Across 101 enterprises, the challenge isn’t accessing AI context—it’s trusting it. Retrieval-augmented generation (RAG) dominates as the leading method to feed AI agents relevant business information, yet many organizations experience their AI confidently providing incorrect answers due to inconsistent or missing context. Despite the rise of provider-native retrieval tools like OpenAI’s file search and Google’s Vertex AI Search overtaking traditional vector databases, a significant trust gap remains. Most enterprises are actively building or piloting a governed semantic layer to address this gap, aiming for hybrid retrieval models that combine accuracy with governance. However, widespread adoption is still in progress, with many caught between the convenience of bundled provider tools and a desire to maintain best-of-breed solutions. This tension highlights that the core issue is not retrieval volume, but the quality, governance, and consistency of AI context—elements critical to AI’s reliable performance in business environments.
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