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.
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