Dun & Bradstreet AI Momentum Survey: AI Is Delivering Returns. Is Enterprise Data Ready to Scale Them?

The latest Dun & Bradstreet AI Momentum Survey highlights a critical gap in enterprise AI adoption: 76% of businesses report measurable returns, yet only 6% say their enterprise data is fully ready to support AI at scale. This suggests that while AI is already delivering value, the foundations needed to scale it consistently are still catching up. Based on responses from 10,000 businesses across 32 countries, the survey tracks AI adoption, investment, deployment, data readiness and business outcomes on a quarterly basis.

AI momentum is building, but enterprise readiness is still catching up

The Q3 2026 findings show businesses are increasingly seeing values from AI and more willing to invest in AI projects. However, enterprise readiness is not developing at the same pace. The data, technology, workforce capabilities and controls needed to turn early AI success into consistent enterprise value are still maturing.

Key Findings from the Q3 2026 AI Momentum Survey

• 34% are scaling AI into production, showing continued progress beyond planning and pilots.

• 76% report measurable ROI from AI, including 48% seeing pockets of ROI and 28% reporting broad or strong returns.

• 6% say their enterprise data is fully ready to support AI at scale.
  
  

Together, the findings highlight a defining tension in enterprise AI: businesses are seeing enough value to move forward, but many have not yet established the foundations required to scale that value consistently.

Why Enterprise Data Readiness Is Critical for Scaling AI

As AI pilots move into production, businesses face a more demanding test: extending the value of individual projects consistently across departments and business processes. Yet with only 6% of businesses saying their data is fully ready to support AI at scale, the data-readiness gap is now a key barrier to turning early AI gains into scalable, repeatable enterprise returns.

Scaling AI requires models to work with business information across teams, systems and use cases. Unless that context is consistent, accurate and reliable, businesses may struggle to reproduce successful outcomes across the enterprise, especially as they move towards more complex agentic workflows.

Gary Kotovets, Chief Data and Analytics Officer at Dun & Bradstreet, points to the gap between model capability and the business context needed to translate AI adoption into enterprise-level returns: "The challenge now is that AI adoption has outpaced data readiness. That is why only a few businesses 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.”

Why Trusted Business Context Is Essential for Sustainable AI Value

The Q3 2026 findings present an encouraging picture: AI is producing measurable returns and businesses are moving projects into production. As AI advances further into production, however, the findings also point to the importance of improving enterprise data readiness. Turning early and uneven returns into more consistent enterprise value will require business context that is complete, current and reliable enough to support decisions across teams, systems and use cases.

The next phase will require businesses to turn early and uneven returns into more consistent value at enterprise scale. Model capability alone is not enough. AI also needs complete, current and reliable business context to support decisions across teams, systems and use cases.

Grounding AI in verified information can help provide this context, enabling facts to be confirmed and integrations to be established as businesses move towards agentic AI workflows.

How D&B.AI Provides Trusted Business Data for Enterprise AI

Dun & Bradstreet provides a verified commercial identity foundation for businesses seeking to deploy AI at scale.

The D‑U‑N‑S® Number is a 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 and validated commercial data.

By pre-resolving crucial business context, the D&B Commercial Graph™ can help reduce the need to repeatedly rediscover and reconcile the same commercial information across departments. It gives AI and intelligent agents verified context about businesses and their relationships, helping businesses build greater trust into their AI-enabled workflows.

D&B.AI brings trusted commercial data into widely used AI and data platforms, including Microsoft 365 Copilot, ChatGPT, Claude and Cursor, etc., helping businesses ground models, agents and workflows in verified business context.

Combined with AI-ready data, intelligent workflow agents and structured, agent-ready delivery, these integrations help businesses apply trusted context across functions and work towards more connected, scalable and trustworthy AI outcomes.

Explore D&B.AI: https://www.dnb.com.hk/solution/hot-topics/generative-ai