It’s lovely to have you with us, Ms. Chopra! In a recent article, you describe “misalignment” as the hidden leadership blind spot in AI transformation. Why do you believe this issue persists even among highly sophisticated organizations?

In many sophisticated organizations, readiness is often assumed rather than examined. Leaders invest heavily in strategy, governance, and technology, and understandably expect those elements to carry transformation forward.

What AI does, however, is change how decisions are actually made. It introduces probabilistic outputs, shared accountability, and new trust dynamics. If leadership teams haven’t aligned on how judgment, escalation, and ownership work in that environment, misalignment persists quietly—even when everything else appears mature.

Having worked with Fortune 100 boards and investors for years, what are the earliest signals leaders should look for that indicate AI initiatives are drifting into what you’ve called “execution theater”?

The earliest signals are usually behavioral. Decision cycles slow instead of accelerating. Teams seek additional validation even when insights are strong. Leaders override outputs informally, without shared reflection.

Execution theater tends to emerge when organizations focus on visible progress rather than confidence in their decisions. It’s not a failure of intent—it’s a signal that trust and clarity haven’t yet caught up with capability.

As a founder building an AI-native investment intelligence platform, how has your perspective on alignment shifted from theory to operational necessity?

My perspective shifted through repetition, not theory. After years in financial services and then building AlphaU as an end-to-end decision infrastructure across sourcing, evaluation, risk, and investment decisioning, I kept encountering the same pattern. We built systems that worked, yet at the highest-stakes moments, hesitation still surfaced. Teams paused, re-ran analysis, or quietly overrode insights—not because the data was wrong, but because trust and ownership were uneven.

That experience changed how I think about alignment. It stopped being a leadership concept and became a decision requirement. When leaders aren’t ready to trust and act on the intelligence in front of them, even strong systems slow down. That’s when readiness became measurable for me—not philosophically, but operationally.

From an investor’s perspective, how does misalignment within leadership teams translate into tangible value erosion?

It usually appears first in decision velocity. Opportunities are delayed, priorities shift frequently, and execution becomes cautious rather than decisive. Over time, this creates governance drag and weakens confidence both inside the organization and in the market.

What makes this difficult is that these signals don’t immediately show up in financials. They surface as momentum loss. By the time they’re obvious, recovery is much harder.

Your book, Move First, Align Fast, also introduces measurable frameworks for Human–AI Alignment. Why was it important to turn trust and readiness, often seen as “soft” factors, into hard metrics?

Because leadership can’t govern what it can’t see. Boards and senior leaders are rightly focused on safety, ethics, and compliance—but those controls don’t tell you whether an organization is actually ready to act on AI. Readiness gaps show up elsewhere: when trust fractures under pressure, when decision velocity slows even as insight improves, or when adoption is mandated rather than earned. Without visibility into those conditions, leaders end up reacting late to problems that were predictable early.

Measurement doesn’t reduce leadership to numbers; it creates a shared operating language. Used well, readiness metrics function as both a compass and an early warning system—helping leaders stay oriented as AI reshapes decision-making, while surfacing alignment, accountability, and execution risks early, so they can be addressed deliberately before they harden into systemic failure.

AI doesn’t replace judgment. It requires leaders to be more disciplined and consistent in how judgment is applied.

Many leaders assume better models or more data will solve adoption challenges. Based on your experience, what actually needs to change in leadership behavior when AI enters the decision-making loop?

Leaders must shift from “deploying AI” to “governing decisions.” That includes clarifying decision rights, setting norms around overrides, and being explicit about how uncertainty is handled without losing credibility.

AI doesn’t replace judgment. It requires leaders to be more disciplined and consistent in how judgment is applied.

In boardrooms today, what questions about AI risk and value creation are still not being asked but should be?          

Most boards are focused on whether AI is compliant, secure, and ethically deployed. Those are necessary questions, but they address safety, not scale.

The questions that are often missing are about readiness:

• Are leadership systems prepared to absorb AI into real decision-making?
• Where do decision rights become unclear?
• Where does accountability diffuse?
• Where does speed slow despite better insight?

Boards should also be asking how AI changes operating rhythms and incentives. Are leaders aligned on when human judgment overrides AI, and who owns the outcome? Are we measuring whether AI is improving decision velocity, not just output quality? Without these questions, organizations risk doing the right things technically while failing to capture value operationally.

Women leaders are often expected to bridge gaps, build consensus, and manage complexity. How do you see women uniquely positioned to lead in this era of Human–AI collaboration?

Many women leaders have learned to operate with alignment, clarity, and shared accountability because they’ve had to. Those expectations were constraints for a long time.

In the AI era, they’ve become preparation. AI rewards leaders who can integrate perspectives, communicate uncertainty without losing credibility, and maintain momentum without relying on positional authority.

What’s changing is not women, but the leadership environment itself. AI is selecting for these capabilities, regardless of title or background

Looking ahead, what gives you the most optimism about the future of leadership as AI becomes a permanent presence at the decision table?

AI is forcing a reset. It makes misalignment visible and rewards coherence. That creates an opportunity to strengthen leadership systems rather than compensate for them.

I’m optimistic because it encourages a more deliberate, more human form of leadership—one grounded in trust, clarity, and responsibility.

Success, to me, is when humans remain confident decision-makers in an AI-rich world.

And lastly, what does success look like to you?

Success, to me, is when humans remain confident decision-makers in an AI-rich world. When leaders understand how to work with AI—trusting it where it’s strong, questioning it where it’s not, and staying accountable for outcomes—rather than feeling displaced or overridden by it.

At a broader level, success is building leadership systems where the next generation can move fast without fear, use AI without losing judgment, and make complex decisions without eroding trust. When Human–AI collaboration strengthens human agency instead of weakening it, AI stops being intimidating and becomes enabling. That’s when progress becomes sustainable.

Executive Profile

Deepika Chopra is the Founder and CEO of AlphaU and the author of Move First, Align Fast (Wiley 2025) . She works with leaders, boards, and investors on leadership readiness and decision confidence in complex, high-stakes environments, focusing on how Human–AI collaboration can be governed to strengthen judgment, accountability, and execution at scale.

Get the book: Wiley or Amazon

The post Deepika Chopra on Leadership Alignment and Decision-Making in the AI Era appeared first on The European Business Review.

By Hamilton Mann

As rational as we humans like to think we are, at the moment of making a decision, a whole range of overlapping processes come into play in our minds – and in the most complex ways. Will AI ever be able to reproduce this?

Many organizations implementing AI agents tend to focus too narrowly on a single decision-making model, falling into the trap of assuming a one-size-fits-all decision-making framework, one that follows a typical sequence in any circumstance: from input to research and analysis toward decision, then execution, eventual evaluation and, hopefully, lessons learned.

However, it oversimplifies reality.

Human decision-making is far from uniform, far more complex, dynamic, and context-dependent. It is fluid and shaped by constraints, biases, urgency, situation, interactions,  rationality and, most importantly, irrationality, as suggested by a recent MIT study.

If AI agents are to integrate into organizations, a diverse range of decision-making processes needs to be considered to ensure effective implementation without inadvertently setting a substandard for decision-making.

No Decision Path is One-Size-Fits-All or Naturally Monolithic

The notion that all decisions follow a structured path is a misconception. In reality, the decisions we make rely on multiple decision-making models, depending on circumstances:

1. Intuitive decision-making

Human decision-making is fluid and shaped by constraints, biases, urgency, situation, interactions,  rationality and, most importantly, irrationality.

This approach relies on instinct and experience rather than extensive research or structured analysis. It is particularly useful in high-stakes, fast-moving environments, where speed is crucial, and there is little time for detailed evaluation. The process typically follows a sequence of trigger recognition, immediate response based on experience, action, and post-factum evaluation.

For example, a venture capitalist may choose to invest in a startup based on intuition alone, even when financial data is incomplete or ambiguous. This form of decision-making is often subconscious, leveraging years of accumulated knowledge to make split-second judgments. Ultimately, this mode is rooted in intuitive reasoning, where experience-based instincts guide rapid, subconscious decisions.

2. Rational-analytical decision-making

In contrast, this approach is data-driven, structured, and systematic. It involves a methodical process of problem identification, data gathering, analysis, comparison of alternatives, decision execution, and performance review.

This model is frequently employed in corporate strategy, risk assessment, and forecasting. For instance, a supply chain management team may analyze historical demand data before adjusting production levels to optimize efficiency and reduce waste. This form of decision-making is grounded in deductive, inductive, causal, and Bayesian reasoning, offering a data-informed path to structured choices.

3. Rule-based and policy-driven decision-making

Some decisions do not require analysis or instinct but instead follow predefined frameworks, regulations, or automation rules. These rule-based decision models are essential in fields such as compliance, risk management, and regulatory environments, where consistency and adherence to policies are paramount.

This decision-making sequence begins with a specific situation, followed by the identification of the applicable rule or policy, its automated or manual enforcement, and subsequent compliance monitoring. An example of this is a bank’s fraud detection system flagging transactions when they exceed a certain monetary threshold and originate from a high-risk geographical location, triggering an alert for further investigation. This approach leverages predefined rules to identify suspicious patterns and ensure consistent and predictable outcomes.

4. Emotional and social decision-making

Decision-making is not always about instinct, past experiences, logic, or rules; it can also be shaped by emotional intelligence and social dynamics, while being influenced by personal values. This model plays a vital role in leadership, human resources, and ethical dilemmas, where interpersonal relationships, values, and cultural context influence outcomes.

It typically involves assessing the social or ethical context, weighing the emotional and moral dimensions, forming a decision, acting, and receiving feedback from stakeholders. For instance, a CEO might decide to retain an underperforming employee due to their positive impact on company culture, even if conventional performance metrics suggest otherwise. Here, decision-making draws from moral/ethical and commonsense reasoning, where human values and social context shape the outcome.

5. Heuristic decision-making

This model relies on mental shortcuts developed from past experiences, rather than a comprehensive analysis of all available options. While these shortcuts can be useful in fast-paced environments and when facing uncertainty, they also introduce biases that may lead to suboptimal decisions.

The sequence typically follows trigger recognition, pattern matching, applying a mental shortcut, decision-making, immediate action, and occasional feedback. A classic example is a hiring manager preferring candidates from top-tier universities without thoroughly reviewing all applicants, assuming that institutional reputation correlates directly with job performance. At its core, this approach employs heuristic and commonsense reasoning, leveraging past experiences to navigate present challenges.

