Most enterprise AI systems built on fixed-pipeline RAG architectures are leaving significant accuracy and reliability on the table. Modular RAG replaces the retrieve-stuff-generate pipeline with a dynamic decision graph, enabling systems to retrieve adaptively, critique their own outputs, and recover from poor retrieval before it determines the final answer. Production benchmarks show 30–40% accuracy improvements on complex, multi-source queries — the exact category of query that drives the most business value and carries the most operational risk. This article explains the architecture, the economics, the governance implications, and the strategic decisions leadership must make to build retrieval infrastructure that is genuinely fit for enterprise production.