When evaluating the results of AI, traditional quality metrics no longer give a full picture. AI can deliver functionality and pass tests, yet still raise the question:

-- Can we trust the outcomes of AI?

As AI adoption grows, we enter complex, uncertain territory where quality metrics must be complemented by trust metrics.

This talk explores how to enhance traditional quality measures with trust-oriented metrics—across code generation and business applications—highlighting when trust metrics matter most and offering 2–3 proven approaches to quantify and apply them effectively.

First presented: Software Quality Days 2026, Vienna