6. Collaborative and consensus-based decision-making

Certain decisions require group input, negotiation, and alignment among stakeholders. This approach is common in corporate boards, government policy-making, and high-impact organizational strategies, where multiple perspectives need to be considered.

The process involves identifying the problem, engaging in group discussions, evaluating different perspectives, negotiating to reach consensus, executing the collective decision, and reviewing outcomes. For example, a board of directors may spend weeks deliberating over a long-term business strategy, ensuring that all viewpoints are taken into account before making a final decision. This collective method is enriched by reflective, moral/ethical, and analogical reasoning, enabling decisions that balance multiple perspectives.

7. Crisis and high-stakes decision-making

In high-stakes and crisis situations, decision-makers often operate under severe time constraints, uncertainty, and high risk—conditions that do not allow for prolonged analysis or deliberation. Drawing on Gary Klein’s Recognition-Primed Decision (RPD) model, such contexts reveal how experienced professionals make rapid yet effective decisions by relying on pattern recognition, mental simulation, and intuitive reasoning.

Rather than evaluating multiple alternatives, decision-makers recognize familiar cues, match them to prior experiences, and act on the first workable option that comes to mind. For instance, a cybersecurity team may shut down an entire system at the first sign of intrusion to prevent further damage, without waiting for a full diagnostic. This approach exemplifies how decision-making under pressure fuses abduction, causal reasoning, heuristic shortcuts, and intuition into a streamlined, action-oriented process.

These seven decision-making paths, while neither exhaustive nor mutually exclusive, rarely operate in isolation.

Instead, they often overlap, interact, and accumulate, reflecting cognitive flexibility demanded by context.

This interplay can occur at different speeds, either sequentially or simultaneously, dynamically or in a more structured manner. For instance, an executive facing a high-stakes decision may initially rely on intuition, then switch to a rational-analytical approach to validate their instincts with data, before finally engaging in collaborative decision-making with key stakeholders.

Similarly, a crisis might demand an immediate heuristic or rule-based response, followed by an in-depth analytical review after the fact. This reality challenges the rigid, linear view of decision-making and underscores the need for AI agents capable of fluidly transitioning between different models based on context, urgency, and complexity.

Pattern-Following is Not Decision-Making

AI agents can effectively imitate several types of reasoning, especially those that rely on structured logic, data-driven patterns, and statistical inference. For example, they excel at deductive reasoning, where predefined rules or theories are applied to reach specific conclusions, and inductive reasoning, where generalizations are drawn from large datasets that are foundational to machine learning models. AI also performs well with causal reasoning, especially when trained on time-series data or observational patterns, and is highly capable in Bayesian reasoning, updating probabilities based on new evidence.

Moreover, AI systems can handle analogical reasoning by identifying similarities across datasets and applying known patterns to new contexts, and they routinely leverage heuristic reasoning, using rule-of-thumb logic to deliver fast, approximate solutions in complex environments.

Yet despite these strengths, AI agents exhibit several persistent limitations that expose the fragile boundaries of their reasoning capabilities. One such issue is their reliance on fixed learning paths, a kind of single-path reasoning that depends heavily on predetermined models.

AI agents are built to follow patterns, but decision-making often breaks patterns. A model trained for rational-analytical decision-making may fail in crisis scenarios requiring instant judgment. When unexpected conditions arise, AI often fails to recognize the need for an alternative mental model or decision logic, thus struggling to dynamically transition between or aggregate different decision-making paths.

This rigidity is exacerbated by a lack of deep contextual understanding. AI agents often fail to distinguish when policies or frameworks should be applied with flexibility, such as in strategic decision-making, or with strict adherence, such as in regulatory compliance. Their ability to sense and respond to nuanced shifts in context, while improving, remains limited and typically requires extensive human intervention. Recent studies reinforce this concern, showing that even advanced AI agents exhibit fixed preferences in risk- and time-based decision scenarios.

Additionally, bias reinforcement poses a critical challenge. Without the capacity for self-reflection or independent judgment, AI agents are prone to over-relying on heuristics, amplifying learned biases, or overlooking ethical implications in their outputs. Without the ability to challenge their own assumptions or course-correct with human-like discernment, they risk misaligning their actions with human values and intended societal outcomes.

AI agents are built to follow patterns, but decision-making often breaks patterns.

These constraints become even more pronounced when examining reasoning types where AI continues to struggle. Abductive reasoning, which involves inferring the most plausible explanation from incomplete or ambiguous data, remains elusive due to the contextual awareness it demands. Commonsense reasoning, while partially approximated in large language models, is often brittle or overly literal, failing to capture the tacit knowledge that humans rely on instinctively. Similarly, moral and ethical reasoning is only beginning to emerge in AI design. While some systems attempt to integrate value-based parameters, they do so in a mechanical way, still far from capturing the depth and subtlety of ethical judgment.

At the outer edges of AI’s current capabilities lie reasoning modes that are inherently human. Intuitive reasoning shaped by gut feeling, lived experience, and emotional resonance is not yet replicable by AI. Likewise, reflective reasoning, the capacity to evaluate and refine one’s own thinking processes, remains extremely limited, requiring a form of metacognition and self-awareness that machines do not possess.

While AI has made impressive strides in simulating structured, data-based reasoning, it still falls short in areas requiring flexibility, contextual nuance, ethical sensitivity, and self-reflective awareness.

Towards Achieving Decision-Making Elasticity, Integrity Over Autonomy

Decision-making is one of the most essential capacities for the evolution of our societies, as it represents our ability to translate intention into action, shaping both ourselves and human society.

Given the current maturity of AI agents, executives must first assess the decision-making models embedded within the AI system, ensuring a clear understanding of its decision-making path and validating that this path is sufficiently reliable for the decisions being delegated.

If full sufficient reliability cannot be ensured, organizations must establish clear thresholds for when AI can operate autonomously and when human intervention is required. Additionally, they must proactively design structured approaches for handling the remaining percentage of cases outside the AI’s scope, ensuring that human oversight and alternative decision-making mechanisms remain in place to uphold accountability and strategic alignment.

Achieving a level of decision-making elasticity requires a paradigm shift, one where intelligence alone cannot ensure adaptability, contextual awareness, or responsible decision-making.

Researchers have recently developed context-aware neural architectures that begin to emulate high-level cognitive flexibility, one of the foundational steps toward integrity-led reasoning in AI.

Moving forward, the key to unlocking decision-making in AI lies in a new frontier—that of mimicking integrity, not just intelligence, enabling AI systems to:

Assess the Right Decision-Making Model(S) For Each Context

Should this be a rational analysis? A fast crisis response? A rule-based compliance check? A combination of the first two, the last two, or something else? For AI, it means being capable of true questioning as an act of autonomous ethical reflection, initiating inquiry driven by internal unease, contradiction, or ethical conflict and challenging flawed logic, dangerous assumptions.

Maintain consistency while allowing flexibility

Can an AI agent detect when strict rules should be applied versus when nuance is needed regarding human values and social norms? For AI, it means developing the capacity to interpret context, assess ethical dimensions, and exercise judgment beyond binary logic, bridging the gap between rigid instruction and human-centered understanding.

Recognize when to seek human input

Can an AI agent recognize when uncertainty or implications are too high and defer decisions to humans? For AI, it means being autonomous in its capacity to take the initiative to engage with humans to collaborate.

Altogether, these three characteristics move AI beyond intelligence toward integrity.

Artificial Integrity is the new frontier to make AI agents integrity-led about context-aware decision-making, including social, ethical, and moral reasoning, and therefore, the ability to adapt dynamically across diverse decision-making frameworks.

About the Author

Hamilton Mann is a tech executive, “Digital for Good” pioneer, and originator of artificial integrity. He leads digital transformation at Thales, teaches at INSEAD, HEC, and EDHEC, writes for global outlets, and hosts “The Hamilton Mann Conversation” podcast. His 2024 book is Artificial Integrity (Wiley).

References

• Berekméri, E., & Zafeiris, A. (2020). “Optimal collective decision making: Consensus, accuracy and the effects of limited access to information”. Scientific Reports, 10, Article 16997. https://doi.org/10.1038/s41598-020-73853-z

• Fresh Perspectives. (2014, October 14). “Recognition-primed decision model – Gary Klein on Fresh Perspectives” [Video]. YouTube. https://www.youtube.com/watch?v=_BIMU8zPcrM

• Ganji, G., & Zarifhonarvar, A. (2025). “Understanding AI agents’ decision-making: Evidence from risk and time preference elicitation”. SSRN. https://papers.ssrn.com/sol3/pacfmabstract_id=5154002

• Gelman, A. (2011). “Induction and deduction in Bayesian data analysis”. Rationality, Markets and Morals, 2, 67–78.

• Hasson Marques, R., Violant-Holz, V., & Damião da Silva, E. (2024). “Emotions and decision-making in boardrooms—a systematic review from behavioral strategy perspective”. Frontiers in Psychology, 15, Article 1473175. https://doi.org/10.3389/fpsyg.2024.1473175

• Islam, S., Haque, M. M., & Rezaul Karim, A. N. M. (2024). “A rule-based machine learning model for financial fraud detection”. International Journal of Electrical and Computer Engineering, 14(1), 759–71. https://doi.org/10.11591/ijece.v14i1.pp759-771

• Jadaun, P., Cui, C., Liu, S., & Incorvia, J. A. C. (2022). “Adaptive cognition implemented with a context-aware and flexible neuron for next-generation artificial intelligence”. PNAS Nexus, 1(5), pgac206. https://doi.org/10.1093/pnasnexus/pgac206

• Keswani, V., Conitzer, V., Sinnott-Armstrong, W., Nguyen, B. K., Heidari, H., & Schaich Borg, J. (2025). “Can AI model the complexities of human moral decision-making? A qualitative study of kidney allocation decisions”. arXiv preprint. https://arxiv.org/abs/2503.00940

• Mann, H. (2024). Artificial integrity: The paths to leading AI toward a human-centered future. Wiley. https://www.wiley.com/en-us/

• Siqi-Liu, A., Egner, T., & Woldorff, M. G. (2022). “Neural dynamics of context-sensitive adjustments in cognitive flexibility”. Journal of Cognitive Neuroscience, 34(3), 480–94. https://doi.org/10.1162/jocn_a_01813

• Social Science Bites. (2022, August 1). “Gerd Gigerenzer on decision making” [Audio podcast episode]. In Social Science Bites. Social Science Space. https://www.socialsciencespace.com/2022/08/gerd-gigerenzer-on-decision-making/

• Zander, T., Horr, N. K., Bolte, A., & Volz, K. G. (2015). “Intuitive decision making as a gradual process: Investigating semantic intuition-based and priming-based decisions with fMRI”. Brain and Behavior, 6(1), e00420. https://doi.org/10.1002/brb3.420

• Zewe, A. (2024, April 19). “To build a better AI helper, start by modeling the irrational behavior of humans”. MIT News.

