AI adoption has accelerated faster than enterprise data readiness. Most organizations now have access to powerful LLMs and agentic frameworks. And yet their underlying data environments were never designed to deliver the semantic consistency, governed context, and distributed access that AI requires to produce trustworthy results.
In this TDWI-hosted expert panel, Donald (TDWI), Dave Mariani (AtScale), Suresh Srinivas (Collate), and Luke Stagoll (insightsoftware) examine how the intelligent data layer, spanning the semantic layer, data catalog, and knowledge graph, brings together the core capabilities enterprises need to make AI work at scale.
Drawing on real-world experience at Uber, AtScale, and enterprise analytics deployments, the discussion moves from organizational design to practical architecture, exploring how federated governance models, composable semantic objects, and code-based semantic modeling enable teams to build an AI-ready data foundation without starting from scratch.
Watch the on-demand session to learn how organizations are building the governance and semantic infrastructure that AI demands.
What You’ll Learn
- Why AI adoption is exposing gaps in enterprise data governance and semantic consistency
- How a hub-and-spoke center of excellence model enables federated ownership of metrics
- Why composable semantic models are the key to preventing duplicate or conflicting definitions
- How pairing a semantic layer with an LLM makes query generation deterministic where it needs to be, without constraining AI creativity
- What a practical, incremental approach to building your intelligent data layer looks like
- How organizations can start with their most critical data assets and build trust incrementally
- Why governance must be designed in from day one, not retrofitted after the fact
Why Watch
As organizations move from BI-driven insights to AI-driven decisions, the cost of semantic inconsistency increases. AI systems cannot detect or self-correct conflicting business definitions. Without a governed semantic layer, LLMs are left to resolve ambiguity on their own, producing outputs that are difficult to trust and impossible to audit.
This webinar focuses on what organizations are doing in practice to address these challenges. It provides an experience-driven perspective on how to build a data foundation that supports both analytics and agentic AI.
Whether you’re responsible for data architecture, AI strategy, analytics governance, or BI delivery, this session offers a practical framework for improving AI readiness from where you are today.
Speakers
Donald Farmer — TDWI
Dave Mariani — Founder & Chief Technology Officer, AtScale
Suresh Srinivas — CEO & Co-Founder, Collate
Luke Stagoll — Chief Technology Officer, insightsoftware
