AI is forcing a redesign of enterprise architecture and infrastructure
For years, progress in AI meant better models. Today, the primary challenge to progress is the layer underneath: infrastructure. Enabling private and sovereign AI requires significant changes. Systems built for centralized, borderless data flows are struggling to support AI that must run in controlled, and increasingly localized, highly jurisdictional environments.
The 2026 Global AI Report: A Playbook for Private and Sovereign AI is the next in a series of content based on our global research.*
Five themes have emerged from our analysis:
- AI is running into a wall — and it’s not the model
- Data jurisdiction is becoming an architectural constraint
- Everyone sees the shift, but few are acting on it
- Leaders redesign early and move decisively, creating competitive divergence
- Private and sovereign AI sound like independence but are built on tightly orchestrated ecosystems
The playbook explores:
- The role of geopolitics and the need for greater data control
- Factors influencing organizations’ AI infrastructure choices
- Why legacy infrastructure is an ever-present constraint
- Why building private and sovereign AI requires outside assistance
- How a cohort of AI leaders is succeeding with private and sovereign AI-first approaches
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* Research findings are sourced from data sets via online questionnaires that ran in September and October 2025. Research was conducted in two phases, drawing on a combined sample of nearly 5,000 senior decision‑makers across more than 30 markets and a dozen industries.
FAQ
What is NTT DATA’s global research?*
All content in this playbook is based on independently sourced research data. Two phases of research were conducted in September and October 2025. Participants were prescreened and selected via random sampling on the basis that they had decision-making authority or influence on their organization’s AI and/or technology strategy. The research spans more than 30 markets in 5 regions, across more than a dozen industries, and is based on a combined sample of nearly 5,000 executives and senior leaders.
What is private and sovereign AI?
Private AI refers to systems built in controlled environments to safeguard sensitive data, models and operations. Sovereign AI emphasizes alignment with national or regional jurisdictions, ensuring infrastructure, data and governance remain under local control. While they often overlap, organizations may pursue one without the other. Our research shows that virtually every enterprise is evaluating these approaches; about one third are succeeding in building what they need.
Who should read this playbook?
This playbook benefits boards, CXOs, business and IT executives, AI practitioners, IT strategists, and anyone with influence over the future of enterprise operations. The insights derived from our global research reveal successful tactics for adopting private and sovereign AI.
Key findings
Recognition of the need for private and sovereign AI is nearly universal. However, the gap between what AI now requires and what existing infrastructure can deliver is widening.
- 95% of organizations say sovereign or private AI is important to their AI strategy.
- 35% of Chief AI Officers say enabling private and sovereign AI is their top barrier to adoption, often requiring significant changes to their existing infrastructure.
- Most AI leaders — nearly 60% — already cite cross-border data restrictions as a major challenge.
- Only one third (29%) of organizations are prioritizing sovereign AI in a concrete, near-term way.
- More than half of respondents (51%) list integration complexity in hybrid environments as a top challenge when running AI workloads in private environments.
- Only 38% of organizations feel highly confident in their cloud security posture — a critical foundation for private and sovereign AI.
- AI leaders embed sovereignty as a core design principle. They modernize infrastructure alongside AI investment, integrate governance early, and reassess hyperscaler relationships to preserve flexibility and control. This approach is delivering stronger revenue growth and better margins.
- AI has entered a new phase. Architecture now matters as much as algorithms. Sovereignty is no longer a constraint on innovation; it is becoming the foundation for trusted, scalable and resilient AI, and a defining factor in long-term business success.
Key insights into AI leaders
more likely to take a sovereign approach
say sovereign/private is extremely important to AI strategy
see sovereign driving competitive advantage
Understand the implications for industries and regions
The 2026 Global AI Report: A Playbook for Private and Sovereign AI draws on insights from nearly 5,000 senior executives across more than 30 markets and more than a dozen industries, including automotive, manufacturing, banking, insurance and more. Explore the unique challenges and opportunities for private and sovereign AI for different regions and industries.
Access the full report or read the press release to learn more.
Additional insights for business leaders
Infrastructure
is becoming the primary challenge to AI innovation and advantage.
AI leaders
treat sovereignty as a core design principle and have stronger revenue and growth margins.
Advantage
depends on designing localized AI environments with rapid, secure data flows.
Success
relies on complex, tightly orchestrated multiprovider AI ecosystems.
Additional insights for AI practitioners
Legacy infrastructure
remains the greatest barrier to deploying AI at scale.
AI ecosystem orchestration
is more than simply assembling technologies.
Hybrid models
dictate where sensitive data resides, and intelligence stays in controlled environments.
Design dimensions
overlap territory, operations, technology, and legal and regulatory requirements.
Get your copy of the playbook
Learn how organizations are designing AI for trust, control and resilience. Get your copy of the playbook and accelerate your private and sovereign AI strategy.
