Growing Data Privacy and Sovereignty Barriers Are Hindering Enterprise AI Progress, According to NTT DATA

MALAYSIA – 4 June, 2026 — As artificial intelligence (AI) continues to transform industries worldwide, enterprises are discovering that the technology’s rapid evolution is exposing fundamental limitations in the digital infrastructure that has supported businesses for decades. According to new research released by NTT DATA, growing demands for data privacy, security and sovereignty are forcing organizations to rethink how AI systems are designed, deployed and governed.

The report, 2026 Global AI Report: A Playbook for Private and Sovereign AI, highlights a growing divide between organizations that are proactively redesigning their AI environments and those attempting to integrate advanced AI capabilities into legacy architectures that were never intended to meet today’s regulatory and operational requirements.

For years, enterprise technology strategies focused on enabling data to move freely across applications, cloud environments and geographical borders. However, the rise of AI is challenging this model. As organizations increasingly rely on sensitive data to train and operate AI systems, concerns surrounding privacy, security and regulatory compliance have become impossible to ignore.

The research reveals that data jurisdiction has become a critical architectural consideration. Businesses must now ensure that data remains within specific legal and geographical boundaries while maintaining compliance with evolving regulations. This shift is driving greater interest in sovereign AI, where AI systems, data and infrastructure operate within defined national or regional control frameworks.

At the same time, private AI solutions are gaining traction as organizations seek greater control over sensitive enterprise information, ensuring that valuable business data remains protected from unauthorized access and external exposure.

While awareness of the importance of private and sovereign AI is nearly universal, implementation remains a significant challenge. NTT DATA’s findings show that more than 95 percent of surveyed organizations recognize the importance of these AI approaches. However, only 29 percent are actively prioritizing sovereign AI initiatives in the near term.

The gap between awareness and action is further highlighted by operational challenges. More than one-third of Chief AI Officers surveyed identified the complexity of building and managing AI models within private or sovereign environments as the biggest obstacle to adoption. Meanwhile, nearly 60 percent of AI leaders cited cross-border data restrictions as a major barrier to scaling AI initiatives.

Compounding these challenges, only 38 percent of respondents expressed strong confidence in their cloud security capabilities, raising concerns about whether existing infrastructure can adequately support the next generation of enterprise AI deployments.

According to NTT DATA CEO and Chief AI Officer Abhijit Dubey, the organizations leading the AI race are looking beyond compliance requirements and focusing on building sustainable operational foundations for future growth.

The report argues that AI performance is no longer determined solely by the sophistication of models. Instead, success increasingly depends on the ability to manage data access, computing resources, governance frameworks and jurisdictional requirements. Organizations that address these foundational issues early are better positioned to deploy AI at scale while maintaining compliance and operational resilience.

The research identifies five major trends shaping the next phase of enterprise AI adoption. First, infrastructure limitations are emerging as a key bottleneck, with many existing systems unable to support the control and locality requirements demanded by modern AI workloads.

Second, data jurisdiction is becoming a defining factor in architectural decision-making, influencing where data is stored, where models are deployed and how AI systems are governed.

Third, although organizations widely recognize the importance of private and sovereign AI, only a minority are taking concrete steps toward implementation.

Fourth, leading organizations are redesigning their infrastructure and governance frameworks early, enabling them to accelerate AI deployment while competitors struggle to adapt.

Finally, despite the perception that private and sovereign AI promote independence, successful implementation relies heavily on collaboration across complex ecosystems of technology partners, cloud providers and infrastructure specialists.

As regulatory scrutiny intensifies and data governance requirements become more stringent, private and sovereign AI are expected to play an increasingly important role in enterprise technology strategies. Organizations that proactively redesign their architectures around control, locality and security are likely to gain a competitive advantage in highly regulated and data-sensitive environments.

Conversely, businesses that continue to layer AI capabilities onto outdated infrastructure may find it increasingly difficult to realize the full value of their AI investments. The message from NTT DATA’s latest research is clear: the future of enterprise AI will be defined not only by innovation, but also by the strength of the foundations that support it.