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AI支出正在重塑企业IT预算。 首席信息干事如何管理不断上升的成本，确定投资的优先次序，衡量生产率收益以外的收益。
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AI投资增长，要求企业重新考虑IT预算。 福雷斯特预测，AI将在2027年占商业技术支出的约20%. 估计包含在10月21日的一份报告中，包括技术供应商的基础设施开支；这并不意味着平均企业将把自己信息技术预算的这一份额专用于AI.
在这部QQA中，福雷斯特公司的分析师布莱恩·霍普金斯(Brian Hopkins)讨论了这给企业预算带来的压力，CIOs如何确定支出的优先次序，以及如何判断AI投资是否带来持久价值。
AI支出增加的原因是什么？
布莱恩·霍普金斯(Brian Hopkins)：几乎所有的增长都来自基础设施和计算能力——服务器，芯片，云服务，AI模型访问和软件订阅。
AI也在改变公司为软件付费的方式。 传统上，各组织可以据此估计申请的年度费用和预算。 有了基因AI，成本更难预测。 供应商采用象征性消费、使用费和信贷。 而agent的成本随任务的复杂性和所需的计算功率而变化。
CIOs在哪里能找到AI投资的钱？.
霍普金斯：企业软件支出的增长比通货膨胀慢，削弱了购买力。
为了补偿，公司正在推动降低IT服务合同的价格，认为供应商应该通过AI驱动的增效，并在内部带来软件开发、支持和客户服务以减少外部成本。
企业应在哪里避免削减技术开支？
霍普金斯：安全是显而易见的。 各组织推迟了安保投资，因为它们将安保视为一个成本中心。 但随着攻击者使用AI agents来寻找和利用我们之前所未见的规模上的弱点，风险变得越来越紧迫。
公司也没有更新长期昂贵的ERP基础设施和平台，如Salesforce. 首席信息干事多年来一直要求在这些领域投资，但企业往往拖延这些决定。
这些长期问题正在趋同，因为AI成本的上升给信息技术预算带来了额外压力。
公司对AI的ROI有什么错误？
霍普金斯：霍普金斯：我们看到三个财务成果——收入增长、效率/成本降低和更好的风险管理——分为三个时间范围：生产力(短期)、参与(中期)和转变(长期)。
生产力是最容易衡量的。 公司可以跟踪AI是减少员工花费在写客户邮件上的时间，还是加快客户服务案件解析。 生产率是最常报道的人工智能的好处，但其底线影响却低得多。
参与涉及改进客户和雇员与企业互动的方式。 例如，帮助客户获得答案，更容易完成交易，可以提高交易频率，提高满意度。
转型涉及重新思考业务流程，加快产品交付，创造新产品或者在竞争者之前进入市场。 这些投资具有更大的风险，但能够创造持久的竞争优势。
仅注重生产力的公司可能丧失参与和转型的更大机会。 取得这些好处还需要各部门之间的协调，并有共同的优先事项和商定结果。
公司如何计算AI不明显成本？.
霍普金斯：我们称之为“信托税”
公司可能期望agent只是将一个过程自动化，但往往还需要测试和红色小组，增加护栏和监测，保持一个有治理审查的集中登记册，如果它与客户或合作伙伴互动，则核实其身份。
这些要求增加了间接费用，业界仍在努力确定各组织应花多少钱。
首席信息干事应在哪些方面优先考虑AI投资？
霍普金斯：霍普金斯：有三个投资重点：安全，数据和AI平台。 但CIOs不能在三个领域全部解决之前推迟AI的通过，特别是在受到快速干扰的行业。
公司应首先制定AI战略，确定它们的主要商业挑战、竞争优势和预期成果。 它们应将该战略转化为可衡量的目标，并建立一个投资组合，由业务主管负责成果，并由投资组合管理人负责供资。
例如，一家石油公司可以通过每桶成本、生产和安全来衡量业绩。 AI投资应与改进这些衡量标准挂钩。 一旦公司了解某项投资将如何影响企业的衡量标准，就可以确定实现这一结果所需的基础工作。
公司是否将继续增加AI预算而没有明确回报？
霍普金斯：组织需要投资于安全，负责任的AI，治理，FinOps和对agent接入和消费的控制，这很可能是2027年的挑战。
一些公司将承认AI通过参与和转型可以实现的价值。 他们将有能力建立商业案例并获得资金。 仍然把AI作为另一个生产力工具的公司更有可能挣扎。
许多公司可能会退缩，而一个较小的集团则推进正确的策略——这一鸿沟在2028–2029年之前很可能无法看到其全部财务影响。
编辑说明：这次采访经过了编辑，以明晰简洁。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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AI spending is reshaping enterprise IT budgets. How CIOs can manage rising costs, prioritize investments and measure returns beyond productivity gains.
