• Josh Bersin
    世界幸福报告能教给我们关于工作的什么? What The World Happiness Report Teaches Us About Work 最新《世界幸福报告》揭示，尽管经济增长，美国幸福感下降。研究强调，高薪并非幸福的关键，而公平薪酬、良好的企业文化才是。特别是年轻人，受到气候变化、政治纷争等影响，幸福感低落。企业需关注文化建设、弹性工作，关照员工心理健康。工作场所的信任、社区感和公平至关重要。我们要反思：真正的幸福是什么？ 我每年都认真研读《世界幸福报告》，今年的报告特别引人深思。以下是我对一些关键发现的解读。 首先，美国的幸福指数（10分满分）降至第23位，比全球最幸福的国家芬兰低了13%。实际上，在过去15年中，美国的幸福度几乎下降了8%，呈现出持续的年降趋势。对于我们这些生活在美国的人来说，这可能并不陌生：坏消息、政治争斗以及人们在价值观上的分歧似乎无处不在。 这一切发生的同时，美国的GDP增长却持续领先世界上大多数主要经济体。这意味着我们作为一个国家正在变得更加富裕，却显著地变得不那么幸福（下文将详细解释）。 从企业角度来看，这个观点很简单：仅仅提高薪资并不能使人们感到更加幸福。尽管每个人都希望得到公平的报酬，但高薪酬并不直接转化为高参与度。我们2023年的《薪酬公平终极指南》发现，与薪酬水平相比，薪酬公平与员工参与度的关联性高出7倍。 其次，报告指出，在美国，年轻人的幸福感明显低于老年人（这一点并非在所有国家都适用，但在大多数发达国家中是这样的）。在美国，30岁以下人群的幸福评分为6.4，而60岁以上人群的评分为7.3，幸福度低了12%。我们对年轻人的这一低幸福评分使美国在全球青年幸福排行榜上仅位列第62位，远低于我们的总体排名。 这反映出我在上周播客中讨论的现象。如今的年轻工作者担忧全球变暖，他们在年轻时就经历了疫情的冲击，他们对于战争、通货膨胀、社会问题以及政治不和感到沮丧。埃德曼信任度量尺表明，年轻人认为相比政府，企业在为社会带来创新方面更值得信赖，高出近20%。但令人担忧的是，这种信任程度也在下滑。 从企业的视角来看，这进一步强化了播客中提到的观点：我们（美国）的劳动力中位年龄现已达到33岁。这表明许多关键员工对生活的热情有所下降，这迫使雇主需要采取更多措施。我们对企业文化、员工福祉、工作灵活性和个人成长的关注，现在比以往任何时候都显得更为重要。这就是像四天工作周、灵活工作时间以及其他诸多福利（如生育支持、儿童看护、心理健康、健身、财务福利）变得越来越普遍的原因。 （最新的劳动统计局数据显示，我们在福利上的支出占工资总额的31.1%，比三年前的29%有所增加。在信息行业，这个比例高达35.5%，是有史以来的最高值。） 此外，重点强调：对企业来说，重振早期职业发展计划至关重要。许多企业在20世纪60、70年代建立了这些计划，但随后这些计划逐渐被忽视。如果你正在从大学招聘顶尖人才，并投资于校园招聘（这一趋势正在上升），那么确保你有一个坚实的1-2年发展计划、工作轮岗以及面向年轻人的群体参与计划是非常重要的。我最近与康卡斯特讨论了他们的计划，他们的早期职业发展计划正在直接为他们的领导力管道做出贡献。 第三，也是最引人注目的一点是，报告强调了社会关系和信任在幸福感中的巨大作用。进行这项研究的学者团队发现，幸福感的“坎特里尔阶梯”（一个简单的“你觉得自己多幸福”的1-10评分问题）可以分解为六个贡献因素： 人均GDP（财富）、社会支持（密切关系的数量和质量）、预期寿命（健康）、生活选择的自由（按个人意愿生活的能力）、慷慨（向他人给予金钱和时间的倾向）以及腐败感知（相信“系统”是公平的）。 这些因素对幸福的贡献度大开眼界。 令人惊讶的是，社会关系是幸福感的最大贡献者，而健康只占大约1.4%。请注意，第二重要的因素是对腐败的感知或者说是公平感，这解释了为什么薪酬公平非常重要。我们再次发现，财富对幸福感的影响相对较小。 这对我们的工作有何启示？ 这里有一些简单的启示： 关系很重要。如果管理层和主管不能建立起团队合作感，员工便会感到不适。尽管我们面临财务和运营压力，但我们仍需抽时间了解员工、倾听他们的声音，并与他们共度愉快时光。通过聚集人员并创建跨功能团队，我们即使在远程工作情况下也能建立社交关系。 信任至关重要。我曾在高层领导贪婪、不忠、不诚实的环境中工作过，公司内的每个人都能感觉到这一点。信任是经年累月建立起来的资产，我们必须不断地进行投资。通过道德、诚实和倾听来培养信任，你的领导模式中包含了这些元素吗？ 薪酬的影响可能比你想象的要小。虽然每个人都希望赚更多钱，但人们更希望感觉到奖励是公平且慷慨的。因此，不应仅仅过度奖励表现突出的员工，而忽视其他人的努力。 生活选择的自由极为重要。众多研究显示，与薪资相比，员工更加重视工作的灵活性，因此，考虑将四天工作周和灵活工作选项作为你的雇佣政策的核心部分是非常重要的。 多年前，我在一个人力资源领导者的大型会议上发表了关于企业公民责任的演讲。我指出，公司就像小型社会一样，如果我们的企业“社会”不公平、不透明、不自由，那么我们的员工就会感受到痛苦。演讲结束时，我不确定听众的反应如何，但来自宜家的一大群人向我走来，给了我一个热情的拥抱。宜家这家公司，深深植根于瑞典的社会主义文化，是地球上最长久的公司之一。他们真心相信集体思维、公平和对每个个体的尊重。 原文来自：https://joshbersin.com/2024/03/what-the-world-happiness-report-can-teach-us-about-work/
    Josh Bersin
    2024年03月22日
  • Josh Bersin
    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。
    Josh Bersin
    2024年03月21日
  • Josh Bersin
    Josh Bersin谈How To Create Talent Density 如何打造人才密度 Josh Bersin发表文章谈到：在过去几年里，我注意到大公司的表现开始不如小公司。我们现在看到苹果和谷歌都出现了这种情况，而微软应对这一挑战也有相当长的一段时间了。 随着公司的发展，帮助我们推动组织绩效的一个重要理念就是人才密度。这篇文章讨论了人才密度的概念，即公司中技能、能力和表现的质量和密度。强调传统的员工绩效评估模型已导致平庸。建议采用人才密度方法，包括招聘增加或乘数效应的人才，基于帕累托分布管理绩效，以及专注于赋权、反馈和领导力。文章强调，为了创新和市场竞争力，尤其在AI和技术进步的背景下，维持高人才密度的重要性。 In this (long) article, I want to talk about a new concept called Talent density. And as I pondered the concept I think it represents one of the more important topics in management. So I hope you find it as interesting as I do. First of all, the concept of talent density, pioneered by Netflix by the way, is simple. Talent Density is the quality and density of skills, capabilities and performance you have in your company. So, if you have a company that is 100% high performers, you’re very dense. If you have a company that’s 20% high performers, you’re not very dense. It’s easy to understand, but hard to implement, because it gets to the point of how we define performance, how we select people to hire, how we decide who’s going to get promoted, how we decide who’s going to work on what project and how we’re going to distribute pay. So before I explain talent density, let’s talk about the basic beliefs most companies have. Most organizations believe that they’re operating with a normal distribution or bell curve of performance. I don’t know why that statistical model has been applied to organizations, but it has become almost a standard policy. (Academics have proven it false, as I explain below.) Using the bell curve, we identify the “mean” or average performance, and then categorize performance into five levels. Number ones are two standard deviations to the right and number fives are two standard deviations to the left. The people operating at level one get a big raise, the people operating at level two get medium raise, the people operating at level three get an average raise, the people operating at level four get a below average raise and the people operating at level five probably need to leave. Lots of politics in the process, but that’s typically how it works. As I describe in The Myth of The Bell Curve, these performance and pay strategies have been used for decades. And at scale they create a mediocrity-centered organization, because the statistics limit the quantity and value of 1’s. If you’re operating at 1 level and you get a 2, you’ll quit. If you’re operating at 3 level, you’re probably going to coast. You get my drift. And since the bulk of the company is rated 2 or 3, most of the managers are in the middle. As the saying goes, A managers hire A people, B managers hire C people. So over time, if not constantly tuned, we end up with an organization that is almost destined to be medium in performance. Now I’m not saying every company goes through this process, but if you look at the productivity per employee in large organizations it’s almost always below that of smaller organizations. Why? Because as organizations grow, the talent density declines. (Netflix, as an example, example, generates almost $3M of revenue per employee, twice that of Google and 10X that of Disney. And they are the only profitable streaming company, with fewer than 20,000 employees and a $240 billion market cap.) The traditional model was fine in the industrial age when we had a surplus of talent, jobs were clearly defined, and most employees were measure by the “number of widgets they produced.” In those days we could swap out a “low performer” for a “high performer” because there were lots of people in the job market. We don’t live in that world anymore. The world we now live in has sub 4% unemployment, a