As artificial intelligence (AI) continues to reshape industries worldwide, many organizations are discovering that their existing infrastructure may not be ready to support the next phase of AI adoption. New research from NTT DATA, a global leader in digital business and technology services, reveals that enterprise AI is advancing faster than the architecture, governance and security frameworks designed to support it.
The findings, published in NTT DATA’s “2026 Global AI Report: A Playbook for Private and Sovereign AI,” highlight a growing divide between organizations that are proactively redesigning their AI environments and those attempting to integrate AI into systems that were never built to handle modern data privacy, security and sovereignty requirements.
For years, enterprise technology strategies focused on moving data seamlessly across applications, cloud platforms and geographic borders. However, AI is exposing the limitations of this approach. As regulatory requirements become more stringent and concerns around data privacy intensify, organizations are increasingly required to maintain tighter control over where data is stored, processed and accessed.
The report found that more than 95 percent of respondents consider private and sovereign AI important to their organizations. Despite this overwhelming recognition, only 29 percent are actively prioritizing sovereign AI initiatives in the near term, highlighting a significant gap between awareness and execution.
According to the research, approximately 35 percent of Chief AI Officers (CAIOs) identify the complexity of building, integrating and managing AI models within private or sovereign environments as their greatest obstacle. Additionally, nearly 60 percent of AI leaders cite cross-border data restrictions as a major challenge in scaling AI initiatives across multiple markets.
Security remains another key concern. Only 38 percent of organizations report having a high level of confidence in their cloud security posture, despite cloud security serving as a critical foundation for successful private and sovereign AI deployments.
NTT DATA distinguishes private AI from sovereign AI by emphasizing their different objectives. Private AI focuses on protecting sensitive enterprise information, controlling access and minimizing data exposure. Sovereign AI, on the other hand, ensures that AI systems, data and operational environments comply with specific jurisdictional, regulatory, national or regional requirements.
Abhijit Dubey, CEO and Chief AI Officer of NTT DATA, noted that organizations leading the AI race are moving beyond compliance and risk management. Instead, they are building robust operational foundations that enable AI to perform effectively across diverse markets, jurisdictions and business environments.
The report identifies several key shifts that are shaping the future of enterprise AI. One of the most significant findings is that AI’s biggest limitation is no longer model performance alone. Organizations now require greater control over computing resources, data access, security and data locality, exposing weaknesses in infrastructure designed for unrestricted and centralized data movement.
Another major trend is the growing importance of data jurisdiction as an architectural consideration. While data can still move across systems and regions, AI applications often require continuous and immediate access to information, making regulatory and geographic constraints increasingly influential in how AI systems are designed and governed.
The research also reveals a growing competitive divide. Organizations that are redesigning their infrastructure, governance frameworks and operating models early are accelerating AI deployment and achieving greater scalability. Meanwhile, businesses that continue to rely on legacy architectures are finding it more difficult to translate AI investments into long-term value.
Interestingly, the pursuit of greater control through private and sovereign AI does not necessarily lead to greater independence. More than half of organizations surveyed identified integration complexity as their most significant challenge, highlighting the increasing importance of collaboration among cloud providers, technology partners, infrastructure vendors and AI platforms.
Overall, the report suggests that the future of enterprise AI will be defined not only by advances in AI models but also by an organization’s ability to build secure, compliant and adaptable operating environments. As AI adoption accelerates worldwide, companies that invest early in governance, infrastructure and data sovereignty will be better positioned to unlock sustainable business value and maintain a competitive advantage in an increasingly AI-driven economy.
