Many enterprises struggle with AI agents providing confident but incorrect answers due to poor business context. Sixty-eight percent of companies have experienced this issue repeatedly. Counterintuitively, companies building or using a governed semantic layer report these failures more often than those without one. This suggests the semantic layer effectively detects rather than causes context defects. The current infrastructure for fixing bad context is revealing the extent of the problem. There is no consensus on the ideal architecture for AI context retrieval. Enterprises are actively building or piloting semantic layers for AI agents and business intelligence. The most common primary context source for AI agents is retrieval, which also reports a high incidence of failures. Buying criteria for context solutions are shifting towards access control and response correctness. Many companies are not feeding AI agents with enterprise data. Large enterprises report more recurring context failures, likely due to better detection mechanisms.