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办公和协同软件市场正在经历从“AI助手”向“AI Agent”和“AI原生工作流”的剧烈转型。各组织正快速从孤立的AI试点项目，转向企业级规模化部署，从根本上重塑人力与智能系统之间的工作分配模式。这一转型凸显了人类独有的核心能力——判断力、创造力与关系管理能力，而AI智能体则负责处理常规性、重复性及知识密集型任务。进入2026年，这个市场的硝烟已经从简单的对话框延伸到了企业的核心业务链条中。OpenClaw这样的AI Agent开源项目的流行标志着AI原生的智能化工作平台在企业数字化转型中的加速落地：其以多Agent驱动、自动化流程和深度生态集成为核心，极大提升了团队协作效率和创新能力。AI Agent不仅重塑了智能化工作平台的用户体验，还推动了市场对AI驱动工作流、安全合规和开放集成生态的高度关注。

国际数据公司（IDC）于2026年6月发布的《中国智能化工作平台In-App AI Agent评估, 2026》对于智能化工作平台领域中的AI助手/Agent能力进行评估，希望通过对中国市场中主要产品技术提供商的产品评测以及对最终用户的客观访谈来帮助市场更加全面地了解中国智能化工作平台AI能力的发展现状，能力特点和应用情况以及未来的技术发展趋势。IDC定义的智能化工作平台是指通过整合企业通讯、协同办公应用、生产力工作套件以及AI助手/AI Agent等数字化工具，帮助个人和组织高效、有效地完成任务或工作，实现目标的综合性平台。评估的内容包括智能办公能力、AI Agent流程自动化能力、集成和跨平台能力、基安全合规/Agent执行安全能力、行业解决方案和客户、AI生态建设等维度。

IDC通过这次评估观察到，智能化工作平台头部厂商的AI能力已经迈向逐渐成熟的发展阶段。领先厂商已经能够支持智能写作、会议纪要生成、文档总结、知识库问答、数据收集和分析、日程与任务建议、跨应用信息检索，以及基于用户指令调用部分办公工具完成稍有难度的任务。这说明AI能力正在从辅助内容生产，逐步走向办公流程中的任务协同。但是从一些评估维度来看，AI Agent流程自动化能力、Agent执行安全和AI生态建设整体得分偏低，说明该领域仍在快速探索阶段。模型稳定性、任务理解能力、流程编排能力、权限控制机制、执行边界管理以及生态伙伴协同机制尚未完全成熟，不同厂商之间的能力差异也比较明显。总体来看，当前市场的竞争核心正在从传统协同工具转向AI驱动的平台能力。未来市场竞争的关键，将不再只是AI功能的数量，而是厂商能否让AI Agent真正进入企业业务流程，并在可控、安全、可审计的前提下实现规模化应用。

基于本次评估情况和对未来趋势的判断，IDC认为，未来中国智能化工作平台市场将有以下发展趋势：

1. AI原生与多Agent驱动的智能化工作平台将成为主流

未来的智能化工作平台将以AI为核心驱动力，原生集成多模态协作、智能代理（Agent）、自动化、知识管理等能力。AI不仅承担任务自动化、智能推荐、内容生成，还将通过多Agent协作实现流程编排、知识流转和业务决策支持。IDC预测，企业将逐步从“AI插件”过渡到“AI原生”平台，AI与人、AI与AI之间的协作将成为提升创新力和敏捷性的关键。Agentic协作将推动企业从单一任务自动化迈向端到端的智能业务流程重塑。

2. 多模态与可视化协作体验持续深化

随着智能化办公场景的复杂化，智能化工作平台正快速融合文本、语音、视频、视觉画布、手势等多种交互方式。可视化协作（如数字白板、流程画布、可视化工作流）和“多人游戏化”体验成为主流。根据IDC全球调研数据，82%的企业认为视觉协作显著提升了创新与决策效率。未来，平台将进一步支持XR、智能硬件（如AI眼镜）、多语言和无障碍访问，实现全员、全场景的沉浸式协作体验。

3. 数据治理、信任与合规成为平台落地前提

随着AI和多方协作的深入，数据安全、隐私保护、合规和AI治理成为平台采购和部署的核心考量。平台需支持细粒度权限，权限继承自动化，数据驻留，合规认证（如GDPR、ISO等），合规审计可追溯，并通过透明的数据政策和治理机制建立员工与组织间的信任。IDC强调，信任是数字协作的基石，缺乏信任的协作空间难以实现持续创新和高效运营。

4. AI治理与FinOps兴起

随着数字员工增多，企业开始担心“AI乱花钱”或“AI越权”。因此，海外出现了专门审计AI Token消耗和AI安全合规的工具。Token成为财务成本，企业开始像审计水电费一样审计AI的Token消耗量。厂商需要推出更精准的ROI分析工具，用来证明AI助理到底帮员工省了多少时间。

5. 平台化与低代码深度融合

智能化工作平台正在从沟通和流程工具，升级为企业统一的工作入口。它可以通过连接ERP、CRM、HRM、财务和供应链等系统，把组织协作、业务流程和数据处理整合到同一界面中。与此同时，低代码能力将与AI和AI Agent深度结合。业务人员可以用自然语言描述需求，由AI辅助生成表单、流程、报表和轻量级应用；AI Agent则可以根据业务规则自动触发流程、分派任务、同步数据和提醒异常。

