Making AI an asset, not an expense
Making AI an asset, not an expense
让 AI 成为资产，而非支出
When customers talk about AI costs, the conversation usually starts with token prices and ends with access to the latest, most capable model in the cloud. Do they always need that level of capability? Not necessarily. But that is often where the conversation goes. 当客户谈论 AI 成本时，对话通常始于 Token（代币）价格，并止于对云端最新、最强模型的使用权。他们真的总是需要那种级别的能力吗？未必。但对话往往会走向这个方向。
As AI moves from experimentation to production, model choice is only part of the equation. When demand becomes steady and business-critical, a consumption-only approach can turn AI spending into a variable monthly line item that is difficult to forecast as usage, workloads, and model requirements change. At that point, the question is no longer simply which model to consume, or which provider offers the lowest token price: It is how to run AI economically, predictably, and at sustained scale. 随着 AI 从实验阶段转向生产阶段，模型选择只是其中的一部分。当需求变得稳定且对业务至关重要时，单纯的“按需付费”模式可能会使 AI 开支变成一项难以预测的月度变动成本，因为使用量、工作负载和模型需求都在不断变化。此时，问题不再仅仅是选择哪个模型，或者哪个供应商提供的 Token 价格最低，而是如何以经济、可预测且可持续的规模来运行 AI。
AI is moving from isolated pilots into production portfolios: assistants, retrieval-and-knowledge systems, and agentic applications. Customer-service, IT, research, and business-process agents can execute multi-step workflows across enterprise systems, creating recurring demand across models, data, and tools. This is already starting to happen. Deloitte’s 2026 State of AI in the Enterprise reflects what many leaders are seeing: worker access to AI rose 5% in 2025, and the share of companies with at least 40% of their AI projects in production is expected to double within six months. AI 正从孤立的试点项目转向生产组合：包括助手、检索与知识系统以及智能体（Agentic）应用。客户服务、IT、研究和业务流程智能体可以在企业系统中执行多步骤工作流，从而在模型、数据和工具方面产生持续的需求。这种情况已经开始发生。德勤《2026 年企业 AI 现状》报告反映了许多领导者的观察：2025 年员工对 AI 的使用率上升了 5%，预计在六个月内，将至少 40% 的 AI 项目投入生产的企业比例将翻一番。
When AI becomes a portfolio of always-on workloads, not a collection of experiments, the economics change. Consumption pricing gives teams flexibility and limits commitment. But when usage becomes steady, predictable, and large enough to keep capacity productive, leaders need to ask a different question: Does it still make economic sense to buy AI one request at a time, or is it time to invest in capacity they can optimize and control? 当 AI 成为一系列“始终在线”的工作负载，而非零散的实验时，其经济逻辑就变了。按需定价为团队提供了灵活性并限制了投入承诺。但当使用量变得稳定、可预测且足以保持产能利用率时，领导者需要提出一个不同的问题：一次一个请求地购买 AI 服务在经济上是否仍然合理？还是说，现在是时候投资于可以自行优化和控制的产能了？
This is not an abstract cloud-versus-on-premises debate. It is a workload-by-workload business decision. Over the next 12 to 18 months, how much AI demand can the company reasonably expect? How consistently will that capacity be used? When multiple workloads share infrastructure, the enterprise can spread fixed costs across more productive use—improving the economics of ownership. 这并非关于云端与本地部署的抽象争论，而是一个基于具体工作负载的商业决策。在未来 12 到 18 个月内，公司可以合理预期多少 AI 需求？这些产能的使用会有多稳定？当多个工作负载共享基础设施时，企业可以将固定成本分摊到更多的生产性用途中，从而改善拥有权的经济效益。
The question is how much you run. Ownership is not automatically the lower-cost answer. It only makes sense when an enterprise can keep capacity productive. Every organization has a crossover point, the level of sustained use at which owning capacity can become more economical than buying it one request at a time. There is no universal number. It depends on the models being used, the balance of input and output tokens, performance requirements, system design, energy costs, and the operating model required to support it. 问题在于你的运行规模。拥有权并不自动意味着更低的成本。只有当企业能够保持产能的生产力时，它才有意义。每个组织都有一个临界点，即持续使用量达到一定水平时，拥有产能比按需购买更经济。这个数字没有统一标准，它取决于所使用的模型、输入和输出 Token 的平衡、性能要求、系统设计、能源成本以及支持该系统所需的运营模式。
