Enterprises Face Trust Gap Blocking Agentic AI Adoption

Denodo released The AI Trust Gap Report, a global survey of 850 executives showing that enterprise readiness for Agentic AI is constrained by data problems. Respondents flagged real-time access, contextual relevance, governance, and security as primary blockers to moving from AI insights to AI-driven actions, with 66% saying real-time data is non-negotiable.

The survey surfaces quantitative pressure points that practitioners must address. Key statistics include:

• •63% identify finding contextually relevant data as a primary deployment barrier
• •66% require real-time data access to consider AI outputs trustworthy
• •67% report inconsistent security and access controls across systems
• •The average enterprise AI initiative now pulls from over 400 data sources, with 20% handling more than 1,000
• •Nearly 60% cite performance bottlenecks for large-scale AI workloads

These numbers imply high cardinality in data integration, weak metadata and lineage coverage, and latency issues across ingestion and feature computation. The report emphasizes live, governed data layers and consistent access control as prerequisites for agents that take automated actions.

The shift from conversational or analytic models to agentic systems raises the operational stakes: a model that executes workflows must rely on fresh, authoritative data and auditable decision trails. This study maps directly onto recurring enterprise pain points: fragmented data estates, sparse governance, and compute-performance tradeoffs. Vendors offering data virtualization, streaming ingestion, feature stores, and unified access control stand to gain, but the work is largely organizational as well as technical. Denodo frames this as a trust problem rather than a pure model problem, which reframes investment priorities for teams building production agents.

Teams deploying agentic workflows should prioritize low-latency data fabrics, comprehensive metadata and lineage, and role-based access with consistent enforcement. Watch for vendor integrations between streaming platforms, feature stores, and policy engines, and for enterprise case studies showing agentic systems operated safely with live governed data.