Why 97% of Companies Invest in AI but Only 5% Feel Data-Ready
Nearly halfway through 2026, enterprises are reaping early rewards from artificial intelligence investments. Yet many are discovering that scaling AI from experimental projects to mission-critical workflows hinges on something far less glamorous than cutting-edge models or benchmark scores: clean, interoperable, and governed data.
According to a new AI Momentum Survey from Dun & Bradstreet, a striking 97% of organizations report active AI initiatives. However, only 5% say their data is ready to support those initiatives effectively. This gap underscores the messy reality of AI adoption as businesses struggle to move beyond pilots into full-scale operationalization.
“You do not need enterprise-wide AI-ready data to launch pilots or isolated AI use cases,” explained Cayetano Gea-Carrasco, chief strategy officer at Dun & Bradstreet. “But you do need it to scale AI reliably across mission-critical workflows and systems.”
The AI Investment Surge: Early Returns Are Real but Uneven
Organizations have embraced AI as a strategic imperative in 2026. The survey—which polled 10,000 businesses worldwide—reveals that over two-thirds (67%) are already seeing “early signs or pockets” of return on investment, while 24% report “broad or strong” returns. Just a year ago, comparable figures were notably lower, indicating rapid adoption and a growing comfort level with the technology.
Nearly three-quarters of companies (56%) plan to increase AI spending in the next 12 months. Around one-third (30%) are scaling AI into production environments, and 26% have operationalized AI across multiple core business processes. Yet these early gains remain uneven, and the survey warns that concerns about data readiness are “even more profound” than in 2025.
Key Data Challenges Holding Enterprises Back
What exactly is blocking progress? The survey highlights five primary obstacles:
- Access to data – reported by 50% of respondents
- Privacy and compliance risks – 44%
- Data quality and integrity issues – 40%
- Lack of integration across systems – 38%
- Shortage of qualified AI professionals – 37%
These data hurdles are compounded by a worrying lack of confidence in risk management. Only 10% of enterprises say with high certainty that they can identify and mitigate AI-related risks. Given that AI decisions increasingly affect compliance, onboarding, risk management, and customer operations, this risk gap poses a serious threat to safe deployment.
The Data Readiness Imperative: Scaling from Pilots to Production
The disconnect between AI investment and data readiness stems from a fundamental reality: launching a copilot or a departmental chatbot is relatively easy. General-purpose models can produce impressive results in a controlled environment with limited datasets. But moving AI into production workflows—where accuracy, accountability, explainability, interoperability, and consistency directly impact business outcomes—requires a solid data foundation.
Gea-Carrasco emphasized this point: “The key question is no longer whether organizations are experimenting with AI. It’s whether they have the data and infrastructure required to deploy AI reliably at enterprise scale.”
Moving Beyond Copilots to Autonomous Operations
As enterprises progress from basic copilots toward more autonomous AI agents, the demands on data become even steeper. Autonomous systems must operate without human oversight, which means they need access to high-quality, governed data that is integrated across silos. Without that, even the most advanced models can produce unreliable outputs that erode trust and expose the organization to risk.
To bridge the gap, companies must invest in data quality, governance, and integration as strategic priorities—not afterthoughts. The 5% who report being data-ready likely have robust data management practices in place, including clear ownership, standardized formats, and automated compliance checks. For the rest, the path to AI maturity runs through data readiness.
Conclusion: The Data-First Path to AI Success
The Dun & Bradstreet survey delivers a clear message: enterprises are all-in on AI, but enthusiasm alone cannot substitute for data preparedness. Without deliberate efforts to clean, govern, and integrate data, even the most promising pilots will stall before they reach scale. Organizations that tackle the data hurdle head-on will be best positioned to realize the full potential of AI—transforming it from a promising experiment into a reliable, value-generating engine for the enterprise.