The firms that pull ahead over the next decade won't necessarily own the best AI. They'll be the ones led by people who can absorb it, redirect around it, and keep their teams moving while the ground keeps shifting. That's a specific profile, and decades of psychological research suggest it's distinct from the one that got most executives promoted in the first place.
Why the familiar profile is the wrong one
The default picture of an effective executive was built for a stable, industrial world: deep domain expertise, a proven playbook, decisive top-down control. In a landmark 2007 paper in The Leadership Quarterly, Mary Uhl-Bien, Russ Marion and Bill McKelvey argued that these top-down, bureaucratic models suit an economy of physical production but fit poorly in a knowledge economy, where a leader's job shifts from issuing commands to enabling the learning, creativity and adaptive capacity of the organization. Their "complexity leadership" describes exactly the conditions AI intensifies: rapid feedback loops, non-linear change, and solutions that emerge from the system rather than the corner office. The executive who wins by knowing the most becomes a riskier bet, because the half-life of what they know is shrinking.
The trait that predicts it: learning agility
If expertise dates faster, the ability to learn becomes the durable edge. Psychologists Michael Lombardo and Robert Eichinger named this "learning agility" in 2000. They defined it as the willingness and ability to learn from experience and apply it to new, first-time situations. It has since become one of the better-evidenced predictors of leadership potential: a meta-analysis led by Kenneth De Meuse found learning agility strongly related to both leader performance and rated potential, with corrected correlations around 0.74. The inverse is just as telling. Morgan McCall's research on executive derailment found that leaders who fail are often defined by the opposite: an inability or unwillingness to adapt, to admit mistakes, or to move past the skills that once made them successful. An AI-shaped market punishes that failure mode faster than a stable one ever did.
The behavior it shows up as: switching gears
Adaptability isn't a single setting. Kathrin Rosing, Michael Frese and Andreas Bausch, writing in The Leadership Quarterly in 2011, described the most effective innovation leaders as "ambidextrous." They can switch flexibly between opening behaviors that invite exploration, experimentation and tolerated failure, and closing behaviors that impose focus, discipline and execution. Leading an AI transition demands exactly that range: the openness to let teams experiment with tools nobody fully understands yet, paired with the discipline to then standardize what works and retire what doesn't. Leaders stuck at either pole, whether endless experimentation or rigid control, tend to stall.
What this means for hiring
The common thread is that the AI-ready executive is defined less by what they already know and more by how they learn, adapt and shift, and those are traits, not credentials. A resume optimized for a stable decade can look impressive and still tell you almost nothing about them. Surfacing the difference takes different questions: less "what did you build," and more "tell me about the last time your expertise became obsolete, and what you did next."
It's why our assessment looks past the track record for the underlying pattern. The executives who matter most through the next decade won't be the ones who happened to have the right answers. They'll be the ones who can keep finding them. You can see how we evaluate for that in our methodology.