For years, learning a new tool at work meant a course. Someone booked a session, a link arrived, and the plan was to sit through the modules and then, eventually, start using what we had learned. The assumption underneath was simple: knowledge comes first, practice comes second. With AI, that order seems to be flipping. In a study of 767 knowledge workers, people who knew at least one person using AI were about three times more likely to have used it themselves in the past week than people who knew no one. Nearly nine in ten participants did know someone. The strongest signal for starting was not a course, a licence or a policy. It was being close to somebody already doing it. That is a time issue, and a bigger one than it looks. The gap between "I should try this" and "I just finished something real with it" is where hours quietly disappear. A month of good intentions is a month of saved time that never started. Ten minutes beside a colleague can close that gap on an ordinary Tuesday afternoon. ------------- Context ------------- Most teams still treat AI adoption as a training problem. Budget goes to courses, prompt libraries and lunch-and-learn sessions. The thinking is reasonable: if we can get everyone through the material, the use will follow. Completion rates become the scorecard, and a high completion rate feels like progress. But courses carry a hidden timing problem. They ask for hours up front and return the payoff later, if at all. Picture a three-hour course spread over two weeks, followed by a gap while the learner hunts for a real task to apply it to. At the end, that person has spent three hours and saved none. Plenty of us have been that learner, and plenty of us never opened the second half. This is where it helps to name a different measure. Call it time-to-first-use: the number of days and minutes between deciding to try something and finishing a real piece of work with it. Courses tend to stretch that distance. Watching a peer shrinks it, because the learner sees the tool working on a real task before they have to figure out the task themselves.

Grab a piece of paper and draw a line down the middle. On the left, write everything you do this week that you'd hand off if you could. The emails, the admin, the repetitive tasks that keep the wheels turning but don't tap into your creativity or your brilliance. The stuff that gets done but doesn't light you up. On the right, write the things you never want to hand off. The work that makes you who you are. The decisions, the relationships, the ideas that only you can bring. Dean's point is that everything on the left side is exactly what AI is coming for, and that's not a threat. That's the opportunity. Because when that list gets handled by someone or something else, you finally get to live fully into the right side. The goal isn't to do less. It's to do more of what you're actually meant for. What's one thing on your left-hand list you'd hand off tomorrow if you could? 👇

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