What happens when AI systems stop acting like assistants and start acting like autonomous decision-makers inside your business? And if those systems are pulling information from fragmented, inconsistent, and poorly governed data environments, how much trust can organizations really place in the outcomes?

In today's episode, I'm joined by Terry Dorsey for a fascinating conversation about the growing gap between AI ambition and enterprise reality. Terry brings decades of experience spanning enterprise architecture, business intelligence, operations, healthcare, utilities, manufacturing, and defense. Long before AI became the headline topic dominating every boardroom conversation, he was already working deeply in semantic modeling, natural language systems, and the architectural foundations that modern AI now depends on.

At the center of our discussion is the new AI Trust Gap report from Denodo, which reveals why so many organizations are struggling to move AI projects from experimentation into reliable production environments. We explore why live data matters so much in an agentic AI world, why "more data" often creates more confusion instead of clarity, and how inconsistent business meaning across systems quietly undermines AI trust inside large organizations.

Terry explains why many enterprises are still operating on architectures originally designed for historical reporting and analytics, while now expecting those same environments to support autonomous AI systems making real-time operational decisions. From semantic sprawl and duplicated business logic to governance failures and fragmented security models, we unpack the hidden technical debt that AI is now exposing at scale.

The conversation also takes a deeper philosophical turn as we discuss why enterprise meaning itself may become the future control plane for AI. Terry shares why provenance, explainability, and semantic consistency are no longer optional concerns reserved for compliance teams, they are becoming foundational requirements for trustworthy AI systems capable of operating autonomously.

We also discuss why governance cannot be bolted on after deployment, how logical data management helps organizations reduce duplication and maintain operational trust, and why the companies that succeed with agentic AI will not necessarily be the fastest movers, but the ones building stable and reusable architectural foundations beneath the surface.

If your organization is rushing toward AI adoption while wrestling with siloed systems, disconnected data, and growing governance concerns, this episode offers a much-needed reality check. Because, as Terry explains, the future competitive advantage may have less to do with the AI model itself and far more to do with the architecture, meaning, and trust frameworks supporting it.

Useful Links

Terry Dorsey LinkedIn
Denodo LinkedIn
Denodo Website
The AI Trust Gap Report — global survey of 850 executives that explores why organizations are investing heavily in AI, but many still can't fully trust the data behind it.
O'Reilly's The Rise of Logical Data Management, by Christopher Gardner — explains what's necessary to enable true self-service data access and 24/7 AI-ready data.
The Enterprise AI and Data Management Glossary — glossary that helps ensure both technical and non-technical professionals can make informed decisions, optimize strategies, and align on best practices for digital transformation.
The ROI of Using the Denodo Platform alongside the Modern Data Lakehouse — Drawing on interviews with numerous global enterprises and applying a comprehensive ROI methodology, this study, conducted by independent analyst Veqtor8, found that by using Denodo alongside their data lakehouse, they realized considerable benefits.
Agentic AI Manifesto — a blueprint for credible autonomy at enterprise scale. Denodo's standard for the next era of trusted, autonomous enterprise AI.

Denodo and The AI Trust Gap: The Enterprise Data Crisis Behind AI Adoption

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Denodo and The AI Trust Gap: The Enterprise Data Crisis Behind AI Adoption