• Corporate Learning
    Josh Bersin:3400亿美元的企业学习的市场将迎来巨大变革 作者:Josh Bersin  本文探讨了企业学习行业的演变,特别是人工智能如何引领这一行业的巨变。企业每年在员工培训和发展上的开支超过3400亿美元,从传统的课堂培训到在线学习,再到以技能为中心的学习,行业一直在不断发展。现在,人工智能预计将彻底改变公司的学习管理系统(LMS)和学习体验平台(LXP),通过个性化和动态生成内容来提高学习效率和效果。文章强调了适应这种变化的重要性,以及AI在企业培训和人才发展中的潜力。 企业在员工培训和发展上的年支出超过3400亿美元,平均每名员工每年花费超过1500美元。这笔巨额开支支撑着一个全球产业,涉及数百家内容和技术公司,现正站在重新定义的风口浪尖。请允许我详细解释这一过程。 从电子学习到集体学习再到自主学习的演变 20世纪90年代末,随着互联网的崛起,以传统教室授课为主的培训产业发生了翻天覆地的变化。企业和内容提供者纷纷开发“电子学习”课程,试图在线复制面对面教学的体验。那是一个充满创新的时期,虽然今天看来有些过时,但它孕育了像Skillsoft(并购了众多竞争对手)、Cornerstone(同样并购了众多竞争对手)以及一大批传统的学习管理系统(LMS,例如Plateau、SumTotal、Learn.com、Pathlore等)公司,这些公司最终都被并购。 如今,LMS市场的规模已超过200亿美元,这一切几乎都是在线培训推动的结果。虽然这些系统可能看起来笨重,但它们对全球每家公司的交易和记录保持都至关重要。 当公司争相购买LMS系统——这是一个投资者非常关注的热门市场时,他们发现一个庞大的课程目录并不实用。因此,他们开始构建一套特征,我称之为“以人才为驱动的学习”,包括基于能力的学习、与职业角色一致的课程和职业发展路径系统。这些特征被添加到LMS中,使得这些系统不仅仅是教育工具,更像是“人力资源系统”,从而促使供应商扩展到更多的人才管理功能。 早期的开拓者Saba和Cornerstone开始推出绩效管理工具。回顾起来,这些尝试可能看起来有些简单,但当时它们代表了一个重大突破。突然之间,公司不再单独购买LMS系统,而是选择购买包含多个功能的“人才管理套件”,这迫使专注于LMS的供应商开始涉足招聘、目标管理乃至薪酬管理。他们可能没有意识到,放弃核心业务最终会导致他们被市场颠覆。 随着Facebook(2004年)、YouTube(2005年)和Twitter(2006年)的相继出现,内容世界发生了巨变。视频、文章和专家意见变得触手可及,那些笨重、以课程目录为导向的LMS系统显得格外难以使用。因此,随着公司寻求新的解决方案,原本投入巨资于人才管理的LMS市场开始显露老态。学习体验平台(LXP)市场随着Pathgather(2010年)、Degreed(2012年)、EdCast(2013年)的诞生而兴起,企业转向这一新兴领域投资。(更多历史,请参阅《从电子学习到集体学习》。) 2010年代初,整个行业的理念是尝试模仿Google,打造一个既具有Twitter式动态性又拥有YouTube式丰富内容的企业学习系统。传统的LMS和人才管理系统逐渐过时,供应商在缓慢的增长中寻求出路,最终合并为几家大型玩家。 随后,微学习的概念兴起。iPhone成为了视频播放平台(2008年),Instagram(2010年)、Snapchat(2011年)及后来的TikTok(2015年)向我们展示了短视频和“微学习”可以是多么的有趣。过长的两小时在线课程变得不受欢迎,因此LXP供应商开始扩展自己的产品线。随着公司将越来越多的内容投入到LXP中,我们意识到需要一种方法来寻找、精准定位并个性化所有这些学习材料。 此变化自然引发了内容市场的爆发。LinkedIn、Coursera、Udemy、OpenSesame、Go1等供应商决定开拓这个领域,推动了新材料的狂热消费。自那以后,内容市场继续繁荣发展,尽管仍然主要由小型玩家主导,但被更大的聚合商所整合,这些聚合商销售并分发多种品牌。 (顺便提一下,Workday在2016年收购了视频公司Mediacore,以抓住这波趋势。由于缺少核心LMS功能,他们花费数年时间将其发展成为一个完整的LMS。) 