AI can reduce time spent locating requirements, comparing documents and tracking assumptions. The same speed can amplify error when evidence provenance, permissions and technical ownership are weak.
1. Start with information architecture
Controlled source material, versioning, access rights and a consistent engineering taxonomy are prerequisites. Without them, AI accelerates document noise.
2. Separate extraction from judgement
Models can identify candidate requirements, conflicts or gaps. A qualified engineer must determine relevance, consequence and action.
3. Make uncertainty explicit
Outputs should show source, confidence, missing evidence and the intended use in a decision. An answer without provenance should not enter a technical basis.
Accountability is not a final approval step. It is designed into the workflow.
4. Review the workflow, not only the output
Technical assurance should test how data entered the system, how transformations occurred and what controls prevented unsupported conclusions.
5. Measure useful speed
The relevant metric is not tokens or summaries produced. It is time saved to a better-evidenced decision, with no loss of traceability or professional responsibility.