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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11AI may automate analysis and carry out more tasks, but that does not by itself answer whether leaders will disappear. Prashant Mishra’s argument is that AI will instead make leadership more visible: it will reveal how leaders set priorities, exercise judgment, take responsibility, and treat people. That is a leadership thesis, not a measured forecast about job losses.
The question Mishra uses to frame the issue is: “What does leadership look like when intelligence no longer belongs exclusively to humans?” His answer is that leadership shifts away from personally controlling every task and toward creating the conditions in which people and AI can act responsibly. In his September 16, 2026, opinion essay for CIO, he describes six related shifts. They offer a way to think about the work leaders still have to do when AI enters organizational decisions.
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Mishra is identified by CIO as group CTO of Walsons Group, a security and facilities services business operating across India. CIO’s author profile says he built and runs the company’s digital operating layer for a frontline workforce of 60,000, and has more than 25 years of experience across engineering, architecture, consulting, and leadership. That context informs his perspective; it does not make the essay an empirical study.
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Why the argument is about leadership, not a job-loss forecast
AI can search information, compare options, generate recommendations, and perform actions within a workflow. But the ability to do those things does not settle who defines the goal, who decides what trade-offs are acceptable, or who answers when a consequential decision harms someone. Mishra’s point is that those choices remain leadership responsibilities even as machines handle more analysis and execution.
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The essay provides no labor-market estimates or quantitative evidence for how many leaders AI might displace. Its claim is better read as a governance challenge: as AI becomes more involved in organizational work, it can make a leader’s priorities and omissions easier to see. A system optimized for a narrow target may expose that the organization never specified what mattered beyond the target.
Six ways AI changes what leadership requires
1. From control to context
Leaders cannot personally inspect every AI output or action as systems spread across organizational platforms. Mishra argues that their role shifts toward setting the purpose, boundaries, values, and working conditions for human-machine interaction. More dashboards may show more activity, but visibility alone does not amount to meaningful control.
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2. From execution management to intent governance
AI systems pursue objectives. If a process is framed only around cost, speed, or productivity, a system may advance those aims while damaging trust, care, or dignity. Leaders therefore need to define both what an AI-supported process should achieve and what it must not sacrifice. The constraint is part of the objective, not an afterthought.
3. From decision-making to judgment
AI recommendations can be useful without being sound. They may reflect flawed assumptions, missing information, bias, or a poor fit with the organization’s circumstances. Mishra calls for “intelligent doubt”: make use of AI, examine its reasoning and context, and override it when warranted. He cautions against both blind acceptance and reflexive rejection. “A leader who blindly accepts AI has abdicated judgment,” he writes.
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4. From authority to accountability
When AI contributes to a decision, responsibility can become difficult to trace. Mishra recommends assigning owners for models, documenting important decisions, defining action boundaries, and making escalation, monitoring, appeal, and override possible. These are his governance recommendations, not a claim that a particular technical standard requires them.
5. From transformation drama to adaptive learning
A pilot or roadmap is not proof that an organization has transformed. AI can amplify existing clarity and capability, but it can also magnify confusion, bias, or weak processes. Mishra’s emphasis is on disciplined experimentation and feedback: use practical deployments to learn where workflows and organizational foundations need attention rather than treating launch as the finish line.
6. From managing people to preserving humanity
Organizational decisions affect dignity, trust, livelihoods, aspiration, and meaning. Mishra argues that leaders must decide where human agency and authority remain important, and resist reducing every valuable outcome to a KPI. Measures can help guide work; they cannot by themselves define what a good outcome means for the people affected.
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Mishra’s recommendations point to a practical sequence for leaders considering a consequential AI use. The sequence below translates his argument into questions to answer before and during deployment; it is not a benchmark for evaluating products.
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- Author: Bungay Stanier, Michael.
- Publisher: Page Two
- Pages: 244
- Publication Date: 2016-02-29
- Edition: 1
- Define the purpose and limits. State what the process is meant to improve, then name the values or outcomes it must not compromise.
- Set decision rights. Identify who owns the AI-supported process, which actions it may take, and which decisions require human review.
- Match oversight to consequences. Consider the stakes, degree of autonomy, reversibility, and people affected. A consequential action that is difficult to undo calls for clearer review and stopping paths than a low-stakes, reversible task.
- Keep the action traceable. Make important decisions reviewable so an accountable person can understand what happened and why.
- Provide routes to challenge and stop. Define how to escalate a concern, appeal an outcome, or override or halt an action.
- Use feedback to improve the work. Observe how the system interacts with real workflows, identify weak assumptions or processes, and adjust the deployment rather than treating a successful pilot as proof of readiness.
Mishra summarizes his position this way: “This is not a technology problem. It is a problem of intent, judgment, trust and accountability.” The phrasing is his interpretation of the leadership challenge, not evidence that technology itself is irrelevant. The practical implication is that technical capability cannot substitute for clear ownership and judgment.
What the essay establishes—and what it does not
The CIO essay is explicitly an opinion piece. It presents a coherent case for how leadership responsibilities may shift as AI takes on more analysis and action, but it does not establish that AI will or will not replace leaders, quantify displacement, or compare governance approaches. Its value is as a framework for asking who sets the objectives, who can intervene, and whose interests count when AI is used in organizational decisions.
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