Accuracy is not enough

Accuracy can go up while business value stays flat. For enterprise AI, the right question is: did we reduce friction or unlock measurable output?

Metrics that matter

Product metrics

• Time-to-answer
• Task completion rate
• Escalation rate

Reliability metrics

• P95 latency
• Incident count
• Recovery time after failure

Business metrics

• Hours saved
• Cost per successful task
• Adoption by target teams

A useful reporting format

Every AI initiative should report a short monthly scorecard:

• One reliability trend
• One adoption trend
• One business impact trend
• One concrete next action

If the scorecard is vague, the system is probably not tied tightly enough to business outcomes.

Related case studies

• DAISI – AI Assistant for Supplier Processes — measurable impact with 13,000 hours saved per year.
• AI Product Photo Detector — KPI-driven deployment with quality gates and production monitoring.