Workday收购HiredScore的意义，这可能颠覆人力资源科技领域Workday计划收购HiredScore，这是人力资源技术领域的一次重大变革。HiredScore是一家领先的基于AI的招聘匹配工具提供商，此举将大大增强Workday在人才智能和招聘方面的能力。这次收购预计将整合HiredScore的专长到Workday的系统中，显著改善其应聘者追踪系统（ATS）、技能云和整体人才智能产品。此战略性收购可能会重塑人力资源软件市场，迫使其他供应商加速他们的AI计划，可能激发一轮新的收购热潮。
以下是原文：
This week Workday announced intent to acquire HiredScore, a leading provider of AI-based matching tools for recruiting (called “talent orchestration”). While it wasn’t discussed much in the earnings call, this deal is a big positive for Workday and could have many implications for the HR Tech market.
Let me explain. (I have not been briefed by Workday yet, so more information will come as I learn more.)
Right now there is a massive marketplace war for high-powered AI-based recruiting tools (estimated at $30.1 billion). Historically dominated by applicant tracking systems (ATS), this market provides essential technology to help every company grow.
The ATS market, which is more than 25 years old, has been rapidly transformed with high-powered AI tools that help with candidate matching, search, skills inference, and sourcing. And now that AI tools are readily available, these systems are becoming big data platforms loaded with billions of employee profiles, running complex AI models to help match people to jobs, projects, and gigs.
Most ATS vendors (including Workday) have slowly extended into this space through matching. The original idea of a resume parser (software that reads a resume and scores it against a job description) has evolved into complex text analysis and AI-powered inference technology, forcing ATS vendors to invest.
As the ATS vendors enhance their AI capabilities, a parallel universe of AI-first Talent Intelligence vendors emerged. These vendors, like Eightfold, Gloat, Beamery, Phenom, Seekout, Skyhive, Retrain, and Techwolf are building skills-centric big data platforms to match people to jobs, gigs, and mentors. These systems do much more than rate matches: they identify skills, find adjacent skills, match people to careers, find mentors, and more. They are essentially open big-data AI platforms built on vector databases that can be used for many enterprise apps (job architecture design, skills planning, internal mobility, pay equity analysis, etc.). In many ways they represent the future of HR Tech.
(Read our Talent Intelligence Primer for more.)
As the Talent Intelligence vendors grow, they start to deliver “HCM-threatening” platforms that impinge on the HCM “System of Record” idea. If you have all your employees, candidates, alumni, and prospects in Eightfold, Phenom, Seekout, or Gloat, for example, Workday or SAP look like a tactical payroll and workflow management system. (ServiceNow also understands this, and is building talent intelligence into its workflow platform.)
Up until now the big HCM vendors like Workday, Oracle, and SAP have struggled to build these new systems, largely because their original architectures were not AI-based. So they’ve attracted customers with offerings like the Workday Skills Cloud or SAP Opportunity Marketplace that aren’t fully completed yet. We have talked with dozens of Workday Skills Cloud customers, for example, and they see it as an important “skills system of record,” but its real AI matching and inference capabilities have been limited.
Along comes HiredScore, a well respected AI-based matching system with 150 employees and 40+ seasoned AI engineers in Israel. These folks are experts at candidate matching (quite a complex problem), and they’ve built a very innovative “orchestration” system to help line managers coordinate activities with HR business partners and recruiters (more on this later). While I’m sure they’ll continue to build out HiredScore, they can also contribute to Workday’s overall talent intelligence offering, improving the entire system – including the Skills Cloud, Workday Learning, Workday’s Talent Marketplace.
As large as the recruiting software market is, the market for internal career tools, talent mobility, skills inference, and corporate learning is five times bigger. This acquisition gives Workday a shot in the arm to accelerate its entire AI platform strategy. (As the Identified acquisition did back in 2014. Identified was the roots of the Workday Skills Cloud.)
Market Implications Of This Move
This move could change the market for HR software in a few significant ways.
First, Workday Recruiting customers will be thrilled. Workday’s ATS now benefits from a first class matching and candidate scoring solution. This helps Workday compete with the bigger ATS players and gives Workday a new revenue source as they sell HiredScore to the existing 4,000+ Workday ATS customers. (Similar to the Peakon acquisition in Employee Experience.) And the talent orchestration features (kind of like a “staffing copilot”) gives Workday a very unique feature set.
Second, this forces Workday’s talent intelligence partners to step up their game. Remember when Apple acquired Dark Sky, the most compelling micro-weather app on the market? Once they integrated it into Apple’s other apps, the market for third party weather apps went away. Workday could limit its partner network to avoid letting HiredScore competitors into the ecosystem.
