Workday 联合创始人 David Duffield 再向康奈尔大学捐赠 3.715 亿美元，累计捐赠达 5.5 亿美元2026 年 1 月 22 日，康奈尔大学宣布获得1962 届本科（电气工程）与 1964 届 MBA 校友、Workday 联合创始人 David A. Duffield 的一笔 3.715 亿美元捐赠承诺。这是该校历史上金额最大的一笔单笔个人捐赠。随着此次捐赠落地，Duffield 对康奈尔大学的 累计捐赠总额已达到约 5.5 亿美元，成为学校历史上最重要的校友捐赠人之一。
作为回馈，康奈尔大学同时宣布将工程学院正式更名为Cornell David A. Duffield College of Engineering（David A. Duffield 工程学院）。
在 HR 科技与企业软件行业，Duffield 是一个极具标志性的人物。他先后创办两家改变全球企业管理方式的软件公司：
PeopleSoft —— 1990 年代企业 HR/ERP 系统核心供应商之一Workday —— 全球主流云端 HCM 与财务管理平台，服务数千家大型企业与机构
其中，Workday 已成为全球人力资本管理（HCM）市场的关键基础设施厂商，被广泛应用于招聘、薪酬、绩效、财务与组织管理等核心场景，是现代 HR Tech 生态中最具影响力的 SaaS 平台之一。
从行业发展脉络看，Duffield 的创业路径几乎完整覆盖了企业软件三次重要转型：本地部署 ERP → 云端 SaaS → 数据与智能化企业管理系统。PeopleSoft 定义了早期 HR 信息化标准，而 Workday 则推动了云原生 HCM 的全球普及。
此次捐赠的资金将主要投向工程与前沿技术创新领域。根据康奈尔大学披露，资金将分配至三个方向：
建立 Duffield Legacy Fund（2.5 亿美元），用于支持工程学院的长期战略发展与核心科研项目。
设立 Duffield Launch Fund（约 7,150 万美元），用于学院设施更新、研究支持与增长机会把握。
教育卓越基金（约 5,000 万美元），将用于加强教学质量、人才培养与创新教育实践。
值得注意的是，2025 年 Duffield 已向康奈尔捐赠 1 亿美元用于扩建 Duffield Hall。本次追加捐赠后，其累计支持金额达到 5.5 亿美元规模，远超多数高校单一校友历史贡献纪录。
对于长期关注 HR Tech 与企业服务软件赛道的从业者而言，这不仅是一笔教育领域的大额慈善捐赠，更是一位企业软件时代代表性创业者的长期资本布局。从 PeopleSoft 到 Workday，再到高等工程教育投资，Duffield 的轨迹持续围绕“企业组织效率与人才体系升级”这一核心主题展开。
在全球 HR 科技产业迈向 AI 与自动化的新阶段之际，这类来自创始人的长期技术教育投入，也折射出企业软件行业对下一代工程与创新人才培养的高度重视。
David A. Duffield, co-founder of PeopleSoft and Workday, has pledged a $371.5M gift to Cornell University — the largest single donation in the school’s history. His cumulative contributions now total about $550M, resulting in the renaming of the Cornell College of Engineering to the Cornell David A. Duffield College of Engineering. Funds will support strategic opportunities, infrastructure, research, and educational excellence.
