Adaptive Leadership as the Value Driver: When There’s No Such Thing as an AI-Ready Culture

Harvard Business Review published an article this week from Korn Ferry’s Sarah Jensen Clayton, Michael Welch, and Khoi Tu. In it, they argue that an AI-ready culture does not exist, because readiness implies a fixed destination and AI keeps moving it. Organizations need the capacity to adapt, built through five shifts. We recently worked with an organization where four of the five showed up in the data, and AQai adaptability assessments and coaching gave us a way to read them.

Activity to impact

“An orientation toward outcomes can make organizations more adaptable because it reduces attachment to existing workflows. If a different approach delivers a better result, the method can change. Adaptation becomes a means to performance rather than a threat to it.”

The authors warn that leaders celebrate pilots and training completion because they are easy to count, while the work itself stays the same. Their test is whether AI is changing how work gets done.

Assessments alone, without context, carry the same trap. Three rounds of AQai® showed Mental Flexibility improving and Resilience reaching High. Those numbers are easy to chart and would have told us to keep building skills. A live adaptive leadership exercise built from the organization’s own strategic plan showed what the scores could not. The same executives produced governance process when escalation was open. Once we removed the current primary decision-maker from a scenario, the team named eight owned workstreams in five minutes. The next leadership level down escalated even when decisions were theirs.

Neither source would have found that alone. The assessment pointed to capability, the leadership exercise pointed to authorization, and the coaching and workshop that followed were aimed at that gap. The result was worth more than the three parts run separately.

Certainty to experimentation

“When conditions shift, adaptable organizations do not wait for uncertainty to disappear before acting.”

HBR cites a Stanford study in which every successful AI project used test-and-learn rather than waterfall planning. Sixty-one percent had a prior failure, and in none of those cases was anyone punished for it. Only half of employees say they feel encouraged to experiment.

In AQ, Change Uncertainty is the brake on the Change Readiness pedal. When uncertainty runs high, it dampens every other input. Experimentation lowers uncertainty because people can test a change at small scale before committing to it.

The exercise worked that way. Setbacks injected into nearly every round gave leaders a place to validate decisions, in a psychologically safe environment, under real pressure. The follow-up workshop to reinforce the lessons extended it. Leaders stated what they would decide without coming back, and a second session reviews what they actually implemented.

Decision makers to decision systems

“Adaptable cultures have fewer decision bottlenecks. Rather than pushing difficult calls upward, they build decision systems that put better intelligence around the people closest to the decision.”

The authors describe an investment firm whose AI challenges deal assumptions using a decade of committee materials, but “the AI does not get a vote.” People keep the decision and get better information around them.

That is the logic behind DeterminALI, our platform serving partners and clients. Its working line is “Context in. Decisions that drive results out.” It runs from research- and experience-backed data to decisions, and informs owned action to results. The approach lets leaders delegate authority cross-functionally, with the right information, and build trust by letting the chosen individual own the outcome. The decision engine provides context and intelligence; the leaders are adaptive.

In this engagement, the missing piece was authority. Some in leadership had the information and the capability, but not the confidence to act on it. So the workshop made permission explicit: mixed groups from two levels of leadership, each leader naming what they could decide alone and what still needed them. Ownership went to whoever was best placed, title aside.

Expertise to learning velocity

“AI is making information abundant. That does not mean expertise no longer matters. It means the half-life of expertise is shrinking, making the cultural ability to learn, unlearn, and relearn increasingly valuable.”

HBR argues that as information becomes abundant, the half-life of expertise shrinks. Credibility then goes to whoever can learn, unlearn, and relearn fastest. The assessment measures an individual’s ability to Reskill: the ability and frame of mind to acquire, apply, and let go of skills, with the agency and a clear pathway to pursue a goal.

Working with this client, the data showed that the team was able to hold multiple competing perspectives and shift between them to make decisions and take action. That is learning velocity moving the right way, and it will outlast any single skill the leaders held at the start.

Takeaway

Organizations that invest in the right training, technology, and tools alongside the right adaptive leadership build the muscle to succeed, regardless of the opportunity or challenge they face.

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Sources. Sarah Jensen Clayton, Michael Welch, and Khoi Tu, “There’s No Such Thing as an AI-Ready Culture,” Harvard Business Review, October 5, 2026. Stanford Digital Economy Lab, The Enterprise AI Playbook.

Read more. How CoAdapta builds the shifts that matter most: There’s no AI-ready culture. There’s an adaptable one.

Originally published on LinkedIn. AQai® is a trademark of its owner.