A report published today by MIT Technology Review highlights how legacy data systems are a major barrier to scaling agentic AI in enterprises. Based on a survey of 300 data and technology executives, the report shows that inadequate infrastructure and fragmented data hinder AI agents from accessing the diverse, real-time information needed to automate or augment business decisions effectively.
The report explains that agentic AI requires seamless access to both structured and unstructured data across an organization’s operational systems, such as supply chain, point-of-sale, and human resources databases. Many existing legacy systems, even those recently updated, struggle to provide this frictionless data flow. This limits AI agents’ ability to make timely, context-aware decisions and take autonomous actions within enterprise workflows.
This issue matters as Gartner predicts AI agents will influence or automate 50% of business decisions by 2027. Without overcoming these data bottlenecks, companies risk underutilizing AI’s potential to transform work processes. The findings underscore the urgency for organizations to modernize data infrastructure to fully leverage agentic AI capabilities and achieve desired returns on investment.
The report’s insights are based on a survey of 300 data and technology leaders, providing a comprehensive view of current challenges in enterprise AI adoption. It calls for strategic investment in data systems to support the growing role of AI agents in business decision-making.