超过一半的首席信息官（CIO）明年可能无法实现其与人工智能相关的投资回报率（ROI）目标，这是根据Forrester的分析得出的结论。该分析机构的《2026年人工智能现状调查》发现，45%的AI决策者需要在一年内看到正向ROI，才会认为其投资成功。部分技术领导者将更善于将AI支出与预期回报挂钩。然而，Forrester预计，成功将局限于孤立的用例，而非整体项目，这将远远不够。
More than half of CIOs are likely to miss their AI-related ROI targets next year, according to Forrester. The analyst's State Of AI Survey 2026 found that 45 percent of AI decision-makers need positive ROI within a year to consider their investments successful. Some tech leaders will get better at tying AI spend to planned returns. However, Forrester expects success to be confined to isolated use cases rather than whole programs, which won't be enough.
“正如以往的新兴技术一样，组织架构、流程和工作方式需要改变，才能利用AI推动可衡量的、全公司范围的成果，”报告称。仅有22%的AI决策者报告称，过去12个月AI对息税折旧摊销前利润（EBITDA）产生了正向影响。“技术采用投入与组织变革之间的这种错位，将推迟更高ROI目标的实现，”报告补充道。无论如何，资金将继续通过IT预算流入。
“As with prior emerging technologies, organizational structures, processes, and ways of working need to change to drive measurable, firmwide results with AI,” the report says. Only 22 percent of AI decision-makers report a positive AI impact on EBITDA (earnings before income tax, depreciation and amortization) over the past 12 months. “This mismatch between the investment into technology adoption and organizational change will delay the realization of heightened ROI targets,” the report adds. The money will keep flowing through IT budgets regardless.
在其《技术与安全预测》中，Forrester称，明年AI将占所有企业技术支出的20%。Forrester首席研究官Sharyn Leaver表示，这将增加领导者证明AI价值的压力，同时要求他们控制成本、基础设施和风险。“与此同时，新兴技术、地缘政治压力，以及AI在技术运营中日益增长的应用，将引入新的复杂性和潜在故障点，”她表示。
In its Technology & Security Predictions, Forrester claimed AI will account for 20 percent of all business technology spend next year. Sharyn Leaver, chief research officer at Forrester, said that will increase the pressure on leaders to prove AI's value while keeping costs, infrastructure, and risk under control. “At the same time, emerging technologies, geopolitical pressures, and the growing use of AI across technology operations will introduce new complexities and potential points of failure,” she said.
当技术团队努力从不断增长的AI支出中获取回报时，他们将试图通过投资开放权重生成式AI模型来获得更高的投入产出比，这些模型可以以极低的成本接近领先专有系统的性能。Forrester预测，开放权重模型将占AI Token总量的一半，但仅占企业支出的5%。
While tech teams struggle to get returns from growing AI spending, they will try to get more bang for their buck by investing in open-weight GenAI models, which can approach similar performance to the leading proprietary systems, at a fraction of the cost. Open-weight models will represent half of AI tokens but only five percent of enterprise spend, Forrester predicts.
“Gemma、Llama 或 Qwen 等开放权重模型已足以应对产生最多 Token 量的企业工作负载，例如文档处理、客户支持对话、内部知识搜索以及智能体工作流中的常规子步骤，”Forrester 表示。前沿模型，包括 Anthropic 的 Claude Opus 5.5 和 Claude Sonnet 5.5、OpenAI 的 GPT-6.1 Sol 和 GPT-6 Astra，以及 Google 的 Gemini 4 Argon，将被保留用于“极少数错误代价高昂的深度知识和关键任务自动化，但 Token 计数将向开放权重倾斜”，Forrester 表示。
“Open-weight models such as Gemma, Llama, or Qwen are good enough for the enterprise workloads that generate the most token volume, such as document processing, customer support conversations, internal knowledge search, and the routine substeps inside agentic workflows,” Forrester said. Frontier models, including Anthropic's Claude Opus 5.5 and Claude Sonnet 5.5, OpenAI's GPT-6.1 Sol and GPT-6 Astra, and Google's Gemini 4 Argon, will be reserved for “the few deep knowledge and critical task automations where errors are expensive, but the token count will tip toward open weight”, Forrester said.