The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
A survey of 101 enterprises reveals a 'context gap' where AI agents are producing confident but wrong answers due to missing or inconsistent context. The majority of enterprises rely on retrieval as their primary context source, with provider-native retrieval leading the way.
What happened
The survey found that 57% of enterprises have experienced AI agents producing confident but wrong answers due to missing or inconsistent context. Retrieval is the primary context source for 38% of enterprises, with provider-native retrieval from OpenAI and Google leading the way. A governed semantic layer is being built to address the context gap, but most enterprises are still in the process of building it.
Why it matters
This context gap is a significant issue for businesses as it can lead to incorrect decisions and actions. The reliance on retrieval as the primary context source also means that the quality of retrieval is critical to the accuracy of AI agent answers. Businesses need to prioritize building a governed semantic layer to ensure consistent and accurate context.
The takeaway
Businesses need to prioritize building a governed semantic layer to address the context gap and ensure accurate AI agent answers. This requires a focus on building a shared understanding of business data and ensuring that AI agents have access to consistent and accurate context.
Our plain-English take, written from public reporting for operational business owners. Always read the original for full context.
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