Nutanix, AI And Containers: Preparing For A Distributed Data Future

Mar 30, 2026

Lee Caswell, SVP of Product and Solutions Marketing at Nutanix, brings enterprise cloud and AI infrastructure expertise. He discusses how AI is accelerating container and Kubernetes adoption. He explains infrastructure gaps like GPUs and governance needs. He covers data sovereignty, shadow AI risks, and practical first steps for preparing distributed AI across edge, on-prem, and cloud.

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INSIGHT

AI Is Driving Rapid Container Adoption

AI is accelerating container adoption because new developers building LLM-powered apps prefer containers and Kubernetes for portability and ease of testing.
Lee Caswell points to edge examples like telemedicine, manufacturing, and wind farms where containerized LLMs run close to distributed data for sovereignty and performance.

INSIGHT

Start AI Internally Before Customer Facing

Enterprises are starting AI with internal use cases like chatbots and support co-pilots before exposing models externally to reduce risk.
Lee warns internal pilots help learn guardrails and then evolve into external agentic AI use cases.

ADVICE

Run Containers In VMs To Leverage Existing Teams

Run containers inside VMs to combine developers' agility with infrastructure teams' virtualization skills and resiliency.
Lee notes public clouds (e.g., EKS) use hypervisors, so this approach lets one team manage containers and VMs together.

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What happens when AI ambition starts moving faster than the infrastructure built to support it?

In this episode, I spoke with Lee Caswell, SVP of Product and Solutions at Nutanix, about the latest Enterprise Cloud Index and what it tells us about where enterprise IT really is right now. There is no shortage of AI headlines, product launches, and promises about what comes next, but this conversation gets behind the noise and into the operational reality that many business and technology leaders are now facing. As Lee explained, AI is not arriving in isolation. It is pulling containers, data strategy, hardware decisions, governance, and application modernization along with it.

One of the biggest themes in our conversation was the growing link between AI workloads and container adoption. Lee made the point that applications still sit at the top of the org chart, and infrastructure exists to serve them.

As more AI-enabled applications are built by developers who favor containers and Kubernetes-based environments, enterprises are being pushed to rethink how they support those new workloads.

We talked about why containers are becoming such an important part of modern application strategy, how they help organizations handle distributed AI use cases, and why many businesses are trying to balance speed and flexibility without giving up the resilience and control they have spent years building into their infrastructure.

We also spent time on the less glamorous side of AI adoption, but arguably the part that matters most. Shadow AI, data sovereignty, unpredictable token costs, and infrastructure readiness are all becoming board-level issues.

Lee shared why so many organizations are realizing that AI cannot simply be layered onto existing systems without deeper changes underneath. New hardware, new software, new governance models, and a more consistent approach across edge, on-prem, private cloud, and public cloud environments are all part of the picture now.

What I enjoyed most about this conversation was that it never framed AI as magic. It framed it as work. Real work that demands better architecture, sharper oversight, and faster decision-making from IT teams that are already under pressure.

So if your organization is racing to adopt AI, are you also building the foundation needed to support it responsibly, and where do you think the biggest risk sits right now? Share your thoughts with me.