AI in QA: how to use Generative AI in testing without creating technical debt

Generative AI (GenAI) isn’t just changing how code is written; it’s reshaping how we define software quality.

As AI tools become more embedded in software development workflows, the role of quality assurance (QA) is shifting from manual gatekeeping to real-time oversight of machine-generated outputs.

The result is a new shared responsibility for accuracy, coverage, and risk where testers keep context and integrity front and center while data scientists help tune models and guard against drift, bias, and hallucinations.

From test case creation to test selection and analytics, the potential to accelerate ...