Josh Bersin:人工智能能战胜人类直觉做决策吗？不可能
多年来，我们一直在争论 AI 是否能用于人类决策，比如：该雇佣谁？该提拔谁？薪酬多少合适？以及数百种其他决策。领导者每天都面临复杂、艰难的抉择——我们能信任 AI 来替我们做决定吗？
我的观点是：不能。这正是我最新一期播客的主题。
什么是直觉？什么是情绪？
我们都知道所谓“第一类思维”（Type 1 Thinking）——也就是直觉反应——在我们日常生活中扮演着主导角色。比如你见到一个人、坐在一个会议中，突然就知道“该雇谁”或“该怎么做”，即使数据很难查证。
我最近深入研究了遗传学、情绪与直觉，并得出结论：再强大的 AI “超级智能”，也无法替代我们的情绪。而这些情绪，来自我们的成长背景、过往经历，甚至基因组成——往往比数据更具洞察力。
作为一名工程师，我当然推崇数据与科学，因此并不是在否定算法与数据驱动决策。但我在人力资本领域的研究一再证明，是“人类直觉”在补充、辅助，并最终确定那些 AI 给出的建议。
AI 做决策的局限性在哪里？
AI 系统依赖“概率神经网络”进行训练，模型会从已有数据中学习，再用来判断新信息——写一段代码、生成一张图、创作一篇文章，它做得确实很出色。这是因为它可以瞬间把所有训练内容当作一个巨大的“数据集”，并用向量计算给出答案。
但这都基于一个假设：数据本身就足够全面，能够包含足够多的观点和洞察。如今，大多数大型 AI 实验室已承认“可索引的数据已经用尽”，所以开始制造“合成数据”——也就是 AI 用已有数据生成新数据，以此来扩充模型。
问题来了：这些数据缺失了什么？
如果你研究情绪理论（至少有六种主流理论），你会发现大多数观点都认为，一个人“对一件事的感觉”源于其生活经历、刺激源（所见所闻所感）以及基因。而“基因”这个维度，则是几百万年人类进化的产物。
所以即使某个商业决策在逻辑上是合理的，但我们每个人对数据的解读都是不同的，而我们的反应也由经验和人性所驱动。这就是为什么在一个高管会议上，大家面对同一组营收与市场数据，却会得出完全不同的结论：
比如一个人说：“我们做得不错，该庆祝！”另一个则说：“为什么没更快增长？我们本可以更好！”
为什么人类决策更有优势？
人类互动千差万别，有人积极进取，有人保守稳重。这种“直觉差异”正是一些公司在市场中脱颖而出的关键。
那这种直觉来自哪里？来自我们几百万年的进化历史与独特的“表观遗传能力”（epigenetic capabilities）。换句话说，人类智能与直觉，源于我们的家族基因、成长经历与历史背景。
以我自己为例：父亲那边是音乐家与科学家，母亲家族是商人。我最终成了一个热爱商业与人力工作的工程师。而因为父母都是企业家，我也成了一个有野心、敢冒险、喜欢挑战的人。
这些人类“能力”，本质上是历史和基因的组合，它们在我们的情感、直觉、性格和智慧中展现出来。
AI 决策能超越人类吗？绝不可能。
很多人用丹尼尔·卡尼曼的书《思考，快与慢》来解释这个问题。书中提出：
“快速思维”是直觉，
“慢速思维”是分析。
尽管这个划分广受欢迎，但现实更复杂。AI 在“慢速分析”方面确实做得不错，但仍然极其“幼稚”。
比如让 Grok 来解释“杰弗里·爱泼斯坦事件”，它会给出一段生硬的描述，但完全没有触及人类直觉所捕捉到的“这是个肮脏、混乱、令人羞耻的丑闻”。
我想表达的是：无论 AI 如何发展，也无论企业在数据中心上投入多少资金，它都无法复制人类在基因、历史与演化层面累积的智能。
举几个例子你就明白了：
当你开车经过街口，看到一个小孩站在路边，你的本能反应是“她可能会突然冲出来”。
当你在会议中感到“这个决策不对”，你会下意识决定“我们先别急，明天再看看感觉”。
而 AI 呢？它只会基于逻辑推演立即给出一个“答案”。
总结：人类直觉，在AI时代更重要
这种“情绪 + 本能 + 遗传”的判断力，正是人类与众不同的关键所在。
正因如此，我们才会有乔布斯与盖茨的不同，马斯克与奥特曼的差异。我们必须正视并尊重这些“人类智能”的组成部分，它们比以往任何时候都更重要。
人工智能
2025年07月27日
人工智能
Is The HR Profession As We Know It Doomed? In A Strange Way, Yes.I just spent a week in London meeting with several dozen companies and most of the discussion was about AI. The overwhelming majority of the conversations were about how companies are struggling, pushing, and agitating about the implications of AI, both within HR and within their teams.