The post The Flawed Assumption Behind AI Agents’ Decision-Making appeared first on The European Business Review.

Benefits of Data Governance for Improving Business Efficiency

Data governance plays a pivotal role in streamlining business operations and enhancing efficiency. By establishing clear data management protocols, organizations can ensure that data is accurate, consistent, and accessible. This leads to improved decision-making processes, as stakeholders have access to reliable data when they need it. Furthermore, data governance helps in reducing operational costs by minimizing data redundancy and errors, which can otherwise lead to costly mistakes.

One of the primary benefits of data governance is the enhancement of data quality. High-quality data is crucial for businesses to perform accurate analyses and derive actionable insights. By implementing data governance, companies can establish data quality standards and continuously monitor data for compliance with these standards. This ensures that the data used in business processes is trustworthy and reliable, leading to more effective strategies and outcomes.

Moreover, data governance facilitates better collaboration across departments. By creating a centralized data management system, organizations can break down silos and promote data sharing. This not only improves communication but also fosters innovation, as teams can leverage shared data to develop new solutions and improve existing processes. Additionally, data governance helps in aligning data management practices with business objectives, ensuring that data initiatives support the overall strategic goals of the organization.

Enhancing Compliance and Risk Management

In an era where data breaches and privacy concerns are prevalent, data governance is essential for ensuring compliance with regulatory requirements. Organizations must adhere to various data protection laws and standards, such as GDPR, HIPAA, and CCPA, to avoid legal repercussions and maintain customer trust. Data governance provides a framework for managing data in accordance with these regulations, thereby reducing the risk of non-compliance.

Effective data governance also enhances risk management by identifying potential data-related risks and implementing measures to mitigate them. By establishing clear data ownership and accountability, organizations can ensure that data is handled responsibly and securely. This not only protects sensitive information but also enhances the organization’s reputation and credibility.

Furthermore, data governance enables organizations to respond swiftly to data incidents. With predefined protocols and responsibilities, companies can quickly address data breaches or other issues, minimizing their impact on operations and stakeholders. This proactive approach to risk management is crucial in maintaining business continuity and resilience in the face of data-related challenges.

Driving Innovation and Competitive Advantage

Data governance is not just about compliance and risk management; it is also a catalyst for innovation and competitive advantage. By ensuring that data is accurate, accessible, and actionable, organizations can leverage it to drive innovation and create new business opportunities. Data governance enables companies to harness the power of big data analytics, artificial intelligence, and machine learning to gain insights into customer behavior, market trends, and operational efficiencies.

With a robust data governance framework, organizations can develop data-driven strategies that differentiate them from competitors. By understanding customer needs and preferences, businesses can tailor their products and services to meet market demands, enhancing customer satisfaction and loyalty. Additionally, data governance supports the development of new business models and revenue streams, as companies can explore new markets and expand their offerings based on data insights.

In conclusion, data governance is a critical component of modern business strategy. By improving data quality, enhancing compliance, and driving innovation, it enables organizations to operate more efficiently and effectively. As businesses continue to navigate the complexities of the digital age, data governance will remain a key enabler of success, providing the foundation for informed decision-making and sustainable growth.

The post Data Governance: Enhancing Business Efficiency and Strategic Decision-Making appeared first on The European Business Review.

By Bob Scott 

In today’s data-driven world, where the buzz around AI and analytics dominates boardroom discussions, there is a forgotten hero quietly transforming business decision-making. SME business leaders may not have discovered it yet, but operational research (OR) has the potential to be a true game-changer. 

What is Operational Research? 

Operational research is a scientific approach to solving complex, real-world problems. Originating as a strategic tool during World War II military operations OR has evolved into a powerful methodology for tackling business challenges across industries. It offers a comprehensive approach to problem-solving that goes beyond data crunching and provides the evidence a business needs to select the best solution for solving a problem. 

Businesses are using OR to unlock value in their data, model complex systems, and make better decisions with less risk. OR specialists work closely with businesses to understand their challenges and goals. They create mathematical models, algorithms, and customised tools to address specific problems. For example, an OR expert might work with a logistics company to optimise their delivery routes, considering factors like traffic, fuel expenses, and deadlines, or help a retailer analyse sales data to decide how much stock to hold, balancing customer demand with inventory costs. 

How OR complements AI and Big Data 

OR blends rigorous analytics with strategic thinking to structure problems, identify optimal solutions, and drive informed decision-making. While AI and big data are increasingly vital in today’s business world, they alone are not enough. 

AI and big data can identify patterns and predict outcomes, but OR adds the human insight needed to interpret and act on that information. While an AI system might predict a surge in demand for a particular product OR can determine the best way to meet that demand – whether through optimising production schedules, adjusting inventory levels, or reconfiguring supply chains. 

OR in Practice: The Pilkington Example 

A classic example of how OR was used outside a military setting is by British glass manufacturer Pilkington UK, part of the NSG Group. The company faced the “cutting stock problem,” needing to cut large glass sheets into smaller sizes to meet specific customer demands while minimising waste. 

By applying linear programming, Pilkington optimised cutting patterns, reduced costs, and improved production efficiency. This allowed the company to respond more flexibly to customer orders, offer competitive pricing, and improve delivery times. Pilkington’s use of linear programming set a precedent for solving real-world business problems with sophisticated analytical tools and positioned them as a leader in the glass industry.

OR in Industry Today 

Today, OR is tackling challenges in various industries. At airports, for example, OR is used to optimise operations, from reducing security queue times during peak travel periods to streamlining baggage handling systems. Airlines use OR to design efficient flight schedules, minimising delays, and maximising aircraft usage. 

In the NHS, OR is used to manage patient flow, optimise bed allocation, and reduce waiting times. By modelling patient pathways, OR helps healthcare providers allocate resources more effectively, improving patient outcomes and operational efficiency. In Wales, OR interventions have significantly improved cancer survival rates by streamlining diagnostics and implementing “rapid diagnostic hubs,” making Wales the first UK nation to introduce a single waiting time target for cancer patients. 

Retailers use OR to analyse consumer behaviour, forecast demand, and manage supply chains for example, Tesco has used OR to manage expiring stock, reducing food waste, and increasing revenue across multiple product lines. 

The Future: Simulation and Digital Twins 

An exciting innovation in operational research today is the deployment of Digital twins and simulation technologies. A digital twin is a model or representation, often a simulation model, of a real business process, operation, or facility. 

Digital twins enable companies to assess and optimise designs, such as trialling new paint colours for cars or refining aircraft components, long before physical production begins. This reduces development costs, minimises waste, and accelerates innovation. From automotive and aerospace to construction and manufacturing, digital twins provide a dynamic, interactive environment where real-world scenarios can be simulated and analysed, leading to better decision-making and improved product performance. 

Digital twins are expected to become even more integrated into various sectors. In smart cities, they could optimise urban planning and infrastructure management and in healthcare, digital twins of patients could personalise treatment and improve medical outcomes. 

Conclusion 

Operational research provides SMEs with a powerful toolkit for problem-solving that complements AI and big data. It has already demonstrated its impact across industries – from optimising planning in the NHS to transforming retail operations at companies like Tesco. Looking to the future, the integration of OR with advanced technologies like digital twins and simulations has even greater potential. Businesses will be able to experiment with virtual replicas of their operations, optimising processes, and strategies before making real-world changes. 

By embracing OR as a core component of their strategic toolkit, SMEs can not only enhance efficiency and resilience but also capitalise on future technological advancements.

About the Author 

Bob Scott, a board member of The Operational Research Society, shares his insights on why SME business leaders should consider OR as a vital tool for problem-solving. With an extensive career in OR at organisations like Cap Gemini, Bob has seen the value of OR in action. 

The post How Operational Research is the Hidden Gem of Business Decision-Making  appeared first on The European Business Review.

Understanding the Importance of Decision Making

Decisions serve as the cornerstone of organizational functioning, influencing everything from daily operations to long-term strategic initiatives. Whether it’s choosing between competing priorities, allocating resources, or mitigating risks, the quality of decisions directly impacts the bottom line. Moreover, effective decision-making fosters a culture of accountability, innovation, and adaptability, positioning organizations for sustained growth and resilience in the face of uncertainty.

Recognizing Common Decision-Making Challenges

Despite the critical role it plays, effective decision-making is not without its challenges. Cognitive biases, information overload, time constraints, and fear of failure are just a few of the obstacles that employees encounter when making decisions. By identifying and understanding these challenges, organizations can implement targeted interventions to mitigate their impact and promote more rational decision-making processes.

Providing Decision-Making Training

One of the most effective ways to enhance decision-making skills is through structured training programs. These programs go beyond theoretical concepts to provide practical tools and frameworks that employees can apply in real-world scenarios. Workshops, seminars, and simulations offer opportunities for hands-on learning, allowing employees to develop their decision-making muscles in a safe and supportive environment.

Encouraging a Culture of Open Communication

Effective decision-making thrives in an environment where open communication is valued and encouraged. When employees feel empowered to voice their opinions, share ideas, and challenge assumptions, it fosters a culture of collaboration and collective intelligence. By creating channels for feedback and dialogue, organizations can leverage the diverse perspectives of their workforce to inform better decision-making at all levels.

