As organizations move AI from proof-of-concept to production, industry leaders are questioning whether enterprises are overemphasizing model selection at the expense of architectural thinking. In a conversation on the Practical AI podcast, Chetan Gupta, Chief AI Officer at Rackspace, argues that successful enterprise AI deployment requires a fundamental shift in organizational priorities—away from the race for the latest and greatest models and toward thoughtful architecture, governance, and operational frameworks. The discussion traces the evolution of enterprise AI from early industrial and physical AI applications to today's generative AI landscape, highlighting how the maturation of the field demands more sophisticated thinking about integration, deployment patterns, and organizational governance. Gupta emphasizes that model choice, while important, should be secondary to solving larger architectural questions around data pipelines, system resilience, and responsible AI governance. The conversation underscores growing concerns about AI sovereignty—the ability of organizations to maintain control and ownership of their AI systems and data in an increasingly consolidated vendor ecosystem.