. Here, technologies like optical character recognition (OCR) and smart scanners play a pivotal role in bridging the gap between physical and digital workflows.

Digitising paper documents is a foundational task. Without it, even the most advanced AI systems cannot function at full capacity. OCR ensures that data is complete, searchable and ready to fuel automated processes. It also supports regulatory compliance by making records accessible and auditable. This step, often overlooked, is essential for unlocking the full potential of AI-powered transformation.

Moreover, the integration of OCR with AI-driven platforms enables SMBs to build systems that are not only efficient but also adaptive. These systems respond to real-time inputs, adjust workflows dynamically and provide actionable insights that inform strategic decisions. The result is a business environment where physical constraints no longer hinder digital progress and where transformation is truly end-to-end.

The convergence of physical and digital capabilities also enhances customer experience. In industries where documentation is central to service delivery, digitisation allows for faster turnaround, fewer errors and more personalised engagement. SMBs that invest in this bridge between worlds are – simply put – elevating their brand and strengthening client relationships.

From efficiency to intelligence

Forward-looking SMBs are moving beyond basic digitisation toward adaptive intelligence. They’re deploying AI to refine decision-making, personalise customer engagement and dynamically adjust operations. No-code platforms, embedded analytics and AI assistants are designed with SMBs in mind, offering enterprise-grade capabilities without the complexity. This evolution marks a shift from reactive operations to proactive strategy.

Transformation is no longer about replacing outdated systems. It’s about reimagining how work gets done. AI enables businesses to anticipate customer needs, optimise resource allocation and uncover patterns that were previously invisible. Real-time analytics provide clarity in decision-making, while automation ensures consistency and scalability. The result is a smarter organisation that can pivot quickly and confidently.

Importantly, these tools are increasingly accessible. Vendors are building solutions tailored to SMBs’ pace, budget and technical capacity. This accessibility removes barriers to entry and empowers smaller firms to experiment, iterate and scale without the overhead of traditional IT infrastructure. The emphasis shifts from technology adoption to strategic integration, where every tool serves a clear business purpose.

In this context, AI becomes more than a productivity enhancer. It becomes a growth engine. SMBs that embrace this mindset are truly transforming – using data to drive innovation, automation to unlock capacity and intelligence to shape the future of their industries.

Transformation as a continuous journey

Viewing digital transformation as a finite project is a strategic misstep. For SMBs, it must be a continuous journey, one that evolves alongside market dynamics, customer expectations and technological advancements. Success lies in cultivating a culture of experimentation, setting clear objectives and measuring outcomes rigorously.

The most successful SMBs don’t chase technology. They pursue clarity and invest in solutions that are intuitive, scalable and directly tied to business outcomes. Whether it’s accelerating operations, predicting market trends or enhancing customer experiences, AI becomes a strategic ally when deployed with purpose.

In today’s hyper-competitive landscape, digital transformation is existential. AI sits at the heart of this shift, unlocking new levels of insight and engagement. For SMBs willing to embrace this evolution, the rewards are tangible: accelerated growth, smarter operations and stronger customer relationships. Ultimately, success is no longer defined by size or resources. It’s defined by adaptability and the courage to innovate continuously. SMBs that embody these traits are leading the charge in a digital-first world.

About the Author

David Malan is the Sales Director for DocuWare, overseeing sales, pre-sales and marketing activities across the United Kingdom and Ireland. With over 18 years of experience in Document Management, David has focused on DocuWare’s Electronic Content Management (ECM) solutions since 2012. Throughout his career, David has developed extensive expertise in business process optimisation, helping organisations improve efficiency and reduce costs by implementing content and document management solutions that streamline operations.

The post The Future of Business Belongs to SMBs that Embrace AI With Clarity and Purpose appeared first on The European Business Review.

By Daniel Trabucchi and Tommaso Buganza

Established companies face persistent challenges of inefficient transactions, shifting customer needs, innovation bottlenecks, underused data, and strategic data gaps. Daniel Trabucchi and Tommaso Buganza of the Politecnico di Milano reveal how platform models address these problems and present a methodology to design, ignite, and grow scalable ecosystems.

The platform economy is no longer an emerging trend—it is an established reality. Yet its geography tells a story of striking imbalance. According to the latest Top 100 Worldwide Platforms ranking, based on market capitalisation, 85.8 per cent of their total value is concentrated in the Americas, 11 per cent in Asia-Pacific, while Europe accounts for a modest 2.2 per cent and Africa just 1 per cent¹. This disparity begs an obvious question: has Europe already lost the platform battle?

Not entirely. Significant examples prove that Europe can still matter in the global platform landscape. Siemens with Siemens Xcelerator, AXA with its AXA Digital Commercial Platform, and Eni with Open-es are three distinct initiatives united by the same logic: leveraging platform models to tackle complex challenges and create new value².

We believe there is still hope. Like a phoenix rising from its own roots, European companies can build on what they already have—brand, customers, relationships, data, and know-how—to innovate and grow.

In our previous article for The European Business Review³, we introduced the concept of Platform Thinking, showing how even traditional firms can adopt it. With our new book, The Digital Phoenix Effect², we explore this dynamic in depth, revealing how legacy companies can reinvent themselves—not by burning everything down, but by using their legacy as the foundation for a new way of creating value.

The Study: Decoding the Digital Phoenix Effect

The Digital Phoenix Effect is grounded in a multi-year research effort on the S&P 500, examining how established firms have approached platform strategies in recent years. Our analysis covered over 800 initiatives, identifying 140 cases that met the essential conditions to be classified as platforms: the presence of at least two distinct groups of customers and the existence of cross-side network externalities².

These two features, which we discussed in our previous The European Business Review article³, are what distinguish platform models from traditional linear value chains. In a linear model, value is created and delivered through a sequence of activities—from supplier to producer to customer—without the interdependent dynamics of multi-sided interactions. In a platform, value emerges from enabling these interactions, and network effects power its growth; the more participants on one side, the more valuable it becomes for the other side, and vice versa^4,5,6.

From the 140 platforms identified, a pattern emerged. Regardless of industry, we found that platforms were not built for their own sake—they were designed to solve pressing business problems that linear models could no longer address effectively. We distilled these into five recurring “business problems” that platforms are particularly suited to tackle²:

• Missing or Inefficient Transactions – cases where opportunities for exchange exist but are hindered by friction or lack of connection.
• Evolving Customer Demands – situations where customer expectations shift faster than traditional offerings can adapt.
• Innovation Bottlenecks – contexts where internal innovation capacity is insufficient to keep pace
  with market needs.
• Underutilised Data – scenarios where valuable data exists but is not leveraged for new value creation.
• Lack of Data for Strategic Insights – situations where key decisions are made with incomplete or inaccessible data.

These categories provide a practical lens for managers. Instead of asking, “Should we build a platform?”, the more relevant question becomes, “Which problem are we trying to solve, and can a platform mechanism address it better than a linear one?” The next section explores each problem through a concrete case.

The Five Problems Platforms Can Solve

1. Kroger – Supplier Hub for Missing or Inefficient Transactions 

Some transactions, while possible and desirable, remain slow, fragmented, or absent. Inefficiencies may arise from organisational silos, non-integrated IT systems, or centralised decision-making that slows the flow of information. These frictions can occur externally—between customers and suppliers, partners and affiliates—or internally—between departments and teams.

Kroger, one of the largest U.S. supermarket chains, faced this challenge in procurement. With thousands of suppliers and hundreds of internal buyers operating across categories and locations, transactions between these two existing groups were frequent but often costly and inefficient.

Moving beyond the traditional “supplier as vendor” mindset, the platform positioned suppliers as active participants in a shared ecosystem.

In 2018, Kroger launched Supplier Hub, a platform designed to streamline these interactions. Moving beyond the traditional “supplier as vendor” mindset, the platform positioned suppliers as active participants in a shared ecosystem. It provided self-service tools, tutorials, dedicated support, and simplified compliance processes.

By enabling more direct, transparent, and efficient exchanges, Kroger unlocked previously hidden value. As more suppliers engaged, the platform generated richer data and greater variety for buyers; as buyers became more active, suppliers gained visibility and efficiency.

This illustrates the power of Platform Thinking: reimagining existing relationships and assets to transform inefficiency into a scalable, self-reinforcing advantage.

2. Royal Caribbean – Royal Caribbean Hotels for Evolving Customer Demands

Royal Caribbean is renowned for world-class onboard experiences, yet many guests face a recurring gap: pre- and post-cruise lodging near ports. Managing this need through external portals is fragmented and can undermine the perceived quality of the entire journey. Rather than buying hotels or locking into a handful of bilateral deals, Royal Caribbean launched Royal Caribbean Hotels, a transactional platform that connects two distinct groups: passengers seeking convenient stays and hotels eager to reach a pre-qualified, high-value audience.

On the platform, hotels are not mere suppliers—they are customers. The more properties participate, the more choice and flexibility travellers enjoy; the more travelers book through the platform, the greater the visibility and occupancy upside for hotels. These cross-side network effects strengthen with every interaction, without diverting the company from its core: delivering great cruises.

By leveraging brand trust, customer relationships, and an existing loyal base, Royal Caribbean extended its ecosystem to solve a fast-evolving customer need, improving end-to-end experience while keeping capital intensity low. The platform turns a service gap into a growth vector—and does so by orchestrating partners rather than owning every asset.

3. Caterpillar – Cat Digital Marketplace for Innovation Bottlenecks

As customers demanded smarter, more integrated solutions, building every digital tool in-house became untenable for Caterpillar: slow cycles, rising costs, and a narrow funnel of ideas. To break the bottleneck, Caterpillar created Cat Digital Marketplace, opening APIs and infrastructure so external developers can build software that augments its machines—fleet management, predictive maintenance, and more.

Developers are platform customers, motivated by access to Caterpillar’s global installed base. More developers mean more solutions for equipment owners; more adoption attracts more developers—a virtuous loop that scales innovation without bloating internal teams.

Crucially, the marketplace enhances (not replaces) Caterpillar’s core. The machines remain the anchor; the ecosystem makes them more valuable, configurable, and future-proof. By activating dormant assets—brand, trust, and a massive installed base—Caterpillar shifted from “doing all the innovation” to enabling it, accelerating time-to-solution while preserving focus on what it does best.

4. Kraft Heinz – Kraft-O-Matic for Unlocking Underused Data

Kraft Heinz, a household name in the food industry, has long enjoyed the trust of millions of consumers and a portfolio of iconic brands. Yet, much of its vast trove of sales, marketing, and feedback data remained locked in silos, underutilised and mostly archival. Recognising the opportunity to transform these dormant assets into real-time strategic insights, the company partnered with Google Cloud to create Kraft-O-Matic, an AI-powered platform designed to aggregate, analyse, and distribute data across the organisation.

In this ecosystem, consumers remain end customers but also act—often unknowingly—as data suppliers through their interactions with the products. Internal R&D and marketing teams become customers of the platform’s insights, leveraging them to shape new products, campaigns, and adaptations with far greater precision.

By reframing data as a multi-use asset, Kraft Heinz shifted from static reporting to a continuous, generative flow of value. Cross-side network effects ensure that the more data consumers feed into the system, the more effective internal decision-making becomes—creating a self-reinforcing cycle of innovation. The result is not just technological upgrade, but a redefinition of relationships, roles, and flows within the company’s value creation system.

5. News Corp – Vertical Video Platform for Strategic Data Gaps

In the fast-evolving media landscape, News Corp faced a critical challenge: acquiring the granular, real-time behavioural data that advertisers demanded, while competing with mobile-first giants like TikTok and YouTube. Traditional audience metrics and market research were too slow, too costly, and too generic to sustain competitiveness.

The solution came in 2022 with the launch of the Vertical Video Platform—a mobile-first, interactive content service designed to attract new audiences and, crucially, to turn engagement into a natural source of data generation. Integrated with Intent Connect, News Corp’s proprietary analytics engine, the platform enables hyper-personalised targeting for advertisers based on in-app behaviours, preferences, and interactions.

Here, users become both customers of an engaging media experience and providers of valuable data; advertisers gain precise targeting capabilities, which in turn makes the platform more attractive for users. This virtuous loop is a hallmark of Platform Thinking: value is exchanged continuously, with each side making the other more valuable.

By leveraging brand trust, existing advertiser relationships, and its established user base, News Corp didn’t just close a data gap—it built an entirely new, sustainable source of competitive advantage.

From the Platform Revolution to the Phoenix Effect, making platform a concrete tool for legacy firms

All five cases presented—Kroger, Royal Caribbean, Caterpillar, Kraft Heinz, and News Corp—are drawn from the U.S. market. This is no coincidence. Given the global scale and maturity of the “platform revolution,” our research focused on the S&P 500, where platforms have long been integral to business models.

Yet the opening examples—Siemens Xcelerator, AXA Digital Commercial Platform, Open-es—show that European firms can play, and win, in this arena. The first part of The Digital Phoenix Effect is dedicated to these and other European success stories, such as Telepass and XOM Materials, demonstrating that geography need not dictate destiny².

The book closes with a practical build-track: the Platform Thinking Journey, a strategic design process structured into four phases—Framing, Design, Ignition, and Growth—and articulated through seven recurring cycles of questions, each supported by clear alternatives, tools, and milestones. The aim is not a linear recipe but a recursive cadence that managers can actually run.

• Framing clarifies where and why to apply Platform Thinking (new service, core activity, or support activity; problem to solve or idle asset to unlock).
• Design translates intent into architecture
  and business model choices across sides and value flows.
• Ignition makes the go-to-market concrete, selecting onboarding strategies and sequencing sides with a roadmap, learnt from a decade of research in start-up-based platforms.
• Growth scales through extension and exploitation strategies, based on the success
  of digital platforms.

This methodology was developed and stress-tested within the Platform Thinking HUB at Politecnico di Milano, a multi-year design science effort that engaged more than 50 managers annually and iterated the journey from a card-based toolkit into a four-phase model—now also supported by a GenAI co-thinker to sustain decisions between workshops. Recent partners include Eni, Leonardo, Prysmian, GS1 Italy, Angelini Industries, and Sisal (over 20 partners in the first three editions).

The “platform revolution” may no longer be an ongoing revolution—it’s the current operating system of competition. The new game is different: established firms can win by building on what they already have—relationships, data, physical assets, and know-how—using platform mechanisms to turn constraints into catalysts. Like a digital phoenix, they don’t burn everything down; they rise by recombining legacy assets into new value-creation logics. That is the opportunity in front of Europe—and any legacy company ready to play it.

About the Authors

Daniel Trabucchi and Tommaso Buganza are, respectively, Associate and Full Professor at the School of Management of Politecnico di Milano. They are featured in the Thinkers50 Radar list 2024.

Their main area of research is Platform Thinking, which focuses on how platforms can be used to foster digital business transformation. They co-founded Symplatform, the international symposium for academics and managers working on platforms, and Platform Thinking HUB, the Observatory where the innovation leaders’ community can explore innovation through platforms. They are co-authors of “Platform Thinking – READ the past. WRITE the future” and “The Digital Phoenix Effect”. You can find out more on their work on platformthinking.eu.

References

1. “Ecodynamics” (Hamidreza Hosseini and Dr. Holger Schmidt). Worldwide Top 100 Platform Companies. Ecodynamics.io, 20 December 2024.

2. Trabucchi, D., Buganza, T., The Digital Phoenix Effect: How Legacy Companies Can Lead the Platform Revolution Without Burning Everything Down, Platform Thinking Publishing, 2025.

3. Trabucchi, D., Buganza, T., “Platform Thinking: What Established Firms Can Learn from Big Tech and Digital Start-ups”, The European Business Review, 2023.

4. Rochet, J.C., Tirole, J., “Platform Competition in Two-Sided Markets”, Journal of the European Economic Association, Vol. 1, No. 4, 2003, pp. 990–1029.

5. Gawer, A., Cusumano, M.A., Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry Innovation, Harvard Business School Press, 2014.

6. Parker, G., Van Alstyne, M., “Two-Sided Network Effects: A Theory of Information Product Design”, Management Science, Vol. 51, No. 10, 2005, pp. 1494–504.

The post The Digital Phoenix Effect: How Established Firms Can Win in the Platform Era appeared first on The European Business Review.

By Dr. Simon L. Dolan, Dr. Mario Raich, and Pedro César Martínez Morán

The best thing ever to happen to humanity – or the worst? The predictions for the impact of AI range from the euphoric to the apocalyptic. Where does the truth lie? Perhaps everything depends on those driving its adoption.

The conversation surrounding AI-driven automation is one permeated with fear and uncertainty, as many visionaries have foreseen the profound disruptions it could inflict on our labor markets. Their words echo with a somber resonance, reminding us of the precarious balance between technological advancement and the well-being of countless individuals.

Albert Einstein once said, “I fear the day that technology will surpass our human interaction. The world will have a generation of idiots.” This stark warning encapsulates a growing concern — that our pursuit of efficiency will undermine the fundamental essence of human contribution in the workforce. Imagine a future where machines replace the very hands that toil, rendering people expendable and relegating their aspirations to dust.

Moreover, the late Stephen Hawking articulated a chilling vision: “The rise of powerful AI will be either the best or the worst thing ever to happen to humanity. We do not yet know which.” His words resonate deeply, trembling with the weight of existential dread. As we rush toward an era driven by algorithms and automaticity, we are left to ponder: who will remain at the heart of the labor market? Will our dreams of productivity lead us to a dark abyss of unemployment, where human touch is deemed obsolete?

Elon Musk, a fervent advocate for caution in the face of AI technology, hauntingly warned, “I think we should be very careful about artificial intelligence. If I were to guess at what our biggest existential threat is, it’s probably that… With AI, we are summoning the demon.” His metaphor serves as a reminder that with our relentless quest for innovation, we might unleash forces we cannot control. As machines increasingly supplant jobs, we risk surrendering not only our livelihoods but also our sense of purpose.

The truth is, the march of technology is relentless, and the toll it has on the workforce can feel insurmountable. As we stand on the precipice of this brave new world, we find ourselves embroiled in a moral quandary: how do we safeguard the dignity of work in an age where the machines are rising? Will we allow the fear of joblessness to transform our society into one where purpose is lost, replaced solely by automation’s cold efficiency? It is a future that beckons with both promise and peril, and it is one that demands our urgent reflection.

Pessimistic Vision: The Fear of Job Displacement

Critics argue that AI-driven automation will disrupt labor markets, rendering millions jobless. Routine tasks in manufacturing, customer service, transportation, and even white-collar sectors like law and finance are increasingly automated. A seminal Oxford study by Frey and Osborne (2013) estimated that 47 per cent of U.S. jobs are at high risk of automation. Nearly 10 years later, “jobs with a high risk of automation constitute approximately 27 per cent on average across OECD countries” (source: https://www.cesi.org/posts/oecd-27-of-jobs-at-high-risk-from-ai/).

AI’s rapid advancement, unlike previous technological shifts, may outpace workers’ ability to adapt, exacerbating inequality. As economist John Maynard Keynes warned in 1930, “technological unemployment” arises when “the discovery of means of economising the use of labour outrun[s] the pace at which we can find new uses for labour.” Critics like Elon Musk caution that AI could create a “jobless underclass,” while Stephen Hawking feared it might become “the worst event in civilization’s history” if mismanaged.

Here are some pessimistic quotes from notable figures who have expressed concerns about AI-driven automation and its potential to disrupt labor markets, leading to widespread job losses:

• Stephen Hawking (theoretical physicist): “The automation of factories has already decimated jobs in traditional manufacturing, and the rise of artificial intelligence is likely to extend this job destruction deep into the middle classes, with only the most caring, creative, or supervisory roles remaining.”
• Elon Musk (CEO of Tesla and SpaceX): “AI is a fundamental existential risk for human civilization, and I don’t think people fully appreciate that. It’s capable of vastly more than almost anyone knows, and the rate of improvement is exponential. We need to be super careful with AI. It’s potentially more dangerous than nukes.” And moreover: “There will be fewer and fewer jobs that a robot cannot do better. These are not things that I wish will happen. These are simply things that I think probably will happen.”
• Bill Gates (co-founder of Microsoft): “Software substitution, whether it’s for drivers or waiters or nurses… it’s progressing… Technology over time will reduce demand for jobs, particularly at the lower end of the skill set.”
• Andrew Yang (entrepreneur and former U.S. presidential candidate): “The truth is that automation is already here, and it’s already destroying jobs. The pace of job destruction is only going to accelerate in the coming years, and we’re not prepared for it.”
• Yuval Noah Harari (historian and author of Sapiens): “As artificial intelligence outperforms humans in more and more tasks, it will replace humans in more and more jobs. Many new professions are likely to appear—virtual-world designers, for example. But such professions will probably require more creativity and flexibility, and it is unclear whether 40-year-old unemployed taxi drivers or insurance agents will be able to reinvent themselves as virtual-world designers.”
• Martin Ford (futurist and author of Rise of the Robots): “The real threat is mass unemployment—or, at the very least, a future in which a great many people simply cannot find work because they have been displaced by machines and there are no new jobs to replace the ones that have been lost.”
• Noam Chomsky (linguist and political activist): “The development of full artificial intelligence could spell the end of the human race. It would take off on its own and redesign itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.”
• Kai-Fu Lee (AI expert and author of AI Superpowers): “AI will increasingly replace repetitive jobs, not just for blue-collar work but a lot of white-collar work. The disruption will be profound, and we need to think about how to prepare for a world where a significant portion of the population is unemployable.”

Optimistic Vision: Adaptation and New Opportunities

History, however, suggests resilience. The Industrial Revolution, despite displacing agrarian workers, birthed factories, railways, and urban services, ultimately raising living standards. Similarly, AI will eliminate some roles but spawn new industries. The World Economic Forum (2020) predicts that AI will create 97 million new jobs by 2025, including roles in AI ethics, data science, and human-machine collaboration. For instance, while self-driving trucks may reduce driving jobs, they will increase demand for logistics analysts and remote fleet managers. And, further, an IMF report found that AI will impact 40 per cent of jobs worldwide. In advanced economies, this figure rises to 60 per cent, with roughly half experiencing negative effects (Cazzaniga et al. 2024).

In a world rapidly transforming through the lens of artificial intelligence, it’s easy to feel a twinge of anxiety over the future of work.

AI’s true potential lies in augmenting human capabilities, not replacing them. Erik Brynjolfsson, an MIT economist, argues that “AI can be a tool to complement human creativity,” enabling workers to focus on complex problem-solving and innovation. Education and reskilling programs will be critical. David Autor, a labor economist, notes that “automation reshapes work, but rarely eliminates it,” emphasizing that technology historically creates more jobs than it destroys.

In a world rapidly transforming through the lens of artificial intelligence, it’s easy to feel a twinge of anxiety over the future of work. Yet, amidst these uncertainties, voices of optimism rise above the noise, reminding us of the boundless opportunities that lie ahead. Some of our greatest thinkers and leaders have expressed powerful sentiments about AI-driven automation, shedding light on a hopeful narrative that encourages us to embrace this evolution with open hearts and open minds.

Consider the words of Satya Nadella, CEO of Microsoft, who once said, “Our industry does not respect tradition—it only respects innovation. We can innovate our way into a better future.” This sentiment speaks to the heart of what AI can accomplish. It is not merely a tool to replace jobs, but a gateway to new realms of innovation. As automation takes over mundane tasks, our creative capacities are freed—from the mundane to the magnificent.

Jacques Attali, the French economist and former advisor to President François Mitterrand, propounded a hopeful vision for the future when he stated, “The future will be about silicon and minds, about man-machine cooperation.” His insight urges us to envision a world where the relationship between humans and technology is one of collaboration, not conflict. The journey ahead is not about pitting man against machine but rather weaving a fabric of teamwork that harnesses the strengths of both to tackle challenges head-on.

And as we stand on the precipice of this adventurous journey, let us hold dear the words of Sheryl Sandberg, COO of Facebook: “Leadership is about making others better as a result of your presence and making sure that impact lasts in your absence.” This philosophy of leadership is crucial in an AI-driven world, where it is our collective responsibility to mentor and uplift one another. We must ensure that the transformation does not lead to division but fosters an inclusive community where everyone can thrive.

The essence of these quotes encapsulates a future filled with promise and possibility. We must remember that automation is not an end, but a beginning—an opportunity to redefine human work and elevate one another. Let us boldly step forward, arms intertwined and hearts open, as we collectively shape the landscape of tomorrow. The power to transform our futures lies in our hands, guided by hope, innovation, and a shared vision of a prosperous world for all.

Here are some additional optimistic quotes from notable figures who have expressed positive outlook about AI-driven automation and its potential to enhance well-being in civilization and create tons of new jobs that will compensate for the routine, boring and manual work:

• Mark Zuckerberg (co-founder and CEO of Meta): “AI is going to make our lives better in the future, and it’s already improving our lives today in many ways. We’re going to have more tools to solve big problems, create new opportunities, and improve the quality of life for people around the world.”
• Satya Nadella (CEO of Microsoft): “AI is one of the most transformative technologies of our time, and it has the potential to help solve some of the world’s most pressing challenges. It’s not about man versus machine; it’s about man with machines. We need to think about how we can use AI to augment human capabilities and create new opportunities for everyone.”
• Fei-Fei Li (AI researcher and professor at Stanford): “AI is not going to replace humans; it’s going to augment humans. The future of work is not about humans versus machines; it’s about humans working alongside machines to achieve things we couldn’t do before.”
• Ginni Rometty (former CEO of IBM): “AI will not destroy jobs; it will change them. Every job will be augmented by AI, and new jobs will be created that we can’t even imagine today. The key is to focus on reskilling and upskilling the workforce to prepare for this future.”
• Andrew Ng (AI pioneer and co-founder of Coursera): “AI is the new electricity. Just as electricity transformed almost everything 100 years ago, today I have a hard time thinking of an industry that I don’t think AI will transform in the next several years. This will create new opportunities and improve productivity across the board.”
• Jensen Huang (CEO of NVIDIA): “AI will create more jobs than it displaces. It will enable entirely new industries and opportunities that we can’t even envision today. The key is to embrace the technology and invest in education and training to prepare the workforce for the future.”
• Erik Brynjolfsson (economist and Director of MIT’s Initiative on the Digital Economy): “Technology is not destiny. The future of work depends on the choices we make today. AI and automation can lead to greater productivity, higher wages, and more leisure time if we manage the transition wisely. The challenge is to ensure that the benefits are widely shared.”
• Reid Hoffman (co-founder of LinkedIn): “AI will not eliminate jobs; it will transform them. The future of work is about humans and machines collaborating to achieve more together than either could alone. The key is to focus on lifelong learning and adaptability.”
• Ray Kurzweil (futurist and inventor): “AI will augment human intelligence and create new opportunities for innovation and creativity. It’s not about replacing humans; it’s about enhancing our capabilities and enabling us to solve problems that were previously unsolvable.”

These quotes reflect a more hopeful perspective on AI-driven automation, emphasizing the potential for collaboration between humans and machines, the creation of new industries, and the importance of education and adaptability in navigating the future of work. While challenges remain, these voices argue that AI can be a force for positive change if managed thoughtfully.

Conclusion and a Strong Message to Future-Shapers

While AI will disrupt certain sectors, humanity’s capacity to adapt is enduring. By investing in education, social safety nets, and policies that promote equitable access to new opportunities, societies can harness AI’s benefits. As with the Industrial Revolution, fear of obsolescence is natural but unwarranted. As economist Joseph Schumpeter observed, technological progress involves “creative destruction”—a cycle of renewal that ultimately drives prosperity. The future need not be bleak if we proactively shape it.

As robots and intelligent machines increasingly take the reins of production and value creation, we stand on the precipice of a profound transformation—one that calls for new mechanisms of value sharing, potentially paving the way for a groundbreaking economy and business paradigm. This shift compels us to contemplate the very essence of work, which has already begun to evolve and will continue to do so. Work should not merely be a means to an end; it is fundamentally about addressing human challenges, delivering essential products and services, and ensuring a dignified quality of life for everyone who desires it.

Quality of life encompasses our deepest values and the fulfillment of our unique journeys in life. It’s about chasing dreams, nurturing our passions, and fostering connections that enrich our existence. Kate Raworth’s groundbreaking work, Doughnut Economics, published in 2017, resonates powerfully in this context. She brilliantly challenges us to rethink our economic systems by posing a vital question: How can we transform economies driven by relentless growth into ecosystems that enable all of us to thrive, irrespective of their growth rates? This is not just an economic inquiry; it’s a heartfelt call to reimagine our priorities, to build a future where the fruit of our labor nourishes our souls and uplifts our communities, ensuring that no one is left behind. Let us embrace this opportunity for change with open hearts and minds, forging a path toward a more humane and nurturing world.

Viewing only paid activities as work is a grave misconception that diminishes the true essence of what it means to contribute to society. We must embrace a broader definition that recognizes all meaningful endeavors—those that enhance life quality and create enduring values—as vital forms of work. This perspective includes the invaluable contributions made through education, nurturing minds and souls for a better future.

Today, the relentless dance of growth and greed continues to ensnare our economy in an unbreakable grip, perpetuating the illusion of endless expansion. Profit has become the singular, dominant measure of success for businesses, often at the devastating expense of the ecosystems upon which our survival depends. Meanwhile, bureaucratic red tape suffocates innovation, fueling the chaos within organizations. Misguided entrepreneurs, chasing the mirage of limitless profit, have turned their enterprises into something akin to a cancer, threatening the very fabric of our economic health.

As we strive to produce faster, cheaper, and more efficiently, we inadvertently strip away the human element—the workers who are also our consumers. Unemployment rises, leading to diminished purchasing power and an increased social burden that affects us all. The stark truth emerges when jobs vanish, so too do consumers and taxpayers. This connection is undeniable: “No work, no economy.”

The fallout isn’t just economic; it’s profoundly social. We witness a dangerous polarization within our communities, leading to fractures that threaten our cohesion and provoke unrest. It’s time for a profound shift—one that redefines our perception of the economy, business, work, and the values that govern our lives. In an age where robots and intelligent machines take over vast swathes of production and value creation, we must forge new mechanisms for sharing the wealth generated by these advancements. We need a revolutionary reimagining of our economy, one that prioritizes human dignity over blind profit and cultivates a future in which every individual can contribute meaningfully, thrive, and find purpose. The journey is challenging but, together, we can pave the way to a more equitable and harmonious world for all.

About the Authors

Dr. Simon L. Dolan is currently a professor and Senior Director of Research and Programs at Advantere School of Management (Madrid) and the President of the Global Future of Work Foundation. He was formerly the Future of Work Chair at ESADE Business School in Barcelona. He taught in many North American business schools, such as Montreal, McGill, Boston, and Colorado. He is a prolific author, with over 85 books on themes connected to managing people, culture reengineering, values, coaching, stress, and resilience enhancement. In 2024 he was awarded a doctorate honoris causa (University of Huelva) and the IFSAM Award for Excellence in Societally Relevant Management Scholarship. He has published over 170 papers in scientific journals. He is an internationally sought-after speaker. His full CV is at: www.simondolan.com.

Dr. Mario Raich is a Swiss futurist, book author, and global management consultant. He has been a senior executive in several global financial organizations and an invited professor to leading business schools, including ESADE (Barcelona). He is the co-founder and Chairman of e-Merit Academy and Managing Director of Raich Futures Studies in Zurich. In addition, he is a member of the advisory board of the Global Future of Work Foundation in Barcelona. Currently, he is researching the impact of cyber-reality and artificial intelligence on society.

Pedro César Martínez Morán is the Director of the Master in Talent Management Faculty at Advantere School of Management, affiliated with Comillas, Deusto and Georgetown Universities (www.advantere.org). He is a professor of Human Resources and researcher and scientific reviewer in Human Resources and Talent Management. Dr. Martínez Morán is an author and speaker on people management.

References

• Autor, D. H. (2015). “Why Are There Still So Many Jobs?”, Journal of Economic Perspectives. vol. 29, issue 3, 3-30

• Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Co.

• Cazzaniga, M., Jaumotte, M. F., Li, L., Melina, M. G., Panton, A. J., Pizzinelli, C., and Tavares, M. M. (2024). “Gen-AI: Artificial intelligence and the future of work”. International Monetary Fund.

• Frey, C. B., & Osborne, M. A. (2013). “The Future of Employment”. Published by the Oxford Martin Programme on Technology and Employment, University of Oxford. extension://efaidnbmnnnibpcajpcglclefindmkaj/https://oms-www.files.svdcdn.com/production/downloads/academic/future-of-employment.pdf

• Keynes, J. M. (1930). “Economic Possibilities for Our Grandchildren”, in Essays in Persuasion (New York: Harcourt Brace, 1932): 358-373 (extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.aspeninstitute.org/wp-content/uploads/files/content/upload/Intro_and_Section_I.pdf)

• Raich M., Klimek J., Dolan, S.L. Cisullo C., and Klimek S. Shaping Our World (Forthcoming Fall 2025)

• Raworth K. (2018) Doughnut Economics: The must-read book that redefines economics for a world in crisis. Random House.

• World Economic Forum. (2020). The “Future of Jobs” Report. (The Future of Jobs Report 2020 | World Economic Forum)

The post Reimagining Employment: Navigating the Promises and Perils of Artificial Intelligence appeared first on The European Business Review.

By Dr. Samer Elhajjar

Digitalization allows us to work remotely and keep up with friends from other parts of the globe. But what about its effect on our emotional stability and happiness? Samer Elhajjar discusses the emotional toll of living in a hyper-connected world and how we can reclaim meaningful connections and emotional healing.

In recent years, the term “brain rot” has gained traction as a way to describe the mental fatigue and cognitive decline associated with the overconsumption of digital content. From endless scrolling on social media to binge-watching low-quality entertainment, the impact on our minds is undeniable. But what about the heart? While we focus on the deterioration of our cognitive faculties, we often overlook the emotional toll of living in an increasingly digital, fast-paced, and disconnected world. Emotional instability, exhaustion, loneliness, stress, and depression are on the rise, and they demand just as much attention as the cognitive consequences of our modern lifestyle.

The Emotional Toll of Over-Digitalization

Despite being more “connected” than ever before, many individuals are feeling emotionally distant and fragmented. Emotional shattering refers to the deepening fragmentation of our emotional health due to excessive digital engagement. The constant exposure to screens and virtual interactions can break down our ability to form meaningful, empathetic connections with others.

Social media, meant to help us stay in touch, often amplifies loneliness, anxiety, and emotional instability. Instead of fostering genuine connection, it creates an environment of comparison and surface-level interaction. Researchers found that higher social media use led to poorer well-being, increasing loneliness and depression.

While we may think we’re connected when we scroll through endless feeds and posts, these interactions are shallow, leaving us feeling emotionally drained rather than fulfilled. Over time, this contributes to emotional shattering, a state where we feel increasingly distant from others and disconnected from our own feelings.

So why is this Emotional Toll More Pronounced Today?

The shift is not just about how often we engage with technology—it’s about how we engage with it and how it’s reshaping our emotional frameworks.

In previous generations, relationships were primarily formed and nurtured through in-person interactions. While there were other distractions and challenges, emotional connections were often grounded in physical proximity, body language, and shared meaningful moments. In contrast, today, technology has become the middleman in nearly every form of interaction. We meet people online, express feelings through texts or social media posts, and seek emotional validation through virtual platforms. But these interactions—despite their frequency—lack the deeper qualities of human connection.

Our emotions are no longer steady or grounded; they have become a rollercoaster, swinging rapidly between highs and lows, with no time to process or catch our breath.

Furthermore, the sheer volume of interactions we have online has made it harder to find the emotional depth we need. With every notification, we are pulled away from the present moment, causing emotional fragmentation. It’s not just the overload of information—it’s the emotional exhaustion that comes from trying to process and respond to so many stimuli without having the emotional bandwidth to do so. We are constantly shifting between conversations, tasks, and obligations, without taking the time to emotionally process each interaction.

What makes the emotional toll of today’s technology so striking is, in particular, the rapid shift it has caused in how we experience emotions. In a world that is always on, we find ourselves constantly bombarded by information, notifications, and interactions. This overstimulation leads to emotional instability—a kind of emotional whiplash that swings us from one extreme to another in a matter of seconds. One moment, we’re elated by a social media post or an exciting news story, and the next, we’re overcome by stress or anxiety from a negative interaction, an upsetting headline, or the overwhelming demands of our digital lives. Our emotions are no longer steady or grounded; they have become a rollercoaster, swinging rapidly between highs and lows, with no time to process or catch our breath.

Over-Digitalization and its Impact on Human Relationships

Over-digitalization of communication has replaced face-to-face interactions with brief, fragmented exchanges. Texting, emailing, and messaging have become our primary modes of communication. Yet, these digital exchanges lack the richness of non-verbal cues—such as body language, facial expressions, and tone of voice—that are crucial for emotional understanding. As a result, conversations often become transactional, shallow, and impersonal.

While the internet and social media allow us to stay in touch with people across the globe, they also breed disconnection. Researchers revealed that social media use is correlated with increased feelings of loneliness, depression, and lower self-esteem. The constant barrage of curated images and updates from others can leave us feeling inadequate and disconnected, as we compare our real lives to others’ seemingly perfect online personas.

This technological disconnection is not limited to social media alone. Digital tools meant to streamline communication—such as instant messaging and video calls—often disrupt the flow of meaningful conversations. When people spend more time on screens than engaging with the people physically around them, it leads to a sense of emotional distance. The emotional intelligence necessary for deep, empathetic relationships is harder to cultivate in an environment dominated by digital noise.

How Over-Digitalization Makes us Emotionless

While technology has made the world more connected than ever, it has also introduced a profound disconnection from our emotions. Social media platforms, designed to facilitate interaction, have instead fostered shallow, performative exchanges. Instead of feeling understood or supported, we’re left craving more—more likes, more attention, more validation from people who are often no more than virtual strangers.

The constant pressure to maintain a curated digital presence—carefully selecting moments to share, projecting an idealized version of ourselves—leads to emotional numbing. We engage less with our authentic feelings and more with the emotionally “safe” versions of ourselves presented online. This endless cycle can lead to what many experts now call “emotional depletion,” where we are left feeling hollow and disconnected despite being constantly “connected” through screens.

The “death of emotions” is particularly evident in the way people experience relationships today. Many digital interactions—whether through social media, messaging apps, or algorithmic dating apps—lack the nuance and depth of face-to-face communication. Emotions are no longer communicated with the richness of voice, body language, or physical presence. Instead, they are reduced to quick texts, emojis, and status updates—superficial markers that fail to capture the complexity of human emotion. These shallow exchanges chip away at our ability to feel deeply, making us emotionally detached and unfulfilled.

The Paradox of AI Chatbots in Personal Relationships

As digital communication becomes more impersonal, a new development has emerged: the use of AI chatbots designed to simulate human companionship. Chatbots like Replika are marketed as a solution for loneliness, offering conversations with an artificial friend who listens and responds empathetically. While these bots can be a temporary distraction, their limitations are stark. They cannot give genuine emotional understanding, empathy, or the complexity that comes with real human relationships.

Many individuals seeking emotional support from AI chatbots may feel heard, but the interaction is ultimately hollow. Research published explored the use of AI chatbots in providing emotional relief, but the findings were clear: while they may help in the short term, they lack the capacity to engage in deep emotional exchanges. AI chatbots are designed to simulate conversation, but they fall short of offering the true emotional connection that humans need.

The emotional void left by AI-driven interactions may eventually lead to deeper feelings of isolation. While these chatbots can be helpful in moments of acute loneliness, they cannot replace the nuanced and reciprocal nature of human relationships. Humans crave real, meaningful connections, and no matter how advanced AI becomes, it will never be able to replicate the warmth and authenticity of a conversation with another person.

The Transformation of Love

The impact of the digital age on love and humanity is profound and multifaceted. Technology has created barriers to genuine connection and emotional intimacy. Dating apps, for example, have commodified love, reducing relationships to a series of profiles and swipes. This “shopping mentality” encourages individuals to seek the next best option instead of investing in meaningful, long-term connections.

The paradox of choice created by these platforms can make it harder to commit, leaving many to struggle with the fear of missing out on a better partner. In this environment, relationships are often judged on superficial qualities like appearance and quick judgments, while deeper factors such as emotional compatibility and shared values take a backseat.