constant shortage of key skills, and a growing shortage of labor. And thanks to automation and AI, the revenue or value per person has skyrocketed, almost an order of magnitude higher than it was 30 years ago. So we need a better way to think about performance in a world where companies with fewer people can outperform those who get too big. Look at how Salesforce, Google, Apple, who are essentially creative companies, have slowed their ability to innovate as they get bigger. Look at how OpenAI, who is a tiny company, is outperforming Google and Microsoft. Today most businesses outperform through innovation, time to market, customer intimacy, or IP – not through scale or “harder work.” How do we maintain a high level of talent density when we’re growing the company and hiring lots of people? Netflix wrote the book on this, so let me give you the story. First, the hiring process should focus on talent density, not butts in seats. Rather than hire someone to “fill a role” we look for someone who is additive or multiplicative to the entire team. Hire someone that challenges the status quo and brings new ideas, skills, and ideas beyond the “job” as defined. Netflix values courage, innovation, selflessness, inclusion, and teamwork, for example. These are not statements about “doing your job as defined.” Netflix’s idea is that each incremental hire should make everybody else in the company and everybody else in the team produce at a higher level. Now this is a threatening thing for an insecure manager because most managers don’t want to hire somebody that could take their job away. But that’s why we have this problem. Second, we need to manage or create some type of performance management process that is built around the Pareto distribution (also called the Power Law) and not the normal distribution. In the Pareto distribution or the power law, we have a small number of people who generate an outsized level of performance, you can call it the 80/20 rule or the 90/10 rule. (20% of the people do 80% of the work) Studies have shown that companies and many populations work this way, and it makes sense. Think about athletes, where a small number of super athletes are 2-3 better than their peers. The same thing is true in music, science, and entertainment. It’s also true in sales and many business disciplines. Research conducted in 2011 and 2012 by Ernest O’Boyle Jr. and Herman Aguinis (633,263 researchers, entertainers, politicians, and athletes in a total of 198 samples). found that performance in 94 percent of these groups did not follow a normal distribution. Rather these groups fall into what is called a “Power Law” distribution. In every population of human beings there are a few people who just have God-given gifts to outperform in the job, and they just naturally seem to be far better than everyone else. Bill Gates once told the company that there were of the three engineers that he felt made the company of Microsoft. And I’ve heard this in many other companies, where one software engineer and the right role can do the work of 10 other people. Now, this is not to say that everybody will fall into one level of the Pareto distribution. At a given point in time in your career, you may be in the 80% and over time, as you learn and grow and find the things that you’re naturally good at, you’ll end up in the 20%. But in a given company this is a dynamic that’s constantly taking place. And that’s what Netflix is doing – constantly working on talent density. What does this mean for performance management? It means that in order to care for a population like this, we have to hire differently, avoid the bell curve, and pay high performers well. Not just a little more than everybody else, a lot more. And that’s what happens in sports and entertainment, so why not in business. If you look at companies like Google, Microsoft, and others, there are individuals in those companies that make two to three times more than their peers. And as long as these decisions are made based on performance, people are fine with it. What obviously does not work is when person making all the money is the person who’s the best politician, best looking, or most popular. And that leads me to item three: In the Netflix culture there’s a massive amount of empowerment, 360 feedback, candor and honesty. You’ve probably read the Netflix culture manifesto: it’s all about the need for people to be honest, to speak truth, to give each other feedback, and to focus on judgement, courage, and accountability. Netflix only recently added job levels: they didn’t have job levels for many years. Giving people feedback is a challenge because it’s uncomfortable. So this has to to start at the top and it has to be done in a developmental, honest way. This does not mean people should threaten or disparage each other, but we all need to know that at the end of a project or the end of the meeting it’s okay for somebody to tell us “here’s what was great about that and here’s what wasn’t great about it.” One of the most important institutions in the world, the US military lives, eats and dies by this process. If you’re in the military and you mess something up, you can guarantee that somebody’s going to tell you about it, and you’re going to get some help making sure you don’t do it again. We don’t have life or death situations in companies, but we can certainly use this kind of discipline. The fourth thing that matters in talent density is leadership and goal setting. One of the things that really gets in the way of a high performing company is too many individual goals, too many siloed projects and responsibilities and people not seeing the big picture. If your goal setting and performance management process is 100% based on individual performance you are sub-optimizing your company. Not only does this work against teamwork, but there really isn’t a single thing in a company that anybody can do alone. So our performance management research continuously shows that people should be rewarded for both their achievements as well as that of the team. (Here’s the research to explain.) Why is talent density important right