Across 101 enterprises, AI agents are frequently fed faulty business context, leading to confident but incorrect answers. Sixty-eight percent of companies reported context-related failures in the past six months, with many experiencing repeated issues. Interestingly, enterprises using governed semantic layers to oversee their AI context report twice as many recurring errors compared to those without such layers. This suggests that governance makes hidden problems visible rather than causing them. Retrieval-based systems remain the primary method for providing context, but no single retrieval architecture dominates; hybrid and pluralistic approaches are tied. Most organizations avoid consolidating context management with a single provider, emphasizing governance, access controls, and answer correctness in purchasing decisions. Despite recurring context failures, companies rate current tools positively, highlighting a gap between expected outcomes and technology capabilities. This research underscores that context layer issues are systemic and detection, not the absence, defines perceived reliability. The evolving landscape calls for focused efforts on improving governance and context quality to enhance AI reliability.
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