LEAD Framework: Differentiating HIL from HIM

I was cruising this month with my family in the Caribbean when I had some interesting conversations around the use of artificial intelligence tools at work. I heard about guardrails, responsible AI guidelines, and involving human in the loop (HIL) with people approving work, code, and build. This was wonderful and I also heard the terms human in the middle and human in the loop being used almost interchangeably. In my mind, the use of humans is for governance accountability and not just monotonous approvals. As I believe in the Accountable AI, I thought how to make this more practicable. The result is the LEAD reference framework. Let me elaborate. 

It is important to realize that the neither the AI as a system or the AI agents as parts of the larger system hold accountability. They may provide evidence, build statistics, predict/forecast, monitor itself, etc. But, none of these things provide accountability to the end users who are impacted by the AI model. That accountability sits squarely with the humans. Here is where I think we need to call out the distinction between human in the loop from human in the middle.

Human in the Loop vs Human in the Middle

Human-in-the-Loop typically means inserting a person somewhere in the AI decision process—reviewing, approving, or overriding an AI-generated outcome. Although this is certainly useful, it can also become a checkbox. In that case, it is not human judgement but a formality involving human approval. Furthermore, as soon as we conceive it as a checkbox, it opens up the dangerous exposure to automating it removing that human connection. So, how is the human in the middle different?

If Human-in-the-Loop (HIL) asks: “Where do we insert a human?” then Human-in-the-Middle (HIM) asks: “Where must humans exercise judgment?” That judgment begins before the AI produces an answer and continues after it does. It includes what we ask AI to do, what data we provide, how we test it, what authority we give it, and what we do when its behavior produces unacceptable consequences.

This is where my LEAD framework becomes practical.

LEAD: Making Human Judgment Explicit

The LEAD framework stands for:

• L — Look beyond the obvious output.
• E — Examine the evidence and its consequences.
• A — Act when the evidence reveals unacceptable risk.
• D — Decide and Demonstrate who owns the outcome and why the decision was made.

The technical professional and the leader will approach LEAD differently. Both must collaborate together to ensure they demonstrate collective leadership in these questions answered.

• The technical professional asks: “Does the system work as intended?”
• The leader must also ask: “Should the system be doing this in the first place, and are we willing to stand behind its consequence"

Illustration with Prompt Engineering

Prompt engineering is a good example of why the AI world challenges some of our traditional Agile assumptions. The second Agile Manifesto principle reads, “Working software over comprehensive documentation.” The intent of this principle was never to eliminate thinking but to reduce unnecessary bureaucracy. With vibe coding and model driven development and powerful AI tools like Kiro, Claude Code, Codex (to cite a few), AI can now generate working software, requirements, test cases, analyses, documentation, and risks at extraordinary speed. So, the pattern is shifting to comprehensive documentation as software development itself is no longer a niche skill (I am sure it may evolve later).

Consequently, those that develop software need to master the art of Prompt Engineering discipline that requires people to think through the specific use cases, deliberate design around the constraints, assumptions, risks, and dependencies (CARD that I keep referring) further evaluating against bias, ambiguity, guardrails, and so on (Ethical considerations). It is important to note that the prompt itself may be small and succinct but the thinking behind the prompt certainly is not. The new pattern therefore is "Ethically Compliance over Faster Delivery."  This thinking makes humans accountable for the outcome rather than an Approval Master in the tool chain.

Let us demonstrate this with an example of using AI to to generate test cases. Given below is the technical responsibility and leadership responsibility across the LEAD dimensions. I am avoiding the specific roles like designer, developer, tester, manager, scrum master, product owner, or project manager, or senior leadership teams in both these areas as the job titles differ across the industry globally. 

LEAD Area	Technical Responsibility	Leadership Responsibility
Look	What scenarios did AI generate? What scenarios did AI miss?	What risks are we not testing? Why are we not testing for them?
Examine	Are the tests reliable and repeatable? Are the tests automation-ready? Are the test sufficiently broad?	Are we optimizing the number of tests rather than meaningful risk coverage?
Act	How we are adding missing tests? How are we improving tests/prompts? How are we analyzing results (e.g.: FP, FN)	How are we verifying additional  human judgement (governance)?
Decide	What evidence increases our confidence?  What risks are we absorbing? What risks are we passing it on to others?	Who accepts the residual and secondary risks? How are we communicating them  to users with autonomy options?

Throughout the AI assisted product delivery value chain, whether it is software or not, the "risk management" is the language through which technical and leadership teams hold each others accountable. So, technical teams govern how the capability works. Leaders govern why it should be used, where it can be used, and what happens when it fails.

