Private cloud adoption is resurging, driven by rising privacy, security, and compliance requirements with AI workloads emerging as a fast-growing contributor to this shift.

This is according to a report from GTT Communications Inc, with a survey conducted by Hanover Research, which shows that in an era of advanced AI, concerns around data, privacy, security, and regulatory compliance are naturally tip of mind for enterprise leaders.

These priorities, alongside a growing need to safely test and deploy AI models, are driving a resurgence in private cloud adoption as organisations seek greater control over where and how sensitive data is handled.

“We know many companies are now shifting their sensitive workloads to private clouds as part of broader multi-cloud and hybrid strategies designed to support agentic AI and other complex AI initiatives at scale,” said Bastien Aerni, VP of strategy and technology adoption, GTT.

“This distributed approach allows enterprises to balance performance, security and compliance requirements while optimizing costs to advance their AI ambitions.”

According to survey respondents, private cloud spending reaching over $10m per year will increase from 43% in 2024 to 53.6% in 2025, reflecting a 24% growth rate, compared to just 12% growth in public cloud spending for these same cohorts.

More than half of all AI workloads already reside in a combination of private cloud and on-premises environments, driven primarily by enhanced security (56%), compliance and regulatory demands (51%), and the specific needs of AI workloads (50%). Cost remains a factor but ranks much lower at 35%.

As enterprises adopt hybrid strategies to support more complex AI workloads, many encounter challenges spanning both public and private cloud environments. For public cloud deployments, challenges around migration of apps and data and technical skills or feasibility are the highest ranking at 4%.

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When migrating workloads to private clouds, the top challenges – each cited by 38% of respondents – are managing apps post-migration, securing hybrid environments and, once again, a lack of technical skills or feasibility.

“Enterprise infrastructure strategies are evolving fast as AI workloads surpass the limits of traditional architectures,” Aerni continued.

“Organisations are refining their cloud environments, whether public, private or hybrid, to support their AI initiatives at scale. But many still underestimate the complexity involved. In our experience, without reengineering connectivity and security architectures, even the most ambitious private cloud strategies can fall short.”