now? Let me mention a few reasons. First, we’re entering a period of low unemployment so every hire is going to be challenging. And thanks to AI, companies are going to be able to operate with smaller teams. What better time to think about how to “trim down” your company so it’s performing at its best? Second, the transformations from AI are going to require a lot of flexibility and learning agility in your company. You want a highly focused, well aligned team to help make that happen. And while AI will help every company improve, your ability to leverage AI quickly will turn into a competitive advantage (think back about how web and digital and e-commerce did the same). (I firmly believe the companies with the most ingenious applications of AI will disrupt their competitors. I’m still amazed at Whole Food’s hand recognition checkout process: I can see self-service coffee, groceries, and other retail and hospitality coming.) Third, the post-industrial business world is going to start to devalue huge, lumbering organizations. Many big companies just need a lot of people, but as Southwest Airlines taught us long ago, it’s the small team that performs well. So if you can’t break your company into small high-performing teams, your talent density will suffer. When the book is written on Apple’s $10 Billion car, I bet one problem was the size and scale of the team. We’ll see soon enough. By the way, I still recommend everyone read “The Mythical Man-Month,” which to me is the bible of organizing around small teams. What if you’re a healthcare provider, retailer, manufacturer, hospitality company? Does talent density apply to you? Absolutely! Go into a Costco and see how happy and engaged the employees are. Then go into a poorly run retailer and you’ll feel the difference. In my book Irresistible I give examples of companies who embrace what I call “the unquenchable power of the human spirit.” Nobody wants to feel like they’re underperforming. With the right focus on accountability and growth we can help everyone out-perform their expectations. Now is a time rethink how our organizations work. Not only should we promote and reward the hyper-performers, the Pareto rule and Talent Density thinking encourage us to help mid-level performers learn, grow, and transform themselves into superstars. Let’s throw away the old ideas of bell curve, forced distribution, and simplistic performance management. Companies that push for everlasting high performance are energizing places to work, they deliver outstanding products and services, and they’re great investments for stakeholders.   AI中文翻译： 在这篇篇幅较长的文章中，我想探讨一个被称为“人才密度”的新概念。思考此概念时，我认为它是管理领域中极其重要的议题之一。希望您能像我一样发现其趣味性。 首先，Netflix首创的“人才密度”概念其实很简单。 人才密度指的是公司内部技能、能力和表现的质量与密集程度。 换句话说，如果你的公司全是高绩效人才，那么你的“人才密度”就很高。如果只有20%是高绩效人才，那么你的“人才密度”就不高。这个概念虽然容易理解，但实际执行起来却颇具挑战，因为它涉及到我们如何定义绩效、招聘员工的标准、晋升决策、项目分配以及薪酬分配。 在详细解释“人才密度”之前，让我们先看看大多数公司的基本信念。许多组织相信，他们的员工表现遵循一个正态分布或钟形曲线。这个统计模型为何被广泛应用于组织之中，我并不清楚，但它几乎已成为标准做法。（实际上，如我下文将解释的，学术研究已证明这一模型是错误的。） 采用钟形曲线，我们确定平均表现（即“平均线”），然后将员工的表现划分为五个等级。表现最好的被归为一级，标准为右偏两个标准差；表现最差的被归为五级，左偏两个标准差。 一级表现者获得大幅度加薪，二级表现者获得中等加薪，三级表现者获得平均水平的加薪，四级表现者加薪低于平均，五级表现者可能就需要离开公司了。虽然这个过程充满了政治操作，但这就是它通常的运作方式。 正如我在《钟形曲线的神话》中所述，这些关于绩效和薪酬的策略已经使用了数十年。而且，当这些策略在大规模下实施时，它们会造成以平庸为中心的组织文化，因为这种统计方法限制了顶尖人才的数量和价值。如果你是一级表现者却被评为二级，你很可能就会选择离职。如果你是三级表现者，你可能就会选择安于现状。你应该明白我的意思了。而且，由于大部分员工的评级为二级或三级，大多数管理者也就处于中等水平。 常言道，A级的管理者招聘A级人才，B级的管理者则招聘C级人才。因此，如果不持续进行优化调整，组织最终几乎注定会变得中庸。 我并不是说每家公司都会经历这一过程，但如果你查看大型组织的员工生产率，通常都低于小型组织的生产率。为什么呢？因为随着组织规模的扩大，“人才密度”往往会下降。（以Netflix为例，其每名员工创造的收入几乎为300万美元，是Google的两倍，是迪士尼的十倍。他们是唯一盈利的流媒体公司，员工不足20,000人，市值2400亿美元。） 在工业时代，人才供过于求，工作职责明确，大多数员工的表现以“生产的产品数量”来衡量。那个时候，低绩效者可以轻松地被高绩效者替换，因为劳动市场上有大量的人才可供选择。 但我们不再生活在那个时代了。在我们现在的世界里，失业率低于4%，关键技能持续短缺，劳动力整体也日益减少。而且，得益于自动化和AI技术，每位员工创造的收入或价值比30年前高出了几个数量级。 因此，在一个人员更少的公司可以超越体量更大的公司的世界中，我们需要一种更好的绩效思考方式。看看Salesforce、Google、Apple这些本质上依靠创新的公司，随着规模扩大，它们的创新能力如何变缓。再看看OpenAI，尽管是一个小公司，却在超越Google和Microsoft。 如今，大多数企业通过创新、市场响应速度、客户亲密度或知识产权而非规模或“更加努力的工作”来实现超越。 在我们不断发展公司并招聘大量人员的同时，我们如何保持高水平的“人才密度”？Netflix在此领域有着开创性的工作，让我来分享一下他们的故事。 首先，招聘过程应专注于提高“人才密度”，而不是仅仅为了填补空缺。我们寻找的不是简单地“填补一个角色”的人，而是能够为整个团队带来正面或倍增效果的人才。我们寻找的是那些能够挑战现状、带来新观点和技能，并超出传统“工作定义”的人。例如，Netflix重视勇气、创新、无私、包容和团队合作等价值观，并不仅仅是“完成既定工作”。 Netflix的理念是，每一次新增的招聘都应该使公司内每个人和团队的每个成员的生产力得到提升。这对于那些缺乏安全感的管理者来说可能是个挑战，因为大多数管理者并不希望招聘可能会取代他们的人。但正是这种思维方式导致了我们当前的问题。 其次，我们需要建立或改进一种围绕帕累托分布（也称作幂律分布）而非正态分布的绩效管理流程。在帕累托分布或幂律分布中，少数人贡献了超出常规的绩效水平，这可以称作80/20规则或90/10规则。（即20%的人完成了80%的工作） 研究显示，许多公司和人群实际上都是以这种方式运作的，这是合理的。想想那些在体育、音乐、科学和娱乐领域表现出色的人，其中少数顶尖人才的表现是同龄人的两到三倍。销售和许多商业领域也是如此。 2011年和2012年由Ernest O’Boyle Jr.和Herman Aguinis进行的研究（涵盖了633,263名研究人员、艺术家、政治家和运动员，共198个样本）发现，这94%的群体的表现并不遵循正态分布，而是呈现所谓的“幂律分布”。 在每个人群中，总有少数人因为天赋异禀，在工作中表现出色，自然而然地比其他人优秀得多。 比尔·盖茨曾经对微软说过，他认为公司中的三名工程师是公司的基石。我也在许多其他公司听到过类似的故事，其中一位软件工程师在合适的位置上可以完成其他十人的工作量。 这并不意味着每个人都将被归入帕累托分布的某一层级。在你职业生涯的某个阶段，你可能处于80%的群体中，但随着你不断学习、成长并找到自己真正擅长的领域，你最终可能进入20%的群体。但在任何一个公司，这种动态都在不断发生。这就是Netflix一直在努力提升“人才密度”的原因。 这对绩效管理意味着什么？这意味着，为了照顾这样一个群体，我们必须采取不同的招聘方式，避免使用钟形曲线，并且为高绩效者提供丰厚的薪酬。这不仅仅是支付比其他人稍微多一点的薪水，而是要多得多。这在体育和娱乐领域已经是常态，那么为什么不可以应用到商业领域呢？ 如果你观察Google、Microsoft等公司，你会发现，这些公司中的个别人物赚取的收入是他们同事的两到三倍。只要这些决策基于绩效，大家通常都能接受它。 当然，不起作用的情况是，赚取高薪的是那些最擅长政治、外表最出众或最受欢迎的人。 这就引出了第三点：在Netflix的文化中，存在着大量的授权、360度反馈、直率和诚实。您可能已经读过Netflix的文化宣言，它强调人们需要诚实、坦诚、互相提供反馈，并专注于判断力、勇气和责任感。直到最近，Netflix才引入了职级制度——在很多年里，他们根本没有职级制度。 提供反馈是挑战性的，因为这会使人感到不适。因此，这个过程必须从高层开始，并以一种促进发展、诚实的方式进行。这并不意味着人们应互相威胁或贬低，但我们都需要明白，在项目结束或会议结束时，对方告诉我们“这是成功之处，这是失败之处”是完全可以接受的。 美国军队是世界上最重要的机构之一，它依靠这种过程生存、发展和克服困难。如果你在军队犯错，你可以确信会有人告诉你，并且你会得到帮助以确保你不会再犯同样的错误。虽然公司里没有生死攸关的情况，但我们完全可以借鉴这种纪律性。 在“人才密度”中很重要的第四点是领导力和目标设定。阻碍高绩效公司发展的一个常见问题是过多的个人目标、孤立的项目和职责，以及员工无法看到整体大局。 如果你的目标设定和绩效管理过程完全基于个人表现，那么你就在削弱你的公司。这不仅阻碍了团队合作，而且实际上没有什么是公司内任何人能够独立完成的。因此，我们的绩效管理研究不断表明，人们应该同时因其个人成就和团队成就而获得奖励。（这是相关的研究。） 为什么“人才密度”在当前尤为重要？我来列举几个原因。 首先，我们正处于一个失业率低的时期，因此每次招聘都将是一个挑战。而且，随着AI技术的帮助，公司将能够以更小的团队运作。在这样一个时刻，有什么比考虑如何“精简”你的公司、使其发挥最佳表现更合适的时机呢？ 其次，随着AI的变革，你的公司将需要极大的灵活性和学习适应能力。你需要一个高度专注、良好协调的团队来实现这一目标。而且，尽管AI将帮助每个公司提高效率，但你快速应用AI的能力将变成一个竞争优势（回想一下网站、数字化和电子商务如何实现了同样的事情）。 （我坚信，那些能够巧妙应用AI的公司将会颠覆它们的竞争对手。我对Whole Foods的手掌识别结账过程仍感到惊讶：我预见到自助服务咖啡、杂货及其他零售和酒店业务的出现。） 第三，后工业时代的商业世界将开始贬低庞大、笨重的组织。许多大公司只是需要大量员工，但正如西南航空所示，小团队的表现通常更好。因此，如果你无法将你的公司划分为小型高效团队，你的“人才密度”将受到影响。 当有关Apple的100亿美元汽车项目的书籍编写时，我敢打赌问题之一将是团队的规模和规模。我们很快就会发现。顺便说一下，我还是推荐每个人阅读《神话般的人月》，对我而言，这本书是关于围绕小团队进行组织的经典之作。 如果你是医疗服务提供者、零售商、制造商或酒店业者，“人才密度”是否适用于你？当然适用！走进一家Costco，看看员工是多么的开心和投入。然后走进一个管理混乱的零售商，你就能感受到区别。 在我的书《不可抗拒》中，我列出了那些拥抱我所称之为“人类精神不可磨灭力量”的公司的例子。没有人愿意觉得自己表现不佳。通过适当的关注责任和成长，我们可以帮助每个人超越他们自己的期望。 现在是时候重新考虑我们的组织如何运作了。我们不仅应该提升和奖励顶尖表现者，帕累托法则和“人才密度”思维还鼓励我们帮助中等表现者学习、成长，并将自己转变为明星。 让我们抛弃旧的钟形曲线、强制分配和简单化绩效管理的想法。不断追求高绩效的公司是充满活力的工作场所，他们提供卓越的产品和服务，并且对投资者而言是极好的投资。