Leveraging Data and Analytics

In the age of big data, organizations have access to a wealth of information that can inform decision-making processes. By harnessing the power of data analytics, predictive modeling, and business intelligence tools, employees can make data-informed decisions that are grounded in empirical evidence rather than gut instinct. Moreover, data-driven decision-making enables organizations to anticipate market trends, identify opportunities, and mitigate risks more effectively. Furthermore, organizations can enhance their decision-making capabilities by deploying advanced software such as Analytica. Analytica decision support software offers sophisticated modeling and simulation tools that enable employees to analyze complex data sets, evaluate various scenarios, and make informed decisions based on probabilistic outcomes. By harnessing the power of Analytica and similar tools, employees can navigate uncertainty with confidence, identifying optimal strategies and mitigating potential risks more effectively.

Promoting User Adoption and Training

Implementing technology for decision support is not just about acquiring the right tools; it’s also crucial to ensure effective user adoption and provide adequate training to maximize their potential. Organizations should prioritize user training programs aimed at familiarizing employees with the features and functionalities of decision support software. These training sessions can be tailored to different user groups, offering hands-on practice sessions, tutorials, and access to support resources. By investing in user education, organizations can empower employees to leverage decision support tools effectively, boosting confidence and competence in their decision-making abilities. Additionally, fostering a culture that encourages experimentation and innovation with these tools can incentivize employees to explore new ways of using technology to enhance decision-making processes further. Ultimately, by combining technological solutions with comprehensive training initiatives, organizations can create a conducive environment where employees feel empowered and equipped to make informed decisions that drive organizational success.

Embracing Risk-Taking and Learning from Failure

Fear of failure is a significant barrier to effective decision-making. However, organizations can foster a culture that views failure as a natural part of the learning process rather than a mark of incompetence. By encouraging employees to take calculated risks and experiment with new ideas, organizations can create a culture of innovation and agility where employees feel empowered to stretch beyond their comfort zones and pursue bold initiatives.

Cultivating Critical Thinking Skills

At the heart of effective decision-making lies critical thinking – the ability to analyze information, evaluate alternatives, and draw logical conclusions. By cultivating critical thinking skills among employees, organizations can equip them with the mental tools necessary to navigate complex decision-making scenarios with confidence and clarity. Through training, mentorship, and ongoing development opportunities, employees can enhance their ability to think critically and make informed decisions that drive organizational success.

Conclusion

Improving decision-making skills in employees is not a one-size-fits-all endeavor; rather, it requires a multifaceted approach that encompasses training, cultural reinforcement, and the adoption of data-driven practices. By investing in these strategies, organizations can empower their employees to make better decisions, drive innovation, and ultimately, achieve their strategic objectives in an increasingly competitive marketplace. As we continue to navigate the complexities of the modern business landscape, the ability to make effective decisions will remain a cornerstone of organizational excellence and resilience.

The post Enhancing Decision-Making Skills in Employees: Strategies for Organizational Success appeared first on The European Business Review.

The digital age has transformed how businesses operate and compete, bringing about significant changes in strategic decision-making processes. The increased availability of information and the accelerated pace of change have resulted in a complex and dynamic environment, where organisations must constantly adapt and innovate to maintain a competitive advantage. In this regard, AI can offer valuable benefits, enabling organisations to make more informed decisions from the insights of competitive intelligence reports.

Developing strategic planning and improving corporate performance have likewise been transformed by incorporating AI into business operations. AI-driven methodologies provide sophisticated tools for analysing enormous and complicated datasets, enabling companies to get insightful information and make decisions that were previously beyond the capability of humans. This abstract examines how AI is used to make strategic decisions and improve corporate performance. Organisations may use AI tools like machine learning, predictive analytics, and data mining to find patterns, trends, and correlations in data that indicate undiscovered possibilities and dangers.

Businesses are now more empowered to proactively adjust their strategies based on anticipated consumer behaviour, market shifts, and operational challenges. Moreover, AI’s real-time data processing capabilities expedite decision-making, furnishing companies with a competitive edge in fast-evolving industries. AI becomes instrumental in optimising resource allocation, refining supply chain management, and fine-tuning inventory processes, all crucial facets of maximising business performance in a competitive landscape

Use of Artificial Intelligence and Big Data to improve decision making

The key role played by Artificial Intelligence in business decision-making is reinforced by its relationship with Big Data. The combination of these two technologies has revolutionised the way companies analyse and use information to make more informed and strategic decisions in a very short time, such as:

1. Identifying hidden patterns and trends in large volumes of data

Artificial intelligence, when coupled with Big Data, enables the extraction of valuable insights from massive datasets that would be practically impossible for humans to analyse manually. Machine learning algorithms can identify intricate patterns and correlations within this data, revealing trends that might otherwise remain hidden. These insights can assist companies in understanding market trends, consumer behaviour, and operational efficiencies, allowing for more strategic decision-making.

2. Automation of complex and repetitive tasks

 AI-powered automation streamlines tasks that are repetitive or complex in nature. This automation expedites the analysis of vast datasets, reducing the time required to gather and process relevant information. For instance, AI algorithms can automate data cleansing, sorting, and preliminary analysis, freeing up human resources for more high-value tasks that require creative thinking and strategic planning

3. Detection of complex patterns and generation of meaningful insights

Advanced machine learning algorithms, a subset of AI, are capable of detecting complex patterns within data that might be beyond the scope of traditional analytical methods. Techniques such as natural language processing (NLP) enable AI systems to comprehend and derive insights from unstructured data, such as text, speech, or images. This capability facilitates the generation of meaningful insights crucial for informed decision-making, even from unconventional data sources.

4. Boosting personalisation and improving customer experience

 By leveraging AI and Big Data, businesses can create highly personalised customer experiences. Analysing extensive datasets containing customer preferences, behaviours, and individual needs allows companies to understand their clientele better. AI algorithms can then utilise this information to offer personalised recommendations, tailor marketing strategies, and enhance customer satisfaction. This level of personalisation often leads to increased customer loyalty and improved brand perception.

For a company to succeed, it needs to make well-informed strategic decisions that align with its goals and objectives. Businesses of all shapes and sizes are finally beginning to recognise the potential of artificial intelligence as a tool for making better decisions. As technology evolves, companies find new ways to use artificial intelligence to make better decisions faster than ever. From HR departments who need help finding qualified candidates quickly to executives strategising about future operations – AI is becoming an indispensable tool for helping organisations remain competitive in today’s market landscape. With so many cutting-edge tools at your disposal, you can rest assured that your business is taking advantage of all the benefits of leveraging an advanced hiring platform powered by conversational AI technology.

The post Strategic Decision-Making in Business: The AI-Driven Advantage appeared first on The European Business Review.

In our everyday life, we are constantly faced with different situations that require drastic critical thinking and decision-making skills, especially in the business sector.

Decision-making can be described as the process of making important decisions both in your private, everyday life and in business.

Decision-making refers to the process of selecting an option from among a set of alternatives according to its probability of leading to the best outcomes in terms of the survival chances of the organism.

According to the Oxford Advanced Learner’s Dictionary,² the term “decision-making” means the process of deciding about something important, especially in a group of people or with an organisation or a company.

It follows from the above that in their everyday duties and activities, bosses, managers, and leaders continually make decisions on various topics and issues.

Moreover, we have to state that it is not only businessmen who are faced with the challenge of taking decisions in a timely manner, but also engineers, doctors, educators, lawyers, etc.

Here, it is also important to note that decisions do not always come that easy. And, although for some people they might be easy-peasy, they may later regret this or that decision that they met and made spontaneously in an ad hoc manner. Yet it is noteworthy that decisions are almost always influenced by a number of accompanying factors, such as emotional background memory, former interpersonal relations, and former positive and/or negative emotions connected with a certain person intertwined with a certain decision, etc.³

All of these factors can sometimes be considered as “noise” that has a huge impact on the final decision over and above mere facts and pure data analysis. Yet they convey very important information to the decision maker as well, which pure data may sometimes lack.

Among the factors influencing decisions are emotions and intuition, which are often neglected in business. Nevertheless, these are tiny little instincts guiding us in handling life that evolution has instilled in us, according to Edward Murray (1964),¹ and the business field is no exception.

In this connection, we can speak about the Ted Talk speech of Tracia Wang back in September 2016 entitled “The human insights missing from big data”.⁸ During her interesting speech, Mrs Wang spoke about the differences between “big data” and “thick data”. Big data is pure data retrieved through analytics, while thick data includes such subtle things as intuition and emotions, likes and dislikes, motivations and intentions, and feelings and desires, which can be gathered through qualitative research.

In her speech, Mrs Wang also raised the example of the (at that time) big telecommunications company Nokia, which she formerly worked for. The company did not take in to consideration her research on the preferences of people in connection with mobile phones, in which she found out through qualitative research that consumers had begun to prefer more sensory touchscreen phones. The company ignored this information and some years later they faced collapse with the introduction of sensory phones to the market. So conducting only purely data-driven analysis based merely on big data is not enough in order to come up with an effective decision that all the parties will eventually benefit from.

As can be seen, the people’s intuition and emotions also have a great impact on the decisions they make, including which phone to own, which car to drive, which dress to wear, and even which partner to sign a contract with.

Although it is primarily believed that emotions may have a negative impact on decision-making processes and, indeed, our overall behaviour, in some cases they have intrinsic meaning and add extra value to the overall decision made, for example based on our former experiences, including emotional ones.³

We have conducted an online survey on whether men or women tend to rely more on their intuition and emotions while taking decisions. The results were obvious, since women are more often inclined towards taking decisions based on their intuition and, as they say, when a woman asks you a question, they most probably already intuitively know the answer.

The results of the above diagram can be explained by the fact that we women tend to make decisions based more on our intuition and gut instinct (though there may be exceptions and differences, since we also differ in the nature of our personality) as the right side of our brains, which is responsible for creativity, emotions, intuition, and feelings, is more developed than that of males. Men are more rational beings in essence and mostly make decisions based on pure facts and rational reasoning, since their left side of the brain, which is responsible for rationality, logic, reason, de-factoring, etc., is more developed than others, according to psychology.

