A new Gartner prediction released May 13 landed like a warning shot for business leaders who think buying an AI tool is the same as having an AI strategy. By 2027, half of all enterprises without a people-centric AI approach will lose their top AI talent to competitors who got this right.

That is not a soft talent risk. That is an operational risk.

The Numbers Tell a Quiet Crisis

The research exposes a gap between what leaders think is happening with AI adoption and what is actually happening on the ground.

Only 27% of executives have a comprehensive AI strategy. Just 20% believe their workforce is truly AI-ready. Meanwhile, 88% of employees who have access to enterprise AI tools are also using their own personal AI tools for work tasks. Not because enterprise tools are bad, but because people are trying to fill gaps wherever they can find them.

The productivity data makes the stakes clear. Employees who are genuinely proficient with AI across multiple use cases are twice as likely to be highly productive, 2.3 times more likely to deliver high-quality work, and 3.2 times more likely to drive meaningful process improvements. These are the people companies cannot afford to lose.

The problem is that 73% of highly productive AI users are managers or executives. Individual contributors, the people doing the largest share of automatable work, are largely underserved with support, guidance, and genuine enablement.

The Enablement Illusion

Gartner calls the core problem the “enablement illusion.” Most organisations are measuring AI adoption by how many people have access to tools, not whether those people actually know how to use them well across different situations.

Giving someone a login is not the same as giving them capability. And capability is what the best talent is actually looking for.

Highly skilled AI professionals are acutely aware of the difference between working at a company where AI is embedded into how things actually get done, and working at a company where AI is a compliance checkbox. They will vote with their feet, and other companies are actively recruiting them.

Culture Is the Variable No One Wants to Measure

Gartner also highlights that employees with a positive outlook toward AI are 3.4 times more likely to be highly productive. That is not a training outcome. It is a culture outcome.

Organisations that treat AI adoption as a change management problem rather than a technology procurement problem tend to build that positive outlook. Organisations that roll out tools without context, without support, and without a clear narrative about how AI helps rather than threatens the people using it tend to produce anxiety and avoidance.

The 88% personal AI tool usage rate is a symptom of this. People are working around official processes because the official processes are not working for them.

What This Means for Business

If you lead a business that is investing in AI and you are wondering why the ROI has not materialised, this research points to where to look. The question is not whether you have deployed AI. The question is whether the people who matter most to your operation have been genuinely equipped to use it well.

That requires a few things that are harder than purchasing software:

Real training, not tick-box compliance. The difference between an employee who uses AI for two things and one who uses it across five different workflows is significant. Structured development programmes that build fluency across use cases, not just introduce a single tool, are what drives that second kind of employee.

Strategy that reaches individual contributors. The managers and executives who are productive with AI are mostly there because they had more time, more access to context, and more visibility into where AI could help. Individual contributors need the same investment, or the productivity gap widens and resentment builds.

A clear narrative about why AI is here. Employees with positive outlooks perform better. Positive outlooks do not emerge from silence. They come from leadership that explains AI’s role, invites questions, and demonstrates that the organisation cares about its people, not just its productivity metrics.

The EDNA Angle

This is exactly the tension we have watched play out across the hundreds of businesses we work with. The companies that see the most from AI investment are not necessarily the ones who moved fastest. They are the ones who brought their people with them.

EDNA Learn exists precisely for this. Structured data and AI literacy programmes, built around real-world application, give teams the kind of fluency that Gartner identifies as a multiplier. The companies that treat learning as infrastructure, not overhead, are the ones building the kind of workforce that stays and performs.

If you are advising leadership teams on AI adoption or building an internal AI programme, the Gartner data provides a strong argument for investing in people as seriously as you invest in platforms.

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