Use cases with demonstrable value
- Test case design: From requirements and acceptance criteria, AI produces draft test cases in minutes, complete with boundary values and negative cases – humans curate instead of typing.
- Requirements analysis: AI finds ambiguities, gaps and contradictions in specifications – you can try the underlying criteria yourself with our free tool RequiCheck.
- Analysis & reporting: Identify defect clusters, merge duplicates, pre-draft test reports.
- Test data: synthetic test data with no GDPR exposure, in any quantity and variance.
- Maintaining automated tests: Selector repairs and refactoring suggestions reduce the upkeep of flaky suites.
The limits – and why humans remain essential
AI results are plausible, but not guaranteed to be correct: when in doubt, models invent convincing-sounding test cases for requirements that do not exist. Without expert review, such artifacts flow unfiltered into the test basis. On top of that, responsibility cannot be delegated – a human must be able to stand behind a release recommendation, in front of auditors, clients and regulators.
Our principle: AI takes on the legwork; senior experts retain assessment, decisions and responsibility. It is precisely this interplay that makes AI use audit-ready.
Compliance: EU AI Act & GDPR
- GDPR: No real personal data in prompts; get data processing agreements and technical measures (Art. 32) cleanly in place; prefer synthetic test data.
- EU AI Act: Document intended uses, ensure human oversight, and meet transparency and governance obligations according to risk class.
- Traceability: Version prompts, models and review steps – otherwise no result is reproducible.
How to start sensibly
- Choose a clearly scoped pilot area (one team, one product) and collect baseline metrics.
- Governance first: Which data may go into which model? Who reviews AI results, and starting when?
- Measure for 10 weeks instead of guessing: A structured pilot like the example engagement delivers reliable before-and-after figures.
- Upskill the team: The ISTQB® CT-GenAI seminar teaches the tools of the trade for AI-assisted testing.
Read more: AI-assisted quality assurance at Loyal Team.
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Test automation
When does automation pay off?
The simple ROI calculation, the right candidates, typical mistakes – and realistic metrics.
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Agile testing
Testing in Scrum & DevOps: whole-team approach, test pyramid, continuous testing and the tester's role.
Read article →Test management
What is test management?
Tasks, risk-based approach, metrics and roles – and when external test management pays off.
Read article →Next step
Use AI in testing – certified
The ISTQB® CT-GenAI seminar prepares your team for generative AI in testing; our AI-QA consulting brings it into your project.