Problem guide
AI requirements gathering should produce evidence, not longer documents.
AI can make software ideas experiential sooner. The discipline is deciding which experience is strong enough to influence scope.
Why AI changes requirements work
Traditional requirements gathering asks people to imagine behavior from language. AI-assisted creation can make a narrow version of that behavior visible and usable much earlier.
That does not eliminate requirements work. It changes the medium. The team can ask stakeholders to react to behavior, not only prose.
The RDD pattern
- Frame the uncertaintyName the operational question the requirement must answer.
- Generate a candidateUse AI to accelerate a provisional working slice.
- Observe real useWatch where intent, rules, roles, and exceptions diverge.
- Write the evidenceRecord accepted behavior and rejected interpretations.
- Hand off responsiblyMove only the evidence-backed slice into Build to Deliver.
What to avoid
Do not treat AI-generated behavior as production-ready because it was fast to create. Do not let a demo replace architecture, security, QA, integration, or operational review.
The useful question is not whether AI can produce software quickly. The useful question is whether the resulting candidate can expose the right uncertainty early.