Did you know that Two-thirds of business leaders say they won't hire someone who lacks AI skills.

Only 39% of professionals know which AI skills they're supposed to develop.

That gap — between what organizations are demanding and what the workforce understands — is the most important career opportunity most leaders are ignoring.

Here's what's actually happening:

AI is not replacing experienced leaders. It is replacing leaders who haven't figured out how to deploy their experience deliberately.

The skills that got you to a leadership position — reading a room, making judgment calls in ambiguous situations, building trust under pressure — are not soft skills.

They are capabilities that the design of AI systems cannot replicate.

But they don't protect you passively. You have to claim them.

Episode 9 of AI Literacy for Leaders is about professional experience as a structural advantage, not as a reassuring idea, but as an architectural reality.

There is a technical role spreading through enterprise hiring right now that most executives have never heard of. It is not a data scientist. It is not a prompt engineer. It is a generative AI engineer — and understanding what one of these people actually builds is one of the most important things a non-technical leader can do right now.

In this episode, Laurence Gill breaks down what a gen AI engineer actually does: the validation layers, the orchestration loops, the drift monitoring, and the accountability structure that determines who is legally and ethically responsible when an autonomous AI system causes harm. Plus — four questions every leader should ask before any production AI system goes live.

Learn more about Laurence at: www.laurencegill.com

Every AI strategy meeting has a translation problem. Leaders are approving systems, signing contracts, and setting policy based on terms they’ve never had defined for them. The vendor speaks. The room nods. The decision gets made and somewhere in the middle, something critical got lost.

This episode fixes that. Not with a glossary. By walking through exactly how an AI interaction works, from the moment you send a prompt to the moment something goes wrong and naming the five terms that reveal what your organization is actually authorizing.

Tokens: the billing unit nobody explained. Context Window: the hard memory limit that silently drops what doesn’t fit. Temperature: the confidence dial that has nothing to do with accuracy. AI Slop: what comes out the other end when the first three are misaligned. And Prompt Injection: the attack that works because someone outside your organization understands these systems better than your leadership team does.

The episode closes with a five-question Boardroom Readiness Diagnostic, one question per term, designed to be asked before your next AI procurement or deployment review.

If you haven’t listened to Episode 3, that episode covers AI hallucinations in depth — start there if that term is still unfamiliar.