    Josh Bersin
    2024年03月10日
  • Josh Bersin
    Is DEI Going to Die in 2024? Josh Bersin 的文章讨论了 2024 年多元化、公平与包容（DEI）项目所面临的重大挑战和批评，特别强调了 "反觉醒 "评论家的攻击和克劳迪娜-盖伊（Claudine Gay）从哈佛辞职的事件。报告探讨了多元包容计划在当前的文化战争中扮演的角色、人们对它的看法以及法律挑战对多元包容计划招聘和投资的影响。尽管存在这些挑战，贝尔辛还是强调了发展型企业的实际商业利益，展示了成功的战略以及将发展型企业融入业务而不仅仅是人力资源的重要性。他认为，应将重点转向在所有业务部门嵌入包容、公平薪酬和开放讨论的原则，并指出，未来的企业发展指数至关重要，但需要适应和领导层的承诺才能茁壮成长。 Is DEI Going to Die in 2024? By Josh Bersin For anyone working in Diversity, Equity, and Inclusion (DEI), it is safe to say that it has been a tough start to 2024. For a while now, there has been a concerted attack on DEI programs, with ‘anti-woke’ commentators and public figures querying their value, worth, and even existence. Those attacks increased enormously in 2024 with the resignation of Claudine Gay from Harvard. While the call to resign was supposedly related to plagiarism, one can’t help but feel that her position as a leading DEI advocate also fuelled the demand. It means that DEI has come under increased and sustained fire, and despite the many benefits provided by a good DEO program – to both employer and employee – there is a feeling that 2024 could be the year that DEI fades away. How likely is this to happen, and what would the impact be if it did? DEI and the culture wars Anyone living and working in the US (or most other countries worldwide) over the past few years will have likely heard of the culture wars. Brought on by declining trust in institutions, growing inequalities, and the proliferation of technology, the culture wars involve opposing social groups seeking to impose their ideologies. All manner of things has been caught up in this, from what’s on the curriculum at schools to taking a knee at sporting events and from definitions of what constitutes a woman to allegations of tokenism in the workplace. DEI has played an unwitting but central part in the culture wars. There’s a perception that DEI programs are ‘woke’ and prioritize ethnicity and gender over achievement and ability. In August of 2023, an attorney filed (and won) a lawsuit against a VC firm that gives grants to black entrepreneurs. Similar suits have been filed against firms with diversity hiring programs, scholarships, and internships. The resignation of Claudine Gay has reinvigorated the commentary around DEI programs. Josh Hammer, a conservative talk show host and writer, wrote on the social media platform X that taking down Dr. Gay was a “huge scalp” in the “fight for civilizational sanity. ” It was described as “a crushing loss to DEI, wokeism, antisemitism & university elitism,” by conservative commentator Liz Wheeler, and the “beginning of the end for DEI in America’s institutions,” by the conservative activist Christopher Rufo, who had helped publicize the plagiarism allegations against Claudine Gay. When something is as consistently criticized and devalued as DEI programs have been, a toll is inevitably taken. That is certainly indicated by the latest hiring data for DEI professionals. According to data from labor market analytics company Lightcast, hiring for DEI positions in the US is down by 48% year over year, in the middle of an economic boom. Clearly, DEI investments are under attack. And when you look at companies doing layoffs, DEI jobs are frequently high on the list of jobs to cut. I even heard a recent podcast with four well-known venture capitalists – three agreed that “doing away with DEI programs” was top on their list. The value of DEI Given this criticism of DEI programs, one could be forgiven for thinking such programs carry no value to HR and the wider business. Yet many companies invest in DEI programs, and the value is high in almost every case I come across. Our Elevating Equity research in 2022 and 2023 found companies focus on diversity and inclusion for very pragmatic reasons, including: An inclusive hiring strategy broadens and deepens the recruiting pool. An inclusive leadership strategy drives a deeper leadership pipeline. An inclusive management approach helps attract diverse customers and markets. An inclusive board drives growth and market leadership. (proven statistically) An inclusive supply chain program improves sustainability of the supply chain. An inclusive culture creates growth, retention, and engagement in the employee base. Organizations are not prioritizing DEI programs because they are woken or as a box-ticking exercise. They do so because DEI provides real and tangible business benefits. Workday, one of the most admired HR technology companies in the market, has pioneered DEI internally and through its products, and the company has outgrown and outperformed its competitors for years. Their product VIBE, an analytics system designed for this purpose, shows intersectionality, and helps companies set targets and find inequities in leadership, hiring, pay, and career development. But some law firms have posited that these types of programs are illegal – is there a case to answer? DEI legality In response, it’s important to consider the massive and complex pay equity problem. Until the last few years, most companies had no problem paying people in very idiosyncratic ways. The Josh Bersin Company looked at leadership, succession, and pay programs worldwide last year and found that there are massive variations in pay with no clear statistical correlation in most larger companies. This problem is called “pay equity,” and when you look at pay vs. gender, age, race, nationality, and other non-performance factors, most companies find problems. Is this a “DEI” program? When we looked at pay equity in detail last year, we found that only 5% of companies have embarked on a strategic equity analysis. While most companies do their best to keep pay consistent