Jonathan Bowder and Joe Folkman
AI is a leadership problem disguised as a technology problem.
The biggest AI leadership failure may be assuming that AI transformation is primarily a technology challenge. As AI absorbs more analytical and executional tasks, the defining question for organizations shifts from “What can AI do?” to “What can only leaders do?” For AI in the workplace to deliver on its promise, organizations need more than better tools and technical expertise. They need leaders capable of coaching people, inspiring change, and building the trust required for adoption.
CEOs from JPMorgan, Microsoft, Citadel, and Klarna have each publicly emphasized the importance of people and leadership as AI reshapes work. But what leadership capabilities actually matter most during an AI transformation?
We turned to our database of more than 2 million assessments of 138,777 leaders to identify the leadership skills that remain uniquely human—the ones AI has not, at least not yet, been able to replicate. To keep the data relevant, we limited our analysis to leaders evaluated from 2023 onward, capturing the period in which generative AI moved from novelty to workplace fixture.
Why AI Leadership Failure Often Starts With Human Skills
Organizations assessing their AI readiness understandably focus on technology, data, infrastructure, and employee AI skills. But an effective AI readiness assessment should also ask whether leaders are prepared to lead people through the transformation.
Our research points to three leadership capabilities that become particularly important as AI takes over more of the work leaders and their teams once performed themselves.
1. The Coaching Paradox
As AI absorbs the doing, coaching becomes the manager’s primary job—the very capability most leaders were never promoted for.
When we asked leaders to rank the importance of Developing Others against 19 other leadership competencies, they placed it 11th. Direct reports ranked it 10th. Not unimportant, but not treated as essential either.
The data tells a different story about its actual impact. Leaders who score in the bottom quartile on Developing Others are rated, on average, in only the 21st percentile for overall leadership effectiveness. Leaders in the top quartile land in the 85th percentile.
That is a 64-percentile difference in leadership effectiveness tied to a capability leaders consistently underrate.
AI can support an employee’s development—surfacing resources, drafting a learning plan, or even simulating a difficult conversation. What it can’t do is notice that someone is ready for a stretch assignment, deliver hard feedback with enough trust behind it to land, and follow up weeks later to reinforce or correct course.
That ongoing loop of observation, feedback, and follow-through is still a manager’s job. And as AI takes on more executional work, developing people becomes less of a side task fitted around the “real work” and more of the real work itself.
2. The Motivation Tax
Across the more than 15,000 leaders we’ve assessed since 2023, Inspires and Motivates Others was the lowest-rated of our 19 competencies.
Yet when direct reports ranked which competencies matter most to their success, Inspires and Motivates came out on top.
By contrast, Drives for Results ranked fourth in both effectiveness and importance.
Put together, this paints a clear picture of leaders who are good at push and weak at pull—effective at driving for results, but far less effective at inspiring the people who have to deliver them.
AI is very good at the push. It can model targets, track progress, identify patterns, and optimize a plan. It cannot supply the meaning, energy, or belief that gets a team to want to execute that plan.
As execution gets automated, the scarce leadership currency shifts from driving work to igniting it.
For AI for business leaders, this distinction matters. Learning how to use the technology is only one side of AI readiness. Leaders must also create a compelling reason for employees to engage with the transformation in the first place.
3. Adoption Runs on Trust
One of the most overlooked AI adoption challenges has little to do with the technology itself: employees need to trust the people leading the change.
Trust in the manager isn’t a soft metric. It’s a leading indicator of retention and effort.
When trust is low, 45% of direct reports are thinking about quitting, only 22% are willing to put forth extra discretionary effort, and just 27% would recommend the organization as a good place to work.
When trust is high, those numbers flip: only 18% think about quitting, 57% offer extra effort, and 62% would recommend the organization.
Our research points to three sources of trust:
- Positive relationships — built through genuine interactions and shared interest, not transactions.
- Consistency — alignment between what a leader says and what a leader does.
- Expertise — confidence that comes from a leader’s demonstrated knowledge and experience.
People are learning to trust AI for what it knows. But trust in a leader is built differently—through relationships and consistency accumulated over time, not just accurate output.
AI can have the facts. It doesn’t have the track record.
Leading Change: Why AI Transformation Is Still a Leadership Challenge
The pattern extends beyond these three skills.
In a related analysis of more than 100,000 leaders, we compared people who scored in the top quartile on Champions Change, Innovates, and Technical/Professional Acumen against their peers to see what else separated top AI transformation leaders from the rest.
Four additional competencies stood out:
- Develops Strategic Perspective
- Establishes Stretch Goals
- Communicates Powerfully and Prolifically
- Inspires and Motivates Others
The overlap is the point.
The capabilities associated with leading change during AI transformation are largely the same capabilities that make a leader effective, period. AI didn’t invent a new leadership model. It raised the cost of neglecting the human one that was already there.
This is one reason AI implementation challenges cannot be solved by technical training alone. Organizations can give employees access to the best AI tools available, but leaders still have to communicate why the change matters, establish ambitious but achievable expectations, help people navigate uncertainty, and motivate them to experiment with new ways of working.
Why AI Projects Fail When Leadership Is an Afterthought
Many conversations about why AI projects fail focus on the technology: poor data, inadequate infrastructure, unclear use cases, or insufficient AI expertise.
Those issues matter. But they leave out another potential source of AI leadership failure: an organization can successfully deploy AI without successfully leading people through the transformation.
Our research suggests that the human capabilities needed for successful adoption are not separate from effective leadership. They are effective leadership.
That reading is reinforced by how employees themselves feel about the shift. Recent workforce research from Workday found that 83% of people believe AI will make human skills more important, not less, and 76% say they want deeper human connection as AI becomes a bigger part of their work lives.
Employees aren’t simply bracing for leaders to become more technical. They’re looking for leaders who can help them navigate what comes next.
Preventing AI Leadership Failure Starts With Developing Better Leaders
None of these findings—coaching, motivation, trust—represent new ideas in leadership research.
What’s new is the leverage.
For decades, developing others, inspiring a team, and building trust could be treated as “nice to have” capabilities alongside the technical and operational work consuming much of a leader’s time.
AI is now taking a real bite out of that operational work.
What remains in the leader’s job description is disproportionately human—and the data shows that many leaders have underinvested in exactly that part of the job for years.
That is the real risk in the AI transition.
AI leadership failure won’t necessarily happen because leaders are replaced by a model. It may happen because the skills separating average leaders from extraordinary ones—coaching, inspiring, and earning trust—matter more than ever, just as organizations pour development resources into technical AI fluency and assume the human capabilities will take care of themselves.
They won’t.
Our data shows a 64-percentile swing in leadership effectiveness tied to one underrated competency alone.
The leaders who thrive in this next decade won’t be the ones who try to out-compete AI at analysis or execution. That’s a race they can’t win—and shouldn’t try to.
They’ll be the leaders who double down on what was always the harder, more human half of the job: growing people, motivating them, and earning their trust one consistent action at a time.
AI will keep getting better at the doing.
The leading has never been more essential.
About our Guest Author: Jonathan Bowder is the founder and director of The S.W.I.T.C.H.Lab, a UK-based leadership development practice and strategic partner of Zenger Folkman. A master trainer for The Extraordinary Leader™, Jonathan works with senior leaders across industries to strengthen leadership effectiveness, navigate change, and build more human-centered organizations.