The digital age has also had a profound effect on our broader sense of humanity and empathy. Constant exposure to tragic news and online outrage has desensitized many individuals to human suffering, leading to emotional numbness. Social media algorithms often create echo chambers, reinforcing existing beliefs and contributing to polarization. This lack of exposure to diverse perspectives can make it harder to empathize with others, further deepening societal divides. The result is a growing sense of disconnection, as the digital world replaces genuine, face-to-face interactions that foster shared humanity. This erosion of empathy extends to our personal relationships, where we struggle to form deeper emotional bonds due to the distractions and superficiality that digital interactions often bring.

Despite these challenges, there is hope for reclaiming love, humanity, and emotional depth in the digital age. By setting boundaries around technology and prioritizing face-to-face interactions, we can restore the authenticity and depth of our connections. Reviving empathy through acts of kindness, active listening, and volunteering can help restore our shared sense of humanity. Embracing the imperfections of love and relationships, rather than striving for idealized versions, allows us to cultivate deeper, more meaningful connections. By building intentional communities—both online and offline—that prioritize emotional support and inclusivity, we can counteract the isolating effects of the digital age and create a more compassionate, connected world.

Love remains the force that defines our humanity, and it’s up to us to nurture it in the face of ongoing digital disruption.

The Stigma around Emotional Vulnerability: A Barrier to Healing

One of the most significant emotional issues many people face today is the stigma surrounding emotional vulnerability. For many, especially in certain cultures and social contexts, there is an overwhelming pressure to suppress emotions. Men, in particular, are often socialized to avoid expressing vulnerability, with phrases like “man up” or “don’t cry” still prevalent in many communities. This expectation leads to the suppression of emotions, which ultimately results in emotional stagnation. The fear of judgment or appearing “weak” prevents people from opening up about their feelings, leading to isolation and an inability to process emotions in healthy ways.

We must normalize conversations about emotional vulnerability and create safe spaces where people feel free to express their feelings without fear of judgment.

The digital world has further exacerbated this stigma. On social media, where people tend to showcase the best parts of their lives, there’s little space for vulnerability. Everyone presents an image of success, happiness, and perfection, but this curated version of reality hides the emotional struggles that many people experience. Those who express feelings of sadness, anxiety, or loneliness often face judgment or lack of support, further discouraging them from speaking out. In this environment, many people, particularly men, feel isolated in their emotional struggles, unable to reach out for help because of fear of being stigmatized.

But this needs to change. We must normalize conversations about emotional vulnerability and create safe spaces where people feel free to express their feelings without fear of judgment. It’s essential to dismantle the cultural and societal pressures that prevent emotional expression and embrace the idea that being open about one’s struggles is not a sign of weakness but a sign of strength. Encouraging people to speak out about their emotions—positive or negative—can foster a sense of connection and support essential for emotional well-being.

Reconnecting with Emotional Well-being in the Digital Age

To address the emotional health issues exacerbated by over-digitalization and AI chatbots, we must take a multifaceted approach. Practical steps are available to individuals to safeguard their emotional well-being in an increasingly digital world.

1. Mindfulness and Emotional Awareness

One of the most effective ways to counter emotional exhaustion is to cultivate mindfulness. Practices like meditation, journaling, and deep breathing exercises help individuals reconnect with themselves and manage stress. By becoming more aware of their emotional state, people recognize when they are becoming overwhelmed or disconnected and take action before burnout sets in.

For example, several apps provide guided meditations and techniques to help individuals manage their emotional health, but the key is for users to incorporate these practices into their daily routines—ensuring they prioritize emotional self-care amid their busy lives.

2. Digital Detox

Taking time away from screens is critical for emotional restoration. Digital detoxes, whether brief or extended, allow individuals to disconnect from the constant demands of technology and reconnect with their surroundings, their thoughts, and, most importantly, other people. Studies show that reducing screen time, especially social media use, can significantly improve emotional well-being, reduce feelings of loneliness, and promote a healthier work-life balance.

For instance, some people set specific boundaries around their use of devices—such as no social media on weekends or limiting work emails to certain hours—to engage more meaningfully with the world around them. These boundaries help people prioritize in-person interactions and focus on deepening relationships rather than merely skimming the surface.

3. Fostering Real Connections

In an era where digital communication often takes precedence, it is important to prioritize face-to-face interactions. Studies show that physical touch, eye contact, and active listening—all elements of in-person communication—are essential for emotional bonding. Humans need genuine, reciprocal relationships to thrive emotionally.

Whether through regular social meetups, community activities, or volunteer work, creating opportunities for deep, real-world interactions can strengthen emotional health. These relationships offer a sense of belonging and connection that digital tools, no matter how advanced, cannot replicate.

4. Leveraging Technology for Emotional Support—With Caution

While AI chatbots may not replace human connection, technology can still play a positive role in supporting emotional health when used thoughtfully. Virtual therapy services, support groups, and mental health apps can offer helpful resources and guidance. However, these tools should be seen as supplements to, not substitutes for, real human relationships.

For example, several platforms allow individuals to seek counseling and emotional support remotely. These services provide an outlet for people who may not have access to traditional face-to-face therapy. However, they should be used alongside efforts to foster real-world emotional connections.

A Call to Reclaim our Emotional Health

The emotional toll of living in a hyper-connected world is a crisis we can no longer ignore. As we navigate this new digital era, it’s clear that technology, while offering immense benefits, also comes with hidden emotional costs. The disconnection, loneliness, stress, and emotional exhaustion many of us experience are real consequences of living in a world that demands constant digital engagement.

One of the biggest limits of AI agents and digital tools is their inability to truly form emotional connections. They might imitate empathy with pre-set responses or sentiment analysis, but they don’t actually feel, understand, or relate. Their “compassion” is programmed, not genuine.

However, the deeper concern is not about what AI cannot do—it’s about what we might slowly lose as humans. If we rely more and more on emotionless systems for companionship, comfort, or communication, we risk weakening our own emotional intelligence. Our capacity to understand others, listen with presence, and handle complex relationships isn’t automatic; it requires ongoing effort with other humans.

The core we stand to lose is the very essence of our humanity. When empathy is handed off and vulnerability is suppressed in favor of quick, digital exchanges, we start to undervalue the emotional effort that keeps societies alive—care, trust, forgiveness, belonging. These qualities cannot be copied by any computer program. If we get too used to artificial interactions, we risk becoming emotionally numb ourselves, unable to build or maintain meaningful human connections.

However, the good news is that we have the power to change this. By setting boundaries, prioritizing real connections, and intentionally caring for our emotional well-being, we can begin to reverse the emotional toll of over-digitalization. Now more than ever, it is essential to protect our hearts from the overwhelming demands of technology, and in doing so, we can reclaim our emotional strength and resilience.

About the Author

Dr. Samer Elhajjar is a Senior Lecturer in Marketing at the National University of Singapore (NUS), where he blends academic rigor with industry expertise. Holding a Doctorate in Marketing, he is an active researcher, consultant, and speaker, with work featured in international journals, conferences, and global media outlets.

The post The Emotional Toll of Over-Digitalization: Preventing “Emotional Shattering” in the Digital Age appeared first on The European Business Review.

By Dr. Anna Rostomyan and Dr. Monika Klein

The metaverse is a virtual reality universe that allows individuals to meet, socialize, work, play, entertain, communicate and even create. The term “metaverse” is a combination of “meta,” meaning transcendence and virtuality, as well as meaning “above,” “over,” “on top,” and “universe.” It is a three-dimensional virtual space inhabited by avatars of real people, where they can engage in various interactions starting from the business sector and even encompassing the education sector.

Introduction

The metaverse has vast possibilities in creating a virtual space where individuals can cooperate and create without seeing each other in real time and interacting with one another with their chosen avatars and holograms. In that universe, we have customized avatars; we can buy property, visit various places, meet people, and spend money (e.g., cryptocurrencies).

As Dwivedi et al. (2022) state, virtual environments and immersive games (such as Second Life, Fortnite, Roblox, and VRChat) have been described as antecedents of the metaverse and offer some insight into the potential socioeconomic impact of a fully functional persistent cross-platform metaverse. The authors classify the definitions of the metaverse into four types—environment, interface, interaction, and social value. Summarizing each characteristic of the metaverse in a similar way to the real world provides a representative example of classifications that distinguish the types of metaverse. There is a realistic environment that faithfully reflects realistic constraints, and an unrealistic environment that gives many degrees of freedom without realistic constraints.

Apart from its vast advantages of creating a space where individuals can create and cooperate as in the real world, the metaverse brings with it challenges, too.

According to Kim & Kim (2023), as individuals became physically isolated from each other as a result of COVID-19, adolescents’ desire for social relationships boosted the online world, including the metaverse. Thus, metaverse services have now assumed an important place in the lives of young people as they affect online classes conducted at schools. Of course, this is a very beneficial tool that can keep students both interested and motivated, in addition to its use for educational purposes. The metaverse can also greatly harm children’s mental health, such as exacerbating depression, exhaustion, loneliness, worsened sleep, anxiety, addiction, self-harm, anorexia or bulimia. Cyberbullying, sexual misbehavior, exploitation of minors online, online gambling, privacy, and security can also become serious social issues among teenagers, as well as other age groups.

All this suggests that, apart from its vast advantages of creating a space where individuals can create and cooperate as in the real world, the metaverse brings with it challenges, too. Thus, the present paper elucidates both its advantages and benefits, as well as its shortcomings and pitfalls, which can be challenging to overcome, in spite of the cute avatars that one can choose when entering it in order to interact with others.

The Metaverse: A Friend or A Foe for Humanity

The launch of Horizon Worlds in 2021 by Meta Platforms and the vision of how the metaverse could potentially shape many aspects of how we work and socialize has engendered an increasing level of questioning and debate from academics and practitioners on the numerous societal implications for many people worldwide (Fernandez & Hui, 2022).

The new metaverse concept as outlined by Mark Zuckerberg describes an integrated immersive ecosystem where the barriers between the virtual and real worlds are seamless to users, allowing the use of avatars and holograms to work, interact, and socialize via simulated shared experiences (“Meta 2022”, seen in Dwivedi et al., 2022).

As stated above, the metaverse has great opportunities that people can benefit hugely from in a great range of disciplines, from education to business, medical, creative industries, and other fields. Yet it is of the utmost importance to note that it has both advantages and shortcomings, which we discuss below.

Positive features: In psychology and psychiatry, the metaverse can create opportunities for individuals to consult mental health professionals uniquely and potentially more comfortably by using avatars (Usmani et al., 2022). This can really be beneficial, since by means of using avatars one can exclude the fear of judgment and this can particularly benefit individuals with high social anxiety and interpersonal trauma levels or even post-traumatic stress disorder (PTSD). In an experimental study by Slater et al. (2019), participants enacted internal dialogue in VR by alternating between two different virtual bodies, one representing themselves and the other representing Sigmund Freud. This method is considered to be possible for example in self-counseling. As we have already stated, it can be really very beneficial for those humans who have social anxiety because of a formerly experienced trauma and / or for those who are are isolated from society for this or that reason (be it during epidemics or pandemics, a childhood trauma, an adolescence trauma, or some other related issue). It can also be beneficial in virtual group therapy, where the people involved will not see each other’s personalities and will only interact with their avatars, which again protects from the fear of judgment. Also, the metaverse can be a place where people are free, can freely create, and can even fly, which can really enhance their innovative skills and provide the stimulus of an experience of something that goes beyond human biological “self,” and even beyond Mother Earth.

Negative features: Since it is mostly teenagers who engage with the metaverse, which is gaining more and more popularity within their circles, we have to look at that aspect. Actually, adolescence is a developmental period during which brain regions undergo significant changes because of biological and environmental factors (Larsen & Luna, 2018). Hence, during this period, the cognitive control system matures progressively. Specifically, the prefrontal cortex, responsible for the regulation of emotions and especially decision-making, is still in its development process (Damasio, 1998). Thus, adolescents’ ability to self-regulate their behaviors, desires, and emotions is still immature (Casey & Caudle, 2013). Moreover, previous studies have indicated that the intensive use of digital media can induce attentional problems (Swing et al., 2010), reduce working memory capacity because of an increased tendency to conduct multitasking, and lower the level and efficiency of comprehension of text written on screens, as compared to paper (Kim & Kim, 2024). Furthermore, we could conclude that their performance levels at school and other higher educational institutions might be lower if they have less self-control and are more driven by the thrill of the experience of avatars in the metaverse. Also, especially with teenagers, an overdose of the metaverse can result in cyberbullying and cybercrime, where there are no rules and they can do what they want. As for the older generation, both in elders and in youngsters, it can result in an antisocial behavior when people mostly interact with one another in the metaverse and unlearn the social skills of humans, the so-called “soft” skills of self-awareness, self-management, social awareness, relationship management, and empathy (Goleman, 1995), which make us humans “humane” and make inter-human cooperation plausible. Moreover, in some cases users might lose their recognition of the external, real world and may even acquire a distorted self-image, identifying themselves with their chosen avatars. Furthermore, in this connection it is also vital to state that it can become an addiction and separate humans from the actual outward external reality of the real world, to the extent that they will no longer be aware of their limited abilities in the real world and think that their metaverse abilities, such as flying, are real in the external reality.

As we have seen, there are more drawbacks than benefits, but still, if we are fully aware of both the advantages and the disadvantages, we can eventually benefit from its possibilities. Especially in the medical field, the metaverse has significant potential to transform the healthcare industry, particularly in the therapeutic fields, making patient-therapist interactions safe, protected, and enjoyable.

To amplify its benefits and reduce its pitfalls, some of the following strategies may be deployed:

1. Before they enter, users should be informed by the platform of the challenges that they might face while using it.
2. Scientists should work on investigating the ways in which the metaverse impacts cognitive abilities, and how to tackle them.
3. Parents and carers should take care that their children do not unlearn their social skills and that they make friendships both in the metaverse and in the real world, for example by limiting the time spent in the metaverse.
4. There should be strategies developed and deployed by psychologists and psychotherapists on how to prevent excessive use of the metaverse and the resultant addiction, which is more difficult to deal with.
5. Educators should help students differentiate between real-world and virtual interactions, organizing some in-class activities, too, so that the students can engage in real-time conversations and interactions that have the potential to raise oxytocin levels, which is one of the “happy” hormones ensuring our emotional and psychological well-being.
6. Platform-provided safety features should be available to restrict unwanted interactions and infringements upon their personal space. It is also essential that young people understand and take advantage of the safety features available within metaverse experiences, including blocking, muting, and reporting functionalities.
7. Consideration should be given by content creators to the ethical implications of their metaverse creations, ensuring that they promote inclusivity, diversity, and respect, and discourage any form of harassment. They should thus strive to make their virtual experiences accessible to users from diverse educational backgrounds, different languages, various cultures, and abilities for all.
8. Strict boundaries should be kept between virtual and real lives, so that users are not carried away by their imagination and social isolation.
9. Older users should be provided with guidance and assistance in finding their way around the metaverse when they encounter it, since they may find acquiring IT, AI, and VR skills more difficult compared with agile and proficient youngsters.

All the foregoing suggests that if we are aware of the risks, biases, potential, and threats of the metaverse and follow some strict guidelines while using it, we can make the best of our interactions in this thrilling space called “the metaverse”.

Conclusion

To conclude with, the metaverse has already begun to be applied in various fields, creating space and increasing accessibility for new experiences and adventures, providing educational resources, and ensuring virtual social interactions, where especially those with a high level of social anxiety can interact with others more easily. Thus, especially in the medical field, the metaverse has significant potential to transform the healthcare industry, particularly in the therapeutic fields, where patients can talk to the avatar of the doctor without fear of judgment. Yet, it brings with it also a number of negative consequences as we have seen in our earlier discussions, such as cybercrime, cyberbullying, and social isolation, as well as addiction. This said, each medal has its flip-side, as they say, and, like everything else, the metaverse, too, has its benefits and pitfalls, its advantages and shortcomings, its assets and drawbacks. However,  if we tackle the challenges efficiently and make the most of the vast possibilities that it brings with it, we can really benefit from this great advancement in science, making sure that our interactions with other humans can take place both in the metaverse and in the real world.

About the Authors

Dr. Anna Rostomyan an assistant professor and certified EI coach, specializes in linguistic-cognitive analysis of emotions and their impacts on life and business. With seven books, over 50 publications, and readers across 100 nationalities, her research highlights the role of emotional intelligence in achieving better business outcomes.

Dr. Monika Klein an award-winning movie producer and design expert, specializes in creative-sector economics, regional development, and business models. With over 80 works, she excels in design thinking, service design, and user-focused solutions. Renowned for leading teams to success, she inspires impactful projects across diverse creative and social spheres.

References

1. Casey, B. & Caudle, K. (2013). “The teenage brain: self control”. Curr Dir Psychol Sci. 2013;22:82–87. doi: 10.1177/0963721413480170.

2. Damasio, A. (1998). “The somatic marker hypothesis and the possible functions of the prefrontal cortex”. In A. C. Roberts, T. W. Robbins, & L. Weiskrantz (Eds.), The prefrontal cortex: Executive and cognitive functions (pp. 36–50). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198524410.003.0004

3. Dwivedi, Y.K. et al. (2022). “Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy”, International Journal of Information Management, Volume 66, 2022, 102542, ISSN 0268-4012, DOI: https://doi.org/10.1016/j.ijinfomgt.2022.102542

4. Fernandez, C.B. & Hui, P. (2022). “Life, the Metaverse and Everything: An Overview of Privacy, Ethics, and Governance in Metaverse”. arXiv preprint arXiv:.01480

5. Goleman, D. (1995). Emotional Intelligence. New York: Bantam Books.

6. Govindankutty, S., Gopalan, S.P. (2024). “The Metaverse and Mental Well-Being: Potential Benefits and Challenges in the Current Era”. In: Chowdhary, C.L. (eds.) The Metaverse for the Healthcare Industry. Springer, Cham. https://doi.org/10.1007/978-3-031-60073-9_7

7. Kim, S. & Kim, E. (2023). “Emergence of the Metaverse and Psychiatric Concerns in Children and Adolescents”. Journal of Korean Academy of Child and Adolescent Psychiatry, 2023 Oct 1;34(4):215–21. doi: 10.5765/jkacap.230047

8. Larsen, B. & Luna, B. (2018). “Adolescence as a neurobiological critical period for the development of higher-order cognition”. Neurosci Biobehav Rev. 2018;94:179–95. doi: 10.1016/j.neubiorev.2018.09.005.

9. Slater, M., Neyret, S., Johnston, T., Iruretagoyena, G., Crespo, MÁC, Alabèrnia-Segura, M. et al. (2019). “An experimental study of a virtual reality counselling paradigm using embodied self-dialogue”. Sci Rep. 2019;9:10903. doi: 10.1038/s41598-019-46877-3.

10. Swing, E.L, Gentile, D.A., Anderson, C.A. & Walsh, D.A. (2010). “Television and video game exposure and the development of attention problems”. 2010;126:214–21. doi: 10.1542/peds.2009-1508.

11. Usmani, S.S., Sharath, M., & M. Mehendale (2022). Future of mental health in the metaverse. Gen Psychiatr. 2022;35:e100825. doi: 10.1136/gpsych-2022-100825.

12. Zhang, X., Chen, Y., Hu, L., & Wang, Y. (2022). The metaverse in education: Definition, framework, features, potential applications, challenges, and future research topics. Frontiers in Psychology, 13, 1016300. https://doi.org/10.3389/fpsyg.2022.1016300

The post The Metaverse as a Universe: Its Benefits and Pitfalls, Psychological Advantages and Challenges appeared first on The European Business Review.

By Claire Snook

In 2025, AI-powered search is poised to become the foundation of how brands connect with their customers. Advances like ChatGPT, SearchGPT, as well as the strategic pivot from traditional search engines toward entity association, are rewriting the rules of brand visibility.   

You’re chatting with an AI assistant, asking it to recommend the best eco-friendly brands, and within seconds, it presents a tailored list of options. No endless scrolling, no guesswork – just immediate answers. This is our reality, and its ubiquity means that AI-powered search is completely reshaping how customers discover brands. 

Tools like ChatGPT, SearchGPT, and Google’s approach to entity association has revolutionised search, transforming it from a tool into an intelligent matchmaker. For brands now, the stakes couldn’t be higher. Visibility in this new AI-driven ecosystem isn’t about simply showing up – it’s about being chosen. The question is, how can brands ensure they’re on the list when AI makes its recommendations? 

This isn’t just about making search smarter; it’s about redefining how brands build authority and reputation and establish visibility when intelligent algorithms now drive consumer discovery. 

The shift away from traditional search 

Increasingly, users are bypassing traditional search engines to ask conversational AI tools for direct, personalised answers. Gartner predicts that by 2026, traditional search engine volumes will drop by 25%, with AI-powered systems like chatbots and virtual assistants taking centre stage. 

Yet, this doesn’t spell the end for all search engines. Bing, for example, is redefining itself through its deep integration with OpenAI technology, making itself a cornerstone of the AI-driven search revolution. Bing employs tools like IndexNow to signal real-time content updates, ensuring search results are always current and relevant. For brands, this shift highlights the need to adapt content strategies to align with Bing’s AI-first approach, ensuring they remain visible in GPT ecosystems. Ignoring this evolution risks leaving brands invisible to the growing number of users relying on these advanced systems. 

Generative Engine Optimisation (GEO) a new frontier 

Enter Generative Engine Optimisation (GEO), the strategy brands must master to thrive in an AI-first world. GEO isn’t about gaming algorithms to secure a spot in search rankings; it’s about shaping how generative AI systems synthesise and present information. 

Unlike traditional SEO, GEO focuses on the unique way AI interprets content. Generative systems don’t just catalogue pages; they weave together insights from multiple sources to provide clear, authoritative answers. For brands, this means creating content that not only meets traditional SEO standards but also aligns with how AI tools synthesise data. 

The key to GEO is having a deep understanding of user intent, thinking beyond keywords and backlinks and focusing instead on crafting narratives that align with user questions, values, and expectations. 

Understanding user intent in the age of AI 

User intent has always been central to search strategies, but AI takes it to a deeper level. The keywords relied on by traditional search engines often result in a scattershot of links that leave users piecing together their own answers. AI search, by contrast, interprets full, natural-language queries to deliver precise, context-rich responses. 

Imagine asking an AI assistant about sustainable travel options. Instead of simply listing websites, the AI might synthesise details about eco-conscious airlines, green hotels, and tips for reducing carbon footprints, all tailored to your preferences. For brands, this demands a shift. Content must not only answer questions but anticipate the nuances behind them. 

This requires a focus on clarity, relevance, and delivering content that adds real value to the conversation. When your content becomes the trusted source that AI platforms turn to, your brand naturally earns visibility and authority. 

Building a unified strategy for AI search success 

The days of siloed strategies for SEO, social media, and paid media are over. Success now hinges on crafting a cohesive digital presence that resonates across all channels. 

AI search systems evaluate not just individual pieces of content but the broader context of a brand’s digital footprint. From blog posts and product descriptions to social media activity and external links, every signal contributes to how your brand is represented in AI-generated results. 

For example, a brand focusing on sustainability must demonstrate this value across platforms: blog content that highlights green initiatives, collaborations with eco-conscious influencers on social media, and product descriptions that detail ethical sourcing. Brands should focus on creating a consistent and authentic narrative that AI systems can easily interpret. 

Technical excellence and continuous adaptation 

Behind every great AI strategy is a technically sound foundation. Generative AI systems, like traditional search engines, rely on structured and accessible content to deliver accurate results. This means optimising websites for mobile-friendliness, load speed, and natural language processing (NLP), ensuring that AI tools (and customers) can process your content without friction. 

Equally important is the need for continuous learning and adaptation. The algorithms powering AI systems are constantly evolving, and brands must stay ahead of these changes. Whether it’s analysing citation patterns in AI-generated responses or exploring new tools that align with generative search behaviours, brands must stay informed. 

Practical steps for brands 

1. Prioritise high-quality, relevant content aligned with E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) principles. Use keyword and semantic research to address traditional and conversational search queries. 
2. Ensure websites meet the technical demands of SEO and GEO. This includes fast load speeds, mobile responsiveness, structured data, and natural language processing readiness. 
3. Tailor content strategies for Bing and GPT-powered ecosystems. 
4. Monitor how AI algorithms evolve and adjust content accordingly. 
5. Actively participate in community platforms and social discussions to enhance brand visibility and authority in AI search.

About the Author

Claire Snook is a digital comms manager at strategic communications agency, AMBITIOUS. With more than 20 years’ experience under her belt in media and PR, Claire brings together digital insights and traditional PR skills. She has worked with top global brands to local start-ups, and everything in between, for agencies and in-house.  

The post AI Search Revolutionises Brand Discovery – Here’s How to Get Noticed appeared first on The European Business Review.

The current pace of change driven by digital innovation can feel overwhelming. With rapid advancements in generative AI and the sheer volume of new tools, platforms, and techniques on offer, many business leaders feel they’re constantly playing catch-up to simply keep pace with their competitors, never mind pulling ahead of them. The market is loud and overflowing with choices, and simply abstaining from engaging with it, is not an option for any organisation. According to a recent report from KMPG which surveyed more than 500 top executives from major global firms, as many as 98% of Global Business Services have either already deployed or are in the process of launching Generative AI tools within the next twelve months.

The key to maintaining this competitive edge is to build a culture of continuous learning within yourself, and within your organisation, and to combine this with a dedication to exploring transformation, innovation and experimentation.

Providing some small comfort, the same report also shares that 79% of these firms also shared that they lack the tech-savvy skills in-house to provide effective digital services. Such figures, and the reality that the pace of technological change is only going to increase as AI and data capabilities grow means that whether you’re a business leader seeking to ensure your company’s survival, or a professional looking to take the next step up the ladder developing a greater acumen in digital application and strategy is vital for sustained success. It also means that, for those that can get it right, there is a significant advantage to be gained.

However, the secret isn’t mastering every new technology that comes along, it’s about knowing just a little bit more than your competitors and leveraging that knowledge to drive competitive advantage.

The key to maintaining this competitive edge is to build a culture of continuous learning within yourself, and within your organisation, and to combine this with a dedication to exploring transformation, innovation and experimentation.

Why Building A Learning Culture Matters

No matter the sector or the focus, organisations that can encourage continuous learning amongst their staff see multiple benefits. Agility is one such strength – creating teams that can identify when industry trends change and quickly pivot to meet them, and skilled employees with a mindset to explore and take advantage of new tools and techniques early-on, rather than shying away from them.

This, in turn increases the potential for innovation, as a structured learning culture brings with it new ideas developed from fresh knowledge, insights and the opportunity to collaborate with colleagues.

Another benefit is a higher level of employee retention. Staff value professional development opportunities. As a result, not only are they more skilled and fulfilled, they are more likely to stay with an organisation that invests in their growth.

Creating a culture of learning starts with leadership. If senior leaders and executives champion the importance of continuous education, the rest of the team will follow. Leaders should engage in the practices they want to see from their staff, such as keeping up their qualifications and sector knowledge, attending webinars or taking courses. They should also provide staff with access to similar resources, and allow them the time to explore these, encouraging experimentation without worry of reprimand or failure.

However, building such a culture in already established skills and disciplines is altogether a more straightforward prospect that embarking on embedding something new, unexplored and untested, as much digital technology is. The key here is found in the attitudes and actions of leadership – being willing to step into the unknown and break new ground.

To do this, organisations and individuals can find value in reaching beyond their industry and their existing networks to benefit from the knowledge of other organisations and institutions. Short Executive Education courses offer the opportunity to both upskill in a new discipline and gain a network that can help further embed a culture of learning for the future.

At Imperial, the Digital Transformation Strategy programme offers exactly this – a short-term, expert-led deep dive into the most prominent, impactful digital transitions impacting industry.

Gaining A Competitive Advantage Through Executive Education

With solid foundations in both business and technological innovation, Imperial offers learners the advantages of a scientifically solid curriculum that is designed to be instantly applicable to the real needs of industry. This gives them the opportunity to build their knowledge as well a means of actioning it in their professional lives.

Taught over five days, the Digital Transformation Strategy programme provides mid-to-senior level leaders with the skills to navigate through the noise of digital innovation. Participants begin by exploring the current technological landscape, understanding its scope and the implications this has on business practice.

With solid foundations in both business and technological innovation, Imperial offers learners the advantages of a scientifically solid curriculum that is designed to be instantly applicable to the real needs of industry.

From here they refine their focus over the following days, identifying the tools, knowledge and techniques to best address their own and their organisation’s needs. To help ensure that learners can be successful in their digital transformation efforts, studies take on a personal approach, auditing strengths and weaknesses and building a robust plan to work from. Learners can build a robust digital transformation strategy for their own organisations with guidance from industry experts.

With academic exploration and a strong foundation in analytics, supported by best practice shared by organisations currently succeeding in this field, the programme is practical at its heart, but instils enough broader knowledge to allow participants to continue learning after the programme’s conclusion.

Crucially, learners also gain an insight into what causes organisations to fail at digital, enabling them to identify potential pitfalls early on in their own work. Here a grounding in data comprehension and analysis can allow organisations to effectively measure the success of their strategies and the ROI they offer, helping to tie digital transformation to financial success and identify further opportunities for development and growth. Because of this, data science also forms a core part of the curriculum.

Who Benefits?

The programme offers mid- to senior-level leaders more than just a world-class education—it provides a powerful network of peers facing similar challenges, and the critical skills many organisations are missing. Learning doesn’t stop at the classroom; faculty and advisors remain accessible for ongoing guidance, while participants also benefit from the broader expertise and resources of Imperial.

In the workplace, the knowledge gained can empower leaders to curate more effective learning resources for their teams—whether through formal training programmes or accessible options like podcasts, YouTube channels, and industry white papers. Leaders can also create tailored initiatives such as internal workshops with industry experts, peer-to-peer learning sessions where team members share insights, or mentorship schemes that connect junior staff with experienced professionals to promote cross-level learning. Learning shouldn’t be a solitary pursuit; by fostering a collaborative learning culture, organisations can ensure that knowledge is not only acquired but actively shared and retained across teams.

It’s not enough to encourage learning – like your digital strategy, you also need to measure its impact. Digital literacy can assist here in demonstrating the return on investment for learning initiatives, tracking how often team members are experimenting with new strategies, tools, or channels, or employee retention rates and satisfaction surveys to see how learning impacts your workplace culture.

Leveraging Learning for Long-Term Competitive Advantage 

Digital transformation is about more that digitisation. Research carried out as part of Imperial Digital Transformation Strategy Programme, looking at over 300 real-world transformations, found that there are 14 areas that organisations need to address digital transformation successfully, and technology is just open of them.

But, true digital transformation can drive cultural change. By combining our approach to education with a structured approach to innovation and experimentation, we can go beyond just trying to stay up to date and create real competitive advantage.

To find out more about the 5 day ‘Digital Transformation Strategy’ programme visit: https://www.imperial.ac.uk/business-school/executive-education/marketing-innovation-strategy/digital-transformation-strategy-programme/

About the Author

Daniel Rowles is an expert in digital transformation strategy, and Programme Director of the Imperial Executive Education ‘Digital Transformation Strategy’ programme.

Outside of Academia, Daniel is the CEO of Target Internet, an online hub for digital marketing career advice and education.

The post How Creating a Learning Culture Can Help Your Company Master Digital Transformation and Gain a Competitive Edge appeared first on The European Business Review.

By Frank Cespedes and Georg Krentzel

Companies need digital technologies in an omni-channel buying world where online and in-person interactions are complements, not either / or substitutes. Multi-channel hybrid sales solutions are required, but what are the key requirements for using the available technology for competitive advantage?

What options does digital technology offer us in order to gain competitive advantage in hybrid sales channels? Based on work with companies, we see lessons from dealing with a humble but pervasive retail outlet: convenience stores and small eating / drinking venues. In the United States, 8 out of 10 people visit weekly one or more of 150,000 convenience stores, 65 per cent of which are independent and not part of a chain such as 7-Eleven or Circle K. The U.S. has one such outlet for every 2,200 people, and the channel is even more important in other countries. In Mexico, there are 800,000 outlets (one for every 160 people), and in Europe the ratios are similar (e.g., Italy has about 350,000 small restaurants, bars, and hotels).

This is a fragmented and high-cost-to-serve channel, but for CPG (consumer packaged goods) companies, these outlets generate up to 50 per cent of their revenue in many countries. Sales productivity is key here and three capabilities are crucial:

Design capability. Begin by answering a core question: What is the role of channel partners in your strategy and the customer buying journey? Is it primarily about cost efficiency: a channel partner can perform important tasks less expensively than your firm can? Or does that partner provide access to certain segments? Or is the partner a necessary part of the solution that customers buy because it provides important complements (e.g., other products or a value-added feature)? These roles have different implications for required interactions and relevant tools.

For instance, about 25 per cent of U.S. apparel shoppers visit Amazon early in their buying journey, but most buy elsewhere. This suggests that online channels are important for search and discovery, but less so for product evaluations and purchase. Moreover, omni-channel customers in this category spend a third more annually than offline-only shoppers. A hybrid solution here should reflect the interactions between online and brick-and-mortar channels for these high-value customers.

The relevant digital investments should also reflect your go-to-market approach. U.S. convenience stores stock up to 3,000 items in as many as 400 categories. “BevCo” delivers direct. Its investment in a proprietary digital sales platform enables retail customers to order at their convenience, receive product information, access their last order and payment details, and track delivery to plan the arrival of goods. The platform allows BevCo to recommend products and stocking levels based on store-specific analyses of buying patterns, complementary products, and the margins to BevCo and the retailer. These recommendations are now constantly refined via AI tools that help BevCo and retail partners target promotions to consumers. The result has been better consumer retention plus increases in average ticket value and purchase frequency.

In contrast, “CPG Co” sells through distributors and its smaller retail customers use distributor platforms for orders. So, CPG Co uses digital tools to promote its products on distributor platforms and provide sales and loyalty rewards. Moreover, because the distributors are not exclusive to CPG Co, its sales force must also build relationships with retail accounts. As in many firms, their contact was determined by a routing system of physical visits. But tools for access to real-time data and data sharing with distributors now allow the sales force to better target business-development activities and, via digital communications, increase interactions and touchpoints without a corresponding increase in physical visits. This has brought more agility to the CPG Co distributor-retailer relationship while improving service and cost efficiencies.

Data capability. Hybrid sales solutions are highly dependent on using timely data for relevant business insights. But two issues often impede this capability.

One is siloed systems. For example, BevCo’s legacy systems included online ordering, sales force automation, dispatching and inventory, flanked by Excel files for prices, promotions, stock transfers, and order management — none of which were linked to each other or to the ERP (enterprise resource planning) system. Further, the initiation of a digital transformation project generated a wish list that added even more complexity. The result was costly delays, organizational cynicism about an already time- and resource-consuming process, and loss of market position.

CPG Co avoided this trap by opting for different work streams, such as sales force productivity, distributor connection, and supply chain management, led by the business areas, with a cross-functional team of digital specialists to connect the different solutions. This approach helped to prioritize relevant tools and avoid diffuse investments. For data sharing with distributors, the responsible team first defined the expected output, such as granular understanding of outlet sales to develop fact-based business plans and prioritize sales force activities with distributors. The desired business result shaped selection of the digital tools – i.e., those which facilitated tracking of segment-specific business development activities and calculating the ROI of different trade marketing activities.

Clarifying the outputs also shaped the data sources and type of data manipulations required. More than 40 internal processes were re-engineered in this way and the approach also helped CPG Co to redefine job positions and descriptions. Equally importantly, the approach clarified how the different systems should interact, such as the flow of data between the SFA (sales force automation), CRM (customer relationship management), and sales force routing analytics. This coordination helped to provide a single version of market truth that aided relations between CPG Co sales teams, distributors, and retail outlets.

A second issue is that, even with relevant systems, data has been hard to find in addressing omni-channel buying behavior. But companies providing this data are emerging, such as Roamler or SharpGrid which use big data techniques, and Asseco, UVE, or Mobilvendor in Latin America which link information between outlets, distributors, and manufacturers. These links help in managing the ecosystem required to deal with omni-channel buying and help companies to understand and manage inherent limitations with market data.

CPG Co developed a customer targeting system with a third-party database on food service outlets that provided their turnover, demographic data about each outlet’s area, and information about its opening hours, menu, prices, products and brands. But CPG Co found that only a minority could be converted into customers. Many outlets turned out not to be high potential after review by the sales force, and others already had exclusive agreements with other manufacturers. But the system still provided benefits. It minimized sales time on false-positive targets and so increased time on other opportunities. Better-quality leads improved business development efforts, enabling distributors to fine-tune CPG Co’s offering by outlet type and, in turn, instigated a continuous improvement cycle in manufacturer-distributor–outlet relations.

Better-quality leads improved business development efforts, enabling distributors to fine-tune CPG Co’s offering by outlet type and, in turn, instigated a continuous improvement cycle in manufacturer-distributor-outlet relations.

This final benefit is important. An often-overlooked data capability is making it easier for channel partners to work with your sales teams. If you sell thru broker channels, for instance, low friction and easy communication are often as important as commission dollars in getting brokers’ attention and commitment to your products. Too often, however, sellers ship products to a channel partner but the material and information needed for effective selling are not provided. Here, digital technology is an enabler. The means for establishing and maintaining partner sites that provide content, online demos, deal registration data, and other relevant sales tools are decreasing in cost and increasing in scope. These tools enable partners to leverage your branding, messaging, and demand-generation knowledge in their selling efforts for your products. The rewards of getting this right are significant. As one executive notes, “Nobody does cartwheels for software. The payoff comes in the form of collaboration. We had no way to share ideas and track this across our offices and multiple partners. Now we can. It’s meant a 30 per cent increase in call volume and productive leveraging of the tribal knowledge in this ecosystem.”

Change capability. Many channel arrangements lag market developments. Managers with quarterly metrics often know what their established channels can deliver in the short term, and a change means transition costs. Also, most producers’ accounting systems can measure product profitability but often lack data needed to assess cost-to-serve and profitability by channel. The result is Catch-22: channel inertia enables new entrants, while change risks retaliation or lost support from established channel partners.

The initial decades of the twenty-first century saw this play out in many consumer product categories. Razors, blades, and other items generated high margins for the established suppliers and retailers, and the incumbents faced a choice in responding to entrants like Harry’s or Dollar Shave Club. Cut prices and you cut profits and earnings; develop online channels for your products and you antagonize retail partners. Within five years of Dollar Shave Club entering its market, Gillette had lost about 13 points of market share. Yet when Gillette finally responded by introducing its subscription service, it required customers to sign up with a retailer rather than directly with Gillette. Consumers bought the way they wanted to buy, not how Gillette wanted them to buy.

What can you do to manage change in hybrid sales models? First, specify and keep up to date the levels of quality control required for a channel function – an area where real-time data from digital tools can help. Too often, managers speak of “distribution” as an undifferentiated category in their marketing plans. But distribution always means a set of discrete activities including demand generation, inventory, delivery, after-sale service, and so on. Depending upon buying behavior and your strategy, different activities require different levels of quality control. Some must be performed flawlessly and others just need to be good enough. Effective hybrid selling reflects these differences in channel design and management.

Second, evaluate periodically the options available for shifting a given activity to a different point in the channel. As products mature, suppliers often find that many service and stocking activities which initially required full-service partners can be performed more efficiently and just as effectively by generic service providers or even self-service. It’s usually better to anticipate these shifts rather than react after the fact. BevCo, for example, significantly reduced visits to retail customers with self-ordering features on its platform.

Third, hybrid approaches require the sales organization to change its way of working. Salespeople must develop partnerships with channel players to access data and work with them to implement the approach. This often places more emphasis on account management tasks (“farming” rather than “hunting,” in common sales parlance), being more disciplined in analyses of consumer and channel data, and managing channel contact beyond physical visits. Processes can be redesigned but the challenge is how to change the habits of sales people. BevCo selected a group of their best sales people to train the rest of the team. This helped to build trust and a deeper understanding of specific concerns, and made a more gradual but enduring change possible.