Coming from the CEO and CFO, HR team are under intense pressure to automate, improve their services, and reduce headcount with AI. Yes, we know AI is a technology for growth and scale, but the main message right now is “hurry up and do some productivity projects.”
And “Productivity,” as you know, is a veiled way of saying “Downsizing.”
So before I get back to HR, let me discuss downsizing.
It’s absolutely true that almost every company we work with has too many people. Why?
We have a sloppy way of hiring people, allocating resources, and managing work. We delegate “headcount” to managers and they go out and hire as many people as they can.
We don’t really teach (or incent) managers how to build “productivity,” we actually do the opposite. We tend to reward them for “hiring more people.”
The result is a problem I just talked about with a large advertising company: too many weird jobs and no consistency or structure to our work. This particular company has around 100,000 employees and more than 60,000 job titles. In other words almost every job is “invented for this person.” It’s insane.
The whole reason we have companies (and not individual craftsmen) is to build scale. If we expect every individual manager to figure out how to scale, we’re more or less designing low productivity into the business.
There are some simple models we use: call centers, global services groups, shared services, capability communities, and centers of excellence. But that kind of high-level productivity design is now becoming obsolete. In this new era of high-powered multi-functional agents, we need to go much further.
Elon Musk likes the “first principles” approach. Fire everyone and start from “first principles,” only hiring the people you urgently need to build, sell, and support your product. That may work in small companies but when you’re big there are too many “support services” to consider.
One of the companies we are working with has “program managers” and “project managers” and “analysts” sprinkled all over the organization in random places. In other words, their core staff don’t know how to manage projects, programs, or data. So there’s a bunch of overhead staff doing this for them. Drives me crazy. This took place because there was no discipline in hiring, so each group “bulked up” with staff.
This is really business as usual. Organization design is an old, crusty, under-utilized domain so most companies barely think about it. IBM told me a few years ago that their “org design” strategy is to “hire a high performing executive and let him or her figure it out.” I hear that, it’s quite common.
The bottom line is this: if we want to get a sound ROI from all these AI tools and agents we have to get a lot smarter about “work design.” And that is not building org charts, it’s the basics of figuring out our workflows, areas of common and uncommon process, and where and how we can automate.
Most of our clients have tons of productivity systems already (ServiceNow, Salesforce, Workday, whatever), but they either don’t know how or don’t have the discipline to use them well. So they just keep hiring people.
As an engineer I see this visibly all the time. It’s very easy to delegate a “problem” to a person, and not think about it as “plumbing.” But it is plumbing. As Tanuj Kapilashrami from Standard Charter put it, we need to focus on plumbing first, then we figure out where to apply AI.
This means we can’t just cross our fingers and hope that the Microsoft Copilot is going to make everyone more productive. We need to look at business processes and skills at the core, and then literally reinvent our companies around these new AI tools.
And skills are very important. The reason companies hire a bunch of “analysts” and “project managers” is because individuals and existing managers just aren’t good at their jobs. We all need to learn how to project manage, schedule, and analyze work. That way these high-powered specialists can work on big things, not sit in staff meetings taking notes (where AI note-takers do this well).
(By the way, I have to guess that we’ll soon have AI agents for project management, program management, and functional analytics, so those staff jobs are going to be automated next!)
How Does This Impact HR
Let’s get back to HR. Given this massive effort to re-engineer and implement AI, where does HR fit?
Well fundamentally HR is tasked to build process, expertise, and advisory services around the “people processes” in the company. That means hiring, developing, managing, paying, rewarding, and supporting people. It’s a big mission, and when we start to focus on “productivity” then HR must be involved.
The general belief is that a “well run” HR team has about a 1:100 ratio to the company. In other words, if you have 10,000 employees you’re going to have around 100 HR people. And the HR team doesn’t just run around doing things, they buy and build HR technology for scale. So HR itself, as a “plumbing” type of operation, needs to be “lean and mean.”
If your CEO wants you to hire 50 top notch AI engineers you can’t just start phoning everyone you know: you must decide precisely how you’re going to do this in a scalable, efficient, and highly effective way. (AI engineers are rare, they’re hard to hire!)