A retrieval-heavy knowledge system can have a very different cost profile from a simple assistant because it may process far more context for every interaction. Agentic workflows can be different again: a single business task may involve repeated reasoning, retrieval, model calls, and tool use. That is why generic cost benchmarks are not enough. Enterprises need to model their actual workloads, understand expected demand, and size capacity accordingly. At the right utilization level, the benefit is not only lower effective cost, but also greater predictability: the ability to manage AI capacity as a strategic infrastructure investment rather than watch a monthly spend line fluctuate with model use and workload demand. 一个重度依赖检索的知识系统，其成本结构可能与简单的助手截然不同，因为它在每次交互中可能需要处理更多的上下文。智能体工作流则更为复杂：单一的业务任务可能涉及反复的推理、检索、模型调用和工具使用。这就是为什么通用的成本基准是不够的。企业需要对其真实工作负载进行建模，了解预期需求，并据此规划产能规模。在合适的利用率水平下，其好处不仅是降低有效成本，更在于提高可预测性：能够将 AI 产能作为一项战略性基础设施投资来管理，而不是看着每月的支出随着模型使用和工作负载需求而波动。
Ownership only works when it is put to work. The capital decision is only half the equation. Even when the economics support ownership, capacity creates value only when the business gets workloads into production quickly and keeps them running. That takes more than installing infrastructure. It takes an operating model that connects the technology to adoption and business outcomes: bringing users and workloads on board, governing how AI is used, reviewing utilization, and continually identifying the next high-value use case. 拥有权只有在付诸实践时才有效。资本决策只是等式的一半。即使经济条件支持拥有权，只有当企业能够快速将工作负载投入生产并保持其持续运行时，产能才能创造价值。这不仅仅是安装基础设施，还需要一种将技术与采用率及业务成果联系起来的运营模式：让用户和工作负载接入系统、管理 AI 的使用方式、审查利用率，并不断识别下一个高价值用例。
The goal is to create value early, then build on it. That means measuring use, identifying underutilized capacity, and bringing additional high-value workloads onto the platform over time. Without that discipline, the business may never realize the economic value that justified the investment. With it, AI capacity becomes a productive asset the business can optimize, expand, and use to create measurable value. 目标是尽早创造价值，然后在此基础上不断积累。这意味着要衡量使用情况、识别未充分利用的产能，并随着时间的推移将更多高价值工作负载引入平台。如果没有这种纪律，企业可能永远无法实现证明投资合理性的经济价值。有了它，AI 产能就会成为一种生产性资产，企业可以对其进行优化、扩展，并利用它创造可衡量的价值。
Three questions to ask. Before committing capital, leaders should ask three questions: Is demand becoming steady, predictable, and large enough to justify dedicated capacity? At what level of usage does ownership make economic sense? Can we keep that capacity productive through adoption, governance, and continued use-case expansion? 需要问的三个问题。在投入资本之前，领导者应问三个问题：需求是否变得稳定、可预测且规模大到足以证明专用产能的合理性？在什么使用水平下，拥有权在经济上是合理的？我们能否通过采用、治理和持续的用例扩展来保持该产能的生产力？
Make the shift deliberately. As AI moves into production, the organizations that create the most value will look beyond token prices and the latest model. They will know when recurring demand calls for a different economic model—and they will have the operating discipline to make that capacity productive. That is when AI stops being an expense and becomes an asset. 审慎地进行转型。随着 AI 进入生产阶段，创造最大价值的组织将不再仅仅关注 Token 价格和最新模型。他们会知道何时持续的需求需要一种不同的经济模式，并且他们将具备使该产能发挥生产力的运营纪律。那时，AI 就不再是一项支出，而成为了一项资产。