Dun & Bradstreet's AI Momentum Survey of 10,000 Businesses Finds Enterprise AI Returns Continue to Advance, But Only 6% Have the Data Ready to Scale Them

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Dun & Bradstreet (DNB) released Q3 2026 results from its global AI Momentum Survey of 10,000 businesses across 32 countries, showing that over three-quarters of enterprises report measurable ROI from AI, but only 6% say their data is fully ready to support AI at scale.

The survey finds 48% see “pockets of ROI” and 28% report “broad” or “strong” ROI across multiple projects. About 34% are scaling AI into production, yet most describe their data as only partially or mostly ready, highlighting a significant data readiness gap for enterprise-wide, repeatable AI returns.

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JACKSONVILLE, Fla., July 28, 2026 /PRNewswire/ -- More than three-quarters of enterprises now report some measurable ROI from AI, according to Dun & Bradstreet's latest AI Momentum survey. The largest share, 48%, report "pockets of ROI" while 28% report "broad" or "strong" ROI across multiple projects. At the same time, only 6% say their enterprise data is "fully ready" to support AI at scale.

The findings suggest that enterprise AI is entering a new phase of maturity, as organizations are increasingly seeing value from AI, but still face a harder challenge in scaling those returns consistently across the enterprise.

That same pattern is visible in how organizations are progressing with AI deployments. More are moving beyond planning and piloting, with the share "scaling AI" rising to 34%. As pilot AI projects move further into production, the central question becomes whether organizations can extend that value across departments. That will depend on closing the data readiness gap.

While the majority of organizations describe their enterprise data as either "partially" (47%) or "mostly" (36%) ready for AI, only 6% say it is "fully ready" to support AI at scale. Realizing the broad ROI across enterprise AI projects will depend on closing the data readiness gap. Models require the business context to be consistent, accurate, and reliable enough to support decisions across teams, systems, and agentic use cases.

"The challenge now is that AI adoption has outpaced data readiness. That is why only a few organizations have turned pilots into P&L-level ROI. Today's frontier models are extremely capable, but getting the context right is the key to effectiveness. Grounding AI in verified information, so facts can be confirmed and integrations can be established, is fundamental to adoption of agentic workflows," said Gary Kotovets, Chief Data and Analytics Officer at Dun & Bradstreet. "That is the opportunity that D&B is built to address. Our identity infrastructure grounds AI in real, verified business context, minimizing hallucinations and giving organizations the confidence to let their agents work for them. The D&B Commercial Graph™ pre-resolves crucial business context for AI, so you don't have to burn tokens rediscovering and reconciling across departments."

Key findings include:

AI ROI is visible, but uneven 

• More than three-quarters of organizations report at least some measurable ROI from AI initiatives.
• The largest share, 48%, report "pockets of ROI," with some pilots showing returns.
• 28% report "broad" or "strong" ROI across multiple projects.

AI deployment is moving into production

• Nearly all organizations report active AI initiatives.
• 34% are scaling AI into production.
• More than half are either scaling AI into production, operationalizing AI across multiple core processes, or using agentic AI workflows.

Enterprise data is not yet fully ready for AI at scale

• Only 6% of organizations report their enterprise data is fully ready to support AI at scale.
• 47% describe their enterprise data as partially ready, while 36% describe it as mostly ready.
• The findings suggest a wide gap between data that can support early AI progress and data that is ready to support scaled, repeatable ROI across the enterprise.

In 2026, Dun & Bradstreet's AI Momentum Survey has shown steady progress in AI adoption and maturity, even as data readiness remains challenged. The Q3 findings suggest the next phase will require organizations to improve data readiness to turn early and uneven ROI into more consistent returns at enterprise scale. That will require confidence in the context behind AI: data that is complete, current, and reliable enough to support decisions across teams, systems, and use cases.

The Dun & Bradstreet AI Momentum Survey is a quarterly global survey of 10,000 businesses across 32 countries that tracks enterprise AI adoption, investment, deployment, and business outcomes over time.

About Dun & Bradstreet

Dun & Bradstreet provides the verified commercial identity foundation for enterprises to deploy AI at scale. The company originated the D‑U‑N‑S^® Number in 1963, now the global standard for identifying commercial entities. Anchored by this identifier, the D&B Commercial Graph structures and connects business identity consistently across systems, enabling AI to operate on accurate, validated data. Since 1841, businesses of every size have relied on Dun & Bradstreet to navigate change and accelerate growth. For more information, visit www.dnb.com.

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SOURCE Dun & Bradstreet, Inc.

FAQ

What did Dun & Bradstreet's 2026 AI Momentum Survey reveal about enterprise AI ROI for DNB?

Dun & Bradstreet reports that more than three-quarters of enterprises now see measurable ROI from AI initiatives. According to Dun & Bradstreet, 48% report “pockets of ROI,” while 28% achieve “broad” or “strong” ROI across multiple projects, indicating expanding but uneven financial benefits.

How many businesses were included in Dun & Bradstreet's AI Momentum Survey cited by DNB in July 2026?

The July 2026 AI Momentum Survey from Dun & Bradstreet covers 10,000 businesses worldwide. According to Dun & Bradstreet, these companies span 32 countries and are tracked quarterly to measure AI adoption, investment, deployment patterns, and business outcomes over time.

What does the Dun & Bradstreet (DNB) survey say about data readiness for AI at scale?

The survey shows only 6% of organizations consider their enterprise data fully ready for AI at scale. According to Dun & Bradstreet, 47% say data is partially ready and 36% mostly ready, underscoring a wide gap between pilot success and scalable, repeatable AI ROI.

How far along are enterprises in scaling AI, according to Dun & Bradstreet's 2026 survey?

According to Dun & Bradstreet, 34% of surveyed organizations are scaling AI into production. The company notes that more than half are either scaling, operationalizing AI across core processes, or already using agentic AI workflows, signaling a shift beyond pilots into broader deployment.

What is the main challenge to enterprise AI maturity highlighted by Dun & Bradstreet (DNB)?

The key challenge is that AI adoption is advancing faster than data readiness across enterprises. According to Dun & Bradstreet, turning early, uneven AI ROI into consistent, enterprise-scale returns requires complete, current, and reliable data to provide accurate business context for AI models and agentic workflows.

How does Dun & Bradstreet position its Commercial Graph in relation to enterprise AI scaling?

Dun & Bradstreet presents its Commercial Graph as infrastructure for verified business identity to support AI. According to Dun & Bradstreet, this graph pre-resolves critical business context, helping minimize AI hallucinations and enabling more confident deployment of agentic workflows across departments and systems.