In a video interview, Nutanix’s Lee Caswell tells how IT customers need to meet changing needs as they embrace AI capabilities.

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Jason Lopez: In between press briefings, Lee Caswell tells The Forecast about some of the biggest IT trends, challenges and innovations explored at the 2025 .NEXT event in Washington, D.C.

Lee Caswell: We're really interested in that customers get value out of AI. And this is one of the things where a lot of customers today are a little bit stalled trying to figure out, well, hey, I don't want to spend too much. I don't want to spend on the wrong thing. How do I get started, right? And how do I also make sure that the AI that I bring to market has the same enterprise level resilience, the same day-to-day operations, the same security and privacy that I bring for all of my traditional apps. How do I make AI just the next enterprise app? And so we're helping customers basically with choice. So the idea is, what are the fast-changing parts? GPUs are changing pretty fast, so we're going to give you the access to the GPUs or CPUs with acceleration, for example. LLMs are changing fast. Last year, we gave you almost an app store, if you will, into LLMs from our partners Hugging Face and NVIDIA. This year, now we're supporting agentic workloads, which is an iterative cycle where it's not enough to just get an answer. Now, I want to iterate on that answer and give you more relevant results, maybe through guardrails, maybe through re-ranking, maybe through embedding. And so we're giving you all of those access points so you can get started today and have a production-ready enterprise experience.

Jason Lopez: Caswell talked about how enterprises are onboarding AI and managing data privacy.

Lee Caswell: Well, I think first off, we're helping customers understand the fast pace of the AI market. So most customers today realize that large language models, or they don't have to be that large, it could be SLMs as well, will be developed either in the public cloud or by some of the largest customers because of the incredible capital expense of training a model. So now customers are looking and saying, well, all right, I'm probably going to get access to a model, but how do I make sure now that my data remains private? It could be, for example, that your model becomes more sensitive than the data itself because the model now starts showing you're inferencing your ideas, your insights into the data that you have. And so that idea of saying, it's going to be important to access large language models and change them out. I want to have access to that. We provide that capability. And then I also want to have this ability to go and have more responsible results and be able to go and manage the infrastructure so that it's actually an enterprise-level experience. That's how we're helping customers basically get started with our Nutanix AI, enterprise AI products.

Jason Lopez: He tells how Nutanix software helps enterprises build and run a future-ready IT operation.

Lee Caswell: Yeah, it turns out, right, you know, the quality of our engineering has been really something that's just been delightful to see as we're bringing all the elements necessary to bring AI into the enterprise. And so that could be, for example, like the storage, right? You have to ingest that data. You've got to run the model. You've got to archive the data. How do you do all of that together? We provide an offer for that. In addition, by the way, many of these models are containerized. And so as you bring containers and Kubernetes, for many customers, these are relatively new concepts into the on-prem world. How do you leverage what you know already today? And here we acquired a company called D2IQ, and we have a terrific set of engineering leadership resources there who are now taking that, what was a production shipping product, and now adding Nutanix data services. It's an incredibly compelling offer and part of the complement of products you need to make AI successful.