Dun & Bradstreet Finds 97% of Enterprises Back AI as Only 5% Have Data Ready
Updated
Updated · Computerworld · May 13
Dun & Bradstreet Finds 97% of Enterprises Back AI as Only 5% Have Data Ready
1 articles · Updated · Computerworld · May 13
10,000 businesses surveyed by Dun & Bradstreet showed AI is nearly universal in 2026, yet only 5% said their data is ready to support enterprise-scale deployment.
67% reported early or localized ROI and 24% saw broad or strong returns, but scaling is being blocked by weak data access, quality, integration and governance rather than model performance.
50% cited data access problems, 44% privacy and compliance risks, 40% data quality concerns and 38% poor system integration; only 10% said they can confidently identify and mitigate AI risks.
30% are scaling AI into production and 26% are operationalizing it across multiple core processes, while 56% plan to raise AI spending over the next 12 months.
Agentic AI is entering production in narrowly scoped, supervised workflows such as onboarding, compliance and research, with human oversight still central in regulated industries.
Why do 95% of companies using AI admit their own data is completely unprepared for it?
With AI project failure rates hitting 85%, is a silent data crisis about to derail the AI revolution?
78% of Enterprises Use AI, But Data Readiness Crisis Threatens Scale: Dun & Bradstreet 2026 Report
Overview
In 2026, enterprises are rapidly adopting AI, but many face a major gap in data readiness. This creates a paradox: while enthusiasm for AI is high, organizations struggle with the practical challenges of preparing their data and infrastructure. As a result, the focus has shifted from simply experimenting with AI to questioning whether companies have the foundational capabilities needed for reliable, enterprise-wide deployment. This disconnect between ambition and operational reality highlights that successful AI integration depends not just on interest, but on robust data and infrastructure, making data readiness a critical hurdle for scaling AI.