Finally, recognize the trade-offs. No channel manages itself. So consider how many partners you can realistically support and work with. A trade-off between control and resources is inherent in a hybrid multi-channel approach. Don’t deny this trade-off. Instead, consider its implications for the best uses of time, people, and digital tools in your go-to-market programs.

About the Authors

Frank Cespedes teaches at Harvard Business School and is the author of books on strategy, sales, and marketing.

Georg Krentzel is a senior partner at Globalpraxis, a consultancy focused on route-to-market and revenue growth strategies.

The post Getting Value from Digital Technologies appeared first on The European Business Review.

This article is part six of an ongoing series – AI Power Plays – that explores the fiercely competitive AI landscape, where tech giants and startups battle for dominance while navigating the delicate balance of competition and collaboration. In this final article, we focus on the ongoing ‘dominant design’ battle among Language Models (LMs) such as ChatGPT, Gemini, and DeepSeek and consider three scenarios how AI LMs might evolve.

The AI LM Inflection Point

AI LMs are approaching an inflection point. Rapid advances in model architecture (DeepSeek’s V3 and R1), computing power (NVIDIA’s Project DIGITS democratising access to AI infrastructure), and autonomous agency (OpenAI’s Deep Research enabling AI to autonomously explore and analyse web information) have propelled AI to the forefront of business agendas, national policies, and everyday life. But the path forward is anything but settled.

The recent breakthroughs in AI LMs reflect fundamental design choices about how AI LMs are built and deployed. We predict that over time, different ‘dominant designs’ of AI LMs will emerge, based on fundamental design choices and the use cases where LMs are applied. Understanding these parameters is crucial for grasping future scenarios for AI LMs.

Design Choices Shaping AI LM Futures

Large vs Small LMs. The relationship between model size and capability is undergoing a fundamental transformation. What began as a simple correlation – “bigger is better” – has evolved into a more nuanced interplay of architecture, efficiency, and specialised expertise. This dichotomy is reflected in many of the design parameters we highlight below.

Scale and Training Cost. Related to size, the general trend for LMs has been towards large models that contain tens of billions of parameters and are trained with vast datasets. This suggests a massive upfront investment in model training and hardware infrastructure and implies a future dominated by tech giants and government-backed corporations. However, technological breakthroughs such as those heralded by DeepSeek may upend this trend and open the door for resourceful startups, academic institutions, open-source communities to enter the market with smaller and specialised LMs. Access to high-quality data for model training may become a key differentiator for LMs.

Operating Costs are shaped by the efficiency of AI hardware and the complexity of user queries. Use cases dominated by simple queries do not require heavy processing, whereas agentic LMs with deep reasoning abilities will be more costly to operate. This has implications for the cost of adding new users and applicable revenue models. We see different use cases emerging, addressed by differently designed LMs that are operated by different players.

Proprietary vs Open LMs. Currently, the LM landscape features a mix of proprietary (e.g., OpenAI’s GPT and o series, Google’s Gemini, Anthropic’s Claude) and more open LMs (e.g., Meta’s LLaMA, NVIDIA’s NVLM, Mistral’s Pixtral, DeepSeek’s R1) made available through Hugging Face. The two approaches represent radically different approaches to the LM business. Whereas proprietary LMs tend to be large and premium ones, models with less restrictive licenses have been adopted by many players and applied to a wide range of use cases. Often the same players offer both proprietary and more open LMs (e.g., Google’s Gemini and BART LMs).

Through model distillation or quantisation techniques, the capabilities of Models can be preserved while reducing resource requirements.

Model architecture and training process: brute force vs efficiency: The Mixture-of-Experts (MoE) architecture exemplifies the evolution from brute force to efficiency. Consider DeepSeek R1: while housing 671 Billion parameters (B), it activates only about 37B for any specific task, while matching the performance of larger models. The future lies not in sheer size, but in the intelligent orchestration of specialised experts. The trend toward efficiency extends beyond architecture to the training process. Low-Rank Adaptation (LoRA allows models to adapt to new domains without significant computational overhead). Through model distillation or quantisation techniques, the capabilities of Models can be preserved while reducing resource requirements.

Regulation. The regulation of AI LMs ranges from tightly controlled regimes that prioritise national security and sovereignty to more open, market-driven regimes that foster rapid innovation and global collaboration. The regulatory dimension shapes strategic dependencies, military applications, government investment, technological and data sovereignty, cultural norms, and trade policies.

The extent of AI regulation directly influences whether a single dominant design will emerge or whether multiple regional standards will coexist. Tightly regulated AI LM environments promote fragmentation, sovereignty-driven divergence, and government-imposed standards, reducing the likelihood of a globally unified design. Flexible regulations foster convergence, enabling a few powerful firms or open-source communities to establish dominant designs through competitive selection.

In the long term, AI LM regulation will determine whether AI LMs follow the trajectory of global technological convergence (as seen in internet protocols and semiconductors) or regional divergence (as seen in telecom standards and cybersecurity models). Beyond technical aspects, the future of AI LM dominant designs will also be shaped by the balance between regulatory intervention, geopolitical constraints, and market forces.

Deployment Architectures shape how AI systems operate and interact with users. This includes both physical architecture (where processing happens) and logical architecture (how systems are controlled and moderated).

For physical architecture, the choice largely depends on computing requirements. Large models typically demand cloud deployment in centralised data centres, creating provider dependencies but simplifying deployment. Edge computing runs smaller models on local devices, offering autonomy but facing computational limits. Apple Intelligence demonstrates an emerging hybrid approach: specialised models handle simple tasks locally while routing complex operations to the cloud, suggesting future systems may emphasise intelligent resource distribution over centralisation.

The logical architecture determines how model capabilities are accessed and controlled. Model providers (e.g., OpenAI, DeepSeek) enforce strict moderation through safety classifiers. Cloud platforms (e.g., Microsoft Azure, Amazon Bedrock, Nebius) offer flexible controls over model behaviour within platform guidelines. Self-managed deployments through rented compute or local installations provide complete control over model boundaries – crucial for enterprises handling sensitive data or requiring specialised behaviours. These deployment choices shape market dynamics and innovation patterns. While integrated cloud solutions attract enterprises seeking reliability, self-managed deployments appeal to those prioritising autonomy. Regional deployments using hybrid infrastructures serve specific market and regulatory needs.

From Design Choices to scenarios

These parameters interact and influence each other, creating the conditions for different possible futures. Based on how these deployment patterns interact with fundamental design parameters—scale of investment, proprietary versus open approaches, and regulatory frameworks—we see three scenarios emerging: (1) Corporate-Led Standardisation, (2) Decentralised Innovation, and (3) Geopolitical Fragmentation. Each scenario arises from specific dynamics—such as the amount of up-front investment required, control dynamics, technology maturity, implementation patterns and operational costs—and provides insight into how AI might evolve over the next decade. By understanding these scenarios, business leaders, policymakers, and technologists can better prepare for whichever future becomes dominant.

Scenario 1: Corporate-Led Standardisation

Dominant design choices at play:

• Upfront Investment: Large
• Deployment Architecture: Cloud
• Proprietary vs open: mainly proprietary
• Operational Costs: High

In this scenario, well-funded technology giants who control or partner with cloud platforms—think Google, Microsoft, OpenAI, and a few others—take the lead in building and maintaining AI technologies. Because the up-front costs are immense, only these behemoths can afford to invest.

These dominant firms would offer AI capabilities via cloud platforms (subscription and API access). Businesses, governments, and individuals gain access to state-of-the-art (SOTA) models but remain heavily dependent on corporate providers, such as Microsoft’s Azure, Amazon’s Bedrock, and various providers available on Hugging Face and OpenRouter. These providers enforce strict validation and safety controls through their official deployments, maintaining tight governance over how their models are used.

As running inference of large AI models eats up large amounts of computing power, a few players who can manage these costs will set the price for accessing AI technologies. Smaller organisations may be priced out, limiting competition and reinforcing a cycle where AI remains an elite tool controlled by a few firms.

An emblematic use case: Industry and Enterprise-specific AI Copilots where large corporations in finance, healthcare, and legal industries rely on AI copilots for tasks like financial analysis or healthcare diagnostics. These systems would be standardised, secure, and integrated with existing enterprise software.

In essence, Scenario 1 paints a world where scale and control of computing power win the day. While it delivers highly efficient, well-tested AI solutions, it risks locking businesses into proprietary ecosystems that limit choice, hamper competition, and concentrate profits and power at the top. The true moat in this scenario isn’t just money – it is the integration of specialised hardware, vast data centres, and proprietary training methods. Like oil refineries of the digital age, these AI factories require both enormous capital and deep technical expertise to operate efficiently.

Scenario 2: Decentralised Innovation

Dominant design choices at play:

• Upfront Investment: Small
• Deployment Architecture: Edge
• Proprietary vs open: mainly open
• Operational Costs: Low

Using clever training approaches rather than brute force computing power, smaller teams proved they could match tech giants’ capabilities.

In this scenario, AI development is driven by vibrant open-source communities and a diverse range of stakeholders rather than a handful of dominant tech companies. Consider how recent breakthroughs by DeepSeek challenged conventional wisdom: using clever training approaches rather than brute force computing power, smaller teams proved they could match tech giants’ capabilities. This suggests a future where innovation comes from unexpected places, as tools and knowledge become more widely accessible by large audiences instead of a few large players.

In this scenario, research collectives, universities, non-profits, startups, open-source linchpins such as Hugging Face, and even some large corporations—such as Meta and Alibaba, which integrate AI into their existing platforms without commercialising AI technologies—collaborate actively. They share new model architectures, training datasets, and software tools through public repositories, fostering transparency and generative innovation by large, distributed developer communities.

With affordable specialised hardware and efficient model training techniques, even small teams can develop and refine AI models (e.g., Mistral and DeepSeek). This accessibility fosters a culture of rapid experimentation, democratising technological and use case innovation.

Open-source projects constantly iterate, pivoting quickly with each new breakthrough. For instance, a new training algorithm discovered by a small research lab can be rapidly adopted worldwide. AI-native platforms like Aimlapi accelerate this pace by providing seamless access to a broad range of foundation models through lightweight, developer-friendly APIs. This enables rapid prototyping and experimentation, fostering a decentralized model of progress—faster, but potentially more chaotic.

Instead of sending data to cloud platforms, most processing takes place on local data centres and installations. This approach reduces reliance on cloud services and can lead to substantial cost savings. Edge-based AI also enhances privacy by keeping sensitive data local, making it particularly valuable in scenarios where confidentiality is key. Users have full control over model behaviour and validation parameters, enabling more flexible and customised deployments, while taking on greater responsibility for safety and governance.

An emblematic use case: Local AI Assistants where individuals run personal AI assistants on their phones and computers (e.g., Apple’s M-series laptops and NVIDIA’s Project DIGITS), without relying on centralised servers. These local AI tools learn from personal data privately, respecting user privacy and control.

On the flip side, Scenario 2 may come with challenges around standardisation. Without a powerful central authority, ensuring consistent security, data governance, and reliability becomes more difficult. Still, this vision highlights an exciting possibility: AI technology that is truly of the people, by the people, and for the people—grassroots, inventive, and broadly accessible.

Scenario 3: Geopolitical Fragmentation

Dominant design choices at play:

• Upfront Investment: Small
• Deployment Infrastructure: Mix of Cloud and Edge
• Proprietary vs open: combination
• Operational Costs: Medium

Unlike the first two scenarios, which emphasise either corporate dominance or grassroots innovation, Scenario 3 places governments at the centre of AI development where nations develop, adopt and customise models, creating relatively Balkanised regional AI ecosystems to safeguard national interests and technological sovereignty.

Medium-sized and large countries in particular tend to want to avoid overreliance on foreign corporations, especially where it comes to strategic technologies. To preserve a degree of technological sovereignty, countries may promote open-source standards not only in LMs but also in related technologies (e.g., RISC-V for microprocessor architectures, Open Computing Project for data centre hardware). They may promote investment in local cloud infrastructures and fine-tune open models to align with regional priorities. This way, different regions may promote distinct “flavours” of AI that reflect their unique characters. DeepSeek’s R1 model, for instance, demonstrates deep understanding of both classical traditions (Tang Dynasty poetry) and contemporary cultural dynamics (Baidu Tieba and RedNote social networks), while Claude and Grok models excel at parsing complex social dynamics on platforms like Reddit and 4chan (from meme culture to community-specific discourse patterns). This could herald a future where regional AI ecosystems diverge, supporting different languages, ethical frameworks, and security protocols.

An emblematic use: France public authorities have introduced its sovereign LM: Albert and it is progressively expanding within the country’s public administration. The system aims to reduce reliance on foreign technologies and reinforce national control over sensitive data. Today, it assists administrative advisors in responding to citizen inquiries with reliable information. Albert is also embedded within the government’s secure messaging system. Albert serves as an API-based infrastructure, providing computational resources and machine learning algorithms for public institutions developing AI-powered solutions. However, the tax authorities prefer to develop their own LM and to avoid using Albert for sensitive data.

For nations pursuing technological sovereignty, Scenario 3 could provide strategic autonomy and localised innovation. But it also risks deepening divisions between regions, making global cooperation on AI ethics, safety, and research more difficult.

Conclusion: Preparing for the AI Worlds Ahead

We expect that multiple dominant designs will co-exist, each optimised for different use cases and constraints.

The three scenarios are not mutually exclusive. We expect that multiple dominant designs will co-exist, each optimised for different use cases and constraints. The different dominant design parameters are also not mutually exclusive and often interact. It is virtually guaranteed that technological breakthroughs will continue to emerge and upend different scenarios and their technological and use case drivers. The evolution will be iterative, and dominant designs will shift over time. We further expect that open-source communities and commercial providers will co-exist in a dynamic equilibrium: corporations continue to adopt open-source breakthroughs, and open-source projects benefit from corporate-funded infrastructure (e.g., LLaMA and DeepSeek models running on Groq servers in Saudi Arabia or on Nebius servers in Finland).

What do these scenarios mean for business leaders, policymakers, and innovators charting their paths today?

Anticipate Power Shifts. In a corporate-led world, forging strong alliances with tech giants and maintaining sufficient capital reserves for AI solutions will be essential. In a decentralised innovation logic, adaptability, open-source collaborations, and edge-based solutions become key. Meanwhile, in a fragmented globe, the ability to understand and comply with diverse national regulations will become a prerequisite to success.

Balance Innovation with Governance. Whichever direction AI takes, companies must keep one eye on short-term performance gains and the other on long-term ethical and regulatory obligations. Stakeholders need to champion responsible data use, equity, and security, or risk public backlash and legal scrutiny.

Balance AI Investments. Given the unpredictability of breakthroughs and the fluid nature of regulations, spreading resources across multiple strategies—corporate partnerships, open-source initiatives, and strategic national collaborations—helps hedge against sudden disruptions. Nonprofit organisations should also prioritise training and governance — adopting an LMS for non profits can scale ethics, compliance, and AI-literacy programs across distributed teams.

No matter which paths AI LMs take, and they will be several, AI’s influence on business, society, and global politics is set to intensify. The key question isn’t just who will own the dominant AI designs—it is how we can guide AI’s development to serve the broadest possible set of human interests. By understanding potential AI LM futures, stakeholders can better position themselves while working toward an AI ecosystem that benefits all society.

About the Authors

Hervé Legenvre is Professor and Research Director at EIPM. He manages education programmes for global clients. He conducts research and teaches on digitalisation, innovation, and supply chain. Lately, Hervé has conducted extensive research on how open-source software and open hardware are transforming industry foundations (www.eipm.org).

Erkko Autio FBA FFASL is a Professor in Technology Venturing at Imperial College Business School, London. His research focuses on digitalisation, open technology ecosystems, entrepreneurial ecosystems, innovation ecosystems, and business model innovation. He co-founded the Global Entrepreneurship Monitor (www.gemconsortium.org), the Global Entrepreneurship Index (thegedi.org), and Wicked Acceleration Labs (www.wickedacceleration.org).

Xule Lin is a PhD Candidate in Management and Entrepreneurship at Imperial College Business School, studying how human and machine intelligences shape the future of organizing. His work received the 2024 Strategic Management Society PhD Paper Prize and research grants from OpenAI, Google Cloud, and Cohere for AI. He co-organizes the “Human & Artificial Intelligence in Organizations” symposium at Imperial (www.haiosymposium.com).

The post The Future of AI Language Models (LMs): Three Scenarios that could Reshape Business and Society appeared first on The European Business Review.

The convergence of Web3 and AI, two of the most promising technologies of our time, has sparked a compelling narrative. This fusion, with its groundbreaking potential, has ignited substantial discourse. Whether it signifies true innovation or is merely marketing-driven hype is at the forefront of this narrative. The interest it has generated across various sectors is undeniable. However, the current conversation, while speculative, is shrouded in uncertainty. The innovative potential of both Web3 and AI is clear, but their integration’s practical application and scalability are still in their infancy. This leads many to wonder whether this hype will translate into real-world impact.

The Promise of WEB3 and AI

Web3’s decentralised architecture offers a unique environment for AI systems to function with greater transparency, security, and autonomy. Theoretically, the decentralised structure of blockchain can distribute ownership of data and computational resources, thereby levelling the playing field for AI. For instance, blockchain-based data marketplaces could empower users to share their data directly with AI systems, bypassing traditional intermediaries and ensuring that individuals maintain control over their privacy and compensation.

Web3’s decentralised architecture offers a unique environment for AI systems to function with greater transparency, security, and autonomy.

In decentralised finance (DeFi), we already observe AI being employed to optimise trading strategies and monitor risks in real time, leveraging the transparency and security that Web3 provides. Smart contracts—another innovative aspect of Web3—have the potential to enable AI systems to autonomously execute tasks within decentralised applications (dApps), eliminating the need for central authorities. This dynamic could enhance efficiency, reduce costs, and foster trust among participants in various sectors, from finance to healthcare.

However, despite these promising developments, most discussions surrounding decentralised AI remain theoretical. The necessary infrastructure for decentralised AI is still underdeveloped, and many current Web3 applications are confined to niche or experimental categories. This gap between potential and practical implementation raises questions about the technology’s readiness to meet real-world demands.

Challenges in Merging WEB3 and AI

The convergence of Web3 and AI is not without its challenges. The foremost among these is the underdeveloped infrastructure. The concept of fully autonomous AI-driven Decentralized Autonomous Organizations (DAOs) is captivating. Still, current technology struggles to manage the complexities that real-world AI applications would demand in a decentralised context. The infrastructure supporting these technologies must evolve significantly to facilitate their synergistic relationship effectively.

Data transparency and ownership present critical hurdles as well. AI models rely on extensive datasets to function effectively, yet the origins of these datasets are often unclear. This lack of transparency leads to significant issues surrounding quality, bias, and ethical considerations. Simultaneously, a handful of tech giants control most global data, creating monopolistic conditions that stifle competition and innovation. Smaller organisations are disadvantaged, struggling to access the data needed to develop AI solutions that could transform industries.

Privacy issues further complicate the landscape. Many AI systems are built on data collected without user consent, violating privacy regulations and raising ethical concerns. Moreover, the data used in AI training may be incomplete, biased, or not representative of diverse populations, resulting in suboptimal outcomes and perpetuating existing inequalities.

Many AI systems are built on data collected without user consent, violating privacy regulations and raising ethical concerns.

Scalability is another critical concern. While blockchain technology promises transparency and decentralisation, it often grapples with scalability issues. AI systems necessitate the ability to process vast amounts of data in real time, a requirement that current blockchain solutions struggle to meet due to their limited transaction throughput. The intricate balance between maintaining decentralisation and achieving the required speed and efficiency for AI applications presents a formidable challenge.

Opportunities for Improving the AI Data Market

To overcome these challenges that AI and Web3 face, it is crucial to fix the broken AI data market first. Centralised data control has resulted in a lack of transparency, the availability of low-quality datasets, and high acquisition costs, all hindering AI development. One promising solution is the creation of decentralised data marketplaces using blockchain technology. These exchanges can incentivise individuals and organisations to share their data by offering financial rewards through tokenisation. Even with today’s blockchain technologies, such marketplaces can ensure that data is shared securely and transparently and empower users to maintain control over how their data is utilised.

Transparency and trust within these marketplaces can be enhanced by establishing robust data provenance systems. By tracking the origins, ownership, and processing history of data, these systems can prevent AI models from relying on opaque or biased datasets, thereby ensuring data transparency and improving the quality and fairness of AI systems. “Federal learning” and “differential privacy” are two ways that make it possible to share just enough and only the most relevant information through a decentralised network to train AI models without comprising user privacy. These approaches allow AI to learn from distributed data sources while protecting sensitive information. Such an environment fosters stakeholder collaboration, encourages innovation, and ultimately leads to better AI outcomes that benefit society.

The Role of Blockchain: A Solution or a Complication?

While blockchain technology undoubtedly offers potential solutions to some of AI’s most pressing challenges—such as data transparency, ownership, and security—it is essential to recognise that it is not a catch-all remedy. Blockchain faces its own challenges, including scalability, energy consumption, and legal uncertainties.

To maximise blockchain’s benefits for AI, we must explore solutions such as Layer 2 scaling and sidechains, which can overcome scalability issues. These technologies facilitate off-chain transactions and data processing, reducing the burden on the primary blockchain and enabling faster, more cost-effective solutions. Moving blockchain to more energy-efficient consensus mechanisms such as proof-of-stake can also help mitigate the environmental impact of combining AI and blockchain technologies.

To maximise blockchain’s benefits for AI, we must explore solutions such as Layer 2 scaling and sidechains, which can overcome scalability issues.

Hybrid models that leverage blockchain for security and data integrity while keeping most data processing off-chain can serve as a pragmatic compromise. Such models can effectively balance the need for decentralisation with the operational demands of AI systems. Furthermore, cross-chain interoperability will enable decentralised AI systems to operate seamlessly across multiple blockchain networks, fostering a more integrated ecosystem.

Data Ownership in the Age of AI

Data ownership in the age of AI presents a complex and evolving issue. Technically and legally speaking, individuals should retain ownership of their data. Yet, very often, these days, sharing data with companies often leads to losing and relinquishing control. This centralised governance creates a power imbalance, as companies wield extensive rights over user data through opaque terms of service agreements that many individuals may need help understanding.

The evolving issue of data ownership in the age of AI underscores the need for further evolution in data governance frameworks. Governments are beginning to intervene with regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), which grant individuals certain rights regarding their data. However, these regulations often need to confer full ownership and may be difficult to enforce. Besides, the terms are frequently incomprehensible for many users, assuming these users even want to read them in the first place. This situation highlights the need for further evolution in data governance frameworks to ensure individuals can exercise meaningful control over their data.

One potential solution is developing community-owned data networks or Data DAOs, in which communities collectively pool their data and negotiate its use. In this model, data governance becomes a communal effort, empowering individuals to make informed decisions about how their data is utilised while ensuring fair compensation and ethical AI development.

It is also possible to create tokenised data markets, where data is assigned a unique value and traded transparently – imagine you can trace the use of the personal data you have given away. This approach fosters a decentralised, trust-based economy surrounding data, allowing individuals to exert greater control over how their data is used in AI systems. Additionally, privacy-preserving AI models can enable AI to train on localised data without centralising it, offering a viable pathway for balancing innovation with data sovereignty.

The Symbiotic Relationship Between WEB3 and AI

AI and Web3 are mutually dependent technologies. AI relies on Web3’s decentralised infrastructure to navigate data ownership, integrity, and privacy challenges. Conversely, Web3 requires AI to address its complexities, scalability challenges, and under-developed user experiences. By working in concert, these technologies can accelerate their adoption and unlock new opportunities across various sectors.

The “killer application” or definitive moment that showcases the synergy between Web3 and AI may be just a few years away. A breakthrough use case—such as decentralised AI marketplaces or AI-enhanced finance platforms—could demonstrate their combined potential and provide concrete evidence that these technologies extend beyond mere hype. Such applications would not only illustrate the practical benefits of merging Web3 and AI but could also pave the way for broader adoption and innovation. By addressing the hurdles and leveraging the unique strengths of Web3 and AI, we can pave the way for a future that embraces innovation while ensuring equitable access and data sovereignty for all. The journey towards realising this vision is only beginning, but the possibilities are limitless.

About the Authors

Andrea Maria Cosentino, MSc, is an Adjunct Professor of Digital Transformation and Entrepreneurship at Hult International Business School and ESCP Business School. Andrea is the founder and CEO of Impact Fundry, a venture capital studio and strategic consulting boutique that accelerates start-ups, SMEs and corporate enterprises to enable growth and sustainable value creation. He lectures at various universities worldwide with publications in LSE Tech Review, World Economic Forum, and MIT Tech Review. He contributed to the book The Internet of Value published by the Blockchain Center at UCL.

Dr. Terence Tse is a globally recognized educator, author, and speaker. He is a Professor of Finance at Hult International Business School and co-founder of Nexus FrontierTech, an AI company. He is also a visiting professor at ESCP Business School and Cotrugli Business School. His latest co-authored book, The Great Remobilization: Strategies and Designs for a Smarter Global Future, was nominated for the 2023 Thinkers50 Strategy Award. Terence co-authored two Amazon bestsellers, The AI Republic and Understanding How the Future Unfolds. The DRIVE framework from the latter earned a nomination for the Thinkers50’s CK Prahalad Breakthrough Idea Award. Terence also authored Corporate Finance: The Basics, now in its second edition.

The post Web3 and AI: Hype Train or the Real Deal? Exploring the Tech behind the Buzz appeared first on The European Business Review.

A Global Research Initiative

MIT CISR, renowned globally for its cutting-edge research, has long been at the forefront of studying how executives can maximize the value of digitization within their ecosystems. The Center’s distinctive approach leverages real-world data from numerous organizations, offering unparalleled insights into what drives performance and value creation.

• By completing the survey, participants will contribute to critical research that explores enterprise AI maturity, platform business models, cultural transformation, real-time business operations, and effective change management practices.
• The findings will provide leaders with evidence-based strategies to stay competitive in the ever-evolving digital landscape.

Take the Survey Here!

Real-Time Business: The Next Competitive Step

A central focus of the survey is the concept of becoming a real-time business (RTB). RTBs execute key processes instantaneously using automated operations, AI, and data-driven decisions supported by governance and risk management. As companies strive to adapt, the survey seeks to identify best practices for embracing real-time business models. Insights from participants will provide a roadmap for organizations to transition toward more agile and efficient operations.

Learn more about RTBs here.

About the Survey

The survey, designed for individuals familiar with their organization’s digital strategy, takes just 20–25 minutes to complete. It addresses essential questions, such as:

• How can traditional companies transition to platform-based business models?
• What are the best practices for leveraging AI to drive business value?
• How can organizations shift focus from traditional industries to customer-centric domains like mobility, wellness, and energy efficiency?
• What role should boards and top management play in overseeing digital and AI initiatives?
• How can companies organize their technical resources to align with a more digital and AI-oriented future?
• What new job opportunities and business models emerge as AI and automation reshape the workplace?

Who Should Participate?

This survey is designed for professionals who are deeply familiar with their organization’s digital strategy and who hold the insights necessary to shape the future of their enterprises.

Complete the Survey Today!

What’s in It for You?

Participants will gain access to advanced research findings, offering unparalleled guidance on navigating the complexities of digital transformation. The survey results will equip leaders with practical strategies to drive innovation, strengthen governance, and achieve sustainable growth in an AI-driven economy.

The post MIT CISR and The European Business Review Collaborate to Uncover Best Practices in Digital and AI for 2025 appeared first on The European Business Review.

Modern organizations across all sectors are grappling with the challenge of harnessing the full potential of their data. The presentations from three organizations in the Data Engineering track at CData’s inaugural Foundations event crystallized several key themes that span across industries and data use cases.

In their sessions, data professionals at NJM Insurance, Commando, and the World Wildlife Fund shed light on the common struggles and how innovative solutions are made possible by effective data integration and replication. This blog pieces out their shared challenges and eventual solutions to equip colleagues to be more efficient data consumers, saving time and money.

The data dilemma: a universal challenge

Regardless of industry or size, businesses are facing similar hurdles when it comes to data integration. The primary barrier is unifying information from a multitude of sources and systems. NJM, for instance, found itself juggling various advertising applications, while apparel manufacturer Commando wrestled with multiple internal systems like BlueCherry ERP, Shopify, and Centric.

This fragmentation of data across different platforms is a significant barrier to efficient decision-making and predictable business growth. For Commando, incomplete data jeopardized order fulfillment accuracy and customer retention, presenting a significant risk to sales targets.

The rise of self-service data solutions

One of the most striking trends emerging from these case studies is the shift toward self-service data solutions. All three speakers emphasized the importance of empowering non-technical teams with the ability to access and utilize data independent of IT oversight.

Democratizing data access can yield significant results—and in a hurry. It allows marketing teams to pull customer insights without waiting for IT support, enables finance departments to generate real-time reports, and empowers product teams to make data-driven decisions on the fly. The result is a more agile, responsive organization that can quickly adapt to market changes and customer needs.

A low-code tool at a fixed cost

In evaluating potential software solutions, the trio of organizations all sought a user-friendly interface and low-code/no-code approach. Not only would these features make data integration and replication accessible to team members across various technical skill levels, they would also accelerate time to value.

The impact was immediate and significant. NJM reported a 90% reduction in time spent gathering data while incurring only 1/3 of the cost compared to its manual pipelines. Commando, too, saw marked improvements in efficiency and decision-making processes. These outcomes underscore the transformative potential of the right data integration tool.

Beyond integration: ensuring data quality and consistency

While connecting disparate data sources is crucial, it’s only part of the equation. NJM’s focus on maintaining data quality and consistency across all its sources highlights another important aspect of effective data management. After all, integrated data is only as valuable as it is accurate and reliable.

This emphasis on data integrity is a reminder that data integration reached beyond consolidation—it also creates a trustworthy foundation for business intelligence and strategic decision-making.

All three organizations are using their data in ways they hadn’t predicted since they implemented CData Sync to manage their data pipelines. Once Commando began integrating its core business systems, it realized that it could streamline product labeling and simplify its shipments to retailers.

Looking ahead: data integration + AI

As these organizations look to the future, the journey of data integration continues to evolve. Commando and NJM’s exploration of AI technologies to further leverage their integrated data points are aimed at improving customer options and overall satisfaction.

At WWF, new ways of resource-sharing are on the horizon, with an eye toward data-driven recommendations for conservation teams working in the field.

To learn more about CData Sync, take a product tour by visiting www.cdata.com/sync/demo or sign up for a free 30-day trial.

The post Foundations 2024: Key Takeaways from Data Engineering Leaders appeared first on The European Business Review.

CData is honored to be recognized in the 2024 Gartner® Magic Quadrant for Data Integration Tools. We are especially proud to be the only new vendor included among the 20 leading providers in this evaluation.

We want to thank each of our 7,000+ direct customers and 150+ OEM customers for your support in reaching this milestone. We’ve drawn directly from your feedback in our community to fuel our innovation across 45 product releases in 2024 alone. Thank you for your partnership, your feedback, and your time. You have shaped CData as a platform and as a company and we are grateful for your investment in us. 

This acknowledgement is personally exciting for me as a validation of our work at CData building a connectivity platform that solves complex challenges for our customers. We feel it also underscores our evolution from a connector company to a trusted vendor in enterprise data integration. 

We know that building on our foundation of connectivity sets us apart from typical integration providers and lets us offer our customers the unique capabilities they need to build a solid data foundation for their businesses. 

Get the Full 2024 Gartner Magic Quadrant Data Integration Report. 

Why CData was recognized 

Our recognition in the Magic Quadrant highlights our strong execution and visionary approach across four products in our portfolio: 

• Sync: ETL/ELT pipelines to replicate any data source to any database or warehouse 
• Connect Cloud: Centralized SaaS platform for governed self-service access to live data in the cloud 
• Virtuality: Enterprise-grade semantic layer 
• Arc: Comprehensive, no-code EDI and MFT 

These four products are built on our best-in-class connectivity – with 300+ sources and destinations – and cover a range of integration patterns and methods needed by today’s enterprises to reliably manage their data at scale. Because our platform was built on this industry-leading connectivity foundation, we support both data movement and live data access – capabilities also recognized in the 2024 Gartner Critical Capabilities for Data Integration Tools. 

With this connectivity foundation, CData can:

• Deliver industry-leading connectivity to enterprises: We offer an unmatched range of depth and breadth in connectivity that scales to cover complex enterprise environments. As a proof point of that range, we provide embedded connectivity for several software vendors like Salesforce, Google Cloud, Atlassian, UiPath, and Collibra – and even powers connectors for other data integration vendors included in the Magic Quadrant. 
• Provide low total cost of ownership (TCO): With native connectivity, our integration products are lower effort to set up and maintain. Because of our highly efficient R&D model, we can also offer unmatched price-to-performance and scalable pricing for customers. 
• Offer both live data access and data movement capabilities: In 2024, we acquired Data Virtuality, adding robust virtualization capabilities to our platform. In contrast with other vendors who support only one integration pattern like ETL/ELT, CData’s platform can support both live data access and data movement integration patterns needed to support enterprise-wide strategies of data and analytics leaders. 

We feel this recognition signifies a clear validation for our approach in a highly competitive and mature market. But this isn’t just about where we stand today—it signals our role as a strong partner to our customers, elevating what they can expect from data integration tools. 

Why it matters 

Based on a recent study by Salesforce, the average enterprise manages 1,000+ systems and that number continues to grow at 25% every year. With an exploding variety of systems and greater demands to access that data across the business, companies need an integration platform that can both connect and integrate data across all the possible source and destination pairings in combination with users’ access needs. 

Traditional vendors are challenged to meet the wide range of connectivity and data integration patterns required to meet this changing landscape. On top of that, as AI permeates into the business world, the need for a solid data foundation is becoming ever more urgent. 

As a result, businesses are turning to data integration vendors that can stand up to the sprawl of systems and data with scalable solutions and sustainable pricing. 

Building on this recognition 

We aim to evolve CData’s platform to fill this market gap and build upon our acknowledgement in Gartner’s report. And we will do that through a relentless focus on unmatched value for our customers. 

We are uniquely positioned to do this for our customers in part because of our underlying connector technology but also because of our efficient operating model. 

As a bootstrapped business building our connector catalogue, we found a way to leverage a common connectivity platform across all our products. As we’ve scaled, this gives us tremendous leverage with our R&D and allows us to be extremely efficient in our product development. We translate that efficiency into a highly performant platform at a sustainable cost for our customers. Our operating model combined with our recent $350M investment means we can keep rapidly innovating and delivering for our customers. 

We are proud to offer unmatched price-to-performance, honored to be recognized in the Data Integration space, and excited to continue to partner with our customers to build products that make a difference for their businesses. 

Gartner Disclaimer 

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 

The post CData Recognized in the 2024 Gartner Magic Quadrant for Data Integration Tools appeared first on The European Business Review.

By Chengyi Lin

The last half-century has been characterized by our unceasing drive to assimilate ever-evolving digital technology into every aspect of our lives. The Paris 2024 Olympic Games provided a revealing example of the huge benefits that getting it right can yield.

The Olympic Games is the world’s most celebrated sporting event. The recently concluded Paris 2024 Games hosted more than 10,000 athletes from 206 National Olympic Committees and attracted more than half¹ of the world’s population to watch the Games as they unfolded over the course of nearly three weeks.

The Paris 2024 Olympic Games provided a revealing example of the huge benefits that getting it right can yield.

Paris 2024 achieved a critical milestone for the Olympic Agenda 2020+5,² which tries to drive more progress to transform the Olympics for youth and the new era. What is the role of digital technologies in this critical transformation? How can we ensure that the Olympics engage with the younger generation who are “digital natives” and value instantaneous communication through various digital channels? Our ongoing research with the key Worldwide Olympic Partners (WOP) examined the last four games: the Pyeongchang 2018 Winter Games, the Tokyo 2020 Olympics, the Beijing 2022 Winter Games, and Paris 2024. We went deeply into the behind-the-scenes technology services to understand how digital delivers value to the Olympic transformation.

Considering that the Olympic Games operate in a high-pressure, high-visibility and high-expectations environment – one that is filled with complexity, uncertainty, and intensity – these lessons are valuable to different types of business around the world who are trying to make technologies work for their own transformation.

Focus on Technology Reliability to Conserve Value

In 2024 alone, the world was subjected to a glut of digital failures. Among the most visible ones were the Meta server outage³ that caused Facebook and Instagram to be down for more than two hours globally, as well as the Microsoft crash in which an outage in its CrowdStrike cybersecurity software affected over 8.5 million⁴ Windows devices worldwide. Even tech giants cannot guarantee the absolute reliability of their technologies.

This is unacceptable for the Olympic Games. As we witnessed during the men’s 100-meter race, American runner Noah Lyles won the gold medal by a 0.005-second margin⁵. Omega’s technology needs to be extremely accurate and reliable to ensure these results. Actually, to eliminate the frustrations and chaos caused by any technological outages, the International Olympic Committee set a nearly impossible goal: that essential technology services, including cloud and timekeeping, should run successfully 99.999 per cent of the time.

How can the technology providers, such as Alibaba, Atos, Deloitte, and Intel, deliver on this near-perfect promise given the complexity, uncertainty, and intensity of the Olympic Games? The answer is redundancies and testing.

First, all teams built sufficient technology backup systems and dedicated support teams. For example, one from the WOP Alibaba Group built sufficient backup systems around their cloud technologies, ranging from hardware to software. They established multi-copies of local storages and built in both active-backup and active-active instances⁶ of relational databases. Additionally, Alibaba used load balancers to distribute traffic to multiple back-end servers. To provide sufficient multi-level redundancies without blowing up the budget, Alibaba Cloud also leveraged existing servers from within and cross regions. This also avoided adding headcount to the new server sites.

The International Olympic Committee set a nearly impossible goal: that essential technology services, including cloud and timekeeping, should run successfully 99.999 per cent of the time.

Testing through scenario simulations is critical to make sure the redundancies and the process work well under stress. To do this, long-term WOP Atos built a 1,000-square-meter Integrative Testing Lab⁷ in Madrid, and completed over 250,000 hours of testing and simulations before June 2024. Similarly, Alibaba also ran five end-to-end rehearsals, including three internal ones for continuous improvements and two technical rehearsals, with the Paris Organising Committee and other technology partners such as Atos, Intel, and Samsung. Intel’s digital twin platform⁸ also allowed event planners to access the simulations remotely from other parts of the world, so that they didn’t need to travel to Paris multiple times but could still rehearse and improve the events simultaneously.

Although these back-end efforts were all hidden from the public eye, they provided the foundation for a smooth and frustration-free experience.

The 99.999 per cent reliability of the technology not only ensured that little to no value was destroyed through mistakes, accidents, and any other uncertainties, more importantly it helped to save tremendous costs for the games. For business leaders, one lesson from the Paris 2024 Olympics is that digital technologies could allow all stakeholders, including the athletes, hosts, volunteers, broadcasters, event planners, team supporting staff, and many more, all to share the same synchronized information, which significantly reduced the chance of crises, thus conserving value for all the end users.

Guide AI to Improve on Quality, Not Just Quantity

Since the launch of ChatGPT in November 2023, generative artificial intelligence (GenAI) has been helping content creators, such as marketers,  to generate various versions of the same idea. The GenAI-generated content may vary in quality, but it compensates in quantity and speed.

The Olympics actually require the opposite use case: less quantity but higher quality.

With 10,714 athletes competing in 329 games across 32 sports⁹, the Olympic Games organizer hosts an overwhelming quantity of broadcasting contents in the cloud. How different media outlets are able to select the right ones in real time to broadcast through multiple streaming channels is a significant challenge. This is where AI, and GenAI in particular, stepped in.

Such efforts actually started with the Tokyo 2020 Games. Historically, before 2020, broadcasters, such as France Télévisions, NBC, and CCTV, would send their own crew to record a specific match, athlete, or angle of interest. For example, for Rio 2016, NBC sent over 2,000 staff¹⁰, including anchors, reporters, editors, camera crew, etc. The BBC, after criticism, sent a reduced number of 455¹¹ for the same year.

