Your leadership team isn’t ready for AI. Here’s a 90-day plan to change that

In March 2026, Coca-Cola CEO James Quincey told CNBC that AI had significantly influenced his decision to step down from his post. The company needed, in his words, “someone with the energy to pursue a completely new transformation of the enterprise.” A few months earlier, Walmart’s Doug McMillon stepped aside for essentially the same reasons: he could, he said, start the next big set of AI transformations, but he couldn’t finish the job. According to McMillon, Walmart needed someone faster to lead them into the AI era and so he was passing the baton on to a new CEO.

These were not failed CEOs being pushed out. Quincey had added more than ten new billion-dollar brands to the Coke stable during his tenure. McMillon had led Walmart for over a decade of sustained growth. These were successful leaders who had both concluded, independently of one another, that the AI era demanded a kind of leadership they could not provide.

What Quincey and McMillon recognized is something most leadership teams have not yet begun to confront: the AI era does not just demand new technology or new strategy. It demands new approaches to leadership. To reap the benefits and avoid the potential pitfalls of AI, leaders require specific skillsets and mindsets that differ from those needed in previous eras.

But there is a critical distinction between what Quincey and McMillon faced and what most organizations need to do. Both CEOs framed the challenge as a personal one — could they, as individuals, transform fast enough? An organization cannot think this way. It cannot step aside and replace itself. It has to develop the leadership it needs, systematically and at scale, or it will fail with the leadership it has. The 90-day plan that follows is designed to start that work.

The 90-Day Plan

Days 1–30: Assess

The goal of this phase is to acquire an honest picture of where your leadership team stands. Not where they think they stand, and not where they told the board they stand — where they actually stand.

1. Understand your leadership team’s AI fluency. Run a structured assessment of every member of the senior leadership team against a defined fluency rubric. The rubric should cover foundational understanding of how AI systems work, awareness of their failure modes, command of the cost and risk implications, and ability to connect AI capability to business strategy.

2. Diagnose mindset gaps. Assess each leader against the behavioral markers of AI-ready leadership: tolerance for ambiguity, willingness to kill their own initiatives, comfort delegating to non-human systems, and bias toward experimentation. The goal is not to grade leaders—it is to surface specific behavioral patterns that will either accelerate or block transformation.

3. Map decision-making patterns. Examine the last ten significant decisions your leadership team has made. How long did each take? How much information was gathered before committing? How often were decisions revisited? How many were reversed? The pattern that emerges from your answers to these questions will tell you whether your leadership team is wired for the demands of the AI age.

4. Stress-test the CEO. The tone is set at the top. If the CEO is not personally fluent in AI, not personally using AI tools, and not personally comfortable with ambiguity and failure, the rest of the organization will not take the transformation seriously. The CEO’s own development plan must be the most rigorous of any member of the leadership team.

By the end of this phase, you should have a clear and evidence-based picture of your leadership team’s AI fluency, their behavioral readiness for the demands of AI-era leadership, and the specific gaps—individual and collective—that the next phase needs to close.

For a detailed analysis of the competencies that AI-era leadership requires, see AI is rewriting the CEO job description: Are you ready?.

Days 31–60: Develop

This phase is about building the capabilities and behaviors that the assessment revealed are missing—not through generic leadership training, but through deliberate, role-specific development tied directly to the decisions each leader is responsible for making.

1. Build individual development plans. Every member of the senior leadership team needs a written development plan tied to the gaps identified in the assessment phase. The plan should specify target capabilities, the activities that will build them, and the measurable outcomes that will demonstrate progress. Generic leadership curricula will not work. The plan must be specific to the leader and specific to the decisions their role requires them to make.

2. Put AI to work. Fluency does not come from reading about AI. It comes from using it. Every senior leader should be actively using AI tools in their daily work by Day 45—drafting texts with them, analyzing data with them, stress-testing their own strategies against them.

3. Run decision simulations. Design AI-era decision scenarios specific to your industry and your strategic priorities, then run your leadership team through them. The scenarios should force the team to confront the decisions they are currently avoiding, such as when to let an AI system make a consequential call autonomously, how to handle workforce transitions, and how to respond when a competitor deploys AI faster than you can. The point of this step is to develop judgment by exercising it under conditions that approximate the real thing.

4. Build peer learning structures. The fastest leadership learning happens in small groups of peers confronting similar challenges. Pair each senior leader with one or two others, inside or outside the organization, who are working through comparable AI decisions. These groups should meet on a defined schedule and work through real-world case examples.

5. Expose leaders to the frontier. Your leadership team must have regular, structured exposure to the state of the art—not to the state of the market, which always lags behind. That means direct engagement with AI labs, leading researchers, and organizations further along in deployment than you are. Leaders who only see what their vendors are selling them will always underestimate what is possible.

6. Realign how leaders are evaluated. If the leadership evaluation framework is unchanged from five years ago, your behavioral expectations have not actually changed. Tie a meaningful portion of leadership evaluation to AI-readiness indicators: experiments personally sponsored, fluency demonstrated in board-level discussions, talent developed in the direction the organization needs to move.

By the end of this phase, every senior leader has a development plan in motion, is using AI tools directly, has been stress-tested through decision simulations, and is being evaluated against criteria that reflect what the organization actually needs from its leadership going forward.

For a deeper look at the leadership capabilities that AI-augmented work demands, see 7 ways leaders must evolve to lead AI-augmented teams.

Days 61–90: Embed

This phase locks the changes into the operating fabric of the organization so that AI-ready leadership becomes a permanent feature rather than a one-off initiative the effects of whic fade away over time.

1. Embed AI fluency into the leadership operating cadence. Every senior leadership meeting should now include an AI component, such as discussion of a decision being tested, a capability being reviewed, or a risk being assessed. This is not a standing agenda item to be skipped when time is running tight. It should be a permanent feature of how the leadership team runs.

2. Rewire succession planning. The leaders your organization needs in three years are not the same as the ones it needed three years ago. Revisit your succession bench against AI-era criteria. Who on the bench is building AI fluency? Who is stuck? The answers will reshape how you invest in talent for the next decade.

3. Build the board’s fluency. A leadership team that is moving faster than its board will eventually slow to the board’s pace. Build a structured AI education program for the board itself. At minimum, the board should have one director with deep AI expertise, a recurring agenda item for AI strategy and risk, and a shared vocabulary that enables substantive oversight rather than surface-level review.

4. Institutionalize the feedback loop. By Day 90, you have evidence. Which development interventions changed behavior? Which leaders moved? Which did not? Use the data. Double down on what is working, and redesign what is not.

5. Confront the hard personnel calls. By this point, you are beginning to learn which members of your leadership team will make the journey and which will not. The longer you avoid the hard calls, the more expensive they become—in strategy, in culture, and in talent retention.

By day 90, your leadership team will be in motion. The gaps will be diagnosed, development will be underway, and the structural changes needed to sustain both will be embedded in how the organization operates. Your leadership will be on its way to being fit for purpose for the disruptive times we live in.

For strategies on sustaining transformational change without exhausting the organization, see How to beat change fatigue.

Conclusion

Quincey and McMillon made the right call. They recognized what the moment demanded, measured themselves against it honestly, and acted. The harder version of that challenge belongs to the organizations left behind. Organizations need to look across their entire leadership teams and make the same honest assessment, not about one person, but about everyone in the room. By Day 90, you will have the evidence needed to begin making assessments like these in an informed way. Some of what that evidence reveals will be encouraging. Some of it will require difficult decisions. The organizations that act on both will be the ones that are still leading when the next transformation arrives.