AI Evaluation Strategy defines how the capability will be tested, measured, and monitored against its intended behavior and outcomes. It translates the expectations established throughout the framework into measurable criteria for determining whether the AI is performing as intended, producing useful results, and creating the value it was designed to deliver.

The outcome is a clear, repeatable approach for evaluating performance before launch and as the capability evolves in production.

What's Included:

• Product Outcomes

• AI Performance

• Evaluation Methods

• Production Monitoring & Feedback

AI Evaluation Strategy turns expected behavior and product outcomes into measurable evidence of performance. The team defines what success means for the product and the AI capability, identifies the behaviors and outputs that must be evaluated, and establishes appropriate methods, thresholds, test cases, and monitoring practices. Evaluation should include both controlled testing before release and ongoing observation in production so that performance can be measured against defined expectations, emerging failure patterns can be identified, and the evaluation approach can evolve alongside the capability.