Not only are the proprietary videos and images captured not shared with other broadcasters, generating a lot of waste, the overwhelming quantity of recordings can take days or even weeks to sift through. They are also difficult to fit into the various formats of social media platforms.

The Tokyo 2020 Games became the first Olympic Games to be migrated into the cloud.

Cloud technologies help tremendously in this regard. The Tokyo 2020 Games became the first Olympic Games to be migrated into the cloud. Alibaba and Deloitte worked with the Olympic Broadcasting Services (OBS) and media rights-holders (MRH) to implement a new practice: OBS Live Cloud¹².

By Paris 2024, two-thirds of booked remote services across 54 broadcasters were onboarded to OBS Live Cloud, which included 379 high-definition live video feeds and 100 audio feeds. Once these recordings were centralized on the cloud, AI could be put to work and was proven to be more than helpful in multiple aspects, including 360 instantaneous rendition and automatic editing into customized versions. The CEO of OBS, Yiannis Exarchos, praised the way that technological innovations push “the way we convey the stories of athletes, sports”¹³. First, AI could provide a high-resolution 360 digital rendition of significant game moments.

Omega deployed a range of cutting-edge digital equipment, including the reliable Scan’O’Vision MYRIA photo finish camera¹⁴, which can take 10,000 digital images per second on the finish line of races to help capture significant game moments. Thanks to AI and its almost instantaneous reconstruction of a 360 rendition of the moment, we got to enjoy the slow-motion, frame-by-frame replay of the 100-meter men’s final, when American athletes Noah Lyles and Fred Kerley and Jamaica’s Kishane Thompson crossed the finishing line nearly at the same time, according to the naked eye.

Based on these successes, the International Tennis Federation is currently considering broadly applying this technology in its future operations for refereeing the challenges and results.

Second, an AI editor like the one deployed by Alibaba¹⁵ can edit the multiple camera recordings almost instantaneously and export the short videos into customized versions. In previous games, editors needed to take minutes or even hours to edit critical moves at high quality for replay and analysis. Now, the Alibaba AI editor can slice and dice the full length recording into various short segments and tag each with highlighted information automatically. With the help of facial recognition and object tracking algorithms, the AI editor can pick the right recordings to feature a certain athlete or highlight the key play.

When digital technology, including GenAI, is guided well, it goes well beyond generating quantity. Like the Olympics organizing committee, managers can think about integrating AI into their work flow to significantly enhance value creation by improving the quality of the content generation, user engagement, and operations.

Drive Added Value through Mass Personalization

Young generations engage with the Olympics much beyond traditional onsite and online live viewing¹⁶. They want to interact with the games, the athletes, and with each other in real time across various social media platforms. According to unreleased official data, the Paris 2024 Olympics was the first Games where online streaming and viewing through digital media surpassed traditional TV viewers. This presents a significant challenge for the highly complex Olympics.

Without inflating the onsite and remote staff, the International Olympic Committee and the WOP looked to digital technologies for assistance. Deloitte, a long-time WOP, has invested heavily in AI and innovations. In addition to assisting athletes and coaches with personalized training videos and data, the fan data platform¹⁷ is another demonstration of the digital value added to user engagements through mass personalization. The data platform collects behaviour data of Olympics super-fans.

At the same time, the GenAI algorithm can simultaneously translate text content into multiple languages, tag the multiple video segments in local languages and, more importantly, combine with Deloitte’s fan data to help MRHs select, edit, and distribute customized content for each country based on their team’s sport categories and the audience popularity. For example, the same women’s singles badminton gold medal match will be broadcast in China by CCTV in Chinese with tailored replay for He Bingjiao, and in Korea by KBS in Korean with tailored content for the gold medallist An Se-young. At the same time, both contents will pay tribute to the Spanish world champion Carolina Marín, who sadly retired from the game due to knee injuries¹⁸.

Integrate Digital with Diversity

One major objective of the  International Olympic  Committee is to leverage AI¹⁹ to ensure equal access to the games and data. For example, Deloitte has built an integrated AI solution²⁰ for coaches and athletes around the world to access all the game videos and data for analysis and training.

After automatically editing out the segments²¹, AI can also edit the same segment into various forms²² that fit the culture, social media channel style, local languages, and preferences. For example, Chinese table tennis fans²³ can celebrate Ma Long’s sixth Olympic Gold Medal²⁴ on the podium through WeChat, Douyin, and Kuaishou. and Brazilian gymnastics fans can celebrate the historical moment in Portuguese on Twitter, Instagram, and Tiktok, when Rebeca Andrade won gold²⁵ in the women’s floor final and created a first all-black Olympic gymnastics podium²⁶ with her teammate Jordan Chiles and the US superstar Simone Biles. The mass personalization can also connect individual fans who love the same sport or spirit, even if they come from different cultures and backgrounds. On social media X, even former US first lady Michelle Obama saluted “this beautiful moment of sisterhood and sportsmanship”²⁷.

Besides producing and distributing diverse content from billions of new materials, we also saw AI play a role in rejuvenating the Olympics’ archive. To support gender parity of the Paris 2024 Olympics, one of the WOP partners, Alibaba, dug into the archives and created a video “To The Greatness of Her”²⁸ with AI-recolored still images and reconstructed video recordings. These efforts brought the audience back to those historical Olympic moments and connected with the heroines in the video.

Recently, GenAI has been widely used to improve productivity and creativity²⁹ in generating documents, images, and videos. So far, these require heavy human intervention through prompt engineering and generate much waste. The GenAI algorithm developed for the Olympics Broadcasting Services is fully automated. It can handle large amounts of inputs, in thousands of hours of full-length videos from multiple cameras, and turn them into precisely targeted highlights.

Post-Olympics, these technologies could be applied by various brands to connect them with their own customer databases. This will help brands, retailers, and e-commerce to better engage with their customers with personalized content and promotions³⁰. For example, some of the GenAI algorithms are already in use within Alibaba’s e-commerce platforms³¹ such as Taobao, TMall, Lazada, and AliExpress, and have generated additional orders³². For managers interested in improving their customer experience, the insights gathered from the consumption data of these personalized contents can further inform their practices around research, design, development, and production, and benefit the entire upstream supply chain.

About the Author

Chengyi Lin is Affiliate Professor of Strategy at INSEAD and a leading expert on digital transformation and sustainability transition. His research focus on strategic impacts of technologies (e.g. GenAI, renewable energies) and effective organisational changes under uncertainty. Professor Lin serves as board member, CEO advisor, and consultant for multi-nationals and start-ups.

Reference:

1. Olympics President: Paris 2024 on track to reach ‘more than half the world’s population’. 07 August 2024. 365. https://www.ibc.org/news/olympics-president-paris-2024-on-track-to-reach-more-than-half-the-worlds-population/11205.article

2. Olympic Agenda 2020+5. 2020. International Olympics Committee. https://olympics.com/ioc/olympic-agenda-2020-plus-5

3. Meta’s Facebook, Instagram back up after global outage. 06 March 2024. Reuters. https://www.reuters.com/technology/metas-facebook-instagram-down-thousands-downdetector-shows-2024-03-05/

4. Microsoft says 8.5M Windows devices were affected by CrowdStrike outage. 20 July 2024. Tech Crunch. https://techcrunch.com/2024/07/20/microsoft-says-8-5m-windows-devices-were-affected-by-crowdstrike-outage/

5. S. Runner Noah Lyles Wins 100 Meter Olympic Gold—By Just 0.005 Seconds. 04 August 2024. Forbes. https://www.forbes.com/sites/mollybohannon/2024/08/04/us-runner-noah-lyles-wins-100-meter-olympic-gold-by-just-0005-seconds/

6. The Enterprise Multi-Active Disaster Recovery System: Construction Ideas and Best Practices in the Cloud-Native Era. 16 December 2021. Alibaba Cloud. https://www.alibabacom/blog/the-enterprise-multi-active-disaster-recovery-system-construction-ideas-and-best-practices-in-the-cloud-native-era_598361

7. Atos supporting athletes and technological innovation on road to Paris 2024. 21 July 2023. International Olympics Committee. https://olympics.com/ioc/news/atos-supporting-athletes-and-technological-innovation-on-road-to-paris-2024

8. Digital Twins Platform Simplifies Venue Planning. Intel. https://www.intel.com/content/www/us/en/customer-spotlight/stories/digital-twinning-olympics-customer-story.html

9. Paris Olympics 2024. International Olympics Committee. https://olympics.com/en/paris-2024

10. BBC staff for Rio 2016 Olympics to be 40% down on 2012 Games. 07 April 2016. The Guardian. https://www.theguardian.com/media/2016/apr/07/bbc-staff-rio-2016-olympics-2012-games-nbc

11. BBC staff for Rio 2016 Olympics to be 40% down on 2012 Games. 07 April 2016. The Guardian. https://www.theguardian.com/media/2016/apr/07/bbc-staff-rio-2016-olympics-2012-games-nbc

12. Alibaba, OBS partner on AI-fueled OBS Cloud 3.0 for Paris 2024. 26 July 2024. TVB Europe. https://www.tvbeurope.com/media-management/alibaba-obs-partner-on-ai-fueled-obs-cloud-3-0-for-paris-2024

13. IOC President praises broadcast operations as Paris 2024 reaches record audiences. 04 August 2024. International Olympics Committee. https://olympics.com/ioc/news/ioc-president-praises-broadcast-operations-as-paris-2024-reaches-record-audiences

14. OMEGA brings its cutting-edge technology to Gangwon 2024 as Official Timekeeper. 23 January 2024. International Olympics Committee. https://olympics.com/ioc/news/omega-brings-its-cutting-edge-technology-to-gangwon-2024-as-official-timekeeper

15. Alibaba Releases New AI Video Editor ‘Aliwood’. 27 April 2018. Alizila. https://www.alizila.com/alibaba-releases-new-ai-video-editor-aliwood/

16. The Olympic change: How young viewers (and athletes) made Olympic media evolve. Anything is Possible. https://aip.media/blog/young-viewers-changing-olympic-media/

17. Power Behind Paris 2024: Deloitte Pushes Olympics Innovation. 05 July 2024. Technology Magazine. https://technologymagazine.com/articles/power-behind-paris-2024-deloitte-pushes-olympics-innovation

18. Paris 2024: Heartbreak for Spain’s Carolina Marín as badminton star faces devastation. 04 August 2024. International Olympics Committee. https://olympics.com/en/news/paris-2024-devastation-spain-badminton-star-carolina-marin

19. Olympic Agenda. International Olympics Committee. https://stillmed.olympics.com/media/Documents/International-Olympic-Committee/AI/Olympic-AI-Agenda.pdf

20. AI at the Olympics. Deloitte. https://www2.deloitte.com/us/en/pages/consulting/articles/ai-and-the-olympics.html

21. Video AI: Next-Generation Intelligent Video Production. 27 November 2018. Alibaba Cloud. https://www.alibabacloud.com/blog/video-ai-next-generation-intelligent-video-production_594220

22. AI at the Olympics. Deloitte. https://www2.deloitte.com/us/en/pages/consulting/articles/ai-and-the-olympics.html

23. Tiktok. https://www.tiktok.com/@zhongguoqingnianbao/video/7401502332749368581

24. Tiktok. https://www.tiktok.com/@tabletennis_malong35/video/7401454403363884296?is_from_webapp=1

25. Tiktok. https://www.tiktok.com/@editss2093/video/7399675990747761925

26. Simone Biles on first all-Black Olympic gymnastics podium: “We knew the impact it would make.” – Exclusive. 04 August 2024. International Olympics Committee. https://olympics.com/en/news/simone-biles-on-first-all-black-olympic-gymnastics-podium-we-knew-the-impact-it-would-make-exclusive

27. I’m still not over this beautiful moment of sisterhood and sportsmanship!. 06 August 2024. Twitter. https://x.com/MichelleObama/status/1820812676819190068?t=uQKF8HeexpsVdQASg561Mw&s=19

28. To the Greatness of HER. August 2024. https://www.youtube.com/watch?v=Aso1wqRN5Io

29. How Generative AI Can Augment Human Creativity. 09 August 2023. Harvard Business Review. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity

30. The Pragmatist’s Guide to GenAI in E-Commerce. 14 June 2024. BCG. https://www.bcg.com/publications/2024/pragmatists-guide-to-genai-in-ecommerce

31. Alibaba bets on gen AI tools for overseas merchants, executive says. 10 July 2024. Reuters. https://www.reuters.com/technology/artificial-intelligence/alibaba-bets-gen-ai-tools-overseas-merchants-executive-says-2024-07-09/

32. Alibaba: Generative AI Tools Drive 30% Increase in eCommerce Orders. 10 July 2024. PYMNTS. https://www.pymnts.com/news/retail/2024/alibaba-generative-artificial-intellilgence-tools-drive-30percent-increase-ecommerce-orders/

The post How Digital Technologies Drove Values for the Olympic Games appeared first on The European Business Review.

As digital transformation accelerates, collaboration between the public and private sectors is essential for improving services and outcomes for citizens. In the UK, the government’s 2022-2025 Digital and Data Roadmap sets the course for a modern, data-driven public sector that delivers seamless, citizen-centric services.  With £2 billion investment for NHS technology upgrades and additional funds for digitising other public services, as pledged in the Autumn Budget, alignment between public and private efforts will secure the successful implementation of these initiatives, driving productivity, innovation and improved service delivery. 

At Civica, we make software that helps deliver critical services for citizens all around the world. In our third annual survey of senior UK civil servants, we assessed progress on the government’s Digital and Data Roadmap. The results show steady improvements, but achieving the full potential of digital transformation requires refocused efforts in five key areas. By prioritising these areas and working together, public and private sector leaders can drive lasting impact and shape the future of public service delivery.  

1. Renewing focus on strategic alignment  

Our findings show that just under half (48%) of civil servants believe their organisation’s digital priorities are aligned with the government’s Roadmap, up from 42% in 2023. Closing the remaining gap will require stronger interdepartmental partnerships and unified digital goals, as well as enhanced knowledge and expertise exchange between the public and private sectors. 

To move forward effectively, the government must focus on fostering stronger alignment between departments, ensuring that digital transformation efforts are directly linked to the delivery of citizen-centred services.  

For business leaders, a unified government digital strategy allows companies to tailor solutions more effectively and be more intuitive to the evolving needs of the public sector, creating a predictable environment for innovation and investment. 

2. Prioritising a single access point 

Citizen engagement with public services should be safe, simple and seamless. Central to the government’s digital strategy, initiatives like GOV.UK One Login aim to streamline access across departments. However, only 16% of departments are actively implementing it, and 60% of civil servants are unaware of rollout plans. To address this, the Department for Science, Innovation, and Technology (DSIT) is leading efforts to improve data sharing and interoperability, as well as communications around it.  

As the government seeks to streamline citizen engagement through initiatives like GOV.UK One Login, there is a growing demand for secure, user-friendly solutions that integrate and simplify access across multiple platforms. This creates space for businesses specialising in automation, identity management, cybersecurity and user experience design, among others, to develop innovative products that address the unique needs of the public sector and contribute to a more cohesive digital ecosystem. 

3. Improving data accessibility   

For digital transformation to succeed, public sector teams must have accessible, interoperable data to make faster, more informed decisions. However, six in ten civil servants (57%) report difficulties in using data from multiple sources, and only 27% rate their department’s ability to leverage data as “quite good” or “very good”. 

The Central Digital and Data Office (CDDO) is addressing these challenges with a Data Marketplace, a central repository for government data. This is a strategic opportunity for businesses offering data management, analytics and cloud solutions to support enhanced decision-making and productivity, positioning themselves as key partners in the public sector’s digital landscape. 

4. Removing barriers and siloes        

When civil servants were asked about the biggest challenges to implementing digital and data initiatives, “siloed working practices” emerged as the top barrier (60%), up from 46% in 2023.  

Another key challenge is “legacy IT infrastructure,” which has risen to second place (47%). This signals the urgent need for the government to accelerate system modernisation through a comprehensive audit of existing infrastructure. With 86% of departments already making progress, sustaining momentum is essential. 

With increasing integration across departments, the demand for technologies that eliminate these barriers is growing. Businesses providing cloud solutions, data integration tools and collaborative platforms are well-placed to support a more unified, data-driven public sector, while opening new markets for solutions that modernise legacy systems and streamline workflows. 

5. Accelerating ethical AI deployment 

As government departments modernise their systems, improve data quality and automate workflows, AI technologies offer a powerful opportunity to drive efficiencies. For example, HMRC is using chatbots to automate routine tasks, while the Department for Work and Pensions leverages AI to detect and prevent fraud. 

Although 70% of civil servants acknowledge AI as essential for boosting productivity, only 31% of departments are currently using it. The private sector can play a key role by working with the government to create an AI adoption roadmap, providing the necessary tools, training and resources to ensure ethical and effective AI implementation that improves productivity and citizen services. 

Partnering for a citizen-centric future 

The digital transformation of the public sector is a unique opportunity to optimise operations, boost productivity and improve citizen outcomes. The UK public sector and private businesses can effectively drive digital transformation in public services, as outlined in the 2022-2025 Digital and Data Roadmap, by combining their strengths. The private sector brings technological expertise, innovation and scalable solutions, while the government provides the regulatory framework, public trust and a deep understanding of citizen needs. 

Initiatives like GOV.UK One Login, the Data Marketplace and ethical AI adoption are prime examples of where business expertise can assist the government in advancing impactful change. Now is the time for public-private partnership to drive innovative, efficient and citizen-focused digital transformation, ultimately shaping the future of public service delivery.

About the Author 

Lee Perkins is CEO of Civica, with over 20 years in the UK tech sector. Formerly Group COO at Sage, he has led SaaS businesses like M247. Known for driving growth and transformation, his expertise spans leadership, cloud services, commercial strategy, and product development. 

The post Driving Public Sector Digital Transformation: The Case for Public-Private Cooperation  appeared first on The European Business Review.

This also has a profound impact on how we ideate and innovate. The rise of AI, especially Generative AI (GenAI) has added to this complexity. With GenAI being the first technology that is now capable of creating and approximating more human-like ideas and combining concepts in novel ways, it is becoming more difficult to discern where human creativity begins and the influence of AI ends.

GenAI, however, is not here to replace human ingenuity or our unique problem-solving abilities. Instead, it prompts a bigger question: How is AI reshaping corporate innovation? What are the opportunities and drawbacks of this technology, and how can organizations position themselves to embrace an AI-driven future?

Seismic Shifts in Corporate Innovation

Innovation has always been a key discipline for organizations to stay relevant in a fast-changing world. While AI has been around for some time, the emergence of large language models (LLMs) like ChatGPT, have sparked concern amongst many businesses, with fears of industry disruption and job losses. While these concerns are partly justified, in the context of corporate innovation, GenAI simply represents another shift in a long history of change.

In the early 20^th century, up until the 1980s, corporate innovation was primarily seen as a form of diversification, rather than as a separate strategic priority. Many executives relied on their personal experiences and intuition to drive company-wide innovation efforts. Towards the late 20^th century, innovation became more integrated into the overarching business strategy, by companies establishing dedicated teams and departments leading corporate innovation efforts, formalizing the approach.

The 21^st century saw a shift from closed innovation to open innovation models, welcoming the input of external parties and assistance through external ideas and technologies. Now, with GenAI, another shift is underway, which if harnessed effectively, can drive businesses to new heights.

AI as a Catalyst for Innovation

The possibilities of AI and GenAI to revolutionize traditional R&D processes as well as corporate innovation, are endless. If used in conjunction with an existing innovation management program, AI and GenAI can transform the innovation journey by accelerating and improving workflows, automating routine tasks, improving decision-making and act as co-pilot for coming up and validating new ideas. At rready, we’ve integrated AI into our innovation management software to help teams move more efficiently from ideation to implementation.

1. Identifying Problems

The first step in innovation is problem identification. Here, GenAI helps innovators as part of the brainstorming process by scanning through data sets and identifying certain trends in these data sets or recognizing possible market opportunities and gaps by analysing competitor activity. Unlike traditional machine learnings models, LLMs can understand and predict human behaviour in a way that is far more advanced than any other previous learning models, leading to more sophisticated outcomes, even as early as the problem identification process.

At rready, the combination of GenAI, our API-first innovation platform and proprietary company data unlocks new possibilities to uncover relevant problems and opportunities. We believe this will set a new standard for corporate innovation and fuel the disruption of innovation discipline as we know it.

Another useful way in which to incorporate GenAI in the innovation process, is by enabling innovators to discover ideas or solutions other innovators in their organization have come up with. Our rready platform features an advanced search function, which allows innovators to discover ideas from others, based on the similarities between ideas. This streamlines the innovation process, helping users avoid duplicating efforts on problems where solutions might already be in the works. We are also currently investigating how AI agents can help to do research and add up-to-date information on an idea level.

2. Creating and Enriching Ideas

Once an idea has been established, it is the task of the innovator to flesh it out. AI agents can analyse large data sets from the web or from internal proprietary company data sources much faster than humans and provide insights that enrich ideas with evidence-based reasoning. From customer preferences or user behaviour to market trends, or scientific research, GenAI helps innovators to synthesize complex information to support or refine their ideas further.

The ability of certain AI integrations to work cross-functionally also offers an opportunity, specifically when using innovation management platforms. The rready platform has AI-powered Dynamic Fields that react with AI integrations to help innovators not only create ideas or descriptions for these ideas, but also enrich these further through co-pilot support.

3. Rapid Prototyping and Simulation

Once an idea is validated, prototyping is essential. By creating a preliminary model, innovators can test, identify potential flaws and gather valuable feedback to iterate their solution. AI can speed up this traditionally time-intensive process, reducing costs and resources.

In product design for example, Generative Design (GD) uses AI-driven software to generate multiple solutions based on a given set of constraints, leading to faster and more efficient problem-solving. To create prototypes and simulations, the rready platform offers the use of AI connectors that facilitate an integration of AI into various tools and systems for challenges extending beyond the platform’s capabilities.

4. Implementation and Commercialisation

After developing a solution, GenAI can assist with market analysis and targeting strategies. By utilizing predictive analytics for example, companies can analyse historical and current trends to predict how a product or service would perform in the market. This allows organizations to adjust their marketing strategies in real-time, to allocate and use resources more efficiently.

5. Providing Oversight and Ensuring Continuous Improvement

AI plays a significant role in tracking and evaluating performance post-launch. For program- and innovation leads, AI helps maintain oversight and optimize programs by tracking key metrics such as KPIs or project ROI. For this, the rready platform offers AI-powered graph architecture to ensure a comprehensive understanding of relationships among ideas, people, and data.

The future of corporate innovation lies in the hands of top-level decision-makers in organizations and how they choose to integrate it. While incorporating AI into the innovation process can be daunting, the benefits are manyfold. Combining GenAI with innovation management tools, offers a comprehensive solution for streamlining and levelling up innovation across a company. This approach augments human creativity, while addressing the shortcomings of traditional human-driven processes, leading to improved product and service offerings.

The post Corporate Innovation in the Age of AI: A Changing Landscape appeared first on The European Business Review.

Once firmly in the province of specialists, 3D printing is now being brought closer to the rest of us, due to the pioneering efforts of printer manufacturers such as Elegoo. We spoke to co-founder Kevin Wang.

Good day, Mr. Wang! It’s a pleasure to have you with us today. Since its founding in 2015, Elegoo has achieved remarkable growth. What key factors do you believe have been instrumental in driving the company’s rapid success?

It’s been quite a journey! Our growth has been driven by a strong focus on our customers. We constantly communicate with our users, gathering feedback and fostering a community where we can co-create products together. We want our users to feel that their voices are heard and that they play an integral role in shaping our products. By understanding and prioritizing their needs and creative aspirations, we’ve built a product lineup that appeals to everyone, from beginners to professionals.

Quality and affordability have also been at the heart of our strategy. We ensure that every product we ship is fully tested and proven in both performance and reliability. We never overpromise. We’ve worked hard to make 3D printing accessible to more people by breaking down barriers to entry. Take our recent Kickstarter campaign for the OrangeStorm Giga, for instance. Having launched it in early November 2023, we offered an early-bird option at just $1,250 for the first 100 backers and $1,500 for others. This pricing strategy has really resonated with the market, making large-format 3D printing more accessible, especially to small businesses that might have been put off by the high costs of traditional industrial machines. The campaign has been a huge success, raising over $3.3 million from more than 2,000 backers.

Another crucial factor in our success is our commitment to innovation. We stay ahead of the curve by continually updating and improving our products. This September, we’re excited to launch the Centauri Carbon, a printer that we believe will shake things up. Many desktop printers can be complicated and expensive, but the Centauri Carbon aims to change that. This agility and responsiveness have been key to Elegoo’s remarkable growth.

Elegoo’s products are now available in over 70 countries. How has your global expansion strategy evolved, and what have been the most significant challenges and successes in this journey?

Our global expansion has been driven by one simple observation: people everywhere want affordable and accessible 3D printing. We saw this demand, especially in key regions like North America and Europe, and knew we had to meet it.

To do this, we’ve expanded our reach to over 70 countries through a mix of direct sales on our official website, partnerships with major e-commerce platforms like Amazon, eBay, and Alibaba, and a strong network of local distributors. But it’s not just about getting our products out there; we’re big on communication. We prioritize clear and consistent interaction with our users and back it up with 24/7 after-sales support to ensure customer satisfaction.

Our global expansion has been driven by one simple observation: people everywhere want affordable and accessible 3D printing.

Of course, going global hasn’t been without its hurdles. One of the biggest challenges has been adapting to different cultures and languages. To tackle this, we’ve invested heavily in building a robust customer support team that understands local needs. We’re also working on establishing local operations and user experience centers to get even closer to our customers and offer the support they need, right where they are.

As for success, the numbers speak for themselves. In 2022, Elegoo’s total revenue surpassed US$118 million. And in 2023, we hit a major milestone, approaching the US$200 million mark. This incredible growth shows that our 3D printing solutions are resonating with users around the globe.

The recent launch of the Saturn 4 Ultra, featuring an AI-powered intelligent detection system, is a major milestone. How do you envision AI transforming the 3D printing experience for users, and what other AI-driven innovations are on the horizon?

AI is set to revolutionize the 3D printing experience by making it more precise, efficient, and accessible than ever before. With the Saturn 4 Ultra’s intelligent detection system, AI steps in to automatically spot and fix common 3D printing issues in real time. This not only boosts the reliability of the printing process but also enables users, even those with little technical know-how, to achieve professional-grade results.

Looking forward, we’re excited about the potential of AI to lower the barriers for everyone, especially those who aren’t familiar with 3D modeling. Imagine being able to generate 3D models using AI. This could open the door for many more people to enjoy 3D printing, expanding our user base and making the technology accessible to all.

We’re actively exploring other AI-driven innovations that could further enhance the user experience, making 3D printing more intuitive and unlocking new levels of creativity.

Could you share what initially attracted you to the field of artificial intelligence and how it has influenced your vision for Elegoo?

As AI started to become a key trend shaping the future of manufacturing, I saw incredible potential in using this technology to push the boundaries of what’s possible. It wasn’t just about jumping on the AI bandwagon; it was about solving real problems and making our products smarter, more efficient, and easier to use.

AI is certainly the future direction for us, but it’s not the end goal. The ultimate aim is to create products that genuinely improve people’s lives, and AI is one of the tools that will help us get there. At Elegoo, we’re committed to using AI and other technologies to make 3D printing more accessible and meaningful, so that everyone can benefit from this amazing technology. This focus on practical, impactful innovation is what drives our R&D and sets Elegoo apart.

Elegoo currently accounts for approximately 15 per cent of the global shipment volume of desktop 3D printers. What strategies do you have in place to sustain and further grow this market share?

To keep growing our market share, we’re homing in on a few key strategies. Continuous product innovation is at the heart of what we do. By investing heavily in R&D, we’re developing the next generation of 3D printing solutions that combine cutting-edge performance, affordability, and ease of use.

We’re also expanding our distribution channels. While our e-commerce model has been a huge success, we’re not stopping there. We’re forging strategic partnerships to get Elegoo products into more brick-and-mortar stores worldwide, making our printers even more accessible.

Finally, cultivating brand loyalty is crucial. When you deliver top-notch products and an exceptional customer experience, people come back. We’re committed to solidifying Elegoo as the go-to choice for desktop 3D printing, whether you’re a professional or a hobbyist. By sticking to these strategies, we’re not just maintaining our market share; we’re setting the stage for even bigger growth.

How do you plan to balance the need for cutting-edge technology with ensuring that your products remain user-friendly, especially for small business owners and individual creators?

At Elegoo, we’re focused on combining cutting-edge technology with user-friendly design, especially for small-business owners and individual creators. We don’t see these as opposing forces; in fact, they go hand in hand. By keeping things simple without sacrificing innovation, we’re making 3D printing accessible for household adoption, which is key to expanding its reach.

Cultivating brand loyalty is crucial. When you deliver top-notch products and an exceptional customer experience, people come back.

To meet the diverse needs of our customers, we’ve developed a range of product lines tailored to different use cases and skill levels. For instance, our flagship Saturn series is packed with advanced features for professional users, while our Mars series offers more accessible and beginner-friendly options at around the $300 price point. Meanwhile, our first industrial-grade printer, the OrangeStorm Giga, is specifically designed for small-business owners who need professional-grade results.

Our products are versatile, with applications ranging from gaming miniatures and garage kits to home decor, jewelry, fashion design, STEM education, and more. Ultimately, our goal is to empower every 3D printing enthusiast, regardless of their technical background, and to foster an environment where creativity knows no bounds.

How do the recent advancements in Elegoo’s product line align with the company’s broader goals for innovation? Could you share how these developments fit into your long-term vision?

The recent advancements in our product line, like the Centauri Carbon, really showcase how we’re aligning innovation with our long-term goals. The Centauri Carbon is designed to be one of the most user-friendly and reliable 3D printers on the market, and we’ve made sure it’s also affordable. We could have launched it earlier this year, but we resisted the urge. We’ve spent months rigorously testing it to ensure that it truly delivers on what we promise. It’s not just about putting out a product; it’s about making sure it fits into our bigger vision of turning 3D printers into household items, where they can function like mini-factories at home. But it’s not just for home use; it’s also a workhorse for professionals, including designers and small businesses.

We’ve noticed a growing trend of people using 3D printers not just for hobbies, but also to produce products that they can sell for a profit. As the technology continues to advance, I think we’re going to see this trend explode to another level. And with that in mind, we’ve been steadily growing as a company. We’re now close to 700 employees, and we’ve maintained a strong CAGR of over 40 per cent for the past three years. Our R&D team alone has seen over 50 per cent annual growth, which really underscores our commitment to innovation and staying ahead in the game.

With your background in marketing STEM kits and promoting 3D printing in STEM education, how crucial is it for Elegoo to continue supporting the integration of 3D printing in educational settings? What future plans do you have in this area?

Supporting the integration of 3D printing in educational settings is a crucial part of Elegoo’s mission, especially since our journey began with producing STEM kits. Education is deeply embedded in our DNA. That’s why we actively collaborate with academic institutions and student organizations worldwide. For example, our partnership with NYU Shanghai’s Interactive Media Arts (IMA) program involves providing cutting-edge 3D printers to help students unlock their full creative potential. By offering these advanced tools, we enable young innovators to seamlessly translate their design concepts into tangible, high-quality prototypes.

Beyond our work with NYU Shanghai, we have a strong history of sponsoring educational institutions and makerspaces globally. Whether it’s supplying 3D printing materials for competitions or equipment for engineering projects, our goal is to foster hands-on learning experiences. We’ve supported teams like the Concordia University Formula EV Racing Team in Canada, the Firenze Race Team from the University of Florence, and the Lenoir-Rhyne Rocketry Team by donating our Neptune 3 Pro and Mars 2 Pro 3D printers. These efforts encourage students to dive deeper into engineering, physics, and aviation, sparking their curiosity and innovation.

You’ve mentioned the role of AI in fostering a circular economy. Could you elaborate on how Elegoo is leveraging AI and other technologies to promote sustainability and reduce environmental impact?

At Elegoo, we’re deeply committed to sustainability and reducing our environmental footprint, and AI plays a key role in this effort. We’re using AI for predictive maintenance on our 3D printers, helping us anticipate issues before they become problems, which maximizes the lifespan of our machines and reduces waste. Additionally, we’re exploring ways in which AI can optimize the 3D printing process itself, suggesting the most efficient parameters to conserve both materials and energy.

Beyond technology, our commitment to the environment is reflected in our product offerings. We provide Elegoo Plant-Based Resin, primarily made from soybean, ensuring that it’s BPA-free with minimal odor and almost no pungent fumes. We’ve also introduced Elegoo PLA filaments, a biodegradable material that supports sustainability.

In 2023, we took another step towards environmental stewardship by partnering with One Tree Planted. Through this initiative, we’ve donated thousands of dollars to help plant trees around the world, contributing to reforestation efforts and reinforcing our dedication to a greener planet.

With your extensive experience in operations and marketing within the consumer electronics industry, what trends do you see as pivotal to the future of 3D printing? How is Elegoo preparing to adapt to these changes?

With my experience in the consumer electronics industry, I see some key trends that are shaping the future of 3D printing, and Elegoo is getting ready to ride this wave.

One of the most exciting trends is the potential for 3D printers to become as common as household items like robot vacuums or other smart home devices. With advancements in technology, 3D printers could very well be the next big thing, much like drones. The best part? You won’t need any technical background to benefit from it.

The second trend I see is the rise of 3D print farms, where small business owners purchase multiple printers to mass-produce customized products at a low cost. This trend is driven by the ease of use and the strong productivity of modern 3D printers. Elegoo is committed to supporting these entrepreneurs by continuing to develop reliable, high-performing printers that make mass production not just possible but efficient and scalable.

Finally, closely related to the rise of 3D print farms, there’s a growing shift where 3D printing is no longer just for prototyping but is becoming a go-to for producing end-use parts. As the technology continues to improve in speed, precision, and material diversity, more industries are turning to 3D printing for functional, final components. At Elegoo, we’re broadening our focus to include not just hobbyists and educational users, but also sectors like gaming miniatures, home decor, jewelry, and fashion design.

Looking ahead, what exciting developments or new products can we expect from Elegoo in the near future?

Looking ahead, you can expect some exciting developments from Elegoo aimed at raising awareness of 3D printing. We’re thrilled about the upcoming launch of our Centauri Carbon, designed to set new standards in the industry. Alongside this, we’ll be releasing new resin printers with larger build volumes later this year to unleash even more creativity. Beyond products, we’re also planning to partner with other brands and influential individuals to launch projects that will inspire and engage a broader audience, bringing 3D printing into the spotlight.

Finally, as a leader and innovator, how do you define success, both personally and for Elegoo? What legacy do you hope to build?

Success is about more than just numbers. While financial growth and market leadership are important, our true goal is to make 3D printers as common as household items. We want them to be cool, innovative, and accessible to everyone.

As a startup, we’re a simple and “pure” team, driven by a genuine passion for 3D printing technology. Our focus is on growing our community and making 3D printing a regular part of everyday life. We aim to create a legacy of innovation that genuinely benefits people and the planet. Ultimately, it’s about making the world a bit more exciting and pushing the future forward with every step we take.

Executive Profile

Kevin Wang joined Elegoo in 2016 as co-founder and Vice President, bringing with him 14 years of professional experience and a bachelor’s degree in International Trade from the Guangdong University of Foreign Studies. He has developed in-depth insights into retail marketing and global e-commerce operations.

Kevin is an experienced leader with a demonstrated professional history in the consumer electronics industry. Earlier at Elegoo, Kevin strategized marketing plans for STEM kits and discovered a market gap as he realized that 3D printing is necessary for STEM education and the maker community. With a vision to create the future with smart manufacturing, Kevin leads a young and vibrant team to democratize 3D printers and enable further exploration of the creative universe.

The post Close Encounters of the 3D Kind: Interview with Kevin Wang of Elegoo appeared first on The European Business Review.

By Jacques Bughin and Duco Sickinghe

As the AI boom continues to accelerate, investors are keenly eyeing the generative AI sector for potential breakthroughs. Jacques Bughin and Duco Sickinghe explore ten key opportunities within the AI value chain that promise to address user challenges and unlock significant market potential.

Since our article of March 2022 (“AI Inside? Five Tipping Points for a New AI-based Business World”),¹ which warned about the big boom in AI, artificial intelligence has indeed become red hot, with the birth of many large language models (LLMs), the launch of Apple AI Intelligence,² and the booming demand for GPUs.

In this mania, investors are not only pouring money into the public AI companies (Nvidia is one of the most valuable public companies, exceeding $3 trillion in market value),³ they are also fighting to invest in the new private AI darlings, with private-equity-backed investment multiplied tenfold in one year.⁴ But the key, as a wise investor, is not to follow the herd as in a gold rush, but to anticipate the most important future opportunities in the AI value chain. The best opportunities are those that ultimately solve user issues and create a major buy-side market. Here are 10 examples.

Ten Investment Opportunities

1. Low-bit quantization

Why? The high computational and memory requirements of modern LLMs is costing generative AI users a lot by consuming a large amount of energy, in addition to generating significant pollution.

To give a sense of this, training a single 200 billion parameter LLM on AWS p4d instances consumes more energy than a thousand households for a year.⁵ In some dense areas in the US, datacenters consume 20 per cent of the grid power, endangering its reliability. Based on current GPU usage, the associated gigatons of CO² emissions, according to figures laid out by the ACM, could well be the equivalent of 5 billion US cross-country flights.⁶

Any player that optimizes sustainable energy in the context of AI will be a big winner.

The opportunity: If these numbers are correct, any player that optimizes sustainable energy in the context of AI will be a big winner, as energy consumption will also be closely watched by sensitive users and regulators, who are already pushing for much better optimization and transparency in this area (c.f. the EU AI Act).⁷ Further, such a reduction in energy consumption might open the low end of the enterprise users market, easily doubling the demand for generative AI.

Examples: Low-bit quantization corresponds to the idea that fixed LLM weighting parameters could be optimized instead of their current “standard” of 32/16 bits in traditional LLMs. Players in low-bit quantization include the big names such as Nvidia and its software TensorRT and cuDNN, or Google TensorFlow Lite, which offers support for quantization-aware training and post-training low-bit quantization. More recently, Microsoft has unveiled its BitNet b1.58 (alluding to a 1.58 bit LLM where every single weight of the LLM is ternary {-1, 0, 1} and is able to match the full precision of 16 bits), demonstrating strong gains, up to 70 per cent down, in energy consumption over traditional LLM models.

The world of startups is also pushing ahead, with low-bit quantization startups including DeepAI, OctoML, and SambaNova Systems, which has established a strong moat through its innovative Reconfigurable Dataflow Architecture, and an integrated hardware-software solutions platform for low-bit quantisation to deliver high-performance AI applications.

2. Liquid neural networks

Why? Neural networks (ANN) have been driving the LLM revolution because of their parallel processing capabilities and their ability to model complex relationships, even with unstructured data. The drawback of ANNs is that they require large amounts of data and computing power, exhibit low explainability, and are still in a race of bigger and larger models.

The issue with ANN is thus a mix of energy cost (see above), a race of concentration whereby LLM models become a key bottleneck resource, and a poor reach, as LLM models cannot be incorporated in thin client layers.

The opportunity: Enter liquid neural networks (LNN), a novel type of neural network architecture inspired by neuroscience, which rely on dynamic connections and weights between neurons.

While LLN technology is potentially revolutionary according to its MIT proponents,⁸ its value is in shifting “the existing big is better“ paradigm to smaller models, where LLN has a potential to replicate fixed ANN performance with at least a 1,000 times lower number of weights. For instance, drones can be guided by a small 20,000-parameter LNN model⁹ that performs better in navigating previously unseen environments than other neural networks with millions of neurons.

Two other features of liquid neural networks are their ability to adapt in real time to new and evolving data without requiring extensive retraining, and their ability to perform continuous learning. This means they can integrate new information on the fly, which is particularly useful in real-world applications where data patterns can change rapidly, such as automotive (autonomous driving), industrial robotics (logistics), healthcare (real-time monitoring of patients), and customer service (chat interactions).

