Enterprise RAG Use Cases That Survive Production: A Decision Framework for IT Teams

This article provides a practical framework for IT teams to select enterprise Retrieval-Augmented Generation (RAG) use cases that can succeed in production. It argues against starting with broad chatbots and instead recommends narrow, governed use cases where source content is authoritative, access boundaries are clear, and users can validate answers. Key qualities of viable use cases include reliance on existing knowledge, natural-language access benefits, and validation against authoritative sources. The article emphasizes treating RAG as a governed knowledge access pattern, not just a model feature, and highlights the importance of retrieval quality, metadata, access control, citations, telemetry, and lifecycle ownership. It includes a use case fit matrix to evaluate potential projects, suggesting operational knowledge assistants, support triage helpers, change planning aids, architecture standards assistants, and evidence retrieval tools as strong starting points, while cautioning against autonomous agents.