AI in QA: What It’s Good At—and What I Will Never Trust It With

AI in QA is getting a lot of attention, and some of it makes sense. Still, we shouldn’t hand everything over to automation, especially when it comes to making decisions.

AI is really good at spotting patterns. It can go through logs, compare results, and find unusual issues in big data sets much faster than a person. That’s why it works well as a support tool.

AI can also help create test data, suggest edge cases, and help with documentation. These jobs take a lot of time but don’t need much judgment.

I don’t trust AI to set priorities. It doesn’t get business risk. It can’t balance user trust with technical debt. It won’t know if something is just a minor issue or a serious problem unless you tell it, and even then, context is important.

AI also has trouble with things that aren’t clear-cut. Real software doesn’t always act in simple ways. People use it in unexpected ways. Requirements shift. Edge cases change over time. That’s where QA judgment comes in.

I think of AI as a junior assistant. It’s quick, never gets tired, and can be helpful, but it still needs someone to watch over it. In the end, people are always responsible for quality.