As we move through 2026, enterprises are pouring resources into artificial intelligence, with nearly all reporting active initiatives. Yet a stark disconnect has emerged: the vast majority lack the data infrastructure needed to scale AI beyond isolated experiments. According to Dun & Bradstreet's latest AI Momentum Survey, 97% of organizations have AI projects underway, but only 5% consider their data truly ready to support them. This gap is critical because scaling AI from pilot to production demands clean, governed, and interoperable data. Below, we unpack eight essential insights from the survey, revealing where enterprises are succeeding, where they're struggling, and what it takes to move from experimentation to operationalization.
1. AI Adoption Is Nearly Universal, but Data Readiness Lags
The survey paints a picture of near-total AI engagement: 97% of organizations are actively investing in AI. However, the same survey reveals that only 5% have data that is ready to support these efforts. This massive gap underscores a fundamental truth—launching a pilot or a simple copilot is easy, but scaling AI across mission-critical workflows demands a solid data foundation. Without it, even the most sophisticated models cannot deliver reliable, consistent results. As Cayetano Gea-Carrasco, chief strategy officer at Dun & Bradstreet, notes, enterprise-wide AI-ready data is not needed for experiments, but it is essential for scaling into production systems like compliance, risk management, and customer operations.
2. Most Enterprises See Some Return on Investment—But It's Uneven
Well over half (67%) of organizations report seeing at least early signs of ROI from their AI projects, and 24% claim broad or strong returns. This is a notable improvement from a year ago, yet the returns remain inconsistent. The fact that two-thirds are still in the 'early signs' phase suggests that while AI is delivering value, many enterprises have not yet cracked the code for large-scale impact. The gap between early returns and full operationalization is partly due to data challenges—companies that can't integrate or govern their data find it difficult to move beyond isolated use cases.
3. Investment in AI Continues to Ramp Up
Despite the data hurdles, enthusiasm for AI is not waning. More than half (56%) of the 10,000 businesses polled plan to increase their AI investment over the next 12 months. Additionally, 30% are scaling AI into production, and 26% are operationalizing it across multiple core processes. This shows a strong commitment to moving beyond experimentation. However, the survey also warns that concerns around data readiness are even more pronounced now than in 2025, suggesting that simply throwing more money at AI without fixing data infrastructure may lead to disappointing results.
4. Data Access Remains a Major Obstacle
Half of all organizations (50%) report problems with access to data. This is the most commonly cited data challenge. Without easy, reliable access to the right data, AI models cannot be trained or used effectively. Enterprises often have data siloed across departments, legacy systems, or even external partners, making it difficult to pull together a comprehensive view. This access problem is closely tied to integration issues—38% of respondents mention lack of integration across systems as a barrier. Solving data access requires both technical solutions and organizational changes to break down silos.
5. Privacy, Compliance, and Quality Concerns Are Widespread
Privacy and compliance risks are a top worry for 44% of enterprises, while 40% point to data quality and integrity concerns. These issues are particularly acute in regulated industries like finance and healthcare, where errors can lead to fines or reputational damage. When AI is deployed in production—say, for risk management or customer onboarding—any flaw in data quality can cascade into serious business consequences. The survey also finds that only 10% of organizations have high confidence in their ability to identify and mitigate AI-related risks, highlighting a dangerous blind spot.
6. Talent Shortage Compounds Data Challenges
Over a third (37%) of organizations cite a shortage of qualified AI professionals as a barrier. This talent gap makes it harder to design, implement, and maintain the data pipelines and governance frameworks needed for scalable AI. Even when enterprises have access to data, they may lack the expertise to clean, label, and manage it properly. The shortage is especially acute for roles that combine data engineering with AI knowledge, which are critical for bridging the gap between raw data and production-ready models.
7. Pilots Are Easy; Production Is the Real Test
Enterprises often find it straightforward to launch copilots, chat interfaces, or departmental AI tools using general-purpose models. These can yield impressive results in controlled environments. But as Gea-Carrasco points out, far fewer organizations are able to deploy AI into production workflows where accuracy, accountability, explainability, interoperability, and consistency directly impact business decisions. Areas like onboarding, compliance, risk management, and customer operations demand a level of data readiness that most enterprises currently lack. This is where the 5% figure becomes most relevant—those that have mastered their data can scale AI with confidence.
8. Data Readiness Is the True Differentiator for AI Success
The survey's key takeaway is that the question is no longer whether organizations are experimenting with AI, but whether they have the data and infrastructure to deploy AI reliably at enterprise scale. The 5% of organizations with data-ready environments are positioned to outpace competitors, while the rest risk hitting a wall as they try to scale. To join that 5%, enterprises must invest in data governance, integration, quality, and security—essentially, the unglamorous but critical groundwork that makes AI actually work in the real world.
In conclusion, the AI momentum is undeniable, but the path to true enterprise-wide AI runs through data readiness. While nearly every organization is investing, only a handful have the data foundation to scale. The gap is not insurmountable, but it requires deliberate focus on the four Ds: data access, data quality, data governance, and data talent. Without addressing these, the AI revolution will remain a series of impressive demos rather than a transformative force for business operations. The message from the Dun & Bradstreet survey is clear: get your data ready, or get left behind.