This is the real transition that needs to happen. Responsible AI asks us to build systems that are transparent, fair, safe, and explainable. Accountable AI asks us to go one step further: Who will act when the evidence tells us something is wrong?

Although a model can be explainable, a prompt can be documented, a test suite can be automated, and a dashboard can show every metric, none of these holds the accountability cards. As we shift from HIL to HIM involving human judgement throughout the value delivery, we demonstrate that "The goal is not slow AI down. The goal is to avoid accelerating irresponsibility." To me, this is the foundation of "Accountable AI" mindset.

I think I landed on something here with the LEAD framework. What do you think? 

Confusion Matrix is for leading governance than reporting metrics

Two different unrelated events this month made me think more about the role of "Confusion Matrix". In one event, I was having some conversations with the clients where discussions emerged about reporting metrics on false negatives and false positives on how accurately AI predicted risks and mitigations for product development. In another event, I was working with doctoral candidates who were not thinking about confusion matrix in the context of ethical decision making. As I reflected on this gap between scholars and practitioners, I felt compelled to emphasize, "A metric can tell us how often an AI system is right or wrong. Leadership must ask who is harmed when it is wrong." This thought process differentiates accuracy, explainability, and explicability that requires leaders to use the confusion matrix as a tool in their congoing governance.

In a nutshell, 

• Accuracy measures performance. 
• Explainability measures the reasoning behind a prediction. 
• Explicability measures ethical and social accountability.

Traditionally, the confusion matrix is used to evaluate the performance of a classification model. It identifies four possible outcomes: true positives, true negatives, false positives, and false negatives. When scholars didn't think of confusion matrix for ethical considerations and practitioners delegated it to be a metric delegated to platform (data collection) and other teams, this means we need to dust off the confusion matrix for leadership. These are more than statistical categories for data scientists to put charts on a dashboard. They represent four different ways an AI system can affect people. Although, the confusion matrix does not make an ethical decision for us, it gives us evidence to make one. This is a leadership level thinking where continuous governance comes into play! 

Confusion Matrix Overview

Imagine an organization uses AI to determine whether a financial transaction is potentially fraudulent. The model reviews 1,000 transactions. In reality, 100 are fraud and 900 are legit transactions.

Suppose the model produces these results:

	Actually Fraud	Actually Legit	Total
AI says Fraud	80 True Positives (TP)	90 False Positives (FP)	170 Transactions
AI says Legit	20 False Negatives (FN)	810 True Negatives (TN)	830 Transactions
Total	100 Fraud Transactions	900 Legit Transactions	1000 Transactions

When reading this table, TP and TN are our win! That is, the model's prediction aligned with the reality. So, the model appears reasonably effective because it correctly identifies 890 (80 TP, 810 TN) of 1,000 transactions, giving it 89% accuracy.

But this accuracy alone hides an important story. The model identifies 170 (80 TP, 90 FP) transactions as fraudulent. Only 80 TP are actually fraudulent. Therefore, its precision is approximately 47%. This is a powerful reminder that when this AI model says something is fraudulent, it is wrong more than half the time! Then, how do we hold ourselves accountable to end users in the product delivery chain? (regardless of your role in using the AI model) You can see the ethical lens evolve. 

Now consider recall, or false positive rate. 

1. Recall is the ratio of TP/(TP+FN). So, the model identifies 80 of the 100 fraudulent transactions. Its recall is 80%. That metric sounds promising and positive until we ask another question: What is the impact of our solution to the people in the 20 fraud transactions the model missed? These are now ethical leadership questions. The organization absorbs the financial risk, while potentially allowing additional fraudulent activity to occur.
2. False Positive Rate is the ratio of FP/(FP+TN). So, the model wrongly classified 90 of the 900 transactions. What would be the impact to customers? Perhaps, they may experience delayed payments, frozen accounts, additional verification, reputational consequences, or loss of access to required timely services.

Connections to Ethics

The 11% (90 FP + 20 FN / 1000 Total) therefore creates two different types of harm. I view the false positives (FP) raise questions about justice, autonomy, non-maleficence, and fidelity while the false negatives (FN) raise questions about beneficence and non-maleficence. If we think of AI as an actor in the solution-engineering, what abuser persona could AI unintentionally be playing? So, the bigger governance questions we should ask as leaders is if this level of error acceptable for our customers when using AI? That question cannot be answered by the model and data. It must be answered by leadership with human insight and intervention.

