Many enterprises are off to the races with agentic AI, snapping up technology in areas such as software development, customer support, IT operations and cybersecurity. But the return on this type of AI investment remains murky for some: A very sizable 94% of respondents said their organizations lacked a consistent, enterprise-wide framework for evaluating AI’s ROI.许多企业在智能体 AI（Agentic AI）领域正全速前进，积极抢占软件开发、客户支持、IT 运维和网络安全等技术高地。然而，对于部分企业而言，此类 AI 投资的回报依然扑朔迷离：高达 94% 的受访者表示，其组织内部缺乏一套统一的、覆盖全企业的 AI 投资回报率（ROI）评估框架。

What’s more, only 16% of respondents said they see a positive ROI on more than half their AI projects, while 31% see ROI on less than a quarter.此外，仅有 16% 的受访者表示其半数以上的 AI 项目实现了正向投资回报，而 31% 的受访者表示其投资回报项目不足四分之一。

Those were some of the key takeaways from our just-published Battery Ventures State of Enterprise Tech Spending survey, which includes responses from 100 senior technology leaders representing more than $66 billion in annual technology spend. Overall, the survey still indicated a robust appetite from enterprises to continue spending on AI. Trends in what types of technology these leaders are evaluating, testing and buying—including new AI tools and platforms—provide key insights for tech startups trying to boost sales and scale their companies.以上结论摘自我们刚刚发布的《Battery Ventures 企业技术支出状况调查》。该调查涵盖了 100 位资深技术领导者的反馈，他们所代表的年度技术支出总额超过 660 亿美元。总体而言，调查表明企业对持续投入 AI 仍保持着浓厚兴趣。这些领导者在评估、测试和采购各类技术（包括新型 AI 工具和平台）时的趋势，为那些试图提升销售额并扩大规模的科技初创公司提供了关键洞察。

Indeed, enterprise spending on new technologies, notably AI, is brisk today: Seventy-six percent of respondents said they’re already in production with generative AI, with 100% planning to deploy AI programs within two years. Not a single respondent said they were cutting AI spending. An impressive 81% plan to increase overall tech spending over the next 12 months, up from 65% in December 2025.事实上，企业在包括 AI 在内的新技术上的支出如今正处于活跃期：76% 的受访者表示已将生成式 AI 投入生产，并有 100% 的受访者计划在两年内部署 AI 项目。没有受访者表示正在削减 AI 预算。令人印象深刻的是，81% 的受访者计划在未来 12 个月内增加整体技术支出，这一比例高于 2025 年 12 月的 65%。

In addition, 49% of respondents said they’re actively deploying agentic AI, up from 33% six months ago. Conviction in this type of tech remains high among buyers, with 50% of respondents saying they’re already scaling agentic workflows across business functions.此外，49% 的受访者表示正在积极部署智能体 AI，这一比例较六个月前的 33% 有所上升。买家对该类技术的信心依然高涨，50% 的受访者表示已在各业务职能中推广智能体工作流。

But the sobering news is that many of these buyers are still struggling with AI ROI, at least at the enterprise level. Of those who said they still don’t have an enterprise-wide, AI ROI framework, 42% said they were measuring ROI inconsistently across the organization; 43% were still defining their measurement approach; and nine percent reported they still have no way to measure ROI yet.然而，令人清醒的现实是，许多买家在 AI 投资回报方面仍面临挑战，至少在企业层面是如此。在那些表示尚无全企业范围 AI ROI 框架的受访者中，42% 的人表示组织内部的衡量标准不一致；43% 的人仍在制定衡量方法；还有 9% 的人表示目前尚无任何衡量 ROI 的手段。

Still, 53% of enterprises saw a clear ROI on AI in general terms, with the industries most convinced of AI’s financial and operational impact being IT and healthcare.尽管如此，53% 的企业认为 AI 带来了明确的总体投资回报，其中 IT 和医疗保健行业对 AI 的财务和运营影响最为认可。

