AI can prepare interviews, structure documents, reformulate requirements, find contradictions and generate test ideas. Domain negotiation, prioritisation and approval remain human responsibilities.
What the concept actually means
Value is high when inputs and quality criteria are clear. Transformations between artefacts are especially suitable: minutes to questions, rule to requirement or requirement to acceptance criteria.
Why it matters in the enterprise
Rapid text production must not destroy traceability. Every derived claim needs provenance, status and accountable confirmation.
A controlled method
The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.
- Define task and impact
- Clarify data, context and permissions
- Review against domain criteria
- Control deviations, approval and evidence
CTPM practice example
CTPM practice example: AI creates candidate requirements from workshop notes. The business analyst checks source, atomic wording, constraint and acceptance criterion; stakeholders confirm content afterwards.
Quality and test criteria
The following criteria make quality observable for this use case:
- Every requirement has source and status.
- Ambiguity and solution bias are reviewed.
- Acceptance criteria are observable.
- Changes remain traceable.
Risks and common misconceptions
Risks include invented stakeholder intent, smoothing over real conflicts, false precision and large volumes of unprioritised requirements.
Example transfer artefact
Transfer artefact: an AI-assisted requirements workflow with artefacts, sources, review questions, approval and traceability.