Enterprise AI agents often provide confident but incorrect answers due to issues with their business context. A significant majority of companies have experienced these errors, traceable to missing or inconsistent information. Retrieval-augmented generation is the primary method for providing context, making retrieval quality crucial. Provider-native retrieval tools from companies like OpenAI and Google are currently leading in adoption, surpassing dedicated vector databases. However, many enterprises express a desire to maintain best-of-breed, independent tools rather than fully consolidating with provider stacks. Hybrid retrieval, which combines embeddings with reranking and access controls, is expected to dominate future RAG systems. The development of a governed semantic layer is seen as a solution to the context gap, with most enterprises either building or planning to build one. Despite the adoption of provider-native tools, companies intend to preserve independence by keeping specialized tools. The focus when selecting retrieval systems is on ease of ingestion and operational simplicity. Once implemented, correctness and security become the primary monitoring concerns.