AI Product & Innovation Leadership
Defining AI Product Metrics That Matter
Product teams report model accuracy and latency. Executives need metrics connecting AI features to customer behavior and business results. The gap between technical and business metrics obscures product performance.
Layer metrics from technical to business
Technical: accuracy, latency, error rates. Product: feature adoption, task completion, user satisfaction. Business: retention, conversion, expansion revenue, support cost. Each layer informs the one above.
Define guardrail metrics
AI features can improve one metric while degrading another—increasing engagement while increasing support volume, for example. Guardrail metrics catch unintended consequences before they compound.
Key Takeaways
- Connect technical metrics to product and business outcomes
- Define guardrail metrics to catch unintended consequences
- Review metric hierarchy in executive product reviews
- Retire features with strong technical metrics but weak business impact
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