One way people can further understand this ethical challenge is by examining performance across populations. Suppose the model performs differently for two customer groups:

Metric	Group A	Group B
Accuracy	92%	87%
Precision	70%	42%
Recall	84%	76%
False Positive Rate	5%	14%
False Negative Rate	16%	24%

The organization's overall accuracy may still look acceptable. But the experience of the two groups is dramatically different. A customer in Group B is substantially more likely to be incorrectly flagged and more likely to have a fraudulent transaction missed. I hope we can see how the algorithmic bias becomes a leadership issue in this case. The leader should not immediately conclude that the model is discriminatory. The numbers alone do not establish causation. Instead, they should trigger investigation.

The governance level leadership questions all of us should think as part of both problem-solving and decision-making involve (examples):

1. Why is the error distribution different?
2. What characteristics or historical patterns are influencing the model?
3. Are those differences legitimate or artifacts of historical inequity?
4. Are some communities bearing a disproportionate share of the system's errors?
5. What happens to people after they are flagged?
6. Can they challenge the decision?
7. Who reviews the decision?
8. How quickly can an error be corrected?
9. What happens if the model continues producing the same pattern six months from now?
10. Who absorbs the risk of our misses?

In my opinion, this is explicability in action. Explainability tells us why the model produced the prediction. This can be on a dashboard but leadership needs explicability that asks whether the prediction and its consequences are ethically defensible. Holding ourselves accountable for the errors our models cause to the people and society is a governance decision. Confusion Matrix is not just a data science technique. It is an ethical lens to risk driven development. This is why I propose that we shift more into the "Accountable AI" than limit ourselves to "Responsible AI." 

What do you think?

Explainability Is the Measure of Accountability: Why Responsible AI Must Evolve into Accountable AI

As I was wrapping up discussions with some people that I had trained for the Project Management Professional (PMP) certification, discussions emerged around Responsible AI. It was interesting to read people's thought about "Responsible AI" from a technical and career enablement standpoint! But, Responsible AI is the start of the leadership for holding oneself accountable for the decisions made. I think therefore, there needs to be an evolution of the next level of AI leadership foundations! 

Undoubtedly, Artificial Intelligence has become remarkably proficient at making predictions. It identifies fraudulent transactions, recommends medical diagnoses, screens job applicants, prioritizes customer requests, and increasingly influences decisions once reserved for human judgment. Yet as organizations race to adopt AI, they continue asking the wrong question. The question is not whether AI is intelligent enough to make decisions. The real question is whether leaders are accountable for the decisions AI helps make. Responsible AI has advanced the conversation around technical reasoning but still has not advanced the ethical leadership. 

It is important to understand that responsibility without accountability remains an aspiration rather than an operational reality. We need to shift our mindset towards the impact of our actions (or lack of it) on the people (employees, customers, end users, societies) and planet (environment) by asking, "How are we accountable for the consequences of an AI-enabled decision?" This mindset is where I believe we move beyond Responsible AI toward Accountable AI. 

• Responsible AI establishes the intention to do the right thing. 
• Accountable AI requires us to explain, justify, and ultimately own what happens.

Consequently, the future belongs to Accountable AI, where both explainability and explicability becomes the mechanism through which accountability is demonstrated.

"Accuracy measures performance. Explainability measures reasoning behind the prediction. Explicability measures ethical and social accountability"
— Dr. Shri Rajagopalan

The distinction begins with three concepts that are often treated as interchangeable: accuracy, explainability, and explicability. 

• Accuracy tells us whether the prediction was correct. 
• Explainability helps us understand the reasoning behind the prediction by understanding data, variables, patterns, features or user behaviors contributed to the model's conclusion. This is essential because an organization cannot govern what it cannot understand. 
• But explainability has a boundary. Knowing why an algorithm reached a conclusion does not tell us whether the conclusion should have been reached, whether the underlying assumptions were appropriate, or whether acting upon that conclusion is ethically defensible. That requires explicability (Floridi & Cowls, 2019). 

Leaders are not faced with explaining the technical architecture or the model differences in the AI system used. That would be explainability based on accuracy and precision. They are faced with answering beyond technical reasoning focused on the impact of the AI system. Hence, explicability takes us beyond technical reasoning toward the ethical and social accountability of AI. It asks not only “Why did the system make this prediction?” but also “Should we accept this prediction, what are its consequences, and can we justify those consequences to the people and communities affected?”

