Requirements reviews are one of the highest-leverage places to use AI in product development. Not because AI can tell you what to build, but because it is good at finding the questions people forgot to ask.

Invite disagreement before a requirement hardens

The biggest risk in requirements is not disagreement. It is unspoken agreement. When a single viewpoint dominates, assumptions stay hidden until implementation.

One way to surface those assumptions is to ask the relevant disciplines to challenge a draft requirement. Keep the conflicting views visible until a person resolves them. AI can help by:

• Generating questions from the perspective of a developer, tester, or business stakeholder
• Highlighting terms that mean different things to different teams
• Suggesting consequences the author may not have considered

The final decision still belongs to a person. The AI's job is to make the trade-offs explicit.

A user story can look complete and still be untestable

Words like "important," "quickly," "relevant," and "user-friendly" hide decisions that acceptance criteria must make explicit. A story that says "the system should respond quickly" sounds fine until you try to test it.

Ask two questions for every requirement:

• What event triggers the behavior?
• How would someone verify that it happened?

AI can scan a set of requirements and flag words that are likely to be untestable. Use it as a way to find questions, not as approval of the requirement.

Practical workflow

1. Draft the requirement as a team.
2. Ask the AI to generate questions from three different viewpoints.
3. Review the questions together and decide which ones matter.
4. Rewrite the requirement with explicit triggers and verifiable criteria.
5. Have a person sign off.

This keeps the human judgment where it belongs while making the review faster and more thorough.