Agentic AI Exposes Data Gap as Enterprises Struggle with Real-Time Demands

Why It Matters

Without real‑time, governed data, autonomous AI actions risk errors, compliance breaches, and eroded user confidence, threatening the ROI of AI investments across sectors.

Key Takeaways

• 66% demand real‑time data for trustworthy AI outputs.
• Only 19% accept data older than one minute.
• Legacy warehouses cause latency for agentic AI decisions.
• Live, governed data fabric essential for autonomous AI scaling.

Pulse Analysis

Agentic artificial intelligence is moving beyond static query‑answering toward autonomous decision‑making, where agents must perceive conditions, decide actions, and execute them across core systems. The Denodo survey of 850 senior leaders shows a clear appetite for immediacy: 66% of respondents say AI outputs must be based on real‑time data, and nearly half insist the data be refreshed instantly. This heightened expectation reflects the growing deployment of AI in frontline functions such as customer service, compliance monitoring, and supply‑chain orchestration, where stale information can undermine both performance and trust.

Traditional data warehouses and lakehouses were engineered for batch‑oriented reporting, relying on extract‑transform‑load pipelines that introduce latency. As a result, many enterprises face a structural mismatch between their data infrastructure and the real‑time demands of agentic AI. Techniques like data virtualization, streaming ingestion, push‑down processing, and intelligent caching are emerging as bridges, allowing operational systems to serve live, context‑rich feeds without sacrificing governance. Vendors that can integrate these capabilities into a unified data fabric are positioned to capture a fast‑growing market as organizations scramble to retrofit legacy stacks.

The business stakes are high. Autonomous AI agents that act on outdated or ungoverned data risk operational errors, regulatory violations, and reputational damage, which can quickly erode the financial justification for AI projects. Companies must therefore invest in live, governed data layers that enforce security policies while delivering sub‑second latency. Early adopters that successfully align their data architecture with agentic AI requirements are likely to achieve superior efficiency, faster time‑to‑value, and a competitive edge in sectors where real‑time insight is a differentiator. The report underscores that bridging the data gap is no longer optional—it is a prerequisite for scaling trustworthy autonomous AI.