Seven Ways Businesses Can Use Dun & Bradstreet Data to Power Agentic AI Workflows

Generative artificial intelligence (Gen AI) has changed knowledge work— now, agentic AI is poised to take that transformation to the next level.   

 While Gen AI can generate content in response to a prompt, AI agents can act independently, enabling them to perform multi-step workflows autonomously, without constant prompting.  

Consider a familiar example. A seller preparing for a meeting might ask a Gen AI chatbot, like OpenAI’s ChatGPT, for background information on a prospect, and the large language model provides its best guess on what the seller is asking for. An AI agent built for sales preparation goes further. The seller enters the company name, and the agent retrieves firmographic details, scans recent developments, identifies potential opportunities, and assembles it into a usable briefing. The work now happens through a repeatable, structured process rather than a one-off prompt.  

This agentic capability depends on two ingredients: trusted, high-quality data and the context needed to interpret it.   

New frameworks, including Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, are breaking new ground by creating standardized processes that allow agents to seamlessly access data and coordinate with one another. As these frameworks mature, organizations can build agents that act consistently across systems and use information in clearer, more governed ways.  

Dun & Bradstreet’s data plays a critical role in these workflows, fueling enterprise AI systems with valuable intelligence on 600M+ business entities and delivering the verification layer that protects against hallucinations, making outputs trustworthy. At the core is the D-U-N-S® Number, the globally recognized identifier that anchors business identity resolution, enabling organizations to confidently link information across sources. When enterprise AI agents are underpinned by verified data, organizations gain the confidence they need to automate more complex tasks and create real efficiencies.  

Below are seven practical ways companies can use D&B data to power agentic AI strategies.  

1. Supercharge Prospecting

Agentic AI thrives on context and so does prospecting. By incorporating D&B’s firmographic data such as size, sector, structure, and location information into agentic workflows, businesses can use AI to identify high-potential leads, segment markets, and draft tailored outreach strategies, freeing up valuable time for sellers.   

2. Benchmark Competitors and Track Market Shifts 

Sales and marketing teams often need to compare performance and positioning against peers. AI agents can help by using MCP servers to access real-time D&B data on market trends, leadership changes, new location openings, and more to help businesses assemble timely profiles of competitors and track market shifts to stay ahead of the curve.   

3. Automate Supplier and Partner Risk Assessment 

D&B supply chain analytics help companies control costs and prevent disruption. By combining internal data with D&B risk indicators, AI agents can evaluate supplier stability, highlight vulnerabilities, and propose mitigation steps. This reduces manual review and creates a more consistent approach to risk monitoring.  

4.Improve Data Stewardship and Master Data Management 

When it comes to AI, quality inputs are required for quality outputs. Many organizations spend significant time reconciling records across systems and cleansing data before it can be used. Agentic AI can streamline this process by leveraging D&B’s entity resolution and matching capabilities with the D-U-N-S Number. Clean, accurate records support compliance, reduce operational friction, and improve the quality of downstream analytics.

5. Enhance Sales Enablement with Contextual Insights 

Imagine an AI agent that not only answers questions but also provides actionable insights. By embedding D&B data into conversational tools, AI agents can answer seller questions with context, surface relevant account signals, and highlight opportunities that might otherwise be missed, and do this naturally as though one is speaking to a colleague. This helps teams prepare more effectively and respond more quickly.  

6. Accelerate KYC and Regulatory Reviews 

Financial institutions and other regulated industries can incorporate D&B data into agentic workflows that support Know Your Customer (KYC) processes. Agents can use D&B data to verify new partners, improve relationship transparency, identify beneficial owners, and monitor for changes in the organizations you do business with. This improves consistency and reduces the time required for compliance activities.  

7. Identify White Space Opportunities 

Growth teams often look for untapped markets or underpenetrated segments. Agents that combine internal performance data with D&B’s market intelligence can point to potential expansion areas across geographies, industries, or product lines. This supports strategic planning and helps organizations focus their efforts where the opportunity is strongest.  

Quality Inputs, Quality Outputs 

Agentic AI systems are only as effective as the data and context that supports them. High-quality, D-U-N-S Number-verified business data provides organizations with the grounding they need to scale agentic workflows with confidence. When D&B business intelligence is integrated into agentic workflows, organizations can deploy AI automation at scale to build intelligent systems that can act independently and efficiently to anticipate needs, mitigate risk, and drive growth.