Leveraging vector databases and RAG (Retrieval-Augmented Generation) to unify structured and unstructured enterprise data. Employees save an average of 1.8 hour
Internal RAG turns organizational knowledge, wikis, drives, tickets, contracts, chats, into a questioned-answer layer with citations and permission awareness, ending the 20%-of-time-searching tax. By 2026 it's the most-deployed enterprise AI pattern and the foundation layer for agents that act on company knowledge.
Knowledge rot and permission leaks are the twin killers: stale documents produce confidently outdated answers, and one ACL mistake exposes sensitive content at AI speed. Successful programs assign content ownership with freshness SLAs, test permissions adversarially, and surface answer-confidence honestly instead of bluffing.
Search returns documents to read; RAG returns answers with citations: synthesized across sources, conversational, and increasingly agentic (acting on what it finds). Retrieval quality still decides everything underneath.
Permission failure: the assistant must enforce existing ACLs perfectly, or it becomes a data-leak engine. Treat permission inheritance and adversarial access testing as launch-blocking requirements.