Consider an AI system used to screen employment applicants. The model may be highly accurate and completely explainable. We might even be able to identify precisely which characteristics contributed to a candidate's rejection. But what if those characteristics reflect historical patterns of discrimination embedded within the training data, such as what happened with the Amazon's AI recruiting tool showing preferential treatment to men over women candidates (Datin, 2018)? Explainability can tell us why the model rejected the candidate. Explicability asks the more difficult question: Should those factors have been permitted to influence the decision at all? This is where algorithmic bias becomes a leadership challenge rather than simply a data science problem. The issue is not merely whether the algorithm works as designed. It is whether the organization designed, deployed, and governed the system in a way that is ethically and socially defensible.

Every participant in the AI value delivery pipeline therefore has a responsibility to ask questions that technology alone cannot answer. The onus now shifts to everyone to think risk based questions on outcome and value! Not just, "Can we get this faster?" but more focused on:

• Who benefits from this technology? 
• Who bears its risks? 
• Who controls the algorithmic system? 
• Whose voices are suppressed? 
• Which communities are marginalized? 
• What meaningful options do we give the users affected by the system? 
• What unintended consequences might emerge over time? 

These questions move us from technical accountability toward societal accountability. They also recognize that AI does not operate in a vacuum. An algorithm can optimize a decision while simultaneously optimizing inequality. It can improve overall performance while imposing disproportionate harm on a particular community. It can be technically correct and ethically wrong. That is why the ethical challenge is not the technology itself but the leadership's governance of its use.

Explicability also gives us a different way to understand the fundamental ethical principles of beneficence, non-maleficence, justice, autonomy, and fidelity. These principles should not be treated as independent boxes on an AI ethics checklist. They are symbiotic conditions of accountability. 

• If an AI system causes preventable harm, non-maleficence has been compromised. 
• But the consequences rarely stop there. If the affected individuals have no meaningful ability to challenge the decision, autonomy is compromised. 
• If the harm disproportionately affects a particular population, justice is compromised. 
• If the technology's promised benefits are not realized or are realized primarily by those already advantaged, beneficence is compromised. 
• And when an organization cannot explain, justify, or take ownership of these consequences, fidelity and trust begin to disappear. 

When one ethical principle is compromised, the others are placed at risk requiring not only understanding the primary risk but also the secondary risks. Ethical accountability therefore cannot be achieved by satisfying five principles independently; it requires recognizing their interdependence. The need for this awareness is why I propose the movement from Responsible AI to Accountable AI (Rajagopalan, 2020). If an end-user was unfairly treated with no alternatives presented, it is not technology failure but leadership failure. Reasoning at that point is too late as the arrow has not only left the bow but hit the wrong person or the wrong place. Explainability, from Responsible AI, provides evidence of reasoning. Explicability, from Accountable AI, provides the basis for ethical judgment. This distinction is critical because a transparent explanation of an unjust decision does not make the decision just. A perfectly explainable harmful outcome is still harmful.

Ultimately, Accountable AI requires humans to remain meaningfully involve, not simply as a Human-in-the-Loop who approves or rejects an algorithmic recommendation, but as Humans-in-the-Middle of an ongoing accountability system. Anyone in the AI based delivery lifecycle shouldn't be afraid of their jobs but should reorient themselves to their new improved leadership responsibility. The future of ethical AI, therefore, should not be measured solely by how intelligent our machines become. It should be measured by how accountable the humans governing those machines remain in our commitment to people, community, and the planet.

“Responsible AI asks whether we are doing the right things. Accountable AI asks whether we are willing to stand behind what those things do.”
— Dr. Shri Rajagopalan

What are your thoughts?

References

Datin, J. (2018). Insight - Amazon scraps secret AI recruiting tool that showed bias against women. https://www.reuters.com/article/world/insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK0AG/

Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Rajagopalan, S. (2020, September). Artificial Intelligence Solutions: Four Considerations extended from Digital Bioethics. https://agilesriram.blogspot.com/2020/09/artificial-intelligence-solutions-four.html

From Agile to AI: Why Culture May Determine the Winners of the AI Revolution

Twenty-five years ago, a small group of software practitioners gathered in the mountains of Utah and produced the Manifesto for Agile Software Development (n.d). Disappointed with the way software projects were developed then, their ideas challenged the dominant project management paradigm of the time: detailed upfront planning, rigid processes, and extensive documentation. Instead, they advocated for adaptability, collaboration, customer feedback, and continuous learning. Little did they really understand the concepts of just-in-time, limiting work in progress, eliminating waste, and progressive elaboration with rolling wave planning that were already foundational to project management where practice created a non-existent theory (Rajagopalan, 2014). 

