The Framework
How AI actually sticks in operations.
Eleven lessons from putting AI to work on a real plant floor — not a vendor deck. Two run through all of them: AI rewards better questions, not more of them; and it runs on your data, not your prompts.
Where it started
It began with a problem I couldn’t solve.
Not a course, not a platform — a seam-integrity problem I didn’t have the statistics for, and a hunch that AI was coming. Working that problem with AI taught me the whole framework below. Here’s that story.
The ten
Ten lessons, in order.
01The head start isn’t the tool — it’s the reps. Start where “AI is coming” meets a problem you can’t solve.
02Define your value narrowly. Hard, Capacity, Capability, or Decision — name which, or it’s cost, not value.
03Adoption has three populations. Builders, Users, Resisters — a few builders package what the many run.
04Your biggest opportunity — and biggest risk to relevance. Same amplifier, two sides. You don’t have to build, just use.
05AI relocates the gap — credentials to adoption. Require understanding, and the lift is real, not rented.
06Train the AI, not just the people. Data structure is how you onboard it to your world.
07Treat AI like a brilliant new hire. Confident even when wrong — supervise it against a source of truth.
08Scale with pull, not push. A proven, packaged win makes the next team ask to join.
09Don’t stall at “What.” Teach What / So What / Now What, and give the AI data that reaches past the What.
10The improvement has to beat the cost of changing. Aim at felt constraints, or people revert to the old way.
The throughline
AI rewards the person who asks better questions — not more of them. The tool got cheap. The questions didn’t.
See it play out on the floor.
Field-tested notes on getting AI adopted in operations — the systems, the processes, and the people.
Browse the field notes →The Operations Bridge
The Operations Bridge 