From counting test cases to catching defects: The QA metrics that matter most in the age of AI

Agentic AI will completely redesign how QA organizations function. QA teams will evolve into centers of innovation, where AI agents work alongside humans as coworkers, taking on the manual work of test creation and freeing testers to focus on more strategic lifts.

This new kind of workforce calls for new metrics and KPIs. Traditional QA metrics, such as test execution rate and automation coverage, will become even less meaningful in the age of AI. AI can easily generate a high volume of output, but the quality of that output is what determines its value. To that end, modern AI-driven metrics will focus on test quality, capturing an agent’s impact on important benchmarks like defect leakage and test case effectiveness. In this webinar, the first of our AI masterclass series, two Tricentis experts discuss how AI is changing the way QA teams measure success. Register now to learn:

• Why some traditional QA metrics will no longer work — and what to measure instead
• How to align AI-driven metrics with business goals to demonstrate AI’s real, measurable impact
• How AI agents will redefine QA roles, with testers taking greater responsibility for guiding strategy and measuring success