This maps cleanly to clinical AI adoption. A dashboard can show training completed, notes drafted, or model calls handled while the real workflow still asks the same people to reconcile sources, own exceptions, and rebuild context before acting.
The useful metric is not whether the change ritual happened. It is whether the next person inherits less unresolved work.
Thanks, Kevin. That goal phrasing matters because it separates adoption theater from workflow inheritance.
In clinical AI, I think the useful proof is whether a tool leaves the next owner with fewer source conflicts, fewer hidden exceptions, and a clearer point where a human can still say no.
This maps cleanly to clinical AI adoption. A dashboard can show training completed, notes drafted, or model calls handled while the real workflow still asks the same people to reconcile sources, own exceptions, and rebuild context before acting.
The useful metric is not whether the change ritual happened. It is whether the next person inherits less unresolved work.
Thanks for the comment. I appreciate your conclusion. That should also be a goal.
Thanks, Kevin. That goal phrasing matters because it separates adoption theater from workflow inheritance.
In clinical AI, I think the useful proof is whether a tool leaves the next owner with fewer source conflicts, fewer hidden exceptions, and a clearer point where a human can still say no.