刚刚:Workday斥资11亿美元收购Sana，打造AI驱动的“未来工作入口” ——此举将重塑Workday在企业学习、知识管理与AI智能体领域的竞争格局
Workday出手11亿美元拿下Sana
2025年9月16日，Workday, Inc.（纳斯达克代码：WDAY）在Workday Rising 2025大会上宣布，已与总部位于瑞典斯德哥尔摩的AI企业知识平台公司Sana签署最终收购协议，交易金额约为11亿美元，将收购Sana全部流通股权。
该交易预计将在Workday 2026财年第四季度（即2026年1月31日前）完成，需满足惯常交割条件。交割完成后，Sana将成为Workday全资子公司，其核心产品Sana Agents和Sana Learn将继续以独立品牌形态发展，并全面融入Workday的AI战略版图。
Sana此次交易的顾问为DLA Piper，Workday方面的财务顾问为Allen & Company LLC，法律顾问为Orrick。
Sana：AI原生的企业知识与学习平台
成立于2016年的Sana，长期专注于AI原生企业知识与学习平台的研发，核心产品包括：
Sana Agents：一套无代码AI智能体构建平台，可用于构建企业内部任务自动化代理，支持文档检索、内容生成、洞察分析、流程执行等全链条工作流，且所有操作通过“Agent System of Record”机制记录，保障安全与合规；
Sana Learn：AI驱动的企业学习管理与内容创作平台，集课程生成、知识库搭建、个性化辅导、互动学习于一体。
目前，Sana已服务全球数百家企业、超过100万名用户。其客户案例包括：
某全球电动车制造商：员工学习参与度提升 275%；
一家拥有7,500名员工的欧洲安装行业分销商：课程开发周期从 4个月缩短至4天；
某全球金融科技公司：内容创作周期从 3周缩短至3小时。
Sana曾获得多轮风险投资支持，投资方包括Merck旗下基金与Polestar创始团队等，凭借其AI原生技术在企业培训和知识管理领域建立了领先地位。
战略意义：打造“未来工作入口”平台
根据Workday产品与技术总裁Gerrit Kazmaier的表述，Sana的加入将成为Workday打造“未来工作入口（front door for work）”战略的核心支点。Workday计划将Sana的知识搜索、智能体和学习系统，与其在人力与财务领域的独特数据上下文深度整合，实现以下目标：
知识与数据融合：实现跨Workday、Google Drive、SharePoint、Office 365等多源数据的统一检索；
智能体驱动工作：通过Sana Agents提供任务自动化、主动推荐、洞察推送、绩效管理等AI服务；
学习与技能重构：利用Sana Learn的AI内容生成与个性化辅导功能，加速员工技能建设、内部流动与再培训计划；
个性化体验升级：基于员工角色、团队构成与绩效数据，主动提供定制化仪表盘、流程工具和学习资源。
届时，全球超过7,500万名Workday用户将能够在同一平台内完成数据获取、流程执行、内容生成与技能学习，极大提升工作效率与体验一致性。
产品互补：强化Workday学习与前端智能化能力
Sana的两大核心产品与Workday现有产品形成互补：
Sana Learn 将为Workday Learning注入AI生成式课程开发、个性化辅导与互动学习等能力，解决企业对“规模化+个性化”学习内容的迫切需求；
Sana Agents 则补足了Workday在前端智能体、跨系统知识检索与工作流执行方面的能力，使员工可以用自然语言发出指令，系统自动完成数据整合、流程审批、绩效分析等任务。
这意味着Workday未来的用户体验将从“提供数据平台”转向“提供智能助手”，从而显著提高员工生产力与系统使用粘性。
行业观察：Workday的AI版图加速成型
长期关注企业HR科技领域的分析师Josh Bersin指出，此次收购不仅是产品线补强，更是Workday整体战略架构的根本性跃迁。通过引入Sana的AI原生架构，Workday将从“后端交易系统”向“前端AI体验平台”转型，从而在以下几个方向强化竞争力：
技术维度：实现AI原生知识图谱、生成式学习内容、智能体平台三位一体的闭环；
用户体验维度：统一知识、数据、行动与学习入口，减少用户在多系统之间切换；
商业维度：借力Sana成熟的企业客户基础与快速迭代的AI产品，将大幅提升Workday的客户粘性与交叉销售潜力。
在全球企业加速投入AI转型、人才再培训与内部流动的大背景下，Workday通过此次收购切入企业知识与学习赛道的“AI上层建筑”，有望显著扩大其在HR科技市场的总体可寻址市场（TAM），并建立长期壁垒。与此同时，SAP收购SmartRecruiters、Oracle强化AI Copilot等竞争者动作频频，Workday此举也被视为正面回应市场竞争的关键举措。
财务与运营前景
Workday目前尚未披露此次收购对未来财务业绩的具体影响，但表示将于后续财报更新指引。本次交易仍需监管及惯常交割程序完成。分析人士预计，此项收购将以增长性投资的形式纳入Workday整体战略支出，短期对利润率或有压力，但将强化其在人才管理与AI企业应用领域的长期增长潜力。
HRTech评论：
这笔价值11亿美元的收购交易，标志着Workday在企业AI平台转型道路上迈出了关键一步。未来，Sana的AI能力与Workday在人员与财务数据的优势结合，将重塑企业用户与员工的交互方式，为全球客户带来更智能、更主动、更个性化的工作体验，也为Workday在AI驱动的下一代企业应用竞赛中奠定领先地位。
值得注意的是，Workday近年已多次通过并购快速补充AI能力：包括2023年收购Helios（AI技能匹配引擎）、2024年收购HiredScore（人才智能与内部流动AI平台），以及2025年初收购Paradox部分团队以强化AI招聘助理功能。此次整合Sana，则进一步补齐了Workday在知识检索、任务自动化与AI学习内容生成上的关键能力模块，显示其正以“拼图式并购”策略加速构建AI时代的企业中台能力。
新闻来源：
Workday 官方新闻稿（PRNewswire，2025年9月16日）
Josh Bersin《Workday Acquires Sana to Transform Its Learning Platform – And Much More》（2025年9月）
AI Platform
2025年09月16日
AI Platform
