The Operational KPIs AI Should Move
A short list: cycle time, cost per unit of work, throughput per person, error/exception rate, and speed-to-response. Measure them at the process level, this queue, this task, where the before-and-after is unambiguous, not as a company-wide average.
The five that matter
Cycle time, cost per unit, throughput per person, error rate, and speed-to-response cover almost every operational AI use case. If an initiative does not move one of these, question why it exists.
Measure at the process level
Company-wide averages hide the truth. A 3% average productivity gain can be a 40% gain in one queue and zero elsewhere. Measure the specific process AI touches, so the delta is real and defensible.
Averages are where AI value goes to die. Pick the exact process, invoice processing, lead follow-up, ticket triage, record its cycle time and cost before AI, then after. That number is defensible in a way "overall productivity is up" never is, and it tells you precisely where to expand next.
Frequently asked questions.
Why not just track overall productivity?
Because averages blend wins and losses into a meaningless middle. Process-level measurement shows exactly which use cases work, so you can cut the losers and scale the winners instead of guessing.