Denodo Finds ‘Trust Gap’ Threatening Agentic AI Adoption
Fragmented Data Sources and Lack of Governance Are Stalling the Shift from AI Insights to AI Action
Denodo, a leader in data management, has just released the AI Trust Gap Report, based on a comprehensive global study conducted by Arlington Research, revealing that the next frontier of Artificial Intelligence (AI)—Agentic AI—is facing a critical trust crisis.
What Denodo Discovered
As AI evolves from passive chatbots to agents capable of making independent decisions and triggering operational workflows, the stakes for data accuracy have never been higher. However, the research highlights that technical hurdles are undermining these initiatives:
- The Search for Context: 63% of organisations say that identifying the most relevant and trustworthy data, or preparing it for consumption, are significant barriers to AI deployment.
- The Need for Real-Time Data: 66% of respondents insist that AI data must be accessed in near real-time to be considered trustworthy.
- The Security Paradox: 67% struggle with AI data security and access controls, a vital requirement for safe agentic operations.
- Scale and Complexity: 42% of organisations say they pull from over 400 original data sources for their AI initiatives.
- Performance Bottlenecks: Nearly 60% of respondents report difficulty optimising performance for the intensive workloads required by large-scale AI.
“AI is rapidly shifting from systems that merely answer questions to systems that take autonomous action, and this transition changes the data requirement entirely,” said Richard Jones, Vice President and General Manager for Asia Pacific and Japan at Denodo. “When an AI agent triggers a business outcome, there is zero room for stale or ungoverned data. To scale agentic AI with confidence, businesses must move beyond static data silos and adopt a foundation of live, governed, and contextually relevant information.”
The Denodo report concludes that the “trust gap” is not a failure of AI models, but a reflection of the underlying data architecture. For organisations to move from experimental AI to automated scale, they must bridge the divide between their disparate data estates and the real-time requirements of agentic systems.
Explore the complete findings of the AI Trust Gap Report.
