Why your AI pilot stalled at the context gap
Blog post from dbt
Many agentic AI initiatives remain stuck in pilot stages because agents often lack reliable access to governed data, with surveys indicating limited enterprise-scale deployment, widespread data-access constraints, and concerns about trust and governance. Without sufficient context, agents may produce valid but incorrect SQL, misinterpret or invent metric definitions, operate without guardrails or audit trails, and generate excessive compute costs through inefficient processing. The proposed solution is machine-readable data governance rather than manual documentation, using data contracts to define and validate datasets, automated tests to maintain data quality, and a semantic layer to centralize metric definitions and lineage. As AI agents increasingly become major consumers of organizational data, dbt positions its platform as infrastructure for producing trusted, governed data that can support more accurate and scalable AI-driven decisions.
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