The evolving role of data intelligence in the age of autonomous AI agents

For the past few years, much of the conversation around AI has focused on what it can generate and how it helps people work more efficiently. Now, AI agents are increasingly participating in workflows, taking on operational tasks and contributing to business decisions, at a speed and scale difficult to achieve manually.

The conversation is no longer just about what AI can do. It’s about whether organizations trust the data behind it.

Can AI rely on the data it finds? Is it accurate? Where did it come from? Has it changed along the way? Is AI allowed to use it for this purpose?

Answering these questions helps build confidence in AI-generated results.

AI agents can amplify the impact of a data issue. When one output becomes another input, a problem can spread across reports, workflows and decisions before anyone notices.

Consider an AI agent working with customer data. Customer records can contain outdated contact details, duplicate profiles or missing transaction data. At the same time, customer churn can appear as cancellation in one system and prolonged inactivity in another.

The agent can produce a recommendation that appears reasonable and is then used in a business intelligence (BI) report. Another AI agent can use that report to inform its next action, while employees rely on those outputs to make business decisions. It’s this cascading effect that makes AI-generated outcomes difficult to trace or debug, forcing organizations to address problems in real-time.

What started as a data quality issue or a misunderstanding of a business term can spread beyond the original source. Organizations often need to identify what was affected, determine which outputs remain reliable and decide where corrective action is required.

Addressing issues at the data level is often much easier than tracing them across a chain of AI outputs and business processes. This reality brings greater attention to how data is prepared for AI use.

Many organizations are already strengthening the data foundations that support AI. According to IDC research sponsored by IBM, 42.1% of organizations cited data quality management as a strategic data investment priority. They also identified data preparation for AI use cases (27.7%) and data governance (25.8%) among their priorities.

These investments point to a broader challenge: preparing enterprise data for AI isn’t only about improving its quality. It’s also about making sure that data is understood, governed and ready for AI use.

IBM watsonx.data® intelligence helps organizations build that foundation by bringing together data quality, business definitions, lineage, governance and data products in a single experience. This approach helps teams find relevant data, understand its meaning, determine how it should be used and see how it connects to other data across the organization.

Agentic Data Intelligence extends watsonx.data intelligence capabilities by making all data easier to access and use for both users and AI agents. Through natural language, users can discover, classify, document and assess enterprise data without needing deep knowledge of the underlying tools and processes. Human oversight remains part of the process for activities that require validation and approval.

Organizations can also expose these capabilities to the AI agents they already use through Model Context Protocol (MCP). Whether it’s IBM watsonx Orchestrate®, IBM Bob™, GitHub Copilot or another MCP-compatible agent, those systems access trusted, governed enterprise data as part of the tasks they perform.

This integration helps AI agents identify data that is appropriate for the task at hand and apply it in line with organizational policies, reducing the risk of misinterpretation or unintended use.

See Agentic Data Intelligence in action

More autonomous AI brings new opportunities, but also greater responsibility for the data it relies on. Bringing data intelligence into the picture early can help organizations keep enterprise data reliable and governed as it becomes part of interconnected AI and business workflows.

IDC Spotlight: Make AI-first data operations a reality

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