How Dun & Bradstreet Rebuilt Its Data Architecture for AI Agents

If you spend any time walking through the Loop or grabbing a coffee near the Merchandise Mart, you know that Chicago isn’t just a city of architecture and deep-dish; it is a global engine for B2B commerce. From the high-stakes trading floors of the Chicago Board of Trade to the massive logistics hubs sprawling around O’Hare, this city runs on the ability to verify who a business is, whether they’re solvent, and who actually owns them. For decades, that “trust layer” was handled by human analysts—people who could look at a messy data set and intuitively figure out that “Acme Corp” and “Acme Incorporated” were the same entity. But as we push further into 2026, that human intuition is becoming a bottleneck. That’s exactly why Dun & Bradstreet just pulled the ripcord on their legacy architecture to rebuild their massive database for a new kind of customer: the AI agent.

The Friction Between Human Intuition and Machine Logic

For over 180 years, Dun & Bradstreet has been the gold standard for commercial data. Their “Commercial Graph” is staggering—covering 642 million businesses globally. To a human credit analyst, a database with 11,000 fields per record is a goldmine. To an AI agent, it’s a nightmare. The problem, as Gary Kotovets, D&B’s Chief Data and Analytics Officer, pointed out, is that the original system was designed for people who could tolerate a bit of ambiguity and wait a few seconds for a query to return. AI agents don’t do “ambiguity,” and they certainly don’t do “waiting.”

When enterprises started integrating agents into their procurement and supply chain workflows, the cracks appeared. The Commercial Graph wasn’t one giant, clean pool of data; it was a fragmented collection of systems held together by custom integrations. If an agent in a Chicago-based logistics firm tried to verify a supplier’s risk profile, it couldn’t navigate those fragments. It needed sub-second latency and absolute precision. You can’t have an autonomous agent making a million-dollar procurement decision based on a “maybe” match.

This is a broader systemic issue that many firms in the Midwest are currently hitting. Whether it’s a legacy insurance giant in the suburbs or a fintech startup in the West Loop, the struggle is the same: the data foundations are too brittle for the AI they want to deploy. As I’ve noted in previous discussions on enterprise AI strategies, you cannot build a skyscraper of intelligence on a foundation of swampy, unnormalized data.

Beyond Static Lines: The Shift to Dynamic Relationships

One of the most critical parts of D&B’s rebuild is the move from static to dynamic relationships. In the old world, a database would show a line connecting a CEO to a company. Simple. But in the real world—and for the purposes of risk management—that’s not enough. If that CEO moves to a different firm, does their track record of success or failure follow them? If a subsidiary in Illinois is bought by a conglomerate in Singapore, how does that shift the risk profile of the local operation?

Agents need to reason across these shifts in real-time. To solve this, D&B migrated to a cloud-based data fabric that normalizes records while keeping regional compliance intact. They’ve also introduced something called the Model Context Protocol (MCP), which essentially gives agents a “skill set” to find the right data without needing to write complex SQL queries. It’s the difference between giving a tourist a map of the entire world and giving them a GPS that knows exactly where the nearest CTA station is.

Then there is the “Identity Problem.” How do you know which agent is asking for the data? D&B has implemented a “Know Your Agent” (KYA) protocol. Just as the City of Chicago Department of Business Affairs and Consumer Protection (BACP) requires business licenses to ensure legitimacy, D&B now requires agents to map to verified IP addresses and registration keys. This prevents “hallucinating” agents from creating chaos in the supply chain and ensures that a “digital handshake” occurs between different agents in a workflow—say, a credit check agent and a KYC (Know Your Customer) agent—to confirm they are both talking about the same company.

Navigating the Data Transition in Chicago

For the business community here in Chicago, this shift is a wake-up call. If your company is relying on legacy spreadsheets or fragmented CRM systems to manage vendor risk, you are essentially speaking a language that the coming wave of AI agents cannot understand. To stay competitive, especially in a hub as data-dense as ours, you need to think about local business growth through the lens of “agent-readiness.”

Given my background in analyzing the intersection of geo-economics and data infrastructure, it’s clear that the “rebuild” isn’t just a D&B project—it’s a blueprint for any enterprise that wants to survive the agentic era. If this trend is impacting your operations in the Chicago area, you shouldn’t be looking for a generalist IT person. You need specialists who understand the plumbing of the new AI economy.

The Local Expert Toolkit

If you’re tasked with preparing your company’s data for AI agents, here are the three types of local professionals you should be scouting for in the Chicago market:

• Enterprise Data Architects (Normalization Specialists): You don’t just need someone who can move data to the cloud; you need an architect who specializes in “schema redesign.” Look for professionals with a track record of consolidating fragmented legacy databases into unified knowledge graphs. They should be able to explain exactly how they handle entity resolution—ensuring that “Company A” is always “Company A” across every department.
• AI Governance & Compliance Consultants: With the introduction of “Know Your Agent” protocols, the legal side of AI is getting complicated. You need consultants who understand both the technical side of API authentication and the regulatory requirements of the Illinois data privacy landscape. Their primary value should be in creating “explainable decision pathways” so you can trace an agent’s decision back to a verified source.
• B2B Workflow Integrators: These are the specialists who can actually implement MCP and A2A protocols. Look for integrators who don’t just “install software” but instead design “multi-agent workflows.” They should be experts in bridging the gap between your internal proprietary data and external trusted sources like the D&B Commercial Graph.

Ready to find trusted professionals? Browse our complete directory of top-rated data experts in the Chicago area today.