Why I Am Telling Enterprise Clients to Reconsider On Premise for AI

Five years ago, if you had told me I would be walking into enterprise boardrooms in 2026 and making the case for on premise AI, I would have laughed. The cloud conversation was over. Everybody I worked with was moving workloads out, not in. The economics, the velocity, the reliability, the regulatory posture, all of it favored the hyperscalers. I said as much in plenty of decks.

I have changed my mind on that, at least for the AI layer, and I want to explain why without overselling it. On premise is not the right answer for every workload and it is not the right answer for most enterprises. The right posture for most companies is still hybrid, with the heaviest agent workloads living where they make the most operational sense. What I am arguing is that the question has come back onto the table for a specific set of reasons, and enterprises that reflexively dismiss it are making a decision that is a year or two out of date.

Here are the five reasons I am telling clients to run the analysis.

Reason one. Execution economics at real scale. When inference was the dominant cost in an AI workload, cloud made sense because the hyperscalers were passing along GPU amortization at a rate no enterprise could beat on its own. That is still true for most companies, and it will continue to be true for a while. But once you start deploying agents that reason through multi step workflows, the cost profile changes. A single agent task can involve dozens of model calls, multiple tool invocations, and retrieval across several knowledge stores. Token volume per unit of work goes up by an order of magnitude. At a certain scale of agent activity, the unit economics of running some of that workload on owned infrastructure start to pencil out. Not for everyone. Not for most workloads. But for the handful of workloads that are volume heavy, latency sensitive, and data gravity locked, the break even point has moved.

Reason two. Action level governance. This is the one I feel most strongly about. When an agent is taking actions on your behalf, you want the audit and policy layer to live somewhere you fully control. Cloud providers are reasonable partners on data governance. They have invested real money in making that story work. Action governance is newer and harder. When you need to prove, to an auditor or a regulator or your own board, exactly what an agent saw, reasoned about, and did, and under what policy, in what order, with what rollback, the control surface has to sit somewhere you can inspect at the lowest level. For a lot of enterprises, that pushes them toward owning at least the governance plane of the AI operating system, even if the heavy compute still runs in the cloud. For some, it pushes them further.

Reason three. Compounding latency. I am not talking about the raw latency of a single model call, which has been getting faster for everyone. I am talking about what happens when an agent has to make a hundred small decisions in a chain, each of which involves a round trip to a hosted model, each of which picks up a little network cost and a little queuing cost. Those numbers compound. For interactive use cases, like a customer facing agent or a production floor workflow, a chain that takes six seconds on a colocated stack can take thirty seconds on a cloud stack that is geographically far from your data. Thirty seconds is a bad user experience, and it is also a window in which other things can go wrong. Moving the agent runtime closer to the systems it is acting on removes that compounding latency. Sometimes that means cloud in the same region. Sometimes it means something closer.

Reason four. Cross system reliability. Agents that work across multiple enterprise systems accumulate failure modes that are different from single system workloads. When an agent is chaining across your CRM, your ERP, your ticketing system, and a data warehouse, any network blip or rate limit along the chain becomes a reliability problem. Running the agent runtime inside the same network boundary as the systems it has to touch reduces the number of failure modes you are exposed to. It does not eliminate them. It takes a class of problems off the table, which matters when the rest of the work is already hard enough.

Reason five. Accountability. This is the softest of the five and the most important. When something goes wrong with an AI system, somebody inside the enterprise has to be able to walk into a room, reconstruct what happened, explain why, and commit to what changes. If the runtime is running on infrastructure you do not control, under a shared responsibility model that you renegotiate every contract cycle, that accountability conversation gets harder. Not impossible, but harder. For some workloads the board wants to be able to say "we own this end to end" in a way that a shared cloud posture does not permit. That is a reasonable position to hold, and it is a conversation I am now having with clients I never expected to have it with.

I want to be honest about what I am not saying. I am not saying cloud is dead. It is not. I am not saying every enterprise should rebuild a data center. Most should not. I am not saying the hyperscalers cannot solve any of these problems. They are all working on all of them, and in 18 months some of this list will read differently. What I am saying is that the reflexive cloud first posture that served us well from 2018 through 2023 deserves a fresh look specifically for the AI layer, and the analysis is no longer as clean as it used to be.

When I was running the AI portfolio at Restaurant Brands International, I had to make this call for real. Some workloads belonged in the cloud and stayed there. Others had enough of the five reasons above stacked up that the answer was not obvious, and the decision matrix we used to evaluate it was more nuanced than the decision matrix I would have used in 2022. I am not going to pretend that every enterprise I talk to ends up on premise for anything. Most do not. What I am going to say is that the ones who do the analysis are building a better AI posture than the ones who refuse to reopen the question.

If you have not asked the on premise question in the last 12 months, I would ask it. You might end up in the same place you were before, which is fine. Or you might find that one or two of your highest value, most governance sensitive workloads belong somewhere you did not expect them to belong. Either way, you will have an answer you can defend.