The shift from passive to proactive AI is underway — and it’s moving faster than most enterprises can adapt. In fact, Gartner projects that at least 15% of work decisions will be made autonomously by agentic AI by 2028, up from 0% in 2024. These new AI agents understand context, make decisions, and execute complex tasks with minimal human oversight. As McKinsey recently reported, “AI agents mark a major evolution in enterprise AI — extending gen AI from reactive content generation to autonomous, goal-driven execution.”
Data is at the heart of this AI evolution, the cornerstone of an agent’s ability to reason, plan, and act autonomously. High-quality, timely, and accessible data enables accurate perception and decision-making, serving as the foundation for AI agents to operate effectively across complex business processes.
The autonomous enterprise
Unlike traditional AI that requires human oversight, agentic AI systems will set their own micro-goals, decide next steps, and execute complex workflows independently. Early adopters are seeing significant results using agentic AI to automate complex tasks. For example, healthcare innovator Genentech automated scientific data analysis for faster drug discovery, and telecom leader Ericsson is advancing autonomous networks through agentic AI.
As AI agents become more prevalent across industries, they expose critical gaps in traditional data architecture. Organizations must rethink their approach to data to harness the potential of agentic AI fully. This isn’t just about collecting more information, but instead creating dynamic, interconnected, and proactive data ecosystems that can support AI-driven decision-making at scale.
As we look at the landscape of agentic AI adoption, three critical shifts in data strategy are emerging. These changes are essential for any organization aiming to stay competitive in an AI-powered future:
1. From static to dynamic data
Consider a customer support agent for a large-scale e-commerce platform. Before making decisions, it needs access to comprehensive product catalogs, customer profiles, and historical sales data. However, this static information alone is insufficient. The agent also requires real-time data on inventory levels, website traffic patterns, and competitor pricing to make optimal decisions about product recommendations, dynamic pricing, and inventory management.
Traditional data architectures that update information in batches every few hours can’t support the level of responsiveness needed for real-time decision-making in fast-paced e-commerce environments. You also need real-time streaming and processing capabilities that provide the continuous flow of data that agentic AI systems require to make informed, up-to-the-second decisions. By building a data foundation that supports real-time streaming, companies can ensure their AI agents have the most current information available, allowing them to respond instantly to changing conditions and drive better business outcomes.
2. From siloed to orchestrated
For agentic AI to be effective, it must access and understand data across your organization. However, most enterprises face a critical challenge: their data is trapped in silos across different departments and systems. Consider a financial services company trying to automate loan processing. Their customer profiles and interaction history are stored in CRM software, financial records and transaction data live in ERP systems, credit histories sit in risk assessment platforms, and historical loan performance data is kept in legacy databases. Without the ability to access and understand this scattered data, AI agents can only see fragments of the complete picture, leading to delayed decisions and missed opportunities.
To solve this challenge, organizations need to orchestrate their data foundation. This starts with establishing unified access across enterprise data sources to break down silos between operational databases, and SaaS applications like CRM, ERP. Organizations must also implement vector capabilities that enable AI agents to search and retrieve relevant information for agents to understand efficiently. Lastly, they must deploy open standards like Model Context Protocol (MCP) servers that act as secure bridges between AI agents and enterprise data, ensuring they can access and understand this unified information while maintaining security and governance controls.
3. From reactive and proactive
In a reactive approach, businesses typically respond to issues as they arise — fixing quality problems after they cause errors, adding capacity when systems slow down, or addressing security breaches after they occur. However, as AI agents make thousands of autonomous decisions daily, this reactive stance puts organizations at significant risk.
The future demands proactive data management that prevents issues before they impact operations. Modern data platforms must automatically detect and correct quality issues before they reach AI agents, while predictively scaling resources to maintain performance during peak demands. For example, in financial services, proactive systems continuously profile incoming transaction data to catch anomalies before they trigger false fraud alerts, rather than dealing with the consequences of incorrect decisions.
Most critically, as AI agents interact with sensitive data in new ways, organizations need governance frameworks that evolve automatically. This means implementing dynamic security controls that adjust based on data sensitivity and usage patterns, while maintaining comprehensive audit trails of agent activities. By shifting from reactive firefighting to proactive management, businesses can ensure their AI agents operate on reliable, high-quality data while maintaining security and compliance at scale.
A fundamental transformation
The shift to agentic AI is a fundamental transformation in business operations. While most organizations are still experimenting with basic AI applications, industry leaders are already building the data foundations to power the next wave of autonomous business operations.
See how your organization can begin the shift toward agentic AI.
This post was created by AWS with Insider Studios.
