Abstract: Imagine this: your QA team is under pressure to test yet another urgent release. There’s a sea of regression tests, multiple environments to validate, and just not enough time. Then, someone suggests, “Why not let AI generate and execute the tests?”

In a matter of minutes, the AI engine spins out hundreds of test cases, runs them across browsers, flags visual bugs, and even self-heals failing scripts. It’s fast. It’s efficient. It’s… impressive.

But then a bug slips into production — a UX quirk that frustrates customers. No red flags were raised. Why? Because while AI followed the rules, it didn’t question them. It didn’t wonder, “Is this really what the user expects?” It didn’t think like a tester.

Cognitive QA- where AI is learning to mimic human testers, but not replace them. We’ll unpack real-world examples of where AI excels: repetitive validations, anomaly detection, and even early-stage defect prediction. But we’ll also confront the areas where it falls short — like exploratory testing, business context, and user empathy.

You’ll walk away with:

• A practical framework for integrating AI into your QA strategy

• Insights on where to trust AI and where to challenge it

• Strategies to reskill your teams and prepare for a future of human-AI testing collaboration

Because in the age of AI, the future of QA isn’t human or machine. It’s human + machine — and the leaders who understand that will define the next chapter of quality.