Invert the Feasibility Order for AI and Establish Acceptance Thresholds
Why traditional software feasibility assumptions collapse on AI, how to invert the evaluation triad (Feasibility first), and calibrate UX to probabilistic boundaries.
Invert the Feasibility Order for AI and Establish Acceptance Thresholds
Traditional Product Managers rely on IDEO's classic innovation triad: Desirability (Do users want this?), Viability (Does this make business sense?), and Feasibility (Can we build this?). In deterministic software, Feasibility is almost always a "Yes" given enough engineering headcount. Consequently, PMs evaluate Desirability and Viability first before handing functional specs to engineering.
In AI product management, this mental model fundamentally collapses.
Running Example: The product team at FinTrack Logistics evaluating two initiatives: real-time "Estimated Time of Arrival (ETA) Prediction" and "Autonomous Cargo Damage Claim Payouts Under $100".