Most organizations approach AI training the same way they approach compliance training: a session, a slide deck, maybe a quiz at the end. Attendance gets logged. Then almost nothing changes.
The problem isn't the content. It's the format.
Awareness isn't adoption
A one-hour overview of "what is generative AI" can raise awareness, but awareness rarely survives contact with a Tuesday afternoon full of real deadlines. If the training doesn't connect directly to the tasks someone does that week, it gets filed away as "interesting" rather than "useful."
We see this pattern constantly: teams that scored well on a post-training quiz, but six months later have never opened the tool they were trained on.
What actually changes behavior
The programs that stick share a few things in common:
- They start from real tasks. Instead of "here's what AI can do," the session opens with "here's the report you wrote last week — let's do it again, faster."
- They're hands-on, not observational. Watching a demo teaches recognition. Doing the task yourself teaches a skill.
- They account for skepticism. Somebody in the room has already tried the tool and had it produce something wrong or embarrassing. Ignoring that experience undermines the whole session.
- They have a next step. Training without a follow-up mechanism — office hours, a Slack channel, a manager check-in — decays within weeks.
Designing around workflows, not topics
When we scope a program, we ask for actual work samples before we build the curriculum: a report, an email thread, a ticket queue. The session is built around adapting that real material, not a generic case study.
This takes more preparation than pulling a standard deck off the shelf. But it's the difference between a training that gets a good feedback score and one that changes how a team works the following Monday.
If your last AI training didn't change anything, the content probably wasn't the problem — the format was.