分析师观点

IDC中国助理研究总监李昭表示，AI在办公场景中的演进，正在从“提升个体效率”加速走向“重构办公执行体系”。以OpenClaw为代表的新一代Agent框架，已不再局限于知识问答、内容生成、沟通协作、会议助手、数据和表格处理、日程规划等传统助手能力，而是进一步具备跨应用调用、连续任务执行和自主编排流程的特征，这标志着企业办公AI正从“会说”走向“会做”。软件不再只是工具，而是数字员工。AI Agent不再是被动响应，而是具备了规划能力，能自主跨应用完成复杂任务。与此同时，近期市场变化也表明，Agent的价值正在快速释放，但其规模化落地已不再只是技术问题，而是进入“技术能力、治理能力与成本模型”三重约束并行的新阶段。

进一步交流

如果您希望进一步了解Agent在企业软件中的落地路径、市场演进趋势或对自身业务的具体影响，欢迎与IDC分析师团队联系(点击此处）。IDC将基于持续的市场跟踪与研究，提供更具针对性的洞察与建议，支持企业与厂商在这一轮变革中做出更有前瞻性的决策。

Lizzie Li

Lizzie Li - Associate Research Director

Analyst Profile
Lizzie Li is Associate Research Director of IDC China's Enterprise System and Software Research that focuses on research and analysis of the China Datacenter, Cloud Computing, and IT infrastructure markets. She also provides intelligence and consulting services in customized projects for…

Salesforce在今年的开发者大会TDX上，正式发布了名为Headless 360的新举措，旨在使得Salesforce平台上的所有功能，均可通过API（应用程序接口）、MCP（模型上下文协议）服务器或CLI（命令行界面）命令的形式对外暴露，从而支持编程智能体或面向特定客户需求的自定义智能体进行调用。

Salesforce的这一动作，并不仅仅是一次产品能力开放，更反映出企业软件正在发生一个根本性变化：应用的核心对象，正在从“人”转向“AI”。当越来越多系统开始以开放接口、连接器以及Agent相关协议而非UI作为主要交互方式时，企业软件的竞争逻辑、产品形态与商业模式，也正在被重新定义。

从应用到Agent，竞争格局与商业模式重塑

企业软件市场正经历一场深刻变革。AI Agent不仅是技术升级，更是市场竞争、商业模式和生态体系的全面重置。IDC最新调研显示，2026年全球72%的企业已将AI Agent投入生产，51.6%已将Agent嵌入核心业务流程。Agent正成为企业软件的“新入口”，未来三年内，Agent接口将超过一半，传统接口将迅速降低接近至零。

传统软件厂商以功能和UI为核心竞争力，如今则被“结果导向、自动执行”的Agent所取代。Agent能够跨系统自动编排任务，推动业务流程从“人操作”转向“意图驱动、自动完成”。IDC预测，到2027年，Agent自动化将增强40%以上的企业应用能力，重塑三分之一的业务流程和工作流。

IDC的核心判断是：Agent并不是一次功能升级，而是在重构企业应用的形态。

应用正在“退居后台”：Agent成为新的执行层

在传统模式下，企业应用的价值建立在“界面+流程”之上。用户进入系统、触发操作、完成任务，软件围绕“人如何使用系统”来设计。

但Agent改变了这个前提。

它可以基于上下文理解需求、跨系统调用能力，并直接完成任务。在越来越多场景中，用户不再需要进入某一个具体系统，任务已经在后台被执行。

这意味着一个关键变化：应用不再是工作的入口，Agent才是。

一旦入口发生变化，应用的角色也随之改变——从“交互界面”转向“能力提供者”，从前台走向后台。

应用边界开始消失：竞争从产品走向生态

当Agent可以跨系统完成任务时，原本清晰的应用边界开始变得模糊。

同一个业务流程，可能同时调用CRM、ERP和供应链系统；而对用户来说，这一切被封装在一次“请求”之中。应用不再以单独系统的形式被感知，而是作为能力被调用。

关键影响在于：竞争逻辑正在发生改变——除了“谁的功能更强”，还需要考虑“谁更容易被调用和整合”。

这也是为什么当前市场中，开放接口、连接器以及Agent相关协议（如MCP、A2A）迅速升温。厂商不仅仅是构建产品，而需要争夺“进入Agent调用链”的位置。

如果无法进入这个链条，即使功能完整，也可能被绕过，逐渐边缘化。

定价体系被重写：从“使用权”到“结果价值”

相比产品形态的变化，更深远的影响正在商业模式层面显现。

过去几十年，企业软件的定价建立在“使用权”之上——按用户数、按模块、按许可收费。但在Agent模式下，这种逻辑开始失效。

一个Agent可以替代多个用户执行任务，自动化程度越高，企业获得的价值越大，但座席数量反而可能下降。

这带来一个根本性问题：当“使用量”不再等于“价值”，软件应该如何收费？

目前市场已经出现一些过渡模式，例如在座席基础上叠加Agent调用量或自动化流程计费，形成混合定价结构。

但更关键的变化在于：

软件行业正在从“为工具付费”，转向“为结果付费”。

而谁能够定义“结果”，并将其转化为可计量、可收费的单位，谁就有机会在这一轮变革中掌握价值分配权。Agent定价需简化，避免复杂结构成为创新和试点的障碍。

IDC观察：三条正在形成的主线

从当前市场演进来看，这一轮变化并不是单点突破，而是沿着几条清晰的主线展开。

首先，企业软件正在从“工具导向”走向“结果导向”。软件不再只是支持人完成工作，而是直接交付结果。

其次，集成逻辑正在从系统层上移到Agent层。过去复杂的系统集成，正在被Agent编排所替代。

更重要的是，竞争的核心正在从功能能力转向价值捕获能力。厂商之间的差距，不再只体现在“能做什么”，而在于“如何从结果中获得收入”。

谁会受益，谁面临风险？

在这一过程中，市场分化已经开始出现。

具备平台能力和生态控制力的厂商，更容易成为Agent的调度中心，从而掌握流量与价值入口。而那些依赖单点功能、定价僵化或生态封闭的厂商，则面临利润下滑和客户流失，被边缘化的风险——即使产品本身依然存在，Agent会绕过其产品，其不再处于用户路径之中。

