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AI may automate analysis and action without eliminating leadership. In Prashant Mishra’s view, its deeper effect is to make leaders’ intent, judgment, accountability, and treatment of people more visible. That is an argument about how leadership should adapt—not a measured forecast that AI will or will not replace managers.
The practical question is not simply whether a system can make a decision. It is who sets its objectives, what limits apply, who is answerable when it acts, and where human judgment remains essential.
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What does “AI will expose leaders” mean?
Mishra’s thesis is that as AI takes on more analysis and action, the choices that shape its use become harder to hide behind routine process or organizational complexity. A system can make a leader’s priorities operational: what the organization rewards, what risks it accepts, and whose needs it considers.
In his September 16, 2026 CIO opinion essay, Mishra frames the question this way: “What does leadership look like when intelligence no longer belongs exclusively to humans?” His answer is not that technology makes leadership obsolete. Rather, leadership shifts from personally directing every task toward defining purpose, setting boundaries, exercising judgment, and accepting responsibility for outcomes. Read Mishra’s essay at CIO.
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The essay offers no labor-market data or quantitative forecast. Its claim should be read as a leadership and governance argument, not evidence that AI will spare or eliminate a particular category of management job.
How AI changes the work of leadership
From control to context
Leaders cannot personally inspect every output or action when AI is embedded across organizational workflows. Mishra argues that their role therefore moves toward establishing the context in which people and systems work: the purpose of a process, acceptable boundaries, relevant values, and conditions for human-machine collaboration.
More dashboards can increase visibility, but visibility is not the same as control. A leader still needs to decide which actions require review, what conditions should trigger escalation, and who can intervene.
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AI systems pursue the objectives they are given. If an organization defines success narrowly as lower cost or higher productivity, a system may advance that target while damaging trust, care, or dignity. Mishra’s recommendation is to pair each objective with explicit constraints: what the process should accomplish and what it must not sacrifice.
For example, a target to speed up service decisions is incomplete unless leaders also decide which cases require human review, how people can challenge an outcome, and what evidence is needed before action.
From decision-making to judgment
AI can search information, compare alternatives, and offer recommendations. Those capabilities do not make a recommendation correct or wise in a specific organizational context. The output may rest on flawed assumptions, incomplete data, bias, or a goal that does not reflect the consequences for affected people.
Mishra calls for “intelligent doubt”: use AI where it helps, examine its reasoning and context, and override it when warranted. He cautions against both blind acceptance and reflexive rejection. As he puts it, “A leader who blindly accepts AI has abdicated judgment.”
From authority to accountability
When a system participates in a consequential decision, responsibility can become difficult to trace. Mishra proposes keeping accountability explicit through named owners, traceable important decisions, defined limits on system action, and clear ways to monitor, escalate, appeal, and override decisions. These are his recommendations, not a cited technical standard.
Before deploying AI in a high-impact workflow, leaders should be able to answer:
- Who owns the process and is accountable for its outcomes?
- Which decisions may the system make or carry out without approval?
- What events require a person to review or stop an action?
- How can an affected person ask for review or appeal?
- What records are needed to understand how a consequential decision was reached?
From transformation drama to adaptive learning
A pilot or roadmap is not, by itself, proof that an organization has transformed. Mishra argues that AI can amplify what is already present: clear processes and capability, but also confusion, bias, and weak practices. Leaders need to use experiments to learn about the workflow and the organization’s readiness, then respond to what the experience reveals.
That means looking beyond whether a demonstration works. A useful experiment asks whether the task is well defined, whether the input data and process are fit for purpose, what changes for the people doing the work, and how errors or unexpected effects will be detected and addressed.
From managing people to preserving humanity
Organizational decisions affect more than measured output. They can shape people’s dignity, trust, livelihoods, aspiration, and sense of meaning. Mishra argues that leaders must decide where human agency and authority matter, rather than treating every valuable outcome as a KPI.
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This does not require keeping people in every step. It does require being deliberate about where automated decisions affect people and what recourse, explanation, or human involvement is appropriate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical way to govern AI-supported decisions
Mishra’s argument can be translated into a decision review before a system is allowed to recommend or take action. The more consequential and difficult to reverse the decision, the stronger the case for human review, clear escalation, and a reliable way to stop the process.
- Define the purpose. State the outcome the process is meant to achieve, not just the activity to automate.
- Set limits alongside the objective. Identify the values, rights, or conditions that must not be compromised to meet the target.
- Assess the decision’s stakes. Consider who is affected, how serious an error could be, and whether the action can be reversed.
- Assign ownership and decision rights. Name the accountable human owner and specify which actions the system may recommend, initiate, or complete.
- Make intervention possible. Establish monitoring, escalation, review, appeal, and override paths appropriate to the consequences.
- Learn from operation. Use feedback and observed failures to revise the workflow, its boundaries, or the decision to use AI at all.
These steps express Mishra’s governance recommendations as a practical review; they are not a claim that one checklist fits every organization or a formal compliance framework.
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Mishra writes as a CIO contributor and group CTO of Walsons Group. CIO’s author profile describes his work with a frontline workforce of 60,000, more than 25 years across engineering, architecture, consulting, and leadership, and his 2025 book The AI Codex: Power, Ethics, and the Human Future in the Age of Intelligent Machines. That background provides context for his perspective; it does not turn the essay into an empirical study.
The essay’s central value is its framework for thinking about leadership as AI expands organizational capabilities. It does not quantify job displacement, compare AI products, or establish that any particular governance practice will guarantee good outcomes. Mishra summarizes his position as: “This is not a technology problem. It is a problem of intent, judgment, trust and accountability.”
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