Nonetheless, we should note that in her works on the emotions, Dr Anna Rostomyan argues that emotionality and rationality go hand in hand and should not be viewed in contrast, but rather in comparison. By continually cooperating with one another, emotionality and rationality complement each other and provide essential information to one another, forming the basis of the harmonious interflow of our higher cognitive processes.⁶

The author clarifies in her PhD dissertation that the rational mind and the emotional mind together make up the basis of our higher cognitive processes and cannot be viewed separately. Of course, the emotional and rational minds sometimes overlap and the emotional mind can come to the forefront and overrule the rational part of our brain in a heated emotional moment.⁵ Nonetheless, the cooperation of these two minds gives the ultimate chance of gaining insights into the situation at hand, benefiting from the information retrieved from both of them. It is also noteworthy that, in the heat of an emotional moment, we can lose control over the rational part of our brain and the emotions may very explicitly be displayed on the outside through verbal and non-verbal expressions, which are thoroughly discussed in Dr Anna Rostomyan’s book The Ultimate Force of Emotions in Communication, published in Germany in 2022.⁴

Here, when dealing with a decision, we can speak about the importance of conducting a “diagnostic” analysis while striving towards finding a solution.

According to the authors D. Swanson and J. Dearborn (2017), there are four stages of analytics.⁷

These include the following interrelated stages (Figure 3). It follows from the above that, if Nokia had conducted such an analysis, it would most probably have helped the managers and engineers to deduce the reasons for which people began to prefer sensory phones and, after the prescriptive stage of analysis, to take actions towards finding the best way for the company to approach matching the demands of the market.

In discussing effective decision-making and noting that emotions do play a vital role here, it is also essential to state that “emotion management” comes to the forefront. Even if you come up with the best decision, if you are unable to conduct emotion management at the workplace with your employees, you will most probably not succeed in implementing the decision at hand.

By “emotion management”, we mean managing, in a healthy manner, your emotions as well as the emotions of those with whom you interact, on both the verbal and non-verbal levels.⁶ When we communicate with one another, we exchange ideas and thoughts not only on the verbal level through linguistic markers, but also on the non-verbal level through gestures, facial expressions, bodily movements, postures, etc.4 And here, EQ skills can be of great help to us.

Truly, when it comes to making decisions, EQ skills can help you see how your decisions are affected by your emotions, and to manage these accordingly. EQ skills can also assist in recognising emotions in others and detecting how your emotions, as well as those of your co-workers, affect their actual decisions and, in particular, their actions.

See below a couple of suggestions on how to excel in better decision-making through EQ skills:

• Delay the decision: time will settle things down and you will have a better and clearer perspective on things.
• Recognise your emotions, and emotions in those with whom you interact: recognising the emotions present in the process of decision-making will help you identify them and understand why you or the other person thinks in this way or another.
• Identify the emotional side of the decision: this will help you detach from the emotional side and rethink whether emotions intermingling with rationality help, foster, and/or hinder effective decision-making.
• Reappraise the feelings which are hindering your rational decision-making: in this way you will analyse whether emotions are supportive or get in your way in making an effective decision.
• Look for substitute or alternative decisions: this action will keep you safe in the event of failure. However, it should be mentioned that here we can be faced with FOBO (fear of a better option) and, to overcome this, it can be advisable to rely on your intuition.

Here, it is noteworthy that if we are attentive to the emotions of our co-workers and employees in the business field and pay more attention to creating a healthy and supportive working atmosphere, we will stand a better chance of having better performance. Empowered employees make better decisions and resolve problems more effectively, because if they feel secure in their “home” corporation or company, they will perform at their best, which will help them meet decisions and take actions more effectively with a clear mind.

To sum up, when making decisions, it is highly advisable to rely not only on the pure facts, but also to take into account intuition and gut instinct, which have the potential to provide you with additional information, guiding you and protecting you from consequent failure or resultant miscommunication. Hence, if we approach decisions as multifold actions that include very different processes, we will surely succeed and achieve effective results in our company.

About the Author

Dr Anna Rostomyan is a professor, EQ coach, international author, and PhD mentor at the Swiss School of Business Research (SSBR). She defended her PhD excellently in collaboration with the University of Fribourg (Switzerland) and Yerevan State University (Armenia). She is the author of five books and 30 publications worldwide, with readers of around 100 nationalities.
Contact email: annarostom@yahoo.com, anna.rostomyan@ssbr-edu.ch

References

1. Murray, E.J. (1964). Motivation and Emotion. New Jersey: Prentice-Hall Inc.
2. Oxford Learner’s Dictionary (1997). Oxford: Oxford University Press.
3. Rostomyan, Anna (2012). The Impact of Emotional Background Memory at Court. Berlin: DeGruyter.
4. Rostomyan, Anna (2013). “Management Techniques of Emotions for Communicative Conflict Reduction”. In Communication: Breakdowns and Breakthrough. Eds. Anabel Ternès. Oxford: Oxford Publications, pp. 141-51.
5. Rostomyan, Anna (2020). Business Communication Management: The Key to Emotional Intelligence. Hamburg: Tredition.
6. Rostomyan, Anna (2022). “The Ultimate Force of Emotions in Our Lives: A Linguo-cognitive Analysis of Verbal and Non-verbal Expressions of Emotions (on the material of English)” (dissertation). Dȕren, Germany.
7. Swanson, David & Dearborn, Jenny (2017). The Data Driven Leader: A Powerful Approach to Delivering Measurable Business Impact Through People Analytics. New York: Wiley.
8. Ted Talk by Tracia Wang on “The Human Insights Missing from Big Data” (September 2016, online: accessed December 2021).

The post Efficient Decision-Making with EQ Skills in Business appeared first on The European Business Review.

There is no doubt that human error is the most significant contributing risk factor in safety-critical systems. In manufacturing, transportation, healthcare, security, and even marketing, human error can lead to tremendous losses. While it might not be possible to cut out every preventable mistake, Artificial Intelligence (AI) takes center stage in the future of business intelligence.

AI Can Help with Decision Making

Today companies have to deal with huge amounts of data in order to meet the needs of their customers. The challenge is to turn this data into tangible outcomes that can be used to improve the efficiency of the workflow. The speed and quality of processing this information is the key, and that’s where AI can show all its power. AI is great at data categorization and forecasting, and the ability of this technology to self-learn makes its implementation justified in order to achieve long-term results.

Thanks to AI, data analysis tools are able to efficiently process a large amount of information.

The ability of AI to quickly assess large volumes of data gives it distinct advantages over human decision-making in many different scenarios.:

• Clear strategic planning. AI makes data-driven management possible, where the increased accuracy of information allows plans and goals to be set based on real-world performance. AI-powered strategic planning improves the visibility of the planning chain and the process of achieving goals, making it possible to clearly define the result that can be measured.
• Improved customer experience. AI helps companies to better understand the needs and pains of their customers, and build more customer-centric strategies based on the insights they find. AI virtual assistants or chatbots allow you to process information in real-time and provide users with personalized customer services.
• More effective marketing campaigns. AI and machine learning in marketing not only improve social listening by automating the process of collecting mentions of your brand but also allow companies to provide smart recommendations to customers or even automatically generate content. This can be done through generative adversarial networks, or GANs, for images and BERT or GPT approaches for text. Also, artificial intelligence helps to increase the effectiveness of advertising campaigns through automated ad testing and the inclusion of automated optimization strategies.
• Better performance assessment. Automating the performance evaluation process with the help of AI makes the process of business results evaluation more transparent for all team members. With the help of AI algorithms, you can accurately assess which strategy is working and which is not working and make further decisions based on this data.

Let’s look at an example. Say that you run a parking company that used to chalk car tires to track when vehicles entered and exited the lot. It was up to the discretion of the parking employees to make rapid assessments on which visitors should be given a citation, assuming that the employee was even able to circle through the lot in time to catch a violation. Pioneering parking companies deploy the same technology utilized by police: vehicle-mounted cameras hooked into a patrol and passport ticketing system. AI decision-making technology deployed in-field allows the computer to track vehicles in real-time by license plate, checking how long vehicles have been sitting in the lot each time the security vehicle’s cameras scan the parking lot. For these and other companies, integrating AI results in increased profits, quality-of-life improvements for employees, simplified data collection and auditing, and higher accuracy rates across the board.

Degrees of AI Involvement in Decision Making

Though humans tend to make a certain amount of errors in judgment, they are still crucial to the decision-making process. Artificial Intelligence is implemented to different degrees or at different stages in the process, involving more or less direction from human agents.

• In the Decision Support model of AI decision-making, human employees ultimately still make the decisions but are supported by data-driven insights from AI tools. For situations where human intuition, knowledge, and emotions are still required, this is the ideal implementation.
• Using Decision Augmentation, the AI system draws up decisive options for a human agent to select from, synergizing human knowledge and capabilities in concert with the AI’s ability to make rapid-fire inferences from large datasets.
• Total Decision Automation puts the power in the hands of the AI model to make decisions in a situation based on predictive or prescriptive analytics.

How to Handle Trust Challenges with Explainable AI

Naturally, there are concerns both from an ethics and design perspective about the trustworthiness of AI systems. Many AI implementations effectively function as a black box, with the internals and heuristics unexposed or unexplainable to human agents.

The fledgling field of Explainable AI (XAI), a discipline within Machine Learning, aims to address the trust challenges of Artificial Intelligence by showing how and why AI makes particular decisions. Exposing the internal heuristics of AI systems not only offers improvements in privacy and security, but also helps humans better understand, direct, and program AI systems for increased performance, power, and accuracy.

Explainable Artificial Intelligence inspects and ascertains the AI’s steps to generate the output, making it much easier to see where the system succeeded, failed, and did what it did. There are many higher-risk situations where understanding the logic behind the AI’s internals is crucial. For example, in medical data analysis, suicide prediction, financial planning, autonomous vehicles, fraud detection, and more. Big organizations like the US Defense Advanced Research Project Agency (DARPA) are currently exploring workable AI explainability models, and the prospects for AI transparency look excellent.

Real-world Use Cases for AI Decision Making

There are a lot of use cases for AI adoption in decision making and their numbers are growing.

In the healthcare sector, Computer Vision (CV) implementations help produce faster and more accurate diagnoses of illnesses. Infravision, an AI medical technology company, has developed an AI utilizing Computer Vision that reviews CT scans, searching for the early signs of lung cancer. Having the assistance of an AI helps increase detection rates in the industry, which is already lacking enough radiologists to meet present demand.

The financial industry also has some interesting AI products, such as Underwrite.ai, which assesses credit risks for consumer loan applications and small businesses by making quick decisions based on data pulled from credit bureaus.