换句话说，未来的竞争不只是“有没有Agent”，而是“是否在Agent体系中占据关键位置”。

结语：这不是一次技术升级，而是一次价值重构

Agent的快速普及，表面上看是AI能力在企业软件中的延伸，但其更深层的影响在于，它正在改变应用的形态、重塑竞争边界，并重新定义价值的获取方式。

这不是一次简单的技术升级，而是一场围绕“执行权”和“价值权”的重构。

对于软件厂商而言，真正的挑战不在于是否引入Agent，而在于——在一个由Agent主导的体系中，自己的产品究竟处于什么位置。Agent不仅是自动化工具，更是业务流程、行业应用和迁移的“新软件”，厂商可通过Agent快速扩展市场份额和收入。

与此同时，企业用户也需要重新评估自身的应用架构、自动化路径以及供应商选择策略，为Agent驱动流程、数据准备和跨应用编排做好组织准备，以确保在这一轮变革中持续获得业务价值与竞争优势。

IDC更多相关研究

如果您希望进一步了解Agent在企业软件中的落地路径、市场演进趋势或对自身业务的具体影响，欢迎与IDC分析师团队联系。IDC将基于持续的市场跟踪与研究，提供更具针对性的洞察与建议，支持企业与厂商在这一轮变革中做出更有前瞻性的决策。

请点击此处与我们联系。

Lizzie Li

Lizzie Li - Associate Research Director

Analyst Profile
Lizzie Li is Associate Research Director of IDC China's Enterprise System and Software Research that focuses on research and analysis of the China Datacenter, Cloud Computing, and IT infrastructure markets. She also provides intelligence and consulting services in customized projects for…

随着生成式AI、大模型及Agent能力持续演进，围绕“AI是否会取代SaaS”的讨论正在快速升温。基于当前企业采用进度、软件支出变化、AI规模化落地情况以及企业工作方式转型趋势的综合观察，IDC的判断是：AI不会在中短期内取代SaaS，但将推动SaaS进入新一轮结构性重构周期。 企业软件市场未来的主要变化，不是SaaS的消失，而是SaaS从“功能交付工具”向“智能执行平台”演进。

当前，AI与SaaS之间并非替代关系，而更接近于“能力叠加”与“价值重定义”关系。随着企业客户从模型试点走向业务落地，从当前市场需求变化看，企业对软件平台的要求，正在从基础数字化和AI集成能力，进一步延伸至以AI Agent为代表的智能能力，包括任务执行、流程协同、系统连接、治理承接及业务结果支撑。

在明确 AI 与 SaaS 并非替代、而是能力叠加的核心关系后，我们可以从企业预算与市场结构的维度，进一步拆解这一演进趋势的现实表现 ——AI 技术的渗透并未削弱软件市场的基本盘，反而在重构企业对软件价值的判断标准与采购逻辑。

观点一：AI预算快速增长，但软件支出并未被替代

IDC数据显示，2025年全球生成式AI支出将达到 1843亿美元，同比增长151.6%。与此同时， 2025年全球IT总支出将达到 6.8万亿美元，其中软件支出将达到1.3万亿美元，同比增长 14.2%。这表明，AI投资扩张并未系统性挤压企业软件预算，反而正在推动企业重新定义软件的能力边界。

从市场结构看，企业并没有因为AI出现而停止采购软件，而是在加速采购“具备AI能力的软件”。这意味着，SaaS的需求基础并未减弱，但产品标准正在提升。未来，缺乏智能能力、缺乏AI与业务流程融合能力以及缺乏数据壁垒的传统SaaS产品，竞争压力将加大；而能够承接AI任务并支撑流程执行的平台型软件，战略重要性将持续提升。

观点二：AI采用率提升很快，但规模化价值兑现仍处于早期阶段

IDC的数据显示，2025年，亚太地区企业平均每年开展17.9个生成式AI概念验证（PoC）项目，PoC转入生产环境的比例为34.5%，也就是说，每10个PoC项目中约有3至4个最终进入生产阶段。在KPI达成率方面，亚太地区平均为36.8%，意味着进入生产环境的GenAI服务中，约37%能够实现预期业务目标。这些最新数据表明，企业对AI试点的热情依然高涨，但从PoC走向规模化生产的转化率，以及业务成效的实际达成率，仍有较大提升空间。

这表明，AI要从“具备生成与回答能力”进一步走向“具备执行能力、可审计性、可复制性和规模化应用能力”，仍需经过持续的场景验证与业务实践。仅依靠AI模型本身，尚不足以形成完整的企业级价值闭环，企业仍需要由业务系统与平台能力持续提供数据访问、权限控制、复杂流程编排、主数据管理及合规治理等关键支撑能力。也正因为如此，SaaS在AI时代并不会被削弱，反而将在任务执行、流程承接与治理支撑等方面承担更加重要的角色。

