Most corporate AI training programs produce the same outcome: employees complete the module, pass the quiz, and change nothing about how they work. The knowledge doesn’t transfer because the program wasn’t designed for transfer — it was designed for completion.
Train for decisions, not awareness
Awareness training — “here’s what AI is, here’s how it works” — has limited value for most roles. What people need is the ability to make better decisions in the specific situations they encounter: evaluating AI-generated output, deciding when to rely on a model versus when to verify, knowing when to escalate.
Design training around the decisions your team actually makes, not around general AI literacy.
Use role-specific scenarios
A product manager evaluating an AI feature and an engineer integrating an API have almost nothing in common in terms of what they need to know and do. Mixed-audience training produces mediocre results for everyone.
When we redesigned our training curriculum, splitting into Product, Engineering, and Leadership tracks produced measurably better outcomes on practical assessments. The investment in differentiation pays off.
Follow-up is the program
A single training session, even a good one, rarely changes behaviour on its own. The sessions that stick are the ones with structured follow-up: a team exercise the following week, a check-in after the first deployment, a retrospective after the first incident.
Budget for the follow-up from the start. If the follow-up isn’t in the plan, the training is probably not worth running.