Examples: Startup companies such as Liquid AI¹⁰ and Vicarious¹¹ are already experimenting with liquid neural networks. The latter aims to create more adaptable and efficient robotic control systems.

3. Quantum computing

Why? Many problems remain too complex to resolve with the current state of generative AI. In parallel, generative AI has boosted a major cybersecurity risk,¹² resulting in the majority of enterprises resisting leveraging generative AI.

Meanwhile, superconducting technologies are now reaching over 120 qubits with IBM’s latest Eagle processor.

The opportunity: The promise of quantum computing lies in its combination with generative AI to solve problems previously thought to be intractable. The combination has the potential to solve a wide range of optimization problems, from logistics to financial portfolios, with unprecedented efficiency.

Quantum computing can not only destroy classical encryption methods, but also develop new, quantum-resistant cryptographic algorithms. In addition, quantum computing can be used to improve the efficiency and effectiveness of cybersecurity solutions.

Examples: Rigetti Computing is an example of a company exploring quantum computing applications in drug discovery. A competitor, PasQal, is also redefining energy efficiency standards in quantum computing through neutral atomic quantum.

4. Knowledge distillation

The promise of quantum computing lies in combining it with GenAI to solve problems previously thought to be intractable.

Why? Besides the energy power issue, the size of LLMs makes companies desperate for GPUs. In this evolution, Nvidia is definitely the big winner, with massive excess demand waiting in the wings. However, in the long run, this slows down the evolution of the market and makes the cost of generative AI too high for a large number of companies.

Opportunities: These lie in optimizing training and usage to reduce the number of GPUs. As we said earlier, liquid neural networks are a major breakthrough if they deliver on their promise, as training will be much easier and models will be much less expensive.

Meanwhile, in the short term, knowledge distillation is a technique that makes ANN run much more efficiently through model shrinking. As first introduced by Geoffrey Hinton in 2015,¹³ the latter technique transfers knowledge from a teacher LLM model to a much lighter student model. During the learning process, a complex neural network is taught to generate meaningful and helpful representations of data. The distillation processes are based on these thorough representations of data, or knowledge, stored by the teacher network in its hidden layers.

Example: distilBERT is a lightweight compressed counterpart of a larger BERT language model, with 60 per cent of the size of the original BERT model while retaining 97 per cent of its performance and being 60 per cent faster.

5. Synthetic data

Why? There are many problems with data, not only because they are increasingly scarce for AI training, but also because data quality can be questionable. There are two possibilities.

The opportunity: Synthetic data. We witness that synthetically trained models are becoming quite powerful and can even outperform models trained on real data.¹⁴ Successful applications are emerging in financial services, healthcare, and retail. In addition, the advantage of synthetic data is its privacy clearance.

Examples:  We have already reported on the Nvidia simulator application15 in its industrial metaverse that successfully leverages synthetic data to train robots. In general, synthetic data can also be used to rebalance samples when the required prediction concerns rare events such as financial fraud or manufacturing defects.

Startups that rely on the power of synthetic data include SBX Robotics,16 whose generated synthetic data are built 10 times faster and more cheaply than annotation services teaches robots to see; or MOSTLY AI, a leading synthetic data platform with a proprietary GenAI model architecture that results in the highest-accuracy synthetic data, and enables sophisticated AI/ML use cases, including multi-variate time series data and relational databases.

6. AI agents

Why? In our current work environment, there has been a lot of talk about how AI can steal our work.¹⁷ In a typical AI model, tasks are well described and automation can be done if the value productivity of humans was lower than the task performed by AI. However, by the end of 2023, the most capable Generative AI could learn many other skills through this process of next token prediction – for example, translation between languages, math and reasoning skills, and much more.

But the most interesting capability is the ability of LLMs to use software tools. ChatGPT, for example, can now browse the web, use a code interpreter plugin to run code, or perform other actions enabled by a developer.¹⁸

Opportunities: In this new environment, AI agents represent a leap from traditional automation in that they are designed to think, adapt, and act independently, rather than simply follow a set of instructions. The assertion is that agentic AI systems could dramatically increase users’ abilities to get more done in their lives with less effort,¹⁹ and with significantly better effectiveness, especially in complex dynamic tasks.

The second opportunity coming from AI agents is real-time data. We are creating more data every year than has been accumulated in the past. One can argue indeed that the collection of data from AI agents will play at the center of task workflows of enterprise processes.

The rush to adopt AI should not be reckless; the use of AI technologies leads to significant risks.

Examples: Microsoft’s Project AutoGen²⁰ demonstrates a multi-agent framework that simplifies building workflows and applications with LLMs. It features specialized agents that can be configured with different LLMs and enables seamless human interaction. Another prime case of the value of AI agents through generative AI relates to the analysis of real-time traffic data from numerous sources, including road cameras, GPS in vehicles, and social media, to optimize traffic flow dynamically. These AI agents analyze real-time traffic data from numerous sources, including street cameras, in-car GPSs, and social media, to dynamically optimize traffic flow. In Hangzhou, China, the system built by Alibaba has reduced traffic congestion by 15 per cent and sped up emergency response times by 49 per cent .²¹

7. Responsible AI

Why? The rush to adopt AI should not be reckless. In particular, the use of AI technologies leads to significant risks due to inaccurate data, privacy and copyright violations, algorithmic bias, or even fake and harmful content. Moreover, agents have specifically been shown to be less robust, prone to more harmful behaviors,²² and capable of generating stealthier content than LLMs, highlighting significant safety challenges. Finally, Europe is clearly moving ahead by passing its EU AI Act, which is rather stringent regarding trustworthy AI compliance.

The risks attached to generative AI are such that the largest bottleneck to date in enterprises adopting generative AI is misbehaving models and agents,²³ on top of cybersecurity risks.

Opportunities: The development of the EU AI law would continue to make companies comply with a large number of responsible practices, for which a large number of them are not ready. In terms of the metrics, the auditing of practices is a significant regtech opportunity, as it is for other regulatory push areas, such as sustainability.

In addition, the intersection of generative AI and regtech presents significant opportunities to improve regulatory compliance processes. The ability to automate, analyze, and predict through AI provides significant value in terms of cost savings, accuracy, and efficiency.

Examples: Key startups in this space, such as Behavox, are pioneering generative AI solutions to help organizations monitor and manage compliance risks. Their flagship product, Behavox Quantum AI, includes monitoring of text and voice communications, reduction of alert volumes and false positives, and high recall rates for detecting compliance violations. In a recent test, ChatGPT detected less than 20 per cent of the intentionally planted phrases, compared with more than 80%²⁴ for Behavox Quantum AI.

8. ML ops

Why? The majority of generative AI bottlenecks inside firms adopting the technology occur at the transition from prototype to production. In fact, research²⁵ has found that 87 per cent of AI projects never make it into production.

Opportunities: MLOps (machine learning operations) is essential for managing the complex life cycle of generative AI models, and hence delivers multiple advantages over scaling. By standardizing workflows and automating repetitive tasks, MLOps ensures consistent performance and reliability of models in a time of strong compliance linked to generative AI.

MLOps tools provide robust capabilities for monitoring model performance, detecting drifts, and triggering alerts for anomalies. This ensures that generative AI models remain accurate and effective over time. Finally, MLOps fosters better collaboration between data scientists, developers, and operations teams by providing a unified framework and tools for managing the entire machine learning life cycle.

Examples: Robust Intelligence is an automated intelligence platform that integrates with ML models to “stress test” AI models prior to deployment, detect vulnerabilities, highlight erroneous data, and identify data issues that could compromise ML integrity.

Comet’s machine learning platform integrates with your existing infrastructure and tools, allowing you to manage, visualize, and optimize models – from training runs to production monitoring.

9. Cybersecurity

While generative AI is a problem, it is also a powerful solution for cybersecurity development.

Why? From former star Cisco buying Splunk to SentinelOne buying Attivo, the M&A race in cybersecurity is a symptom of the rush to provide a global platform for all cybersecurity issues. These issues have only grown exponentially in the last few years, and they are experiencing a major uptick through LLM. Currently, it reflects email phishing attacks, but with the rise of multimodal LLM, the threats will only expand and diversify. With quantum AI, the cryptographic elements may also be at risk.

Opportunities: While generative AI is a problem, it is also a powerful solution for cybersecurity development. Some specific opportunities in cybersecurity software and management that leverage generative AI are: a) automated threat detection and response (AI models can generate threat signatures and response strategies in real time, enabling faster and more effective incident response, while AI-powered tools can automatically filter out phishing emails and generate alerts for suspicious messages, reducing the risk of successful phishing attacks); b) vulnerability management (AI models can generate reports on potential vulnerabilities and suggest patches or mitigations, helping organizations to proactively address security weaknesses); c) malware analysis and generation (AI can assist in creating honeypots and decoys that mimic vulnerabilities, attracting and analyzing malware to better understand and mitigate threats).

Examples: There are a number of startups emerging from Jericho that are looking at better security, from governance to security solutions such as providing AI firewalls and threat detection and response solutions to detect malicious behavior that attacks generative AI models. For example, Vectra AI uses AI to identify and stop cyberattacks by analyzing network traffic and detecting malicious behavior.

While there are many companies in the space, opportunities remain, especially for more real-time risk detection and more sophisticated detection models.

10. Thin-client generative AI

Why? By 2024, barely 10 per cent of smartphones are GenAI capable²⁶ but, as in the first wave of the internet, the market truly exploded when the internet migrated to mobile and became the reference access for everyone. The battle is on,²⁷ with Samsung taking the lead in the GenAI smartphone market and Apple’s recent entry into the artificial intelligence space.

Opportunities? Generative AI is not yet widespread on thin clients, due to significant computational, storage, and latency challenges, on top of mobile network infrastructure interfacing with typical other IP networks. Finally, the user experience on mobile is tied to the multimodal experience.

Regarding mobile networks, it seems that many standardization activities currently exist for AI functionality in RAN and core parts of mobile networks.²⁸ Yet, the user experience may require the roll-out of 5G or more. The opportunity will thus manifest itself as a mix of the model and architecture optimization we have touched upon earlier, such as low-bit quantization, and LNN, on top of advances in edge computing, for more feasible deployment of generative AI on mobile and other lightweight devices. Opportunities are not only consumer-based, but also enterprise-based. Digital twinning²⁹ for training, and mobility is, for instance, a major opportunity under inspection by many players in the mobile ecosystem.

Examples: Chip set manufacturers like ARM and Qualcomm have already made significant strides in launching thin-client GenAI-powered chip sets. But startups are also making inroads in this large market: Syntiant, on the other hand, develops ultra-low-power AI chips for edge devices that use advanced quantization techniques to enable efficient AI processing in power-constrained environments.

Conclusions

The world of generative AI is only just opening. It is still complex and faces major hurdles to being deployed. Still, these hurdles imply a major fix opportunity to scale the market significantly. Investors should pay close attention to how solutions will impact the market, and whether solution providers offer an opportunity to be exploited in exchange for a large market (TAM) in the making.

About the Authors

Jacques Bughin is the CEO of MachaonAdvisory and a former professor of Management. He retired from McKinsey as a senior partner and director of the McKinsey Global Institute. He advises Antler and Fortino Capital, two major VC / PE firms, and serves on the board of several companies.

Duco Sickinghe founded Fortino Capital in December 2013 and has overseen Fortino’s growth to a recognized technology VC firm. Before Fortino, Duco was CEO of Telenet, INED of CME, and is currently Chairman at KPN. Other positions that Duco held are General Manager at Wolters Kluwer, founder of Software Direct, Product & Channel Manager at HP, and VP Marketing & General Management at NeXT Computer, where he was a contemporary of Steve Jobs. He holds a degree in Civil and Commercial Law and obtained an MBA from Columbia Business School.

References

1. AI Inside? Five Tipping Points for a New AI-based Business World. March 2, 2022. The European Business Review. https://www.europeanbusinessreview.com/ai-inside-five-tipping-points-for-a-new-ai-based-business-world/.

2. Introducing Apple Intelligence, the personal intelligence system that puts powerful generative models at the core of iPhone, iPad, and Mac. June 10, 2024. Apple. https://www.apple.com/newsroom/2024/06/introducing-apple-intelligence-for-iphone-ipad-and-mac/.

3. Everybody Loves … Nvidia. March 19, 2024. The European Business Review. https://www.europeanbusinessreview.com/everybody-loves-nvidia/.

4. Private equity-backed investment surge in generative AI defies 2023 deal slump. March 1, 2024. S&P Global. https://www.spglobal.com/marketintelligence/en/news-insights/latest-news-headlines/private-equity-backed-investment-surge-in-generative-ai-defies-2023-deal-slump-80625128.

5. Measuring GPU Energy: Best Practices. July 24, 2023. ML.ENERGY Blog. https://ml.energy/blog/energy/measurement/measuring-gpu-energy-best-practices/.

6. GenAI: Giga$$$, TeraWatt-Hours, and GigaTons of CO2. ACM Digital Library. August 2023. https://dl.acm.org/doi/pdf/10.1145/3606254.

7. The EU Coordinated Plan on AI: Expectations for the Energy Transition. August 12, 2022. EEIP. https://ee-ip.org/en/article/the-eu-coordinated-plan-on-ai-expectations-for-the-energy-transition-1-5799.

8. “Liquid” machine-learning system adapts to changing conditions. January 28, 2021. MIT CSAIL. https://www.csail.mit.edu/news/liquid-machine-learning-system-adapts-changing-conditions.

9. Robust flight navigation out of distribution with liquid neural networks. April 19, 2023. Science Robotics. https://www.science.org/doi/10.1126/scirobotics.adc8892.

10. “Liquid” machine-learning system adapts to changing conditions. January 28, 2021. MIT News. https://news.mit.edu/2021/machine-learning-adapts-0128.

11. MIT Technology Review. https://www.technologyreview.com/video/604683/ais-next-leap-forward-artificial-intelligence-at-work/.

12. Cybersecurity in the age of generative AI. September 10, 2023. McKinsey & Company. https://www.mckinsey.com/featured-insights/themes/cybersecurity-in-the-age-of-generative-ai.

13. Distilling the Knowledge in a Neural Network. March 9, 2015. arXiv. https://arxiv.org/abs/1503.02531.

14. James, S., Harbron, C., Branson, J., & Sundler, M. (2021). Synthetic data use: exploring use cases to optimise data utility. Discover Artificial Intelligence, 1(1), 15.

15. 2024: What is the Near Future of Generative AI? December 28, 2023.The European Business Review. https://www.europeanbusinessreview.com/2024-what-is-the-near-future-of-generative-ai/.

16. SBX Robotics. Y Combinator. https://www.ycombinator.com/companies/sbx-robotics.

17. Does artificial intelligence kill employment growth: the missing link of corporate AI posture. November 17, 2023. Frontiers. https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1239466/full.

18 .CodePori: Large Scale Model for Autonomous Software Development by Using Multi-Agents. February 2, 2024. arXiv. https://arxiv.org/pdf/2402.01411.

19. Practices for Governing Agentic AI Systems. https://cdn.openai.com/papers/practices-for-governing-agentic-ai-systems.pdf.

20. AutoGen. Microsoft. https://www.microsoft.com/en-us/research/project/autogen/.

21. Hangzhou Smart Traffic. Bing. https://www.bing.com/search?q=Hangzhou+Smart+Traffic&cvid=2c83c1abd93d4190b810da24f94c5d2f&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBBzQzMGowajSoAgiwAgE&FORM=ANAB01&PC=LCTS.

22. The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey. April 17, 2024. arXiv. https://arxiv.org/html/2404.11584v1.

23. Adoption and impacts of generative artificial intelligence: Theoretical underpinnings and research agenda. April 2024. Science Direct. https://www.sciencedirect.com/science/article/pii/S2667096824000211.

24. AI Showdown: Behavox AI Outperforms ChatGPT in Compliance. April 4, 2023. Behavox. https://www.behavox.com/blog/behavox-outperforms-chatgpt-compliance/.

25. Why do 87% of data science projects never make it into production? July 19, 2019. Venture Beat. https://venturebeat.com/ai/why-do-87-of-data-science-projects-never-make-it-into-production/.

26. The Future of GenAI Smartphones: A New Era of Personalized Experiences. April 16, 2024. Smartphone Magazine. https://smartphonemagazine.nl/en/2024/04/16/the-future-of-genai-smartphones-a-new-era-of-personalized-experiences/#:~:text=By%202024%2C%20it%20is%20estimated%20that%2011%20percent,total%20of%20over%20550%20million%20units%20(Counterpoint%20Research).

27. The race to bring generative AI to mobile devices. March 15, 2023. Financial Times. https://www.ft.com/content/6579591d-4469-4b28-81a2-64d1196b44ab.

28. Generative AI in mobile networks: a survey. August 17, 2023. Springer Link. https://link.springer.com/article/10.1007/s12243-023-00980-9.

29. Demo: Scalable Digital Twin System for Mobile Networks with Generative AI. https://drive.google.com/file/d/1g8im3L7nLBu0TJXUtSAQK0ze0CJyYeg9/view.

The post Sorting Out the AI Gold Rush? Ten Opportunities in the Generative AI Value Chain appeared first on The European Business Review.

What motivated you to start Tripsider, and what were the key factors that drove you to enter the tech startup space? 

I have always been an avid traveler who meticulously planned my own trips. However, a disappointing vacation in China with a package tour exposed me to numerous issues, including a 5-hour flight delay and an unprofessional tour guide. As I noticed that the tour organizers seemed indifferent to the tourists’ experiences, treating them as mere parts of an assembly line, I realized there was a need for personalized travel services. 

Determined to find a better solution, I discovered that expert-led, small group trips offered a much richer experience, but there was no single platform where travelers could find these tours. Therefore, I decided to create one myself. It was an attractive business proposition, since the adventure travel sector was actively growing — around 15% annually — and expected to be a $2 trillion industry by 2032. Hence, after returning from China, I partnered with two like-minded co-founders, and we invested $10,000 of our own savings into developing and promoting Tripsider, aiming to provide a more tailored and enjoyable travel experience for others. 

How has your vision for Tripsider evolved since its inception, and what role has mentorship played in shaping that vision? 

Adventure travel is the antidote to cheap dopamine. Tackling challenging tasks builds confidence, happiness, and resilience in difficult times. Our core concept has stayed the same since we defined our mission and vision in 2018. Initially, we focused on building a marketplace, but now we’re putting more effort into helping travel experts develop their businesses. This shift has allowed us to create more personalized and high-quality travel experiences. Our mentorship efforts mainly target C-level executives, especially because they handle a lot of data and responsibilities, which is crucial for our growth. 

As a leader, mentoring other leaders often means confronting my own ego. When something goes wrong, it’s important not to jump in and fix things immediately. Mistakes are inevitable, and they provide valuable learning opportunities. By giving people the chance to learn and grow on their own, we foster a more resilient and capable team.

How do you mentor and support your executives to help them grow and succeed within the company? Can you provide an example of a success story?  

In our company, each employee has a Personal Development Plan (PDP) that is supported by C-level leaders from different departments. Everyone has room for growth in areas that might hold them back from achieving their ambitions. Based on these, we create a plan to help our team members set the right goals for their personal and professional development. 

For example, one of our employees, who initially worked with us as a sales manager, expressed a desire to transition into product management. We greatly valued her enthusiasm and commitment to growth, so we decided to support her on this journey. I developed a tailored development plan for her, which included training, mentorship, and hands-on projects to help her gain the necessary skills and experience. 

Even though the shift took four years, now, she works with us as a product manager and is extremely happy with her decision. Her journey is a testament to our commitment to nurturing talent and supporting our team’s professional development. In fact, over the past six months, she has made significant progress and has even started mentoring other employees herself.  

What challenges have you faced as a woman leader in the tech industry, and how have you overcome them to thrive in a male-dominated field? 

As a woman leader in the tech industry, I encountered the same amount of difficulties as males, but they were different. Early on, I learned to establish clear boundaries, which has helped me navigate the typical challenges women face in business. This proactive approach has enabled me to earn respect and trust, allowing me to concentrate on driving innovation and achieving success in a predominantly male field.  

Can you share your insights on how women can navigate and succeed in leadership roles, particularly in industries where they are underrepresented? 

For women aspiring to succeed in leadership roles, especially in male-dominated industries, here are a few key pieces of advice that have helped me: 

Firstly, develop self-confidence. Believing in your abilities and taking on responsibilities is crucial. My background in professional sports instilled this quality in me. It was especially useful during the pandemic when, as CEO, I had to quickly adapt our business strategy. This not only helped us navigate the crisis but also earned me the trust of my team. 

Secondly, trust in your expertise, as well as in other people’s. You don’t need to be an expert in everything, but it’s important to surround yourself with knowledgeable people and trust them. Seeking out mentors is also vital. Consulting with industry experts when hiring for key positions has allowed our company to grow rapidly. Personal mentors have helped me avoid mistakes and plan strategically. 

Thirdly, build professional connections. For me, actively participating in networking events and conferences has opened up many growth opportunities. 

Lastly, maintain a work-life balance. It’s essential to find sources of energy and motivation that help you thrive in your role. Discovering how to recharge is also crucial. 

By following these tips, you can significantly enhance your chances of success in any field. 

What are some key strategies you believe are essential for women leaders to implement in order to build and sustain successful businesses? 

One important strategy is for women leaders to confidently acknowledge their accomplishments. Men often highlight their individual contributions with “I” statements, while women tend to credit their success to teamwork. It’s crucial for women to speak up about their achievements without fearing they’ll be seen as too aggressive. 

Another vital strategy is to be willing to ask for help. Many women hesitate to seek assistance because they worry it might make them seem less independent. However, successful leaders—regardless of gender—know when to leverage other people’s support and expertise. 

Lastly, women should not settle for less than they deserve. While compromise is sometimes necessary, consistently accepting less can hinder long-term success. Successful leaders are often those who stand firm on their principles and negotiate assertively. 

In summary, women can build and sustain successful businesses by confidently owning their achievements, seeking support when needed, and negotiating effectively to achieve their goals. These strategies empower women to overcome challenges and lead their businesses to success.   

What words of inspiration would you like to share with other women aspiring to take on leadership roles in their respective fields?  

Effective and happy personal development relies on two crucial components: a high level of awareness and stable self-esteem. By cultivating awareness, you can accurately evaluate cause-and-effect relationships, as well as formulate and execute actions based on data rather than emotions. Stable self-esteem enables you to feel whole and act according to your goals, rather than simply aiming to win a competition. 

Remember, leadership involves not only leading yourself but also understanding others and the environment in which you operate. Embrace each moment with mindfulness to build meaningful connections and navigate challenges confidently. Your journey to leadership begins with self-awareness and recognizing your potential to create positive change.

Executive Profile

Olga Bortnikova is a seasoned entrepreneur, travel expert, and the co-founder of travel tech startup Tripsider. Under her leadership, the companies have grown into booming platforms with over $30M GMV and more than 500,000 monthly users. Olga is a certified mentor at UN Women and holds an ICF Certified Coach designation. 

The post Breaking Barriers: Olga Bortnikova on Women Leading in Tech appeared first on The European Business Review.

Implementing benefits technology into an organization is a great investment. Automating a variety of administrative processes and minimizing errors when coordinating employee benefits is a great help for businesses. 

However, many businesses face a number of challenges when implementing and maximizing the use of their benefits technology. Still, by applying some helpful strategies, you can ensure that you’re making the most of your technology investments.  

Overcoming Resistance to Change 

It’s natural for employees and even leadership teams to be resistant to change. Even if a process is inefficient, completing tasks the same way you always have brings a certain familiarity and comfort. 

However, when you’ve invested in a benefits technology solution, you will need additional training and an adjustment period to familiarize yourself with the new way of doing things. This disruption to the normal flow of business can cause many employees to be overly critical of new solutions and may not put effort into using the platform to its fullest.. 

This is why it’s important for managers to remind the HR team of how these new solutions will make their lives easier in the long run. Talk about the direct impact it will have on redundant processes and time-wasting activities. This helps to make the solutions more relevant for each team member, allowing them to place more value on their implementation and be less abrasive to changes. 

Seamless Integration with Existing Systems 

When organizations take the time to invest in a benefits technology solution, they’ll often be looking to prioritize a handful of objectives. They may be focused on a specific functionality or improvement they’ve been looking for   

Regardless, however, there are any number of benefits technology solutions out there, and they’re not created equal. Organizations should partner with a technology vendor that will connect them to the best-fit solution for their unique needs and challenges. The right partner will assist with implementation, ensuring everything is set up correctly.  

It’s important for your employee benefits solution to offer seamless connectivity between This is typically facilitated through API connections and the right selection of supporting cloud services. However, if the solution provided doesn’t have the right integrations pre-established, it may be lacking in the level of efficiency and automation  

Tackling Data Migration Issues 

Successfully migrating data across various systems can be one of the more challenging components of implementing benefits technology. This typically involves a methodical process that involves planning and coordination with both internal and external teams. 

In order for your migration to happen successfully, it’s important to prioritize careful planning during the implementation stages. Whether you’re working with outside consultants or have internal project managers, you’ll need a clear strategy for roll out..  

A big part of making your benefits technology implementation a success is doing the prep work ahead of time to “clean” your data – making sure to get rid of duplicate or outdated data that can impact the integrity of your solution.   

It’s also a good idea to set up a regular meeting cadence with your technology solutions provider since they’ll be able to help you prioritize your efforts and ensure you get maximum value out of your solution. 

Ensuring System Dependability 

The last thing a business wants to see is that their benefits technology fails when they depend on it for open enrollment or when finalizing their pay periods. However, this is unfortunately fairly common when not working with a qualified benefits technology partner.  

Management teams should thoroughly vet their partnerships to help ensure they’re choosing reputable solutions specialists who will ensure their systems are reliable long-term. Technical issues can happen without any warning, and it’s important that you’re able to receive the support you need to get things back up and running. 

Measuring Success 

While it can be tempting to simply apply a “set it and forget it” mentality to the implementation of your solution, this rarely works out long-term. While benefits solutions on their own can provide a fair amount of value, that value can only be confirmed if you’re measuring its performance.  

To do this properly, it’s important that you’ve established primary goals to hit even before you’ve started the implementation process. Maybe your main focus is to reduce administrative workloads or improve your benefits enrollment efficiency with your employees. Whatever the primary goals, it’s important to always have this at the forefront of your mind when evaluating the effectiveness of your solution.  

Data collection can be an important part of benchmarking your solutions and help you make the right decisions regarding how they can be improved. Benefits technology solutions with advanced reporting and analytics features can help you monitor trends and determine what’s working and develop strategies to get better results.   

Maximizing the Value of Your Benefits Technology  

Most businesses that put the right benefits technology solutions in place experience a variety of advantages – especially when it comes to better efficiency and productivity in their administrative processes. However, in order to get the most out of your investment, it’s critical to have the right implementation plan in place, along with the right partners.  

By following the steps provided, you can help avoid the common challenges that come up when implementing new technology while making sure you’re getting the support you need to get the most out of your investment.

About the Author

Frank Mengert continues to find success by spotting opportunities where others see nothing. As the founder and CEO of ebm, a leading provider of employee benefits solutions. Frank has built the business by bridging the gap between insurance and technology driven solutions for brokers, consultants, carriers, and employers nationwide.

The post Addressing the Common Challenges that Managers Face When Implementing Benefits Technology  appeared first on The European Business Review.

By Peter Weill & Stephanie L. Woerner

Top-performing firms reorganise several times to effectively use digital to capture value. In a series of CEO interviews, we identified four successful levers for maximising value from digital. Then, in a global survey, we found that the companies in the top quartile of effectiveness on these four levers were also top financial performers, growing 12 percentage points above their industry average, and leaders in innovation, with 45 per cent of their annual revenue coming from new products introduced in the last three years – a huge premium. In this paper, we describe the four levers and the reorganisation required, illustrating with examples including Standard Bank Group, the largest bank in Africa. To become a top performer takes persistence, as companies must perform organisational surgery – reorganising on average seven times to create the industry-leading value. It is like solving an organisational Rubik’s cube, with a big payoff.

How many major organisational changes has your company been through in the last five years, and did those changes create value? At MIT CISR, we studied over 700 companies to understand how companies unlock new digital value.¹  We found that a company must perform organisational surgery, often reorganising many times to create the value. The top-performing companies in our research underwent, on average, 7.2 major organisational changes in the preceding five years, but the results were worth the disruption, as the companies grew well above their industry average.

Historically, organising a company to maximise value from digital started with the technology leader looking out of the IT organisation to understand what the business needed.

We found that the companies in the top quartile of effectiveness at using these four levers were also top financial performers, growing at almost 12 percentage points above their industry average, and leaders in innovation, with 45 per cent of their annual revenue coming from new products introduced in the last three years. From the interviews, we learned that taking a top-executive perspective rather than a tech leader perspective can enable the kind of persistence, organisational buy-in, and change needed to unlock industry-leading digital value enterprise-wide.

In this paper, we describe the four levers and illustrate them with examples from companies including Standard Bank Group and ANZ, and discuss how to move from the technology-led governance to the enterprise-wide governance that is now needed to succeed.

The Four Levers to Create New Digital Value

Historically, organising a company to maximise value from digital started with the technology leader looking out of the IT organisation to understand what the business needed. But in today’s world of technology everywhere, it’s time to take, first, a CEO perspective and, then, an enterprise-wide one to design the organisation to maximise value from digital. To understand how top-performing companies organise for digital, we began by interviewing eight CEOs of large organisations and then followed up with their colleagues, to learn what organisational levers were used to create new digital value. Four levers to unlocking value emerged. Each of these levers needed to be supported by CEO involvement to drive the necessary changes in company and employee behaviour. Then we surveyed executives from 721 companies to understand best practices and the impacts of employing the levers on company performance.

Companies focused on these four levers to unlock new digital value:

1. Customer: Identifying and delighting the most important unique customer types.
2. Capability: Providing and reusing a shared capability as a service across customer types.
3. Commercialisation: Commercialising what the company is great at to generate new revenue.
4. Component: Designing, embedding, and reusing digital modules of self-contained business capabilities.

Each lever produced a specific type of value to help drive top performance:

1. Value from customer – focus: Customer loyalty and increased revenue per customer via tailored customer journeys and customer focus.
2. Value from capability – scale: Consistency and efficiency across different customer types while capturing key data.
3. Value from commercialisation – new revenues: New revenues from providing services to other companies based on what the company is great at.
4. Value from component – speed via reuse: Faster time to market using best practices and decentralised governance with better compliance.

Let’s go into more detail on each of the levers.

Identifying Unique Customer Types

To unlock new digital value from customers, a company’s senior executive team must first identify their set of unique customer types to focus on, describing each type’s persona, customer journey, data model, channels for engagement, and more. In financial services, customer types typically include home buyers, small business enterprises, corporations, and high-wealth individuals and families. Developing an understanding of its most important customer types helps a company to really empathise and focus on meeting customer needs. The top-quartile performers on growth focused on an average of 8.9 unique customer types, typically describing for each type how they preferred to engage with the company, the typical products and solutions needed, the kinds of offers found attractive, and the associated data profile and systems that made the customer journey easy. In our interviews, CEOs reported that each customer type needed a senior executive who had both decision rights and accountability for success and, increasingly, customer journeys were supported by providing curated access to complementary and partner service providers.

Driving a Shared Capability as a Service

Leveraging business capabilities as a shared service helps a company to generate speed and efficiency. Here, senior executives must first identify what capabilities are common across customer types. Standardising, automating, branding, and reusing these capabilities allows the company to drive consistency, which both provides the customer with a common experience across products and increases efficiencies for the company. Shared capabilities can lead to better insights, because the data collected is more consistent and in one place. For example, a key shared capability in banking is a unified customer profile that details a customer’s current assets, products, and a forecast of their future needs, along with their identity, credit score, risk tolerance, and other factors.

The top-quartile growth companies provided an average of 6.3 separate business capabilities as a service across (almost) all their customer types. Because this lever can be hard to implement politically, as they were often centralised, top performers were selective about which services to share across customer types, thereby ensuring that the services were strategically important and there were a manageable number.

Commercialising What the Company Is Great At

The top-quartile growth companies selected internal capabilities they were great at – their crown jewels – and commercialised them as a service to produce a new revenue stream. This anything-as-a-service model, which we call XaaS, is becoming an important growth area for many companies as digital connections between companies become easier. Examples of XaaS that some banks have developed include anti-money-laundering (AML), payments, know your customer (KYC), and foreign exchange (FX). Often, such services are essentially selling compliance as a service, allowing the company to derive more value from its own efforts to address increasing compliance costs and create increasing scale.

Australian bank ANZ has recently focused on providing XaaS in areas including international payments and anti-money-laundering (AML). ANZ CEO Shayne Elliott described the bank’s AML efforts:

We saw one major player exit this business as a result of some AML issues, which meant their customers had sixty days to find another provider. Of those, there were seventeen major mandates and we won sixteen of them. That took our [AML] market share from the low 40s to 58 per cent.² 

In our top-quartile companies on growth, an astounding 56 per cent of revenues were generated using the XaaS approach, unlocking a lot of previously untapped value.

Embedding, Nurturing, and Reusing Digital Modules

Embedded digital modules, sometimes called components, create new digital value for the company by driving consistency, compliance, and speed to market. Digital modules are “atomic” business capabilities, in that they are fully self-contained and right-sized. They are fine-tuned, automated, and reused in every possible application in the company, and nurtured by their owners to ensure they maintain best practice.

In financial services, typical examples of such modules are establishing the customer identity, onboarding a new customer, establishing or accessing a customer’s credit score, assessing risk, assessing compliance, and many other often-reused business capabilities. Often the motivation for these modules is to increase speed to market of different groups, while meeting compliance with regulations via consistency of approach and common reporting. Modules are built into the other three levers – customer types, shared capabilities, and XaaS – as well as other opportunities for reuse. The top-quartile companies on growth were 80 per cent effective at digital module reuse, improving their time to market and helping to generate an industry-leading percentage of revenues from new products introduced in the last three years.

Once a company has identified which business capability to modularise, it typically uses decentralised governance and APIs or some other kind of digital service to create the module and share it easily.

Top Performers on Growth and Innovation Used All Four Levers Effectively

Companies that were more effective at using the four levers individually grew faster than their peers. And the companies that were in the top quartile of effectiveness of all four levers combined grew even faster, at 11.7 percentage points above industry average.

Standard Bank Group, the largest financial services group in Africa,³  has used all four levers to unlock new digital value as part of the bank’s digital business transformation.

Unlocking New Value at Standard Bank Group

In its strategic transformation plan, Standard Bank Group describes serving the needs of clients in financial services and beyond by “banking the ecosystem” – i.e., providing financial services in all the ecosystems the bank is targeting. Behind this vision is Standard Bank’s inspiring purpose: “Africa is our home, we drive her growth.”⁴

The bank started by focusing on three client segments (Customer): consumer and high-net-worth clients, business and commercial clients, and wholesale clients. It identified client acquisition and engagement as the drivers for sustainable growth. The bank also initially targeted 10 ecosystems to operate in (Customer) – five that it would drive, such as agriculture and trade, and five that it would participate in, such as energy and education. It has since narrowed its focus to ecosystems where it is able to achieve the most competitiveness, including trade and home services. Standard Bank’s participation in an ecosystem typically involves offering B2B financial services the bank is great at (Commercialisation), such as FX and payments.

We found that in a digital / AI everywhere world, companies should rethink the traditional model of the technology organisation.

To enable shared capabilities as a service (Capability), the bank created a new group, called Client Solutions, that serviced the client segments with banking, insurance, and investment services. However, as the transformation progressed, the bank found that it was more efficient to provide these services within the client segments and reverted the segments to being more traditional business units.

Finally, a great deal of effort went into architecting modularity (Components). Standard Bank’s modularity relies on standardisation and simplification, as well as the technological capability to connect both internally and with partners, enabled by API readiness and integration and scalable and interoperable platforms. The bank calls developing modularity in this way “unpacking the honeycomb”, and tracks the number of digital solutions as a percentage of total solutions it has achieved. In 2021, 24 per cent of the bank’s banking solutions and 22 per cent of all solutions were digital solutions, and it was aiming for a target of 50 per cent across all solutions by 2025.

Standard Bank is making great progress toward its transformation goals, with the bank’s 2022 results demonstrating record revenue and earnings.⁵ The positive impacts continue in the first half of 2023, when the bank’s cost-income ratio (a common measure of efficiency) improved from varying between 55 and 58 per cent over the previous 10 years to 50.5 per cent.⁶

The Importance of Lever Governance in Unlocking Value

To unlock its value, each lever needs to be governed and nurtured differently (see figure 1). The Customer lever is typically owned and governed by business unit heads with responsibility for engaging each customer type. The governance of the Capability lever, because it spans different customer types, is typically owned centrally by a shared services group, COO, or CIO who operates those services for the rest of the company, perhaps on a chargeback basis. We have also seen leader-follower models, where one business unit takes the lead on a particular service and then provides it to the other business units. The ownership of the Commercialisation lever frequently belongs to a combination of people who sell business-to-business solutions and the specific business service owner (e.g., payments), often using a two-in-a-box model.⁷ Finally, components are typically owned and governed by the business owner of the business capability embedded in the component, such as risk management (owned by the head of risk), know your customer (the head of compliance), credit scoring (the CFO), or payments (the head of the payment service), often in partnership with a technology leader.

The Key to Realising Value from the Four Levers

We found that in a digital / AI everywhere world, companies should rethink the traditional model of the technology organisation. Instead of taking a technology-led perspective, we recommend taking the CEO, enterprise-wide perspective on designing the technology capability to unlock maximum value from digital. To be a top-quartile growth company in the digital era requires focusing on four levers to create digital value. But companies must iterate several times to get the levers to work together to unlock that value. And they have to implement an ownership and governance model that encourages nurturing and reuse of the four levers. They also need very good real-time metrics that measure the effectiveness of the levers, their impact on performance, and the capabilities needed to deliver them, shared widely via a dashboard. Finally, they need the support and vision of the CEO and top management team, along with the board, to help exploit the levers throughout the company. It is like solving an organisational Rubik’s cube with a big payoff.

This paper draws on Weill, Peter and Stephanie L. Woerner. “Unlocking New Digital Value.” MIT Sloan Center for Information Systems Research, Research Briefing, XXIII-7, July 2023.

About the Authors

Peter Weill, PhD, is an MIT Senior Research Scientist and Chairman of the Center for Information Systems Research (CISR) at the MIT Sloan School of Management, which studies and works with companies on how to transform for success in the digital era. MIT CISR has approximately 75 company members globally who use, debate, support and participate in the research. Peter’s work centres on the role, value, and governance of digitisation in enterprises and their ecosystems and has coauthored 10 books. Ziff Davis recognised Peter as #24 of “The Top 100 Most Influential People in IT” and the highest-ranked academic.

Stephanie L. Woerner, PhD, is a Principal Research Scientist at the MIT Sloan School of Management and Director of MIT CISR. She is a renowned researcher and speaker, and coauthor of Future Ready: The Four Pathways to Capturing Digital Value and What’s Your Digital Business Model? Six Questions to Help You Build the Next-Generation Enterprise, both published by Harvard Business Review Press. Stephanie studies how companies use technology and data to create more effective business models as well as how they manage the associated organisational change and governance and strategy implications. Stephanie’s research has appeared in MIT Sloan Management Review, Harvard Business Review, CNBC, Forbes, Chief Executive, and CIO.

References

1. This research is based on the MIT CISR 2022 Future Ready Survey (N=721), plus interviews with eight CEOs conducted in 2021–2 and case vignettes of five large companies in manufacturing, medical technology, financial services, and real estate.

2. Shayne Elliott, “Elliott: Accelerating Our Strategy”, ANZ bluenotes, 27 May 2021, https://bluenotes.anz.com/posts/2021/05/anz-ceo-shayne-elliott-banking-future.

3. Standard Bank Group was the largest financial services group in Africa, based on Tier 1 capital, in 2023; see T. Minney, “Africa’s Top 100 Banks in 2023”, African Business, 4 October 2023, https://african.business/2023/10/finance-services/africas-top-100-banks-in-2023

4. “Purpose and Values”, Standard Bank Group, https://www.standardbank.com/sbg/standard-bank-group/about-us/who-we-are/purpose-and-values.

