A smiling engineer in glasses works on a laptop in a dark data hall, surrounded by racks of glowing green status lights.

The data center
checklist for
scaling AI

See where your data center strategy stands across six critical areas.

Most AI strategies get built around models, data, and talent. The facility running underneath them rarely makes the list until it's the reason a plan stalls. Many existing data centers were built for general-purpose workloads, years before today's AI-scale demands existed.

Use this checklist to see where you stand on power, cooling, deployment, operations, security, and cost as AI places greater demands on your data center strategy. Check off each item as you go. We'll tally your results at the end.

Scroll to begin

Six areas. 24 statements. One readiness score.

Each section below covers one area of AI readiness. Select each statement that describes your organization. Your total shows where you stand and where to focus next.

Your progress

0 / 24

statements selected

01 / 06

Power

AI racks draw far more power than the infrastructure most facilities were built around. Industry estimates put rack densities climbing from roughly 12 kilowatts to 100 kilowatts, part of a wave of capital investment estimated at $1.8 trillion industry-wide. This is the first challenge many AI initiatives encounter.

Close-up of a server rack full of drive bays with green-lit release handles.

Here's where to start:

You know your current per-rack power capacity and how it compares to what your planned AI workloads actually need.
Your power distribution and backup systems are sized for sustained high-density draw and resilient enough to maintain operations if the primary supply is interrupted.
You have a plan for phased power increases as AI workloads grow, rather than a single upgrade.
You've confirmed power availability with your utility or colocation provider for planned AI expansion.

0 of 4 selected

02 / 06

Cooling

Traditional air cooling runs out of headroom well before AI-density racks do. Immersion, direct-to-chip, and rear-door heat exchange are now in active production, not experimental. Are your facility, budget, and team ready for liquid cooling?

Motion-blurred view down a blue-lit data center aisle with bright light strips along the server racks.

See how your facility measures up:

You know which liquid-cooling approach (immersion, direct-to-chip, or rear-door heat exchange) fits your current and planned rack density.
You've assessed whether your facility can support liquid-cooling infrastructure without a full rebuild.
You have a plan beyond installation for maintaining and monitoring cooling systems.
You've modeled how cooling costs scale as rack density increases.

0 of 4 selected

03 / 06

Deployment and infrastructure support

AI infrastructure is only as reliable as the people and processes maintaining it.

A technician checks a tablet behind a perforated server rack door lit by green indicator lights.

Before workloads go live, make sure your deployment and support model can keep pace:

You have on-site deployment capacity, or a partner with certified technicians, to rack, stack, and commission AI and GPU infrastructure at the speed your rollout requires.
Your hardware support coverage extends to high-density compute and storage platforms in addition to standard server and network equipment.
You have a life cycle management plan for GPU servers, storage platforms, and HCI environments, including firmware currency and scheduled maintenance.
Your break-fix model for AI infrastructure includes defined response times and on-site coverage alongside remote diagnostics.

0 of 4 selected

04 / 06

Operational readiness

AI environments involve more signals, more systems, and more ways for small issues to compound before anyone notices them. Visibility and automated response need to be built in from day one, since operational complexity tends to outpace ad hoc monitoring as AI workloads scale.

Two technicians in a data hall: one holds a laptop and a bundle of cables beside a rack while the other works at a monitor on a cart.

See how your operations hold up:

You have end-to-end visibility across AI infrastructure, including GPU environments and supporting systems.
You use monitoring and observability tools to proactively identify performance and capacity issues.
You have automated workflows and response processes in place to reduce operational complexity as AI workloads scale.
You have defined escalation paths and clear ownership across the teams managing infrastructure, security, and AI operations.

0 of 4 selected

05 / 06

Security

AI workloads carry more sensitive data, involve more models, and create more ways for something to go wrong. A security posture built for yesterday's threat model won't cover model manipulation, data leakage, or unauthorized access to training data.

Blurred close-up of server racks with bright green indicator lights streaking across the frame.

Where does your posture stand today?

Your physical and network security controls extend to AI-specific infrastructure, including GPU clusters and high-density racks.
You have visibility into who and what can access AI training data and models, beyond the infrastructure hosting them.
You've accounted for the regulatory and compliance requirements specific to AI use in your industry.
Your incident response plan covers AI-specific risks, including model manipulation and data leakage.

0 of 4 selected

06 / 06

Cost planning

Some organizations need predictable, consistent data center costs. Others have room to flex as opportunities shift. Understanding your position is crucial for effective budget management.

Glowing teal circuit-trace pattern on a dark green background.

Take stock of where you are:

You know whether your organization needs cost predictability or has an appetite for variable investment as AI scales.
You've modeled the total cost of running AI workloads at scale, not just the upfront infrastructure investment.
You're applying FinOps-style cost governance to data center and AI infrastructure spend.
You've identified where consolidating vendors or services could reduce overhead as AI adds complexity.

0 of 4 selected

of 24 checked

Here's where you stand

Start checking items above, and we'll tally where your data center strategy stands across all six areas.

Where Unisys fits

Working through this checklist surfaces six critical areas. Unisys helps you build operational resiliency across every layer through data center management, field services, liquid cooling and immersion, and device support. Independent analysts and partners agree:

Dell Technologies named Unisys its 2026 Global Alliances Americas Data Center Partner of the Year.

ISG named Unisys a Leader in the 2026 Private/Hybrid Cloud – Data Center Services Provider Lens, for the sixth consecutive year.

NelsonHall named Unisys a Leader in the 2026 Vendor Evaluation & Assessment Tool for AI-Enabled Cloud Infrastructure Management Services.

Ready to close the gaps you found?

Talk to Unisys about where your data center stands today and what it will take to get it ready for what's next.

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The data center
checklist for
scaling AI