The Learning Paradox, or why you can't push a team into learning AI

Hi Reader,

Today, I want to tell you about the AI learning paradox. I've been noticing it lately, and you might have seen that as well.

The people who are most worried about AI (the ones convinced it's coming for their jobs) are usually the last ones to learn it.

You'd think it would go the other way. If you're afraid AI might replace you, the smart move is to get good at it. To make yourself the person who uses it well, not the person it makes redundant. But that's not what happens. The fear doesn't push people toward the tool. It pushes them away from it.

So the people who most need the skills are the ones avoiding them.

A recent study on AI and workforce learning explains this phenomenon. It asked what makes someone willing to learn AI, and found that it has less to do with how skilled they already are and more to do with trust.

Whether they trust the tool. Whether they feel secure enough to try.

Job insecurity does the opposite. People who feel their role is under threat are the least motivated to learn at the exact moment learning would help them most. Fear doesn't make people adapt. It makes them freeze.

And you can't push them out of it.

The same research found that pressure-driven learning doesn't stick. Mandates, deadlines, "everyone needs to be using AI by the end of the quarter," can produce compliance, but they don't build capability. What lasts is the other kind of motivation: curiosity, the sense that this is actually worth learning.

The thing that makes the difference is psychological safety. The plain, unglamorous permission to be bad at something new before you're good at it.

I wrote about this in my Forbes Council article — the first piece in my AI Transformation Gap series is about exactly this: how fear blocks AI adoption before it even starts. Most organizations treat AI as a technology problem. But the thing that's holding them back usually isn't the technology. It's the unaddressed fear about what the technology means for the people using it.

So if your team isn't moving on AI, it's worth asking why before you push harder. Pushing harder is the obvious move, but it usually makes things worse. The block is rarely about the tool. It's about whether people feel safe enough to be beginners again.

But there's another interesting part. When teams do start using AI and are trying to get the most out of it, they often fall into the other extreme — using it too much, for the wrong kinds of tasks, and losing their human judgment along with it. I wrote about that in the second piece of the series, The AI Transformation Gap: Preventing Cognitive Dependency.

Fear keeps one team out. Overuse dulls the other's judgment. So the real question isn't how much your team uses AI — it's what part of the work they still truly own.

See you next Thursday.

Daria

P.S. Two new episodes of Built by People Leaders are out. Why Most L&D Fails (And How to Fix It) with Jason Aydelott, Fractional Chief Learning Officer — on why so much learning and development misses the mark, and what works instead. And Hiring Executives in 2026: What Startups Keep Getting Wrong — with John Pezoulas, Co-Founder of Ready Set Exec — on the mistakes startups keep making when they bring in senior leaders. Both worth a listen.