Enterprise AI Hits the Wall: NTT DATA Research Reveals Growing Privacy and Sovereignty Barriers

NTT DATA, a global leader in AI, digital business and technology services, today released new research showing that enterprise AI is outgrowing the architecture and infrastructure beneath it as data privacy and sovereignty requirements tighten. The research finds a widening split between enterprises that are redesigning AI for control, locality and security, and organizations still layering AI into environments that were not built to support these requirements.

For years, enterprise architecture moved data across systems, clouds, applications and borders with increasing speed and efficiency. AI is exposing the limits of that model. Sensitive data must be protected, workloads must run inside defined jurisdictions, and models must be governed under tighter controls. Data cannot always move with the speed and fluidity many AI systems expect, making jurisdiction a core architectural constraint. As a result, private and sovereign AI have become critical considerations.

NTT DATA’s 2026 Global AI Report: A Playbook for Private and Sovereign AI reveals a gap between what organizations know they need and what they are ready to build:

• More than 95% of respondents say private and sovereign AI are important, but only 29% are prioritizing sovereign AI in a concrete, near-term way.
• About 35% of CAIOs identify building, integrating and managing complex AI models in private or sovereign environments as their top barrier to adoption, and nearly 60% of AI leaders cite cross-border data restrictions as a major challenge.
• Only 38% report high confidence in their cloud security posture—a critical foundation for both private and sovereign AI.

Private and sovereign AI are related, but distinct. Private AI focuses on protecting sensitive enterprise data, controlling access and limiting exposure. Sovereign AI focuses on ensuring that AI systems, data and operating environments meet jurisdictional, regulatory or national and regional control requirements.

"As AI evolves, private and sovereign approaches are testing enterprise readiness," said Abhijit Dubey, CEO and Chief AI Officer, NTT DATA, Inc. "The organizations that are succeeding are going beyond regulatory compliance and risk mitigation. They are building the operating foundation for AI that can perform across markets, jurisdictions and business environments. Our research shows AI leaders are pulling ahead by treating architecture, infrastructure and governance as strategic requirements."

The report identifies five shifts defining the next phase of enterprise AI:

Together, private and sovereign AI are changing how AI systems are built, governed and scaled. Organizations that redesign early are better positioned in regulated, distributed and data-sensitive environments. Those that layer AI into architectures that were not built for control, locality or data-flow constraints may struggle to turn their AI ambition into durable value.

The report draws on two studies engaging a total of nearly 5,000 senior decision-makers across more than a dozen industries, more than 30 markets and five regions. It is part of NTT DATA's global research series on strategies that separate AI leaders from the market.