Speed is winning the AI race in EMEA, but at the cost of control, visibility, and clarity on compliance.
A new study commissioned by Veeam Software suggests that many organizations across EMEA are accelerating AI deployments even as they lose visibility into the data powering those systems.
The research found that while 99% of enterprise decision-makers consider data sovereignty important, nearly three-quarters of organizations prioritize AI initiatives over sovereignty efforts. As a result, AI-related data has emerged as the largest visibility challenge facing enterprises, with 40% of respondents identifying data used for AI and analytics as their biggest operational blind spot.
The findings come as AI adoption reaches mainstream enterprise scale. According to Veeam's broader research, 88% of organizations are already operating AI agents, yet only 7% believe they are fully prepared to manage them.
That gap between deployment and governance is creating growing concerns around compliance, security, and operational oversight.
“Organizations across EMEA are accelerating AI adoption, recognising its potential to drive innovation and growth,” said Tim Pfaelzer, general manager and senior vice president for EMEA at Veeam. “But many now face a critical trade off: move quickly with AI without fully understanding, protecting and managing their data, or slow progress to meet sovereignty requirements.”
Regional approaches differ, but visibility challenges persist
The research highlights significant differences in how organizations across the region approach data sovereignty and AI adoption.
In the UK, risk reduction remains the primary motivation for sovereignty initiatives. More than half of respondents said preventing data breaches is the main driver behind their efforts. Yet UK organizations also reported the highest AI-related visibility concerns in Europe, with 45% identifying AI and analytics data as their largest blind spot.
Germany appears to be taking a more aggressive innovation-first approach. The study found that 82% of German decision-makers prioritize accelerating AI development over implementing stronger data controls, underscoring the pressure many organizations face to remain competitive in the AI race.
France presents a different picture. Rather than focusing primarily on sovereignty itself, French organizations are more concerned with protecting intellectual property and sensitive business information, reflecting the priorities of innovation-driven industries.
Meanwhile, organizations across the Middle East and Africa reported the highest level of maturity in implementing sovereignty strategies, with 60% saying their programs are fully operationalized. However, the region also reported the greatest dependence on third-party vendors and ecosystems, creating additional complexity around data governance and oversight.
Compliance still drives action
Although data sovereignty is widely viewed as strategically important, the research suggests many organizations remain reactive rather than proactive.
Reducing the risk of data breaches and gaining greater control over data ranked among the leading motivations for sovereignty efforts. However, action is most often triggered by compliance audits, regulatory reviews, or expansion into new markets rather than long-term governance planning.
The findings also reveal a growing knowledge gap around emerging regulations. While organizations expressed strong confidence in established frameworks such as GDPR, their understanding of newer requirements, including the EU AI Act, was considerably lower.
That disconnect could become increasingly problematic as regulators introduce stricter oversight of AI systems and the data used to train and operate them.
Data control is falling behind AI adoption
The findings highlight a growing tension facing enterprise leaders: balancing the urgency to deploy AI with the need to maintain control over increasingly complex data environments.
For many organizations, AI promises competitive advantages through automation, efficiency, and innovation. However, deploying AI without clear visibility into underlying data assets may increase exposure to regulatory penalties, security incidents, and operational risks.
The challenge is particularly relevant as governments introduce new AI governance frameworks that place greater emphasis on transparency, accountability, and data management practices. Organizations that prioritize AI speed today may gain short-term advantages, but they could also face higher compliance costs and remediation efforts later if governance controls fail to keep pace.
The emerging AI trust gap
The research points to a broader trend across enterprise technology strategies. Companies no longer view AI as an experimental technology; they see it as a business imperative. That urgency is driving faster deployments, but governance frameworks are evolving more slowly.
What makes the findings notable is not that organizations value data sovereignty less. On the contrary, nearly every respondent acknowledged its importance. The issue is execution. Businesses understand the risks but are increasingly willing to accept them in pursuit of AI-driven growth.
This creates what industry observers often describe as a trust gap: organizations are deploying powerful AI systems faster than they can fully govern the data, infrastructure, and third-party ecosystems supporting them.
As AI becomes embedded across business operations, the companies most likely to succeed may not be those that deploy the fastest, but those that can combine innovation with visibility, governance, and operational control.
Also read: The EU’s tech sovereignty package targets cloud, chips, and AI infrastructure as Europe tries to reduce dependence on US and Chinese technology suppliers.