AI is increasingly delivering demonstrable results. The latest AI Momentum Survey by Dun & Bradstreet shows that more than three-quarters of organizations now achieve measurable returns from AI. At the same time, only 6% say their data is fully ready to support AI at scale.
The survey is conducted every quarter among 10,000 companies in 32 countries. This clearly shows how quickly AI is developing, but also where growth is starting to encounter obstacles. Returns are increasing sharply, while the AI-readiness of the underlying data is barely keeping pace.
AI returns rise sharply in a single quarter
In the previous edition, 60% of organizations reported at least some measurable return from AI. One quarter later, that figure has risen to more than 75%. The share of organizations achieving broad or strong returns across multiple AI projects also increased, from 24% to 28%.
AI is therefore increasingly being assessed based on the value it actually delivers. However, much of the return is still limited to specific applications and pilot projects. A successful application within one team does not automatically mean that the same value can be achieved across multiple processes.
Interesting read: The next phase of AI: From experimentation to ROI
More organizations are scaling AI
Organizations are also moving AI into production more frequently. The share of organizations actively scaling AI rose from 30% to 34% in a single quarter. More than half are now scaling AI, using it across multiple core processes, or working with agentic AI applications.
This places greater demands on the underlying data. A clearly defined pilot can often still work with a single dataset and additional checks. When scaling, however, AI must be able to reliably combine information from different systems and processes. Customers, suppliers, and corporate relationships must be identified consistently across all of them.
Interesting read: Agentic AI: from hype to practical reality
The AI readiness of data is lagging behind
While returns and scaling are increasing, data readiness is barely keeping pace. Of the organizations surveyed, 47% describe their data as partially ready for AI and 36% as largely ready. Only 6% have data that is fully ready to support AI at scale, compared with 5% one quarter earlier.
This limited increase shows that AI maturity and data maturity do not automatically progress at the same pace. An organization can achieve results relatively quickly with a targeted application. Cleaning, connecting, and keeping business data up to date across multiple systems and departments requires more effort.
As AI is scaled further, data issues also have a greater impact. Duplicate company records, outdated information, and missing corporate relationships no longer affect just one analysis, but multiple processes and decisions at the same time.
What is needed to sustain returns from AI?
The quarterly figures show that AI is delivering value increasingly quickly, but also that the underlying data does not always keep pace with this development. This becomes particularly apparent as AI is scaled further. AI must not only have access to data, but also understand which legal entity it relates to, how organizations are connected, and whether the information being used is still up to date.
That is why AI readiness is not about having perfect data, but about building a data foundation that grows alongside the role of AI within the organization. Within the D&B.AI Ecosystem ,we combine reliable business data, technology, and advisory services to support AI applications with up-to-date, verified business information and the right business context.
Would you like to discuss your organization’s AI readiness and what is needed to scale AI further? Schedule a conversation with our AI experts.
