Most organisations have now 'done' AI training. A vendor came in, ran a slick session full of impressive demos, and left. A week later, almost nobody is using AI any differently. The budget was spent; the behaviour didn't change.
After running 40+ workshops, the pattern is clear: AI training fails for the same handful of reasons every time. The good news is that each one is fixable.
1. It's a demo, not a practice
Watching someone use AI is not the same as using it yourself. Demos create the illusion of learning — people nod along, impressed — but skill only forms when hands hit the keyboard on real work. If your team didn't build something during the session, they didn't learn.
2. It's generic, not role-specific
A recruiter, a finance analyst and a marketer need completely different AI workflows. Generic 'intro to AI' training gives everyone the same shallow examples that map to nobody's actual job. Adoption happens when someone solves a task they had on their plate this week.
3. There are no guardrails, so people don't trust it
Without a clear, simple policy on what data can go into which tools and where a human must review, cautious professionals (rightly) stay away. Responsible-use guardrails aren't a brake on adoption — they're what makes confident adoption possible.
What actually sticks: learn by doing
The training that changes behaviour shares four traits. Build these in and adoption follows.
- ▹Hands-on on real work — participants bring a live task and leave having done it with AI.
- ▹Role-specific use cases — examples that map to each person's actual job.
- ▹Guardrails first — a responsible-use framework people can trust on day one.
- ▹Capability transfer — a take-home kit of prompts and workflows, so the skill outlives the session.
This is the entire design philosophy behind kenai's workshops and the 3-day bootcamp: less watching, more building. When people leave having shipped something real, AI stops being a novelty and becomes a tool they reach for on Monday.