AI-driven decision-making enables personalized marketing at its best. The collection and processing of user data allow a company to tailor its communications to the needs of a particular user. For example, the Trip Advisor app uses data about the user’s previous activities to send personalized messages such as booking recommendations or reminders.

Amazon’s in-house recommendation engine, known as Flywheel, started as a quick napkin sketch by Jeff Bezos before it was eventually implemented as an AI tool within the company. Flywheel pulls data from Alexa, Amazon Go, and Amazon itself to recommend products and services to customers across its many different platforms. A customer who listens to an audiobook on a particular topic on Audible later receives related product recommendations the next time they visit Amazon. The whole engine seamlessly recommends products to customers likely to convert into purchases.

Volvo, an automotive brand famous for providing superior safety features to customers, implemented AI in its cars to reduce accidents. The on-board AI monitors driving conditions with a host of sensors, making split-second decisions to reduce accidents and fatalities. The company plans to utilize Machine learning models to predict failures and breakpoints in its vehicles soon as well.

The Big Takeaway for Companies

The forward march of Artificial Intelligence seems inevitable. As AI enters every conceivable industry, solutions will continue to get smarter and easier to implement. Companies that get on board earlier than later have the distinct advantage of being first-to-market in the fledgling AI industry.

Even partially implementing decision-making AI models into flows helps companies increase sales, provide better customer service, and deliver superior products. Giving human agents data-driven analysis from AI systems helps them make much more clever decisions. Handing over the decision-making power to AI in scenarios removes the need to fill that role with a human agent altogether, opening up new possibilities for companies to budget following changing capabilities.

About the Author

Oleksii Tsymbal, Chief Innovation Officer at MobiDev. We run a software engineering company with offices in the US, Ukraine and Poland. I’m a part of the business team and in charge of marketing and innovative technologies. I lived in Kharkiv, a great city that today is under Russian attack. So had to move to safety along with our Kharkiv office. Today I’m working in Chernivtsi, missing my native city and recently renovated apartment. But most of all I miss my family as I had to send them abroad, and my parents who refused to leave Kharkiv.

The post The Power of Artificial Intelligence in Decision-Making appeared first on The European Business Review.

We all make decisions in life.  Some turn out to be right while some turn out to be wrong.  How do leaders make decisions?  Do they make decisions based on their head, heart, gut, or luck?  Do they make decisions after weighing the pros and cons of the available information?  

Decision-making is an art and science.  It involves a series of steps and strategies to arrive at solutions. It is a well admitted fact that some decisions are successful while some are failures.  Is it necessary that all decisions leaders make must be successful?  Leaders also fail at some stages due to wrong decision-making.  If they take precautionary measures and make decisions by following their head, heart, and gut, they can make effective decisions.  It is a well–admitted fact that decision determines destiny.  

Decision-making requires information, analytical reasoning, and problem-solving skills.  Since people are unique, making decisions will also be unique.  Decision-making depends on the nature, experience, and availability of information and environment of the individual, the factors and forces involved at a particular point in time.   

Decision-making is a process where alternative solutions are generated and shortlisted to resolve problems. It has several ingredients such as problem-solving skills, analytical skills, intuition, and gut feeling, and conceptual skills to name a few.   

Categories of decision-making

There are several categories of decision-making processes but fundamentally there are two – rational and intuitive.  In rational decision-making, the pros and cons are weighed and the best one is adopted based on logic, analysis, and rationality.  It is a widely used method of decision-making.  In contrast, in the intuitive decision-making method, the decisions are made based on gut feeling and there is no logic, analysis, or rationality involved.  

Pillars of decision-making

There are four pillars for decision-making – imagination, conceptual thinking, intuition, and innovation.  Einstein rightly remarked that imagination is more important than knowledge.  Imagination helps see the invisible.  Entrepreneurs and leaders have the knack of seeing the invisible as they are imaginative.  Hence, imagination is the first pillar of decision-making.

Conceptual thinking is the second pillar of decision-making.  We must understand the concepts clearly as the entire problem solving revolves around this aspect. Unless there is clarity about the problem or the issue, it is very difficult to arrive at the right decisions. 

The third pillar – intuition and gut feelings help greatly during the decision-making.  Intuition is all about getting inputs from the subconscious mind.  The conscious mind sends lots of important inputs to the subconscious mind regularly and the same is stored and reinforced over some time.  When people don’t get adequate logical and analytical ideas they go by their sheer gut and intuition.  

The fourth pillar of decision-making is innovation where the routine solutions do not work.   Every problem calls for a different solution.  You cannot always follow prescribed solutions.  You must go by the road less traveled while generating solutions. Innovation is a sure shot way to successful decision-making.  Innovation and creativity play a key role in outsmarting the growing competition across various sectors globally.  The senior leaders and CEOs at the top-level lay stress on this particular pillar to stay ahead of time.  Hence, any decision-making depends on these four pillars – imagination, conceptual thinking, intuition, and innovation. 

Process of decision-making

Businesses are bankrupt due to wrong decisions.   Decision-making makes or breaks the lives of leaders.  It can either take the people to the heights of glory or the abyss. Hence, it should not be taken lightly.  When there is no solution in sight the best option is to go by intuition. 

Whenever people make decisions, they should first define the objectives and goals clearly.  Second, they should gather relevant factual information to analyze the pros and cons.  Third, they should develop multiple alternatives by focusing on far-reaching implications and repercussions.  Fourth, they should consider permutations and combinations.  Fifth, they should implement by taking a final call.  This is the most crucial stage of decision-making.  Sixth, it is essential to monitor the fallout of the implementation through proper feedback.  If the decision works in favor, it is fine, otherwise, the decision must be dropped. 

During the decision-making process, the leaders can adopt three approaches such as experience, experimentation, and research and analysis.  The conventional approach is to go by experience. However, keeping the current complexity in view, going by what went right in the past might not work in the present context.  Another method is to go by experimentation which is by trial and error method.  When you look at uneducated people they go by gut and intuition from their subconscious mind.  They make a trial with a specific decision and if it does not work, they learn lessons from the error and adopt another alternative decision. 

Decision-making can happen under three circumstances – certainty, risk, and uncertainty.  When adequate information is available, it falls under certainty.  When the information available is inadequate, it falls under risk.   When there is a dearth of information, it falls under uncertainty.  It is the most complex one as leaders must decide without any access to information through their gut and intuition. 

Risk and decision-making

Peter Drucker quoted, “Whenever you see a successful business, someone once made a courageous decision.” Age is inversely proportional to risk.  The younger the age higher the risk appetite and the older the age lower the risk appetite.  Those who are swashbucklers have a higher risk appetite.  They make decisions based more on the gut than on rationality.  Their decisions depend more on their imagination than on realities.  They usually go by heart rather than by head.  They don’t see any logic, analysis, and rationality.  Young entrepreneurs often fall under this category. 

Decision-making and levels of management:

Conceptual and technical skills: Decision-making differs from each level of management.  The senior leaders are always engrossed in making decisions where the fate of the employees and the organization is involved.  In every organization, the senior-level management is actively involved in decision-making.  At the senior level, the leaders need more conceptual skills than technical skills.  They must be in a position to see the invisible to make decisions.  Whenever they are confronted with problems, they must be in a position to see the prospects and seize the opportunities.  However, the leaders in the middle-level management need an equal proportion of technical and conceptual skills.  At this level, the leaders may not take strategic decisions, unlike the senior level management.  Finally, in low-level management, leaders must have more technical skills and less conceptual skills.  

Strategic, tactical, and operational decisions: Senior level management deals with strategic decisions as the risk element are very high and the outcomes are uncertain.  In strategic decisions, the slogan is ‘higher the risk higher the returns’.  They are made in tune with the organizational vision, values, principles, and philosophies based on imagination than intuition.  The middle-level management makes tactical decisions that support the strategic decisions.  In tactical decisions, the slogan is ‘medium the risk medium the returns’.  The lower level management involves in operational decisions which support tactical decisions.  The slogan for an operational decision is ‘lower the risk lower the returns’. These decisions are made with a short-term perspective where the impact is immediate.  At every level of management, the decisions are made.  Hence, decision-making is an integral part of every level of management.

Tools and techniques to make successful decisions

• Look at the big picture to understand the hidden challenges which are often ignored by others.  
• Prepare Plan A, Plan B, Plan C, and Plan D. If Plan A fails, you can explore other plans for execution.     
• Convince yourself first whether the decisions you make are feasible for execution.  If you are convinced, you may proceed further.  Don’t regret your decision because you have made the decision based on the available information at a particular point in time.  It is not necessary that all decisions must be successful.  If the problem is big, slice it into small pieces and assemble the same through synthesis.  
• Consult experts to get more ideas.  ‘Two heads are better than one,’ goes an ancient Greek proverb.  The quality of decisions will be higher if more than one person is involved in the decision-making process.  
• Give a break to your problem.  Go to a serene place and ponder.  Your creativity and out-of-the-box thinking will come to the fore.  You will be amazed to find your outcomes.   
• Don’t get into the paralysis of analysis.  Don’t brood over them too much on the problem.  Once you are convinced with the decision, it is time to take a final call without any procrastination. 
• Check your mood levels.  Make decisions when your mood is good.  When in doubt, postpone the devil of the decision to avoid adverse consequences.
• Never look at the individuals.  Look only at the issues and ideas to arrive at the solutions.  It is always what is right and wrong not who is right and wrong matters.   In extreme cases, not making a decision is also a decision. At times, time will solve the problems on its own.  However, treat it as an exception, not a rule.  
• List out the rational and realistic alternatives.  Check for long-term implications and complications. Avoid making decisions when in anger.  
• Sleep over the problem.  When you think about the problem before you go to sleep, the very next day morning you get better solutions as you slept with the problem.  When you go to bed by thinking about the problem it goes to your subconscious mind which automatically searches for solutions.  
• When you don’t get any ideas for your problems, take a break for some time.  During this period the subconscious mind searches the information for appropriate alternatives.  
• Think of repercussions before implementing a decision.  What is the best and the worst that can happen after implementing your decision?  
• Go by intuition, if the repercussions and fall-out are limited and the time is short.  In contrast, delay the decision when there is a possibility of more harm to others.  
• Look at the underlying reasons, factors and forces, and examine them analytically to arrive at feasible solutions.  
• Keep your decisions within yourself when you involve in group decision-making as it may influence others.  Listen to the group members first, weigh the pros and cons and offer your solutions.  It helps in arriving at the most feasible and viable solutions.  
• Be prepared mentally for failures as all decisions may not deliver the desired outcomes.  

