Leading with AI Starts with Learning to Think with It

AI is already embedded in everyday work. Employees use it to research subjects, summarize documents, prepare presentations, analyze information, write code, and improve communication. In many organizations, this activity is extensive, although much of it has developed through individual tools and informal experimentation.

 

The leadership challenge has therefore moved well beyond encouraging people to try AI. Leaders now need to understand how it is being used, where it is creating value, and how it can contribute to better thinking and stronger decisions across the organization.

 

That requires more than familiarity with the tools. It requires AI fluency.

 

From Using AI to Thinking with AI

 

In his book Lead with AI, Robert Moutal describes three broad responses from leaders. Some remain cautious and wait for greater certainty. Others use AI to complete occasional tasks but have not changed how they approach their work. A third group is learning to incorporate AI into how they think, communicate, and make decisions.

 

This third group brings genuine business questions into the conversation. They provide context, question the response, test competing views, and refine the output. Their own knowledge and experience remain active throughout the process.

 

The difference is significant. Asking AI to draft an email can save time. Asking it to challenge the reasoning behind an important proposal can improve the thinking that shapes the email, the meeting, and the eventual decision.

 

This is where AI begins to function as a thought partner. Its value comes from combining its breadth and speed with the leader’s understanding of the organization, the people involved, and the consequences of different choices.

 

Why Leaders Need Firsthand Experience

 

Executives do not need to become AI specialists. They do need enough firsthand experience to recognize what effective use looks like, evaluate where it creates genuine value, and provide informed direction.

 

Without this fluency, an executive can approve an impressive-sounding proposal without understanding its weaknesses or dismiss a useful application because its value is not immediately apparent. Practical experience helps leaders ask better questions and engage more confidently with employees, technical specialists, and outside advisors.

 

It also moves the organizational conversation beyond which tools people are using and toward how AI is affecting the quality of work.

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AI Fluency Requires Active Judgment

 

AI can produce a polished and persuasive response even when the underlying reasoning is weak, important context is missing, or some of the information is inaccurate. Fluency includes recognizing that confidence in a presentation is not evidence of quality.

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Leaders need to interrogate the output. They can ask AI to explain its assumptions, identify missing information, present an opposing view, or reconsider the issue from the perspective of a customer, employee, regulator, or competitor.

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AI can widen the range of possibilities considered, reveal gaps in the available information, and expose assumptions that might otherwise go unchallenged. Its value depends heavily on the context, questions, and judgment the leader brings to the exchange.

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Using AI to Improve a Decision

 

One useful application from Robert’s book is the 4A Pre-Mortem.  Before finalizing an important decision, a leader uses AI to examine four areas:

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• Assumptions: What am I taking for granted that could be wrong?

• Absent information: What relevant information am I missing?

• Adversarial case: What is the strongest argument against this position?

• Arm’s-length view: What would I advise someone else to do in the same situation?

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This process does not transfer the decision to AI. It gives the leader a structured way to examine the decision more thoroughly.

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The original position may remain unchanged. Even so, the leader is likely to understand the reasoning more clearly, anticipate objections, and communicate the decision with greater confidence.

 

Turning Individual Use into Shared Capability

 

Personal fluency is the starting point. Leaders must also create an environment where people can discuss how they are using AI and what they are learning.

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Teams should be able to share processes that have improved their work, identify outputs that proved unreliable, and raise questions about confidentiality, accuracy, transparency, and human review. These conversations help an organization learn from the experimentation already taking place.

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They also give leaders access to valuable knowledge from employees who have developed strong practical skills. Effective leadership in this environment includes being willing to learn from those people and helping their individual experience become part of the organization’s wider capability.

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Start With Work That Matters

 

Leaders can begin with a real decision, strategic issue, or communication challenge. Give AI the relevant context, question its response, test alternative views, and notice whether the process changes the quality of your thinking.

Then discuss the experience with your team. Ask how they are using AI, what they have learned, and where clearer direction would help.

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Leading with AI starts with learning to think with it.  From there, individual fluency can develop into stronger decisions, shared learning, and an organization better equipped to use AI for what comes next.

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