Third, this forces HCM vendors to accelerate their AI. Since HiredScore is such a well-respected product (every client we talk with adores it), it will become part of Workday demos and sales proposals quickly. Workday’s HCM competitors will start scratching around to find a similarly mature AI vendor to acquire. And that could kick off another round of acquisitions, similar to the frenzy that took place in the mid 2010s.
Finally, there’s one more scenario, and I give this good odds. Not to be outdone by Workday, the Talent Intelligence vendors may just expand their ATS capability and decide to go “full stack.” I wouldn’t be surprised to see this happen.
Why Is AI-Based Candidate Matching So Important
Why is this technology so important? Well if you’ve ever tried to recruit on Indeed or LinkedIn, you know why. The quality and reliability of “candidate matching technology” is a lynchpin of a talent platform. Just as Google Search crushed Yahoo, Excite, and Inktomi, a powerful next-gen matching tool adds an enormous amount of value. Not only does it speed talent acquisition, it fuels all the internal mobility, career portals, skills, and eventually learning and pay systems.
Why do I say this? A “match” is a sophisticated problem. Unlike a Google search which looks at text and traffic, when you search for a person to fill a role you have to think about dozens of complex relationships. What are this person’s skills and capabilities? What are their credentials or certifications? Who else are they connected with? How likely will they fit into the job, role, and company? What is the impact of their industry experience? What tools and technologies do they understand?
And it gets much more complex. The Heidrick Navigator platform (built on Eightfold), uses AI to assess functional skills for management and leadership, identifies a person’s “ability to drive results,” and more. This important application of AI powers many of the most important decisions we make in business.
That’s why the Talent Intelligence space is growing so fast. As of this week there are more than 1,800 Director or VPs of “Talent Intelligence” in LinkedIn, and that number is up almost six-fold from one year ago.
Can Workday take the lead in this emerging space? It’s impossible to tell at this point, but the horses have left the gate and the race is on. This deal sets the players in the right lanes and feels like the earthquake to shake things up.
Corporate Learning
2024年03月01日
Corporate Learning
Autonomous Corporate Learning Platforms: Arriving Now, Powered by AIJosh Bersin 的文章通过人工智能驱动的自主平台介绍了企业学习的变革浪潮，标志着从传统学习系统到动态、个性化学习体验的重大转变。他重点介绍了 Sana、Docebo、Uplimit 和 Arist 等供应商的出现，它们利用人工智能动态生成和个性化内容，满足了企业培训不断变化的需求。Bersin 讨论了跟上多样化学习需求所面临的挑战，以及人工智能解决方案如何提供可扩展的高效方法来管理知识和提高学习效果，并预测了人工智能将从根本上改变教学设计和内容交付的未来。推荐给大家：
Thanks to Generative AI, we’re about to see the biggest revolution in corporate learning since the invention of the internet. And this new world, which will bring together personalization, knowledge management, and a delightful user experience, is long overdue.
I’ve been working in the corporate learning market since 1998, when the term “e-learning” was invented. And every innovation since that time has been an attempt to make training easier to build, easier to consume, and more personalized. Many of the innovations were well intentioned, but often they didn’t work as planned.
First came role based learning, then competency-driven training and career-driven programs. These worked great, but they couldn’t adapt fast enough. So people resorted to short video, YouTube-style platforms, and then user-authored content. We then added mobile tools, highly collaborative systems, MOOCs, and more recently Learning Experience Platforms. Now everyone is focused on skills-based training, and we’re trying to take all our content and organize it around a skills taxonomy.
Well I’m here to tell you all this is about to change. While none of these important innovations will go away, a new breed of AI-powered dynamic content systems is going to change everything. And as a long student of this space, I’d like to explain why. And in this conversation I will discuss four new vendors, each of which prove my point (Sana, Docebo, Uplimit, and Arist).
The Dynamic Content Problem: Instructional Design By Machine
Let’s start with the problem. Companies have thousands of topics, professional skills, technical skills, and business strategies to teach. Employees need to learn about tools, business strategies, how to do their job, and how to manage others. And every company’s corpus of knowledge is different.
Rolls Royce, a company now starting to use Galileo, has 120 years of engineering, technology, and manufacturing expertise embedded in its products, documentation, support systems, and people. How can the company possibly impart this expertise into new engineers? It’s a daunting problem.
Every company has this issue. When I worked at Exxon we had hundreds of manuals explaining how to design pumps, pressure vessels, and various refinery systems. Shell built a massive simulation to teach production engineers how to understand geology and drilling. Starbucks has to teach each barista how to make thousands of drinks. And even Uber drivers have to learn how to use their app, take care of customers, and stay safe. (They use Arist for this.)