Although the adaptive Agile ways eventually spread across industries and continents, its adoption was far from uniform. Some organizations embraced it enthusiastically, while others struggled or rejected it altogether. The reasons were often deeper than process or technology; they were cultural. While later Agile practitioners called any Agile Transformation to be a practice rooted in change management to modify the team and organizational culture, the adoption of culture was an after-thought! It shows the myopic foresight and the lack of diversity of the practitioners which is evident from the second manifesto principle "working software over comprehensive documentation" limiting the agile approaches to software development. 

Based on numerous years of research, the Dutch social psychologist Geert Hofstede (1980) provided a comprehensive lens to view the geographical culture across five dimensions first and later adding the sixth dimension. These dimensions include: Power Distance, Individualism, Motivation towards achievement and success (which was originally called Masculinity), Uncertainty Orientation, Long-Term Orientation, and Indulgence (sixth dimension added later). More details of these cultural elements can be found in Hofstede's Culture Factor Group (n.d) or in other scholar-practitioner materials (Hofstede & Bond, 1988; Hofstede, 2011).

In summary, countries with lower power distance often found it easier to embrace self-organizing teams and decentralized decision-making. Cultures with lower uncertainty avoidance were generally more comfortable with experimentation, iterative delivery, and evolving requirements. Highly individualistic societies often adapted quickly to Agile's emphasis on empowered individuals, while collectivist cultures sometimes preferred greater structure and consensus-building. None of these approaches were inherently better or worse, but they influenced how Agile was interpreted, implemented, and sustained. When Agile was promoted because people attended a Scrum Master workshop or gained an online training, little to no attention was given to the cultural considerations of the team members, their hybrid cultures due to long travel, immigration, expatriate arrangements, etc. So, agile didn't fail but we failed agile. 

Author's attempt to compare four countries (May 2026)

Today, organizations face another transformative wave: Artificial Intelligence. Much like Agile, AI is frequently presented as a universal solution capable of improving productivity, accelerating innovation, and transforming business models. The landscape of existing roles are being refined or even redefined where people have began question why do we need developers as AI tools can write them, why do we testers because tools can help with automation, and why do we need project managers, product owners, or scrum masters because AI can replace them! Organizations and leaders have not yet learned the lessons that Agile adoption taught. Extending these observations, AI adoption will not be determined solely by technological capability. As a popular saying goes, "A fool with a tool is still a fool," successful AI adoption needs people to realign AI ways of working with the individuals and team culture, organizational culture, leadership philosophies, risk tolerance, and societal values. The same cultural dimensions that shaped Agile adoption are likely to shape AI adoption as well.

Consider uncertainty avoidance, which is the orientation to risk. Organizations and societies with high uncertainty avoidance often demand predictability, governance, and clear accountability. Such environments may be slower to adopt generative AI systems whose outputs can be probabilistic and occasionally unpredictable. Conversely, cultures more comfortable with ambiguity may experiment aggressively with AI, accepting occasional failures as part of the learning process. Similarly, power distance influences whether AI is viewed as a democratizing force that empowers employees or as a centralized tool controlled by senior leadership. Long-term oriented cultures may prioritize investments in AI capabilities that take years to mature, while short-term oriented cultures may focus on immediate productivity gains and rapid return on investment.

However, AI introduces a dimension that Agile largely avoided: ethics at scale. While Agile transformed how work was organized, AI has the potential to transform how decisions are made. This shift requires leaders to move beyond questions of efficiency and effectiveness toward questions of responsibility and societal impact. Ethical frameworks such as Utilitarianism ask whether AI creates the greatest good for the greatest number. Deontological perspectives ask whether certain actions remain wrong regardless of outcomes. The Indian concept of Lokasamgraha (Singh & Awasthy, 2023) reminds us that decisions should contribute to the welfare and stability of society as a whole. Likewise, the bioethical principles of beneficence, non-maleficence, justice, and autonomy provide valuable guidance for evaluating AI systems that increasingly influence human lives.

One of the most important lessons from Agile is that transformation cannot simply be imposed through frameworks, certifications, or technology investments. Many organizations adopted ceremonies without embracing values, resulting in what practitioners often call "Agile in name only." AI faces a similar risk. Organizations may deploy chatbots, copilots, and predictive algorithms without addressing trust, transparency, governance, workforce readiness, or ethical safeguards. Just as Agile required cultural adaptation rather than procedural compliance, AI adoption requires organizations to align technology with values, leadership behaviors, and stakeholder expectations.

