Your company bought the licences. The announcement went out. Six months on, three people use them properly, most use them occasionally for boilerplate, and nobody can tell you whether the spend was worth it.

This is the most common outcome of an AI rollout, and it is almost never a tooling problem. The industry data is blunt: 84% of workers report real benefit when they actually use these tools, but only 13% say their employer provided any training. That gap is where the money goes.

This session is about closing it — drawn from designing AI enablement programmes for enterprise teams and building 40 hours of AI curriculum for a university MBA.

You'll leave with:

Why adoption stalls at the same four points every time, and what each one actually needs
How to capture a baseline in week one, because without it no improvement can be defended in a budget conversation and every claim of value stays anecdotal
The metrics worth tracking for an engineering team, and the vanity metrics that will mislead you
Why a champion per team beats a mandate from leadership, and how to pick one
How to run a weekly clinic that does more for adoption than any formal course

If you're a lead being asked whether the AI spend is working, this is the session that gives you an answer with numbers behind it.