RAG is often treated as a packaging step: index the documents, connect a retriever, and improve answers.
Inside real enterprise environments, that framing is usually too shallow.
Retrieval quality depends on more than embeddings or ranking logic. It depends on whether critical knowledge is actually visible to the system in the first place. That includes:
- source coverage across systems that matter
- freshness of indexed content
- document quality and consistency
- permission boundaries
- the gap between what is documented and what operators actually do
If the underlying knowledge is fragmented or stale, RAG does not become strategic memory. It becomes a faster way to surface partial truth.
That is why readiness has to be evaluated before retrieval is sold as a solution category. A credible assessment needs to look at:
- coverage of the knowledge surface
- ability to retrieve useful answers for real questions
- confidence in the freshness and authority of the retrieved material
Those are diagnostic questions before they are implementation questions.
In practice, RAG readiness is one part of broader AI readiness. It sits alongside workflow observability and execution safety, not above them.