Computerworld: 5% say data ready

Computerworld reported on May 13 that Dun & Bradstreet’s latest AI Momentum Survey found a sharp gap between enterprise AI ambition and the data foundations needed to run it at scale. Dun & Bradstreet said 97% of organizations now have active AI initiatives, while only 5% say their data is adequately ready to support those efforts reliably at enterprise scale. The survey covered 10,000 businesses globally in the first and second quarters of 2026. Cayetano Gea-Carrasco, Dun & Bradstreet’s chief strategy officer, said the issue is no longer whether companies are experimenting with AI, but whether they have the data and infrastructure to deploy it across mission-critical workflows. ### How wide is the gap between AI activity and AI readiness? Dun & Bradstreet’s May 4 release put the split in simple terms: 97% of organizations reported active AI initiatives, but only 5% said their data was fully ready for AI. The same release said 30% are scaling AI into production and 26% are operationalizing it across multiple core processes. (computerworld.com) Computerworld and CIO, both citing the Dun & Bradstreet survey, reported that many companies are moving from pilots toward broader deployment even as core data problems remain unresolved. Gea-Carrasco said enterprises do not need enterprise-wide AI-ready data to launch pilots or isolated use cases, but they do need it to scale AI reliably across critical systems. (prnewswire.com) ### What are companies saying they are getting back from AI so far? Dun & Bradstreet said 67% of organizations are seeing “early signs or pockets” of return on investment, while 24% reported broad or strong returns. Its May 4 release separately broke that out as 20% with multiple projects delivering ROI and 10% with strong ROI, indicating that gains are emerging but remain uneven across organizations. (computerworld.com) The survey also found that 56% of organizations plan to increase AI investment over the next 12 months. That combination of rising spending and limited readiness suggests companies are continuing to fund deployments while still working through data access, quality and governance constraints, according to the survey findings reported by Computerworld and CIO. (computerworld.com) ### Which data problems are showing up most often? Dun & Bradstreet said 50% of respondents cited limited data access as a leading obstacle, 44% identified privacy and compliance risks, and 40% pointed to data quality and integrity concerns. Another 38% reported a lack of integration across systems, and 37% cited a shortage of skilled AI professionals. (computerworld.com) Computerworld reported that those issues have become more pronounced as adoption has accelerated in 2026. The publication said enterprises are finding that clean, interoperable and governed data matters more than model performance once projects move beyond controlled pilots. ### Why does this become a bigger problem in production than in pilots? (prnewswire.com) Gea-Carrasco said it is relatively easy for enterprises to launch copilots, chat interfaces or departmental AI tools using general-purpose models and get strong results in controlled environments. He said far fewer organizations are able to deploy AI into production workflows, where accuracy, accountability, explainability, interoperability and consistency directly affect business decisions. (computerworld.com) Only 10% of enterprises said they have high confidence in their ability to identify and mitigate AI-related risks, according to Dun & Bradstreet. That figure adds another constraint as companies try to move from experimentation to systems that must be auditable and dependable across departments. (computerworld.com) ### What should readers watch next? May 4 is the date Dun & Bradstreet released the AI Momentum Survey that underpins the Computerworld report, and the company said the results were drawn from Q1 and Q2 2026 responses from 10,000 businesses. Over the next 12 months, 56% of respondents said they plan to increase AI investment, making that spending cycle the next concrete test of whether companies can close the gap between AI activity and data readiness. (prnewswire.com)