If your test automation looks impressive in demos but falls apart the moment a UI changes, consider this: every fragile test quietly increases release risk, rework, and the number of uncomfortable questions you’ll have to answer later.

AI is now baked into software testing. Expectations are higher. Pressure is higher. But for many teams, the work hasn’t actually improved. Scripts still break on minor changes. Maintenance still eats into every sprint. Coverage still feels uncertain when it matters most.

That gap between promise and reality is the problem.

Many “AI testing” tools haven’t changed the fundamentals. They
automate faster, but not smarter. They look good in controlled demos, then struggle in complex environments with legacy systems, compliance constraints, or limited access.

This session focuses on what actually holds up in the real world. Based on enterprise experience and the latest Gartner Magic Quadrant, we’ll look at why platforms fail at scale and what a modern testing platform must do to reduce risk, not shift it.

This isn’t about hype. It’s about confidence. In your coverage. In your releases. In the decisions you’re making as a tester or QA leader.

What you’ll learn:

• How to recognize AI-labelled tools that still behave like brittle script runners


• What practical, scalable AI support looks like in complex environments


• The capabilities that genuinely matter when quality, speed, and accountability collide