5. See “Overview of Financial Results”, Investor Relations, Standard Bank Group, https://reporting.standardbank.com/overview-financial-results-2022/ .

6. https://reporting.standardbank.com/about-us/key-performance-indicators/

7. Two-in-a-box is a management model in which two (or more) people are given equal leadership authority and responsibility for a task or set of tasks, often in complementary roles. Read about a two-in-a-box model in use at DBS in S.K. Sia, P. Weill, and M. Xu, “DBS: From the ‘World’s Best Bank’ to Building the Future-Ready Enterprise”, MIT CISR Working Paper No. 436, 19 March 2019, https://cisr.mit.edu/publication/MIT_CISRwp436_DBS-FutureReadyEnterprise_SiaWeillXu.

The post How Top-Performing Firms Needed to Reorganise Seven Times for Digital appeared first on The European Business Review.

By Daniel Trabucchi & Tommaso Buganza

The platform revolution has already taken place, but we link the idea of platforms with Big Tech like Amazon or Meta and digital-native start-ups like Airbnb and Uber. In this article, we explore the concept of Platform Thinking, perceiving platforms as a tool to foster digital business transformation for established firms like Telepass, John Deere, and Klöckner.

Over the last two decades, the business landscape has changed radically. We can prove it easily with two lists:

• 2003: Microsoft, General Electric, ExxonMobil, Walmart, Pfizer, Citigroup, Johnson & Johnson, Royal Dutch Shell, BP, IBM
• 2023: Apple, Microsoft, Alphabet, Amazon, Nvidia, Meta, Tesla, Berkshire Hathaway, Eli Lily, TSMC

These are the 10 companies with the highest market capitalisation in 2003 and 2023¹. We can get three main insights by comparing the lists: only one company remained in the Top 10 two decades later (Microsoft), and the industries had shifted quite clearly from product and energy companies to tech companies. Finally, of course, five out of the 10 companies in 2023 are “platform companies”.

The Big Tech MAGMA (Microsoft, Apple, Alphabet-Google, Meta, and Amazon) established their leadership by applying platform models and became, along with “younger giants” like Airbnb and Uber, flagship cases of the platform revolution. But what are platforms? Can we really compare these companies under the same label? And, more interestingly, is it all a matter of digital native companies? How can established – non-digital-native – companies exploit platform thinking and leverage the learning from (younger) digital companies?

What is a platform?

The question “what is a platform?” is not an easy one to answer. We recently wrote a book, Platform Thinking, which dedicates entire chapters to the peculiarities of the various typologies of platforms. Let’s start by defining what a platform is not. Not every value-creation mechanism based on a linear value chain is a platform. Porter described the linear value chain as a sequence of primary activities to transform raw materials from suppliers into finished products for the market, plus all the other activities needed to support these primary ones (e.g., training or hiring). This model easily applies to product or service companies such as General Electric, Johnson & Johnson, FedEx, and many others.

However, if we look at the aforementioned MAGMA cases, this definition doesn’t seem able to fully describe their value-creation mechanisms. We need to introduce the concept of platform (and, more precisely, different typologies of platforms) to describe their value-creation mechanisms.

Key features that allow us to define the concept of the platform: 1) the presence of two (or more) groups of interdependent customers and 2) the presence of cross-side network externalities that drive the growth and potential scale of platforms.

Microsoft (and, more precisely, the Windows operating system) is the typical case of an innovation platform² – and, indeed, one of the very first of these. Innovation platforms are technological systems open to two different customers: computer users on the one hand and “complementors” on the other. Complementors are organisations or individuals that can foster innovation on top of the platform, delivering their own products to the end users. In other words, Microsoft offers Windows to the final users but, at the same time, it offers its APIs and software development kits to software developers who wish to code on top of the operating system. In a nutshell, both the end users and the software developers (like Adobe or Autodesk) are customers in Microsoft’s eyes. Moreover, innovation platforms are subject to the so-called bidirectional cross-side network externalities³: the more users, the more value for software developers, and vice versa.

Amazon is a different and very varied case. It started off as a linear value chain company delivering books and, even now, still has important revenue sources managed as linear value chains, like AWS^4,5. However, if we focus on the Amazon Marketplace, we can see a perfect case of a transactional platform^6,7. We are clearly customers any time we log on to Amazon Marketplace to buy anything we need, from a book to a stabiliser, and we correctly perceive the merchant selling the stabiliser as a provider. From Amazon’s point of view, though, there are no customers and providers, but only customers and customers. People buying any product on Amazon are obviously customers, but companies selling those products are also Amazon’s customers. They receive the chance to reach one of the widest potential markets in the world, a delivery service, payment services, and much more. To underline their customer role, the sellers pay Amazon a fee for each product sold (if they were providers, they would be paid instead).

In this case, the platform is not the basis upon which to develop new and innovative products but rather an enabler of one-to-one transactions. There are two customers (buyers and sellers) and, again, bidirectional cross-side network externalities that make this platform so valuable; the more buyers, the more potential value perceived by the sellers, and vice versa.

Meta is the last platform typology we present. The reason we consider Facebook, as well as Instagram by Meta, a platform is mainly the presence of advertisers. Advertisers pay Facebook to reach as many viewers as possible and target them with incredible precision, thanks to the data generated by the users themselves. This makes Facebook an orthogonal platform^8,9. As in the previous cases, there are two customers (the end users and the advertisers) but, here, there is not a one-to-one transaction. On the contrary, the second side, the advertisers, see the first side as both a target for commercial purposes (but the transaction will happen somewhere else) and as a source of valuable information to target them more accurately. There are still externalities, but only unidirectional; the more end users, the more value for advertisers. The other way around (more advertisers, more value for end users) is just not verified.

These three non-linear value-creation mechanisms are very different, but they share two key features that allow us to define the concept of the platform: 1) the presence of two (or more) groups of interdependent customers and 2) the presence of cross-side network externalities that drive the growth and potential scale of platforms.

(Non-digital-native) established firms fostering innovation through Platform Thinking

So far, all the examples we have provided are Big Tech and / or digital-native start-ups mainly headquartered in Silicon Valley.

However, it would be a big mistake to think that these are the only companies that can benefit from platform-based value-creation mechanisms.

With the concept of “Platform Thinking“, we refer to the ability to foster innovation by seeing possible platform mechanisms everywhere. It might seem strange but, once unlocked, the platform way of thinking can foster innovation even in established, industrial, non-digital companies, as in the cases of Telepass (Italy), John Deere (USA), and Klöckner (Germany).

Telepass and Telepass Pay

From 1989, Telepass, once incorporated into Autostrade per l’Italia, the Italian motorway company, revolutionised the driving experience with its automated toll collection system.

The system enabled motorists to glide through toll stations, offering a seamless journey without the “stop and go” of traditional toll booths. Telepass is an example of how platforms can revolutionise the entire company’s value-creation mechanism¹⁰.

Initially, the Telepass service was a typical linear value chain, directly linking the company to the customer through a unique service offering.

This all changed in 2017, when Telepass expanded its horizons with the launch of Telepass Pay, a multiple-service smartphone app based on a transactional platform mechanism. Now, drivers could pay not only for tolls but also for parking, fuel, and even car services like washing or maintenance through a unified system. The introduction of Telepass Pay brought in additional customer groups – parking facilities, fuel stations, and service providers – and transformed Telepass into a platform enabling service transactions between them and drivers.

This strategic move turned Telepass into a multi-sided transactional platform leveraging cross-side network externalities; the more drivers, the bigger the value for service providers, and vice versa.

A start-up wanting to introduce a similar platform would encounter the so-called “chicken and egg” paradox: drivers are attracted by service providers who are attracted by drivers, but none of these sides are on board at the beginning. On the contrary, when launching Telepass Pay, Telepass already had 8 million users from the linear value chain service (toll payment) and had no paradox to face.

John Deere and the Operations Center

John Deere, founded in 1837, is well known for producing heavy-duty agricultural machinery. It’s an example of how incorporating Platform Thinking can extend the value-creation capabilities of a traditionally product-centric company relying on data¹¹.

Besides the obvious challenges of changing corporate culture, John Deere was able to leverage its brand, know-how with regard to agricultural equipment, and presence in approximately one-third of American arable acres. A start-up could hardly match these assets.

Initially, John Deere made its machinery “smart” by integrating sensors, GPS, and AI. Farmers used these sophisticated tools to collect detailed data, enhancing their agricultural productivity through “MyJohnDeere“. This smart equipment represented an advanced linear value chain, optimising activities like seeding and fertilisation through real-time data.

The transformative moment came when John Deere began seeing farmers not just as equipment users but as data providers. In 2013, Deere opened up MyJohnDeere through its “Operations Center“, a platform providing aggregated and anonymised farmers’ data to a number of third-party providers. This shift added a two-sided model with network externalities to the traditional linear value chain of the company (the production of heavy-duty agricultural machinery).

Previously, farmers used John Deere’s system to optimise seed planting. Transitioning to a platform model, John Deere managed to enable entities like Bayer to access a wealth of anonymised agricultural data. Bayer might analyse this data to create advanced seeds or fertilisers tailored to the identified conditions, establishing a feedback loop where farmers, utilising these products, enhance the platform’s collective intelligence.

This strategic move transformed John Deere into an orthogonal platform, allowing third parties to innovate using the aggregated data. It not only amplified the farmers’ capabilities but also catalysed a network effect, broadening the platform’s scope and attracting new participants to this knowledge-rich ecosystem.

Besides the obvious challenges of changing corporate culture, John Deere was able to leverage its brand, know-how with regard to agricultural equipment, and presence in approximately one-third of American arable acres. A start-up could hardly match these assets.

Klöckner and XOM Materials

Klöckner is an independent German producer-distributor of steel and metals. It operates between the big crude-steel suppliers, such as ThyssenKrupp and Tata Group, and the customers, such as construction companies, car makers, and phone and appliance manufacturers.

In the last decade, they have revolutionised a long-lasting and hard-to-change market with two moves.

In 2014, Klöckner launched Kloeckner Connect, a digital service allowing customers to make orders from any device, check on past orders, see what’s available in stock, look through the entire Klöckner catalogue, and place online custom orders. Although this represents a major innovation in the market, we can still consider it to be a (digital) linear value chain service.

The game-changer came with the launch of XOM Materials in early 2017. XOM is a transactional platform where Klöckner’s customers (later joined by many others) form the demand side, and the supply side is enriched not just by Klöckner’s products but also by offerings from various third-party vendors, competitors, and service providers^12,13.

This strategic move turned them from a metal producer and distributor into one of the leading steel service centre companies worldwide.

XOM Materials leveraged Klöckner’s established assets, like their extensive customer network, to generate cross-side network externalities. As more customers and suppliers joined, the value and efficiency of the network grew for all, demonstrating the potential of established companies to pivot into a transactional platform model even more successfully than start-ups.

Takeaways for Platform Thinkers

These six stories let us define platforms (Microsoft Windows, Amazon Marketplace, and Facebook) and how platforms can help established firms foster digital business transformation (Telepass Pay, Operations Center, XOM Materials).

At this point, we can leave behind the usual preconception about platforms. They are not just for digital native start-ups or Big Techs. We can, indeed, define Platform Thinking as the ability to use platform-based mechanisms to unlock digital business transformations¹⁴, and unveil the three key insights of this article:

1. “Platform” is a broken word. We need more labels – like “innovation“, “transactional“, and “orthogonal” – to capture the complexity of the value creation models around us.
2. Platforms are for everyone. Established traditional companies based on a linear value chain can also leverage Platform Thinking to foster digital business transformation.
3. Platform Thinking builds on established firms’ idle assets (like data, customers, brand, or existing relationships), opening avenues for value exploitation and innovation.

About the Authors

Daniel Trabucchi and Tommaso Buganza are, respectively, Senior Assistant Professor and Full Professor at the School of Management of Politecnico di Milano. They are featured in the Thinkers50 Radar list 2024. Their main area of research is Platform Thinking, which focuses on how platforms can be used to foster digital business transformation. They co-founded Symplatform https://symplatform.com/, the international symposium for academics and managers working on platforms, and Platform Thinking HUB, the observatory where the innovation leader community can explore innovation through platforms. They are the authors of Platform Thinking – READ the past. WRITE the future, published by Business Expert Press in 2023. You can find out more about their work on platformthinking.eu https://platformthinking.eu/.

References

1. https://en.wikipedia.org/wiki/List_of_public_corporations_by_market_capitalization

2. Cusumano, M. A., Gawer, A., & Yoffie, D. B. (2019). The business of platforms: Strategy in the age of digital competition, innovation, and power (Vol. 320). New York: Harper Business.

3. Katz, M. L., & Shapiro, C. (1985). “Network externalities, competition, and compatibility”. The American Economic Review, 75(3), 424-40.

4. For a detailed analysis of the evolution of Amazon as a platform: Trabucchi, D., & Buganza, T. (2023). Platform Thinking: Read the past. Write the future. Business Expert Press.

5. For a detailed analysis of Amazon evolution: Kenney, M., Bearson, D., & Zysman, J. (2021). “The platform economy matures: measuring pervasiveness and exploring power”. Socio-economic Review, 19(4), 1451-83.

6. Rochet, J. C., & Tirole, J. (2003). “Platform competition in two-sided markets”. Journal of the European Economic Association, 1(4), 990-1029.

7. Trabucchi, D., & Buganza, T. (2022). “Landlords with no lands: a systematic literature review on hybrid multi-sided platforms and platform thinking”. European Journal of Innovation Management, 25(6), 64-96.

8. Filistrucchi, L., Geradin, D., Van Damme, E., & Affeldt, P. (2014). “Market definition in two-sided markets: Theory and practice”. Journal of Competition Law and Economics, 10(2), 293-339.

9.  Trabucchi, D., Buganza, T., & Pellizzoni, E. (2017). “Give Away Your Digital Services: Leveraging Big Data to Capture Value”. Research-Technology Management, 60(2), 43-52.

10. Farronato, C.; Denicolai, S. & Mehta, S. (2021). “Telepass: From tolling to mobility platform”. Harvard Business Publishing Teaching Case.

11. Joachimsthaler, E. (2020). The interaction field: The revolutionary new way to create shared value for businesses, customers, and society. Hachette UK.

12. Joachimsthaler, E. (2020). The interaction field: The revolutionary new way to create shared value for businesses, customers, and society. Hachette UK.

13. Kominers, S. D. & Knoop C.I. (2020). “Klöckner & Co: Steeling for a Digital World”. Harvard Business Publishing Teaching Case.

14. Trabucchi, D., & Buganza, T. (2023). Platform Thinking: Read the past. Write the future, Business Expert Press.

The post Platform Thinking: What Established Firms Can Learn From Big Tech and Digital Start-Ups appeared first on The European Business Review.

By Stephan Kudyba and Agnel D’Cruz

The power of generative artificial intelligence (GEN AI) has organisations of all types intrigued and clamouring to leverage its functionality to enhance productivity, improve their financial bottom line and maintain market share or possibly achieve a competitive advantage. Increasing the speed and robustness of information assets presents ample opportunities for process applications. However, the jury is still out on a few issues for implementing this technology to achieve truly valuable results. Issues involving the data that must be accessible for large language models (LLMs), verifying the output generated and where to apply the platform to operationalise it for a sustained production environment introduces difficulties in its adoption.

Individuals who understand GEN AI capabilities and best applications in a given area need to collaborate in order to optimise potential roll-outs.

The following steps provide a high-level methodology on how to best approach the implementation of GEN AI to produce effective results that will justify the cost of the strategic initiative.

Throughout the step-wise approach, a major factor must be emphasised, and this is the involvement of expert knowledge of workers at all levels. It is an essential element to achieving success. A phrase that sums up the process is best stated….true value in Generative AI involves a collaborative environment integrating top-down strategy and bottom-up implementation.

1. Establish knowledge transfer to strategic management and SMEs regarding the functionality of GEN AI.

This goes beyond simply presenting generic prompting and content creation but involves prominent process applications.

In other words, knowledge experts within a given organisation must be informed of what GEN AI can actually do. Examples include:

• Devising marketing correspondence for a production environment
• Examining and extracting information from complex and lengthy documents
• Creating computer code and reverseengineering legacy systems
• Creating imagery
• Identifying gaps and new, evolving elements in existing content

Possible GEN AI functionality can help maintain parity with the market given its adoption by competitors. Competitive advantage could be achieved through faster time to market of custom information creation (e.g. combining internal data with that of open source).

2. Conduct collaborative knowledge transfer among informed knowledge experts along with GEN AI technicians (e.g. vendors) to achieve optimal organisational applications.

Knowledge creation is best achieved through an open collaboration of knowledge assets (e.g. SMEs). Individuals who understand GEN AI capabilities and best applications in a given area need to collaborate in order to optimise potential roll-outs. Diverse perspectives and cross-functional input can uncover both routine and innovative opportunities to create value with LLMs.

For example, leverage customer feedback from voice and online sources to enhance product attributes according to consumer (sizing, delivery, price, etc.).

This step not only entails the identification of best areas of GEN AI applications but also should involve estimations of potential returns. In the case above, adjusting product attributes yields increased customer satisfaction, sales, repeat buying, etc. Or in the case of a more routine application (streamlining marketing resources), estimate the expected reduction in labour and the value of re-allocated labour to more productive activities in the firm.

3. Once realistic GEN AI applications that prove tangible value have been identified, cost estimates need to be generated to measure implementation versus potential gains.

Technology And Technology Labour Costs

Data resources that must be accessed by LLMs must be identified, which involves the incorporation of data engineers when considering internal data or the combination of open source and internal data for competitive advantage. These engineers must estimate the time for alignment and optimisation of required data (e.g. cloud-based, internal repository based).

Computer processing costs entailed in LLMs utilisation must be included.

Editing And Verification Costs

True value in Generative AI involves a collaborative environment integrating top-down strategy and bottom-up implementation.

Costs required for the selection of editorial staff time must be initiated. This entails labour resources required to authenticate created content (e.g. safeguarding against adverse, incorrect, outdated content or hallucinations) and potential copyright infringement. This mitigates the risk of costly legal or accountability issues.

Total cost must then be weighed against the expected value created by GEN AI incorporation. The estimation of value must be recognised with variances, given the uncertainty of outcomes achieved, where probabilities should be considered.

4. Value estimation consideration: 

“Reduction” in cost (e.g. savings in a reduction in labour hours through streamlined processes).

“Enhanced” market share through competitive advantage in producing timely custom content. This may include knowledge-enhancing content for internal processes (e.g. adjusting product attributes to consumer needs) or creating custom content of timely information for sources external to the organisations (e.g. customers, suppliers).

“Reduction” in risk of losing market share to competitor activities who adopt GEN AI .

Risk Management is Key

As is the case with many innovative technologies available to the entire market, organisations must not only consider if the functionality is a fit for their organisation but whether they are exposed to loss of market position by competitors effectively adopting it. One of the main advantages of GEN AI is speed, or how quickly content can be created. This speed element includes the ability to generate ideas (identify critical content initially not thought of etc.). Speed also entails vast processing capabilities of information resources to streamline operations. Both of these can increase the exposure of losing market share.

Risk also involves the exposure and accountability of missing adverse content created (e.g. a missed hallucination or simply including inaccurate sources for creating content). This opens exposure to legal ramifications and loss of market share by producing content violating copyright laws, or releasing inaccurate content to customers.

5. Prioritise projects according to value and risk reduction.

After consideration of the previous steps, organisations must answer a major question. Is the project really worth it?

This considers the company’s risk profile and cost/value analysis. If the answer is YES, then move on.

It is time to select the application that best fits the organisation’s situation. What this involves is an examination of some previous steps. Prioritisation of a GEN AI implementation should focus on the following elements:

1. How exposed is the company to loss of market share by not taking action?
2. How significant is the process that will be augmented with GEN AI (will the implementation make a real difference to its performance?)

To illustrate these points, consider a hypothetical case in the insurance industry.

The application of GEN AI may augment an insurance provider’s ability to identify the elements required for custom coverage to an entity (individual, group, etc.) in a timelier manner than industry standards.

The ability to provide a quote for custom coverage faster than the industry norms may increase market share, given the augmentation of the customer experience. The risk exposure of not moving on GEN AI may be high. However, an additional risk that must be considered is that of devising coverage elements with inaccurate information.

6. The final stage is the roll-out of GEN AI for the application.

At this stage, data sources should be aligned, LLMs trained through the engagement of data scientists and prompters, and SMEs should be in place to measure accuracy. An overall assessment of the entire performance of the platform must take place, where the critical elements to focus on are the accuracy of content and time to production. The new process must evolve regarding new data sources and LLM accuracy.

About the Authors

Stephan Kudyba is a professor of analytics and information systems at the Martin Tuchman School of Business, New Jersey Institute of Technology. He has held senior management positions at prominent organisations and has been a researcher, professor, and practitioner of AI applications in business for over 20 years. Email: SKudybs@gmx.com

Agnel J D’Cruz is a seasoned data professional who is currently a principal of data strategy and partnerships at ZoomInfo. Agnel has been published by HBR for his outstanding work at Honeywell International where he led large-scale data and AI implementations.

The post Strategic Excellence: Steps to Maximise ROI in GEN AI Implementations appeared first on The European Business Review.

By Jacques Bughin and Jeroen Van Hautte

What do companies as diverse as Booking.com, Accenture, Revolut, GSK, Walmart, or Unilever have in common? Answer: they have been on a common journey to migrate their organisation to a skills-based organisation (SBO).

At a time when the skills mix of workers is exploding and the future of work is dramatically changing with work-from-home, automation, and AI, front-running companies are moving their organisations to a world of better skill use and continuous learning. But what they mostly find is that AI skill tech is a critical software solution to support the journey to an SBO.

1. The Causes and Rewards of SBO

The concept of the SBO represents a paradigm shift in the traditional working model. Instead of rigid job-centric structures, these organisations prioritise human skills, defining work by breaking down roles into tasks and activities based on required competencies. This transformative approach fosters an environment that values employee expertise, continuous learning, and adaptability over traditional siloed structures.

The trend towards skills-based organisations (SBO) is now well established and is inevitable for at least three reasons.

The first is that digitisation and other trends are shifting the skill set needs for the workforce. One would argue that skill shift has always been there. For example, coal miners in the past used to carry out heavy physical and manual tasks requiring gross motor skills and physical strength. Today, they increasingly operate machines that do the heavy and dangerous toil, and need to apply more complex skills by monitoring equipment and problem solving. Nurses in 1957 were required to administer medicines, monitor patients by taking their pulse and temperature, and help with therapeutic tasks, including bathing, massaging, and feeding patients. Today, they still administer medicines to patients but also help perform diagnostic tests and can analyse the results, employing skills and filling roles that were more common to doctors half a century ago. But our research with Nobel Prize recipient Chris Pissarides shows that the skill shift has been accelerating in recent years, and the skill obsolescence rate has been doubling in the last decade.

Digitisation and other trends are shifting the skill set needs for the workforce.

Second, the number of skills the workforce needs to master is only getting larger, not smaller, per individual. The skill set is moving to soft skills and, under a digital lens, skills that are also notoriously absent from the main scope of traditional educational systems. The result is an increasing mismatch where the skills of workers are badly utilised As a case example, consider taxi drivers. While, in 1970, fewer than 1 per cent of US taxi drivers had a college degree (meaning they master some clear cognitive skills), the proportion had risen to nearly 15 per cent by 2013 and is now reaching 17 per cent , with close to 10 per cent of them with a business and  engineering degree. Sure, those skill sets may be useful elsewhere.

Other research by OECD and other academic labour market scholars, using the PIACC skill taxonomy, concluded that skill mismatch affects 30 per cent  of workers in any of the 34 countries it analysed.

Third, AI itself is radically building a major skill shift and a new organisational model of the workforce where workers must augment their skills with technology while seeing tasks automated. Finally, using the catalyst of the COVID-19 crisis, a lot of organisations have been testing and promoting new work models, such as remote work. What we recently found is that, in general, the difference between using and avoiding fully agile work environment has been associated in the last three years with 3.1 points of extra revenue growth annually for large companies worldwide. for large companies worldwide.

Given those trends, companies which have adopted an SBO are demonstrating some clear rewards. A plethora of research mentions among other things that SBOs are 52 per cent more likely to innovate and 57 per cent more likely to anticipate and respond effectively to change. They have a 98 per cent likelihood of retaining their top talents.

2. AI Tech Is a Must-Have to Power the SBO Transition

From the above, pivoting to an SBO is one of the most robust proven ROI cases. In fact, a typical mismatch of skills of 20-30 per cent at the level of the firm is not unusual, and may translate to a gap (versus a perfect match) of more than 5-6 per cent of productivity loss. On a global basis, this is a 5 trillion GDP gap, linked to misuse of labour skills, according to consultancy BCG. And this does not take into account the fact that employees may feel frustrated, especially high performers.

Evidently, the organisational pivot is a fantastic opportunity for the CHRO, but  it is nevertheless a massive enterprise-wise task for which the CHRO may have operational accountability. Fortunately, this is where AI tech comes to the rescue.

AI itself is radically building a major skill shift and a new organizational model of the workforce.

While AI adoption has seen a staggering 70 per cent increase across business over the past five years, the spotlight has often been on supporting customer services and supply chain optimisation, but is now also moving into a key untapped potential in the field of so-called “skills tech”. This emerging market, with pioneers like TechWolf, Workday, or Skillate, is at the forefront of delivering AI solutions and tools to unleash the power of the skill-based organisation, storing and defining skills, inferring competencies across the workforce, and predicting / recommending training needs using AI and machine learning.

Powered by machine learning and data-driven tools, companies can then exhaustively map the current skills of their workforce. This involves creating skills matrices to identify existing skill sets within the organisation, highlighting strengths and weaknesses. This information then enables strategic planning of skills enhancement and renewal initiatives, ensuring that employees remain relevant and equipped for the jobs of tomorrow. AI skills technology acts as a visionary force, predicting future workforce needs, also identifying areas where additional training or recruitment may be required to meet the demands of future work.

3. Mapping the HR Tech Journey

If a company has not already embarked on the journey, then here are five key important steps.

Step 1: Get ready

SBO is a pivotal change. Hence, only a CHRO who has the drive, the vision, and the support of the board can make it happen.

Step 2: Select your skill tech partner

As discussed, SBO must be implemented and facilitated by skill tech. While some large providers will claim to have the right solutions, the best of these are coming from AI-native firms that can support a comprehensive AI factory for HR, including cleaned and accurate skill data, AI algorithms that fit HR needs, and AI Ops that make AI easy to use by employers and employees. On top of those qualities, (mostly cloud) app-based solutions allow ease of integration and access, and providers must have strong proof of data security.

Step 3: Clarify  

One major issue of the SBO is that CHRO and organisations will jump without having built the definition and taxonomy of skills they want. Having one unique definition of skills is a critical first step. The second step is the main objective of the transition, whether it is mismatch resolution and hiring speed-up, a skill-based internal labour market with the enterprise, etc.

Step 4: Implement and build workflows around skills

Step 5: Move from employer-centric to employee-centric: develop the best skill portfolio for each employee

If all this is clear, it is time to launch that SBO.

What are you waiting for?

About the Authors

Jacques Bughin is the CEO of MachaonAdvisory and a former professor of Management. He retired from McKinsey as a senior partner and director of the McKinsey Global Institute. He advises Antler and Fortino Capital, two major VC / PE firms, and serves on the board of several companies.

Jeroen van Hautte is a co-founder and CTO of TechWolf, one of the fastest-growing AI companies in Europe. The company leverages AI to help organisations understand the skills of their workforce and works with some of the world’s biggest brands. Jeroen developed his expertise in AI at Cambridge University and is recognised by the World Economic Forum and Forbes Under 30 as a leader in technology.

Additional References:

• Bughin J. (2020). “Preparing for the Coming Skill Shifts, The Digital Future Of Management”, in “How AI Is Transforming the Organization”, MIT Sloan Management Review, The MIT Press, https://doi.org/10.7551/mitpress/12588.003.0007

• BCG Henderson Institute (2020). “The Global Workforce Crisis: $8.5 Trillion in Growth at Risk”, retrieved from: https://www.bcg.com/publications/2020/impact-of-skills-deficit-on-global-economy

• Pissarides, C. (2019). “The Changing Nature of Work”, Nobel Prize Lecture, retrieved from: https://www.nobelprize.org/prizes/economic-sciences/2010/pissarides/lecture/

• (2019). “Skills Matter: Further Results from the Survey of Adult Skills”, retrieved from: https://www.oecd.org/skills/piaac/Skills-Matter-Further-Results-from-the-Survey-of-Adult-Skills-9789264258051-en.htm

• “Skills Management & Skills Intelligence”, retrieved from: https://techwolf.io/skills-intelligence/

• “Workday Skills Cloud”, retrieved from: https://www.workday.com/en-us/solutions/talent-management/skills-cloud.html

The post How AI Liberates the Transition to a Skill-Based Organisation appeared first on The European Business Review.

While the first full year of operation of ChatGPT, 2023, gave a foretaste of the enormous impact that AI is going to have on us all, 2024 shows every sign of boggling the mind even more. Here are some things to look out for.

GenAI has taken a leading role in supporting and enhancing activities, drawing on cognitive functions in many facets of our society. Unlike the robotic revolution that impacted mostly blue-collar workers in the manufacturing and assembly industries of the end of the 20^th century, GenAI has most directly impacted white-collar and creative workers over the past year.

OpenAI’s ChatGPT was the first major GenAI out of the gate in late 2022. It started an avalanche of entries in the field from start-ups to the leading large tech corporations of Microsoft, Google, Meta, IBM, and more. Now, with more than 100 million weekly users, as well as more than 92 per cent of the Fortune 500 companies,⁹ OpenAI remains in the lead of this massive movement to integrate artificial intelligence in nearly all aspects of business, industry, and commerce.

In one of the earliest academic studies of the implementation of GenAI, Harvard University, the University of Pennsylvania Wharton School, and MIT collaborated to analyse the impact of making the ChatGPT tool available to 758 consultants at the prestigious Boston Consulting Group. Given 18 realistic consulting tasks, the GenAI-equipped consultants, who used GPT-4, completed on average 12.2 per cent more tasks, 25.1 per cent more rapidly. Additionally, 40 per cent of the trial group were judged to have produced higher-quality results.¹⁹

Based on more than 4,700 interviews of business executives at the World Economic Forum held in Davos, Switzerland earlier this year, 46 per cent of the leaders believed that GenAI would boost profits in 2024. Also, 25 per cent of the chief executives expected GenAI to lead to headcount reductions of at least 5 per cent this year.⁷

Clearly, GenAI has the potential to be a game-changer in the coming year. In this article, we will examine a number of the key changes, challenges, and opportunities that can be expected by the end of the year.

Where is the technology today and where is it leading?

With a focus on artificial general intelligence (AGI), GPT-5 is expected to exhibit enhanced cognitive abilities, enabling it to comprehend and respond to a broader range of complex queries and tasks in a more human-like manner.

Any update on this technology has to carry the caveat that it is changing day by day and that research and development is, in most cases, months ahead of what is available to the general public. We are now in a period of highly competitive one-upmanship in the features, speed, security, and reliability of GenAI products. As the top dozen or so competitors seek to build consumer and corporate markets, we will see usage expand. Currently, business and industry has effectively applied the technology to marketing, accounting, industry research, product development, trend analysis, report writing, and predictive applications.

This rapidly changing environment will continue to make retraining and updating of staff and applications a necessary practice until a level of stability is reached. However, the changes are resulting in consistently improved performance that will make the updating cost-effective through more efficient and expanded performance in many cases.

OpenAI is expected to soon release a version 5 of their GPT large language model (LLM) that will include a host of new capabilities. Didier Hope writes in Medium, “The transition from GPT-4 to GPT-5 is anticipated to showcase significant advancements in generative potential, language understanding, and contextual reasoning, further consolidating GPT-5’s position as a leading AI model in the industry. With a focus on artificial general intelligence (AGI), GPT-5 is expected to exhibit enhanced cognitive abilities, enabling it to comprehend and respond to a broader range of complex queries and tasks in a more human-like manner.”¹⁵ GPT-5 will likely be seen as an incremental step toward AGI. We are not likely to see a robust version of AGI without a quantum computing platform, a technology that, itself, continues to develop and will likely host fully robust AGI. However significant developments can be expected in the coming months.

Capabilities to expect in 2024

This year, after a dizzying 2023, global business leaders will be able to lay the required groundwork for long-term AI adoption more thoughtfully and intentionally. Organisations will take a step back to evaluate and reskill their workforces, assess data infrastructure needs, and update cybersecurity practices in ways that consider AI needs and risks. As dependency on AI grows, chief information officers will play a pivotal role.¹³ Their expertise will be critical to proper procurement and implementation decisions impacting legacy systems.

The generative AI landscape is shifting from large language models towards smaller, open-source, often multimodal models that combine text, images, video, and more.20 This is important because these new models will be better able to specialise and solve niche use cases. This will lower barriers to experimentation and promote pilot testing, as these more precise and less costly models allow businesses to innovate faster. Market leaders across sectors will harness this trend to accelerate advancements in personalisation, supply chain optimisation, customer service, and many other business operations.²

Finally, in 2024, enterprise software providers will begin to integrate generative AI capabilities and features directly into at least some of their product offerings, making the technology’s transformative powers readily available to the masses within tools that they are already familiar with and use daily. Companies that leverage prepared workforces, modernised data, secure systems, and up-to-date software tools will gain sustained competitive advantages.¹⁷

Strategies to follow in 2024

McKinsey and Company estimates that GenAI could contribute between $2.6 trillion and $4.4 trillion annually to the global economy by 2030. Despite the excitement, they emphasise the importance of companies focusing on cautiously scaling their AI applications and undergoing fundamental organisational changes to fully and wisely leverage the benefits of AI transformations.¹⁶

There are many factors impacting the decision of whether to retain workers in the wake of significant new efficiencies and competencies afforded by GenAI.

Cprime’s “Generative AI in 2024: A Strategic Guide for Global Enterprises” explains that the economic impact of generative AI on enterprises will be profound, redefining operational efficiencies and cost structures across various industries such as healthcare, marketing, and legal. It will unlock new business value and spark significant advances across organisational functions by processing and extracting value from unstructured data, which has been difficult or impossible up to now.⁴

Apotheker and colleagues3 discuss the impacts of GenAI on businesses based on a survey of 1,400+ C-suite executives. While GenAI is rapidly changing business operations, 90 per cent of leaders are still waiting for the hype to subside or are pursuing only limited experimentation. The survey reveals that 66 per cent of leaders are ambivalent or dissatisfied with their progress on AI and GenAI, and only 6 per cent have begun upskilling meaningfully. Unfortunately, because of the exponential speed with which these technologies are unfolding, laggards will fall behind quickly as brave adopters jump on board.

How to handle your workforce – upskill or lay off / terminate?

Of course, there are many factors impacting the decision of whether to retain workers in the wake of significant new efficiencies and competencies afforded by GenAI. In order to best confront this decision, it may be best to engage GenAI itself, to assist with the priority considerations in decision-making. On 8 February, I asked the newly named Google Gemini to provide a list of considerations. Gemini prefaced its remarks with, “This is a complex issue with ethical and financial ramifications, so consider these factors closely.” It then proceeded to enumerate factors including:

1. Financial Considerations

1. 1. Cost of severance packages
   2. Cost of retraining programmes
   3. Long-term ROI for both scenarios

2. Skills and Adaptability

1. 1. Assessment of impacted employees
   2. Alignment of new opportunities
   3. Company culture and values

3. Prioritisation of employee well-being

1. 1. Long-term reputation
   2. Employee morale

4. Ethical Considerations

1. 1. Responsibility to employees
   2. Potential bias in AI systems
   3. Societal impact: What does the decision mean for the overall community?
   4. Consider how lay-offs may impact families and the local economy

Gemini provided additional details in each of these areas and went on to urge consultation with HR, legal experts, employee representatives, communication transparency, and more. Gemini is linked to the Web and updates its responses accordingly.¹⁰

Some may be surprised at the depth and relevancy of responses. Gemini also provides three different responses to the prompt. Follow-up prompts may provide even more relevant and useful information.

As suggested by the GenAI app, there is no single answer for this challenging aspect of successfully achieving efficiencies and competencies through inexpensive or no-cost AI. Google Gemini does not have a monopoly on perspectives; in confronting such situations, you may want to access multiple apps for a further diversity of options.

What to expect from new graduates as regards AI

Beginning in 2024, we should anticipate a new generation of graduates equipped with AI skills, reshaping the workforce.^6,12 Immersed in nascent AI applications, these graduates exhibit baseline fluency through daily use. Most considered AI’s trajectory in selecting their majors, with over 75 per cent factoring labour market implications into their decision-making.⁶ However, doubts persist regarding workforce automation. Many desire integrated curricula that blend technical and humanities disciplines to prepare them for AI collaboration and a hybrid new world.²¹

Reassuringly, this cohort remains hopeful about AI’s possibilities. Unlike previous technological shifts, these digital natives see AI as a tool to boost critical thinking, creativity, and productivity, not a replacement for human roles.²³ As Handshake¹² notes, Gen Z seeks to drive AI initiatives within organisations, suggesting robust understanding, a willingness to innovate, and a commitment to integrate AI solutions into business processes.

Expect cohorts attuned to AI’s risks and rewards, comfortable with constant reskilling, and motivated to direct these technologies toward equitable ends that improve society.6 Rather than displacing roles, their biggest anxiety is not being empowered to steer this wave of change.¹² Wise leaders will embrace their input on AI implementation, offering tailored upskilling initiatives and collaboration opportunities.

How the ecosystems of higher ed and business have changed

Business and industry thrive on maintaining an agile, responsive culture that is highly sensitive to the changing needs and wants of their market; higher education is notoriously known as the “ivory tower” that is insular and slow to change. Each of the two has a different ecosystem.

Business is dominated by serving the client or customer while generating a profit; higher education is ruled by serving students and, up until now, to a far lesser extent on serving employers of graduates.

The worldwide environment has changed across the two fields. With fewer students entering college, enrolments in Europe have declined by nearly 5 per cent over the past decade,¹⁴ and enrolments in the US have declined by 10 per cent over the same period.²⁴ Amid rising expenses for college attendance, students have looked to alternative credentials and directly entering careers in lieu of the traditional baccalaureate degree. In order to maintain tuition and fee revenue to cover operating expenses, the colleges and universities on both sides of the Atlantic must cultivate greater enrolments.

Meanwhile, the advent of GenAI and associated technologies has shifted the needs of employers. The new technologies can accomplish tasks historically handled by middle managers, accountants, human resources specialists, supervisors, marketers, computer programmers, legal department workers, and many more office positions. Yet, GenAI has opened whole new areas of workers in prompt engineering, AI trainers, sentiment analysers, AI integration specialists, AI ethicists, AI art directors, AI security specialists, and many more.⁸

It is in the nexus of education and employment that these two ecosystems merge. The interests of both universities and corporations are best served by successful careers for the students as they become new employees. The Boston Consulting Group has advocated for partnerships between higher education and business: “Partnerships between higher education institutions and employers can be invaluable for helping businesses respond to growing talent needs. They can offer employers a reliable way to cultivate an educated and trained workforce.”¹⁸ Serving their mutual interests, such partnerships will advance both ecosystems in the years ahead.

Strategies to stay on top of the changes

Forget the zero-sum-game mentality. Hamood11 advocates “extreme information-sharing”, particularly around failures and challenges. Nothing is a “mistake”, as long as learning is acquired. Fostering a culture of openness encourages cross-company collaboration, accelerates learning, and minimises redundant efforts. Share your AI journeys, successes, and setbacks with industry peers and participate in open-source communities. The collective knowledge exchange fuels innovation and propels the AI ecosystem forward.

The pace of AI development is relentless. As LinkedIn contributors1 advise, staying informed can be daunting. Cultivate a culture of continuous learning within your organisation.

