Scaling AI capabilities from a promising Proof-of-Concept (POC) to a widely adopted, production-ready product has become one of the most important and complex organizational 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 vast amounts 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 develop a pragmatic, tactical approach to the ―scaling AI‖ problem. We believe that scaling AI to production 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 and proven business value, operational maturity and cross-functional alignment. The framework shared here describes a four-phase lifecycle (Strategic Pilot, Operational Crucible, CrossCompany Scaling, Monetization) where the product manager needs to ―own the whole stack‖ of the execution in order to methodically de-risk scaling. The product manager is the chief integrator and orchestrator of technical feasibility, human-centric design, business strategy and operational pragmatism. The goal is to productize AI to transform it from an interesting science experiment to a sustainable core differentiator and engine of profit for the company.