So your little HR team has to think about productivity. Should we outsource this? (Which is a cheap and dirty way to look productive.) Should we buy a talent intelligence or sourcing system? Should we hire three high-powered recruiters? You know where I’m going. We have to find a way to “be productive” while we try to “make the company productive.”
This means we, as a support and advisory function (HR professionals spend a lot of time coaching and supporting managers) have to stop creating forms and checklists and implement AI agents as fast as we can. Why? Because so much of our work is transactional, workflow-oriented, and administratively complex. And AI can do a lot of amazing things, like “assessing the skills of an AI engineer” for example.
(Our AI Galileo can literally evaluate a recorded interview and give you a pretty good assessment of an individuals skills, mapped against the Lightcast, SHL, and Heidrick functional and leadership models.)
Let’s assume we do this well, and HR technology vendors give us good products. We wind up with amazing recruiting agents, AI agents for employee training, onboarding, and coaching, AI agents that help with performance management, AI agents for succession and careers, and AI agents that deal with all the myriad of personal benefits and workplace questions people have. Where do we end up?
Do we “automate away” our own jobs?
Well, in a way the answer is yes.
AI, through its miraculous data integration and generation capabilities, can probably do 50—75% of the work we do in HR.
All this is far from built out yet, but it’s clearly coming.
(We just talked with a large pharmaceutical company that is “all-AI” and they manage a team of 6,000+ scientists and manufacturing experts with only ten people in learning and development. They’ve automated training, compliance tracking, onboarding, leadership support, and all the details of training operations.)
Could you do all that for a fast-growing 6,000 person company with 10 people? I doubt it. Most companies would have more than 10 people in sales training and sales enablement alone.
So here’s my point. HR, like other functional areas in our companies, is going to have a real-life identity crisis. If you can’t figure out how to move your HR function up the maturity level quickly (check out our Systemic HR maturity model) someone’s just going to cut your headcount (the Elon Musk approach). Then you’ll be figuring out AI in a hurry.
(Galileo can assess your HR maturity with its “consulting mode,” by the way.)
I’m not saying this is easy. The AI products we need barely exist yet. But the pressure is on.
You shouldn’t wait for the CFO to point his “productivity gun” in your face, you have to get ahead of this wave. Start pushing yourself to fix plumbing, check out the new tools in the market, get your IT team involved, and redesign your work using your own expertise. Many surprisingly good things will happen.
Let me give you an example.
A few years ago Chipotle adopted an AI-based agent system for recruiting, effectively automating a complex workflow for hiring. Not only did it save millions of dollars, the “speed and quality” of hiring went up so high the CEO talked about it as their top “revenue driver” with Jim Cramer on CNBC.
In other words this “identity crisis” in HR is a good thing.
Our recruiting, training, and employee services groups are too big. AI can automate enormous amounts of this work. So my advice is this. As the AI wave sweeps across your company, get out your old “org design” book and start redesigning how your HR team operates right now. Then you can go to the AI vendors and tell them what you want. That’s the secret to keeping HR in tip-top shape.
Will HR go away? Well a lot of the process, data management, and support roles will absolutely change. And yes, employees and job candidates will happily use intelligent bots instead of calling their favorite HR manager.
But as a Superworker, you, as an HR professional will do more interesting things. You’ll become a consultant; you’ll manage and train AI systems; and you’ll have much more real-time information about the strength and weaknesses of your company. We’re just going to have to lean into this AI wave to get there.
As AI agents arrive, it’s time to seriously re-engineer HR. And this time it’s not a transformation, it’s a reinvention.
Bottom line is this. Don’t wait for Workday, SAP, or some other vendor to “invent” a tool that changes your HR operation. You should do it yourself first and bring your IT people with you. That way you’ll buy and build the AI systems you need, and the result will be a new career, an even better HR function, and the opportunity to help your company move from “hiring” to “productivity” in the future.