观点三：AI正在改变企业应用软件的交互方式与价值交付逻辑

传统SaaS的核心价值，在于以模块化、标准化的方式承载企业流程，并通过菜单、表单、权限和审批机制推动业务数字化落地。但随着生成式AI和AI Agent能力不断渗透，企业软件的交互逻辑正在发生明显变化：从过去的 “用户操作功能” ， 逐步转向 “用户提出需求、系统理解意图并执行任务” 。在这一变化过程中，软件价值的衡量标准也在同步改变。企业关注的重点，正在从功能是否齐全，转向任务完成效率、流程自动化水平，以及对实际业务结果的支撑能力。

IDC预测，到2027年，约有50%的企业将在核心业务流程中引入AI agents，重塑人与机器之间的协作方式。这一趋势表明，AI agents正在从辅助工具逐步演变为企业工作体系的重要组成部分，而不再只是单点能力的补充。这也意味着，未来的软件平台，无论是SaaS还是企业级PaaS，都不再只是承担数据录入和流程流转的基础角色，而将进一步承担任务编排、系统集成、过程留痕和治理支撑等职责，成为AI执行任务时所依赖的业务底座。

与此同时，AI也正在重塑企业软件的交付方式与商业模式。基于用量的定价、智能体驱动的交互界面，以及基于结果的价值衡量，正在对传统的按席位订阅模式形成冲击。这种变化并不只停留在概念层面，而是已经在市场中逐步发生。

观点四：将被替代的更可能是低壁垒、浅流程的SaaS产品

从竞争格局变化看，AI对SaaS市场的影响并非均匀分布。预计首先受到冲击的，将是那些功能相对单一、流程嵌入程度有限、差异化不足，且主要依赖界面交互提供价值的轻量型SaaS产品。随着AI Agent逐步具备任务理解、流程拆解与自动执行能力，这类产品的独立价值空间可能被进一步压缩。

相比之下，深度嵌入企业核心流程、掌握关键业务数据、具备权限控制与合规支撑能力，并拥有行业知识积累的平台型软件，在AI时代更可能提升其战略重要性。这类平台不仅更有条件承接AI Agent的任务执行与流程协同，也更有可能在未来企业软件架构与采购体系中占据核心位置。

IDC中国企业级应用软件市场高级研究经理徐文婷认为，未来2-3年，企业将不会简单以AI替代现有软件系统，而是要求软件平台具备AI原生能力，并能够支撑AI Agent在业务场景中的应用。预计市场竞争焦点将逐步从功能覆盖转向任务执行、流程自动化和业务结果支撑能力。对SaaS厂商而言，关键不在于是否面对AI技术本身，而在于能否将AI有效嵌入企业级数据、业务流程和治理框架。从当前市场发展看，AI正在重塑SaaS的产品定义，但尚未改变企业对系统平台的根本需求。随着AI Agent从试点走向生产环境，SaaS将进一步向平台化和智能化演进，并承担更多任务执行与治理支撑职能。

为了更好的帮助用户了解企业级软件AI的发展动态和未来趋势，IDC正式启动《中国AI-Enable ERP市场份额研究，2026》报告、《中国企业资源管理（ERM）市场AI Agent技术厂商评估报告，2026》报告研究，欢迎大家与我们保持沟通交流，与IDC共同开展更多前瞻性与实践性研究。

IDC相关研究报告：

Worldwide AI and Generative AI Spending Guide 2025

Worldwide Black Book 2025

Automation, AI, and Agentic AI for FoW: Worldwide Tech Buyer Perspective Doc#：EUR154249426

IDC Survey: Enterprise AI Trends – the Latest Survey Insights Doc#：AP53632426

IDC 2026年软件和服务领域研究计划：

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

In the early 2020s, most IT dashboards looked deliciously green – until you cut them open. That “watermelon problem” summed up the gap between what SLAs said and how people actually felt at work: 99.8% uptime on paper, but slow logons, clunky multi-factor authentication, and chatbots that couldn’t understand what anyone really wanted. Experience was an afterthought, AI was a sideshow, and creativity was nowhere to be found in the contract.​

When SLAs ruled the world

Back then, three things defined the status quo. AI was narrow and local, sitting on the edge of workflows answering FAQs or routing tickets rather than orchestrating work. Experience measurement lagged reality, with annual or quarterly surveys surfacing issues long after the damage was done. And creativity simply didn’t exist in the metrics; contracts cared about uptime, not whether people had the cognitive space to experiment or innovate.​

The result was a strange split-screen. On one side, leaders proudly cited their SLA success. On the other, employees wrestled with friction that didn’t fit any KPI: context-switching between tools, re-entering the same data, and watching “helpful” chatbots miss the point. XLAs were occasionally piloted  (an NPS here, a satisfaction score there) but rarely changed actual design or investment decisions.​

Now: XLAs as control towers for human-AI work

Fast forward to 2026, and AI is no longer the sidekick; it is the backbone of digital work. GenAI assistants, low-code agents, and orchestration platforms now sit inside service desks, digital workplace platforms, and line-of-business apps. XLAs have emerged as the language that decides whether all this AI is genuinely helping humans do better work or just adding more noise.​