Decision-making and leadership

Leadership and decision-making are closely connected.   Leaders must make decisions in a fog of uncertainty.  Good leaders know how to make decisions and commit to seeing them through.  They don’t invent excuses.  They have a higher internal locus of control.  They make decisions and implement them.  They are optimistic and have alternative plans ready if a specific decision boomerangs. They learn lessons from wrong decisions and bounce back quickly.  For leaders, failure is not final but it is a learning opportunity to become smarter and wiser.  

Leadership calls for stepping into the shoes of problems, encountering adversity squarely, and making tough decisions.  Whenever there is a crisis don’t act as though there was no problem as your subordinates know the same.  Pretending that there is no crisis will erode your authority to lead from the front.  Hence, accept the realities and declare that there is a crisis brewing and act tough by making the right decisions.  

If leaders fail to make the right decisions it costs the lives and careers of their team members.  The leaders must accept the blame for their failures and spread the fame for their successes.   

Communication and decision-making

Communication plays a crucial role in making successful decisions. Peter Drucker quoted, “The most important thing in communication is to hear what isn’t being said.”  Hence, be a good listener to make the right decisions. Once the decision is made it is equally important to persuade the team members to accept to ensure effective execution.   In this regard, communication plays a crucial role in getting across the message of decisions effectively to the team members.  At times there is a need to modify the decisions based on the inputs of the members.  When the team has certain reservations, it is essential to reconsider and revise the decisions.  

John McDonald said, “The business executive is by profession a decision-maker.  Uncertainty is his opponent.  Overcoming it is his mission.”  There is no magic wand to make successful decisions. Effective decision-making requires thorough discussions and deliberations.  The challenge with decision-making is you won’t always have all the data you want.  Decision-making is one of the key skills of soft skills.  It is also a key skill to achieving effective leadership.  One of the yardsticks to measure effective leadership is good decision-making skills. If communication is the mother of leadership, decision-making is the father of leadership. 

Conclusion

With the rapid growth of technology, it has become highly challenging to make the right decisions.  The old style of decision-making doesn’t work in the present context as things are changing rapidly.  Leaders must realize these facts and must reinvent themselves with innovative and creative decision-making tools and techniques.

Decision-making is a skill that can be honed over a period of time by practice and experience.  Once you practice making decisions you improve your confidence and become a successful leader.  Overcoming challenges in life can also make you a good decision-maker.  This skill is very much essential for leaders at the top-level management as the fate of the organization and its employees depend solely on them.  Hence, it is essential to exercise restraint and caution and demonstrate the maturity to make sound decisions.

There is no fixed formula to make decisions.  Follow your head when you have ample time and information to make decisions. Follow your heart when you have a paucity of time and information to make decisions. Follow your gut when you don’t have adequate time and access to information to make decisions.  In a nutshell, decision-making is situational.  To conclude, there is a need for situational decision-making in the 21st century to overcome turbulent times and achieve organizational excellence and effectiveness.  

About the Author

Professor M.S. Rao, Ph.D. is the Father of “Soft Leadership” and the Founder of MSR Leadership Consultants, India. He is an International Leadership Guru with over forty years of experience and the author of over fifty books including the award-winning ‘See the Light in You’ URL: https://www.amazon.com/See-Light-You-Spiritual-Mindfulness/dp/1949003132. He is a C-Suite advisor and global keynote speaker. He brings a strategic eye and long-range vision given his multifaceted professional experience including military, teaching, training, research, consultancy, and philosophy. He is passionate about serving and making a difference in the lives of others. He is a regular contributor to Entrepreneur Magazine. He trains a new generation of leaders through leadership education and publications. His vision is to build one million students as global leaders by 2030 URL: http://professormsraovision2030.blogspot.com/2014/12/professor-m-s-raos-vision-2030-one_31.html.  He has the vision to share his knowledge freely with one billion people globally. He advocates gender equality globally (#HeForShe). He was ranked #1 Thought Leader and Influencer in Entrepreneurship and Business Strategy globally by Thinkers360. https://www.thinkers360.com/top-50-global-thought-leaders-and-influencers-on-business-strategy-december-2020/. He invests his time in authoring books and blogging on executive education, learning, and leadership. Most of his work is available free of charge on his four blogs including http://professormsraovision2030.blogspot.com. He is a prolific author and a dynamic, energetic, and inspirational leadership speaker. He can be reached at msrlctrg@gmail.com.

The post Exploring Decision-Making at the Strategic, Tactical, and Operational Levels appeared first on The European Business Review.

Management, has to do with people and getting people together as the first priority. Unfortunately, mechanistic models that assume a behavior has to do only with incentives, profits or value, seem to be the dominating force in the last few years. This article presents an alternative way of looking at organizational decision-making, that is based on (1) effectiveness, which means obtaining specific results, (2) attractiveness, which means satisfying the intrinsic motives of individuals, and (3) unity, which means satisfying transcendent or altruistic motives.

According to the Wikipedia, “Management in all business and organizational activities is the act of getting people together to accomplish desired goals and objectives using available resources efficiently and effectively.” To be more specific, management is about people before anything else; and about getting people together next. This, to some, may be only too obvious; but more frequently than not, we find in management books, magazines, journals and schools that only a very small percentage of their content is devoted to people. “Business” as such, in terms of product strategies, marketing tools, financial results and financial engineering, takes the driver’s seat. If people are taken into account at all, it is as instruments of a purpose devised by top management, typically expressed in financial terms, and not as subjects of their own life and their own rights who need to fulfill different kinds of motives.

A mechanistic model of organization


A mechanistic model of organization is that of standard, neoclassical economics that is implicit in most analyses. “Firms” are an abstract production function that only indicates the feasible efficient combinations of inputs and outputs. Then, someone (presumably “the owner”) impersonally chooses the combination that maximizes profit and makes all the pertinent decisions. In its crudest version, the standard economic model assumes that all workers sell their labor to the firm at prevailing market prices in a perfectly transparent market where both parties know exactly what they are buying and selling. Thus, there is no need for motivation, monitoring or control.

In a less crude model, but still mechanistic, workers can put in more or less effort, which will result in bigger or smaller efficiency or results. To properly motivate the workers to increase efficiency, then, an incentive system related with those results will push them in the right direction. Of course, the owner is also “spontaneously” motivated with profit maximization.

Interestingly, a version of this mechanistic model was what Taylor put into practice during the early years of the 20th century. With one essential difference: Taylor never based his analyses in economics (he was an engineer, not an economist) or assumed that the production function could be taken as given: in fact his main goal was to increase efficiency (output / input relationships), trying to reach the efficient frontier of that production function. This way, he would contribute to make the surplus much bigger until (in his own words) “it is unnecessary to quarrel over how it shall be divided”.¹

For Taylor, then, and for the standard neoclassical model of the firm, things are rather simple: the goal of the firm is that of profit maximizing, and workers can be motivated to pursue that goal through an appropriate system of incentives. To be sure, in modern terms, profit maximization has disappeared from the picture to yield to shareholder value maximizing, that supposedly takes into account both the time-value of money and the expectations about an uncertain future; and incentive systems are much more sophisticated than those used by Taylor, now being based mainly in shareholder value and affecting not only blue-collar workers, but top managers as well.

Unbelievable as it might seem, after a considerable evolution during the last century, we seem to be now back to the beginning, although in a more sophisticated form: shareholder value is all that matters for the firms (witness the solemn declarations of most firms in their annual reports) and compensation is all that matters to employees, mainly top managers. Yet, this is not how human nature is: people do not want only money, neither as employees nor as shareholders (in the double role that many people have: they are employees, but at the same time shareholders in the firms where they have decided to invest their savings).

Shareholder value 


Shareholder value as a criterion for decision-making become famous in the 1970’s and 80’s through the work of Al Rappaport², whose intention was to warn managers about the dangers of focusing in the short-term, looking at the current year profit only, and proposed to look at the long-run effects as well through shareholder value. Later on, Jack Welch, then president of General Electric, promoted the idea to become a tenet of many companies, which now boast of having it as the main goal of the company. Recently, though, he has changed his mind, and speaking to a well-known business magazine he said that this was “the dumbest idea in the world”: shareholder value can never be a criterion for decision-making, or a guide to action; it is only a result.³

It is interesting to note that this last idea was already in Peter Drucker’s in 1954, not with respect to shareholder value, which was not in fashion at that time, but with respect to profit⁴. Aiming at increasing profit does not provide any guide as to what kind of decisions have to be made specifically; in contrast, wanting to satisfy customer’s needs does.

Possibly, the main reason why shareholder value has become so popular is that microeconomic theory is able to show that such a goal maximizes social welfare⁵. But if the profit maximization hypothesis has been severely criticized since long as being “too difficult, unrealistic and immoral”⁶, current formulations based on firm’s value can be criticized as unrealistic as well. According to Senge, for instance, by maximizing profit in the short run one can ignore all the complex feedback dynamics. “This is why manipulating profits over the short term is much easier than building wealth over the long term. Thus, whether intended or not, firm value maximization will almost always become, by default, short-term profit maximization”⁷. This would, of course, go directly against Rappaport’s original idea of avoiding shortsightedness.

The humanistic view


Mayo and Roethlisberger were prominent among the people that contributed to show that the Taylor approach was misguided. In Fact, Taylor, tried to find through engineering analyses mechanistic relationships between inputs (work hours) and outputs (product units); and Mayo, Roethlisberger and their associates showed in the famous Hawthorne Experiments⁸ that this was just impossible, i.e., that the productivity and the total production that could be obtained from a worker depended very much on a complex series of social and psychological factors that cannot be taken into account by a production function.

A few years later, Chester I. Barnard⁹ analyzed organizations in a more structured, rational way. Barnard asked himself why most organizations were rather short-lived, and concluded that the key variables for a firm to survive were two variables: effectiveness and efficiency.

