AI agents stalled by legacy data systems; leaders see 3× higher trust

The survey reveals a stark divide in agentic AI readiness. Most organizations are investing in AI agents without first modernizing the data infrastructure those agents depend on. The constraint is not the AI itself but the foundational systems—legacy databases and data architectures that were designed for human decision-making, not autonomous agents that must access diverse, real-time data across the enterprise. Data leaders have recognized that agents require a different data environment: one where structured and unstructured information from supply chains, point-of-sale systems, and HR platforms is immediately accessible and properly contextualized.

This gap matters precisely because adoption is accelerating. Gartner's projection that agents will influence half of business decisions by 2027 is driving urgency, yet most firms are unlikely to achieve that vision without addressing data system bottlenecks first. Organizations that move quickly to grant agents access to a broader data estate—and to build trust through improved governance—will see faster scaling and better decision outcomes. The data leaders already report dramatically fewer constraints: only 8% cite legacy system issues, versus two-thirds of laggards. The message is clear: modernizing data infrastructure is not optional for agentic AI success.