AI discussions have moved past which model to run or which use case to tackle first. Enterprises developing their own private AI now face a more consequential question: where that AI should live. Drifting into public cloud because it feels familiar or delivers quick wins, without asking whether the environment meets AI's specific demands, tends to store up problems that compound with time. AI is not a transient workload. It is persistent, data-hungry, and deeply sensitive, drawing on proprietary information and embedding itself in critical workflows. AI depends on an organization's most valuable data to operate effectively, so control, privacy, and security cannot be treated as secondary considerations. Business systems and intelligence are best safeguarded in an environment the organization owns and governs. Enterprises building AI programs to improve business operations are making that choice, opting for private cloud infrastructure they can architect and evolve on their own terms. They are building AI a home, not checking it into a hotel. Why AI is different from other cloud workloads Most cloud workloads are reasonably portable and can be scaled, moved, or switched off without much consequence. Public cloud suits those portable workloads well, but AI is different. The days of AI serving one-time data analysis and decision support are over ; it now operates as a data-intensive workload driving performance, with security directly linked to where data resides. A well-functioning AI program improves through use, drawing on proprietary data and embedding itself in business processes. That creates real dependencies: on where the data lives, who has access to it, how the model is governed, and how the environment evolves. As Oliver Rowell, solution architect at Xtravirt, notes, the data sovereignty question organizations need to ask themselves is "who has the keys to your data?" Get the infrastructure wrong and the consequences compound. The more deeply AI embeds itself in workflows, the harder and more expensive it becomes to untangle governance issues, address spiraling costs, or move workloads to a more suitable environment. AI needs more than borrowed space As enterprise AI moves from pilot to production, organizations are rethinking where those workloads should run. Broadcom's Private Cloud Outlook 2026 found that 56 percent of enterprises are running or planning to run production AI inferencing in private cloud environments, while public cloud usage for the same workloads has fallen from 56 percent to 41 percent in a single year. The shift reflects a growing recognition that AI places different demands on infrastructure. Public cloud helped accelerate the first wave of AI experimentation, but production AI demands more compute power alongside low-latency connectivity, and generates sensitive data flows and new governance requirements that call for greater control over the environment in which it runs. Data sovereignty remains a central consideration for businesses handling sensitive information. Even when data is stored locally, foreign legal jurisdictions may retain access rights through the cloud provider, which is why organizations are placing greater value on ownership, predictability, and control. As Will Rodbard, master architect at Broadcom, explains: "As soon as you give parts of control away, somebody else has the encryption keys or access to the data, and you lose overall control. You can only control cost if you are in charge and in control over who can do what and when." For organizations running business-critical AI workloads, the benefits of full control extend beyond security and governance. Full control also provides greater visibility into how resources are consumed, which helps create a more predictable cost model as AI adoption scales. The closer AI sits to the systems, data, and policies that govern the business, the easier it becomes to manage risk, maintain compliance, and control long-term operational costs. Building a home for AI Building a home for AI does not mean abandoning cloud strategies; it means applying cloud principles in an environment designed around the organization's requirements. Private cloud, whether in an internal datacenter, a co-location facility, or a managed service provider environment, gives greater influence over how infrastructure is designed, how data is governed, and how AI services evolve over time. Platforms like VMware Cloud Foundation (VCF) make this practical. Automation, self-service provisioning, and policy-driven governance deliver the agility organizations expect from cloud, while retaining visibility and control over the infrastructure underpinning critical AI workloads. Private cloud also creates room for change. AI strategies, models, and use cases will continue to evolve rapidly over the coming years, and the organizations primed for private AI success will be those that build a flexible foundation capable of adapting as requirements shift. Where private AI creates real value The most effective deployments rarely begin with ambition; they begin with a clearly defined problem. "Those companies really succeeding with AI" says Rowell, "are those that identify an actual use case and nail that use case down." One clear starting point is documentation and knowledge search*.* Most organizations sit on vast stores of data that are fragmented across different systems and formats, which makes access difficult. Retrieval Augmented Generation (RAG) offers a useful solution: it is a practical way to unlock internal insights, delivering contextual answers from existing documentation without data leaving the environment. Xtravirt has driven rapid returns through RAG deployments for clients running on VCF. A second strong use case is secure coding environments. Development teams in regulated or air-gapped settings need AI-assisted coding support that stays within the perimeter. Private AI delivers the productivity benefits of AI-assisted development without the compliance risk of routing proprietary code through a public endpoint. The competitive case for acting now Much of the conversation around AI is driven by the fear of being left behind, and the gap between organizations that get their infrastructure right and those that do not will widen quickly. As Rodbard explains: "If you've got two businesses doing roughly the same thing and one has orchestrated and automated a lot of their business processes while another is still using manual labour, they're not going to be as reactive, not going to be as fast, not going to keep pace." IT teams have spent years being asked to do more with less. Private AI is one of the clearest opportunities to deliver on that promise, freeing people from high-volume, repetitive work so they can focus on what moves the business forward. Organizations that apply AI to practical business challenges can respond faster to change, make better use of existing resources, and create capacity for higher-value work. What to do next As AI moves into production, infrastructure becomes a strategic decision. The environment organizations choose today will shape AI's performance, governance, and resilience. Enterprises should assess where AI workloads should run, including the suitability of private cloud, the data they rely on, and whether the chosen environment supports long-term business goals. Working with an experienced partner such as Xtravirt can help organizations identify the environment best suited to their AI ambitions and operational requirements. From AI readiness assessments and cloud strategy development through to deployment, governance, and ongoing optimization, Xtravirt supports enterprises at every stage of AI adoption. Whatever an organization's starting point, the most important step is understanding where AI can create genuine business value and then building the right foundation to support it. AI needs a home, not a hotel. Visit xtravirt.com/own-your-cloud to explore the Own Your Cloud hub or get in touch to discuss where your AI program should call home. Contributed by Xtravirt.
