Where the Data Actually LivesEvery enterprise I have worked with hits the same wall. Mountains of data. Warehouses, ticketing systems, PDFs, old email archives. Most of that data is not ready for AI to use. Leadership wants a chatbot that can answer questions about policy and product specs. But no one knows where the data lives or how to get it ready. A bigger model will not fix that.  It gets solved by data engineering. The Pattern That Shows Up Again and AgainA chatbot wired straight to a foundation model, no retrieval layer at all. It answers from memory and gets the specifics wrong. Documents sitting in five different systems, none of them governed the same way. Embeddings computed once, at launch, and never refreshed again. A RAG pipeline built without anyone checking who has write access to the source documents.The gap is never the model. It is what happens before the model ever sees the question.