"Before enterprises can deploy AI agents that actually work, they need something most organizations don't have: a single, authoritative source of truth."
"Joe DosSantos, VP of Enterprise Data and Analytics at Workday, joins Robb and Josh for a wide-ranging conversation about canonical knowledge, the semantic layer, and why data governance, a concept from the 1990s, has suddenly become essential for AI deployment."
Enterprises require a single, authoritative source of truth to enable reliable AI agent deployment. Canonical knowledge structures align definitions, reduce ambiguity, and provide consistency across systems. A semantic layer translates business concepts into machine-understandable forms and bridges data stores with AI agents. Mature data governance establishes policies, ownership, lineage, and quality controls that ensure trusted inputs for models and agents. Data governance practices originated in the 1990s but have become critical again because AI amplifies errors from inconsistent or unmanaged data. Without canonical knowledge, semantic standardization, and governance, AI agents will produce unreliable or conflicting outputs.
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