进入技能的世界。 你可能不会相信,但“技能记录系统”的概念最初出现在LXP领域,供应商如Degreed和EdCast建立了一个搜索术语数据库,并用“技能”一词标记内容。在消费者市场,我们能接收到成百上千的信号来推荐广告,但LXP供应商只有少数工程师,因此他们的“技能分类”相对简单。这个概念迅速走红,公司开始专注于构建基于“技能”的培训,随后是招聘和人才战略。 同时,L&D领域正处于创造性混乱之中。出现了如360 Learning、Fuse Universal、Kineo等数百家内容创作和分享系统的供应商,旨在帮助公司创作、分享视频内容,并按角色、技能或职能进行组织。这些并非严格意义上的LMS系统,但它们位于LMS前端,使员工能够轻松创建和消费动态内容。 这一时期,从2018年至今,成为L&D领域的热潮。市场充斥着各式各样的视频内容工具,同时像STRIVR和Talespin这样的先锋公司开始为虚拟现实(VR)构建工具和内容系统。自创内容平台、视频平台和VR平台正在满足重要需求,而LMS市场则变得更加固定、枯燥和无趣。(Talespin最近被Cornerstone收购。) 顺带一提,我仍然认为“能力学院平台”市场具有巨大潜力(这类平台提供综合的专业能力和小组学习功能,例如我们的Josh Bersin Academy)。Docebo、Learn-In、Nomadic、NovoEd和Intrepid等供应商仍在增长,但随着时间推移,这些系统可能被整合进人才市场。这一领域一直是行业的一个亮点。(想了解更多,请阅读《能力学院:L&D的未来方向》。) 作为分析师,我得诚实说,过去几年对我来说有些单调。我们帮助了数百家公司决定该选择哪种L&D系统,但通常我们发现这些组织有太多平台,内容分散杂乱,缺乏一致性的数据处理,以及在这一领域的过度投资。因此,这个静态期代表了过去3到5年的趋势,是企业整理过去十年购买历史的好机会。 世界突然再次发生变化。技能分类的理念迅速蔓延,同时新兴的人才智能系统,如Eightfold、Gloat、Fuel50等纷纷涌现。这些新兴系统使公司能够按技能寻找人才、根据技能推荐职位和机会,并按技能动态规划职业路径,再次与L&D领域发生碰撞,促使我们将所有内容“整合”进这些新平台中。(更多信息,请阅读《人才智能入门》。) 本周我刚与我最喜爱的L&D专家之一通话(他即将在我们的会议上演讲),他向我展示了他所在的大型制药公司如何将其LMS、LXP和人才市场融合成一个无缝、端到端的体系。他可能略微超前于当前趋势,但这正是事物发展的方向。 然而,故事还在继绀。又一场变革已经到来,这一次的影响力与YouTube、Instagram或iPhone相媲美,甚至更大。没错,就是AI。 AI,如许多人所预料,将彻底颠覆这个行业。正如我们在电子学习和人才管理时代所见证的那样,这意味着供应商生态将彻底改变。 AI如何改变一切 让我不夸大其词地告诉你。在这30年的故事中,有一点始终未变:企业培训关注的核心始终是内容。是的,我们希望内容更简短、更快速、能在手机上查看——但如果内容本身没有实用价值,不切实际,不易于消费,它就无法发挥作用。你们中有多少人为了得到学分而快速点击通过那些以页面为基础的合规课程,但实际上几乎没有注意内容?这正是我们面临的挑战。所有这些向视频、微学习、大规模开放在线课程(MOOCs)以及其他形式的转变,都是为了解决这个问题的尝试。 比如,假设企业学习系统能识别你是谁,你只需提出一个问题,它就能生成答案、一系列资源和一组动态学习对象供你消费。有时候,你可能只需快速获取答案即可。其他时候,你可能会深入研究内容。还有时,你可能会浏览整个课程,并花时间学习所需的知识。 假设这一切都是完全个性化的。这意味着你不会看到一个“标准课程”,而是根据你当前知识水平定制的特殊课程。 这就是AI即将带给我们的。而且,这已经在今天开始发生了。 不仅生成式AI能够回答问题和吸收内容(例如,Galileo™已经容纳了我们25年以上的每一项研究,包括视频、播客和文章),它还能生成视频、测试、测验甚至整个课程。它可以作为技术课程的教学助手,也可以作为领导力项目的教练或导师,并且能够进行语言转换。 AI能够根据你的身份动态生成内容,这意味着什么? 那么,LMS市场、LXP市场、VR学习市场以及所有内容提供商将如何呢?