Subvolts take AI agents fail because of bad data, not bad models. Fix your data strategy. Jul 2, 2026 - Developers
- Learn how data architecture impacts AI agent performance and explore solutions beyond traditional models.
- Business
- Understand common reasons for AI project failure and how to build a robust data foundation for AI success.
This episode offers a practical, data-centric perspective on AI implementation challenges directly from an industry expert.
AI Agents Are Failing and It's Almost Never the Model's Fault | Alberto Pan, Denodo
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Craig Smith interviews Alberto Pan, CTO of Denodo, on the Eye on AI podcast. Their discussion, based on Denodo's AI Trust Gap Report, reveals that enterprise AI agents fail due to data issues like staleness and missing context, not model flaws. Pan explains how traditional data architectures hinder AI performance and proposes Denodo's logical data management as a solution for scalable AI.
Imagine AI agents are like assistants needing information. If their reference books are outdated or disorganized, they make mistakes. This episode explains that AI failures usually stem from messy company data, not the AI's 'brain' itself. Fixing data access helps AI work better.
This episode targets enterprise leaders and data professionals struggling with AI implementation. It explains why AI projects falter, attributing failures to data management rather than core AI models. This is relevant as frontier labs like Google Gemini and Microsoft Copilot push for more integrated, data-aware AI systems. Understanding these data bottlenecks is crucial for any organization aiming to deploy AI effectively at scale, ensuring agents have reliable access to information.
- HostCraig S. Smith
- GuestAlberto PanCTO, Denodo
- Guest: Alberto Pan, CTO of Denodo
- Show: Eye on AI
- Length: 42 minutes
- Report Basis: Denodo's AI Trust Gap Report
- Survey Size: 850 enterprise data leaders
- Failure Cause: Stale data, missing context, inconsistent semantics
After two years of AI pilots, enterprises are finally diagnosing what went wrong, and the answer keeps coming back to data. Alberto Pan, CTO of Denodo, joins Craig Smith to walk through the findings of the company's AI Trust Gap Report: a survey of 850 enterprise data leaders that reveals the dominant failure modes of enterprise AI agents are almost never the model's fault. They're caused by stale data, missing context, and inconsistent semantics across the hundreds of data sources agents need to access to do real work.
Pan explains why traditional data warehouse and lake house architectures - built for analytics, not real-time decision-making - are creating an invisible ceiling on AI performance, and how Denodo's logical data management approach lets agents query data where it lives without centralizing it first, while enforcing consistent governance across every source in one place. The conversation also identifies two specific traps most organizations fall into as they try to scale AI - over-centralizing data into a single system, or building custom ad hoc data layers for every agent - and why both approaches collapse in a multi-agent world where agents need to cooperate, share context, and work from a common semantic foundation.
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- AI agents
- Alberto Pan
- AI programs designed to perform tasks autonomously.
- data warehouse
- Alberto Pan
- A central repository for storing large amounts of data for analysis.
- lake house
- Alberto Pan
- A modern data architecture combining data lakes and data warehouses.
- logical data management
- Alberto Pan
- A way to access and manage data without moving or centralizing it.
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WithAndrii Hrytseniuk
Questions people ask
- Why do enterprise AI agents fail?
- AI agents typically fail due to issues with their data, such as stale information, missing context, or inconsistent data meanings across various sources, rather than flaws in the AI model itself.
- What data architectures hinder AI performance?
- Traditional data warehouse and lake house architectures, designed for analytics, create limitations for AI agents that require real-time decision-making and access to diverse, up-to-date data.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Craig S. Smith.