Abstract: Scaling AI capabilities from a promising Proof-of-Concept (POC) to a widelyadopted, production-ready product has become one of the most important and complexorganizational challenges of our time. Recent studies have indicated a failure rate of over 80%for AI projects not making it past the pilot phase and into scaled production, resulting in vastamounts of talent and resources being consumed with no value delivered to the organization.This comprehensive review will serve as an expansive guide to help product managers developa pragmatic, tactical approach to the ―scaling AI‖ problem. We believe that scaling AI toproduction is first and foremost a product-led orchestration problem. AI scaling is a multifaceted problem that must be solved in parallel with respect to ―bleeding edge‖ technology andproven business value, operational maturity and cross-functional alignment. The frameworkshared here describes a four-phase lifecycle (Strategic Pilot, Operational Crucible, CrossCompany Scaling, Monetization) where the product manager needs to ―own the whole stack‖ ofthe execution in order to methodically de-risk scaling. The product manager is the chiefintegrator and orchestrator of technical feasibility, human-centric design, business strategy andoperational pragmatism. The goal is to productize AI to transform it from an interesting scienceexperiment to a sustainable core differentiator and engine of profit for the company.