我刚刚在伦敦与数十家企业进行了为期一周的交流，大部分讨论都围绕着AI展开。绝大多数对话的主题是：公司在应对AI带来的影响时，感到焦虑、推动、甚至焦躁不安，这种焦虑不仅体现在HR部门，也体现在各业务团队中。
在CEO和CFO的压力下，HR团队正被要求加速自动化、优化服务、并通过AI实现人员精简。虽然我们都知道AI是一种能够促进增长和规模化的技术，但当前传递出的主要信息是：“赶紧推动生产力项目。”
而所谓的“生产力”，实际上就是“裁员”的委婉说法。
先谈谈裁员
几乎我们接触的每一家企业，都的确存在人员过剩的问题。这是为什么呢？
因为我们的招聘、资源配置和工作管理方式本身就非常低效。我们将“编制名额”下放给各级管理者，而他们则倾向于尽可能多地招聘人员。
我们并没有真正教导或激励管理者如何构建高效的生产力，反而往往奖励他们“扩大团队规模”。结果就是，像我最近在一家大型广告公司看到的那样，组织中充满了各种各样的职位，但缺乏统一性和结构性。这家公司有约10万名员工，却设有超过6万个不同的岗位头衔——几乎每个职位都是为某个人量身定制的，这种做法显然荒谬。
企业存在的根本目的，是为了实现规模化。如果每个部门经理都各自为战，自行搭建团队架构，那无异于将低效深植于企业之中。
虽然我们有一些基本的组织效率模型，比如呼叫中心、全球服务中心、共享服务、能力中心等，但这些传统设计在当下正逐渐过时。在高性能多功能AI代理全面普及的时代，我们必须走得更远。
从“第一性原理”重构组织？
Elon Musk 推崇“第一性原理”方法——即解散现有团队，只从零开始招聘最核心、最迫切需要的人员。这种方法在小型公司或许奏效，但在大型企业中，由于存在大量“支持服务”，简单地“砍掉重建”并不可行。
现实中，很多公司在各个角落散布着项目经理、程序经理、分析师等职位，因为核心员工缺乏管理项目、推进计划、或进行数据分析的能力。由于招聘过程中缺乏严格的标准和规划，各部门纷纷自行扩编，导致组织臃肿、效率低下。
组织设计本来就是一门古老且被严重忽视的学问，多数公司对此缺乏系统化思考。IBM 曾表示，他们的组织设计策略是“聘请一位高绩效高管，让他/她自己摸索出解决方案”——这实际上是行业普遍现象。
AI真正改变的，是“工作设计”
如果我们希望从AI工具和代理中获得真正的投资回报率，就必须彻底重新思考“工作设计”——不仅仅是画组织结构图，而是要厘清工作流程、标准化与非标准化的业务环节，并找出可以自动化的领域。
尽管大多数企业已经部署了大量的生产力系统（如ServiceNow、Salesforce、Workday等），但由于缺乏使用这些系统的能力或纪律，反而持续地通过“增加人手”来解决问题。
作为一名工程师，我对此体会尤深。将问题推给某个人远比优化底层“管道”来得容易。然而，管理工作流程就像修建城市水管系统——如果基础设施不合理，再先进的AI工具也无济于事。
正如渣打银行Tanuj Kapilashrami所说：“必须先修好管道，才能合理应用AI。”
这意味着，我们不能指望微软Copilot之类的工具神奇地提升员工生产力。我们必须从根本上重新审视业务流程与员工技能，并围绕AI重新设计整个企业运作模式。
员工技能，未来的关键
企业之所以聘请大量“分析师”和“项目经理”，往往是因为普通员工和管理者缺乏项目管理、时间安排、数据分析等基本技能。未来，所有人都需要掌握这些能力，而不再依赖大量辅助人员。高阶专业人才应当专注于重大事务，而不是出席会议做会议记录（AI记录工具早已能胜任此事）。
（顺便提一句，我预测很快就会出现AI项目经理、AI程序经理、AI数据分析师——这些岗位也将逐步被自动化！）
那么HR会怎样？
回到HR领域，当企业致力于重塑流程、导入AI时，HR的角色至关重要。
HR的本质任务是构建并管理围绕“人”的各项流程：招聘、培养、管理、薪酬、激励与支持等。这项使命极为庞大，当公司将焦点转向“提升生产力”时，HR必须积极参与。
一般认为，一个运作良好的HR团队与公司整体人数的理想比例是1:100。也就是说，一家拥有1万名员工的公司，大约需要100名HR人员。而优秀的HR团队不仅自己高效运作，更会采购、搭建技术系统，以实现规模化管理。
举例来说，如果CEO要求你招聘50名顶尖AI工程师，你不能只是随便打几个电话，而是要设计一套高效、可扩展的方法。这可能包括外包、引进人才情报系统、招聘高端猎头，等等。总之，HR自身也必须成为高效运作的样板。
