AI can help turn requirements into candidate scenarios, prepare test data, draft automation and organise failure information. The useful question is where it improves the testing process and how the result will be checked.
Keep business rules, review and evidence at the centre of your QA process.
Start with a representative workflow, agreed acceptance criteria and a comparison with the current approach. Expand when the evidence supports it.
Ask AI to identify ambiguities, suggest boundaries and propose positive and negative cases. Compare the output with actual business rules. Trace each accepted test to the requirement or risk it covers.
AI can suggest combinations and draft synthetic records. Check constraints and important exceptions. Agree the handling of confidential and personal information before using it with an AI tool.
Review, execute and maintain generated code like other test code. Check that assertions verify meaningful outcomes, failures remain visible and tests run reliably in the delivery process.
Summaries of errors and proposed reproduction steps can organise investigation. Verify them against logs and test evidence. People still need to explore unexpected questions and business consequences.
Suggested locator updates or test repairs need a review trail. Check whether the application changed as intended or a genuine defect appeared. Weakened assertions and skipped failures can hide problems.
Measure useful coverage, review effort, repeatability, defect detection and maintenance. More generated tests alone do not demonstrate better quality. Read the practical workflow.
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