Role-Specific Enablement: One Size Doesn’t Equip All

AI training fails when it tries to scale too broadly. One workshop, one LMS module, one prompt guide — delivered to hundreds of people, across functions, with no context. The result? Everyone’s technically “trained,” but no one’s materially more effective. Fluency doesn’t spread. It stalls. Harvard Business Review calls this the difference between literacy and fluency — the former is easy to tick off, the latter is what actually moves performance.

That’s because AI impact is hyperlocal. It lands differently in a marketing team than it does in legal, procurement, or support. Each role encounters a different pace of decision-making, a different risk surface, a different relationship to process. The tools might be consistent — but the way they create value isn’t. McKinsey research shows that adoption accelerates when training maps directly to day-to-day roles, not abstract categories.

Organisations that understand this aren’t pushing generic enablement. They’re building role-specific fluency.

Sales leaders learn to use AI to qualify leads faster and prep comms in context. Product managers learn to generate user stories and test cases from insight. Compliance teams learn how to audit models, not just talk about ethics. That’s why we design enablement programs function by function — because fluency scales only when it’s grounded in the work people actually do.

This is the real shift: AI enablement is no longer about tooling. It’s about operational augmentation — giving people the judgment, workflows, and language to integrate AI into the way their function runs.

If you want performance, stop teaching AI. Start teaching people how AI changes their job. Not the category. Not the industry. Theirs.

When teams see themselves in the design of the training, they use the system. And when they use it fluently, they don’t just adopt AI — they outperform with it.