AI agents (like Zapier or ChatGPT) have been researched for their automatic test case, report, and documentation creation or inspection abilities. Thus, I would like to know if anyone in this group has first-hand experience of involving AI in their QA processes for generating test cases, reviewing comments, or condensing test results?
Which tools or configurations are really bringing in the value and which ones are just passing phase?
I wouldn’t hesitate to ask for sharing your experiences, wins, or even hard way knowledge.
From my experience, the most effective way to tackle a large piece of work with the help of AI is to break it down repeatedly:
Chunk the task into manageable pieces.
Sub‑divide each chunk until the resulting units are small enough to be tackled independently.
Once a sub‑task reaches a size where it can be solved quickly, you can consider bringing AI into the loop. At that point the AI acts like a junior teammate: you define the problem, supervise the output, and verify the result.
Remember, when you use AI you’re essentially managing a virtual intern. The responsibility for quality, correctness, and alignment with project goals stays with you, and you don’t get paid for supervising the AI.