Start an AI Requirements Validation Service

1. 1

   Define the exact problem you catch

   AI generates plausible-looking requirements and stories that can be incomplete, ambiguous, internally contradictory, or confidently wrong, and teams building on them do not notice until it is expensive. Your service is the expert human check that catches those flaws before the build. Name that risk sharply, because the danger of AI output is its false confidence.

2. 2

   Build a rigorous validation method

   Create a repeatable review process: completeness, testability, consistency, traceability, and alignment with real business need. A structured checklist makes your review credible and repeatable, not just an opinion. The method is what you are selling as much as the judgment.

3. 3

   Position as insurance, not obstruction

   Frame the service as cheap insurance against building the wrong thing, not as slowing teams down. The pitch is that a fast AI draft plus an expert check is faster and safer than either alone. Speed with a safety net is the value proposition.

4. 4

   Sell to teams already using AI

   Your buyers are exactly the teams adopting AI to draft requirements at speed and quietly worried they are shipping flaws. That worry is your market, and it grows as AI adoption grows. Meet the anxiety created by the tools flooding the field.

5. 5

   Keep it lean and expert

   This is a low-capital, high-margin service resting on senior judgment: no software to build, just your expertise and a validation method. Price on the cost of the errors you prevent. Keep overhead near zero and value high.

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Best for: Senior analysts with the judgment to catch what AI-generated requirements miss

Not for: Not for junior analysts, since the whole value is senior judgment that catches what AI misses. Not for someone who cannot articulate a rigorous, repeatable validation method, because buyers need more than an unstructured opinion.