All these challenges are fun to think about. Instructional designers and training managers create fascinating training programs that range from in-class sessions to long courses, simulations, job aids, and podcasts. But as hard as they try and as creative as they are, the “content problem” keeps growing.
Right now, for example, everyone is freaked out about AI skills, human-centered leadership, sustainability strategies, and cloud-based offerings. I’ve never seen a sales organization that does quite enough training, and you can multiply that by 100 when you think about customer service, repair operations, manufacturing, and internal operations.
While I always loved working with instructional designers earlier in my career, their work takes time and effort. Every special course, video, assessment, and learning path takes time and money to build. And once it’s built we want it to be “adaptive” to the learner. Many tools have tried to build adaptive learning (from Axonify to Cisco’s “reusable learning objects“) but the scale and utility of these innovations is limited.
What if we use AI and machine learning to simply build content on the fly? And let employees simply ask questions to find and create the learning experience they want? Well thanks to innovations from the vendors I mentioned above, this kind of personalized experience is available today. (Listen to my conversation with Joel Hellermark from Sana to hear more.)
What Is An Autonomous Learning Platform?
The best analogy I’ve come up with is the “five levels of autonomous driving.” We’re going from “no automation” to “driver assist” to “conditional automation” to “fully automated.” Let me suggest this is precisely what’s happening in corporate training.
If you look at the pace of AI announcements coming (custom GPTs, image and video generation, integrated search), you can see that this reality has now arrived.
How Does This Really Work
Now that I’ve had more than a year to tinker with AI and talk with dozens of vendors, the path is becoming clear. The new generation of learning platforms (and yes, this will eventually replace your LMS), can do many things we need:
First, they can dynamically index and injest content into an LLM, creating an “expert” or “tutor” to answer questions. Galileo, for example, now speaks in my own personal voice and can answer almost any question in HR I typically get in person. And it gives references, examples, and suggests follow-up questions. Companies can take courses, documents, and work rules and simply add them to the corpus.
Second, these systems can dynamically create courses, videos, quizzes, and simulations. Arist’s tool builds world-class instructional pathways from documents (try our free online course on Predictions 2024 for example) and probably eliminates 80% of the design time. Docebo Shape can take sales presentations and build an instructional simulation automatically, enabling sales people to practice and rehearse.
Third, they can give employees interactive tutors and coaches to learn. Uplimit’s new system, which is designed for technical training, automatically gives you an LLM-powered coach to step you through exercises, and it learns who you are and what kind of questions you need help with. No need to “find the instructor” when you get stuck.
Fourth, they can personalize content precisely for you. Sana’s platform, which Joel describes here, can not only dynamically generate content but by understanding your behavior, can actually give you a personalized version of any course you choose to take.
These systems are truly spectacular. The first time you see one it’s kind of shocking, but once you understand how they work you see a whole new world ahead.
Where Is This Going
While the market is young, I see four huge opportunities ahead.
First, companies can now take millions of hours of legacy content and “republish it” in a better form. All those old SCORM or video-based courses, exercises, and simulations can turn into intelligent tutors and knowledge management systems for employees. This won’t be a simple task but I guarantee it’s going to happen. Why would I want to ramble around in the LMS (or even LinkedIn Learning) to find the video, or information I need? I”d just like to ask a system like Galileo to answer a question, and let the platform answer the question and take me to the page or word in the video to watch.
Second, we can liberate instructional design. While there will always be a need for great designers, we can now democratize this process, enabling sales operations people, and other “non-designers” to build content and courses faster. Projects like video authoring and video journalism (which we do a lot in our academy) can be greatly accelerated. And soon we’ll have “generated VR” as well.
Third, we can finally integrate live learning with self-directed study. Every live event can be recorded and indexed in the LLM. A two hour webinar now becomes a discoverable learning object, and every minute of explanation can be found and used for learning. Our corpus, for example, includes hundreds of hours of in-depth interviews and case studies with HR leaders. All this information can be brought to life with a simple question.
Fourth, we can really simplify compliance training, operations training, product usage, and customer support. How many training programs are designed to teach someone “what not to do” or “how to avoid breaking something” or “how to assemble or operate” some machine? I’d suggest its millions of hours – and all this can now be embedded in AI, offered via chat (or voice), and turned loose on employees to help them quickly learn how to do their jobs.
