Nine in ten organisations in Asia Pacific say private and sovereign artificial intelligence are important to their strategy — yet only 30% are taking concrete near-term steps to implement sovereign AI, according to new research from NTT DATA. The findings reveal a widening gap between ambition and execution that could leave regional enterprises exposed as data governance requirements tighten globally.

NTT DATA’s 2026 Global AI Report, subtitled A Playbook for Private and Sovereign AI, draws on two studies covering nearly 5,000 senior decision-makers across more than 30 markets and five regions. The Singapore-issued research focuses on the structural barriers constraining enterprise AI deployment in APAC.

A 64-point gap between intent and action

The APAC data is stark. While 94% of regional respondents affirm the strategic importance of private and sovereign AI, only 30% have prioritised sovereign AI in a concrete, near-term way — a 64-percentage-point gap. Nearly two-thirds cite cross-border data restrictions as a major challenge to AI adoption, the highest operational friction point in the region. Only 40% of APAC organisations report high confidence in their cloud security posture, which NTT DATA identifies as a foundational requirement for any private or sovereign AI environment.

Thirty per cent of APAC CIOs and CTOs identified building, integrating, and managing complex AI models in private or sovereign environments as their single greatest barrier to adoption — ranking ahead of budget constraints, talent shortages, and regulatory uncertainty.

Infrastructure, not models, is the competitive frontier

The report argues that the dominant narrative around AI model capability is obscuring a more consequential shift. Enterprise AI is outgrowing the centralised, borderless architectures it was built on, and organisations that redesign early are gaining measurable advantages in readiness and scale. Those that continue layering AI onto legacy infrastructure risk being unable to convert AI ambition into durable business value.

NTT DATA identifies five defining shifts: the constraint moving from model performance to infrastructure control; data jurisdiction becoming a core architectural parameter; widespread acknowledgement of the shift without corresponding action; early movers pulling decisively ahead; and the paradox that greater AI control increases ecosystem interdependency and integration complexity.

“The organisations 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.” — Abhijit Dubey, CEO and Chief AI Officer, NTT DATA, Inc.

The full report is available via the NTT DATA global campaign page.