The pace of AI development is relentless. As LinkedIn contributors1 advise, staying informed can be daunting. Cultivate a culture of continuous learning within your organisation. Voracious learning in diverse formats – from online courses and conferences to hands-on experimentation – expands competency and comfort with evolving tools.1 Invest in employee training programmes that equip your workforce with AI knowledge and skills. Encourage experimentation and support internal or external “hackathons” or innovation labs to explore emerging AI applications.

Finally, the AI Readiness Quotient, a diagnostic tool from Wharton, is an invaluable resource for businesses. It helps organisations assess their readiness for AI integration, identifying areas of strength and opportunities for improvement. By understanding their current position in the AI landscape, companies can develop targeted strategies to enhance their AI capabilities, ensuring that they can effectively leverage AI technologies.22

The world of business – more than ever before – belongs to the informed, agile, and fearless

As we progress through this second full year of GenAI, the ways in which we work, do business, and prepare for the future have changed. Our new tools have redefined many of the middle management positions in accounting, personnel, legal, customer relations, research, marketing, sales, and more. Those businesses that have embraced the new technologies are already reaping the benefits in more efficient operations, more creative approaches to tasks, more informed staff members, and new abilities to gather, analyse, predict, project, and apply data. The upside in operations is enormous at very low cost.

It is unprecedented that such advantageous tools enabling such valuable information, insights, and knowledge are available with a trivial investment. Over time, customised applications will become available to corporations at an increasing cost. However, now is the time to take advantage of building a base of utilising GenAI upon which the competitive future will depend. This requires a steady source of upskilled and new employees who have the understanding, knowledge, and experience to optimally utilise GenAI.

Universities, more than ever in recent times, are now pressured by declining enrolments to offer more relevant and useful foundations to all students. The key to relevance in today’s job market is knowledge and facility with utilising GenAI. Clearly, partnerships between corporations and industry associations with colleges and universities is a promising solution to the challenge of developing an AI-savvy workforce. It is in working together that we will be able to smoothly transition to the GenAI economy of 2024.

About the Authors

Ray Schroeder is a respected leader in higher education. He is the Professor Emeritus of Communication at the University of Illinois Springfield (UIS) and a Senior Fellow at UPCEA, the Online and Professional Education Association. Ray is a frequent author, speaker, and presenter on technology in education.

Katherine Kerpan is an accomplished marketing, communications, and product leader with over 20 years of experience driving complex initiatives at large non-profits. Currently, she works at her alma mater, Loyola University Chicago, as a project manager on the university’s enterprise marketing and communications team.

References

1. AI + LinkedIn contributors. (n.d.). “How do you keep up with the latest trends and innovations in AI?” LinkedIn. Retrieved 10 February 2024, from https://www.linkedin.com/advice/3/how-do-you-keep-up-latest-trends-innovations-704 1842070671556608

2. Althoff, J. (2024, January 29). “Embracing AI Transformation: How customers and partners drive pragmatic innovation to achieve business outcomes with the Microsoft Cloud”. Official Microsoft Blog. Retrieved 10 February 2024, from https://blogs.microsoft.com/blog/2024/01/29/embracing-ai-transformation-how-custo mers-and-partners-are-driving-pragmatic-innovation-to-achieve-business-outcomes-wit h-the-microsoft-cloud/

3. Apotheker, J., et al. (2024, January 12). “From Potential to Profit with GenAI”. Bcg.com. Retrieved 10 February 2024, from https://www.bcg.com/publications/2024/from-potential-to-profit-with-genai

4. Cprime (n.d.). “Generative AI in 2024: A Strategic Guide for Global Enterprises”. Cprime.com. Retrieved 10 February 2024, from https://www.cprime.com/resources/blog/generative-ai-in-2024-a-strategic-guide-for-global-enterprises/

5. Crouse, M. (2023, November 30). “Deloitte’s Tech Predictions for 2024: Generative AI Will Continue to Shape Chips Market”. TechRepublic. Retrieved 10 February 2024, from https://medium.com/next-top-writers/gpt-5-release-date-what-to-expect-from-the-latest-openai-model-6f9cc4739612

6. Flaherty, C. (2024, January 10). “Survey: How AI Is Impacting Students’ Career Choices”. Insidehighered.com. Retrieved 10 February 2024, from https://www.insidehighered.com/news/student-success/life-after-college/2024/01/10/survey-college-students-thoughts-ai-and-careers

7. Fleming, S. (2024, January 15). “Generative artificial intelligence will lead to job cuts this year, CEOs say”. Financial Times. Retrieved 8 February 2024 from https://www.ft.com/content/908e5465-0bc4-4de5-89cd-8d5349645dda

8. Forbes Councils Member. (2023, July 6). “20 New And Enhanced Roles AI Could Create”. Retrieved 10 February 2024 from https://www.forbes.com/sites/forbestechcouncil/2023/07/06/20-new-and-enhanced-roles-ai-could-create/?sh=76c351eb6f04

9. Gartenberg, C. (2023, November 6). “ChatGPT already has ‘tens of millions’ of active users, developer conference reveals”. The Verge. Retrieved 3 February 2024 from https://www.theverge.com/2023/11/6/23948386/chatgpt-active-user-count-openai-dev eloper-conference

10. Gemini (2024, February 10) Considerations for Layoffs vs. Upskilling/Retraining.

11. Hamood, J. (2023, September 17). “Growing With AI Not Against It: How To Stay One Step Ahead”. Informationweek.com. Retrieved 10 February 2024, from https://www.informationweek.com/machine-learning-ai/growing-with-ai-not-against-it- how-to-stay-one-step-ahead

12. Handshake (n.d.). “Report: The Class of 2024 sets its sights on the future: A new cohort of seniors charts a path to AI fluency, financial stability, and work-life balance”. Joinhandshake.com. Retrieved 10 February 2024, from https://joinhandshake.com/network-trends/gen-z-career-goals-ai-economy/

13. Hansen, B. (2023, December 22). “Generative AI Trends for 2024”. Dataversity. Retrieved 10 February 2024, from https://www.dataversity.net/generative-ai-trends-for-2024/

14. Hanson, M. (2024, January 10). “College Enrollment & Student Demographic Statistics”. Retrieved 8 February 2024) from https://educationdata.org/college-enrollment-statistics

15. Hope, D. (2023, November 30) “GPT-5 release date: What to expect from the latest OpenAI model”. Medium: Next Top Writers. Retrieved 5 February 2024 from https://medium.com/next-top-writers/gpt-5-release-date-what-to-expect-from-the-lates t-openai-model-6f9cc4739612

16. Jackson, A. (2023, December 21). “GenAI will continue to dominate the 2024 business landscape”. Aimagazine.com. Retrieved 10 February 2024, from https://aimagazine.com/data-and-analytics/genai-will-continue-to-dominate-the-2024-b usiness-landscape

17. Janakiram MSV (2024, January 2). “Exploring The Future: 5 Cutting-Edge Generative AI Trends In 2024”. Forbes. Retrieved 10 February 2024, from https://www.forbes.com/sites/janakirammsv/2024/01/02/exploring-the-future-5-cuttin g-edge-generative-ai-trends-in-2024/?sh=2efb0119206e

18. Litman, et al. (2022, October 19). “How Higher Ed and Employers Can Partner to Power Talent Pipelines”. Retrieved 10 February 2024 from https://www.bcg.com/publications/2022/bridging-the-talent-gap-by-partnering-with-hig her-ed-institutions

19. Martinez, C & Mezitis, T. (2023, October 13). “Harvard Business School Partners with BCG on AI Productivity Study The Harvard Crimson”. Retrieved 30 January 2023) from https://www.thecrimson.com/article/2023/10/13/jagged-edge-ai-bcg/

20. Ranger, S. (2024, January 24). “Tech Trends for 2024: Generative AI Models Will Get Smaller, An iPhone’s Average Life Will Be 8 Years”. TechRepublic. Retrieved 10 February 2024, from https://www.techrepublic.com/article/tech-trends-2024/

21. Sloyan, T. (2023, August 23). “To Keep Up With The AI Revolution, We Need To Change How We Learn”. Forbes.com. Retrieved 10 February 2024, from https://www.forbes.com/sites/forbestechcouncil/2023/08/23/to-keep-up-with-the-ai-re volution-we-need-to-change-how-we-learn/?sh=44486cd4b6d4

22. Snyder, S. A. (2024, January 12). “What’s Your Company’s AI Readiness Quotient?”. Informationweek.com. Retrieved 10 February 2024, from https://knowledge.wharton.upenn.edu/article/whats-your-companys-ai-readiness-quoti ent/

23. Threlkeld, K. (2023, November 15). “Indeed Hiring Lab: 5 Trends That Will Make or Break the 2024 Labor Market”. Indeed.com. Retrieved 10 February 2024, from https://www.indeed.com/lead/indeed-hiring-lab-5-trends-that-will-make-or-break-the-2 024-labor-market

24. Welding, L. (2023, August 16). “U.S. College Enrollment Decline: Facts and Figures”. Retrieved 10 February 2024 from https://www.bestcolleges.com/research/college-enrollment-decline/

The post Generative AI Update for 2024 appeared first on The European Business Review.

Semiconductors are positioned at the forefront of innovation and digital transformation. With companies like Nvidia leading the charge and the semiconductor sector potentially reaching a staggering valuation of $1 trillion in the next five years, we contemplate the bright future and emerging trends shaping this critical industry. 

The Future Looks Bright for Semiconductors 

For those who may not remember, “Everybody Loves Raymond” ran for nine seasons on CBS in the US, and was (apparently) voted the 35th-best sitcom of all time by Rolling Stone magazine. Borrowing the title to include Nvidia is not far-fetched. As the Financial Times recently reported, Nvidia ranks 5th in terms of the number of hedge funds holding shares and, most importantly, it is the stock that has added the most this year. 

This interest is clearly linked to the fact that the digital revolution is finally putting all the pieces together, with the cloud, big data and AI. But for this to run, one needs semiconductors. 

Since their inception, semiconductors have radically changed the course of technology, with the successful demonstration of the first transistor in the 1940s. The use of semiconductors as the base material for optical fibres was then widely introduced in 2000. The role of semiconductors in electronic circuits and lasers demonstrates their undeniable importance in our modern world. As the world moves into the next phase of digitalisation and the Web 3.0 era, semiconductors are once again at a crucial inflection point. This is not just due to geopolitical factors such as TSMC-Taiwan and China, or supply chain disruptions caused by the COVID-19 pandemic, which has led to delays in various industries, including automotive. Instead, it is being driven by the shift towards electronic and electric vehicles, the transition to 5G/6G wireless networks and, mostly, artificial intelligence.  

The recent surge in the share price of Nvidia testifies to the enthusiasm for semiconductors. The SMH index of 25 industry leaders is up around 25 per cent this year, with a lower beta than most technology and artificial intelligence stocks. These trends point to a bright future for semiconductors, with forecasts suggesting that the sector could reach a valuation of US$1,000 billion over the next five years. 

Bubble or beyond Moore? Five trends to consider 

However, this optimism begs the question: is this growth sustainable, or is it simply a bubble? For some players, such as Nvidia, their share price performance is closely linked to their return on assets (ROA) and return on equity (ROE), which have expanded significantly in recent months. Nevertheless, as the semiconductor industry continues to evolve at a rapid pace, it is important to identify and manage new dynamics around at least five emerging trends. 

• Trend 1: Hello, (generative) AI. How will demand evolve, especially with the emergence of generative AI models driving semiconductor demand? 
• Trend 2: Product evolution. Is silicon still the reigning champion, or will compounds such as gallium nitride (GaN) dominate the landscape, thanks to their superior electrical properties and energy efficiency? 
• Trend 3: Dual transformation. Can sustainability and digitalisation coexist harmoniously, or is the energy-hungry nature of digital technologies a stumbling block? 
• Trend 4: Hyper-competition. How will the competitive landscape evolve as technology giants increasingly design their own chips? 
• Trend 5: Battle of the platforms. The rise of the ARM architecture is challenging the dominance of the x86 architecture. How will this reshape the semiconductor ecosystem, particularly in terms of chip architectures and supplier dynamics? 

Trend 1: Generative AI 

One of the key drivers of semiconductor demand in recent months has been the development of powerful generative AI models to complement already-burgeoning AI applications such as deep learning, computer vision, robotics, and Internet of Things (IoT). 

For these models to work, a special type of chip – AI accelerator chips (or deep learning processors) – is needed to speed up AI computations, making them significantly faster and more energy efficient than using general-purpose processors. These AI accelerators often have multiple cores and focus on low-precision arithmetic, are optimised to process data with reduced precision using new dataflow architectures, and efficiently process data through specialised pipelines. 

Key supplies include Nvidia’s Tensor Processing Units (TPUs) and AMD’s Radeon Instinct accelerators. These specialised chips are optimised for deep learning tasks and are widely used in data centres for AI inference and training. Nvidia’s Tesla GPUs, for example, are powering AI applications in sectors ranging from healthcare to autonomous vehicles, demonstrating the important role of semiconductor companies in meeting the evolving demand for AI. 

The uncertainty does not come from the surge in AI demand; it comes from the fact that there is no dominant design (yet?), and the evolution of AI in terms of horizontal / vertical LLMs is not yet defined. 

Trend 2: Is silicon here to stay? The rise of gallium nitride (GaN) 

GaN is a compound semiconductor with superior electrical properties that will usher in a new era of energy-efficient electronics. GaN has a very hard crystalline structure and a wide bandgap, making it more suitable for high-power, high-frequency optoelectronic applications such as blue LEDs, microwave power amplifiers, and space applications (e.g., solar panels on satellites). 

However, it is increasingly being used in power supplies for electronic devices, converting alternating current from the grid into low-voltage direct current. GaN technology can handle larger electric fields in a much smaller form factor than silicon, while offering much faster switching. GaN is becoming indispensable, for example, in power conversion platforms, where silicon has reached its limits, or in the transition from mobile computing to Web 3.0. GaN chips are also easier and faster to manufacture than silicon chips, a major drawback of semi-finished products in the recent past, so companies are turning to GaN for smaller, more efficient electronic devices. 

Trend 3: The promise of dual transformation 

Dual transformation is the hope that sustainability and digitalisation are highly complementary. For example, digital technologies can enable people to work efficiently from home, reducing the environmental impact of commuting. At present, however, the vision of dual transformation is not justified, because digitalisation is an energy-intensive process. A single semiconductor factory can consume up to 1 TWh of energy per year and 2 to 4 million gallons of ultra-pure water per day. Semiconductor manufacturers have understood the challenge and, like native digital players, are unveiling their sustainable development initiatives. These include moving cloud workloads to GANs with access to renewable energy or improving semiconductor design. However, moving to GANs is a game changer, because it radically reduces energy consumption. 

Trend 4: Hyper-competition? 

Until now, the AI mega-users (e.g., Google, etc.) have outsourced the value chain, buying chips from third parties. But this is changing. Many tech giants, such as Apple, Tesla, Google, and Amazon are now making their own chips, designed specifically for their products. Google has just unveiled its new Pixel 6 and Pixel 6 Pro phones, which use Tensor, the first chip designed by Google to bring AI capabilities to its range of mobile phones. Apple’s new MacBook Pros 2021 are based on the company’s own M1 chips. Importantly, this development could challenge the current horizontal model of AI players such as Nvidia. 

This move towards in-house chip development could challenge the current model of outsourcing semiconductor production to third-party manufacturers. In particular, the trend could disrupt the dominance of companies such as Nvidia in the horizontal AI accelerator market, as technology giants seek greater control over their semiconductor supply chains. 

Trend 5: Platform battle: chip architectures 

The x86 architecture has dominated the microprocessor industry for more than 50 years. However, this is changing with the growing popularity of the ARM architecture. While this architecture was born out of the need for low-power chips for vertical applications, it is beginning to establish itself not only as a low-power solution, but also as a high-performance competitor to the established x86 players. 

Google and AWS have decided to build their own chips, choosing the ARM architecture for its performance and low power consumption, which has become so important for power-hungry data centres, consumer products, and sustainability efforts. This growing shift to the ARM architecture is changing the dynamics of the semiconductor ecosystem. Unlike the x86 platform, where companies can buy from one or two suppliers, ARM has become a broker, making its intellectual property available to multiple companies. 

About the Author

Dr. Jacques Bughin is the CEO of Machaon Advisory and a professor of Management. He retired from McKinsey as senior partner and director of the McKinsey Global Institute. He advises Antler and Fortino Capital, two major VC/PE firms, and serves on the board of multiple companies.

The post Everybody Loves … Nvidia appeared first on The European Business Review.

It’s great having you around again, Mr Cox! In our last interview, you mentioned the potential risks associated with generative AI, such as deep fakes and misinformation. What safeguards do you think are necessary for businesses adopting AI to ensure ethical use and mitigate risks?

Thanks for having me back!

Jumping into AI is like navigating a minefield with huge rewards but equally big risks, such as deep fakes and misinformation. It’s crucial for businesses to be picky about where and how they use AI, similar to conducting a risk assessment of ISO271001 security standards. Ask yourself: Is the planned use of AI ethical? What are the reputational and financial risks? And importantly, does it benefit your customers and staff?

Also, with laws constantly evolving, staying updated is key. For instance, the US just banned AI robocalls with deep fake voices, as this threatened the upcoming election. It’s a wake-up call for businesses using any similar tech in their services, for instance in a customer service environment.

Lastly, stick with a machine learning model that’s got street cred, something reputable and off-the-shelf. If you try to cut corners or go off the beaten path without really knowing what you’re doing, you’re opening up a can of worms. Bad data is not just a headache, it’s a fast track to a whole mess of risks.

As a founder of Enate, can you share some key lessons or insights you’ve gained from building a SaaS platform that facilitates operational efficiency through automation and AI?

We find that a lot of businesses think they’re ready for automation and AI but, the truth is they’re often not. You’ve got to have your house in order first. If you don’t know who’s doing what and with what resources, jumping into automation is like putting the cart before the horse. You’ll just end up redoing everything a year later.

Then there’s the real talk about automation. It’s not just about slapping some tech on a process and calling it a day. It’s a whole business shift. We’re talking about fundamentally changing how people work and interact with their jobs. Sure, the tech part is cool, but the real magic happens when you see how it changes the work dynamic for the better, though for some, the idea of it can be a bit daunting.

Finally, building a company like Enate has taught me that you’re only as good as your team. You need folks who aren’t just brilliant but also gel well together. It’s those relationships and that team spirit that really drive a company forward.

So, in a nutshell, get your basics right, understand that automation is a change maker, not just a tech upgrade, and build an extraordinary team that loves working together.

With the evolution of human roles alongside AI, how do you envision the collaboration between human workers and AI evolving, and what steps can companies take to facilitate a smooth transition and collaboration between the two?

AI is set to become the ultimate co-pilot, taking over tedious tasks and freeing humans up for work that adds real value – tasks like enhancing customer engagement. To make this transition smooth, companies should engage those excited about AI, providing a framework but avoiding unnecessary micromanagement. It’s crucial not to rush everyone at the same pace; some will embrace AI quickly, while it’s only natural that others will take a while to warm up to its benefits.

Understanding “the art of the possible” with AI is vital, but it’s changing literally weekly at the moment. Companies should bring in experts to demystify AI’s potential, making the magic of technology accessible to all. Knowledge plays a key role in this transition; it can either empower or intimidate. By educating and inspiring employees about AI, companies can ease fears and foster a collaborative future where humans and AI work together seamlessly, enhancing productivity and creativity.

As businesses explore AI integration, what role do you think the collaboration between industry leaders and AI developers plays in driving impactful innovations for operational enhancement?

Most industry leaders have little understanding of “the art of the possible” and most AI developers have little understanding about what businesses need. This is where intermediaries such as Enate play a key role. We’re not direct AI developers; instead, we serve as the essential link, enabling collaboration between industry leaders, who often lack insight into AI’s potential, and AI developers, who may not fully understand business challenges.

The key to driving impactful innovations lies in this collaboration, facilitated by intermediaries. Unlike big consultancies or global systems integrators (GSIs) that may not provide the necessary freedom for exploration, entities like Enate offer a platform for truly innovative solutions. By demonstrating how generative AI can solve real business problems, we not only bridge the knowledge gap but also foster operational enhancements that are both meaningful and practical. This collaborative approach ensures that AI integration is not just about technological advancement but about creating tangible value for businesses.

What simple yet impactful steps can businesses take to make their AI implementations more transparent and understandable to employees?

To make AI implementations more transparent and understandable, businesses should empower their employees to take the lead on these projects. The key is to move beyond seeing AI as simply a to-do list for the IT department. When off-the- shelf AI models are made accessible to business users, they can directly apply these tools to their work areas, enabling a deeper understanding and ownership of the technology.

For instance, at our company, we’ve embraced this approach by purchasing and deploying an AI testing platform that our testing team (not the IT department!) has configured and installed. This hands-on involvement demystifies AI, allowing employees to see firsthand how it can be tailored to meet their specific needs and challenges. By enabling those who are directly impacted by AI to lead its operationalisation, businesses can create a more inclusive, transparent environment that encourages everyone to engage with and understand AI technologies.

How do you believe that businesses can effectively integrate AI innovations to streamline operations and remove manual work, fostering increased productivity?

Orchestration is the backbone of any automation endeavour. You need to start with an end-to-end workflow tool such as Enate, which will enable you to stand back and view your entire service line from start to finish, get the right task to the right worker at the right moment, and enable you to identify bottlenecks and make impactful changes.

The main steps involved in any service delivery life cycle can be distilled into three main steps: 1. understanding the request (what is the customer asking?); 2. gathering the necessary data (get the data to do it); and 3. executing on the task (go and do it). The first two steps must be meticulously completed to ensure that the third step is executed effectively. Implementing a robust orchestration tool facilitates this process, ensuring that AI and automation technologies can be implemented and leveraged to their fullest potential to boost efficiency across the board.

What quick wins can businesses achieve by leveraging AI in their day-to-day processes?

AI is essentially a productivity super-boost. It enables people to do fewer of the mundane, repeatable tasks and focus their attention on the more challenging and rewarding aspects of work, such as customer success. For instance, with Enateʼs AI capabilities, businesses can automatically categorise emails, check the sentiment of communications, intelligently extract data from a range of documents, and automate queries. The time saving is massive.

For example, in email classification environments alone, using AI saves 30 hours per 1,000 emails. For a mid-sized operation, that’s a huge saving over the course of a year.

On a personal note, how would you define success?

Feeling happy. I don’t view success as a specific number or end point. It’s much more personal and immediate than that. I define success by satisfaction – mine and that of the people around me. If I’m happy and I can see that my actions or achievements bring happiness to others, then I consider that a success. On the flip side, if the people and team around me are not experiencing satisfaction, then it signals to me that there’s still work to be done.

Executive Profile

Kit Cox is Enate’s Founder and CTO. Kit has been obsessed with technology from a young age, he began coding at the age of 10 and is an engineer by trade. Kit built Enate’s workflow orchestration and AI platform to help businesses run operations smoothly, automate manual tasks and deliver SLAs on time. Today, global businesses such as TMF and EY rely on Enate to work efficiently and seamlessly.

About Enate

Enate is an end-to-end orchestration platform designed to help businesses run operations smoothly and produce consistent work on time, view, manage, and track the flow of all work, identify automation opportunities, assign tasks to the right resource, and become more efficient.

EnateAI, powered by GPT-4, is the latest product release from Enate. It’s integrated into the platform and offers five exciting features to help businesses leverage AI in operations, including: categorise – automatically categorise emails to create the right ticket category; data extraction – extract data from your emails and auto-populate forms; sentiment analysis – identify the emotional tone of communications from your clients; thank you analysis – know whether incoming “thank you” emails need action; foreign language fluency – understand and process foreign-language emails.

The post What can AI do? The Art of the Possible: Interview with Kit Cox, Founder and CTO at Enate appeared first on The European Business Review.

Congratulations on the recent breakthrough in quantum computing! Could you explain, in simple terms, what the discovery of logical qubits means and how it helps solve the problem of high error rates in quantum computing?

A critical challenge preventing quantum computing from reaching its enormous potential is the noise that affects qubits, corrupting computations before reaching the desired results. Quantum error correction overcomes these limitations by creating “logical qubits”, groups of physical qubits that are entangled to store information redundantly. This redundancy allows for identifying and correcting errors that may occur during quantum computations. By using logical qubits instead of individual physical qubits, quantum systems can achieve a level of fault tolerance, making them more robust and reliable for complex computations.

The announcement mentions the sensitive nature of atoms causing high error rates. How does the concept of grouping qubits into logical qubits address this sensitivity and improve the stability and reliability of quantum computers?

Quantum error correction has been developed to counteract this sensitivity. It involves grouping several physical qubits to form a single logical qubit. This approach significantly enhances the stability and reliability of quantum computers.

The concept of grouping qubits into logical qubits introduces redundancy, akin to the classical repetition code used in traditional computing. In a classical repetition code, information is replicated across multiple bits to protect against errors; similarly, in quantum error correction, the state of a logical qubit is distributed over multiple physical qubits. If one or some of the physical qubits experience an error, the overall state of the logical qubit can still be maintained and determined based on the remaining, unaffected physical qubits. This redundancy provides a buffer against individual qubit errors, leading to increased stability.

Additionally, logical qubits allow for the implementation of error detection and correction algorithms. These algorithms can identify when and what type of error has occurred. Once an error is detected, specific quantum operations can be applied to correct it, restoring the logical qubit to its intended state. By using logical qubits, quantum computers can therefore manage and mitigate the effects of errors more effectively, paving the way for more reliable and robust quantum computing systems.

The collaboration with Harvard University, MIT, and NIST/UMD is highlighted in achieving quantum error correction in 48 logical qubits. Can you share with us how these partnerships contributed to this milestone?

The collaboration between Harvard University, QuEra Computing, MIT, and NIST/University of Maryland played a pivotal role in achieving this significant milestone in quantum computing.

Harvard University led the experiments, which were performed in the Harvard labs. QuEra supplied critical electronic components and know-how. QuEra, MIT, and Harvard are in a long-time close collaboration. In fact, QuEra was founded by several Harvard and MIT professors.

The term “logical qubits” might be new to many. Can you break down what logical qubits are and why achieving quantum error correction in 48 of them is such a big deal?

Logical qubits are groups of physical qubits that are entangled to store information redundantly. Until now, previous demonstrations of error correction have showcased one, two, or three logical qubits. Our research demonstrates quantum error correction in 48 logical qubits, enhancing computational stability and reliability while addressing the error problem.

This breakthrough has achieved the creation and entanglement of the largest logical qubits to date, enabling the detection and correction of arbitrary errors. Larger code distances imply higher resistance to quantum errors.

Additionally, our research showed for the first time that increasing the code distance indeed reduces the error rate in logical operations. By realising the use of 48 small logical qubits to execute complex algorithms, this research has surpassed the performance of the same algorithms when executed with physical qubits.

Moody’s Analytics recognises the potential to revolutionise data analytics and financial simulations. Can you provide a simple example of how everyday tasks, like data analysis or financial predictions, might be positively affected by this quantum computing breakthrough?

Moody’s Analytics’ recognition of the potential of quantum computing to revolutionise data analytics and financial simulations is a testament to the transformative power of this technology. To understand how quantum computing could positively affect everyday tasks like data analysis or financial predictions, let’s consider a simplified example:

Imagine a financial analyst working for an investment firm, tasked with predicting stock market trends to make informed investment decisions. In the classical computing world, the analyst relies on algorithms that process historical data, market indicators, and economic factors. However, as financial markets are incredibly complex and influenced by countless variables, classical computers can take a considerable amount of time to analyse data and often struggle with accurately predicting market behaviours, especially under volatile conditions.

Now, introduce quantum computing into this scenario. Quantum computers, with their ability to handle and process vast data sets simultaneously, can speed up this analysis or perform this analysis while considering a wider range of variables, and provide more nuanced insights into potential future market movements. This could lead to more accurate and timely predictions, enabling the investment firm to make better-informed decisions, manage risks more effectively, and potentially achieve higher returns.

Another example is the potential to enhance weather forecasting, which, especially in predicting the severity of major weather events, is a crucial application with far-reaching implications for consumers, as well as for insurance companies. Classical computers, currently used for weather modelling, face limitations due to the sheer complexity and dynamic nature of atmospheric systems. Quantum computers could allow for more precise and faster modelling of complex weather phenomena. Improved accuracy in forecasting the paths and impacts of events like hurricanes, tornadoes, and floods could lead to more effective emergency planning and response, ultimately saving lives and reducing property damage.  

How does this achievement accelerate the timeline for practical quantum applications in the short term, and what kind of applications could we see sooner than expected?

This announcement sets us on a path culminating in a system with 100 logical error-corrected qubits which we will have ready in 2026. This development, capable of deep logical circuits, will push quantum computing beyond the limits of classical simulation.

The unique transversal gate capability of logical qubits prevents error propagation across qubits, making them inherently error-resistant. They simplify quantum error correction by allowing errors to be corrected independently for each qubit. This system establishes the groundwork for error-corrected quantum computing.

The announcement mentions solving problems previously considered intractable by classical computing. Can you give an example of a problem that was once impossible to solve but may now be within reach thanks to this breakthrough?

One example of a problem that was previously considered intractable by classical computing but may now be within reach, thanks to the breakthrough in quantum computing, is the optimisation of large-scale logistics networks. Classical computers struggle with this problem due to its immense complexity and the exponential growth of possible solutions as the size of the network increases.

Consider the scenario of optimising a global logistics network for a major shipping company. The company needs to determine the most efficient routes and schedules for its fleet of trucks, ships, and planes, considering numerous variables such as delivery deadlines, fuel costs, vehicle capacities, weather conditions, and traffic patterns. This is a classic example of a combinatorial optimisation problem, where the number of possible combinations of routes and schedules grows exponentially with each added variable, quickly becoming too vast for classical computers to analyse effectively.

Quantum computers can explore a much wider range of potential solutions in parallel, significantly reducing the time it takes to identify the most efficient logistics plan. This capability could lead to substantial cost savings, reduced environmental impact, and improved service quality for the shipping company.

Looking ahead, how do you see this impacting our daily lives? Are there specific areas or industries where the average person might see the positive effects of this quantum computing advancement?

Advancements in quantum computing are poised to impact our daily lives in several significant ways, particularly as this technology becomes more integrated into various industries and applications. Here are some specific areas where the average person might see the positive effects of quantum computing.

Healthcare and medicine: Quantum computing has the potential to revolutionise drug discovery and personalised medicine. By accurately simulating molecular interactions, quantum computers can help develop new medications and treatments more quickly and cost-effectively.

Financial services: Quantum computing can enhance risk assessment, portfolio optimisation, and fraud detection in the financial industry. This means more secure transactions, better financial products, and potentially lower costs for consumers. Improved financial models could also lead to more stable and efficient financial markets.

Supply chain and logistics: As mentioned earlier, quantum computing can optimise logistics and supply chains, making them more efficient and environmentally friendly. This could result in faster delivery times, lower costs, and reduced carbon footprint for the products we use every day.

Weather forecasting and climate research: Quantum computing can provide more accurate and timely weather forecasts, helping us better prepare for natural disasters. It can also enhance climate modelling, leading to more-informed decisions about environmental policies and practices.

Energy sector: Quantum computing can optimise energy production and distribution, leading to more efficient use of renewable resources and a reduction in energy costs. This could accelerate the transition to sustainable energy sources, benefiting both the environment and consumers.

Executive Profile

Yuval Boger is the CMO of QuEra, the leader in neutral atom quantum computers. He served as CEO and CMO of frontier-tech companies in markets including quantum computing software, wireless power, and virtual reality. In “The Superposition Guy’s Podcast”, he hosts thought leaders in quantum computing, quantum sensing, and quantum communications to discuss business and technical aspects that impact the quantum ecosystem.

The post A Logical Solution for Quantum Computing: Interview with Yuval Boger, Chief Marketing Officer at QuEra appeared first on The European Business Review.

By Hervé Legenvre

DeepTech technologies have significant effects on the economy and are an avenue to solve many problems humanity faces. However, the vitality of DeepTech projects is linked to the innovation ecosystems in which they are embedded. In this article, Hervé Legenvre explores the role of innovation in DeepTech success using the ATTRACT programme.

Innovation is the art of abolishing distances. Innovation thrives by combining distinct, diverse, and previously unconnected capabilities and resources to solve problems. This is particularly true for DeepTech Innovation. ATTRACT is an initiative that created a Pan-European DeepTech ecosystem that spans detection, imaging, and computing technologies. It assembled around 170 DeepTech projects in an ecosystem comprising scientific research institutions, universities, business networks and hundreds of students from across Europe. ATTRACT takes the art of innovation to its next level by steering open and interdisciplinary cooperation across a diverse network of stakeholders.

Innovation as the art of combining the power of distant objects 

Adam Smith’s book, The Wealth of Nations is renowned for the description of the division of labour, where the production process is segmented into a series of small, specialised tasks, each carried out by a distinct worker, enhancing overall efficiency. Smith’s illustration of this through a pin factory is well-known. Yet, the subsequent passages in this book that portray innovation as the art of linking disparate resources and expertise are less cited. He noted.

“Many improvements have been made by […] philosophers or men of speculation, whose trade it is not to do anything, but to observe everything; and who, upon that account, are often capable of combining together the powers of the most distant and dissimilar objects.”

In today’s era of high specialisation and continuing knowledge expansion, philosophers are scientists, and men of speculation are entrepreneurs, but innovation remains the art of combining disparate capabilities, resources, and expertise to solve problems. This art is essential to convert scientific discoveries into economic and social benefits. Bringing DeepTech projects to market requires uniting many elements that are diverse and distinct and initially disconnected.

What are DeepTech technologies? 

DeepTech solutions require long and uncertain development cycles, significant capital investment, and a deep understanding of the underlying scientific principles.

In the rapidly evolving world of technology, the term “DeepTech” describes technologies that are rooted in groundbreaking scientific discoveries and high-tech innovations. These are not the apps or software we use every day at work or in our lives; they are technologies born out of significant scientific advancements and discoveries. DeepTech projects can sometimes be taken to market by new ventures but also by collaborative projects where large companies and scientists work together. The applications of DeepTech innovations are often not confined to a single industry. They are often pervasive and impact a wide array of sectors. They have broad-ranging effects on the economy, improving efficiency and productivity across many different industries. They are also regarded as an avenue to solve some of the most pressing problems humanity faces. Unlike consumer-focused apps or software, DeepTech solutions require long and uncertain development cycles, significant capital investment, and a deep understanding of the underlying scientific principles. DeepTech ventures often struggle with raising money due to their longer gestation periods and higher research and development (R&D) investments. Entering different markets is a major hurdle for DeepTech projects. Different countries and sectors have their own unique culture, language, and business practices.

The role of innovation ecosystems and public policies in DeepTech success 

With such challenges, DeepTech projects need support from powerful innovation ecosystems and public policies. DeepTech projects can gain considerably from supportive public policies. Governments can allocate funds for DeepTech R&D. This can take the form of grants, tax incentives, or direct investment in research initiatives. Such funding helps DeepTech projects overcome the initial capital-intensive phase of development where private investment might be risk-averse due to the long-time horizons and uncertain outcomes associated with these projects. Public policies also support education and training at the intersection of technology and the economy to enlarge the talent pool available to DeepTech companies. DeepTech challenges and opportunities transcend national borders; it is therefore key to have public policies that promote international collaboration so DeepTech projects can connect a more global innovation network. In a nutshell, governments play a crucial role in shaping the ecosystem within which DeepTech projects operate.

The vitality of DeepTech projects is linked to the innovation ecosystems in which they are embedded. These ecosystems bring together complementary resources and expertise to nurture these projects. Research Institutions and universities are the seedbeds of science and DeepTech innovation. They are also the training grounds for the next generation of scientists, engineers, and entrepreneurs. Large companies act as partners for DeepTech projects, providing the scale, resources, and industry expertise necessary to bring DeepTech innovations forward. Startups can also emerge out of DeepTech projects, they are often the vehicles that take nascent technologies out of the lab into the market. Venture capital firms, angel investors, and other financing entities can also play a role when the time is right. These investors also bring their business acumen, mentorship, and networks that can help DeepTech entrepreneurs.

The origin of ATTRACT 

ATTRACT is a pioneering initiative bringing together Europe’s fundamental research and industrial communities to lead the next generation of detection and imaging technologies. Funded by the European Union’s Horizon 2020 programme, it aims to help revamp Europe’s economy and improve people’s lives by creating products, services, companies, and jobs. By funding this project, the European Union aims to foster innovation from the earliest stages of technological development to market entry and beyond and to create positive societal impacts.

The ATTRACT initiative represents a strategic and collaborative effort to harness the innovative potential of pan-European research infrastructures and their associated communities. The consortium that orchestrates ATTRACT activities includes Aalto University, the European Organization for Nuclear Research (CERN), the European Industrial Research Management Association (EIRMA), the European Molecular Biology Laboratory (EMBL), ESADE Business School, the European Southern Observatory (ESO), the European Synchrotron Radiation Facility (ESRF), the European X-Ray Free Electron Laser Facility (European XFEL), and the Institut Laue-Langevin (ILL).

The core objective of ATTRACT is to bridge the gap between fundamental research and marketable solutions by engaging entities capable of translating high-level research into societal and commercial gains. ATTRACT has created a rich ecosystem that meshes Pan-European research infrastructure, universities and industry representatives. Research infrastructure initiatives and universities are hotbeds for advanced scientific inquiry and technological development. They are equipped with state-of-the-art facilities and are staffed by researchers working at the frontiers of knowledge across various disciplines. Industry players bring a commercial perspective to the table. They can help create connections between the generation of knowledge and its practical application.

A structured innovation process for DeepTech projects 

ATTRACT offers a structured process to identify and develop breakthrough technologies in the field of detection and imaging. By selecting 170 potential projects, ATTRACT focusses resources on the most promising ideas that have the potential to address societal challenges and fill gaps in the market. In its first phase, ATTRACT helped validate and test these concepts through a gradual and phased approach as illustrated in Figure 1.

In the context of ATTRACT, a demonstrator is an early version of the technology that is used to demonstrate its potential, performance and viability to stakeholders including early investors or partners. A prototype is a more advanced application of the technology, dedicated to a specific market. Prototypes help test and refine the functionality of a product, identify issues, and present the concept to stakeholders including potential users.

The first phase of the ATTRACT Project also serves as an initial financier backed by the European Union Horizon 2020 programme. A €20 million funding from Horizon 2020 supports the selection of 170 technology concepts via an Open Call to consortia comprising Research Infrastructures (RIs), academic institutions, Research and Technology Organisations (RTOs), and industry partners. Each successful proposal received a one-time payment of €100,000 to initiate the first steps of the project.

The second phase of ATTRACT helps bring XX project closer to a market-ready stage. Here, there is a focus on the proven and most promising breakthrough technology concepts from the previous phase showing strong potential for scientific, industrial and societal applications.

A springboard for interdisciplinary education 

ATTRACT focusses resources on the most promising ideas that have the potential to address societal challenges and fill gaps in the market.

The ATTRACT Programme also adopted an “Open Science to Open Innovation” approach by training students in an interdisciplinary manner. This closes a gap in the current education landscape which often limits students to specialised activities without interdisciplinary interaction or entrepreneurial training. The goal is to move beyond the traditional, siloed approach to research and instead imbue students with Design Thinking and similar methodologies. Using these methodologies, groups of students coming from different horizons envision how the DeepTech projects of ATTRACT can be used to solve real-world problems. This fosters an entrepreneurial and co-creation mindset among students, demonstrating how these approaches can mesh scientific research and social innovation. The initiative is designed not only to enrich the educational experience of students but also to contribute to a more innovative and evenly distributed logic of innovation.

What is ATTRACT’s secret recipe? 

Innovation is the art of abolishing boundaries and distances. Innovation often emerges from the combination of different and previously unrelated elements such as specialised knowledge, practical skills, advanced equipment, financial backing, and real-world challenges across various industries.

The ATTRACT initiative is about fostering a community as much as it is about advancing technology. It’s not merely a source of funding; it’s a networked environment where sharing ideas and support is standard practice. Central to ATTRACT is the idea that a diverse range of contributors can enable the development of DeepTech innovation. This community includes research institutions and universities that lay the groundwork for new ideas through fundamental research and provide the intellectual and physical resources to develop these ideas further. Students and business networks contribute new perspectives and energy, while public funding, companies and venture capitalists bring the necessary investment and business expertise to help turn promising ideas into market-ready products.

