AI adoption owned by the operating committee and priced per use case. In our data the reporting line predicts AI value better than the technology does — so the practice starts with ownership, not models.
A year of pilots has produced demonstrations, a platform bill, and no verified operating result.
AI sits with IT while the decisions it should change — ordering, pricing, service — sit elsewhere.
The board wants an AI position; what exists is a tool list and a risk register.
One or two use cases genuinely work, and nobody can say why those, or how to repeat it.
Every candidate priced against the operating decision it changes — with a value case, a baseline and a kill test.
AI moved to the operating committee, with decision rights and adoption accountability written down.
Use cases drawn in phases like any other commitment — three or fewer at a time, verified before the next draw.
Per-use-case delivery measured against month-0 baselines, under the same standard as everything else we ship.
Candidate use cases traced to the operating decision each would change. No named decision, no funding.
Surviving use cases get value cases and baselines; platform and data work is costed inside them, never separately.
Ownership installed at the operating committee; adoption obligations named per function.
First use cases released into production under oversight, verified at fixed cadence — then the next draw.