Legacy Data Systems Hamper AI Agent Adoption, Report Reveals

The era of agentic artificial intelligence (AI) is undeniably here, with business and technology leaders widely acknowledging its transformative potential for the workplace. Organizations are accelerating their adoption of AI agents, and few executives question the technology's capacity to revolutionize how work is done. However, a significant challenge persists for many: realizing the desired return on investment (ROI) from AI initiatives is heavily contingent upon establishing the right foundational infrastructure and data management practices. Inadequate systems and data accessibility are frequently cited as major blockers to successful AI agent deployment.

Agentic AI introduces substantial new demands on enterprise data systems. Unlike traditional AI that primarily answers questions, agentic AI is designed to take actions. This fundamental shift requires AI agents to access data from across the entire enterprise, encompassing both structured and unstructured information, and crucially, imbued with the correct business context. To enable real-time decision-making and autonomous action, these agents also necessitate frictionless access to an organization's operational systems. This includes systems that store vital information such as supply chain logistics, point-of-sale transactions, and human resources records. Unfortunately, many legacy data systems, even those that have undergone updates in recent years, are struggling to meet these increasingly sophisticated demands.

As AI agents become more deeply embedded within enterprise operations, the imperative to overcome the limitations imposed by legacy data systems grows more urgent. The implications are substantial: Gartner, a leading research and advisory company, predicts that AI agents will augment or automate 50% of business decisions by the year 2027. If this projection proves accurate, organizations that fail to eliminate data bottlenecks risk depriving their AI agents of the essential data required to make timely and accurate decisions, thereby hindering their effectiveness and scalability.

This critical challenge is explored in a recent report that surveyed 300 data and technology executives. The findings underscore how legacy systems are actively limiting the effectiveness of AI agents in a broad spectrum of organizations. The report identifies a select group of organizations, termed 'data leaders,' that are demonstrating greater success with agentic AI. These leaders are experiencing fewer data limitations directly attributable to legacy systems. Their experiences offer valuable guidance for other organizations aiming to create the optimal data environment for AI agents to flourish and to build trustworthy systems that can be scaled effectively. A key finding from the survey reveals that a limited number of companies currently provide agentic AI with ample access to their enterprise data. Across all surveyed organizations, AI systems have access to only a fraction of the available enterprise data, highlighting a pervasive data accessibility gap that must be addressed for the widespread and successful deployment of agentic AI.