Retrieval-Augmented Generation (RAG) has long been positioned as the way to make enterprise AI reliable: retrieve the right internal context, then generate accurate answers. In practice, RAG often falls short once scaled to millions of fragmented documents across PDFs, tables, images, and legacy formats. Organizations frequently spend months re-engineering data and pipelines, yet still struggle to achieve consistent accuracy.

This article highlights recent benchmarking results suggesting a meaningful shift. A new benchmark tested retrieval systems at multi-million document scale using intentionally diverse, enterprise-like datasets. Traditional retrieval pipelines struggled under these conditions, either returning incorrect context or introducing noise that degraded output quality. By comparison, more advanced retrieval systems reportedly delivered up to 23% higher answer accuracy, maintained stable performance as datasets scaled beyond 10 million documents, and avoided the need for disruptive data migrations or schema rewrites.

The core argument is that accuracy is the real threshold for production AI. In compliance investigations, customer service, and internal knowledge workflows, unreliable answers create risk and erode trust. With retrieval becoming more dependable at enterprise scale, organizations can move from pilots to production and deploy AI where correctness and trust matter most. The question shifts from “Can we make RAG work?” to “Where should we deploy it first?”

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