Field notes
Notes on making AI enablement actually land, from someone who ran it inside a large enterprise.
Almost every AI enablement program stalls in the same place, and it is not a training problem or a tooling problem. Good practice gets written down and then never reaches the people who would use it. Here is what that looks like, and what closing the gap actually takes.
License counts and usage dashboards tell you a tool is being touched, not that anyone changed how they work. Here are the four measures that actually tell you whether AI adoption is happening, and how to start reading them without new tooling.
Most AI Centers of Excellence drift into being a documentation team or a governance team, and both quietly fail at the one job the CoE exists to do. Here is what that job is, and the single thing it cannot fake.
Across a team, the same task gets prompted a dozen different ways at a dozen different quality levels, and nobody knows which version is the good one. The cost is not the minutes. It is the variance, and it compounds.
The pilot succeeds, leadership greenlights the rollout, and then almost nothing happens the way it did in the pilot. The gap between "it worked for one team" and "it is how we work" is where most AI programs stall. Here is what that middle actually requires.