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.热情无法运行 AI 工作负载——基础设施才能做到。随着越来越多的组织采用 AI，现有设施的局限正在显现：电力不足、冷却短缺、运营复杂以及新的安全风险。如果这些需求得不到解决，AI 举措就会停滞、难以扩展，更糟的是还会使敏感数据面临风险。

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.AI 能够推动增长和长期竞争力，但前提是领导者必须对照现有基础、投资战略以及未来需求计划，诚实地评估就绪程度。判断错误并非小挫折；它决定了 AI 投资是产生回报，还是永远无法启动。在投入预算之前，请通过以下五个步骤查明需要在何处改进基础设施。

1. Set the Right Foundation1. 奠定正确的基础

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.AI 性能取决于其背后的基础设施。服务器、存储和网络必须能够处理高计算密度和大型数据流，而不会过热或发生故障。如果基础要素滞后，即使功能强大的 AI 系统也会表现不佳。

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.以生产线上的质量检测用例为例。摄像头、存储和计算必须以实时推理匹配传送带的速度。如果该流水线无法跟上，缺陷产品就会漏过。同样的原则也适用于数据中心内部：更快的 GPU 集群也可能因存储吞吐量、网络拥塞或散热限制而性能受限。

This requires businesses to evaluate how systems work together rather than in isolation.这要求企业评估系统如何协同工作，而非孤立运行。

2. Prioritize Power and Cooling Capacity2. 优先考虑电力与冷却容量

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.必须仔细核查电力容量和冷却基础设施，以确定组织的技术基础设施能否支撑 AI 工作负载所需的重负荷。为 5 kW 设计的机架无法服务于 20-kW 的 AI 节点。没有充足的电力支撑，AI 部署甚至可能在开始前就陷入停滞。

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.可靠供电与容量同样重要，组织必须评估当前环境能够持续供应多少电力，以及其冷却系统能否处理产生的额外热量。电力供应与冷却必须同步扩展，以维持持续的 AI 处理。两者中任何一方面存在缺口都会削弱系统。

3. Establish Security Across the AI Environment3. 构建覆盖整个 AI 环境的安全体系

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.AI 应用通常处理敏感和专有数据。保护这些资产需要多层安全措施，包括实体安全、网络防御和访问控制。这些安全层中任何一层存在缺口，都可能波及其他层，使敏感信息面临风险。

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.组织还应评估 AI 特有风险，包括模型操纵和数据泄露，以及所在行业的任何法规和政策义务。全面的方法可同时保护底层基础设施及其支持的 AI 环境，将安全视为基础而非附加功能。

4. Decide Between Predictable and Flexible Costs4. 在可预测成本与灵活成本之间做出决策

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.不同组织对成本可预测性的重视方式各不相同。一些组织需要稳定、可预测的支出来管理预算，而另一些组织则优先考虑敏捷性，能够接受变化。两种方法没有对错；选择取决于组织的基础设施战略和财务优先事项，这些又决定了应如何处理 AI 支出。

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.不断变化的云定价可能增加不确定性。应尽早推动财务与工程团队协调一致，避免错配——要求预算确定性的组织不应因波动的账单而措手不及，重视灵活性的组织也不应为不需要的保障支付过高费用。

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.从一开始就做好这种协调，意味着 AI 支出决策会支持更广泛的财务战略，而不是与之相悖。随着 AI 应用增长，FinOps 原则有助于设置护栏，同时保留创新空间。

5. Build Room to Grow5. 预留增长空间

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.AI 工作负载很少保持静止。随着组织扩展现有应用、推出新用例，或因整体组织增长而使用量增加，需求可能会增长。为当今工作负载构建的基础设施很快就会成为明天的制约因素。

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.这要求组织提高处理能力和存储容量，同时扩展支撑它们的网络和冷却系统。可扩展性应从一开始就成为基础设施设计的一部分。构建这种灵活性，可让组织在 AI 需求演变时有能力作出响应。

From Readiness to Results从就绪到成果

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.AI 投资依赖于从初始部署到未来增长都能为其提供支持的基础设施。IT 领导者可以使用这个由五部分组成的框架，找出当前环境的不足之处，并优先安排支持其 AI 计划所需的改进。在进行下一笔 AI 采购之前，先评估就绪度并弥补差距——否则就有可能成为各项举措无法交付成果的原因。

About the Authors关于作者

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.Patrycja Sobera 是 Unisys 数字工作场所解决方案业务部的高级副总裁兼总经理。在这一关键职位上，她领导多个备受瞩目的团队，致力于持续改进并打造现代化、差异化的终端用户体验，从而推动客户实现突破并促进业务发展。

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).在 20+ 年的职业生涯中，她曾在 Marsh McLennan、Getronics、Computacenter 和 Compaq（HP 旗下公司）等机构，跨银行、资本市场和电信等多个行业交付前沿技术解决方案。

Patrycja holds a Master of Arts degree in Modern Languages and French Literature from the University of Wroclaw.Patrycja 拥有弗罗茨瓦夫大学现代语言与法国文学文学硕士学位。