评估自身AI基础设施就绪度的五种方法

Enthusiasm doesn’t run AI workloads – infrastructure does. As more organizations adopt AI, limitations in existing facilities are surfacing: insufficient power, cooling shortfalls, operational complexities, and new security risks. If these needs aren’t addressed, AI initiatives will stall, become hard to scale, or, worse, put sensitive data at risk.

AI can enable growth and long-term competitiveness, but only when leaders honestly evaluate readiness against what’s already in place, the investment strategy, and the plan for future demand. Getting this wrong isn’t a minor setback; it’s the difference between an AI investment that delivers and one that never gets off the ground. Before committing budget, use these five steps to pinpoint where infrastructure improvements are needed.

1. Set the Right Foundation

AI performance depends on the infrastructure behind it. Servers, storage, and networks must handle high compute density and large data flows without overheating or failing. Even capable AI systems underperform if foundational elements lag.

Consider a quality inspection use case on a production line. Cameras, storage, and compute must match the conveyor’s speed with real-time inference. If the pipeline can’t keep up, defective products slip through. The same principle applies inside the data center: a faster GPU cluster can be throttled by storage throughput, network congestion, or thermal limits.

This requires businesses to evaluate how systems work together rather than in isolation.

2. Prioritize Power and Cooling Capacity

A careful examination of power capacity and cooling infrastructure is necessary to determine whether an organization’s technology infrastructure can support the heavy lifting required by AI workloads. A rack designed for 5 kW won’t serve a 20-kW AI node. Without enough power behind it, an AI rollout can stall before it even starts.

Reliable power delivery matters just as much as capacity, and organizations must assess how much power their current environment can consistently supply and whether their cooling systems can handle the additional heat generated. Power supply and cooling must scale together to sustain continuous AI processing. A gap in either undermines the system.

3. Establish Security Across the AI Environment

AI applications often process sensitive and proprietary data. Protecting those assets requires several layers of security, including physical security, network defenses, and access controls. A gap in any of these layers can expose the rest, putting sensitive information at risk.

Organizations should also assess AI-specific risks, including model manipulation and data leakage, as well as any regulatory and policy obligations for their sector. A comprehensive approach protects both the underlying infrastructure and the AI environment it supports, treating security as foundational rather than an add-on.

4. Decide Between Predictable and Flexible Costs

Different organizations value cost predictability in different ways. Some need steady, predictable spending to manage budgets, while others prioritize agility and can accept variability. Neither approach is right or wrong; the choice depends on the organization’s infrastructure strategy and financial priorities, which in turn determine how to approach AI spending.

Shifting cloud pricing can add uncertainty. Align finance and engineering early to avoid mismatches – organizations that require budget certainty shouldn’t be surprised by variable bills, and those that value flexibility shouldn’t overpay for guarantees they don’t need.

Getting this alignment right from the outset means that AI spending decisions support the broader financial strategy rather than work against it. As AI adoption grows, FinOps principles help set guardrails while preserving room for innovation.

5. Build Room to Grow

AI workloads rarely stay static. Demand can grow as organizations expand existing applications, introduce new use cases, or experience increased usage tied to broader organizational growth. Infrastructure built for today’s workload can quickly become a constraint tomorrow.

This requires organizations to increase processing capacity and storage while also expanding the networks and cooling systems that support them. Scalability should be part of the infrastructure design from the outset. Building that flexibility gives the organization a way to respond as its AI requirements evolve.

From Readiness to Results

AI investments depend on infrastructure that supports them from initial deployment through future growth. IT leaders can use this five-part framework to identify where their current environment falls short and prioritize the improvements needed to support their AI plans. Before committing to the next AI purchase, assess readiness and close gaps – or risk becoming the reason initiatives fail to deliver.

About the Authors

Unisys

Patrycja Sobera is the Senior Vice President and General Manager of the Digital Workplace Solutions business unit at Unisys. In this pivotal role, she leads high-profile teams dedicated to continual improvement and creating modern, differentiated end-user experiences that drive client breakthroughs and propel the business forward.

Her 20+ year career includes delivering cutting-edge technology solutions across diverse industries, including banking, capital markets, and telecommunications, at organizations such as Marsh McLennan, Getronics, Computacenter, and Compaq (HP company).

Patrycja holds a Master of Arts degree in Modern Languages and French Literature from the University of Wroclaw.