Enterprise AI projects often demonstrate impressive capabilities in a prototype but face very different challenges when they move into real-world environments. This session explores the product and governance considerations involved in taking AI systems from experimentation to enterprise adoption. Drawing on experience across enterprise B2B SaaS, AI/ML product strategy, data-driven products, and AI governance, the discussion will cover RAG, data quality, access and oversight, evaluation, human-in-the-loop controls, and measuring business value. The focus is on practical decisions teams can make to build AI systems that are useful, trustworthy, and scalable without allowing governance to become an afterthought.