因此，HR团队必须迅速引入AI代理，取代大量重复性事务，尤其是那些依赖工作流、流程管理和行政性处理的工作。比如，我们的Galileo系统已经可以自动评估候选人的面试表现，并将其技能映射到Lightcast、SHL和Heidrick的领导力模型。
未来，HR工作会消失吗？
某种程度上，答案是肯定的。
凭借出色的数据整合和生成能力，AI可以完成50%-75%的HR工作。目前这些AI系统尚未完全成熟，但趋势已经非常明显。
我们刚刚与一家大型制药企业交流，他们已经基本实现了“全AI化管理”，以仅10人规模的学习与发展团队，服务6000多名科学家和制造专家。他们通过AI自动完成了培训、合规追踪、入职辅导、领导力支持等任务。对于大多数公司来说，这种效率简直是难以想象的。
HR将迎来身份危机
未来，HR必须迅速向更高的成熟度迈进（可以参考我们提出的Systemic HR Maturity Model）。否则，就会像Elon Musk那样，被大规模裁员，并被迫在短时间内仓促上马AI项目。
我并不是说这条路轻松易行。事实上，市面上真正成熟的AI HR产品还非常有限。但压力已经到来。
HR不能等着CFO拿着“生产力枪”指着自己，必须主动出击，修好内部“管道”，试用新工具，联合IT团队，重新设计工作模式。这样，你将能主动选择适合自己公司的AI系统，并构建一个全新的、充满机遇的职业未来。
结语：HR的重塑与再创造
让我们看看Chipotle的案例。他们通过部署基于AI的招聘代理，成功自动化了复杂的招聘流程，不仅节省了数百万美元，还大幅提升了招聘速度和质量。甚至在接受CNBC采访时，CEO将这一成果称为公司的“主要营收驱动因素”。
这场HR身份危机，其实是一个难得的机遇。
我们今天的招聘、培训、员工服务团队规模普遍过大。AI将能够自动化其中大量工作。我的建议是：在AI浪潮席卷而来之前，立即拿起你尘封已久的组织设计手册，重新设计HR团队的运作方式。这样，当面对AI供应商时，你可以主动提出自己的需求，而不是被动接受他们的产品。
未来HR不会消失，但大量传统流程、数据管理与支持岗位将发生剧变。员工与候选人也会越来越习惯通过智能机器人，而非人力HR来解决问题。
不过，真正优秀的HR专业人士，将会变成超能型人才（Superworker）——你将成为企业战略顾问、AI系统训练师，并且能够实时掌握公司人才与流程的整体健康状况。
这次，不再是简单的“转型”，而是真正意义上的“再创造”。
背调巨头First Advantage以22亿美元完成对Sterling Check的收购,
2024年10月31日，美国亚特兰大 — 作为全球就业背景筛查、身份和验证解决方案的领先供应商，First Advantage Corporation（纳斯达克代码：FA）今天宣布，公司已经成功完成对Sterling Check Corp的收购。此次交易的总价值达到了惊人的22亿美元，包括承担Sterling现有的债务。这一战略性收购不仅显著扩大了First Advantage的服务范围，也增强了其在全球背景筛查和身份验证市场的竞争力。
First Advantage的总裁兼首席执行官斯科特·斯台普斯（Scott Staples）在宣布收购完成时表示：“我们非常高兴能欢迎Sterling的才华横溢的团队加入First Advantage。通过整合双方的业务和共享文化属性，我们将更好地满足客户需求，并为我们的股东创造价值。这次合并将使我们能够通过提供高质量、成本效益的解决方案，增强我们的价值主张，帮助客户更智能地招聘，更快地入职，并保护他们最重要的资产：人才。”
此次合并将结合两家公司在背景筛查和身份验证领域的领先技术平台和创新解决方案，以交付更优的客户体验，并扩展及多元化First Advantage的垂直和地理市场覆盖，创造一个更加平衡的业务组合。并且，该交易预计将实现50至70百万美元的常年协同效应，立即对每股收益产生双位数的增长。
Sterling的加入，增强了First Advantage在提供移动优先、高度直观且数据驱动的客户和申请人体验方面的专业能力。这一优势将通过加速创新投资进一步发挥，客户将获得更广泛的产品和解决方案套件以满足其需求，这将推动合并公司的增长。合并后的公司预计在客户细分、行业和地理多样性方面拥有更大的收入分布，减少季节性波动，并提高资源规划和运营效率。
