Marty Cagan and Marily Nika outline what makes AI product management distinct from conventional product management, framing it through the lens of four critical product risks: feasibility, usability, value, and viability. They argue that just as "Mobile PM" was once a specialized skill that eventually became table stakes, AI product management will soon be expected of all product managers. The article focuses specifically on AI-powered applications rather than underlying infrastructure, drawing a parallel to the platform-versus-experience product distinction.

The authors detail how each risk category intensifies with AI. Feasibility risk is heightened because generative AI is probabilistic, not deterministic — making training data quality, error rates, and appropriate use-case matching paramount. Usability risk demands new approaches to transparency, explainability, and trust-building in the user experience. Value risk requires product managers to ensure AI features deliver genuine incremental benefit rather than serving as marketing checkboxes. Viability risk encompasses unit economics (still uncertain for many AI products), data provenance and copyright concerns, legal liability for probabilistic outputs, and ethical considerations around bias, hallucinations, and misuse by bad actors. The piece underscores that these viability questions fall squarely on the AI product manager's shoulders, making deep knowledge of users, data, business context, and technology more critical than ever.