Scaling agentic AI means trusting your data - here's what most CDOs are investing in

Editor's take

Chief data officers report that data quality and retrieval challenges are hindering the widespread adoption of agentic AI, with 50% of organizations currently implementing these systems facing such obstacles. This highlights a critical bottleneck: sophisticated AI agents, designed to autonomously execute tasks, are fundamentally reliant on accurate, accessible, and relevant data to function effectively. Without robust data governance and retrieval mechanisms, the promise of agentic AI remains largely theoretical, impacting not only the CDOs tasked with data management but also the business units expecting to leverage these advanced capabilities.

The current situation underscores the persistent importance of foundational data infrastructure in the AI era. Organizations must prioritize investments in data cleansing, cataloging, and efficient search technologies, rather than solely focusing on the AI models themselves. Future advancements in agentic AI will likely hinge on breakthroughs in how these systems can dynamically understand and utilize imperfect or incomplete data, or conversely, on more effective strategies for ensuring data readiness. Observing how companies tackle this data deficit will reveal the true pace and scope of agentic AI's integration into enterprise workflows.