AI Performance Monitoring is moving from experiment to operating layer. Most programs fail because they optimize for demos instead of outcomes.
What works
- Start with low-risk, high-value workflows
- Use production data, not synthetic demos
- Create operator runbooks and escalation paths
- Review model behavior weekly
Outcome
With strong governance, ai performance monitoring becomes reliable infrastructure.