Completion and corrections
Record whether representative work reached its defined outcome and what people or agents had to correct.
AI-agent pilot scorecard
What should an enterprise AI-agent pilot measure?
An enterprise AI-agent pilot should measure task completion, corrections, human intervention, adoption, cycle time, and operating cost for representative work. It should also record exceptions, approval decisions, and recovery evidence so the team can decide whether to stop, revise, repeat, or expand the workflow without turning activity into an unsupported ROI claim.
The pilot scorecard
Measure the operating loop, not only model output or the number of agent runs.
Human intervention
Track where judgment, approval, exception handling, or recovery required a person.
Adoption and continuity
Observe whether the responsible team uses the workflow and can continue the work from its retained context.
Cycle time and operating cost
Compare matched work within an explicit scope; do not convert incomplete activity evidence into ROI.
AI-agent pilot measurement FAQ
Are run counts and token totals enough?
No. They show activity and consumption, not whether the workflow completed useful work with acceptable corrections, intervention, adoption, and cost.
What if a metric is unavailable?
Record it as unavailable with the reason and keep a specific measurement task. Do not silently convert missing evidence into zero.
How should the team use the scorecard?
Agree on the measures before the pilot, review representative cases, and use the evidence to make an explicit stop, revise, repeat, or expand decision.
Define the pilot evidence
Choose the representative cases, measures, approval evidence, and decision threshold for one operating room.
Architecture conversation
Tell us what your team needs to control
Share your deployment boundary, number of agents, work surfaces, and governance requirements. We will reply by email to arrange a focused technical discussion.
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