This collaborative approach within ATTRACT is designed to encourage openness and interdisciplinary cooperation, making it easier for people to learn from one another and work together effectively. By facilitating connections among these varied elements, ATTRACT helps to develop nascent ideas into mature projects. ATTRACT speeds up the development and application of new technologies, broadening their potential impact. This approach showcases the value of enabling collaboration across different disciplines and sectors to drive innovation forward.

Example of projects supported by ATTRACT 

ULTRARAM 

ULTRARAM is a new type of computer memory that uses very little energy with the speed of DRAM and the non-volatility of flash memory. It’s designed to save a lot of power in places like data centres and satellites in space. The next step for the project is to test ULTRARAM’s ability to hold data for a long storage time and its endurance level. This could change how all kinds of devices, from tiny sensors to big data centres, store and use data. The ULTRARAM team is led by Manus Hayne from Lancaster University, it is now supported by the award-winning startup Quinas Technology as it won the title of “Most Innovative Flash Memory Startup” in Silicon Valley.

RANDOM POWER 

The Random Power taps into the quantum characteristics of semiconductors to create a secure and continuous supply of random data bits, crucial for encryption. In its first phase, the project team which included research institutions and companies, successfully engineered a compact, credit card-sized device that generates these random bits and passed US National Institute of Standards and Technology tests, confirming its practicality for real-world use. The project led by Massimo Caccia from the Università dell’Insubria has since grown to include a new company formed from the original team and has broadened its expertise by collaborating with other projects. The goal now is to develop a range of True Random Bit Generators that can be used across various industries, from large-scale infrastructure to the IoT and automotive sectors, enhancing security with quantum-level unpredictability.

The author would like to thank the ATTRACT team for their support and for making this article possible. This includes Pablo Garcia Tello, Markus Nordberg and John Wood. 

About the Author

Hervé Legenvre is a Professor and Research Director at EIPM. He manages educational programmes for global clients. He conducts research and teaches on digitalisation, innovation, and supply chain. Lately, Hervé has conducted extensive research on how open-source software and open hardware are transforming industry foundations. Hervé is part of the project advisory committee and part of the Independent Committee for the Socioeconomic Studies call in ATTRACT (www.eipm.org). 

The post Innovation, the Art of Abolishing Distance: Building the ATTRACT DeepTech Ecosystem  appeared first on The European Business Review.

By Ram Ramalingam, Teresa Tung, Nitu Kaushal & Shalabh Kumar Singh

Edge is an essential component of the cloud computing model and has the potential to help enterprises increase speed of action, reduce network costs and boost efficiency. But rapid adoption or higher investment is not enough to get the best value from edge computing. Integration of edge with the enterprise’s overall cloud adoption strategy is the way forward.

KEY TAKEAWAYS

1. 83% of our survey respondents believe that edge computing will be essential to remaining competitive in the future but only 65% are using edge today.
2. Super Integrators—edge adopters that tie edge to business in transformation adoption—comprise just 6% of edge adopters across the globe and 5.5% of those in Europe, but gain the most in terms of efficiency, cost reduction and capitalising on revenue opportunities.
3. Our three-step framework for optimal edge adoption includes strategising for edge, scaling edge across the enterprise and strengthening allied capabilities such as operations and human resources.

Edge computing, in step with an overall cloud strategy, can play a major role in bending enterprises’ innovation curve. Much of innovation today comes from companies that adopt solutions informed by the staggering amounts of data generated in branch offices, on individuals’ health trackers, retail stores, remote oil rigs, manufacturing plant sites, hospitals and even satellites. In most cases, it is inefficient to move all of this data back to a central data centre for real-time analysis. And real-time complex analytics right where the data is produced – be it on the factory floor, on an individual’s health tracker or at the store checkout counter – have the potential to drive the next wave of performance improvement across industries.

We see several examples of edge adoption in many industries. Take the case of a UK-based international energy services company that designs, builds, operates, and maintains oil, gas and renewable energy assets. It wanted to increase efficiency, productivity and safety at its project sites during the critical construction and commissioning phases. The Internet of Things (IoT) enabled the collection of data from sensors, equipment and workers. Edge computing enabled onsite analysis of this data to drive real-time improvements in operations, resiliency, worker safety and security.

In retail, Starbucks combines in-store IoT capabilities with cloud computing to run real-time analytics on the machines used by baristas for personalising orders and for predictive maintenance.¹

In auto and transport, Tesla has created its own chipset to custom-build a supercomputer that trains the AI systems within the car, which will rely on data from at-scale AI training across its fleet.² Heathrow Airport is experimenting with AI combined with 3D scanning on edge to prevent wildlife trafficking.³ 

For our research report, “Leading with Edge Computing,⁴” we surveyed 2,100 C-level executives across 18 countries to explore the extent of edge adoption and factors that influence success. We found that while enterprises are convinced about the possibilities edge enables, adoption is yet to reach optimal levels — 83% of our survey respondents believe that edge computing will be essential to remaining competitive in the future but only 65% are using edge today.

However, as we have seen, enterprises across industries are rapidly adopting edge computing to improve performance and reduce latency, essential for deploying levers of reinvention such as AI, including generative AI. While the trend is recent, it is catching up, as our research below shows, and European companies are keeping pace with the rest of the world.

Global spending on Edge is expected to be $208 billion in 2023, a 13.1% jump from 2022. Enterprise and service provider spending on hardware, software and services for Edge is forecast to sustain this pace of growth through 2026 when spending will reach nearly $317 billion.⁵

Edge in Europe 

For European companies, the enhanced data security that edge entails is a major attraction. Concerns about data privacy, supply chain vulnerabilities and uncertainty about where critical data is stored and processed in the cloud are driving many countries to treat digital sovereignty as a regulatory, responsibility and reputational matter. An increasing number of European companies are considering sovereign cloud investments to comply with numerous regulations, but also find it complex to implement.⁶  

Edge is one way to help companies meet data protection regulations, such as GDPR, by restricting the sharing of data over cloud for specific requirements and across limited servers—all with the consent of the user. Accenture’s Sovereign Cloud Survey revealed that to ensure cloud sovereignty, 40% of European enterprises have taken actions to secure their sensitive data at the edge, while 55% have plans to do so in the next 2-3 years.

As Europe focuses on taking manufacturing to the next level by establishing smart factories, edge computing can come into play in many ways. It can improve quality assurance by fine-tuning automation and reducing variation in output through onsite analysis of data. Manufacturers that need to locate computing closer to the site of production to enable smart, connected systems – which can be cumbersome and expensive – could look to edge computing for a solution. Edge is also critical for incorporating artificial intelligence, including generative AI, in industry, enabling data analysis in real or near real-time and delivering key business insights.

Edge computing is also a step towards sustainable computing. Data centres and transmission networks consume a significant 1-1.5% of the world’s electricity produced today, and their energy use continues to grow.⁷ While data centres are moving towards greener practices such as more efficient cooling mechanisms and renewable energy usage, edge can contribute too by reducing unnecessary data traffic between devices and the cloud.⁸ Gartner predicts that with edge computing, just 25% of enterprise data will need to be uploaded to the cloud by 2025.⁹

Aiming for an integrated approach to edge adoption 

Getting the most out of edge requires more than mere installation and investment. Accenture’s research shows that edge adoption challenges conventional management wisdom. The fast adopter doesn’t necessarily have the greatest advantage overall. Half of the companies we studied adopted edge as a standalone technology on ad-hoc projects (following the Ad-hoc or Tactical approaches in the figure below) to achieve quick improvements to their bottom line and address immediate pain points rather than strategic improvements. While this enables learning and some smaller, faster outcomes, these are not the companies that achieved the best results. Our cluster analysis shows that neither early adoption led by a centralised digital team nor higher investments to address specific business needs led to optimal outcomes. 

The other half, which applied edge across all parts of their business (the Integrated or Super Integrated approach), saw better outcomes. They operated with a view that edge increases the value of their digital core and enables the integration of artificial intelligence into their core business. Edge helped them accelerate innovation and reduced costs, improved efficiency, led to new revenue opportunities and enabled better customer experience. The differentiating factor was the strategic approach to edge adoption and its integration with a broader cloud strategy.

Unsurprisingly, the Super Integrated approach delivered the best results. Super Integrators comprise a small group of companies – 6% of edge adopters across the globe, and 5.5% of those in Europe.  Our research shows that Super Integrators are 4x more likely to achieve accelerated innovation, 9x more likely to increase efficiency and nearly 7x more likely to reduce costs than those that follow the Ad-hoc approach.

This is because Super Integrators made the best use of the digital core’s range of technologies – cloud, data, AI, applications and platforms – using edge to make quick innovation decisions. They could also enhance digital capabilities with human talent.

Not all companies can or need to aspire to be Super Integrators, but any level of integration provides future-proof resilience as their tech footprint and digital core expand.

How to unlock the value of edge 

Edge implementations often begin with relatively narrow objectives but should be designed with broader applications in mind.

The oil and gas enterprise mentioned earlier believes that edge will be a key part of its broader strategy in the future, which includes AI and blockchain. With these technologies, it hopes to achieve its goal of being a net-zero energy business by 2050.

It realised early on that though deploying edge had to initially be geared towards local conditions where the use case would be customised to factors such as geographic location and weather, the successful use case would need to be scaled across multiple locations globally.

The company has created a horizontal function that focuses on standardising use cases and now has approximately 40 edge assets. It used multiple providers for cloud services as well as outside consultants for edge implementation and turned to key technology original equipment manufacturers (OEMs) for edge-related systems and devices.

The result: reduced unplanned downtime, which can cost the company up to 10% of its revenues.

It also launched its edge talent strategy by working with a leading systems integrator while simultaneously establishing an edge Centre of Excellence to build a pool of relevant internal talent. This approach gives the company the flexibility to adjust the internal/external talent ratio as conditions warrant.

A three-step framework

The future of edge 

Current trends indicate that edge adoption is gaining momentum globally. By 2028, 38% of the edge infrastructure footprint will be in Asia Pacific, with 29% in Europe and 21% in North America.¹⁰ The full potential of technologies like IoT and AI can only be harnessed through the simultaneous strategic deployment of edge computing. This is a major opportunity for European enterprises to be at the forefront of technology deployment and establish themselves as lead players in their respective industries.

Most of the companies that participated in our research believe that edge will be a transformative force—one that leads to innovative business models over the next three years.

However, the right approach is an important distinguishing factor between making the most of this technology versus using it in stop-gap solutions. Aligning edge with the business strategy, integrating it with the digital core and garnering support from all stakeholders is the key to successful adoption.

The authors would like to thank Jai Bagmar, Toms Bernhards Callahan and Ramani Moses for their contributions to this article. 

About the Authors

Ram Ramalingam is the senior managing director and global lead for software & platform engineering and intelligent edge at Accenture. 

Teresa Tung is managing director and global CTO of Cloud First, data and AI, at Accenture. 

Nitu Kaushal is managing director and Europe practice lead, intelligent edge, at Accenture.

Shalabh Kumar Singh is the principal director and global lead for sustainable technology and cloud-related thought leadership at Accenture. 

References

1. ¹ Starbucks Turns to Technology to Brew up a More Personal Connection with its Customers, Microsoft, https://news.microsoft.com/source/features/digital-transformation/starbucks-turns-to-technology-to-brew-up-a-more-personal-connection-with-its-customers/ 

2. ² Tesla Starts Production of Dojo Supercomputer to Train Driverless Cars, The Verge, July 2023, https://www.theverge.com/2023/7/19/23800854/tesla-driverless-dojo-supercomputers-production

3. ³ HRH The Duke of Cambridge Visits Microsoft’s UK Headquarters to Learn about Project SEEKER as Part of His Work with The Royal Foundation, Microsoft, November 2021, https://news.microsoft.com/en-gb/2021/11/18/first-of-its-kind-multispecies-ai-model-to-detect-illegal-wildlife-trafficking-is-ready-to-roll-out-to-airports/#:~:text=HRH%20The%20Duke%20of%20Cambridge%20met%20Microsoft%20UK,work%20with%20The%20Royal%20Foundation%E2%80%99s%20United%20for%20Wildlife.

4. ⁴ Leading with Edge Computing. Accenture. https://www.accenture.com/content/dam/accenture/final/accenture-com/document-2/Accenture-Leading-With-Edge-Computing.pdf#zoom=40′

5. ⁵ New IDC Spending Guide Forecasts Edge Computing Investments Will Reach $208 Billion in 2023, IDC, February 2023, https://www.idc.com/getdoc.jsp?containerId=prUS50386323

6. ⁶ Sovereign Cloud Comes of Age in Europe, Accenture, https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-Sovereign-Cloud-PoV-Short-2023-24-May-FINAL.pdf#zoom=40

7. ⁷ Data Centers and Data Transmission Networks, IEA, https://www.iea.org/energy-system/buildings/data-centres-and-data-transmission-networks

8. ⁸ Uniting Technology and Sustainability, Accenture, Uniting Technology and Sustainability | Accenture https://www.accenture.com/content/dam/accenture/final/a-com-migration/pdf/pdf-177/accenture-tech-sustainability-uniting-sustainability-and-technology.pdf#zoom=40

9. ⁹ Computing on the Edge Can Be Transformative – But Look Before You Leap, Forbes, March 15, 2021, https://www.forbes.com/sites/forbestechcouncil/2021/03/15/computing-on-the-edge-can-be-transformative—but-look-before-you-leap/?sh=64836186f3a5

10. ¹⁰ LF Edge’s State of the Edge 2021 Report Predicts Global Edge Computing Infrastructure Market to be Worth Up to $800 Billion by 2028, the Linux Foundation, March 2021, https://www.linuxfoundation.org/press/press-release/lf-edges-state-of-the-edge-2021-report-predicts-global-edge-computing-infrastructure-market-to-be-worth-up-to-800-billion-by-2028 

The post Edge Computing in Europe: A Key Driver of Business Innovation  appeared first on The European Business Review.

By Jack McGuire, David De Cremer, Leander De Schutter, and Yorck Hesselbarth

In the contemporary digital era, innovations such as artificial intelligence (AI) are profoundly transforming the business landscape (De Cremer, 2020). The buzz surrounding ChatGPT, coupled with recent assertions about the sentience of Google’s LaMDA, a large language model, underscore the prominence of chatbot technology in these advancements (Adamopoulou & Moussiades, 2020; Ryu & Lee, 2018; Tiku, 2022). Customer-oriented chatbots, an emergent application of this tech, offer unparalleled efficiency and cost-effectiveness, operating ceaselessly and responding to client inquiries in real time (Salesforce, Research, 2019). Yet, amidst these advantages lies an ethical conundrum. Customers cherish genuine human interaction and can become quickly disillusioned when they realise they’re communicating with a bot, not a person (Ciechanowski, Przegalinska, Magnuski & Gloor, 2019). Balancing this desire for authenticity with the allure of operational efficiency poses a challenge, making it tempting for businesses to deceive customers by blurring the lines between human and machine.

Specifically, organisations nowadays are confronted with a reality where chatbots demonstrate remarkable human-like qualities (Collins & Ghahramani, 2021; Leviathan & Matias, 2018). This reality makes the choice to cut costs by adopting human-like chatbots a rational one. However, this choice is not so straightforward for organisations to make. After all, customers prefer the real thing (i.e., interactions with a human) over the artificial one, and therefore making the rational choice requires organisations to adopt a strategy of deceiving their customers by not disclosing to them that chatbots are used.

However, what are the risks when firms use chatbots without disclosure? What happens to the reputation of organisations engaging in these deceptive acts when customers find out what is really happening? And, even more important, what happens to the employees working for those organisations? When deception is found out, organisations are likely to suffer reputational damage, but will it also tarnish the careers of their employees? Several high-profile tech companies have faced backlash over the unethical use of emerging technologies.

Consider the fallout from the Theranos fraud and misconduct scandal. While the company suffered legal and reputational damage, employees faced a backlash, too. Several of them reported difficulties in job transitions, with potential employers associating them with the scandal (Lapowsky, 2021). As companies carry responsibility for their employees, it is imperative from an accountability point of view that they are aware of any potential effects on the careers of their employees before succumbing to the allure of deploying chatbots under a veil of deception. To test whether employees indeed suffer in their career prospects when the organisation they work for engages in deceptive chatbot practices, we conducted several experimental and field studies (McGuire, De Cremer, De Schutter, Hesselbarth, Mai & Van Hel, 2023).

The Ripple Effect on Careers 

First of all, our research unsurprisingly finds that organisations employing undisclosed chatbots are perceived as less ethical by customers when found out. Obviously, if you work for an organisation that is seen as unethical in its use of emerging technologies, it will affect your work identity. If this is the case, how will it affect the judgements and subsequent actions of these employees? The Uber scandal involving the suppression of sexual harassment allegations presents some useful insights regarding how to respond to that question. Employees at Uber, even those uninvolved, experienced that the company’s ethical breaches overshadowed their individual reputations and motivated many of them to resign (Kosoff, 2017).

Organisations that deceive their customers by pretending to have humans handle customer enquiries are judged to be unethical by both customers and the employees working for those organisations.

To validate this idea, we ran a series of experimental studies where employees in a simulated company were asked to facilitate deceptive chatbot use. Putting employees in this situation made them more likely to perceive their organisation as cultivating a culture of making unethical requests to their workforce. In turn, because of these perceptions, we found that those employees wanted to quit their job more.

So, organisations that deceive their customers by pretending to have humans handle customer enquiries are judged to be unethical by both customers and the employees working for those organisations. As a result, customers will show no loyalty to those organisations, and employees want to leave them. But where can those employees go? Are they contaminated for the job market? With today’s rapid transmission of information online, a company’s unethical practices can become widely known, and thus impact employees’ professional trajectories.

To study this phenomenon, we conducted two more studies, where we assessed how those employees are seen by recruiters. Our results showed that employees that had worked for an organisation known to use chatbots deceptively were perceived by recruiters to be less trustworthy, were less likely to be offered a job, and were given a lower salary when offered one. The deceptive use of chatbots therefore has widespread repercussions. It harms not only the company, but also the people who work there.

The Responsibility of Tech Professionals: A Call to Action 

The case is clear. Tech professionals must champion ethical AI use. The broader societal implications of our creations cannot be ignored. Advocating for transparency and ethical guidelines protects both the company’s reputation and your own professional standing. The findings from our research offer two actionable takeaways:

1. The role of leaders. Leaders must recognise the lasting harm of deceptive practices. Ethical technology use can bolster company reputation, morale, and customer trust.
2. The role of employees. Employees should be proactive, voice concerns about unethical technology use, and leave companies using deceptive practices before those deceptions are revealed. Communicating these concerns anonymously, in private with your manager, or publicly in team meetings and town hall sessions are all useful and should be considered.

In conclusion, as AI’s role in business grows, its ethical use is critical. It’s not merely about company profits; it’s about the careers and reputations of those who make up the organisation. Prioritising ethical AI practices isn’t just a business imperative; it’s a career necessity.

About the Authors

Jack McGuire is Jack McGuire is a Postdoctoral Research Associate at the D’Amore-McKim School of Business at Northeastern University (Boston). He received his PhD in Management & Organization from the National University of Singapore Business School and his MSc from University College London. Prior to this, he was an Experimental Lab Manager and Research Assistant at the University of Cambridge, Judge Business School. Jack’s research examines the psychological consequences of artificial intelligence and its increasing application in the workplace. This work has been published in Journal of Business Ethics, Computers in Human Behavior, International Journal of Human–Computer Interaction, and Harvard Business Review, among others. 

David De Cremer is currently the Dunton Family Dean of D’Amore-McKim School of Business and professor of management and technology at Northeastern University (Boston), and an honorary fellow at Cambridge Judge Business School and St. Edmunds College, Cambridge University. Before moving to Boston, he was a Provost chair and professor in management at National University of Singapore and the KPMG endowed professor in management studies at Cambridge University. He is the founder and director of the Center on AI Technology for Humankind (AiTH) in Singapore, which was hailed by The Higher Education Times as an example of interdisciplinary approaches to AI challenges in society. He is one of the most prolific behavioral scientists of his generation, and a recognized global thought leader by Thinkers50. He is a best-selling author, including “Leadership by algorithm: Who leads and who follows in the AI era?”, and his newest book “The AI-savvy leader: 9 ways to take back control and make AI work”, which will be published by Harvard Business Review Press in 2024. 

Leander De Schutter is assistant professor at the Vrije Universiteit Amsterdam, the Netherlands. He is interested in leadership and decision-making in the workplace. 

Yorck Hesselbarth is building foundational models with European values at Nyonic AI, contributing to digital sovereignty on the continent. Previously, he conducted research in the field of human-computer interaction and led several cutting-edge AI projects for the German Armed Forces. 

References 

• Adamopoulou, E. & Moussiades, L. (2020, June). “An overview of chatbot technology”. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 373-83). Springer, Cham. 

• Bogost, I. (2022). “Google’s ‘Sentient’ Chatbot Is Our Self-Deceiving Future”. The Atlantic. Retrieved from: https://www.theatlantic.com/technology/archive/2022/06/google-engineer-sentient-ai-chatbot/661273/ 

• Collins, E. & Ghahramani, Z. (2021, May 18). “LaMDA: our breakthrough conversation technology”. Google Blog. Retrieved from: https://blog.google/technology/ai/lamda/ 

• De Cremer, D. (2020). Leadership by Algorithm: Who leads and who follows in the AI era. Harriman House. 

• Kosoff, M. (2017, March 20). “Uber’s President Resigns as Employees Head for the Exits”. Vanity Fair. Retrieved from: https://www.vanityfair.com/news/2017/03/ubers-president-resigns-as-employees-head-for-the-exits 

• Lapowsky, I. (2021, August 31). “What became of Theranos employees?”. Protocol. Retrieved from: https://www.protocol.com/newsletters/sourcecode/theranos-on-trial 

• Leviathan, Y. & Matias, Y. (2018, May 8). “Google Duplex: an AI system for accomplishing real-world tasks over the phone”. Retrieved from: https://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html 

• McGuire, J., De Cremer, D., De Schutter, L., Y. Hesselbarth, Mai, K.E. & Van Hiel, A. (2023). “The reputational and ethical consequences of deceptive chatbot use”. Scientific Reports, 13, 16246. 

• Ryu, H. S. & Lee, J. N. (2018). “Understanding the role of technology in service innovation: Comparison of three theoretical perspectives”. Information & Management, 55(3), 294-307. 

• Tiku, N. (2022). “The Google engineer who thinks the company’s AI has come to life”. The Washington Post. Retrieved from: https://www.washingtonpost.com/technology/2022/06/11/google-ai-lamda-blake-lemoine/ 

The post Why the Ethical Use of AI Matters for Your Career appeared first on The European Business Review.

The digitalisation of global supply chains is unstoppable and there is no doubt about the upside potential it brings in terms of efficiency gains. Yet, the rapid progress in digital technologies, artificial intelligence, and data-driven decision-making exposes organisations to an elevated risk of systemic cyberattacks. And the material impact of cyber events such as the 2020 SolarWinds cyberattack in the US, as well as more recent incidents like XPlain and Concevis in Switzerland, highlights the interconnected nature of cyber threats within global supply chains, demanding increased attention from business leaders and policy makers. Against this backdrop, this article underscores the critical need for resilient cyber supply chain risk management (C-SCRM) practices across industry sectors, including those traditionally less associated with high-profile cyber threats. 

Introduction to supply chain cyberattacks

The SolarWinds breach serves as a compelling case study, revealing the widespread consequences of vulnerabilities within supply chains. Drawing parallels with incidents in Switzerland, where the financial sector remains a prime target, this article aims to outline the complexities of safeguarding supply chains against evolving cyber threats. 

While the Swiss financial sector has responded with comprehensive regulations, the spotlight shifts to broader industrial supply chains where similar regulations either do not exist or are not followed with adequate priority. Examining examples of supply chain cyberattacks on manufacturing companies unveils the vulnerabilities and challenges faced by these critical sectors. 

In the United States, many industries have experienced targeted supply chain cyberattacks, disrupted operations, and compromised sensitive data. Recent incidents have illuminated the need for robust risk management strategies tailored to the unique characteristics of industrial supply chains. For example, in 2021, Colonial Pipeline, an oil pipeline system that carries jet fuels and petrol, encountered a ransomware attack that disrupted its operations, leading to a temporary shutdown. The focal point of the attack was the billing infrastructure, rather than the critical oil pumping systems, which remained operational. The decision to halt pipeline operations was attributed to the inability to bill customers. Colonial Pipeline took this precautionary measure to prevent potential further attacks on vulnerable sections of the pipeline, prompted by concerns that hackers might possess information enabling additional attacks. In a bid to restore network functionality, the company ultimately opted to pay a ransom of $4.4 million. 

As depicted in figure 1, a typical supply chain attack focuses on a third-party software provider. The goal is to obtain unauthorized access to a larger network of suppliers and customers. The hackers achieve this by infiltrating the automated update servers of the targeted software provider. The pernicious aspect of such attacks lies in the fact that the affected companies, those relying on the software provider for updates, are often unaware that they are inadvertently installing malware onto their servers. Consequently, the malware can then spread throughout the network, potentially compromising the security of numerous interconnected organisations within the supply chain. This method allows the attackers to exploit the trust established between the targeted company and its software provider, using the update process as a Trojan horse to gain access to a more extensive supply chain network. 

Hence, the lack of a proactive approach to C-SCRM can leave organisations and their supply chains susceptible to cyber risks, drawing attention to the importance of industry-wide collaboration, regulatory frameworks, and the adoption of international standards. And despite proven significance, the adoption of ISO certifications, such as ISO 27001, within industrial supply chains remains limited. This raises questions about the readiness of organisations to confront cyber risks head-on, especially in an era where cyber threats continue to evolve in sophistication. Furthermore, there are gaps in academic literature, necessitating action from both researchers and practitioners. This article underscores the imperative for a holistic approach to cybersecurity, in alignment with the evolving concept of cyber resilience. 

Leveraging ISO standards and NIST frameworks for cyber supply chain risk management 

In the intricate landscape of C-SCRM, two globally recognised frameworks – the ISO 27001 (International Organisation for Standardisation) standard¹ and the NIST (National Institute of Standards and Technology) framework² – play pivotal roles in guiding organisations toward robust risk management practices. 

1. The ISO 27001 standard 

While the ISO standards do not explicitly define C-SCRM as a standalone topic, the principles embedded in ISO 27001 provide valuable insights for managing risks associated with the buyer-supplier relationships. Key considerations within this standard advocate for comprehensive risk assessments along the entire supply chain. This necessitates a fundamental understanding of potential vulnerabilities and threats arising from supplier interactions.

Furthermore, the standards outline various security measures that organisations should implement to ensure information security. These measures offer flexibility, enabling adaptation to control risks associated with supplier interaction, and sensitive information exchange. Like customer relationship management, ISO 27001 recommends establishing clear security requirements and obligations in contracts or agreements with suppliers. This ensures adherence to security measures and compliance standards by both parties. 

Regular monitoring and review of supplier and customer-related processes and security measures are crucial. This proactive approach enables organisations to swiftly identify potential risks or deviations from established security protocols. 

Finally, robust incident response plans and business continuity measures mitigate risks arising from supplier-related incidents, minimising disruptions in operations. 

While the term “C-SCRM” may not be explicitly detailed in ISO 27001, the principles of risk management, security controls, contractual obligations, and monitoring can be effectively applied to managing risks associated within the supply chain. 

2. The NIST framework 

NIST focuses on C-SCRM through various publications. However, the NIST Special Publication 800-161 deals with C-SCRM specifically. Key elements covered in NIST publications include guidelines for identifying, assessing, and managing risks within the supply chain, understanding potential vulnerabilities and threats throughout the supplier lifecycle. 

The standard recommends evaluating and selecting suppliers based on their security practices, compliance with standards, and commitment to cybersecurity. It encourages information exchange and collaboration among supply chain stakeholders to effectively address and mitigate emerging threats. This may include sharing best practices, threat data, and security-related information. 

The NIST approach to C-SCRM focuses on empowering organisations to establish comprehensive cybersecurity practices suited for dynamic and modern supply chains. Through detailed guidelines and recommendations, the NIST framework serves as a crucial tool for organisations to strengthen their capabilities in identifying, assessing, and effectively managing risks within their supply chain environment. The NIST framework outlines a range of factors fundamental for effective C-SCRM, with a particular emphasis on cultural and awareness-related components. 

The role of insurance in cyber supply chain risk management 

Cyber insurance can help businesses in several ways to manage cyber supply chain risk. First and foremost, it can provide reimbursement for losses incurred due to supply chain cyberattacks, such as data breach notification costs, regulatory fines, and business interruption losses. However, many cyber insurance providers offer risk assessment services to help businesses identify and address vulnerabilities in their supply chains. They may also provide access to cybersecurity experts who can assist in developing and implementing risk mitigation strategies. It can be used as a tool to incentivise suppliers to adopt stronger cybersecurity practices. Businesses can require suppliers to carry cyber insurance and meet certain cybersecurity standards as a condition of doing business. 

When selecting cyber insurance coverage, businesses should consider several factors, including the size and complexity of their supply chain, the type of data and assets stored or transmitted within the supply chain, the industry they operate in, and their overall cybersecurity posture. 

Cyber insurance is an essential component of a comprehensive supply chain risk management strategy. By providing financial protection, risk assessment services, and contractual leverage, cyber insurance can help businesses mitigate the impact of supply chain cyberattacks and protect their bottom line. As cyber threats continue to evolve, businesses must adopt cyber insurance as a strategic shield to safeguard their supply chains and ensure business continuity. Major insurance companies offer tailored cyber risk coverage that is accessible to both global organisations and SMEs. 

Best practices for implementing a C-SCRM programme 

In the intricate realm of cyber threats within the supply chain, organisations can bolster their defences by adopting a comprehensive C-SCRM programme. These practices, combined with the seven steps outlined in table 1, offer a roadmap for safeguarding the supply chain and effectively managing cyber risks associated with suppliers.  

Table 1. The seven steps to kick-start your C-SCRM programme.

On a more technical note, initiating the process with a basic cybersecurity questionnaire for suppliers or harnessing the latest technologies, such as incorporating external cyber risk scores from firms like OneTrust or SecurityScorecard, can significantly streamline the risk assessment process. This is particularly beneficial for companies dealing with a multitude of suppliers, providing a more efficient and comprehensive approach to evaluating and managing cyber risks across a broad supplier base. Alternatively, collaborating with supplier risk assessment partners like GRMS, who provide tailored solutions and supply chain risk analysis capabilities, is another effective approach. 

Conclusion – your supplier’s cyber risks are your risks 

The examples of cyberattacks, such as the one on SolarWinds in 2020 and similar incidents in Switzerland in 2023, underscore the necessity of improving cybersecurity in buyer-supplier relationships.  

The holistic approach outlined in this article, focusing on collaborative risk management, fundamental cyber hygiene, and the strategic integration of cyber insurance, empowers organisations to fortify their supply chain resilience against evolving cyber threats. By actively engaging with suppliers, businesses can create a robust line of defence that protects the entire supply chain ecosystem from potential cyber attacks. The imperative remains unwavering – implement known strategies to safeguard the intricate web of the digital supply chain ecosystem. 

About the Author 

Dr. Kamil J. Mizgier is the former Global Supplier Relationship and Risk Management Leader at Dow with 15 years of experience in implementing risk management strategies across industry sectors. Before this role, he led enterprise risk modelling projects and teams, among others, at BNY Mellon and UBS. He has published more than twenty academic and practitioner journal articles on risk management and is a frequent public speaker. He obtained his master’s degree in applied physics at the Warsaw University of Technology and a PhD in supply chain management at ETH Zurich.  

References: 

1. Boyens, J., Smith, A., Bartol, N., Winkler, K., Holbrook, A., & Fallon, M. (Oct, 2021). Cybersecurity Supply Chain Risk Management Practices for Systems and Organizations. (N. I. Technology), access https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-161r1-draft2.pdf 
2. International Standard ISO/IEC 27001. (Jan. 2022). Information security, cybersecurity and privacy protection – Information security management systems – Requirements (Third edition 2022-10), access https://www.iso.org/standard/27001 

The post A Practical Guide to Kick-starting Your Cyber Supply Chain Risk Programme appeared first on The European Business Review.

Cloud computing is a sector of Information Technology where computing services are offered and delivered over the Internet. In traditional IT, people relied on local servers and personal devices such as laptops, phones, and PCs to store data and handle applications. Due to the latest innovations in cloud computing, it has become so easy for people to access and utilize a shared pool of computing resources such as databases, servers, storage, analytics, networking, and software through the Internet. This feature has revolutionized the way businesses operate and leverage technology. In that form, companies are empowered to improve processes, reduce capital expenditure, and enhance faster development cycles.In industry developments, cloud computing has been integrated into many companies, with healthcare emerging among the top.The integration of cloud computing has significantly changed healthcare through many services, such as telemedicine and healthcare data storage.This article explores the power of cloud computing integration in healthcare institutions by studying a health institution called HealthBridge that has successfully implemented cloud technologies.

Cloud Computing Paradigm in Healthcare

As discussed above, cloud computing makes access to services through the Internet. This means that patients and doctors can access various resources, data, and applications without being bound by physical location or device limits and constraints. Therefore, a cloud platform is an efficient, scalable, and flexible platform for storing and managing large amounts of data, which aids healthcare providers with the tools to analyze medical records and come up with helpful insights to improve the provision of healthcare. Additionally, the central location of data makes it easy to share data among healthcare providers, enhancing a more interrelated and coordinated approach to medical attention towards the patients and thus improving the speed and accuracy of diagnoses.

 Case study: The HealthBridge Initiative

Challenges

HealthBridge is a health service provider focusing on remote and rural communities. Unfortunately, this institution confronts significant challenges in providing effective and prompt healthcare when the distances within the area are substantial and the level of specialists is lower. To tackle this, the organization engages in a strategic exercise that targets leveraging the power of the cloud to overcome the problems.

Objective

The fundamental aim of HealthBridge is to overcome the significant obstacles that hinder the provision of health services that are crucial and appropriate to the people who live in rural areas and remote communities. Due to patients being far from medical facilities and needing specialists in these areas, the goal is to take advantage of the cloud technologies offered to overcome these problems. The abiding difficulties are ensuring good healthcare in inaccessible places, getting around the dimension limits, and ensuring instantaneous availability of medical advice.

Cloud technologies are among the fast growing innovations that elevate companies and industries to the next level. The most popular is IaaS, which stands for Infrastructure as a Service and is famously known for moving healthcare systems to the cloud. As a game changer, integrating and implementing Cloud technologies requires careful planning and consideration of key components. HealthBridge considered the following elements when implementing the cloud-based system to handle the challenges of remote diagnosis and patient care.

1. Telehealth Services– Telehealth, which is the field that involves using digital communication methods to deliver medical care, presents one of the solutions to the deficit of medical services in rural areas. These services apply multiple media ways of communication, e.g., video calls, phone conversations, messengers, and various others. The Virtualization concept supports when healthcare specialists and their patients interact virtually, while telemedicine includes electronic services such as appointments, mental health support, and diagnostic tests.
2. Remote Monitoring– Remote monitoring is the process of sharing data and information through several technological devices such as sensors, phones, and computers. These devices can be interconnected to gather information about a patient without them being physically in hospitals. This network of interconnected physical devices is called the Internet of Things.
3. Electronic Health Records– Before the adoption of digital technologies, most patient’s data was stored and documented on paper and in files. In cloud systems, patient’s data is stored in the form of electronic records that can be stored in the cloud. This data is stored in a central platform that makes it easily accessible. It is updated in real-time, and healthcare providers can collaborate remotely to offer advice and treatments.
4. AI and Machine learning– As data is stored in a central platform, analyzing the data becomes quite significant to retrieve insights. AI and machine learning provide libraries and programming frameworks to build algorithms to analyze the data proficiently. Besides data analysis, AI aids in diagnostic support, personalized treatment plans, data security, and drug development.

Implementation

The cloud-based service is built to be user-friendly for patients and healthcare providers, which is intended to involve simplicity of use. Furthermore, it involved intense preparation and attention to cybersecurity issues to ensure patient information security. It further augmented the system with AI-driven diagnostic instruments and machine learning algorithms to support the health providers in assessing days, finding patterns, and making better choices. Precautions regarding security protocols, such as end-to-end encryption, secure access controls, and regular security audits, were implemented.

Outcomes

Cloud-powered healthcare initiatives showed that it was possible to overcome the initial problems faced by HealthBridge Healthcare. Thanks to telemedicine, individuals can consult a specialist or connect with healthcare workers without the necessity of travel. Introducing AI and machine learning algorithm-based equipment has massively enhanced diagnostic accuracy. Frequent check-ups and swift interventions have improved chronic illness management and provided better health solutions to patients. The project has also shown cost-effectiveness by cutting the infrastructure cost and in-person visits. Through the integration of the cloud-based system, a patient diagnosed with a rare cardiac condition was healed after receiving a timely disease detection, followed by continuous monitoring and a personalized treatment plan through a remote consultation with a cardiologist.

Challenges of cloud-based systems

Cloud based systems are powerful revolutions with their significant benefits. Despite the advantages, these systems face challenges in the implementation process. These challenges includes:

• Data Security and Privacy: Based on this requirement, preventive security measures and strict regulations were needed to secure private data.
• Technology Adoption: Having patients and healthcare providers as the keys to successfully implementing new technologies requires outreach and support mechanisms.
• Connectivity Issues: Providing reliable internet access was one of the main conditions for a remote area to run the telehealth services successfully and monitor from there.
• Interoperability: It is essential to ensure newly introduced cloud systems interact appropriately with the present health information technologies and EHR systems to provide patients with comprehensive care.
• Scalability: The cloud eventually needs to bring elasticity into the picture and scale accordingly to welcome the increasing volume of ecosystem data and services.

The Future of Cloud-Powered Healthcare

According to the achievement of HealthBridge, it is evident that several characteristics of cloud systems will shape future trends. Adopting these ideas into business and institutions enables flexibility, scalability and also efficiency.

• Wider Adoption of Telehealth: With technology being developed and changes in regulations, it is anticipated that telehealth services will, in the long run, be a critical asset of healthcare delivery.
• Integration of Emerging Technologies: With the help of technologies like blockchain for showing secure data and enhanced telehealth accounts through augmented reality, they may be part of the cloud-based healthcare process.
• Global Health Equity: The advent of cloud-transformed medicine may be the most important thing to working towards global health equity by providing the tools for treating the areas that lack quality health care.
• Greater Integration of AI: With AI technology development, more AI is being integrated into healthcare systems on a cloud platform, which enriches and refines diagnostics tools, supports personalized patient care, and develops advanced treatment options.
• Expansion of Remote Monitoring: The ubiquitous nature of IoT devices will bring advances in remote monitoring sophisticated technologies, paving the way for more advanced and preventative healthcare approaches.

Conclusion

The HealthBridge case study clearly shows how cloud-based healthcare can reduce geographical boundaries, increase patient care quality, and improve access to medical services. Cloud powered healthcare evolutions promises telehealth adoption, integrations of technologies, global health equity, and remote monitoring advancement. Cloud computing will surely be a critical factor in determining how the healthcare sector develops in the future.

About the Author

Keyur Patel is a seasoned Lead Solution Architect, who boasts a remarkable blend of expertise in Data Engineering and Artificial Intelligence, underpinned by over twelve-plus years of consulting experience.  His career is marked by the successful execution of critical projects across distinguished organizations and Fortune 500 companies, establishing him as a leader in his field. His academic credentials include a Master’s in Biomedical Engineering, showcasing his ability to blend technical skills with impactful healthcare insights. Keyur’s work exemplifies the intersection of technology and health, highlighting the potential of AI and data engineering to revolutionize healthcare strategies and outcomes.

The post Cloud-Powered Healthcare: A Revolution in Remote Diagnosis and Patient Care appeared first on The European Business Review.