AI-Driven Quality: Shifting Beyond Traditional Test Metrics

Software quality is getting harder to manage. AI is driving 10x more output, faster cycles, and constant change. What used to signal “ready” (green tests, high coverage, passing checks) doesn’t hold the same weight. Your release confidence is suffering because these signals only give you one part of the story. 

Teams built most QA systems for simpler demands and more predictable workflows. AI can help testers, but only if the foundations are right. Otherwise, it just scales bad practices faster. 

This calls for a system-level rethink. The opportunity to redesign your quality architecture is now, before the gap widens further, visibility breaks down, signals fragment, and it becomes harder to maintain visibility across manual, automated, and agentic workflows. 

In this session, we’ll cover: 

• How to gain the confidence to scale –  Build environments that shift your teams from speed optimization to flow engineering.  

• Ways to drive better decision making –  Use SmartBear QMetry to unify workflows and get shared visibility from one system of record that delivers consistent, real-time analysis of coverage, risk, and release readiness. 

• Steps for building production-ready software –  Replace rigid testing with continuous measurable assurance that signals where your risk is, not just where tests actually fail and why.