with performance, these studies always find problems. Would it be considered illegal to analyze pay by race or nationality and then fix the disparities? The future of DEI DEI is undoubtedly a complex issue, and many organizations will be uncertain about the best course of action. Despite the current wave of criticism, there has been vast investment in DEI strategy over recent years, and business leaders are highly unlikely to let that fade away. Despite the anti-woke movement, political debates, and the inability of Harvard, Penn, and other universities to speak clearly on these topics, businesses will not stop. Affirmative Action was not created to discriminate; it was designed to reduce discrimination. At the University of California, where Affirmative Action was halted in 1995, studies found that earnings among African American STEM graduates decreased significantly. So, one could argue that they were making a real difference. DEI will not die – it is far too important for that to happen. However, it’s time to do away with the “DEI police” in HR and focus on embedding the principles of inclusion, fair pay, and open-minded discussions across all business units. Senior leaders must take ownership of this issue. In the early 2000s, companies hired Chief Digital Officers to drive digital technology implementation, ideas, and strategies. As digital tools became commonplace, the role went away. We may be entering a period where the Chief Diversity Officer has a new role: putting the company on a track to embrace inclusion and diversity in every business area and spending less time pushing the agenda from a central group. In every interview we conduct on this topic, we see overwhelming positive stories from various DEI strategies. Each successful company frames DEI as a business rather than an HR strategy. While HR-centric DEI investments are shrinking, it’s more like them migrating into the business where they belong. 中文翻译如下，仅供参考： 2024年，多样性、公平与包容（DEI）将走向消亡吗？作者：Josh Bersin 对于那些致力于多样性、公平与包容（DEI）领域的人士来说，2024年的开端无疑充满挑战。近期，DEI项目遭到了前所未有的集中攻击，包括一些“反觉醒”评论员和公众人物对其价值、意义乃至存在的质疑。 特别是随着Claudine Gay从哈佛大学的辞职，这种攻击愈发激烈。尽管她的辞职表面上与剽窃事件有关，但不难察觉，她作为DEI领域的领军人物，这一身份似乎也是辞职呼声高涨的一个重要因素。 这意味着，DEI正面临着前所未有的挑战。尽管高效的DEI项目能够为雇主和雇员带来众多益处，但人们仍担忧2024年可能成为DEI逐渐淡出视野的一年。这种情况发生的可能性有多大？如果真的发生，又会产生何种影响？ DEI与文化战争 近年来，无论是在美国还是全球其他大多数国家，你可能都会听说过“文化战争”。这场战争源于对机构的信任下降、不平等现象的加剧以及技术的广泛传播，涉及到试图强加自己意识形态的社会对立群体。 从学校课程内容、体育赛事中的下跪行为，到对“女性”定义的争议、以及工作场所中的代表性指控等，无一不被卷入这场文化战争。而DEI，在这场战争中虽不愿意却占据了核心位置。 人们普遍认为DEI项目倾向于“觉醒”，过分强调种族和性别因素，而忽视了成就和能力。2023年8月，一位律师成功对一家支持黑人创业者的风险投资公司提起诉讼。类似的诉讼也针对那些实施多样性招聘、奖学金和实习计划的公司提起。 Claudine Gay的辞职再次引发了对DEI项目的广泛讨论。保守派脱口秀主持人和作家Josh Hammer在社交媒体平台X上表示，击败Gay博士是“为文明理智而战的一大胜利”。保守派评论员Liz Wheeler称之为“对DEI、觉醒主义、反犹太主义及大学精英主义的沉重打击”，而保守派活动家Christopher Rufo则称这是“DEI在美国机构中走向终结的开始”。 如此一致的批评和贬低无疑对DEI项目造成了重创。根据劳动力市场分析公司Lightcast的数据显示，尽管经济蓬勃发展，但美国DEI相关职位的招聘量同比下降了48%。显然，DEI正面临严峻挑战。 当提到公司裁员时，DEI相关职位往往是裁减名单上的重点。我最近听到一个播客，四位知名风险投资家中有三位认为“取消DEI项目”是他们的首要任务。 DEI的价值 面对如此批评，人们或许会误以为DEI项目对人力资源和更广泛的商业活动没有任何价值。然而，实际上，许多公司对DEI项目的投资极具价值，几乎每个案例都能证明这一点。 我们在2022年和2023年的《提升公平研究》中发现，公司出于实际原因关注多样性和包容性，这包括： 包容性招聘策略扩大了招聘范围。 包容性领导力策略深化了领导力储备。 包容性管理方式吸引了多元化的客户和市场。 包容性董事会推动了市场增长和领导地位（这一点已通过统计数据得到证明）。 包容性供应链项目提升了供应链的可持续性。 包容性文化促进了员工的增长、留存和参与。 组织之所以优先考虑DEI项目，并非仅仅因为“觉醒”，或者作为勾选式行动。他们这样做是因为DEI确实带来了实际和有形的商业利益。例如，Workday这样的HR技术公司在市场上备受尊敬，它不仅在内部推广DEI，在其产品中也体现了这一点，多年来一直超越竞争对手的增长和表现。它们的产品VIBE，一个专门设计的分析系统，展示了交叉性，帮助公司设定目标，找出领导力、招聘、薪酬和职业发展中的不平等。 然而，一些律所提出这类计划可能违法——这是否成立呢？ DEI的合法性 面对这一问题，我们不得不考虑到复杂且广泛的薪酬公平问题。直到最近几年，大多数公司在个性化支付薪酬方面并未遇到太大问题。Josh Bersin Company去年对全球的领导力、继承计划和薪酬计划进行了研究，发现在许多大公司中，薪酬存在巨大差异，且大多没有明显的统计相关性。 这个问题被称作“薪酬公平”。当涉及到性别、年龄、种族、国籍等非绩效因素时，大多数公司都存在问题。那么，分析基于种族或国籍的薪酬差异并加以解决，这会被认为是非法的吗？ DEI的未来 DEI无疑是一个复杂的议题，许多组织对于采取何种措施感到不确定。尽管面临当前的批评浪潮，但近年来对DEI策略的巨大投资表明，商业领袖们不太可能让这一切付诸东流。 尽管存在反觉醒运动、政治辩论，以及哈佛、宾夕法尼亚大学等教育机构在这些议题上的模糊立场，但商界不会因此而停滞不前。平权行动的初衷不是为了歧视，而是为了减少歧视。例如，在加州大学，自从1995年停止实施平权行动以来，研究发现非洲裔美国人STEM专业毕业生的收入显著下降。因此，可以说这些措施确实产生了积极的影响。 DEI不会消亡——它对此太重要了。然而，现在是时候取消人力资源部门中的“DEI警察”，转而专注于在所有业务单元中嵌入包容性、公平薪酬和开放性讨论的原则。高级领导层必须对这一议题负起责任来。 回顾21世纪初，许多公司聘请首席数字官来推动数字技术的实施、创意和战略。随着数字工具成为常态，这一角色逐渐消失。我们可能正处于一个新的时期，首席多样性官的角色也在发生变化：不再是从中心团队推动议程，而是引导公司在每一个业务领域都拥抱包容性和多样性。 通过我们在这个话题上的每次采访，我们都能看到各种DEI策略的积极故事。每个成功的公司都将DEI视为一项业务策略，而非仅仅是人力资源策略。虽然以HR为中心的DEI投资正在减少，但这更像是它们向业务领域的转移，这正是它们应有的归属。  
    Josh Bersin
    2024年02月23日
  • Josh Bersin
    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 
    Josh Bersin
    2024年02月18日
  • Josh Bersin
    公司雇用大量员工，好＞坏？ 公司招聘人数越多公司发展越快？为什么不会集中团队的公司落后更快？在人工智能时代如何制造高集中团队，提高追求生产力的全球战略位置？这个新战略是什么样的？Josh Bersin提出了五大想法。本周，我们见证了多年来最令人惊叹的商业故事之一。Meta 宣布裁员22%，收入增长25%，净收入为140 亿美元，同比增长203%。这意味着 Meta 是一家价值160+ 亿美元的公司，税后产生35%净利润（高于谷歌、苹果和Microsoft）。 这真是太神奇了。该公司解雇了近四分之一的员工，财务业绩飙升（Meta 的市值周五上涨了17亿美元）。 我们在这里学到了什么？ 很简单，公司无须雇用这么多人就可以以惊人的速度增长。 公司为什么过度招聘？ 我们退一步想。为什么公司会过度招聘，我们该如何避免？未来几年，随着就业市场变得更加紧张，公司需要在没有员工线性增长的情况下实现增长。 我们正在进入一个“人员过多的公司”将表现不佳的时代，这改变了以往的思维。 顺便说一句，请注意，2024年普华永道CEO调查发现，高管认为他们公司40%的时间浪费在非必要事情上。十大问题中有三个与人力资源有关。同一项调查还显示，三分之二的CEO认为人工智能将把行政效率提高5%或更多，我同意这一点。这就是我在2024年预测报告中谈论“全球追求生产力”的原因。我们正在进入这样一个时代——人均收入较高的小公司将在执行，操纵和发展等方面超越多员工的竞争公司。由于招聘中太多的层次和挑战，那些没有学会如何集中团队（和员工人数）的公司将落后。 这个新战略是什么样的？这里有五大想法。 #1.不要再认为招聘是一种增长战略。 许多领导者仍然认为“雇用更多的人将使公司发展壮大”。换句话说，如果你想“快速做大”（硅谷的口头禅），你就尽可能快地招聘。更多的销售人员将产生更多的收入。更多的工程师将生产出更多的产品。更多的营销人员将产生更多的潜在客户。更多的服务人员将服务更多的客户。 这些都是有缺陷的假设。在每个职能领域，都有高绩效者（超能力工人）和低绩效者。当你匆忙招聘时，你迫使招聘人员大量招聘，而不是专注于适合。其结果就是我所说的“削弱每个员工的生产力”。您每雇用一个额外的人，就会减慢已经到位的其他人的速度。 是的，公司必须替代离职人员并增加员工。但是，当一家公司快速招聘时，入职和新员工的剪切量迫使经理放慢速度，员工放慢速度，许多现有流程也放慢速度。这意味着每增加一个“新员工”都会降低整体生产力。 我们最近采访了领先的电池制造商之一松下。高级人力资源主管发现（通过分析）直线经理过度招聘，他们的产出放缓，而员工预订了更多的加班时间。虽然经理们不同意（见#2），但当她分享数据时，他们突然意识到了问题所在。 数据显示，一旦一条生产线的调度和人员配备超过50人，生产力就会下降。这是由于收益曲线递减，即增加超过最佳点的工人会导致每个工人的产出减少。 这种人员过剩导致了成本的增加，也导致了更高的缺陷率和材料浪费，因为生产线上的人越多并不一定等同于更高的效率或更好的质量。而生产经理们在直接看到数据之前并不相信这一点。 医疗保健提供商是医疗领域最先进的提供商之一。鉴于护士和临床专业人员的巨大短缺（未来三年将有超过200万个工作岗位短缺），这些公司将行政工作自动化，将临床护理分解为亚专业，并培训护士在执照的顶端操作。 例如，普罗维登斯（Providence）和斯坦福医疗保健公司（Stanford Healthcare）精心设计了护理角色（通过减少管理任务和使用人工智能进行调度），以减少每位患者的人员配备，而不会降低患者的治疗效果。你怎么确定自己在这条曲线上的位置？ 您可以查看每个员工的收入或产出。当这个指标开始下降时，你就是在曲线的右侧操作。在许多组织中，我们已经走上了下坡路。 我经常比较细分市场中同行公司的每位员工收入，而数字较低的公司几乎总是在其市场中落后。顺便说一句，这就是为什么私募股权公司几乎总是在收购公司后立即放人的原因。 #2.重新定义HR处理员工人数需求的方式。 我们面临的第二个问题是大多数公司的招聘方式。 据我所知，几乎每家公司都有一个年度或季度的员工分配流程。