WeTest.SKG

The traditional QA lifecycle is built around a familiar division of responsibilities: people interpret requirements, design tests, investigate failures, and ultimately decide whether a product can be trusted. Artificial intelligence is beginning to blur each of these boundaries.
If AI can question requirements, propose scenarios, generate automation, and interpret results, are we simply accelerating the existing lifecycle or creating a fundamentally different one? What does a test represent when it can be generated and adapted dynamically? Where does accountability belong when decisions are shared between humans and machines?
This session reconsiders the QA lifecycle not as a fixed sequence of activities, but as a continuous system of decisions, feedback, and trust. It explores the shift from writing tests to shaping quality intelligence, from automating actions to orchestrating reasoning, and from owning test artifacts to owning confidence in a product.
The AI era does not eliminate the human role in quality. Instead, it makes human judgment, skepticism, context, and accountability more important. The central question is no longer simply how AI can help us test faster, but how it changes what testing means—and what QA professionals must become.