Josh Bersin: When Will The Trillions Invested In AI Pay Off? Sooner Than You Think.近年来，生成式人工智能（GenAI）的投资已达数万亿美元，但围绕其回报问题的争论不断升级。一些分析师，如麻省理工学院教授达隆·阿西莫格鲁（Daron Acemoglu）和纽约大学心理学与神经科学教授加里·马库斯（Gary Marcus），对AI的经济影响持悲观态度，认为其对美国生产力和GDP增长的推动作用有限，甚至可能导致市场崩溃。相反，另一派如高盛的全球经济学家则乐观地认为，AI有望在未来十年内大幅提高生产力。然而，文章指出，生成式AI的真正价值在于其特定领域的应用。例如，Paradox和Galileo等HR技术平台通过高度专业化的解决方案，显著提升了招聘和人才管理的效率。最终，文章强调，AI行业仍处于早期阶段，成功的关键在于找到具有专注性和精确性的创新解决方案。
In the last few weeks there has been a lot of concern that Gen AI is a “bubble” and companies may never see the return on the $Trillion being spent on infrastructure. Let me cite four analyst’s opinions.
Will Today’s Massive AI Investments Pay Off?
MIT professor Daron Acemoglu estimates that over the next ten years AI will impact less than 5% of all tasks, concluding that AI will only increase US productivity by .5% and GDP growth by .9% over the next decade. As he puts it, the impact of AI is not “a law of nature.”
On a similar vein, Gary Marcus, professor emeritus of psychology and neural science at New York University, believes Gen AI is soon to collapse, and the trillions spent will largely result in a loss of privacy, increase in cyber terror, and a lack of differentiation between providers. The result: a market with low profits and big losses.
Goldman Sachs Head of Equity Research Jim Covello is similarly pessimistic, arguing simply that the $1 Trillion spent on AI is focused on tech that cannot truly automate complex tasks, and that vendors’ over-focus on “human-like features” will miss the boat in delivering business productivity. (He studies stocks, not the economy.)
And Goldman Sachs Global Economist, who is a fan, estimates that AI could automate 25% of work tasks and raise US productivity by 9T and GDP by 6.1% over the next decade. He follows the traditional business meme that “AI changes everything” for the better.
What’s going on? Quite simply this new technology is very expensive to build, so we’re all unsure where the payoffs will be.
Buyers Are Looking For A Return Soon
If we discount the work going on at Google, Meta, Perplexity, and Microsoft to build AI-based search businesses, which make money on advertising (Zuckerberg essentially just said that in a few years AI will guarantee your ad spend pays off), corporate IT managers are asking questions.
An article in Business Insider pointed to a large Pharma company that cancelled their Microsoft Copilot licenses because the tool was not adding any significant value (Chevron’s CIO was quoted similarly in The Information).
Another quoted a Chief Marketing Officer who stated Google Gemini’s email marketing tool and the new AI-powered ad-buying tool performed worse than the human workers it was intended to replace (or support).
Given that these tools almost double the “price per user” for the productivity suites, I think it’s fair that CIOs, CMOs, to expect them to pay for themselves fairly quickly.