首席财务官知道经理对招聘的需求是无限的，因此根据业务部门的财务状况向业务部门“发布员工人数”。这些申请被分发给经理，人力资源团队开始工作。 然后，HR 像订单接受者一样运作，招聘组织开始处理申请。我们开发招聘信息、寻找候选人、购买广告和雇用招聘人员。我们开始筛选、面试和评估候选人。并且进行了大量的日程安排、讨论候选人和决策工作。 所有这些努力都需要宝贵的时间却不经过深思熟虑，而且被首席执行官评为#3“最官僚”的过程。 这个招聘应该由内部候选人填补吗？这份工作应该是全职的还是兼职的，可以作为共享工作吗？这项工作是否应该外包，因为它没有战略意义？这个团队的流动率高吗，那么我们是否应该讨论为什么这个职位空缺？ 这些都是重要的战略对话，除非高级人力资源业务合作伙伴（或人才顾问）参与，否则它们不会真正发生。招聘经理是老板，他们可能不希望人力资源人员问他们关于他们如何管理团队的各种问题。 那么会发生什么呢？人才招聘团队急于填补职位空缺，几乎没有机会讨论内部发展、工作轮换、兼职或任何其他重要选项。没有真正的过程来思考我们如何“设计”这个团队以实现增长，然后团队会招聘更多的人。 正如我们在系统人力资源研究中所讨论的那样，如果我们采用 4R（人才招聘、人才保留、更新技能、重新设计）的招聘方法，这一切都可以避免。这就是为什么越来越多的招聘团队正在与L&D整合，公司正在购买人才市场平台，大多数首席人力资源官都在努力提高内部招聘比例并制定内部职业管理战略。 #3.为内部流动制定战略、文化和工具。 许多年前，我意识到你可以将公司分为两种类型：一种是相信“向上或向外”的工作模式（他们经常使用堆栈排名），另一种是相信“辅导和发展”工作模式。 第一种公司相信“竞争绩效”，总是通过绩效的视角来看待员工。将人员分组到绩效桶中，随着新机会的出现，专注于这些重要角色的“HiPO”。 第二种公司相信“持续学习”和成长心态，他们为每个人提供成长机会、发展任务和指导。从某种意义上说，这些公司只是本着“任何人都可以被培养来做更多事情”的理念运营，他们专注于永无止境的技能发展。 顺便说一句，今天，我们研究的公司中有三分之二以上属于第二类，但大多数公司都像第一类一样“思考和运营”。因此，我们正处于从“要么执行，要么被解雇”模式到“执行，我们将帮助您成长”的模式的全球过渡。 好吧，在劳动力短缺的情况下运营的唯一方法（现在平均需要 45 天才能招聘，某些职位需要 70 天或更长时间）是转向第二种模式。多亏了人工智能工具和人才智能，我们现在可以发现，拥有市场营销数学学位的营销经理可以在相当短的时间内成为一名数据科学家。 当然，不是每个人都想转行，我们大多数人都害怕做一些新的事情。但是，如果你想在不雇用和流失人员的情况下发展你的公司，并且你想将员工从表现不佳的产品领域转移到增长领域，你必须做到这一点。而强大的人才流动性的结果是什么？您不必按周期招聘（和解雇），您可以培养深厚而持久的技能，并且工作满意度和保留率可以飙升。 #4.重新定义管理者的角色。 从广义上讲，有两种管理模式：作为主管运作的管理者，以及作为“在职教练”运作的管理者。虽然这因工作和角色而异，但高效公司很少有领导者既不“指挥”又不“实践”。 正如WL Gore的人力资源主管多年前告诉我的那样（扁平化、高效管理的先驱），“经理管理项目，员工管理自己。换句话说，如果你想避免中层管理人员臃肿的官僚主义，你必须增加控制范围，并将“管理”定义为教练、项目领导、发展和调整。 当你这样做时，人们会站出来，在团队中担任领导职务。从某种意义上说，解放生产力的途径是“少管理，多领导”。 正如我们新的领导力研究发现的那样，伟大的领导者专注于愿景、灵感、专注和变革。这些是特殊人员的角色，他们可以设定方向并帮助其他人弄清楚如何到达那里。他们协调团队，帮助人们避免时间浪费，并明确分配责任。他们拥抱并鼓励变革，他们树立了榜样，永远帮助和指导他人。 虽然这些想法很好理解，但快速招聘往往让这变得不可能。当我在“快速招聘”（而不是“快速增长”）的公司工作时，我发现经理们对人事问题不知所措：入职、培训、辅导和解决问题。当你慢慢成长并保持广泛的控制范围时，你会发现同龄人会挺身而出，为这些任务负责。这有助于公司的发展。 再次回到医疗保健。有几十人向护士主管打报告并不罕见，因为这些员工训练有素，对自己的工作很清楚，而且积极性很高。这是一个高度可扩展的模型示例，我们都必须一直致力于这种转换。 #5.专注于你的核心。 避免“人员膨胀”的最后也是最重要的方法是集中精力。我的经验是，组织（团队或业务部门）一次只能专注于两到三件事。 但专注于什么？大多数大型公司在世界各地拥有数十个项目、数百个产品和业务部门。在我们的人力资源领域，这意味着做我经常称之为“清理厨房抽屉”的事情。今天，使用新的人工智能工具，我们可以将精力集中在少数重要的事情上。 上周，我们会见了几个人力资源领导团队，他们中的许多人有20个或更多的项目。虽然这听起来可能雄心勃勃，但实际上效率低下。你应该作为一个领导团队聚在一起，决定什么是必要的，什么不是。当 Meta 让22%的员工离开时，我猜许多项目都停止了。尽管这可能很痛苦（每个重大举措都有一个赞助商），但它可以促进增长、盈利和创新。 几年前，在 Sybase（最初是一家高性能数据库公司），我们进入了一个不专注的时期。该公司正在开发工具、中间件、行业解决方案和专业服务。高层领导认为，“成为一家更大的公司更好”。但唉，事实并非如此。 由于失去了对核心数据库的关注，Microsoft和Oracle迎头赶上。很快，“箭后面的木头”变得虚弱，我们的销售和营销分心，最终公司被卖掉了。 去年，我们采访了麦当劳的招聘团队，随着年轻人的职业生涯发展，麦当劳不断招聘新员工。通过“简化主义思维”的过程，在 Paradox 的帮助下，他们将商店职位的招聘时间从25天缩短到6天。这减少了75%的工作量。因此，麦当劳的招聘团队可以专注于招聘质量、目标定位、保留和店内职位管理。对于麦当劳来说，这家公司雇佣了一些世界上最难找到的职位，这是一个奇迹。 公司有数百个机会可以关注。与您的团队聚在一起，优先考虑真正重要的事情。当百事可乐询问他们的员工在大流行期间公司“最官僚、最浪费时间的流程”是什么时（使用他们称之为“流程粉碎机”的众包工具），绩效管理被评为最浪费时间的流程。每家公司都有阻碍的事情，今年是指出它们的原因。 最后：进行对话 底线是这样的。没有公司再一开始就确定什么最重要，哪个团队太大了等方面达成一致。但你必须进行对话。 在当今的经济中，招聘比以往任何时候都更难，人手过剩的公司只会表现不佳。请记住，“少即是多”，并帮助您的整个领导团队思考如何提高生产力、简化主义和专注，无论您走到哪里。 Source JOSH BERSIN
    Josh Bersin
    2024年02月04日
  • Josh Bersin
    HR Predictions for 2024: The Global Search For Productivity 2024年的HR预测强调了生产力和AI在商业和雇佣实践中的关键作用。这篇文章讨论了公司在动态的经济条件和不断变化的劳动力市场背景下，如何适应他们的人才管理和招聘策略。强调了员工赋权的增加，劳动力市场的变化，以及技能发展的重要性。文章还探讨了劳动力囤积、混合工作模式和员工激活等关键概念。此外，还涉及领导力挑战、薪酬公平、DEI计划，以及可能的四天工作周。 一起来看Josh Bersin 带来新得见解 For the last two decades I’ve written about HR predictions, but this year is different. I see a year of shattering paradigms, changing every role in business. Not only will AI change every company and every job, but companies will embark on a relentless search for productivity. Think about where we have been. Following the 2008 financial crisis the world embarked on a zero-interest rate period of accelerating growth. Companies grew revenues, hired people, and watched their stock prices go up. Hiring continued at a fevered pace, leading to a record-breaking low unemployment rate of 3.5% at the end of 2019. Along came the pandemic, and within six months everything ground to a halt. Unemployment shot up to 15% in April of 2020, companies sent people home, and we re-engineered our products, services, and economy to deal with remote work, hybrid work arrangements, and a focus on mental health. Once the economy started up again (thanks to fiscal stimulus in the US), companies went back to the old cycle of hiring. But as interest rates rose and demand fell short we saw layoffs repeat, and over the last 18 months we’ve seen hiring, layoffs, and then hiring again to recover. Why the seesaw effect? CEOs and CFOs are operating in what we call the “Industrial Age” – hire to grow, then lay people off when things slow down. Well today, as we enter 2024, all that is different. We have to “hoard our talent,” invest in productivity, and redevelop and redeploy people for growth. We live in a world of 3.8% unemployment rate, labor shortages in almost every role, an increasingly empowered workforce, and a steady drumbeat of employee demands: demands for pay raises, flexibility, autonomy, and benefits. More than 20% of all US employees change jobs each year (2.3% per month), and almost half these changes are into new industries. Why is this the “new normal?” There are several reasons. First, as we discuss in our Global Workforce Intelligence research, industries are overlapping. Every company is a digital company; every company wants to build recurring revenue streams; and soon every company will run on AI. Careers that used to stay within an industry are morphing into “skills-based careers,” enabling people to jump around more easily than ever before. Second, employees (particularly young ones) feel empowered to act as they wish. They may quietly quit, “work their wage,” or take time out to change careers. They see a long runway in their lives (people live much longer than they did in the 1970s and 1980s) so they don’t mind leaving your company to go elsewhere. Third, the fertility rate continues to drop and labor shortages will increase. Japan, China, Germany, and the UK all have shrinking workforce populations. And in the next decade or so, most other developed economies will as well. Fourth, labor unions are on the rise. Thanks to a new philosophy in Washington, we’ve seen labor activity at Google, Amazon, Starbucks, GM, Ford, Stellantis, Kaiser, Disney, Netflix, and others. While union participation is less than 