The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
A recent survey of 101 enterprises reveals a significant "context gap" in AI systems, where agents often provide confident but incorrect answers due to unreliable or inconsistent business context. While many organizations are building a governed semantic layer to address this issue, the adoption of provider-native retrieval systems is outpacing the development of reliable context sources, leading to a reliance on tools that enterprises are not fully confident in.
The key insight for you, considering your focus on AI infrastructure and deployment, is the emergence of a "context gap" in enterprise AI agents. This gap reflects the disparity between the confident outputs of AI agents and the inconsistent or missing business context that underpins them, leading to incorrect results. The actionable takeaway is the urgent need to develop and deploy a governed semantic layer, as most enterprises are currently building but have not yet completed this infrastructure, which is crucial for ensuring reliable and accurate AI agent outputs.