Scaling AI agents with trustworthy data

Business and technology leaders are rapidly adopting agentic AI, but realizing ROI hinges on the right data foundation. Agentic AI shifts from answering questions to taking actions, requiring data from across the enterprise in structured and unstructured forms, with business context and frictionless access to operational systems like supply chain, point-of-sale, and HR. Legacy data systems struggle to meet these demands, and with Gartner predicting AI agents will augment or automate 50% of business decisions by 2027, eliminating bottlenecks has become urgent.

The report is based on a survey of 300 data and technology executives, exploring how legacy systems limit agent effectiveness. It categorizes organizations into "data leaders" and "data laggards" based on agent access to enterprise data, and compares their outcomes.

Key findings: AI has access to only 45% of company data on average, falling to 30% or less for laggards, while leaders ensure access to over 70%. Only about half of all surveyed organizations trust their agents' decisions, but 100% of data leaders trust theirs. Two-thirds of laggards say legacy systems limit agent scaling (66%) and prevent fast decisions (68%), while only 8% of leaders report either constraint. Within two years, 100% of respondents plan to make their data estates agent-ready.

The data leaders' approach provides a guide for creating the right data environment for agentic AI to flourish, suggesting that reliable AI requires a reliable data foundation.