When enterprises originally began building AI strategies, the default assumption was straightforward: AI would run in the hyperscaler cloud. The APIs were ready, the GPU capacity was building out, and the inertia of a decade of public cloud investment pointed in one direction. The Private Cloud Outlook 2026 report reveals a clear shift: as enterprises look to scale, the direction has changed.
The Private Cloud Outlook 2026 - The AI Tipping Point, which Broadcom published today, is based on a blind, global survey of 1,800 senior IT leaders across eight countries. Now in its second year, the report tracks a shift in cloud strategy that is no longer something in the future. It is happening in production workloads, capital budgets, and board-level priorities. Enterprise AI has found its infrastructure home. And it is private cloud.
Production AI Is Moving to Private Cloud
Last year, 56% of enterprises used public cloud as the primary environment for production AI inference. This year, that number has fallen 15 percentage points to 41%. Meanwhile, 56% of enterprises are now running or planning to run production inferencing in a private cloud.
The shift goes deeper than the top-line numbers. Forty-three percent of enterprises actively repatriating workloads are specifically moving AI training, large language models, and inference out of the public cloud. That AI category did not exist in last year's study. The broader repatriation trend has accelerated sharply as well: 83% of enterprises are now considering repatriation, up from 69% in 2025, and half have already moved at least some workloads, a 15-point jump in a single year.
What is driving enterprise AI to the private cloud? The same forces that drove storage, security-sensitive applications, and regulated data there before it. Security, control, cost, and governance did not become more important because of AI. The consequences of getting them wrong simply became much harder to absorb at production scale. In fact, when looking at workload placement, high stakes workloads like high security, latency sensitive, business critical, and data-intensive have a preference for private cloud.
The Bill for AI Infrastructure Has Arrived
For the first time in this study, cost has overtaken security as the top concern about public cloud. That reflects a familiar reality for enterprise IT leaders: public cloud costs were already difficult to forecast and manage, and AI workloads have made that problem substantially worse.
Nearly all IT leaders surveyed (97%) believe some portion of their public cloud spend is wasted, and more than half (52%) say that waste exceeds 25% of their total spending. Generative AI and agentic workloads are compounding the pressure. Sixty-two percent of IT leaders report being very or extremely concerned about AI infrastructure costs.
Enterprises are revising their investment strategies accordingly. Net intent to increase private cloud investment over three years has risen from 51% to 72%. Private cloud investment is now growing at more than twice the rate of public cloud. Cost predictability has become the second biggest driver, cited by 39% of the organizations.
Enterprises that built AI ambitions on variable, consumption-based public cloud pricing are recalculating. Private cloud, with its predictable economics and direct IT control over infrastructure, is increasingly where the budget decisions are landing.
Sovereignty Has Become a Board-level Priority
Geopolitics has entered the infrastructure conversation in a significant way. Eighty-six percent of IT leaders say geopolitical and regulatory factors are now directly affecting their IT strategy and operations. Data sovereignty and residency requirements are the top concern, cited by 54% of respondents, followed by jurisdiction-specific compliance requirements at 51%.
For enterprises operating across borders, decisions about where data lives carry direct implications for where workloads can run. AI workloads that process sensitive, regulated, or proprietary data require infrastructure that provides governance and control from the ground up. Security and compliance remain the single most important factor in workload placement decisions, cited by 32% of respondents. On top of existing obligations, AI is introducing new ones: data protection and privacy (37%) and security and control (36%) are now the leading infrastructure requirements that AI brings to the table.
Private cloud provides the governance architecture to meet those requirements by design, built in from the start rather than bolted on after deployment.
Complexity Is a Platform Problem
Running production AI at enterprise scale is an operations challenge as much as an infrastructure one. The #1 skills gap cited by IT leaders is AI infrastructure and operations, named by 40% of respondents, followed by cloud security operations at 38% and Kubernetes operations at 37%. To close the skills gap, the greater majority – 81% of enterprises, now fully outsource or use professional services for cloud-related needs.
Along with having the right technology partners, operational simplification is crucial to close the skill gap. Enterprises that standardize on a unified, well-governed private cloud platform address the AI skills challenge with fewer specialists, less operational fragmentation, and clearer organizational accountability. A platform-centric approach reduces the surface area that teams have to manage, and that is where the real operational leverage is.
The Tipping Point Is Here
The Private Cloud Outlook 2026 confirms what the data has been building toward for two years. Enterprise IT has reached the AI tipping point. Private cloud is the preferred platform for production AI because it addresses what AI at scale actually demands: security, cost predictability, data sovereignty, and governance that enterprises cannot treat as optional.
VMware Cloud Foundation 9.1 is built for this environment. It provides a unified platform for running AI and traditional workloads together, with the performance, cost controls, and security capabilities that production AI at enterprise scale requires. The research shows where enterprise AI is heading. VMware Cloud Foundation is the platform built to get organizations there.