AI Success Depends on Governance, Infrastructure and Data Control, New Research Finds

Artificial intelligence is entering a new phase of maturity, one where success is no longer measured solely by the performance of AI models. According to NTT DATA’s latest global research, organizations are increasingly realizing that the future of AI depends just as much on infrastructure, governance and data control as it does on innovation.

The company’s 2026 Global AI Report: A Playbook for Private and Sovereign AI reveals that enterprises around the world are facing mounting pressure to rethink how AI systems are built and deployed. As regulations evolve and concerns around data privacy intensify, organizations are being forced to balance rapid AI adoption with greater accountability and control.

For many businesses, traditional technology architectures were designed for a world where data could move freely across clouds, applications and geographic boundaries. However, AI’s growing dependence on vast amounts of data is exposing weaknesses in those models, particularly in regions where strict regulations govern how and where information can be stored and processed.

The report found overwhelming support for private and sovereign AI initiatives, with more than 95 percent of surveyed organizations recognizing their importance. Yet despite this broad awareness, only 29 percent are actively prioritizing sovereign AI implementation in the near term, highlighting a significant disconnect between strategic intent and operational readiness.

Organizations are also facing technical challenges as they work to establish secure AI environments. More than one-third of Chief AI Officers identified the complexity of building and managing AI systems within private or sovereign frameworks as a major obstacle. Meanwhile, nearly 60 percent cited cross-border data restrictions as a key challenge affecting AI scalability and deployment.

Cloud security remains another area of concern. Despite growing investment in AI, only 38 percent of organizations expressed strong confidence in their cloud security capabilities. This finding underscores the importance of strengthening foundational technology environments before pursuing large-scale AI initiatives.

According to NTT DATA, private AI focuses on safeguarding sensitive enterprise data through controlled access and enhanced protection measures. Sovereign AI extends this concept further by ensuring that AI systems and data comply with local regulatory, jurisdictional and national requirements.

Abhijit Dubey, CEO and Chief AI Officer of NTT DATA, believes organizations that are leading in AI adoption are taking a more holistic approach. Rather than viewing governance and compliance as obstacles, these companies are treating them as strategic enablers that support long-term growth and innovation.

The report also identifies an emerging divide between AI leaders and laggards. Organizations that are proactively modernizing their infrastructure and governance models are moving faster from experimentation to deployment. Those that delay these investments risk slowing their AI transformation journey and losing competitive ground.

Interestingly, greater control does not necessarily mean greater independence. As enterprises embrace private and sovereign AI, they are becoming increasingly reliant on complex ecosystems involving cloud providers, technology vendors and integration partners. More than half of organizations surveyed cited ecosystem integration as one of their biggest challenges.

As AI continues to reshape industries worldwide, the report suggests that future success will depend on much more than advanced algorithms. Organizations that invest in secure infrastructure, effective governance and responsible data management today will be better equipped to unlock sustainable value from AI in the years ahead.