The future may belong not to the organizations that adopt AI the fastest, but to those that adopt it the wisest. Agile taught us that successful transformation occurs when methods align with culture and when people are empowered to learn and adapt. AI extends that challenge from teams and organizations to society itself. As leaders navigate this next era, the question is not simply whether AI can improve performance. The more enduring question is whether our cultural values, ethical principles, and leadership choices can ensure that AI improves humanity as well.

References

The Culture Factor Group (n.d.) Retrieved from https://www.theculturefactor.com/

Hofstede, G. (1980). Culture's Consequences: International Differences in Work-Related Values. Beverly Hills, CA: Sage. 

Hofstede, G. & Bond, M. H. (1988). The Confucius connection: from cultural roots to economic growth. Organizational Dynamics, 16, 4-21.

Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1).

Manifesto for Agile Software Development (n.d.). Retrieved from https://agilemanifesto.org/

Rajagopalan, S. (2014). Review of the myths on original software development model. International Journal of Software Engineering & Applications, 5(16), 103-111.

Singh, D. & Awasthy, R. (2023). Lokasamgraha: An indigenous construct for social entrepreneurship. IIMB Management Review, 35(4), 344-358.

Mechanical Empathy: A New Leadership Competency for the AI Era

I have been experimenting with some of the AI tools in developing some new functionality! When I asked it if something can be done, it started doing it! I responded with a prompt, "Don't jump to conclusions dragging me with you! Instead, stop and engage me with powerful questions so that we can collaborate on the solution!" I treated the interaction with the AI tool as a coach would engage with the person coached. Not commanding with prompt engineering as many prompt engineering schools of thought emphasize with the STAR (Situation, Task, Action, Results) method, for instance. During my interactions with AI, I realized the need for "Mechanical Empathy."

The irony of the modern workplace is striking. I have seen people complain, “Gen AI sucks!” In one of the interactions with participants at my talk in Melbourne on "Modern Cost of Quality", people asked, “Why should we worry about workload for robots? They are not living beings.” Yet, I see everyone emphasize empathy, inclusion, psychological safety, and sustainable work practices for human teams. Wait, did we somehow in our rush toward the “AI race,” forget a critical leadership principle: the way we interact with technology shapes the quality of the outcomes we receive. We may not need emotional attachment to machines, but certainly need something deeper and more practical: mechanical empathy. Not empathy for feelings, but empathy for systems, limitations, constraints, context, and capability. As the AI race continues, the more we fail to realize this new leadership competency, the more we will extend that same thoughts on humans. Would that shape a better future?

Consider how we treat children learning to communicate. We do not ask a six-year-old fifty complex questions in thirty seconds and then ridicule them for misspelling a word. We do not hand a new employee the workload of five people and then publicly shame them for underperforming. If we did, most leadership experts would identify the root problem not as incompetence of the child or the employee, but failure in parenting, coaching, management, and system design. Yet this is precisely how many people interact with AI systems today. Users bombard AI with vague prompts, contradictory instructions, overloaded requests, insufficient context, unrealistic expectations, and then critique the results with remarkable confidence. The issue is often not that the machine lacks capability, but that humans lack intentionality in how they engage with it.

Mechanical empathy is not about pretending AI is human. It is about recognizing that every system — human or machine — operates within constraints, patterns, architecture, and context. High-performing leaders already understand this intuitively when working with people. Leadership-level empathy requires presence in the moment, careful observation, appreciation of ideas and situations, curiosity expressed through thoughtful questions, reflection before reaction, risk awareness, ethical consideration, and constructive feedback designed to stimulate improvement rather than humiliation. Ironically, these same leadership behaviors improve AI interactions as well. Clear prompts, structured context, iterative refinement, workload balancing, validation mechanisms, and feedback loops often produce dramatically better outcomes than impulsive criticism. In many ways, effective AI utilization is exposing how poorly many individuals communicate, delegate, and think critically.

There is also a broader organizational implication. As agentic AI systems increasingly become part of the workforce — scheduling work, analyzing data, drafting communications, automating workflows, and even coordinating decisions — organizations may need to rethink the meaning of workforce management itself. Leaders already understand that burned-out human teams make more mistakes under chaotic conditions. Similarly, overloaded AI ecosystems operating with poor governance, weak data quality, fragmented instructions, and unrealistic dependency expectations can amplify errors at scale. Mechanical empathy therefore becomes a form of operational intelligence. It requires leaders to design environments where both humans and machines can perform sustainably and responsibly. The question is no longer simply whether AI is intelligent, but whether humans are interacting intelligently with AI.