Three big shifts define the “now.” Agentic AI makes XLAs real-time and contextual, correlating technical signals like latency and crashes with human signals such as sentiment, task completion, and time to productivity. It can trigger automated remediation, from self-healing endpoints to conversational agents that guide users through fixes, and spotlight experience hotspots for specific personas or workflows. IDC’s 2025 Future of Work survey shows 79% of organizations now actively measure the relationship between employee and customer experience, with two-thirds having proof of causal linkages, while 94% of AI-enabled work adopters report productivity gains and over half see significant improvements.​

Making creativity a measurable outcome

The most interesting XLAs no longer treat creativity as a fuzzy aspiration. They track uninterrupted focus time per persona, link AI automation to freed-up hours, and measure innovation throughput:  ideas submitted, prototypes built, experiments completed. Instead of only asking if AI is fast or accurate, organizations track “human-plus” metrics: how much better decisions, proposals, and options become when humans and AI work together.​

Governance grows up

This evolution is forcing governance structures to grow up fast. AI-focused Centers of Excellence increasingly use XLA dashboards as strategic instruments, challenging deployments that look great on technical metrics but poor on human outcomes. They prioritize changes that build trust and agency, such as better explainability, robust feedback loops, and human override capabilities, and retire tools that consistently score badly on ease of use or learning curve.​

Metrics are diversifying accordingly: about 69% of organizations use productivity scores such as task-based speed and throughput to assess AI, while 42% also track employee satisfaction and 44% monitor skills proficiency. XLAs have become a proxy for hard questions: Are we making it easier for people to solve novel problems? Are AI tools empowering experts or boxing them in? Where is digital friction quietly killing initiative?​

Tomorrow: XLAs as the OS for co-creation

Looking ahead, XLAs are set to become the operating system for human/AI co-creation. Emerging “experience-risk” indices predict burnout or disengagement, while creativity capacity scores combine focus time, use of exploratory tools, and psychological safety indicators. Agentic AI will increasingly use XLAs as experience-intent parameters  – goals like maximizing focus time for data scientists or ensuring frontline staff resolve most issues in under three minutes  – and autonomously orchestrate tools, notifications, and workflows to hit them.​

Contracts will catch up too, moving from green dashboards to models that reward innovation, protect against “experience debt,” and explicitly safeguard time and cognitive bandwidth for meaningful work. For service providers, the mandate is clear: anchor XLAs on outcomes only humans can deliver, make creativity visible on the dashboard, build strong feedback loops, and use XLAs as guardrails against over-automation. XLAs are no longer just a friendlier way to measure IT; they are becoming the central platform for keeping human potential at the center of an AI-driven future of work.

For more information see IDCs upcoming research documents: “Measuring What Matters: XLAs and the 2026 Digital Workplace” and “Control Towers for Human Potential: The Growing Importance of XLAs in the Age of Agentic AI”.

If you have a question about this or any other IDC research, drop it in here.

Meike Escherich - Associate Research Director, European Future of Work - IDC

Analyst Profile
Meike Escherich is an associate research director with IDC's European Future of Work practice, based in the UK. In this role, she provides coverage of key technology trends across the Future of Work, specializing in how to enable and foster teamwork in a flexible work environment. Her research looks at how technologies influence workers' skills and behaviors, organizational culture, worker experience and how the workspace itself is enabling the future enterprise.

By Bo Lykkegaard, Associate VP for Software Research Europe with advice and review by Ewa Zborowska, Research Director, AI, Europe

Providers of SaaS solutions across the world have been through the market capitalization bloodbath during the past six months. Despite presenting solid indicators of growth and margins for 2025, almost all publicly traded companies have seen share price reductions 10% to 60% with the average reduction being in the 30-35% range.

Forget about looming trade wars, recession fears, missed revenue goals, and other conventional share price depressants. This is about AI disruption of the current SaaS user experience, licensing model, and product architecture. Investors are starting to fear that the SaaS ‘rental model for software’ will become invisible ‘featureware’ inside an AI agent layer.

What Are the Market Cap Reductions Telling Us?

We have examined the market cap reductions of public traded SaaS vendors over the past six months. Based upon this, we can make the following observations:

  • All SaaS vendors are affected across solution areas, geographies, size of vendor, recent growth KPIs, and size focus (SMB vs. enterprise). This means that investors are reexamining their assumptions related to SaaS growth prospects in general.
  • Vendors of workflow automation solutions and vendors targeting small and medium-sized businesses appear particularly exposed. Commercial workflow software is seen as exposed to replacement by new AI agent technologies. Also, vendors targeting small businesses are seen as more exposed to churn and price pressures.
  • SaaS vendors headquartered in EMEA do not appear harder hit than those headquartered in North America and the market cap correction has hit the largest as well as the smaller SaaS vendors.

Changes that All SaaS Vendors Are Facing

Firstly, the conventional SaaS user experience must change. In a conventional SaaS application, the user executes tasks manually within defined workflows. In an AI-powered application, the system adds to these structured workflows with probabilistic outputs, where it generates, predicts, recommends, or executes. Also, AI-powered applications can accept and react to all kinds of conversational user inputs. Furthermore, just like today’s LLM-based apps, business applications understand context and remember past interactions, which make recommendations and predictions more relevant and precise. Finally, AI-powered business applications are more proactive in nature and help users with monitoring tasks and relevant notifications.