Vendors Watch Out
This shift is about as disruptive as Tesla has been to the big three automakers. Old LMS and LXP systems are going to look clunkier than ever. Mobile learning won’t be a specialized space like it has been. And most of the ERP-delivered training systems are going to have to change.
Sana and Uplimit, for example, are both AI-architected systems. These platforms are not “LMSs with Gen AI added,” they are AI at the core. They’re likely to disrupt many traditional systems including Workday Learning, SuccessFactors, Cornerstone, and others.
Consider the content providers. Large players like LinkedIn Learning, Skillsoft, Coursera, and Udemy have the opportunity to rethink their entire strategy, and either put Gen AI on top of their solution or possibly start with a fresh approach. Smaller providers like us (and thousands of others) can take their corpus of knowledge and quickly make it come to life. (There will be a massive market of AI tools to help with this.)
I’m not saying this is easy. If you talk with vendors like Sana, Docebo, Arist, and Uplimit, you see that their AI platforms have to be highly tuned and optimized for the right user experience. This is not as simple as “dumping content into ChatGPT,” believe me.
But the writing is on the wall, Autonomous Learning is coming fast.
As someone who has lived in the L&D market for 25 years, I see this era as the most exciting, high-value time in two decades. I suggest you jump in and learn, we’ll be here to help you along the way.
About These Vendors
Sana (Sana Labs) is a Sweden-based AI company that focuses on transforming how organizations learn and access knowledge. The company provides an AI-based platform to help people manage information at work and use that data as a resource for e-learning within the organization. Sana Labs’ platform combines knowledge management, enterprise search, and e-learning to work together, allowing for the automatic organization of data across different apps used within an organization.
Docebo is a software as a service company that specializes in learning management systems (LMS). It was founded in 2005 and is known for its Docebo Learn LMS and other tools, including Docebo Shape, its AI development system. The company has integrated learning-specific artificial intelligence algorithms into its platform, powered by a combination of machine learning, deep learning, and natural language processing. The company went public in 2019 and is listed on the Toronto Stock Exchange and the Nasdaq Global Select Market.
Uplimit is an online learning platform that offers live group courses taught by top experts in the fields of AI, data, engineering, product, and business. The platform is known for its AI-powered teaching assistant and personalized learning approach, which includes real-time feedback, tailored learning plans, and support for learners. Uplimit’s courses cover technical and leadership topics and are designed to help individuals and organizations acquire the skills needed for the future.