Other findings:其他调查发现：

• Humans remain firmly in the AI loop today. Of our respondents, 70% said humans were overseeing or making final decisions on their AI projects; only five percent said they expect fully autonomous systems to be deployed in their enterprises in the next 12 months.目前，人类在 AI 流程中仍占据核心地位。在我们的受访者中，70% 表示人类负责监督或对 AI 项目做出最终决策；仅有 5% 的人预计在未来 12 个月内会在企业内部部署全自主系统。
• Not all spending on AI is net new. Seventy-eight percent of the survey’s respondents said they are funding AI at least partly by reallocating existing tech budgets. Forty-six percent said they blend net-new spending with reallocation.并非所有 AI 支出都是全新的增量资金。78% 的受访者表示，他们的 AI 资金至少部分来自对现有技术预算的重新分配。46% 的受访者表示，他们采取了新增支出与预算重组相结合的方式。
• With AI technology changing so rapidly and enterprises still experimenting with use cases, enterprise contract lengths have shortened significantly. Only 29% of our respondents said they were signing tech contracts of 25 months or longer. Historically, 64% of organizations signed contracts of 25 months or .由于 AI 技术迭代极其迅速，且企业仍处于用例试验阶段，企业合同期限已显著缩短。仅有 29% 的受访者表示签署了 25 个月或以上的技术合同。而在过去，64% 的组织会签署 25 个月或更长的合同。
• Still, enterprises seem to believe AI represents a core, long-term, structural shift in their operations, particularly in software development. In the survey, 84% said they expect AI to materially reshape their software-delivery cycle within two years. This is one factor driving increasing technology budgets overall.尽管如此，企业似乎认为 AI 代表了其运营中核心的、长期的结构性变革，尤其是在软件开发领域。调查显示，84% 的受访者预计 AI 将在两年内实质性地重塑其软件交付周期。这也是推动整体技术预算增长的因素之一。
• And AI has risen to the top of our respondents’ list of their top five enterprise priorities. Right now, those priorities are generative AI; agentic AI; cloud infrastructure; data warehousing; and cybersecurity.AI 已跃升至受访者五大企业优先事项清单之首。目前，这些优先事项包括：生成式 AI、智能体 AI、云基础设施、数据仓库以及网络安全。
• Finally, we saw some emerging trends in which LLMs enterprises are increasingly turning to. A strong 75% of respondents ranked Claude, from Anthropic, among their top three model choices, ahead of Microsoft (72%) and OpenAI (58%). Still, Microsoft’s Azure OpenAI/Copilot product remains the single most-used model.最后，我们观察到企业在倾向使用的大语言模型（LLM）方面出现了一些新兴趋势。高达 75% 的受访者将 Anthropic 旗下的 Claude 列为首选模型前三名，超过了微软（72%）和 OpenAI（58%）。不过，微软的 Azure OpenAI/Copilot 产品仍然是目前使用最广泛的模型。
• Code generation and testing are the top engineering re-evaluations by a wide margin: 82% of enterprises are re-evaluating code generation and refactoring, and 75% are re-evaluating testing and QA automation. These are the two highest numbers in the entire re-evaluation dataset. Code security and vulnerability detection is at 70%. Together these three paint a clear picture of where AI is restructuring the engineering stack and where opportunities for AI startups exist.代码生成和测试是工程领域重估程度最高的项目，遥遥领先：82% 的企业正在重新评估代码生成和重构，75% 的企业正在重新评估测试和质量保证（QA）自动化。这是整个重估数据集中的最高两项。代码安全和漏洞检测占比为 70%。这三者共同清晰地描绘了 AI 正在重构工程技术栈的版图，也揭示了 AI 初创公司的机遇所在。
• Security review is the biggest breaking point in the AI SDLC, according to the survey. When asked to name the biggest SDLC breaking points as AI agents become more common in the delivery pipeline, respondents named security review and vulnerability management as their top concerns, followed by compliance and auditability and developer skill degradation.调查显示，安全审查是 AI 软件开发生命周期（SDLC）中最大的断点。当被问及随着 AI 智能体在交付流水线中日益普及，最大的 SDLC 断点是什么时，受访者将安全审查和漏洞管理列为首要担忧，其次是合规性与可审计性，以及开发者技能退化问题。
• AI governance is moving to the C-suite, but ownership is still fragmented. Twenty-five percent of enterprises now have a “chief AI officer”, up from 23% in Q3 2025. But 54% still have AI sitting under existing tech leadership with no dedicated owner, and seven percent have no formal AI leadership role at all.AI 治理正在进入高管层，但所有权依然分散。25% 的企业现已设立了“首席 AI 官”，高于 2025 年第三季度的 23%。但 54% 的企业仍将 AI 职能置于现有技术领导层之下，且没有专门的负责人；另有 7% 的企业根本没有任何正式的 AI 领导角色。

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The information contained in this market commentary is based solely on the opinions of Scott Goering, Evan Witte and Nick Elsner, and nothing should be construed as investment advice. This material is provided for informational purposes, and it is not, and may not be relied on in any manner as legal, tax or investment advice or as an offer to sell or a solicitation of an offer to buy an interest in any fund or investment vehicle managed by Battery Ventures or any other Battery entity. The views expressed here are solely those of the authors.本市场评论中包含的信息仅基于 Scott Goering、Evan Witte 和 Nick Elsner 的个人观点，不应被视为投资建议。本材料仅供参考，在任何情况下均不得被视为法律、税务或投资建议，也不得被视为出售或招揽购买 Battery Ventures 或任何其他 Battery 关联实体所管理之基金或投资工具权益的要约。此处表达的观点仅代表作者个人。

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