What’s Going On? The Big Wins Will Be Domain Specific
As with all new technologies that enter the market quickly, “the blush on the rose” is over. We’ve been dazzled by the power of ChatGPT and now we’re searching for real solutions to problems. And unlike the internet, where research was funded by the government, there’s going to be a lag (and some risk) between the trillions we spend and the trillions we save.
Given that ChatGPT is less than two years old and OpenAI has morphed from a research company into a product company, it’s easy to see what’s happening. Every vendor and tool provider is narrowing its AI “strategy” and not just pasting little AI “stars” on their websites, looking for useful things to do. And this process may take a few years.
In the world of HR, I think we can all agree that a “push the button job description generator” is a bit of a commodity. However if the AI analyzes the job title, identifies the skills needed through a large skills engine, and tunes the job description by company size, industry, and role, then it’s a fantastic solution. (Galileo does this, as does SeekOut, SAP, and some other vendors.)
The more “specific” and “narrow” the AI is, the more useful it becomes. Generic LLMs that aren’t highly trained, optimized, and tuned to your company, business, and job are simply not going to command high prices. So while we all thought ChatGPT was Nirvana, we’re now figuring out that highly specialized solutions are the answer.
Let me give you some examples.
The first is the platform built by Paradox, a pioneering company that started work on AI-based recruiting agents in 2016. Paradox, now valued at around $2 Billion, delivers an end-to-end recruitment platform that automates the entire process of candidate marketing, candidate experience, assessment, selection, interview scheduling, hiring, and onboarding. Most people believe its a “Chatbot” but in reality it’s an AI-powered end-to-end system that radically simplifies and speeds the recruitment process in a groundbreaking way. Companies like 7-11, FedEx, GM, and others see massive improvements in operational efficiency and both candidates, managers, and recruiter adore it. It took Paradox eight years to build this level of integrated solution.
The second is our platform Galileo. Galileo, which is now licensed by more than 10,000 HR professionals, is a highly tuned AI agent specifically designed to help HR professionals (leaders, business partners, consultants, recruiters, and other roles) do the “complex work” HR professionals do. It’s not a generic LLM: it’s a highly specialized solution designed specifically for HR professionals, and we’ve added specialized content partners and are building special integrations with other HR platforms. Our clients tell us it’s saving them 1-2 hours a day.
The third is the platform HiredScore, that was recently acquired by Workday. Founded in 2012, the HiredScore team built tools to help identify “fit” between individuals and jobs, and tuned its AI to be highly explainable, unbiased, and very easy to use. It took Athena Karp and the team a few years to nail down the use-cases and user interface but now HiredScore is considered one of the most powerful recruitment “orchestration” tools in the market, and is also used for internal hiring and many other applications. Every customer I talk with tells me it’s essential and saves them months of manual, error-prone effort.
The fourth is the platform Eightfold, which was invented in 2016 as a way to build “Google-scale” matching between job seekers and jobs. Through many years of engineering, product management, and ongoing sales process the company has become the leader in a new space called “Talent Intelligence,” now a billion dollar rapid-growing category. The company is about ten years old and now has some of the world’s largest companies building their hiring, career management, and talent management processes using AI. Companies like EY, Bayer, and Chevron now use it for all their strategic talent programs.
Each of these vendors, including others like Gloat, Sana, Arist, Lightcast, Draup, Uplimit, Firstup, and hundreds of others have patiently taken the power of Generative AI and applied it with laser precision to their solutions. Each of these companies is different, and as we work with them we see lightning bolts of innovation: not in AI itself, but in finding new ways to solve problems and do what I call “crawling up the value curve.”
This is the path for AI in the coming years. As with all new technologies, the “trough of disappointment” is always followed by the “bowling pin” of hitting the nail on the head. Innovators, entrepreneurs, and startup founders are the ones who will take GenAI and apply it in unique ways to solve problems. And soon enough, “AI-powered” will be a phrase we barely even need to say.
The Best Solutions Will Be Narrow Not Wide
GenAI solutions require a large “platform” of data, infrastructure, and software. That alone is not where the value resides. Rather, the big productivity advantages come after years of effort, focusing the data sets and working with customers to find the features, UI designs, and data sets that add enormous value. And we are still in the early stages.
If you want to learn more about HR Technology and AI, join me at the HR Technology Conference on September 24-25 in Vegas, or at Unleash in Paris in October 16-17. While I can’t predict who will win the core AI platform game (Microsoft, OpenAI, Google, Meta, Amazon will fight it out), I can predicts this: Generative AI will deliver massive improvements in business productivity. You just have to shop around a bit and wait for just the right solutions to arrive.
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