在未来几年,我们将见证一场巨大的行业洗牌。 供应商正在采取的行动 虽然我无法确切知道每个L&D供应商正在做什么,但可以肯定,变化正在迅速进行中。 Docebo Shape能够从文档中生成高效的互动式培训材料(Arist也能做到这点)。Uplimit构建了一个完整的L&D平台,采用AI智能体和课程中自动生成的内容。我们的合作伙伴Sana不仅能自动生成内容,还围绕AI核心建立了一个完整的LMS系统。Cornerstone通过收购Talespin,能够动态创建角色模拟和几乎可以无限配置的场景。快速增长的“精确技能”供应商Growthspace,可以根据1100种具体的商业技能,为你匹配一个“技能教练”,与你的具体目标对齐。 LMS市场不会消失,但正如人才智能系统正在逐渐取代应聘追踪系统(ATS)和人力资源管理系统(HRMS)一样,AI驱动的内容平台将逐步侵蚀LMS市场。我的制药公司朋友希望他的LXP能成为他们的“动态内容系统”,但坦白说,我不确定LXP供应商是否已经准备好迎接这个挑战。许多供应商,从LinkedIn到Microsoft,将不得不重新考虑他们如何成为“动态学习”系统,以及他们希望在其中扮演什么角色。 正如所有技术转变一样,通常情况下,从头开始构建的系统会超越旧有系统。对于Cornerstone或Docebo这样拥有数千客户的公司来说,当新技术出现时,他们不能简单地“替换”他们已经建立的系统。因此,新兴的AI驱动学习系统可能会由新的供应商推出,并随着这些公司的发展,开始取代和竞争现有的系统。 尽管看上去简单,学习技术实际上非常复杂。Workday几乎花了十年时间从Mediacore发展到一个相对健全的LMS,并且他们才刚刚开始尝试AI。因此,不要期望你现有的供应商能够一夜之间彻底改变。 但有一件事我可以确定:颠覆即将来临。就像Plateau、Saba和SumTotal在2000年代初期时“市场上最热门的供应商”一样,它们很快就成为了过时系统和收购目标,当市场变化时同样的情况也可能发生在今天。新兴供应商如Sana、Growthspace、Uplimit、Docebo、LMS365等将崭露头角。 尽管风险资本家通常对这个市场持谨慎态度,但往往是那些拥有最佳管理团队的公司最终胜出。大型供应商如LTG、Cornerstone和Skillsoft拥有充足的资金,因此随着市场的发展,任何事情都有可能发生。但对我来说,一件事是明确的:前方是一个巨大的增长周期。 AI的机会是真实的,而且极为巨大 想象一下我们公司中的遗留内容量。全球必然存在价值超过一万亿美元的  合规培训、销售培训、运营培训、安全培训和领导力发展内容。如果AI能够在大规模上“重新利用”和“再创造”这些内容,我们将看到这个巨大的市场向新系统转变,最终实现知识管理和学习的完美结合。 我来举一个简单的例子。我们的一位Galileo客户是一家拥有百年历史的大型航空航天公司,他们在工程、产品设计、航空和国防技术方面有着丰富的积累。他们构建了喷气引擎、导弹、核潜艇以及各种系统。对于一名新工程师,他们需要超过三年的时间来完成“入职培训”,因为需要掌握大量的知识产权、设计专长和系统操作。他们的资深工程师们都在逐渐退休! 他们在我们的帮助下,开始了一个以AI为中心的试点项目,把多年累积的内容放到一个新平台中,供年轻工程师使用。我相信,这将带来翻天覆地的变化。Galileo将协助处理管理层面的问题,而一个类似的AI助手将帮助工程师学习、寻找文档、观看视频并参加相关课程。传统的LMS和HRMS工具可能不会在这一过程中发挥重要作用。 考虑一下你的公司。你们囤积了多少内容、专业知识和旧有的培训资料?AI可以“释放”这些资源给你的员工,使其以前所未有的方式变得可用。这是一个激动人心的新时代,充满了即将到来的变革。
    Corporate Learning
    2024年03月21日
  • Corporate Learning
    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 AI Josh 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。
    Corporate Learning
    2023年12月13日