10 Critical Insights on Enterprise AI Adoption and Data Readiness in 2026

As we speed through 2026, enterprises are pouring billions into artificial intelligence, with nearly every organization running some form of AI initiative. Yet a stark reality check emerges from the latest Dun & Bradstreet AI Momentum Survey: while 97% of companies have active AI projects, only 5% believe their data is truly ready to support those efforts. This gap between ambition and operational reality defines the current state of enterprise AI—a landscape of early victories tempered by profound data challenges. Below, we unpack the ten most important findings from the survey, revealing what it takes to move from experimentation to reliable, scaled AI deployment.

1. AI Investment Is Universal, But Data Readiness Lags

Nearly every enterprise—97%—now reports active AI initiatives, according to the D&B survey of 10,000 businesses. However, only a meager 5% say their data is prepared to support these initiatives. This disconnect underscores a fundamental hurdle: organizations can launch pilots or narrow use cases with general-purpose models, but scaling AI into mission-critical workflows demands clean, interoperable, and well-governed data. As Cayetano Gea-Carrasco, D&B’s chief strategy officer, puts it, “You do not need enterprise-wide AI-ready data to launch pilots, but you do need it to scale AI reliably.” This first insight sets the stage for understanding why data readiness is the linchpin of AI success.

2. Early Returns Are Emerging, But Unevenly

Despite data challenges, early signs of return on investment are common. The survey reveals that 67% of organizations are seeing “early signs or pockets” of ROI from their AI efforts, while 24% report “broad or strong” returns. This marks progress compared to a year ago, yet the gains remain uneven. Many companies are still in the pilot phase, achieving isolated wins without widespread impact. The unevenness highlights the gap between controlled experiments and production-grade deployments, where accuracy, consistency, and compliance become non-negotiable. As investments grow, the pressure to convert these pockets of ROI into enterprise-wide value intensifies.

3. The Majority Plan to Increase AI Spending

Confidence in AI’s potential is driving further investment. More than half (56%) of surveyed enterprises say they will increase their AI budget over the next 12 months. This bullish outlook reflects a belief that AI is not a passing trend but a mission-critical imperative. However, the same survey notes that data readiness concerns are “even more profound” than in 2025, suggesting that simply throwing more money at AI without fixing underlying data problems will lead to wasted resources. Companies must align spending with data infrastructure improvements to avoid scaling flawed systems.

4. Scaling Into Production Remains a Challenge

While many talk about AI, fewer have operationalized it. The survey finds that 30% of enterprises are scaling AI into production, and 26% are integrating it across multiple core processes. That leaves nearly half still in experimentation or pilot stages. Moving from copilots and chat interfaces to production workflows—such as onboarding, compliance, risk management, and customer operations—requires data that is accurate, explainable, and interoperable. Without that foundation, scaling attempts often stall or produce unreliable results, as Gea-Carrasco notes: “That’s where data readiness becomes critical.”

5. Data Access Is the Top Roadblock

When asked about specific data challenges, 50% of enterprises cite difficulties with data access. This means they cannot easily retrieve or use the data they need for AI models. Coupled with privacy and compliance risks (44%) and data quality and integrity concerns (40%), it’s clear that the data ecosystem is fragmented. Many organizations have data siloed across departments, legacy systems, or cloud environments, making it hard to feed AI models consistently. Improving access often requires significant investment in data integration platforms and governance policies.

6. Integration and Talent Shortages Compound Issues

Beyond access, 38% of respondents report a lack of integration across systems, preventing smooth data flow. Additionally, 37% say there is a shortage of qualified AI professionals who can manage both the technology and the data. This talent gap means companies struggle not only to build models but also to maintain the data pipelines that support them. The combination of poor integration and limited expertise creates a bottleneck that slows AI maturity. Addressing both areas—through training, hiring, or partnerships—is essential for progress.

7. Confidence in Risk Mitigation Is Alarmingly Low

Perhaps most concerning, only 10% of enterprises express high confidence in their ability to identify and mitigate AI-related risks. This includes risks like bias, security vulnerabilities, regulatory non-compliance, and model drift. With regulations tightening worldwide, the low confidence level signals a potential crisis waiting to happen. Companies deploying AI at scale without robust risk management frameworks expose themselves to legal and reputational damage. The survey suggests that risk mitigation capabilities must become a priority alongside data readiness.

8. The Shift Beyond Copilots Demands Data Readiness

As organizations move from simple copilots and chat interfaces to more autonomous AI systems, data readiness becomes even more critical. Autonomous AI—such as systems that make decisions in onboarding, compliance, or customer operations—requires not just accuracy but also explainability and consistency. Gea-Carrasco emphasizes that while impressive results are possible in controlled environments, deployment into production workflows where outcomes affect business decisions is a different ballgame. The survey shows that only a fraction of enterprises have the data governance needed for such autonomy.

9. Investment in AI Must Be Paired with Data Infrastructure

The key takeaway from the survey is that AI success hinges on data infrastructure. Without clean, governed, and interoperable data, scaling AI is impossible. Enterprises that rush to deploy models without first addressing data quality, access, and integration will likely see diminishing returns. Those that invest in data lakes, catalogs, governance platforms, and monitoring tools will be better positioned to turn AI pilots into enterprise-wide value. The report urges leaders to treat data readiness as a strategic priority, not an afterthought.

10. The Path Forward: From Experimentation to Operationalization

The ultimate challenge for enterprises is moving beyond experimentation to full operationalization. The survey indicates that while many have started, few have mastered this transition. To succeed, companies must foster a culture of data stewardship, invest in continuous data quality improvement, and build cross-functional teams that include data engineers, AI specialists, and domain experts. The 5% who report data ready today are the trailblazers; the rest must accelerate efforts to close the readiness gap. As AI becomes ubiquitous, data readiness will separate the leaders from the laggards.

In conclusion, the Dun & Bradstreet AI Momentum Survey paints a vivid picture of an industry at a crossroads. Nearly every enterprise is betting on AI, but only a handful have the data foundation to win. The 5% data readiness figure is a wake-up call, reminding leaders that the race is not just about who builds the most advanced models, but who can deploy them reliably. As we look ahead, investment in data infrastructure, talent, and risk management will be the true drivers of AI success. The organizations that take this lesson to heart will not only survive the AI revolution—they will lead it.