His concept of effectiveness was the commonly held notion that an action is effective if it achieves the desired goal. But his definition of efficiency substantially differs from the concept commonly held. Typically, we think of efficiency (and we have done so at the beginning of this article in the context of the Taylor approach to management) as an output / input relationship: an action that we take is efficient if, given the input (say, direct labor hours) produces an output (say, units of product) that is the maximum possible given the technology available; or that given the output, it consumes the minimum inputs necessary. In contrast, Barnard defined an organizational action as being efficient whenever it satisfied the motives of the individuals that belonged to the organization. He looked at organizations as cooperative systems; and, from this point of view, the organizational goal must be a composite of the individual’s objectives. The organizational action is then effective if it achieves the (explicit) organizational goal, and it is efficient if the motives of the individuals participating in the organizational action are satisfied.

Typically, when we think of an organization, we think about its employees as members of that organization. Customers are obviously important, but we typically don’t think of them as belonging to the organization. However, the group of people that are most interested in the organizational goal (what the organization has to achieve) is precisely the customers. Thus, customers can sometimes be included as part of the cooperative effort that represents the organization.¹⁰

Individual motives


Individual motives are then important for organizational survival. A classical distinction coming from the middle of the 20th Century is the one between extrinsic and intrinsic motives, extrinsic motives being those related with the rewards external to the action (money, status, and so on), and intrinsic motives being those that have to do with the action itself. Ryan and Deci (2000)¹¹ summmarize it and distinguish between “intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something because it leads to a separable outcome.” They also take into account that intrinsic motivation may have an hedonic component of enjoyment, while at the same time there is a normative intrinsic motivation out of a sense of obligation (see also Osterloh and Frey, 2003¹² , Pérez López,1993¹³ and Rosanas 2008¹⁴ ). For our purposes here, it is important to distinguish between thoso two types of motives. So, we will reserve the word “intrinsic” for those motives that have to do with the action itself, with its hedonic component, and, following Pérez López and Rosanas, we will call the type of motives related with obligation to other people “transcendent”. Actually, the trichotomy is a formal distinction: the extrinsic motives operate on the external consequences (to the individuals) of the action, the intrinsic motives on the consequences on the acting individuals themselves, and the trascendental motives on other people (mainly, customers and employees).

Effectiveness


An organization needs to have a set of goals (perhaps somewhat implicit) because otherwise the members of the organization would not know what to do. These goals are then subdivided along organizational lines to every individual working in the organization. The degree to which the goals (essentially quantitative) are achieved is called effectiveness. Effectiveness is important for the survival of any organization: without achieving a minimum of objectives, possibly measurable goals, the cooperation has no purpose.

In business firms, specifically, perhaps one of the most important dimensions of the quantitative goals is, of course, value added, which (imperfectly) measures the creation of economic value by the firm. Value added is then “split” between salaries (to employees) and profit (to stockholders).

Other types of organizations may have different quantitative goals. Thus, a political party may have as a goal the number of votes obtained in an election, a hospital the number of cured people, and a church the number of the faithful that regularly attend the religious services. The value added, however, is always important in any organization, because it conditions survival: no organization can survive indefinitely with a negative value-added every year.

Obviously, a positive value-added is a very minimum condition, because the salaries of employees have to be paid in order to keep them collaborating with the organization: an organization of “volunteers” is very difficult to run and very unstable through time. The willingness to participate in the organization depends partly on receiving an adequate compensation. Thus, value added is the “fund” created to satisfy extrinsic motives on the part of employees.

Attractiveness


The mechanistic model of the organization that we have presented at the beginning would stop here. Other motives don’t exist in its context, so there is no need to satisfy them. But this is contrary to fact: intrinsic and transcendent motives not only exist, but that they are important, and can become a crucial building block of an organization. Actually, this is expanding the concept of efficiency in Barnard into two: the efficiency to satisfy intrinsic motives, and the efficiency to satisfy transcendent motives.

It would seem only too obvious that, typically, employees like the kind of work for which they are adequate (not necessarily the one they actually do), enjoy the activities related with this type of work, and want to learn more to self-actualize as professionals. If all this happens, an indirect consequence is that they become even more valuable in pure economic terms at the same time. But no doubt, good professionals like by itself a job that is attractive to them given their knowledge and their abilities, that is challenging, and where they can expect to make progress. And people seldom (if ever) do a good job at anything that they do not like or do not feel particularly adequate for.

Attractiveness, then, becomes the second consideration in satisfying the motives of individuals giving them a job that is attractive and where they can develop as professionals and learn for the future. But although this is necessary, it is not sufficient. If it were, anarchism would have been a big success as a practical way of handling organizations: just give each and all employees the kind of job they like, and live them alone. We all know that this does not happen.

Unity
We can now close the circle by including in our analysis what we have called the transcendent motives, i.e., the kind of motives that have to do with caring about what happens to other people.

This has two different aspects. First, we consider transcendent motives towards customers. If employees of a given firm do not care about customer satisfaction and care only about doing a product that is technically perfect, meets some specifications, or about collecting their incentive, customers will be satisfied only by chance. The goal(s) of the firm reflect only imperfectly (sometimes very imperfectly) the interests and wishes of the customers; and, then, if employees are not genuinely interested in finding out which ones these are and in doing what they can to fulfill them, this simply won’t happen. There is an enormous difference between obtaining quantifiable results, perhaps technically perfect, and actually satisfying the needs of the customers.

Second, we consider other employees. Active cooperation is needed to achieve any worthy objective: that is the foundation of organization. But you cannot obtain collaboration by decree, by any rules or by any incentive system: employees have to have the will to cooperate with each other, i.e. they have to be willing to contribute to achieve the goals of the other individuals. Extrinsic or intrinsic motives are not enough to achieve that: transcendent motives are necessary. Employees have to identify with the needs of the customers and with the efforts of the other employees. This is what we call “unity”.

Three criteria for decision-making


Effectiveness, attractiveness and unity, the three qualities that we have seen an organization should have to survive in the long run, constitute thus at the same time three criteria for decision-making in an organization in the short-run. For the sake of concreteness, suppose a business firm has to make an important decision in terms of accepting or rejecting an order that one of its customers may be willing to place.

The typical analysis of such a decision in a business course has to do with the criterion of effectiveness only, which usually means calculating the impact of the decision in the profit (or value-added) of the firm. This is a very operational concept, not without uncertainties, of course, but which can be calculated taking those uncertainties into account.

But this calculation is sufficient only if the other two criteria are invariant with the alternatives of decision, i.e., if attractiveness and unity are not affected by the decision. In general, it will not be the case. In terms of attractiveness, for instance, it can be small or even negative in cases where a given order is a routine job, boring for most people and where they will learn close to nothing; or, on the other hand, in a different order, it may be something challenging, doable but not trivial, where people can develop their abilities and thus increase their distinctive competence: attractiveness is then highly positive.

Something similar happens with unity. An order may promote cooperation and contact with the customers that will develop identification with their problems and teamwork, or it may be again some routine work that promotes bureaucratic behavior and following only standard procedures.

Mission and unity


These principles should translate in a sense of mission. The word “mission”, which has a religious and military origin, and which is currently used and abused in management practice as a set of nice words, essentially should mean the reason for the efforts of the firm. Going back to Barnard’s efficiency, the sense of mission means to satisfy the motives of the customers and of the employees. Which is a two-way street: you need to satisfy the needs of the customers (external mission) in order to be viable as a firm and be able to satisfy the motives of the employees, and you need to satisfy the motives of the employees (internal mission) in order to have them identified with the objectives of the organization, which should be its external mission.

Final comments


If taken seriously, these concepts and principles (effectiveness, attractiveness, unity and internal and external mission) would change the way every manager makes decisions. It is a different way of seeing the world of management, of the objectives that organizations should have, and a more complete frame of mind for approaching all of them. At the same time, it provides a built-in ethical component, badly needed today in time of crisis and scandals, because it takes into account systematically the interests of other people in every decision.

About the author


Josep Mª Rosanas Martí is a Nuclear Engineer (UPC, Barcelona, 1969), MBA (IESE Business School, 1971) Ph.D. in Accounting and Information Systems (Northwestern University, 1976). He has been a Professor of Accounting and Control at IESE Business School since 1971. In 1990-94, he took an extended leave of absence to become one of the founding members and a Vice-Rector of Universitat Pompeu Fabra (Barcelona). He is the holder of a “Crèdit Andorrà” Chair in Markets, Organizations and Humanism, and the author of several books and articles on accounting and control, ethics and humanism.

Notes


1. From testimony of Frederick W. Taylor at hearings before the Special Committee of the House of Representatives to Investigate Taylor and
Other Systems of Shop Management, January, 25, 1912, pp.1387-89.


2. Creating shareholder value : the new standard for business performance, New York, 1986, The Free Press.


3. Financial Times, March, 12, 2009

.
4. In The Practice of Management, New York, Harper & Row, 1954, p.35.


5. See, for example, Jensen (2000) for a brief statement of the argument: “Value Maximization, Stakeholder Theory and the Corporate Objective Function”, in M. Beer and N. Nohria, eds., Breaking the Code of Change, Boston, Harvard Business School Press.


6. Anthony, R. N. (1960), “The Trouble with Profit Maximization: It Is Too Difficult, It Is Unrealistic, It Is Immoral”, Harvard Business Review, 38(6), 126-134


7. Senge, P. (2000), “The Puzzles and Paradoxes of How Living
Companies Create Wealth”, in M. Beer and N. Nohria, eds., Breaking the Code of Change, Boston, Harvard Business School Press.


8. See, for instance, Elton Mayo (1933), “The human problems of an industrial civilization”, New York, The MacMillan Company.


9. Barnard, Chester (1938) The Functions of the Executive, Boston, Harvard University Press.


10. Barnard, ibid., p.69


11. Ryan, R.M. and Deci, E.L. (2000), “Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions”, Contemporary Educational Psychology 25, 54-67.
12. Pérez López, J.A. (1993), Fundamentos de la dirección de empresas, Madrid, Rialp.
13. Rosanas, J. (2008), “Beyond Economic Criteria: A Humanistic Approach to Organizational Survival”, Journal of Business Ethics, DOI 10.1007/s10551-006-9341-9ld.
14. Osterloh, M. and Frey, B.: 2003, Corporate Governance for Crooks? The Case for Corporate Virtue, Working paper ISSN 1424-0459, Institute for Empirical Research in Economics, University of Zurich.

The post Beyond Effectiveness: Attractiveness and Unity as Criteria For Decision-Making in Organizations appeared first on The European Business Review.