Data Infrastructure Barriers Delay Agentic AI Deployments

Enterprises are increasingly finding that their existing data infrastructure is a major barrier to deploying and scaling agentic AI, according to separate studies from GoogleMIT and Cloudera. The research indicates that while interest in AI agents is high, projects are frequently delayed or canceled due to problems with data access, context, and governance.

The GoogleMIT report, based on a survey of 300 IT executives, found that more than half of respondents have paused or delayed AI agent deployments to address foundational data issues. Similarly, Cloudera's research, conducted with Wakefield Research and surveying 1,500 enterprise architects, revealed that 95% had delayed or canceled AI projects in the past year due to data governance, compliance, or regulatory challenges.

Both studies highlight a fundamental mismatch: enterprise data systems were built for human access, but AI agents require real-time, context-aware, and multimodal data to function effectively. Legacy systems often create silos, store data in inaccessible formats, or rely on batch processing that cannot support the high-velocity decisions agents need to make. "Legacy data systems struggle to meet these...