Why AI pilots die in week three
The pattern repeats in almost every SME I walk into: enthusiasm in the first week, doubts in the second, silence in the third. The problem is almost never the model.
The pilot started from a tool, not from a task
Someone saw a demo, ordered licences and handed them out to the team. Nobody defined which task has to get faster, or how long it takes today. Without that number there is no way to know whether the pilot is working — and what isn't measured gets dropped.
Pick one concrete task, time it for a week, and only then decide on the tool.
The new work landed on someone who had no time left
The pilot is usually handed to the most capable person on the team — who keeps every one of their previous responsibilities. For the first two weeks they compensate with sheer effort. In the third, the day job wins and the AI goes back to being a tab left open in the browser.
Explicitly take a task away from whoever will run the pilot, and carry that cost in the plan.
Nobody knew what they were allowed to type into the chat
With no written data rules, each person decides for themselves. The cautious ones avoid using the tool for real work; the less cautious paste in what they shouldn't. Both behaviours kill the pilot — one through disuse, the other through the fright when somebody notices.
One page of rules: business accounts, training switched off, and the list of what never leaves the company.
What I do instead of a pilot
- One small workflow in production, with an owner and a date — instead of a pilot opened up to the whole team.
- A before and after measurement on the same task, using numbers the company already had.
- Training built on people's real documents, not on generic examples.
- A three-week review put in the calendar on day one.
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