As AI agents become increasingly integrated into enterprise operations, organizations must overcome the limitations of traditional data systems. According to Gartner predictions, AI agents could power or automate 50% of business decisions by 2027. To make that possible, companies must remove data bottlenecks and ensure AI agents can access the accurate, relevant information they need to make timely decisions.
Based on a survey of 300 data and technology executives, this report explores how legacy data systems are limiting the performance and scalability of AI agents. The findings reveal that a small group of organizations—known as data leaders—are achieving stronger results by addressing legacy data challenges and improving access to trusted information. Their approach offers a practical roadmap for building a modern data environment where AI agents can operate effectively and enterprise systems can scale with confidence.
Key findings from the report include:
Few organizations give AI agents sufficient access to enterprise data. Across all surveyed organizations, AI agents can access an average of just 45% of corporate data. Among “data laggards,” access falls to 30% or less. By comparison, data leaders provide AI agents with access to more than 70% of their data and report significantly greater success with agentic AI initiatives.
Confidence in AI agent decisions depends on data readiness. Only about half of the organizations surveyed trust AI agents to make accurate and appropriate decisions. Among data leaders, however, 100% express confidence in their agents’ decisions. This finding highlights a fundamental principle: reliable AI requires a reliable, well-governed data foundation.
Data leaders make it easier to scale AI agents and accelerate decision-making. Two-thirds of data laggards say legacy data systems restrict the scalability of AI agents (66%) and prevent them from making decisions quickly (68%). Data leaders have largely overcome these barriers through modern data strategies, with only 8% reporting significant constraints.
The demand for agent-ready data is growing rapidly. Within the next two years, all respondents plan to use AI agents, and 69% expect to use them extensively. Unless organizations address data access, quality, governance, and integration challenges, AI agents may not deliver the speed, efficiency, and business value enterprises expect.
Data access and business context are essential to scaling AI agents. For organizations at every stage of data maturity, the most important priority is improving AI agent access to structured and unstructured data. Strengthening data governance and adding business context are also critical. Data leaders are going further by automating data management and creating a trusted foundation for scalable, production-ready AI.
Source: www.technologyreview.com