4 Min Read
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Growing investment in AI is challenging enterprises to rethink IT budgets. Forrester forecasts that AI will account for about 20% of business technology spending in 2027. The estimate, included in an Oct. 21 report, includes infrastructure spending by technology vendors; it doesn't mean the average enterprise will devote that share of its own IT budget to AI.
In this Q&A, Brian Hopkins, an analyst at Forrester, discusses the pressures this is putting on enterprise budgets, how CIOs can prioritize spending, and how to judge whether AI investments deliver lasting value.
What's driving the increase in AI spending?
Brian Hopkins: Almost all of the growth comes from infrastructure and computing capacity — servers, chips, cloud services, AI model access and software subscriptions.
AI is also changing how companies pay for software. Traditionally, organizations could estimate an application's annual cost and budget accordingly. With generative AI, costs are harder to predict. Vendors are introducing token-based consumption, usage charges and credits. And agent costs vary with task complexity and the computing power required.
Where can CIOs find money for AI investments?
Hopkins: Enterprise software spending is rising more slowly than inflation, eroding purchasing power.
To compensate, companies are pushing for lower prices on IT services contracts, arguing providers should pass on AI-driven efficiency gains, and are bringing software development, support, and customer service in-house to cut external costs.
Where should enterprises avoid cutting technology spending?
Hopkins: Security is the obvious one. Organizations have delayed security investments because they view security as a cost center. But risks are becoming more urgent as attackers use AI agents to find and exploit vulnerabilities at a scale we haven't seen before.
Companies also haven't modernized longstanding, expensive ERP infrastructure and platforms such as Salesforce. CIOs have been asking for investment in these areas for years, but businesses have often delayed those decisions.
These longstanding issues are converging just as rising AI costs put additional pressure on IT budgets.
What are companies getting wrong about the ROI of AI?
Hopkins: We see three financial outcomes — revenue growth, efficiency/cost reduction, and better risk management — organized into three time horizons: productivity (short term), engagement (medium term), and transformation (long term).
Productivity is easiest to measure. Companies can track whether AI reduces the time employees spend writing customer emails or speeds up customer service case resolution. Productivity is the most commonly reported AI benefit, yet its bottom-line impact ranks much lower.
Engagement involves improving how customers and employees interact with a business. For example, helping customers get answers and complete transactions more easily can increase transaction frequency and improve satisfaction.
Transformation involves rethinking business processes, accelerating product delivery, creating new products or entering markets ahead of competitors. These investments carry more risk but can create a lasting competitive advantage.
Companies that focus only on productivity risk missing the larger opportunities in engagement and transformation. Capturing those benefits also requires coordination across departments, with shared priorities and agreed-upon outcomes.
How are companies accounting for AI's less visible costs?
Hopkins: We call those costs the "trust tax," and agentic AI is bringing them to the forefront.
Companies might expect an agent to simply automate a process but often need to also test and red-team it, add guardrails and monitoring, maintain a centralized registry with governance review, and verify its identity if it interacts with customers or partners.
These requirements create additional overhead, and the industry is still trying to determine how much organizations should expect to spend.
Where should CIOs prioritize AI investments?
Hopkins: There are three main investment priorities: security, data and the AI platform. But CIOs can't delay AI adoption until all three areas are fully addressed, especially in industries undergoing rapid disruption.
Companies should start with an AI strategy that identifies their major business challenges, competitive advantages and desired outcomes. They should translate that strategy into measurable targets and create an investment portfolio, with a business executive accountable for results and a portfolio manager responsible for funding.
For example, an oil company might measure performance through cost per barrel, production and safety. AI investments should be tied to improving those metrics. Once a company understands how an investment will affect a business metric, it can identify the foundational work required to achieve that outcome.
Will companies continue to increase AI budgets without clear returns?
Hopkins: Organizations need to invest in security, responsible AI, governance, FinOps and controls over agent access and consumption in what is likely to be a challenging 2027.
Some companies will recognize the value AI can deliver through engagement and transformation. They'll be able to build business cases and secure funding. Companies that remain focused on AI as just another productivity tool are more likely to struggle.
Many companies may pull back, while a smaller group pushes forward with the right strategy — a divide whose full financial impact likely won't be visible until 2028–2029.
Editor’s note: This interview has been edited for clarity and conciseness.
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