93% of Leaders Encourage AI Use — But Few Apply It Strategically
Across virtually every industry, AI adoption has shifted from a buzzword on a conference slide to a line item in the budget. Tools are being deployed. Roadmaps are being drafted. Pilot programs have graduated into full rollouts. On the surface, the momentum looks undeniable. But beneath the headline numbers, a more uncomfortable reality is taking shape — one that should concern every executive, learning leader, and organizational strategist paying close attention.
A recent survey of more than 500 senior leaders found that 93 percent actively encourage their teams to use AI, and 82 percent report regular AI use across their organizations. Those are striking figures. They suggest broad cultural buy-in, institutional support, and genuine progress toward becoming AI-enabled enterprises. Yet those same leaders reveal a critical blind spot when the questions get more specific.
Only 27 to 28 percent of those leaders report applying AI to genuinely strategic work — things like scenario planning, organizational design, or financial modeling. That is not a rounding error. That is a structural problem.
What the AI Competency Gap Actually Means
The term "AI competency gap" describes the distance between how prepared leaders believe their organizations are to operationalize AI — and how prepared they actually are. It is the space between encouraging adoption and understanding what adoption is supposed to achieve. Between deploying a tool and knowing how to embed it into high-stakes decision-making.
For most organizations right now, that gap is wide and growing. Leaders are succeeding at the relatively easy part: getting people to use AI for routine, task-level work. Drafting emails. Summarizing reports. Generating first drafts. These are productivity wins, and they are real. But productivity at the task level is not the same as transformation at the organizational level.
Strategic AI use — the kind that reshapes how organizations plan, compete, and adapt — requires a fundamentally different skill set. It requires leaders who understand not just what AI tools can do, but how to frame the right questions, interpret outputs critically, and integrate AI-generated insights into complex, high-consequence decisions. That capability is still rare, and the data confirms it.
The Leadership Bottleneck No One Planned For
One of the most revealing findings from the survey is not just that the gap exists, but where it exists. The breakdown is happening at a specific and consequential layer of organizational leadership: vice presidents.
VPs occupy one of the most critical positions in any large organization. They sit at the intersection of executive vision and operational execution. They are the ones responsible for translating a C-suite AI strategy into something teams can actually act on. And they are falling behind.
Only 73 percent of VPs have completed AI training, compared to 88 percent of directors. That gap alone is significant. But it becomes even more striking when you look at leadership-specific AI training in the past year: just 55 percent of VPs have participated, versus 80 percent of directors.
This is not just a skills story. It is a translation story. When the people responsible for converting strategy into reality are less equipped than the people reporting to them, the entire AI initiative loses coherence. Teams receive vague direction. Priorities become unclear. Adoption stalls not because employees resist AI, but because they are waiting for someone to tell them how AI fits into the work that actually matters.
Why Broad Encouragement Is Not Enough
There is something almost paradoxical about the 93 percent figure. Organizations have done an impressive job of creating psychological permission for AI use. Employees are not afraid to experiment. Leaders are openly supportive. The cultural resistance that slowed adoption in earlier phases has largely been overcome.
But encouragement without direction creates a specific kind of dysfunction: high activity with low strategic impact. When teams know they are supposed to use AI but do not know what problems AI is supposed to solve at the organizational level, they default to the safest and most obvious applications. They use AI for what it is easiest to use AI for, not necessarily for what would create the most value.
This dynamic is visible in the numbers. Eighty-two percent of organizations have regular AI use. Twenty-seven percent apply it strategically. The gap between those two figures is not explained by resistance or access. It is explained by the absence of a coherent, leader-driven framework for what strategic AI use actually looks like in practice.
Closing the Gap: What Learning Leaders and CLOs Need to Do Now
For Chief Learning Officers and learning leaders, the AI competency gap is not an abstract organizational challenge. It shows up concretely: in stalled digital transformation initiatives, in uneven adoption across business units, and in the frustrating experience of deploying AI tools that never seem to reach their full potential.
Closing the gap requires interventions that are targeted, not generic. A few priorities stand out:
- Prioritize VP-level AI development immediately. The data is clear that this is where capability is breaking down. Learning programs need to reach this layer of leadership with content that is specific to their role — how to use AI in strategic planning, how to evaluate AI-generated recommendations, and how to lead AI-enabled teams effectively.
- Shift from tool training to decision-making training. Most current AI learning programs focus on how to use specific tools. What is missing is training on how to integrate AI into leadership judgment — how to ask better questions, how to stress-test AI outputs, and how to communicate AI-informed decisions to stakeholders with confidence.
- Create use-case clarity at the organizational level. Leaders cannot drive strategic AI adoption if the organization has not defined what strategic AI use looks like in its specific context. CLOs, working alongside C-suite sponsors, should help develop and communicate concrete examples of high-value AI applications tied to business outcomes.
- Measure outcomes, not activity. Organizations that track AI adoption by usage metrics alone will continue to mistake activity for progress. Learning leaders should push for outcome-based measurement — how is AI affecting the quality and speed of strategic decisions?
The Real Opportunity in the AI Competency Gap
It would be easy to read this data as discouraging. Ninety-three percent of leaders encouraging AI use, but only a fraction applying it where it matters most, suggests a system that is spinning without fully engaging. But the opportunity is real and substantial for organizations willing to face the gap honestly.
The foundational work — building awareness, reducing resistance, creating cultural permission — has already been done. The harder and more valuable work is now possible: building the leadership capability to use AI in service of the decisions that actually shape the future of the organization. That is the work of the next phase of AI adoption, and the organizations that get it right will have a durable competitive advantage over those still celebrating their usage metrics.
The question is no longer whether your organization encourages AI. The question is whether your leaders know how to use it strategically — and what you are doing to close the gap if they do not.