Secondly, the conventional SaaS licensing model must evolve. The talk of the town these days is ‘outcome-based pricing’, i.e. the notion of pricing an application on outcomes (e.g. number of invoices issued) as opposed to number of users. If agentic workflows increasingly automate core business processes in the future, the user of a, say, financial application will be an agentic workflow as opposed to a human user. As AI agents increasingly become users of business applications, the user-based revenue model of SaaS application collapses. Investors are looking for SaaS vendors to at least align licensing better to business outcomes.

Thirdly, the conversional SaaS product architecture must be rethought. Adding AI to a conventional SaaS solution in the form of a chatbot or other form of AI-generated add-on does not make a meaningful difference. Real modernization requires rethinking the SaaS workflow from the ground up. AI changes all levels of the SaaS product stack and needs foundation model(s), embedding layer, vector database, retrieval-augmented generation (RAG), orchestration layer, guardrails, monitoring, and prompt/version management.

AI is making several other significant changes in SaaS. Development and maintenance as well as running costs have become more volatile and unpredictable. Data management requires new approaches, as application data now serves as a key source for training AI-powered SaaS solutions. Product roadmaps and release cadences are increasingly driven by AI model upgrades rather than traditional update schedules. Software vendors face new risk management challenges related to hallucinations and regulatory compliance. And both vendors and end-user organizations need to adapt their teams with new sets of skills. And most importantly, the overall competitive landscape has shifted, with AI-based startups and hyperscaler offerings emerging as new challengers.

The changes above certainly apply to SaaS vendors in Europe. However, in addition, vendors in Europe – as they adapt solutions and business models to become AI-driven – must pay particular attention to four areas in order to successfully transform.

Firstly, there is the GDPR, NIS2 and EU AI Act compliance, often accompanied by various national or industry-specific regulations. If they cannot document and showcase complete compliance to customers, they cannot sell their AI-powered solutions to compliance-sensitive European organizations.

Secondly, increasingly we see data residency requirements from customers in Europe, particularly in public services, financial services, and healthcare. Buyers in such industries can require EU-hosted data and sovereign cloud guarantees and approaches and can seek to avoid subjection to the US CLOUD Act and to exposing data for foundation model training.

Thirdly, Europe is multi-lingual and buyers require multi-language model performance. A conversational SaaS application is great but only if the conversation happens in the local European language where the application is deployed. We have seen many cases where non-English conversational capabilities are years behind English.

Fourth, European AI-powered SaaS vendors should expect higher demand for transparency and explainability. European customers have a strong preference for understanding how AI systems make decisions, a need often reinforced by regulations like GDPR and the EU AI Act. This means vendors must provide clear logic behind decision criteria, bias mitigation documentation, human oversight mechanisms, and comprehensive audit trails. Black box AI approaches such as “Pick this candidate because the recruiting application assigned a high AI score” simply will not fly in Europe, where trust is key and it heavily depends on being able to trace and justify how conclusions are reached.

Join the Conversation

At IDC, we help you navigate these changes with deep market research, robust data analytics, and tailored custom solutions. Whether you need strategic insights, benchmarking, or support in adapting your business model, our experts are ready to guide you.

Contact us to discuss your unique challenges and discover how IDC can empower your next steps in the evolving, AI-disrupted European software landscape.

Sources:

Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.

过去一年，生成式AI迅速从“前沿技术”演变为企业讨论中的常规议题。从董事会到业务一线，关注点已经不再是“要不要用AI”，而是企业在不同发展阶段，应该如何选择落地路径、如何判断投入节奏，以及如何尽量降低不必要的试错成本。

从市场实践来看，企业的AI探索并不存在统一范式：有的企业从具体应用场景切入，有的优先推动流程自动化，也有企业选择先夯实数据和平台基础。这些选择背后，往往与行业属性、组织能力、数字化成熟度和管理目标密切相关，并不存在绝对正确的先后顺序。

在这一过程中，企业级应用、企业级服务以及数据库与数据管理，往往以不同形式、不同权重出现在企业的AI实践中。IDC开展相关研究，并非试图将这些因素“硬性绑定”为成功前提，而是希望更真实地反映市场的复杂性，帮助企业理解不同路径下可能面临的机会与约束。

从“加AI功能”到“用AI做事”：企业级应用的重构正在发生

AI Agent正在改变企业应用的基本形态

在很多企业中，生成式AI最初的落地方式是“功能叠加”：写文案、生成报表、自动摘要。这类能力提升了效率，但并没有改变应用的本质。

IDC的研究发现，真正具有颠覆意义的变化来自AI Agent（智能体）的引入。企业级应用正在经历从“被动工具”到“主动参与业务执行”的转变：

  • 应用不再只是被人操作，而是能够理解目标、拆解任务并自动执行
  • 用户界面逐渐从复杂菜单，转向自然语言和流程驱动
  • 应用之间开始通过Agent进行协同，而非人工串联

IDC将这一变化总结为企业级应用的“Agentic演进路径”，并指出未来几年内，Agent将从辅助角色逐步走向主导角色。

哪些业务场景最先受益？

从企业实际落地情况来看，生成式AI和智能体的应用并未集中在单一部门，而是优先出现在高频交互、高度标准化或知识密集型的业务与技术场景中，包括：

  • 客户服务与智能联络中心：AI被广泛用于自动应答、坐席辅助、工单分流与服务质量监控，在不完全替代人工的前提下，提高响应效率和服务一致性。
  • 办公自动化与知识管理：会议纪要、文档整理、企业知识问答等场景逐步由AI承担基础工作，降低员工获取信息和跨部门协作的成本。
  • 内容生成与市场营销：从内容和素材生成，延伸至客户洞察、活动优化和线索管理，营销决策开始更多依赖数据与模型驱动。
  • 职能流程自动化：在财务、供应链、HR、采购、法务等职能领域，AI被用于规则明确、重复性高的流程自动化、合规检查和风险识别。
  • 研发与IT运维：代码生成、测试、故障定位和运维自动化成为AI落地的重要方向，直接影响研发效率、系统稳定性和运维成本。

