EP 269 - Why One-Off AI Training Fails (and What to Do Instead)

If your organization ran an "AI 101" lunch-and-learn… and nothing changed after, this episode is for you. Host Susan Diaz explains why one-off workshops create false confidence, how AI literacy is more like learning a language than learning software buttons, and shares a practical roadmap to build sustainable AI capability.

Episode summary This episode is for two groups:

teams who did a single AI training and still feel behind, and

leaders realizing one workshop won't build organizational capability.

The core idea is simple: AI adoption isn't a "feature learning" problem. It's a behaviour change problem. Behaviour only sticks when there's a container - cadence, guardrails, and a community of practice that turns curiosity into repeatable habits.

Susan breaks down why one-off training fails, what good training looks like (a floor, not a ceiling), and gives a step-by-step plan you can use to design an internal program - even if your rollout already happened and it was messy.

Key takeaways One-off AI training creates false confidence.

People leave either overconfident (shipping low-quality output) or intimidated (deciding "AI isn't for me"). Neither leads to real adoption.

AI literacy is a language, not a feature.

Traditional software training teaches buttons and steps. AI requires reps, practice, play, and continuous learning because the tech and use cases evolve constantly.

Access is not enablement.

Buying licences and calling everyone "AI-enabled" skips the hard part: safe use, permissions, and real workflow practice. Handing out tools with no written guardrails is a risk, not a training plan.