Arist is a company that provides a text message learning platform, allowing Fortune 500 companies, governments, and nonprofits to rapidly teach and train employees entirely via text message. The platform is designed to deliver research-backed learning and nudges directly in messaging tools, making learning accessible and effective. Arist’s approach is inspired by Stanford research and aims to create hyper-engaging courses in minutes and enroll learners in seconds via SMS and WhatsApp, without the need for a laptop, LMS, or internet. The company has been recognized for its innovative and science-backed approach to microlearning and training delivery.
BY JOSHBERSIN
Corporate Learning
2024年02月18日
Corporate Learning
人工智能正在以比我预期更快的速度改变企业学习AI Is Transforming Corporate Learning Even Faster Than I Expected在《AI正在比我预想的更快地改变企业学习AI Is Transforming Corporate Learning Even Faster Than I Expected》这一文中，Josh Bersin强调了AI对企业学习和发展（L&D）领域的革命性影响。L&D市场价值高达3400亿美元，涵盖了从员工入职到操作程序等一系列活动。传统模型正在随着像Galileo™这样的生成性AI技术的发展而演变，这改变了内容的创建、个性化和传递方式。本文探讨了AI在L&D中的主要用例，包括内容生成、个性化学习体验、技能发展，以及用AI驱动的知识工具替代传统培训。举例包括Arist的AI内容创作、Uplimit的个性化AI辅导，以及沃尔玛实施AI进行即时培训。这种转型是深刻的，呈现了一个AI不仅增强而且重新定义L&D策略的未来。
在受人工智能影响的所有领域中，最大的变革也许发生在企业学习中。经过一年的实验，现在很明显人工智能将彻底改变这个领域。
让我们讨论一下 L&D 到底是什么。企业培训无处不在，这就是为什么它是一个价值 3400 亿美元的市场。工作中发生的一切（从入职到填写费用账户再到复杂的操作程序）在某种程度上都需要培训。即使在经济衰退期间，企业在 L&D 上的支出仍稳定在人均 1200-1500 美元。
然而，正如研发专业人士所知，这个问题非常复杂。有数百种培训平台、工具、内容库和方法。我估计 L&D 技术空间的规模超过 140 亿美元，这甚至不包括搜索引擎、知识管理工具以及 Zoom、Teams 和 Webex 等平台等系统。多年来，我们经历了许多演变：电子学习、混合学习、微型学习，以及现在的工作流程中的学习。
生成式人工智能即将永远改变这一切。
考虑一下我们面临的问题。企业培训并不是真正的教学，而是创造一个学习的环境。传统的教学设计以教师为主导，以过程为中心，但在工作中常常表现不佳。人们通过多种方式学习，通常没有老师，他们寻找参考资料，复制别人正在做的事情，并依靠经理、同事和专家的帮助。因此，必须扩展传统的教学设计模型，以帮助人们学习他们需要的东西。
输入生成人工智能，这是一种旨在合成信息的技术。像Galileo™这样的生成式人工智能工具 可以以传统教学设计师无法做到的方式理解、整合、重组和传递来自大型语料库的信息。这种人工智能驱动的学习方法不仅效率更高，而且效果更好，能够在工作流程中进行学习。
早期，在工作流程中学习意味着搜索信息并希望找到相关的东西。这个过程非常耗时，而且常常没有结果。生成式人工智能通过其神经网络的魔力，现在已经准备好解决这些问题，就像 L&D 的瑞士军刀一样。
这是一个简单的例子。我问Galileo™（该公司经过 25 年的研究和案例研究提供支持）：“我该如何应对总是迟到的员工？请给我一个叙述来帮助我？” 它没有带我去参加管理课程或给我看一堆视频，而是简单地回答了问题。这种类型的互动是企业学习的大部分内容。
让我总结一下人工智能在学习与发展中的四个主要用例：
生成内容：人工智能可以大大减少内容创建所涉及的时间和复杂性。例如，移动学习工具Arist拥有AI生成功能Sidekick，可以将综合的操作信息转化为一系列的教学活动。这个过程可能需要几周甚至几个月的时间，现在可以在几天甚至几小时内完成。
我们在Josh Bersin 学院使用 Arist ，我们的新移动课程现在几乎每月都会推出。Sana、Docebo Shape和以用户为中心的学习平台 360 Learning 等其他工具也同样令人兴奋。
个性化学习者体验：人工智能可以帮助根据个人需求定制学习路径，改进根据工作角色分配学习路径的传统模型。人工智能可以理解内容的细节，并使用该信息来个性化学习体验。这种方法比杂乱的学习体验平台（LXP）有效得多，因为LXP通常无法真正理解内容的细节。
Uplimit是一家致力于构建人工智能平台来帮助教授人工智能的初创公司，它正在使用其Cobot和其他工具为学习人工智能的技术专业人员提供个性化的指导和技巧。Cornerstone 的新 AI 结构按技能推荐课程，Sana 平台将 Galileo 等工具与学习连接起来，SuccessFactors 中的新 AI 功能还为用户提供了基于角色和活动的精选学习视图。
识别和发展技能：人工智能可以帮助识别内容中的技能并推断个人的技能。这有助于提供正确的培训并确定其有效性。虽然许多公司正在研究高级技能分类策略，但真正的价值在于可以通过人工智能识别和开发的细粒度、特定领域的技能。
人才情报领域的先驱者Eightfold、Gloat和SeekOut可以推断员工技能并立即推荐学习解决方案。实际上，我们正在使用这项技术来推出我们的人力资源职业导航器，该导航器将于明年初推出。
用知识工具取代培训：人工智能在学习与发展中最具颠覆性的用例也许是完全取代某些类型培训的潜力。人工智能可以创建提供信息和解决问题的智能代理或聊天机器人，从而可能消除对某些类型培训的需求。这种方法不仅效率更高，而且效果更好，因为它可以在个人需要时为他们提供所需的信息。
沃尔玛今天正在实施这一举措，我们的新平台 Galileo 正在帮助万事达卡和劳斯莱斯等公司在无需培训的情况下按需查找人力资源信息和政策信息。LinkedIn Learning 正在向 Gen AI 搜索开放其软技能内容，很快 Microsoft Copilot 将通过 Viva Learning 找到培训。
这里潜力巨大
在我作为分析师的这些年里，我从未见过一种技术具有如此大的潜力。人工智能将彻底改变 L&D 格局，重塑我们的工作方式，以便 L&D 专业人员可以花时间为企业提供咨询。
L&D 专业人员应该做什么？花一些时间来了解这项技术，或者参加Josh Bersin 学院的一些新的人工智能课程以了解更多信息。
随着我们继续推出像伽利略这样的工具，我知道你们每个人都会对未来的机会感到惊讶。L&D 的未来已经到来，而这一切都由人工智能驱动。
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