AI agents struggle with enterprise data because most organizational datasets lack the context required to interpret business metrics correctly. Definitions for metrics, table relationships and business logic are often scattered across documentation, dashboards and individual analysts' knowledge. 

Additionally, much enterprise data exists in formats that require significant transformation before an agent can work with it. And the more transformation required, the more likely the agent is to fail or misinterpret the data. Without semantic context describing how data relates and how metrics are calculated, AI agents may generate technically valid queries that still produce misleading results.