Ultimately, the conversation about empathy toward AI is not really about protecting machines. It is about improving humanity’s relationship with technology and reflecting on our own behaviors. The faceless mechanical workforce may not possess emotions, but our interactions with it reveal our patience, discipline, ethics, critical thinking, and leadership maturity. The organizations and individuals who succeed in the AI era will not necessarily be those with the fastest tools, but those who develop the wisdom to engage thoughtfully with both human and mechanical systems. Mechanical empathy may become one of the defining leadership capabilities of the next decade — not because machines demand it, but because effective leadership always begins with understanding the nature, limitations, and potential of the systems we seek to influence.

What are your thoughts? Please share.

AI isn’t replacing leaders - It reveals the leadership gaps

Recently, I took part in a strategy discussion about the need to incorporate AI in designing good practices and preparing the next generation training them on AI tools. I reasoned the focus should not be on execution efficiency but strategic effectiveness. A gentle reset is required understanding the role of AI in future than rushing to adopt AI everywhere! If we look back into the present from a future, is this what we want our next generation to have? 

Artificial Intelligence is often celebrated as the ultimate accelerator of efficiency—automating workflows, optimizing operations, and reducing human error. And to be fair, it delivers on that promise. Organizations today can execute faster, cheaper, and at scale in ways unimaginable a decade ago. But beneath this efficiency boom lies an uncomfortable truth: AI is not closing leadership gaps—it is exposing them. The more we rely on AI for execution, the more visible our shortcomings become in strategic thinking, business acumen, and long-term value creation.

I can certainly say that AI excels at anomaly detection, pattern recognition, and fault prevention, far beyond the mundane automation tasks it is commonly used for. I myself have used AI to create things that would have taken a lot of time! So, yes, it can identify fraud in milliseconds, predict equipment failures before they happen, and surface trends hidden deep within complex datasets. Yet, do these capabilities inherently translate into better strategic decisions? 

Recognizing a pattern is not the same as interpreting its meaning in a volatile market. Detecting anomalies does not equate to understanding their business implications. Drafting even an email does not necessarily connect with the cultural connation of the way the message may be perceived. The gap here is not technological; it is cognitive. Leaders must still ask: Which patterns matter? Which risks are worth taking? Which signals should shape our strategy? AI informs decisions; it does not make them wise. So, do we prepare people for leadership role? Does the use of AI in their responsibilities make someone a leader?

Consider facial recognition technologies. AI systems have reached remarkable levels of accuracy in identifying individuals, yet they continue to struggle with bias. This is not a failure of algorithms alone; it is the lack of risk management thinking leading to the  failure of governance, ethics, and oversight. Bias in AI reflects bias in data, which in turn reflects bias in human systems. Leadership gaps in ethical frameworks, inclusive thinking, and accountability become amplified when scaled through AI. In my mind, AI there is an amplifier of our gaps mainly on leadership level strategic thinking. The question is no longer whether AI can recognize faces, but whether leaders can recognize and correct systemic inequities embedded in their organizations.

Similarly, AI’s prowess in pattern recognition has not guaranteed success in market or product development. Companies have access to unprecedented consumer insights, yet many still fail to create products that resonate or strategies that endure. Why? Because strategy is not just about identifying trends—it is about making choices under uncertainty. It requires judgment, intuition, and the courage to deviate from data when necessary. AI can suggest what is happening, but it cannot define what should happen. That responsibility lies fully with leadership, and it is here that capability gaps—particularly in strategic thinking, customer-centric innovation, process oriented sustainment considerations, alternative impact oriented thinking inherent in risk and people oriented change management become glaringly evident.

Even in highly automated environments like aviation, autopilot systems have not eliminated the need for pilots. Instead, they have elevated the role. Pilots are no longer just operators; they are decision-makers in critical moments when systems fail or unexpected conditions arise. In the healthcare setting, AI enabled systems can identify tumors but have not removed the need for diagnostic image operators, radiologists, physicians, or surgeons. It has only made their role more important. 

The same principle applies to business leadership in the age of AI. As execution becomes increasingly automated, the expectation for leaders shifts toward higher-order capabilities: governance, risk management, ethical judgment, and continuous capability building. AI does not replace leadership and it raises the bar for it. The organizations that will thrive are not those that adopt AI the fastest, but those that close the widening gap between technological capability and strategic leadership maturity.

What are your thoughts? Please comment.

With AI here to stay, what kind of humans will autonomous systems and mechanical robots demand?

