Goldman Sachs published a projection that stopped a lot of executives in their tracks: roughly 50% of current jobs could be automated by 2045. That number is debated, but the direction of travel is not. The nature of work is changing, and it is changing faster than most organizations are built to absorb.
The instinctive leadership response to that kind of disruption is to scramble. Buy tools. Launch initiatives. Hire consultants. Announce a transformation program. And then wonder, eighteen months later, why the needle has not moved.
What I see in my work with executives is that the leaders who handle AI disruption well are not the ones who move the fastest. They are the ones who stay the steadiest while still moving. That is adaptability — and it is the first pillar of the AI Leadership Triad.
What Adaptability Actually Means in an AI Context
When most leaders hear "adaptability," they think it means being flexible, open-minded, or willing to change. Those things help. But they are not a definition that is actionable enough to lead on.
Here is the definition I use with every executive I coach: adaptability is the ability to maintain mission clarity while continuously evolving your methods.
That distinction — mission versus methods — is everything. Leaders who lack adaptability conflate the two. When methods come under pressure, they feel like the mission itself is threatened. So they either dig in and resist change (to protect what they think is the mission) or they abandon structure entirely and chase every shiny object (because "everything is changing anyway"). Neither works.
The leaders who adapt well in AI adoption share a specific mindset: there is always a better way to accomplish what we are here to accomplish. That belief is not naive optimism. It is a competitive orientation. It means they approach AI tools not as threats or saviors, but as potential improvements to a process they already understand deeply.
This is why adaptability outranks technical skill as a leadership advantage. A technically skilled leader who is not adaptive will pick a tool and defend it. An adaptive leader who is not technically skilled will find the right tool, or hire someone who can. Adaptability compounds. Technical skill, without the disposition to keep learning, depreciates.
The Three Signs a Leader Is Not Actually Adaptive
In working with leaders across industries, Joel Salinas has identified three patterns that look like adaptability but are not.
The first is reactive urgency. This is the leader who adopts every new AI model the week it launches, not because it solves a problem, but because not adopting feels like falling behind. They are responsive, but not adaptive. Their methods change constantly while their understanding of why stays shallow.
The second is performative flexibility. This leader talks a great game about being open to change. They attend every conference, share every article about the future of work, and genuinely believe they are adaptive. But when a direct report proposes changing an actual workflow — one that has been in place for five years — the answer is always "let's table that for now." They adapt their language without adapting their decisions.
The third is strategic paralysis dressed as caution. This one is common in mission-driven organizations and nonprofits. The leader genuinely cares about getting it right, so they study every option, consult every stakeholder, and wait for perfect information before making a move. By the time they decide, the landscape has shifted again. Caution is not adaptability. Caution with a bias toward action is.
Three Practices That Build Real Adaptability
Adaptability is not something you have or do not have. It is something you build through deliberate practice. Here are three specific approaches I recommend to the executives I work with.
1. Run quarterly method audits on your team's workflows.
Once a quarter, sit down with your team leads and ask one question about each major workflow: "If we were designing this from scratch today, knowing what AI can now do, would we do it this way?" The answer will not always be no. But the habit of asking keeps the methods permanently in question — which is exactly where they belong. It also normalizes the conversation about change before urgency forces it. Leaders who only review their methods in a crisis are not adaptive. They are reactive.
2. Build 30-day experiments into your AI strategy instead of big-bang rollouts.
One of the clearest signals that a leader is not adaptive is the all-or-nothing AI rollout. They spend six months evaluating tools, another three months building internal consensus, and then launch an organization-wide implementation — only to discover that the tool does not actually fit the workflow it was meant to improve.
Adaptive leaders do something different. They treat AI adoption as a series of small, time-boxed experiments. Pick one team. Pick one workflow. Give them 30 days with a specific tool and a clear success metric. Review the results honestly. Then decide whether to expand, pivot, or move on. This approach is faster, cheaper, and it builds the organizational muscle for continuous change — which is the actual goal.
3. Study leaders who adapted successfully — and pay attention to what they kept, not just what they changed.
Most case studies about organizational transformation focus on what changed: the new technology, the restructured teams, the updated processes. What they gloss over is what did not change — and understanding that is often more instructive.
When you study a leader or organization that navigated a major disruption well, ask: what did they refuse to abandon? What values, practices, or commitments remained fixed even as everything around them shifted? That is usually the key to their success. They were adaptive because they were clear about what was non-negotiable, which freed them to change everything else without losing their footing.
As I explore in my article on leading your team through AI adoption, the leaders who bring their organizations through AI transitions successfully are almost always the ones who communicate mission stability while modeling method flexibility. The team needs to see both at once.
Why This Is the First Pillar
The AI Leadership Triad — Adaptability, Innovation, and Creativity — is a framework built around a specific insight: the skills that determine whether AI serves your mission are leadership skills, not technical ones. Adaptability comes first in the Triad because without it, the other two do not land.
Innovation without adaptability produces organizations that announce a lot of new initiatives and complete very few. Creativity without adaptability generates ideas that never get implemented because the environment is not ready to absorb them. Adaptability is the foundational capacity that makes the other skills operational.
Joel Salinas works with executives across sectors who are navigating exactly this challenge — leaders who are smart, committed, and capable but find themselves overwhelmed by the pace and volume of AI change. Adaptability is almost always the first thing we work on, not because it is easy, but because it is foundational. Get it right and everything else becomes easier to build.
The question worth asking yourself right now is not "am I adaptable?" Most leaders believe they are. The more useful question is: in the last 90 days, what method did I change based on new information? If you cannot name it, that is where to start.
If You Only Remember This
- Adaptability means holding mission steady while continuously evolving your methods. Leaders who conflate the two either resist change entirely or chase every trend — neither leads to effective AI adoption.
- Quarterly method audits, 30-day experiments, and studying what successful leaders chose not to change are three concrete practices that build real adaptability — not the performative kind.
- Adaptability is the first pillar of the AI Leadership Triad because without it, innovation and creativity cannot take root. It is the capacity that makes everything else possible.