AI is no longer the “cool demo” part of the roadmap. Broadcom’s Private Cloud Outlook 2026 found that 56 percent of enterprises are running or planning production AI inferencing in private cloud environments, while public cloud usage for the same workloads fell from 56 percent to 41 percent in a single year.

That is the core change: production AI behaves like a long-term resident, not a guest. Enterprises moving from experimenting to deploying have discovered that the environment where the model runs becomes tightly coupled to data control, privacy, security, and operational cost. When teams drift into public cloud because it feels familiar, or because pilots are easier to start, they risk compounding issues over time, because AI is persistent, data-hungry, and embedded in business workflows.

Why AI changes the cloud math Most cloud workloads are portable. They can scale up and down, be moved, or even be switched off with relatively little consequence. AI is different. It is a data-intensive workload where security is directly linked to where the data resides, and where the model keeps improving through use. Once an organization starts using proprietary data to power AI systems and then wires those systems into real processes, the dependencies stack up. The location of the data is not a detail anymore. It determines who has access, how the model is governed, and how the environment can evolve as business requirements change.

As Oliver Rowell, solution architect at Xtravirt, frames it, organizations need to ask, “who has the keys to your data?” That question sounds philosophical until you try to untangle governance after the fact. The more deeply AI embeds itself in workflows, the harder and more expensive it becomes to address tangled governance issues, spiraling costs, or the practical reality of moving workloads to a better-suited environment later.

AI “in public cloud” also raises sovereignty questions that don’t stay theoretical The swing away from public cloud is partly about control, but also about the hard edges of sovereignty and compliance. The source notes that data sovereignty remains central for businesses handling sensitive information. Even when data is stored locally, foreign legal jurisdictions may retain access rights through the cloud provider. That is why organizations place greater value on ownership, predictability, and control.

Will Rodbard, master architect at Broadcom, explains the underlying mechanism plainly: “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.” The point is not that public cloud is “unsafe” in some generic sense. It is that AI production increases the blast radius of every permission and every architectural choice.

For CFOs and boards, cost predictability is the second-order issue Security and governance are the headline issues. But the second-order effects matter just as much when the models graduate from prototype to ongoing production. The more AI scales, the more visibility an organization wants into how resources are consumed so it can build a predictable cost model.

The source links this to control and placement: 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. In other words, “where it runs” becomes part of your financial model, not just your technical stack.

Private cloud is being treated less like a compromise and more like a platform decision “Building a home for AI” does not mean abandoning cloud strategies. The source is explicit: it means applying cloud principles in an environment designed around the organization’s requirements. Private cloud can be internal data center, co-location, or a managed service provider environment. The common thread is influence over infrastructure design, data governance, and how AI services evolve over time.

Platforms like VMware Cloud Foundation (VCF) are mentioned as making this practical. The described capabilities include automation, self-service provisioning, and policy-driven governance, aiming to deliver the agility organizations expect from cloud while preserving visibility and control over the infrastructure that underpins critical AI workloads. This matters because AI strategies, models, and use cases are expected to evolve quickly. The organizations primed for private AI success are those building a flexible foundation that can adapt as requirements shift.

Where private AI creates value fast: start with a use case that belongs inside the perimeter The source also gets practical about adoption. It argues that the most effective deployments rarely start with ambition; they start with a clearly defined problem. Two examples are highlighted. First is documentation and knowledge search. With RAG, teams can unlock internal insights by delivering contextual answers from existing documentation, without data leaving the environment. Second is secure coding environments. In regulated or air-gapped settings, private AI can provide AI-assisted coding support that stays within the perimeter, avoiding compliance risk from routing proprietary code through a public endpoint.

The competitive pressure is also real, and it is not just about tech talent Much of the AI conversation is driven by fear of being left behind. But the source frames a sharper competitive gap: organizations that orchestrate and automate business processes with AI will be more reactive, faster, and better able to keep pace than those still relying on manual labor.

That connects directly to how boards should think about “AI readiness.” IT teams have spent years being asked to do more with less. Private AI is presented as one of the clearest opportunities to deliver on that promise by freeing people from high-volume, repetitive work so they can focus on what moves the business forward.

So what should decision-makers do next? As AI moves into production, the environment chosen today shapes AI’s performance, governance, and resilience. The source’s recommended next step is an assessment of where AI workloads should run, including suitability of private cloud, the data they rely on, and whether the environment supports long-term business goals. It also notes that working with an experienced partner like Xtravirt can help with readiness assessments, cloud strategy development, deployment, governance, and ongoing optimization.

The underlying message is simple and urgent: understand where AI can create genuine business value, then build the right foundation for it. AI needs a home, not a hotel.