斯台普斯继续指出：“Sterling的收购定位First Advantage于长期价值创造，解锁效率并为额外增长和新技术解决方案的投资提供机会，包括AI驱动的自动化，同时进一步多样化我们的业务以增强韧性。展望未来，我们致力于促进企业文化的无缝整合，继续向我们的客户提供世界级的解决方案，快速有效地执行我们的协同计划，并去杠杆化我们的资产负债表。我们期待在即将到来的2024年第三季度财报电话会议中分享关于我们未来组织结构和战略的更多细节。”
此外，为完成这项交易，First Advantage获得了J.P. Morgan Securities LLC的领导财务顾问服务。Barclays Bank PLC、BofA Securities, Inc.、BMO Capital Markets Corp.、Jefferies Finance LLC、RBC Capital Markets、Citizens Capital Markets、HSBC、KKR Capital Markets LLC、Stifel和Wells Fargo Securities, LLC也为First Advantage提供了财务咨询服务。Simpson Thacher & Bartlett LLP担任交易的法律顾问。
对于Sterling，Goldman Sachs & Co. LLC和Citi提供了财务顾问服务，而Fried, Frank, Harris, Shriver & Jacobson LLP则担任其法律顾问。此次合并强化了First Advantage作为行业领导者的地位，使其在全球范围内提供更为高效和全面的就业背景筛查与身份验证服务的能力进一步提升。
随着市场对背景筛查服务的需求持续增长，First Advantage通过这次战略性收购，不仅能够扩大其市场份额，还能通过引入更先进的技术解决方案来提升服务质量和效率，满足客户的需求，并进一步巩固其在全球背景筛查市场的领导地位。
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2024年10月31日
人工智能
Cornerstone Galaxy: Acquisition Of SkyHive Could Pay OffCornerstone在人力资源技术领域长期以来一直是学习管理系统（LMS）的领导者。公司最近推出了Galaxy，这是一个集成了人工智能的全新人才管理平台。这一重大进展是在一系列收购之后实现的，尤其是最近收购了SkyHive，显著增强了公司的数据处理能力。Galaxy平台通过提供全面的技能发展、绩效管理和员工晋升系统，为HR技术空间树立了新标准。
Galaxy区别于市场上其他基于技能的或智能平台，例如Eightfold主要从人才获取开始，而Gloat着眼于人才流动性。Galaxy则从另一个角度出发，即员工发展，这是由Cornerstone在学习与发展（L&D）领域深厚的背景所支撑的。Galaxy系统内置了完整的用户界面，能够推断技能，让员工标记和评估自己的技能，帮助员工找到并完成各种学习形式，管理合规性和认证程序，通过任务、评估或管理辅导提升技能。
通过整合性能管理、发展计划、继任计划，以及招聘过程，Galaxy使公司能够通过绩效管理推动技能发展。在收购SkyHive之前，Cornerstone试图仅使用其LMS信息的数据集来实现这一目标，但这些数据并不足以构建完整的人工智能语料库。通过这次收购，Cornerstone获得了一个完整的劳动力市场数据系统、一个公司中立的职位架构以及大量行业技能，使Galaxy能够与其他主要的人才智能和人才市场供应商直接竞争。
Cornerstone spent the last decade acquiring LMS and talent software companies, all in a goal to build an integrated skills platform. Finally, after years of hard work and integration, the company introduces Galaxy, an advanced offering in the world of AI-powered HR systems.
