Business and technology leaders need no convincing that the time of agentic AI is here. Organizations are rapidly adopting agents, and few executives doubt the technology’s potential to transform work. But many organizations find that realizing the desired return on investment (ROI) from AI hinges on having the right foundation, with inadequate infrastructure and data being major blockers.

Agentic AI places considerable new demands on enterprise data systems. The shift from answering questions to taking actions means AI agents need data from across the enterprise, in all its structured and unstructured forms, and with the right business context. Legacy data systems, even those updated just a few years ago, struggle to meet these demands. If Gartner’s prediction that AI agents will augment or automate 50% of business decisions by 2027 proves correct, organizations must eliminate bottlenecks.

A report based on a survey of 300 data and technology executives finds that across all surveyed organizations, AI only has access to an average of 45% of company data. That number falls to 30% or less in “data laggards.” A select group, “data leaders,” ensures access to over 70% of their data. Today, only around half of surveyed organizations trust that the decisions their AI agents make are accurate and relevant. By contrast, 100% of the data leaders trust their agents’ decisions, a strong indicator that reliable AI requires a reliable data foundation.

Data leaders find it easier to achieve agent scale and speed. Within two years, 100% of respondents plan to be using agentic AI, with 69% expecting to use it widely. Without removing data system constraints, agentic AI will fail to deliver the desired speed and efficiencies it promises.