NTT DATA has released its 2026 Global AI Report: A Playbook for Private and Sovereign AI, examining how tightening data sovereignty requirements are exposing infrastructure limitations across enterprise AI deployments. The report focuses on the growing operational gap between organizations redesigning AI systems around jurisdictional control, security, and locality requirements, and enterprises attempting to scale AI on architectures built for unrestricted data movement across clouds and regions.
The research positions sovereign AI as an infrastructure and governance challenge rather than a model-performance issue. According to the findings, enterprises are increasingly constrained by where data can reside, where AI workloads can run, and how models are governed under national or regional regulations. This shift is forcing organizations to reconsider centralized cloud architectures that depend on continuous cross-border data flows.
The report draws on research involving nearly 5,000 senior decision-makers across more than 30 markets, spanning multiple industries and regions. More than 95% of respondents said private and sovereign AI are important to their organizations, yet only 29% said sovereign AI initiatives are being prioritized in a concrete near-term way.
NTT DATA distinguishes private AI from sovereign AI in operational terms. Private AI is focused on protecting enterprise data and limiting exposure through controlled environments and access restrictions. Sovereign AI extends those requirements into jurisdictional compliance, ensuring AI systems, infrastructure, and operational environments remain aligned with regional regulatory frameworks and data residency obligations.
The findings suggest enterprises are struggling to operationalize these requirements at scale. Around 35% of Chief AI Officers surveyed identified the complexity of building and managing AI systems in private or sovereign environments as the primary barrier to adoption. Nearly 60% of AI leaders cited cross-border data restrictions as a major challenge, while only 38% reported strong confidence in their existing cloud security posture.
The report argues that enterprise AI has reached a stage where infrastructure design has become as important as model capability. Rather than focusing exclusively on training performance or inference efficiency, organizations are being forced to redesign around compute placement, localized data access, and governance controls. Data jurisdiction is increasingly shaping architectural decisions, including workload placement, cloud strategy, and partner ecosystems.
NTT DATA also identifies a widening competitive divide between organizations redesigning early for sovereign AI and those delaying infrastructure changes. According to the report, enterprises aligning governance, infrastructure, and operating models earlier are moving more quickly from pilot programs to scaled production deployments. Others remain constrained by legacy architectures that were not designed for locality controls or regulated AI operations.
The research further highlights the growing complexity of enterprise AI ecosystems. More than half of respondents cited integration complexity as a major challenge, particularly as organizations attempt to coordinate cloud providers, infrastructure vendors, AI platforms, governance tooling, and security controls within sovereign operating models.
The report reflects a broader shift underway across enterprise AI adoption, particularly in regulated and data-sensitive industries. As governments introduce stricter controls around data handling and AI operations, sovereignty requirements are becoming a core infrastructure consideration rather than a compliance afterthought. For enterprises operating across multiple jurisdictions, AI deployment strategies are increasingly tied to localized governance, cloud segmentation, and operational control over data movement.