Member-only story
A QA engineer’s honest take on where AI genuinely helps automated testing — and where it still needs a human in the loop.
(Updated 2026) Every few months, a new tool promises to “revolutionize” test automation with AI. Some of that is real. A lot of it is marketing.
After more than a decade in Quality Assurance — working across frameworks like Robot Framework, Pytest, Cypress, and Playwright — I’ve learned to separate the two. This article is my honest take on where AI genuinely moves the needle in test automation, and where it still needs a human paying close attention.
Where AI actually helps #
1. Speeding up test case creation
Instead of starting from a blank file, AI can generate a first draft of test scenarios from a user story, an API spec, or existing code. It won’t get edge cases right on its own, but it turns a blank page into a starting point — and that alone saves real time.
2. Reducing maintenance pain
One of the most tedious parts of automation is fixing tests that break because a locator changed or a UI…