Why Canonical Knowledge Is the Foundation for Enterprise AI ft Joe DosSantos, VP at Workday

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, Workday’s VP of Enterprise Data and Analytics, 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.Large language models are predictive engines modeled to anticipate what users probably likely mean. For B2C applications where multiple interpretations are acceptable, this works fine. But enterprises need deterministic truth, not probabilistic guesses. The trio outline a solution in three layers: establishing canonical knowledge, building a semantic layer to translate between human definitions and machine-readable formats like YAML, and using LLMs as an interface to deterministic back-end systems.For leaders evaluating AI investments, this episode clarifies what actually needs to be built before agents can deliver value: not flashy use cases, but the unglamorous, essential work of data governance and semantic translation.---------- Support our show by supporting our sponsors!This episode is supported by OneReach.aiForged over a decade of R&D and proven in 10,000+ deployments, OneReach.ai’s GSX is the first complete AI agent runtime environment (circa 2019) — a hardened AI agent architecture for enterprise control and scale. Backed by UC Berkeley, recognized by Gartner, and trusted across highly regulated industries, including healthcare, finance, government and telecommunications.A complete system for accelerating AI adoption — design, train, test, deploy, monitor, and orchestrate neurosymbolic applications (agents). - Use any AI models- Build and deploy intelligent agents fast- Create guardrails for organizational alignment- Enterprise-grade security and governanceRequest free prototype: https://onereach.ai/prototype/utm_source=soundcloud&utm_medium=social&utm_campaign=podcast_s7e2&utm_content=1 ---------- The revised and significantly updated second edition of our bestselling book about succeeding with AI agents, Age of Invisible Machines, is available everywhere: Amazon — https://bit.ly/4hwX0a5Chapters - 0:00 – Welcome to Invisible Machines1:28 – Why AI Agents Fail Without a Source of Truth2:34 – Canonical Knowledge Is More Than Feeding Data to an LLM3:16 – LLMs Are Good at Language, Not Truth4:16 – The Convergence of Governance and Generative AI5:48 – Implicit vs Explicit Knowledge Explained7:31 – Why Accuracy Breaks Down in AI8:37 – The Real Launchpad for AI: Get the Facts Right9:42 – Alignment, Not Intelligence, Is the Hard Problem10:53 – Semantic Layers: Teaching Machines Meaning12:38 – LLMs Are Interfaces, Not Systems14:26 – Routing Questions: Inference vs Deterministic Answers16:21 – Canonical Knowledge Requires Human Ownership18:16 – There Is No ROI for Data (It’s the Foundation)23:59 – From Use Cases to Systems ThinkingEpisode Credits:Robb Wilson - HostJosh Tyson - HostElias Parker - Executive ProducerVishal Menon - ProducerMaksym Zlydar - Audio/Video EditorMykhailo Lytvynov - Audio/Video Editor Eugen Petruk - Graphic DesignAlla Slesarenko - Copy Vira Prykhodko - Web Development #InvisibleMachines #Podcast #TechPodcast#AIPodcast#AI #AgenticAI#AIAgents#DigitalTransformation#AIReadiness #AIDeployment#AISoftware#AITransformation#AIAdoption#AIProjects#EnterpriseAI#CanonicalKnowledge#DataGovernance#SourceOfTruth#AIArchitecture#DeterministicAI