What's the biggest reason enterprise RAG projects fail?

I feel like we need a collective support group for anyone trying to scale an internal RAG setup past the "cool internal demo" phase right now.

You take a couple hundred PDFs, run them through a basic recursive character splitter then use embeddings into a vector database, and build a neat little streamlit app. Then you push it to production workflows with live enterprise data and the entire system completely loses its mind.

From what i’ve seen over year, when enterprise RAG fails the immediate reaction is always to blame the model or the prompt engineering. Teams spend weeks swapping out frontier APIs but the model can only reason over the context it's handed.

The real failure points are almost always upstream in the information architecture:

- Context Fragmentation: standard RAG indexes every data source as an isolated silo where a thread in slack, clause in sharepoint, and the updated status in salesforce exist as disconnected chunks in a vector store.

- The Versioning Nightmare: in a live enterprise env, the same client guide exists in multiple different formats across drives, half of them outdated from years ago and without massive filter the freshness at retrieval time, the vector search will blindly pull a 2022 chunk right alongside a current guideline causing total context collision.

We’ve been following how architecture trends are trying to solve this by moving away from raw semantic retrieval entirely and shifting toward a managed enterprise context layer and some teams are outsourcing the memory stack to setups like 60x. Their approach maps an underlying relationship graph across connected systems meaning the data ingestion layer resolves the temporal traces before the model even touches it.

So i'm curious to know more from those who have actually deployed RAG to a large user base, thanks!