Before I explain Galaxy, the history is important. Founded in 1999, Cornerstone started as an e-learning platform company (CyberU). The company established a foothold in the emerging LMS market and grew through strong marketing, sales, and product innovation. Since then the company has gone public, reached a $5.2 billion valuation, and was then acquired by a private equity firm (Aug. 2021, three years ago).
The new management team continued to acquire companies (EdCast, SumTotal, Talespin, and most recently SkyHive) and has now stitched these systems together into a unified platform called Galaxy. Galaxy, as I show below, is a skills-powered integrated talent management platform, built around the core of learning management. And this is what makes it unique.
The other talent intelligence or skills-based platforms started elsewhere. Eightfold started in talent acquisition; Gloat started in talent mobility; SeekOut started in recruiting; Beamery started in CRM; and players like Retrain.ai and NeoBrain started in more vertical domains. Each of these companies use large-scale profile data to infer skills, give companies tools to find and match candidates, and eventually to deliver learning.
Cornerstone, with deep background in L&D, is coming at this from another direction: employee development. The Galaxy system, which is built into a complete user interface, infers skills, lets employees tag and assess their skills, helps employees find and complete many forms of learning, manage compliance and certification programs, and advance skills through gigs, assignments, assessments, or management coaching. And since Cornerstone is an integrated talent suite, the system lets companies drive skills through performance management, development planning, succession planning, and also recruiting.
Before the acquisition of SkyHive, Cornerstone was trying to do this with its own data set of LMS information. This data, which includes billions of learning records, was simply not sufficient to build out the entire AI corpus. By acquiring SkyHive, Cornerstone gained an entire labor market system of data, a company-neutral job architecture, and lots of industry skills. This brings Galaxy into direct competition with the other major talent intelligence and talent marketplace vendors.
I have not yet talked with Galaxy customers, but the user experience is integrated and shows the sophistication of thinking under the covers. Remember that Cornerstone acquired Evolv, Clustree, and EdCast before acquiring SkyHive, so the team has been building AI capabilities and use-cases for several years. And now that Cornerstone has a VR platform for learning, more use-cases are coming.
While I don’t know Cornerstone’s revenues, the leadership team assures me that the company is growing and the profitability is high. This means the company has long-term sustainability and despite its many acquisitions, is likely to evolve to “Oracle-like” status. (Oracle has acquired hundreds of companies over the years and now looks at M&A as one of its core strengths).
Here’s the major play in the market. With 7,000+ customers, Cornerstone has many customers shopping for new tools. If Galaxy is as solid as it looked in the demos, some percentage of these buyers could upgrade to Galaxy and avoid the purchase of Gloat, Eightfold, or another LMS. While we cannot be sure where Galaxy will play, for companies that want to deploy a skills architecture across all talent practices, it looks like a solid option.
Cornerstone Vision:
Cornerstone User Experience
Cornerstone Career and Talent Marketplace
Cornerstone Performance Management
Skills in Goal Management
Why Cornerstone Still Matters
Cornerstone has a massive customer base. The users of Cornerstone, Saba, SumTotal, Lumesse, and Halogen include many of the world’s largest companies and thousands of mid-market organizations as well. These organizations have invested billions of dollars into learning infrastructure, content, and user portals to reach employees. If Cornerstone Galaxy delivers on its promise, the company can help many of these organizations avoid buying lots of standalone new tools. And given Cornerstone’s size, the company could become, as I mentioned above, the “Oracle” of the space.
And note, by the way, that a recent survey by HR.com found that the top rated HR tech issue to address is L&D infrastructure, so this issue is on everyone’s mind.
While the market is highly competitive and there are many skills-based tools in the market, Cornerstone’s focus on L&D is unique. None of the other major LMS vendors have the skills infrastructure of Cornerstone today.
If your skills strategy is focused on building skills, Galaxy may be the answer.
More to come as we talk with more Galaxy customers.
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