I was traveling in India where I had discussions with family and friends around the revolutionary AI landscape. I could sense a feeling of paranoia and confusion. So, I asked myself, "Imagine a future meeting where scheduling is automated, risks are predicted in real time, stakeholder sentiment is analyzed instantly, and portfolio trade-offs are simulated before anyone speaks. The dashboards are perfect." In such a utopian world, what happens when the contextual decisions are problematic and the forecasts are probabilistic. Would the robots turn to the human in the room and say, “Optimization complete. Strategic ambiguity unresolved. Ethical trade-off undefined. Human intervention required.” In my mind, that is not science fiction. That is trajectory.

AI is extraordinarily good at optimization. It can reduce noise in medical images, prevent aircraft drift through autopilot, activate ABS braking systems in milliseconds, and execute trades at speeds no human can match. It detects patterns, flags anomalies, and recommends mitigation paths. But optimization is not direction. Prediction is not purpose. Progress is not value. Algorithms can simulate ten efficient options; they cannot define which future is worth pursuing. They cannot decide what the organization should value when speed conflicts with sustainability, or profit conflicts with reputation.

In such a world, project management does not disappear—it mutates and reemerges. The coordinator of tasks becomes the architect of decisions. The status reporter becomes the framer of ambiguity. The future competency is not mastering more tools; it is mastering judgment. It is rethinking the current process and workflow rather than fall victim to an old tool. It is the ability to think and address risks much before it materializes. It is the ability to define value under uncertainty, to reconcile competing incentives, to make trade-offs that algorithms surface but cannot morally resolve. AI will compress execution layers. What remains, and expands, is decision architecture.

If Agile were written in an AI-native era, it might read differently. Not “responding to change over following a plan,” but conscious human judgment over blind automation. Not velocity metrics over everything else, but strategic intent over algorithmic efficiency. Agile was always about adaptability in complex environments. AI increases complexity. It accelerates data. It amplifies consequence. It does not eliminate the need for leadership—it sharpens it.

The uncomfortable truth is this: AI will not replace project leaders. It will expose those who never moved beyond tools. In a room full of autonomous systems, the only human invited to stay will be the one who can answer: Why are we doing this? Who benefits? What risks are we willing to accept? What future are we choosing? Leadership begins where optimization ends. And in that moment—when the machines pause and wait—the human who can think will matter more than ever.

What are your thoughts?

Has AI killed Agile and Project Management?

Is Agile dead? Do we even need project managers in an AI-driven world? 

These questions surfaced every time technology discussions around AI came up in the last 3-4 months. But before we declare the end of a profession, we must pause and examine a deeper truth: First, AI isn’t new. I was exposed to Expert Systems in 1991-92 when I designed rules based first responder system  using Prolog. If I have exposure to it almost 3 decades back, then, I am sure many others have used it in numerous ways. 

In my experience subsequently, I have found that we have trusted it for years. It already flies planes through auto-pilot systems, prevents skidding through ABS braking, and executes trades in milliseconds through algorithmic platforms. In healthcare, it enhances diagnostic images and flags clinical risks long before the human eye can detect them. Yet in every one of these domains, humans remain accountable. Why? Because context, risk, and ethics cannot be automated. Judgment cannot be outsourced.

The question to ask here is did AI eliminate pilots, drivers, traders, or physicians? No. It elevated them. It removed repetitive execution and exposed the higher-order responsibility of decision-making. The same shift is happening in project management. AI can optimize schedules, analyze risks, summarize meetings, and generate reports. But it cannot align conflicting stakeholders, resolve strategic trade-offs, or lead teams through ambiguity and resistance. It cannot sit in a room where political tension exists, apply context, and choose courage over convenience. It cannot balance short-term delivery pressures with long-term enterprise value. Project management was never about tasks; it was always about decisions.

So, Agile is not dead and neither is project management. What is dying is cargo-cult Agile and checklist-driven project management. Frameworks were never meant to replace thinking but promote it. When we confuse process compliance with leadership, we diminish the profession. The uncomfortable reality is this: AI will not replace project managers, but it will expose those who never learned to think strategically. It will surface who understands value and who only understands velocity. It will reveal who can translate uncertainty into direction and who relies solely on templates.

In Leadership Unleashed, I argue that leadership begins when we move beyond tools and into conscious choice. This moment in history is not a threat to project management; it is a clarifying force. AI optimizes execution. Humans create meaning. AI accelerates data. Leaders shape direction. In a world of increasing complexity, the need is not for fewer project leaders—it is for stronger ones. The future does not belong to those who manage tasks. It belongs to those who can think, decide, and lead when certainty is absent.