The widespread adoption of AI agents in business is encountering significant hurdles due to the inadequacy of existing enterprise data infrastructures. According to a new report by MIT Tech Review Insights in collaboration with Google, traditional data systems struggle to provide seamless access to the structured and unstructured data that AI agents need to make real-time decisions.
New Demands Placed on Data Infrastructure by Agentic AI
The role of artificial intelligence in the business world has evolved from simply answering questions to actively executing business processes and making decisions. This shift necessitates instant AI access not just to text-based information, but to critical operational data from supply chain, human resources, and point-of-sale systems.
However, legacy data systems still used by many companies were not designed to manage this intense data flow and business context. Gartner's projection that 50% of business decisions will be supported or automated by AI agents by 2027 increases the urgency of this infrastructure transformation. Inadequate data foundations stand out as one of the biggest factors preventing companies from achieving desired return on investment (ROI) despite rising infrastructure spending.
Sectoral Reflections and Infrastructure Transformation
For organizations looking to achieve success in AI projects, the most critical step is to modernize their data architectures. Industry pioneers are investing in integrated infrastructures that enhance data reliability, enabling agents to make fast and accurate decisions. The autonomous operation of agents depends on breaking down data silos and presenting data with consistent context across all departments.
Frequently Asked Questions
Exactly how do companies' legacy data systems restrict the performance of AI agents?
Because legacy systems cannot gather data from different departments—such as supply chain, finance, and human resources—centrally and in real time, AI agents experience delays in decision-making or are forced to work with incomplete context.
What priority steps must businesses take in their data infrastructure to increase return on investment (ROI)?
Businesses must focus on modern architectures that consolidate all structured and unstructured data, eliminate data silos, and provide AI agents with frictionless access to operational systems.
*This news article was prepared based on data published by MIT Tech Review Insights.
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