IDC之所以持续追踪这些细分场景，是因为企业在做AI投资决策时，往往需要回答一个现实问题：哪些应用场景已经具备规模化条件，哪些仍处在早期探索阶段。这类研究的价值，在于帮助企业避免“平均用力”，而是将有限资源投入到最有可能产生业务回报的方向。

没有服务能力，AI很难真正跑起来

一个在客户中反复出现的共识是：AI Agent的成败，不仅仅是模型本身，很大程度还取决于项目实施过程中对于数据治理，安全合规，流程重塑，平台整合等环节的设计和把控，以及后期的运营和维护。

企业在推进过程中普遍会遇到：

  • 业务流程是否适合被Agent接管
  • 多个Agent如何协同、治理和监控
  • 如何持续评估ROI，而不是一次性交付

这也是为什么企业级服务在AI时代的重要性被显著放大。IDC在软件与服务研究中，将AI咨询、Agent设计与开发、系统集成、运维与持续优化视为一个完整闭环，而非单一项目 。

对企业而言，这类研究的价值并不仅在于“推荐某一家供应商”，而在于帮助管理层理解能力建设的先后顺序：哪些能力需要长期内生，哪些可以借助生态伙伴补齐，从而避免“试点成功、规模失败”的常见陷阱。

AI走得多远，取决于数据和数据库走得多稳

数据库正在从“后台系统”走向“AI基础设施”

如果说企业级应用决定了AI“做什么”，那么数据库和数据管理决定的则是AI“能不能做、做得好不好”。

在生成式AI快速演进的同时，中国数据库市场也正在经历深刻变化：一方面，AI对数据实时性、多模态和向量能力提出更高要求；另一方面，国产化进程推动本土数据库厂商在功能和市场份额上持续提升 。

AI for Data：让数据库更智能

IDC在数据库研究中发现，AI正在反向赋能数据库自身：

  • 自动调优与容量预测
  • 基于AI的异常检测和安全防护
  • 更智能的运维和资源调度

这些能力直接降低了数据库复杂度，使企业能够用更少的人力支撑更复杂的业务和AI负载。

Data for AI：让AI真正可用

更关键的是，数据库正在成为AI应用的“能力上限”：

  • 向量引擎和多模数据管理决定了Agent是否具备“长期记忆”和上下文理解能力
  • 数据治理和权限体系决定了AI是否可信、可控
  • 实时数据能力决定了AI是否能够参与业务决策，而不仅是事后分析

IDC在数据库管理系统市场的研究中强调：未来企业AI竞争的本质，是数据架构和数据能力的竞争。

为什么IDC要持续开展这些研究？

IDC之所以持续在企业级应用、企业级服务以及数据库与数据管理领域投入研究，一方面，这些领域是企业AI价值真正发生的位置：应用决定AI是否进入业务流程，服务决定AI能否规模化运行，数据库管理和数据治理决定AI是否长期可持续。任何一环缺失，AI都很难从“亮点项目”走向“稳定能力”。

从企业决策者视角看，这些研究真正解决了什么问题？

通过持续的市场数据、趋势判断和实践洞察，IDC希望帮助客户：

  • 看清AI技术和应用的成熟节奏
  • 了解行业发展的最新趋势和最佳案例
  • 对于热点领域和技术的评估和参考实践

在生成式AI引领的新一轮技术升级中，真正具备长期优势的企业，往往不是最早“尝鲜”的企业，而是那些能够构建高质量数据资产、完善AI治理体系、深度重塑业务流程并持续融合行业Know-how，实现数据驱动的敏捷创新与可持续落地的企业。这正是IDC持续开展相关研究的出发点，也是客户能够从这些研究中获得的长期价值。

IDC 2026年软件和服务领域研究计划：

如需进一步了解与研究相关内容或咨询 IDC其他相关研究，请点击此处与我们联系。

Lizzie Li - Associate Research Director - IDC

Lizzie Li is Associate Research Director of IDC China's Enterprise System and Software Research that focuses on research and analysis of the China Datacenter, Cloud Computing, and IT infrastructure markets. She also provides intelligence and consulting services in customized projects for local and multinational corporation (MNCs) IT vendors. Lizzie’s research domain covers Datacenters, Cloud Computing, Virtualization, and her duties include providing consulting proposals to IT vendors on sales, marketing, and research fields. Lizzie Li has seven years of experience in the IT industry, including Internet datacenters, cloud computing services, mobile telecommunication systems, and enterprise markets. Prior to joining IDC, Lizzie Li worked for 21vianet, Nokia Siemens Networks, and Huawei, and was responsible for sales analysis, project management, and technical support. Lizzie graduated from Huazhong University of Science and Technology with a Master’s degree in Pattern Recognition and Intelligent Systems.

In December 2024, one year ago, Microsoft CEO Satya Nadella declared on the BG2 podcast that “SaaS is dead.” The comment set off a shockwave across the technology industry and many felt provoked. After all, software-as-a-service (SaaS) has defined enterprise computing for nearly two decades, representing a massive share (over 10% according IDC’s Black Book) of IT spending in 2024 and forming the backbone of digital transformation strategies worldwide.

