AI is forcing leaders to operate in an environment where expertise is distributed, answers change quickly and nobody has a complete roadmap. Strong leadership increasingly depends on the ability to acknowledge uncertainty while still providing direction, learn openly and create an organization in which people can challenge assumptions and contribute what they know.
CEOs Have to Join the Learning Curve
One of the more interesting observations in McKinsey’s recent discussion of how CEOs are responding to AI came from Eric Kutcher, the firm’s North America chair. He described leaders becoming more comfortable acknowledging that they do not know the answer to every question, and argued that showing greater vulnerability can make leaders more approachable and organizations more willing to learn.
The observation matters because AI creates an unusual leadership situation. Seniority no longer guarantees that the person at the top understands the technology, its possibilities or its implications better than everyone else in the organization. A younger employee experimenting with AI every day may understand a particular tool far better than the CEO. A team close to a customer may discover an application that senior leadership never considered. The implications of the technology itself continue to change quickly enough that yesterday’s confident answer may already require revision.
For leaders accustomed to being rewarded for expertise and certainty, this requires an adjustment. The CEO has to become part of the learning curve rather than standing above it.
Uncertainty Does Not Remove the Need for Direction
Acknowledging uncertainty should not be confused with abandoning authority. Employees still need leaders to establish priorities, make decisions and provide a credible sense of where the organization is going. The leadership challenge is to provide that direction without pretending that every step of the journey is already known.
This distinction becomes particularly important during AI transformation because employees are navigating uncertainty of their own. They are trying to understand how their work will change, which capabilities will remain valuable and what the organization will expect from them. A leader who responds to that uncertainty with artificial confidence can quickly lose credibility when reality proves more complicated. Yet a leader who simply announces that nobody knows what will happen provides little more reassurance.
At MLC Advisory, we believe the stronger leadership position combines clarity of direction with openness about uncertainty. Leaders can be explicit about the outcomes the organization is pursuing, the principles that will guide decisions and the capabilities it intends to build, while remaining equally explicit about what still needs to be learned. This creates stability without requiring false certainty.
Leaders Need to Make Learning Visible
The way senior leaders behave also influences how the rest of the organization approaches AI. If the CEO treats unfamiliarity as something to conceal, managers and employees quickly learn that admitting uncertainty carries status risk. People become more reluctant to ask elementary questions, challenge assumptions or reveal that an experiment failed, precisely when the organization needs those behaviors most.
Leaders can create a very different environment by making their own learning visible. Asking someone more junior to explain how they are using a tool, acknowledging when new information has changed a view, or openly testing an assumption signals that learning is part of competent performance rather than evidence that competence is missing. The objective is not performative vulnerability. It is creating the psychological conditions in which information can move toward the people who need it, regardless of hierarchy.
That matters because AI transformation depends heavily on distributed learning. Useful knowledge is emerging simultaneously across functions, teams and levels of seniority. Organizations need that knowledge to travel quickly enough to influence decisions, and leadership behavior plays a substantial role in determining whether it does.
Managers Need the Same Capability
This challenge extends well beyond the CEO. Middle managers occupy perhaps the most difficult position in an AI transformation because they are expected to help employees navigate changes that they themselves are still learning to understand. Many will be leading teams in which individual employees know more about particular AI applications than they do.
Organizations therefore need to prepare managers for a form of leadership in which expertise is increasingly distributed. That means being able to ask good questions without feeling that authority is threatened, draw knowledge from the team, respond constructively when employees challenge an assumption and provide direction even when the answer remains incomplete. It also requires managers to distinguish between situations where decisive leadership is necessary and situations where exploration will produce a better decision.
These capabilities rarely appear in an AI implementation plan, yet they influence how quickly an organization learns. Technology can be deployed centrally. The behavioral adaptation required to use it well happens through thousands of everyday interactions between leaders, managers and employees.
Leadership Capability Is Part of AI Readiness
This is why we believe organizations should assess leadership preparedness alongside technological preparedness when considering their readiness for AI. Providing managers with access to tools and teaching them how those tools work addresses only one part of the transition. Organizations also need to examine whether leaders can operate effectively when expertise is distributed, create enough psychological safety for people to challenge existing assumptions, learn from employees regardless of hierarchy and maintain clarity of direction while circumstances continue to evolve.
These are established leadership capabilities, but AI is increasing their strategic importance. Organizations will inevitably make mistakes as they experiment with new ways of working. Their ability to learn from those mistakes depends heavily on whether information travels upward, whether leaders revise decisions when evidence changes and whether employees believe that questioning an established view is worth the risk.
For CEOs, that begins with a deceptively demanding shift in posture. Leadership in the AI era does not require knowing everything before everyone else. It requires creating an organization capable of learning faster than any individual leader possibly could, while providing enough clarity and direction for that learning to accumulate into meaningful business progress.