NTT DATA Finds AI Growth Limited by Data Sovereignty Rules

JAKARTA, Jakartaweekly.com — Growing demands for data privacy, sovereignty, and tighter governance are reshaping how enterprises deploy artificial intelligence, according to new research released by NTT DATA.

The company’s latest study, 2026 Global AI Report: A Playbook for Private and Sovereign AI, found that many organizations are struggling to adapt legacy infrastructure to support modern AI systems amid increasingly complex regulatory environments and cross-border data restrictions.

The report highlights how enterprise AI is rapidly outgrowing architectures originally designed for centralized and borderless data flows. As governments and regulators tighten rules around data localization, organizations are being forced to redesign systems to ensure data remains protected and processed within approved jurisdictions.

According to the report, more than 95% of respondents acknowledged the importance of private and sovereign AI. However, only 29% said they are prioritizing sovereign AI initiatives in a concrete and near-term way.

The findings indicate a widening gap between companies that are proactively redesigning infrastructure and governance frameworks for AI, and those still attempting to integrate AI into systems not built for modern data sovereignty requirements.

“AI is exposing the limits of traditional enterprise architectures,” said Abhijit Dubey, CEO and Chief AI Officer, NTT DATA, Inc. “Organizations that are succeeding are treating infrastructure, governance, and security as strategic priorities rather than secondary considerations.”

The report distinguishes between private AI and sovereign AI. Private AI focuses on protecting enterprise data, limiting exposure, and controlling access to sensitive information. Sovereign AI, meanwhile, emphasizes compliance with national or regional regulations governing where data is stored, processed, and managed.

The research found that nearly 60% of AI leaders cited cross-border data restrictions as a major challenge in scaling AI adoption. In addition, about 35% of Chief AI Officers identified the complexity of building and managing AI models in private or sovereign environments as the top barrier to deployment.

Security readiness also remains a concern. Only 38% of organizations surveyed reported having high confidence in their cloud security posture, despite cloud infrastructure serving as a critical foundation for secure AI implementation.

The report outlines five major shifts shaping the next phase of enterprise AI adoption.

First, organizations are realizing that AI limitations are no longer driven solely by model performance. Instead, challenges increasingly stem from infrastructure issues such as compute control, data access, security, and geographic restrictions.

Second, data jurisdiction is becoming a key architectural constraint. AI systems rely on continuous data movement and access, but growing regulatory requirements are determining where data can reside and where models can operate.

Third, while awareness around private and sovereign AI is high, concrete implementation remains limited. Many organizations recognize the importance of sovereignty but have yet to allocate resources or redesign systems accordingly.

Fourth, companies moving early to redesign infrastructure and governance are gaining competitive advantages. These organizations are reportedly progressing faster from pilot projects to full-scale AI deployment, while competitors struggle with outdated systems.

Finally, the report notes that despite the goal of achieving greater independence and control, private and sovereign AI environments often require highly coordinated ecosystems of partners and technology providers. More than half of surveyed organizations identified integration complexity as their biggest operational challenge.

NTT DATA stated that organizations operating in highly regulated, distributed, and data-sensitive industries are likely to face increasing pressure to adopt private and sovereign AI strategies in the coming years.

The report was based on two separate studies involving nearly 5,000 senior decision-makers across more than 30 markets, five regions, and over a dozen industries globally.