11% of the US workforce, it’s much higher in Europe and this trend is up. What does all this mean? There are many implications. First, companies will be even more focused on building a high-retention model for work (some call it “labor hoarding.”) This means improving pay equity, continuing hybrid work models, investing in human-centered leadership, and giving people opportunities for new careers inside the company. This is why talent marketplaces, skills-based development, and learning in the flow of work are so important. Second, CEOs have to understand the needs, desires, and demands of workers. As the latest Edelman study shows, career growth now tops the list, along with the desire for empowerment, impact, and trust. A new theme we call “employee activation” is here: listening to the workforce and delegating decisions about their work to their managers, teams, and leaders. Third, the traditional “hire to grow” model will not always work. In this post-industrial age we have to operate systemically, looking at internal development, job redesign, experience, and hiring together. This brings together the silo’d domains of recruiting, rewards and pay, learning & development, and org design. (Read our Systemic HR research for more.) What does “business performance” really mean? If you’re a CEO you want revenue growth, market share, profitability, and sustainability. If you can’t grow by hiring (and employees keep “activating” in odd ways), what choice do you have? It’s pretty simple: you automate and focus on productivity. Why do I see this as the big topic in 2024? For three big reasons. First, CEOs care about it. The 2024 PwC CEO survey found that CEO’s believe 40% of the work in their company is wasted productivity. As shocking as that sounds, it rings true to me:  too many emails, too many meetings, messy hiring process, bureaucratic performance management, and more. (HR owns some of these problems.) Second, AI enables it. AI is designed to improve white-collar productivity. (Most automation in the past helped blue or gray collar workers.) Generative AI lets us find information more quickly, understand trends and outliers, train ourselves and learn, and clean up the mess of documents, workflows, portals, and back office compliance and administration systems we carry around like burdens. Third, we’re going to need it. How will you grow when it’s so hard to find people? Time to hire went up by almost 20% last year and the job market is getting even tougher. Can you compete with Google or OpenAI for tech skills? Internal development, retooling, and automation projects are the answer. And with Generative AI, the opportunities are everywhere. What does all this mean for HR? Well as I describe in the HR Predictions, we have a lot of issues to address. We have to accelerate our shift to a dynamic job and organization structure. We have to get focused and pragmatic about skills. We have to rethink “employee experience” and deal with what we call “employee activation.” And we are going to have to modernize our HR Tech, our recruiting, and our L&D systems to leverage AI and make these systems more useful. Our HR teams will be AI-powered too. As our Galileo™ customers already tell us, a well-architected “expert assistant” can revolutionize how HR people work. We can become “full-stack” HR professionals, find data about our teams in seconds instead of weeks, and share HR, leadership, and management practices with line leaders in seconds. (Galileo is being used as a management coach in some of the world’s largest companies.) There are some other changes as well. As the company gets focused on “growth through productivity,” we have to think about the 4-day week, how we institutionalize hybrid work, and how we connect and support remote workers in a far more effective way. We have to refocus on leadership development, spend more time and money on first line managers, and continue to invest in culture and inclusion. We have to simplify and rethink performance management, and we have to solve the vexing problem of pay-equity. And there’s more. DEI programs have to get embedded in the business (the days of the HR DEI Police are over). We have to clean up our employee data so our AI and talent intelligence systems are accurate and trustworthy. And we have to shift our thinking from “supporting the business” to “being a valued consultant” and productizing our HR offerings, as our Systemic HR research points out. All this is detailed in our new 40-page report “HR Predictions for 2024,” launching this week, including a series of Action Plans to help you think through all these issues. And let me remind you of a big idea. Productivity is why HR departments exist. Everything we do, from hiring to coaching to development to org design, is only successful if it helps the company grow. As experts in turnover, engagement, skills, and leadership, we in HR have make people and the organization productive every day. 2024 is a year to focus on this higher mission. One final thing: taking care of yourself. The report has 15 detailed predictions, each with a series of action steps to consider. The last one is really for you: focus on the skills and leadership of HR. We, as stewards of the people-processes, have to focus on our own capabilities. 2024 will be a year to grow, learn, and work as a team. If we deal with these 15 issues well, we’ll help our companies thrive in the year ahead. Details on the Josh Bersin Predictions The predictions study is our most widely-read report each year. It includes a detailed summary of all our research and discusses fifteen essential issues for CEOs, CHROs, and HR professionals. It will be available in the following forms: Webinar and launch on January 24: Register Here (replays will be available) Infographic with details: Available on January 24. Microlearning course on Predictions: Available on January 24. Detailed Report and Action Guide: Available to Corporate Members and Josh Bersin Academy Members (JBA).  (Note you can join the JBA for $495 per year and that includes our entire academy of tools, resources, certificate courses, and SuperClasses in HR.)
