AI training ends with interesting examples and several deliverables open on the screen. The following week, the team goes back to the usual urgencies. For the learning to continue, one application needs a place, an owner and a moment when it is used.
Choose an activity that already happens: preparing a meeting, reviewing material or gathering information for a decision. That way the team can see what changed without creating a task just to justify the course.
Record a run someone else can repeat
If a team uses AI to prepare the monthly project review, the record should show which documents go in, how the analysis is requested, what a good deliverable looks like and which points need checking.
Also keep one relevant correction. If the AI mistook a forecast for a confirmed deadline, explain how the team noticed and fixed it. The next person learns something that would never show up in the final file. The record does not need to cover every situation; it needs to let someone pick the work back up.
Watch the second and third uses
The first experience usually gets exceptional attention. Continuity brings other conditions: different documents, less time, someone else doing the task. That is when the team finds out whether the application fits the routine.
Agree on a review after a few uses. Did the deliverable help the meeting? Did the corrections repeat? Was there extra work to keep the process going? These answers guide the decision to keep, adjust or drop it.
The leader's role
Following this transition is a concrete way to connect education and work — two of the threads in Dani Hayoshi's work. The sign of continuity is the ability to repeat the application, explain its conditions and improve the result.
In AI Thinking, this is the Rebuild move: taking the choice into the routine with an owner and a review date. To plan the training itself, see the AI learning path for your team.
Dani Hayoshi is the author of AI Thinking. Her work connects artificial intelligence, education and entrepreneurship. About Dani.