NTT DATA Report Reveals Growing Gap Between AI Ambitions and Enterprise Readiness
5 min read
Artificial intelligence is rapidly becoming a core driver of enterprise transformation, but new research from NTT DATA shows that many organizations are struggling to keep pace with the infrastructure demands required to support it.
In its 2026 Global AI Report: A Playbook for Private and Sovereign AI, NTT DATA highlights a growing tension between the speed of AI adoption and the readiness of enterprise systems to support secure, compliant and scalable AI environments.
As businesses expand their use of AI across industries and regions, traditional data architectures are being pushed beyond their original design limits. Systems built for seamless, borderless data movement are now facing increasing restrictions due to privacy laws, regulatory frameworks and national data sovereignty requirements.
The report reveals that while AI adoption continues to accelerate, enterprises are being forced to rethink how data is stored, accessed and processed. This shift is driving stronger interest in private AI and sovereign AI models, which prioritize security, governance and jurisdictional compliance.
Despite this growing awareness, there remains a significant gap between recognition and execution. More than 95 percent of organizations acknowledge the importance of private and sovereign AI, yet only a small portion are actively prioritizing their implementation in the near term.
The research also points to operational barriers that are slowing progress. Many organizations report difficulty in building and integrating AI systems within controlled environments, while others highlight cross-border data limitations as a major constraint on scaling AI initiatives globally.
Security readiness remains another critical concern. A relatively low level of confidence in cloud security indicates that many enterprises may not yet have the foundational safeguards required to fully support advanced AI workloads.
Private AI focuses on protecting enterprise data through controlled access and restricted exposure, while sovereign AI extends this approach by ensuring compliance with specific national or regional regulations governing data and AI operations.
According to NTT DATA leadership, organizations that are succeeding in this environment are those treating AI infrastructure, governance and compliance not as technical challenges, but as strategic business priorities.
These leaders are investing early in redesigned architectures that allow AI to operate securely across multiple jurisdictions while maintaining full control over sensitive data. This approach enables faster scaling from pilot projects to enterprise-wide deployment.
The report also highlights a growing ecosystem dependency, where organizations pursuing greater control are increasingly reliant on coordinated networks of cloud providers, technology partners and integration specialists.
Ultimately, NTT DATA concludes that the next phase of AI maturity will not be defined by model innovation alone, but by how effectively organizations can align infrastructure, governance and data strategy. Those that act early are likely to gain a lasting competitive advantage in an increasingly regulated and data-sensitive digital economy.
