While 75% of learners say that the training they receive aligns with their knowledge level, more than half of learners feel that training isn’t very relevant to their role. This is a stat our report, the AI Readiness Gap: The 2026 Enterprise Learning Wake up Call, unearthed from more than 2000 learning leaders and learners. 

In other words, learning may make sense but isn’t useful. 

Organizations are spending real money building and deploying training that learners can follow, understand, and complete, but their learners are walking away without changing how they actually work.

But that’s what the learners are saying, what do leaders think about their content?

During our recent webinar discussing the AI Readiness Gap report, we polled attendees about how confident they were that their training actually changes how people work. Surprisingly, only 20% said “very confident.” A solid 64% landed on “somewhat confident.”

As Koreen Pagano, CEO of Talent Rewire, put it during the webinar: That somewhat confident answer reflects the optimistic side of not really knowing. L&D teams are doing their best, and they believe in what they’re delivering. But belief and evidence are two different things, and right now, most organizations don’t have the data to tell the difference.

This is a critical issue because when you can’t confidently connect training to performance outcomes, making the case to your executive team for continued investment becomes a much harder conversation.

AI training is a perfect case study in the relevance problem

Nowhere is the relevance gap more visible than in AI training specifically. When we asked webinar attendees how they’d describe the AI training their organization currently delivers, the results were telling: 39% said general awareness and literacy, 36% said they don’t have a formal AI training program at all, and only 4% said their training was role-specific and use-case driven.

Four percent. In 2026. When AI fluency is the number one learning priority for enterprise organizations.

This points to a structural problem. As Kyle Forrest from Deloitte pointed out in the webinar, most organizations started with AI 101: learn to prompt, understand the basics, get familiar with the tools, which was a reasonable first step two or three years ago. But many organizations never took the second step. And now the stakes have risen. What “basic AI literacy” even means keeps evolving as the models get more complex, and the use cases more specific. In essence, the training hasn’t kept up.

The data from the report backs this up: 85% of learners say the training they receive does not help them fully understand or use AI in their role.

What’s holding relevance behind?

The AI Readiness Gap report found that 79% of learners say their learning experience is not fully personalized, and 63% of learning leaders acknowledge the same gap. 

To personalize effectively, you need to know what skills your people actually have right now, what skills their roles require, where the gaps are, and how those gaps connect to real business outcomes. Most organizations have none of that picture in a clean, usable form.

Without that skills visibility, personalization becomes guesswork. You’re building learning paths based on assumptions about what learners need rather than evidence of what they’re missing. And when learning isn’t built on accurate skills data, it ends up only being appropriate for the average learner in the average role, whatever that means. In the end, it’s just not useful because it’s not specific.

That’s the exact gap the 75% stat is describing. Training that works in theory, but doesn’t land in practice.

There’s another dimension to this that the report surfaces and the webinar conversation reinforced. It’s not just that training lacks relevance to the learner’s current role. It’s that learners can’t see how training connects to their future.

Fewer than half of learners clearly understand how learning contributes to their career progression. And in a moment when AI anxiety is pervasive, when people are genuinely unsure what their role will look like in two years, that lack of visibility impacts engagement and retention.

Kyle Forrest made the point clearly: When organizations don’t explicitly connect learning to shifts in how work is changing and what that means for career paths, workers fill in the blanks themselves. And the blanks they fill in are usually much more alarming than the reality. The result is resistance, disengagement, and a workforce that sees AI training as something being done to them rather than something being done for them.

Forty percent of learners in the report said career growth was their primary motivation for completing training, nearly double the 22% motivated by job requirements. People want to grow. They want to see the path forward. The problem is that most training programs don’t show it to them.

Where to start

By making skills visible, connecting learning to real business outcomes, and building the kind of continuous feedback loops that let training evolve as quickly as the work around it does.

The organizations getting this right aren’t necessarily the ones with the biggest L&D budgets. They’re the ones that treated skills data as a strategic asset, brought learning into the conversation before the transformation decisions were made, and built learning systems designed for continuous change, not one-time deployment.The 4% of organizations delivering role-specific, use-case-driven AI training aren’t unicorns. They’re just further ahead on a path that every organization needs to start walking now.

You can read more about them in The AI Readiness Gap: The 2026 Enterprise Learning Wake-Up Call. Download the report here.