AI Moves Fast. Your Infrastructure Needs to Move Faster.
Key Takeaways From the 2026 Nutanix Enterprise Cloud Index
By Lee Caswell
AI is transforming every business. And in every customer conversation I’m having lately, the message is the same: competitiveness now depends on how quickly you can adopt it. The hurdles they face—in cost, sovereignty, hardware, and software—will define the difference between winning and losing.
The 2026 Nutanix Enterprise Cloud Index (the “ECI report”) captures this shift, and honestly, the numbers are striking. 85% of IT leaders say AI is accelerating their adoption of containers, even as 82% say their on‑prem infrastructure isn’t fully ready for AI workloads.
That gap between acceleration and readiness is where competitiveness will be decided.
This is the new reality: AI isn’t just another workload. It’s a stress test for your entire platform.
Hardware Is Moving Faster Than Traditional Infrastructure Can Absorb
I remember when hardware decisions were long‑term commitments. You standardized on Intel or AMD. You picked a memory configuration. You bought servers from Dell or Lenovo and expected to run them for a predictable lifecycle.
AI breaks that pattern.
New GPUs, accelerators, memory technologies, and server designs are arriving faster than most organizations can evaluate them.
The ECI data reinforces this urgency. 82% of IT leaders say their on‑prem infrastructure is not fully ready to support AI workloads, a number that jumps even higher in sectors like healthcare.
In almost every conversation I’ve had this year, customers ask me the same questions:
- How do I adopt new hardware without re‑architecting everything?
- How do I avoid locking myself into a vendor or configuration?
- How do I make sure my infrastructure doesn’t slow down innovation?
My answer is always the same: you need a platform that can absorb hardware changes.
If you choose the right foundation, you can take advantage of the latest capabilities without re-architecting your environment every time the industry moves. That flexibility is becoming essential at the bottom of the stack.
Software Is Evolving Just as Quickly—Especially LLMs
Hardware isn’t the only thing accelerating. The software ecosystem around AI is moving even faster.
What I’m seeing across enterprises is that the LLMs they deploy today won’t be the ones they rely on six months from now. Many of those models run in containers, even though the rest of the environment is still VM‑based. Frameworks update. Dependencies shift. GPU requirements change.
This creates a new operational challenge:
How do you adopt new AI software without creating fragmentation?
The ECI report shows why this matters. 85% believe AI is accelerating their adoption of containers, and 83% are already building new applications in containers. That’s because container adoption gives teams a way to package the fast‑moving software dependencies that AI workloads require.
If every new model requires a separate team with separate skills, you end up with an environment that becomes harder to manage over time. I often describe this as flipping an “AI switch.” Once it’s on, you’re running a different class of workloads — and your platform either supports that transition or it doesn’t.
If You Don’t Control Your Data, You Don’t Control Your AI
Even if you can keep up with hardware and software, there’s a third dimension that has become unavoidable: data sovereignty.
Customers want to know:
- Where their data lives
- How it’s protected
- How it’s replicated
- How it’s governed across jurisdictions
This isn’t just about compliance. It’s about trust. It’s about cost predictability. And it’s about avoiding the rise of Shadow AI—models trained on data that was never meant to leave a particular boundary.
The ECI report is blunt on this point: “Most IT executives (87%) believe that the use of AI tools and agents outside of official oversight creates business risk.”
And sovereignty is now a top priority. 80% of IT leaders say data sovereignty is a high‑priority or must‑include factor in infrastructure decisions.
I say this often because it’s true:
If you don’t control your data, you don’t control your AI.
Distributed sovereign cloud architectures are emerging as the way forward. They allow organizations to keep data local, enforce policy, and maintain consistency across environments. But they only work if the underlying platform behaves the same way everywhere.
Without that consistency, sovereignty is simply unattainable.
Shadow AI Is a Symptom of Infrastructure That Can’t Keep Up
One of the most striking findings in the ECI report is how widespread shadow AI has become. 79% of IT leaders encounter AI applications implemented by employees in non‑IT functions.
This isn’t happening because people are being reckless, far from it. It’s happening because the business wants AI faster than IT can safely deliver it.
Silos make this worse. “82% believe silos between business units and IT make it difficult to effectively execute technology initiatives,” according to the ECI report.
When different parts of a company aren’t aligned, enterprise AI adoption becomes fragmented—and risk increases.
A consistent and unified platform reduces that risk. It gives teams a sanctioned, governed way to innovate without going around IT.
The Hidden Cost: Time Lost to Maintenance
There’s another factor that doesn’t get enough attention. Historically, by my own estimate, enterprises spent 60–80% of their time on maintenance, including patching, upgrading, and troubleshooting. That’s time not spent building new applications or deploying new AI capabilities.
AI raises the cost of that lost time.
Every hour spent maintaining legacy infrastructure is an hour not spent:
- Training a model
- Deploying a new service
- Integrating new hardware
- Updating to the latest LLM
- Improving data governance
AI rewards speed. Maintenance slows it down.
Organizations that shift their time from upkeep to innovation will move faster. Those that don’t will fall behind.
Where This Leaves Us
From my perspective, it comes down to this: AI is moving too quickly for static infrastructure. Too quickly for siloed teams. Too quickly for architectures that weren’t designed for constant change.
Your ability to compete will depend on how quickly you can:
- Adopt new hardware
- Integrate new LLMs
- Maintain sovereignty
- Prevent shadow AI
- Run everything on a consistent, repeatable platform
The companies that build for adaptability will lead. The ones that don’t will spend the next decade trying to catch up.
Read the 2026 Nutanix Enterprise Cloud Index to find out more about the future of AI, containers, and sovereignty in enterprise IT.
Background and Research Goals
The Nutanix Survey was conducted by Wakefield Research among 1,600 IT & engineering executives, with a minimum seniority of manager, at companies with a minimum of 500 employees across the following markets: Australia, Brazil, France, Germany, India, Italy, Japan, Mexico, Netherlands, Kingdom of Saudi Arabia, Singapore, Spain, United Kingdom, and the United States, with an oversample of 100 Federal U.S. workers, between November 13th and November 23rd, 2025, using an email invitation and an online survey.
Results of any sample are subject to sampling variation. The magnitude of the variation is measurable and is affected by the number of interviews and the level of the percentages expressing the results. For the global interviews conducted in this particular study, the chances are 95 in 100 that a survey result does not vary, plus or minus, by more than 2.38 percentage points; for the United States 4.9, and all remaining countries 9.8, from the result that would be obtained if interviews had been conducted with all persons in the universe represented by the sample.
1 A survey of 1,600 cloud, IT, and engineering executives conducted by Wakefield Research for Nutanix which assesses where they’re running their apps, how their infrastructure priorities are shifting, and what pressures are driving their decisions.
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