Measuring operational value from AI
Without a baseline and a defined measure, it is impossible to know whether an AI system is actually improving the operation.
AI initiatives are frequently evaluated on model accuracy alone. Accuracy matters, but it is not the same as operating value: a highly accurate recommendation that nobody acts on, or that duplicates existing effort, creates little real benefit.
A more useful measurement approach starts before the system is built: establish a baseline for the target workflow (time taken, error rate, cost, or outcome quality), define the measure that will indicate success, and set a value gate that decides whether the initiative scales, is adjusted, or is retired.
Post-deployment, the same measures should be tracked on a regular cadence, alongside adoption data. A system with high accuracy and low adoption is not succeeding; a system with moderate accuracy and strong, sustained adoption inside a well-bounded workflow may be delivering real value.
Measurement discipline is what separates a durable AI programme from a portfolio of interesting but ultimately unaccountable pilots.