AI Investment Surges as Data Readiness Lags: Only 5% of Enterprises Prepared
AI Investment Soars, But Data Readiness Drops to 5%
A new survey from Dun & Bradstreet reveals that 97% of organizations now have active AI initiatives, yet a mere 5% deem their data ready to support them. This stark contrast highlights the widening gap between ambition and infrastructure, as enterprises rush to deploy AI at scale.
"You do not need enterprise-wide AI-ready data to launch pilots or isolated AI use cases," said Cayetano Gea-Carrasco, chief strategy officer at Dun & Bradstreet. "But you do need it to scale AI reliably across mission-critical workflows and systems."
Early Returns Uneven, Data Hurdles Persist
Despite the data readiness crisis, 67% of the 10,000 businesses surveyed report early signs of ROI from AI, and 24% cite broad or strong returns. Over half (56%) plan to increase AI investment in the next year, while 30% are scaling AI into production and 26% are operationalizing it across multiple core processes.
However, concerns around data readiness are "even more profound" than in 2025, according to Dun & Bradstreet. Key barriers include access to data (50%), privacy and compliance risks (44%), data quality and integrity (40%), lack of system integration (38%), and a shortage of qualified AI professionals (37%). Only 10% of enterprises express high confidence in identifying and mitigating AI-related risks.
"The key question is no longer whether organizations are experimenting with AI," Gea-Carrasco emphasized. "It's whether they have the data and infrastructure required to deploy AI reliably at enterprise scale."
Background: From Pilots to Production
Enterprises have rapidly moved from experimentation to mission-critical AI deployment. While copilots and chat interfaces yield impressive results in controlled settings, scaling to production workflows—such as onboarding, compliance, risk management, and customer operations—demands clean, interoperable, governed data.
The Dun & Bradstreet AI Momentum Survey, fielded in early 2026, underscores that the initial euphoria around AI is giving way to the messy reality of operationalization. As adoption accelerates, the gap between investment and data readiness widens, threatening to stall progress in the second half of the year.
What This Means: The Data Imperative
For enterprises, the takeaway is clear: AI success at scale hinges on data readiness, not just model sophistication. Without a foundation of reliable, integrated, and compliant data, even the best AI tools will falter in real-world business decisions.
Gea-Carrasco noted that launching pilots is relatively easy, but deploying AI into production workflows—where accuracy, accountability, explainability, and consistency matter—requires a level of data maturity most organizations lack. "That's where data readiness becomes critical," he said.
Companies that invest now in data governance, integration, and quality will be best positioned to capture the full value of AI. Those that neglect this foundation risk falling behind as AI becomes a competitive necessity.