Yet, when we cast a cold IDC analytical eye beyond the provocative statement, a crucial truth emerges: SaaS, as we know it, is being disrupted, not by decline but by evolution.

The Status Quo: SaaS at Its Peak

Today, most of the world’s leading software vendors are, in some form, SaaS companies. Among the ten most valuable software players, including Microsoft, Salesforce, Oracle, SAP, and Shopify, SaaS delivery models dominate. Enterprises have grown dependent on the SaaS ecosystem, licensing countless applications to manage HR, payroll, CRM, expenses, and vertical industry workflows.

However, the sheer sprawl of SaaS adoption has created complexity for business users. Employees navigate dozens of interfaces daily, shifting context between multiple systems that rarely communicate smoothly. Despite efforts to simplify workflows through integrations and APIs, SaaS remains a patchwork of interfaces and data silos, forcing users to adapt to the software rather than the other way around.

The Complexity Problem and the AI Opportunity

This complexity is the Achilles’ heel of the SaaS model. Each SaaS application demands its own learning curve and user interface, often used sporadically and inefficiently. In this environment, AI offers a compelling remedy.

Instead of navigating multiple dashboards, users could interact with agent-driven, conversational interfaces that perform tasks across systems. Imagine instructing an AI agent to “approve last week’s expense reports” or “generate next quarter’s sales forecast” and having the agent orchestrate workflows across HR, finance, and CRM systems behind the scenes.

This agentic, “flow-of-work” user experience could replace much of today’s direct interaction with SaaS applications. The result? AI as the new interface layer, which is one that abstracts away complexity, automates repetitive processes, and redefines how enterprises consume software.

The Disruption: From Seats to Outcomes

Such a shift has profound implications for how SaaS is bought and sold. The traditional per-user, per-month licensing model becomes increasingly obsolete as digital labor replaces manual interaction. IDC predicts that by 2028, pure seat-based pricing will be obsolete, with 70% of software vendors refactoring their pricing strategies around new value metrics, such as consumption, outcomes, or organizational capability (please see IDC FutureScape: Worldwide Agentic Artificial Intelligence 2026 Predictions, IDC #US53860925, October 2025).

This agentic IT disruption will impact IDC’s existing forecasts for the various levels in the IT stack differently as shown below. Also, the impact will change over time, as for examples SaaS Applications and IT Services will feel a negative impact in the short term, while recovering if we look five years out to 2030.

For infrastructure hardware, IDC sees a different impact with a short term boost, followed by headwinds as inference costs drop exponentially.

Source: Charting the Agentic Future: 10 Vision Statements for 2030 (IDC #US53909225, November 2025)

Inside the enterprises, this evolution changes the economics of enterprise software. Companies optimizing AI agent development to reduce licensing costs will need to revisit their roadmaps as vendors adjust to these emerging pricing paradigms. Meanwhile, process owners may gain more flexibility, designing application-neutral operational efficiencies that transcend the limitations of current SaaS systems.

Business and IT Implications

The rise of AI agents doesn’t just alter pricing, it transforms how technology functions within organizations.

From a business perspective, enterprises may initially lose the tactical benefit of reduced software costs but gain strategic control over innovation and process optimization. Process teams will design workflows around end-to-end outcomes rather than application silos, supported by a new breed of “headless” software modules accessible via APIs and marketplaces.

From an IT standpoint, this means a fundamental re-architecture of the enterprise tech stack. Where today’s stack is built around SaaS interfaces, tomorrow’s will revolve around AI agents that interact with modular backend services. Data lakes and live data connections become critical enablers, while vendor relationships evolve from UI-centric engagement to agentic enablement partnerships.

Guidance for Technology Buyers

For IT and procurement leaders, this transformation demands foresight and experimentation. Buyers should assume that software vendors will increasingly position their offerings to accommodate or counteract the impact of digital labor.

Before adopting agentic systems, IDC advises enterprises to:

  • Build proofs of concept (POCs) and define clear ROI metrics around cycle time, productivity, and revenue improvements.
  • Evaluate end-to-end process efficiency, not just individual task automation.
  • Explore packaged AI agents offered by existing SaaS vendors, integrating them as part of broader operational redesigns.

In other words, the transition to AI-driven enterprise software should be intentional, data-backed, and aligned with measurable business outcomes.

The Road to 2030: SaaS Reimagined

By the end of this decade, the enterprise technology landscape will look radically different. The AI agent will become a new enterprise SKU, purchased via marketplaces and powered by modular backend capabilities rather than monolithic SaaS platforms. User interfaces will still be critical to productivity but so will orchestration of more-or-less autonomous workflows.

SaaS is not dead, but it is metamorphosing. The software industry is entering a new chapter defined by AI, automation, and outcome-based economics. For vendors, it’s a challenge to reinvent their business models. For buyers, it’s an invitation to rethink how software delivers value.

Either way, the next generation of enterprise technology will be less about screens and more about agents.

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Bo Lykkegaard - Associate VP for Software Research Europe - IDC

Analyst Profile
Bo Lykkegaard is associate vice president for the enterprise-software-related expertise centers in Europe. His team focuses on the $172 billion European software market, specifically on business applications, customer experience, business analytics, and artificial intelligence. Specific research areas include market analysis, competitive analysis, end-user case studies and surveys, thought leadership, and custom market models.