AI can analyse requirements, generate test cases, write scripts and assess large volumes of test results. Teams can work faster and achieve broader test coverage. But faster testing does not automatically mean better quality.
Traditional software usually produces the same output for the same input. AI functionality is different. Responses may vary, models are updated, and the quality of an outcome depends on the prompt, context, data and configuration. A test that passes today may produce a different result tomorrow even though the application code has not changed.
The question is no longer just: “Does the functionality work?” We also need to ask:
- Does the system produce sufficiently reliable outcomes?
- Under what circumstances does it fail?
- Is the data representative, and may it be used for this purpose?
- Can decisions and outcomes be traced afterwards?
- Can a human still intervene?
- What happens when the model, prompt or context changes?
Quality Engineering for AI therefore demands more than test automation. It calls for risk-based evaluations, recorded model and prompt versions, security testing, monitoring in production and clear boundaries for acceptable behaviour.
In my view, AI does not make testers redundant. On the contrary: the ability to exercise professional, critical judgement about quality becomes even more important.