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

Generative artificial intelligence (Gen AI) has transformed how businesses create content, analyse information, and support decision-making. But the next phase of enterprise AI is already taking shape.

Unlike generative AI, which responds to prompts and generates outputs, agentic AI can take action. AI agents can plan, reason, make decisions, and execute multi-step workflows autonomously, reducing the need for constant human intervention.

Consider a familiar sales scenario. A seller preparing for a meeting might use a generative AI tool to gather background information on a prospect. The AI generates a summary based on available information.

An AI agent takes that process further.

Instead of simply answering a question, the agent gathers firmographic information, reviews recent business developments, identifies potential opportunities, prioritises actions, and assembles a briefing automatically. The process becomes structured, repeatable, and scalable.

This shift from generating content to driving outcomes is what makes agentic AI one of the most important developments in enterprise AI today.

As AI moves closer to decision-making and workflow execution, the quality of the underlying data becomes increasingly important.

Agentic AI doesn't just need information. It needs context.

For an AI agent to act effectively, it must understand:

• Who a business is

• How it is connected to other entities

• Whether information can be trusted

• What actions should be taken next

This is where AI and data become inseparable.The rise of frameworks such as Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol is accelerating this trend. These frameworks create standardised ways for AI agents to access data sources, exchange information, and coordinate actions across systems.

As these ecosystems mature, organisations will increasingly rely on trusted business data to fuel intelligent workflows.

Dun & Bradstreet helps provide that foundation.

With intelligence on more than 650 million businesses globally, anchored by the D-U-N-S^® Number, Dun & Bradstreet helps organisations establish trusted business identity, resolve entities across systems, and provide the context needed for AI agents to operate with confidence.

Below are seven practical ways organisations can use Dun & Bradstreet data to power agentic AI workflows.

1. Supercharge Prospecting

   Agentic AI thrives on context, and so does prospecting.

   By integrating Dun & Bradstreet's firmographic data, including company size, industry, location, ownership structure, and growth indicators, AI agents can identify high-potential prospects, prioritise accounts, segment markets, and draft personalised outreach strategies.

   Instead of spending time researching accounts, sales teams can focus on building relationships and closing opportunities.

2. Benchmark Competitors and Monitor Market Shifts

   Competitive intelligence is often fragmented across multiple sources.

   With access to Dun & Bradstreet data through agentic workflows, AI agents can monitor leadership changes, new locations, company expansions, acquisitions, and market developments.

   The result is a more dynamic view of competitors and emerging market opportunities, helping organisations stay ahead of change.

3. Automate Supplier and Partner Risk Assessment

   Supplier risk management is becoming increasingly complex as organisations operate across larger and more interconnected ecosystems.

   By combining internal business data with Dun & Bradstreet risk indicators, AI agents can continuously assess supplier stability, identify potential vulnerabilities, and recommend mitigation actions.

   This creates a more proactive and scalable approach to risk monitoring while reducing manual review processes.

4. Improve Data Stewardship and Master Data Management

   Quality AI starts with quality data.

   Many organisations spend significant time matching records, removing duplicates, reconciling systems, and maintaining master data.

   Agentic AI can streamline these activities by leveraging Dun & Bradstreet's entity resolution capabilities and the
   D-U-N-S^® Number to identify, match, cleanse, and enrich records automatically.

   The result is cleaner data, stronger governance, and better downstream analytics.

5. Enhance Sales Enablement with Contextual Insights

   Imagine an AI assistant that not only answers questions but also provides recommendations.

   By embedding Dun & Bradstreet data into sales workflows, AI agents can surface prospect signals, identify changes within accounts, recommend next-best actions, and provide contextual insights during customer engagements.

   This allows sales teams to spend less time searching for information and more time acting on it.

6. Accelerate KYC and Regulatory Reviews

   For regulated industries, verification and transparency are critical.

   Agentic AI workflows supported by Dun & Bradstreet data can help automate Know Your Customer (KYC) processes by verifying business identity, identifying beneficial ownership, monitoring organisational changes, and improving relationship transparency.

   This helps improve consistency while reducing the time and effort required for compliance reviews.

7. Identify White Space Opportunities

   Growth opportunities are often hidden within existing markets, customer bases, and geographies.

   By combining internal performance data with Dun & Bradstreet market intelligence, AI agents can identify untapped opportunities, emerging segments, and underpenetrated markets.

   This helps organisations prioritise resources more effectively and focus on opportunities with the highest growth potential.

Agentic AI systems are only as effective as the data and context that support them.

As organisations scale enterprise AI, the need for trusted business identity, connected data, and explainable decision-making will continue to grow.

The D-U-N-S^® Number, together with Dun & Bradstreet's business intelligence, provides the verification layer that helps organisations reduce uncertainty, improve trust, and enable AI agents to operate with greater confidence.

When trusted business data is integrated into agentic workflows, organisations can move beyond experimentation and deploy intelligent systems that help anticipate needs, mitigate risk, uncover opportunities, and drive growth.

As part of the D&B.AI™ portfolio, organisations can access Dun & Bradstreet data through MCP-enabled services and leading AI ecosystems.

Businesses can leverage trusted business identity, commercial intelligence, and entity resolution capabilities to power AI agents across sales, marketing, risk, compliance, and data stewardship workflows.

Because in the age of agentic AI, success depends not only on the intelligence of the agent, but on the quality and trustworthiness of the data behind it.

Trusted Context. Confident Decisions. Powered by D&B.AI.