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SETDA AI Outcome Discovery Register

A product team running AI discovery gets a structured register of candidate AI outcomes, each correctly categorized (revenue / cost / risk) and mapped to a Sense/Explain/Think/Decide/Act method, with needs and requirements broken out per the SETDA framework.

You receive: A JSON object {productName, outcomes:[{challenge:string, valueCategory:enum['generateRevenue','reduceCost','manageRisk'], setdaMethod:enum['sense','explain','think','decide','act'], needs:[string], requirements:[string]}]}.

Part of Choose Business Model

What's verified: STUD verifies the register is structurally complete, each outcome is tagged with a valid value category and a valid SETDA method, all three categories are covered if required, and needs/requirements meet the minimum counts. STUD does NOT verify that the SETDA method is the *right* one for the challenge or that the outcomes are genuinely valuable or achievable with AI.