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 organizations identify "finding relevant data," within specific business contexts, as a primary barrier to AI deployment.
• The Need for Real-Time Data: 66% of respondents insist that AI data must be accessed in real time to be considered trustworthy.
• The Security Paradox: 67% struggle to maintain consistent security and access controls across systems, a vital requirement for safe agentic operations.
• Scale and Complexity: The average enterprise AI initiative now pulls from over 400 data sources, with 20% of organizations juggling more than 1,000 sources.
• Performance Bottlenecks: Nearly 60% of respondents report difficulty optimizing 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 Dominic Sartorio, vice president of Product Marketing 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 report concludes that the "trust gap" is not a failure of AI models, but a reflection of the underlying data architecture. For organizations 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. 

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Explore the complete findings of the AI Trust Gap Report conducted by Arlington Research.