    Josh Bersin
    2024年01月19日
  • Josh Bersin
    Josh Bersin:2024: The Year That Changes Business Forever (Podcast) The podcast "2024: The Year That Changes Business Forever" by Josh Bersin explores anticipated transformations in business by 2024. It highlights the impact of AI, labor shortages, and evolving organizational structures. The podcast delves into the 2023 economic performance, changes in employee engagement, and the necessity for businesses to adapt strategically. It emphasizes a shift towards dynamic, flatter organizations and the critical role of systemic HR practices in shaping future business landscapes. Josh Bersin探讨了2024年企业预期的转型。这些转型由AI的应用、劳动力短缺和组织结构的变化驱动。播客讨论了2023年的经济表现、员工参与度的变化以及企业为应对未来挑战所需的适应策略。它强调了向动态、扁平化组织的转变和系统性人力资源实践在塑造未来商业环境中的重要作用。 In this podcast I recap 2023 and discuss the big stories for 2024, and to me this year is a tipping point that changes business forever. Why do I say this? Because we’re entering a world of labor shortages, redesign of our companies, and business transformation driven by AI. We’ll look back on 2024 and realize it was a very pivotal year. (Note: In mid-January we’re going to be publishing our detailed predictions report. This article is an edited transcript of this week’s podcast, so it reads like a conversation.) Podcast Begins: Interestingly, the entire year 2023 people were worried about a recession and it didn’t happen. In fact, economically and financially, we had a very strong year. Inflation in the United States and around the world went down. We did have to suffer rising interest rates, and that was a shock, but it was long overdue. I really think the problem we experienced is we had low interest rates for far too long, encouraging speculative investment. Now that the economy is more rational, consumer demand is high, the business environment is solid, and the stock market is performing well. The Nasdaq is almost at an all time high, the seven super stocks did extremely well: the big tech companies, the big retailers, the oil companies, many of the consumer luxury goods companies did extremely well. And the only companies that didn’t do well were the companies that couldn’t make it through the transformation that’s going on. On the cultural front we had the Supreme Court overturning affirmative action in education, which led to a political backlash on diversity and inclusion. The woke mind virus by Elon Musk and similar discussions further pushed back DEI programs, which has made chief diversity officers life difficult. We’re living through two wars, which have been very significant for many companies. I know a lot of you have closed down operations in Russia, and anybody doing business in Israel is having a tough time. And we’ve had this continuous period where every piece of data about employee engagement shows that employees are burned out, tired, stressed. They feel that they’re overworked. Despite this employee sentiment, wages went up by over 5% and people who changed jobs saw raise wages of 8% or more. The unemployment rate is very low so there are a lot of jobs. You could ask yourself, why are people stressed? I think it’s a continued overhang of the pandemic: the remote work challenges, the complexities and inconsistencies in hybrid work. And something else: the younger part of the workforce, those who are going to be living a lot longer than people who are baby boomers, are basically saying I don’t really want to kill myself just to get ahead. I want to have a life. I want to quietly quit. If my company don’t take care of me, I’m going to work my wage, meaning I’m going to work as hard as I’m paid, no more than that. And that mentality has created an environment for the four-day work week, which I think is coming quicker than you realize. And unions, which are politically in favor, are rising at an all time increase in about 25, 30 years. Inflation and the need to raise wages to attract talent leads to pay equity problems. This domain is more complex than you think. You can read about it in our research and in 2024 it belongs on your list. 2024 will also see enormous demand for career reinvention, career development, growth programs, coaching, mentorship, allyship and support amongst the younger part of the workforce. And that means that if you’re in retail, healthcare, hospitality, or one of the other industries that hires younger people you have to accommodate this tremendous demand for benefits. These are things that became very clear in 2023. But let’s talk about the elephant in the room:  the biggest thing that happened in 2023 was AI. AI has transformed the conversations we have about everything from media to publishing to HR technology to recruiting to employee development to employee experience. As you probably know, I’m very high on AI. I think it’s going to have a huge transformational effect on our companies, our jobs, our careers, and our personal lives. AI will improve our health, our ability to learn, the way we consume news (note that the NYT just sued OpenAI and Microsoft for copyright infringement). Almost every part of our life will be transformed by AI. I know from our conversations that most of you are trying to understand it and see where it fits. And many of you have been told by your CEO, “we need an AI strategy for the company as well as in HR.” And the AI strategy in HR is one thing, but the bigger topic is the rest of the company. So HR is going to have to be a part of this transformation: the new roles, jobs, rewards, and skills we need. This year I’m very excited that we introduced Galileo™, which about 500 or so of you have been using. We’re going to launch the corporate version for everybody in the corporate membership in February, so corporate members stay tuned (or join). Galileo brings AI to HR in an easy-to-use, safe, and high-value way, so it will help you get your strategy together. It’s basically ready to go. Then later in the year we’ll launch a version to the JBA community and more. AI, despite all the fear-mongering, is already a very positive technology. Where are we going next? Well as the title of this article states, I think this is the year that changes business forever. And I’m not trying to be hyperbolic, I really see a tipping point. Let me give you the story. For about a decade I’ve been writing about the flattening of organizations, breaking down of hierarchies, creating what I used to call the networked organization. And this is now mainstream and we’ve decided to call it the Dynamic Organization. And what we mean by this, as you read about in the Dynamic Organization research or in the Post-Industrial Age study, is that the functional hierarchies of jobs, careers, organizations and companies are being broken down for really good reasons. The reason we have functional hierarchies, job levels and siloed business functions is because they’re patterned after the industrial age when companies made money by selling products and services at scale. The automobile industry, the oil and gas industry, the manufacturing industries, the CPG industries, even the pharmaceutical companies are essentially building things, bringing them to market, launching them, selling them, and distributing them in a linear chain. And that “scalable industrial business model” is how we designed our organizations. So we built large organizations for R&D, large organizations for product management and product design and packaging, large organizations for marketing, large organizations for sales, large organizations for business development and distribution, supply chain, and so on (including Finance and HR). And all these ten or fifteen business functions had their own hierarchies. So you, as an employee, worked your way up those hierarchies. When I graduated from college in 1978 as an engineer, I went into one of those hierarchies. For each employee you were an engineer, a salesperson, a marketing manager, or whatever and you worked your way up the pyramid. And at some point in your career you crossed over and did other things, but that was fairly unusual. That wasn’t really the career path. You worked about 35-40 years in that profession and then you retired. And a lot of companies had another construct: management and labor. Management decided “what to do” and labor “did it.” And all of these designs helped us build most of the HR practices we use today, including hiring, pay, performance management, succession, career management, goal setting, leadership development, and on and on. Today, if you look at how the most valued companies in the world, they don’t operate this way any more. Why? Because it slows them down like molasses. If you have to traverse a functional hierarchy to come up with a new idea it takes months or years to create something new. Today value is created through innovation, time to market, closeness to customers, and unique and high-value offerings. The “hierarchy” wasn’t designed for this at all. Here are a few dogmas to consider. We used to think that all new ideas come out of R&D. That’s crazy. Of course R&D is important, but some of the most innovative companies in the world don’t even have R&D departments, they have product teams. The Research Department at Microsoft didn’t even invent AI, the company had to partner with OpenAI, a company that has less than a thousand employees. Here’s another one to consider. Deloitte consultants used to talk about “innovation at the edge,” otherwise known as “skunk works.” We used to advise clients to “separate the new ideas from the scale business” so they new ideas don’t get crushed or ignored. Well today all the new ideas come from the operating businesses, and we iterate in a real-time way. So there’s another industrial organization structure that just no longer applies. So what we’ve been going through in the dynamic organization, and we’ve studied this in detail, is that we’ve got to design our companies to be flatter. We’ve got to simplify the job titles and descriptions so people can move around. We have to organize people into cross functional teams, we have to motivate and train people to work across the functional  silos. We have to build agile working groups, we have to redo performance management around teams and projects, not around individual goals and cascading goals. We need to build pay equity into the system so you’re paid fairly regardless of where you started. Let’s talk about pay. One of the problems with the hierarchy is you get a raise every year based on your performance appraisal. And after a few years your pay may have been quite a bit different than somebody sitting next to you simply because of your appraisals. But you may not be delivering any more than them. That wasn’t fair. If you came into the company with a background in marketing, you made less money than somebody who came into the company with a background in engineering. But five years later you might be doing the same stuff but making different amounts of money. And then there’s gender bias, age bias, and other non-performance factors. In a “skills meritocracy,” as we call it, pay equity has to get fixed. We’ve got to have developmental careers and talent marketplaces and open job opportunities and mentoring for people. And these people practices are the facilitation of becoming more dynamic. And the problem of not being dynamic is what happened at Salesforce, Meta, and other tech companies last year. Salesforce hired thousands of salespeople during the last upcycle after the pandemic, and then a year later laid most of them off. Meta did the same thing. Google’s probably next. These companies, operating in the industrial mindset, thought that the only way to grow is to hire more salespeople, more engineers, or more marketing folks. But the quantity of people in one of these business functions doesn’t necessarily drive growth and profitability. What matters is how they work together and what they do, not how many of them there are. This old idea that we’re going to grow the company by hiring, hiring, hiring is gone. It doesn’t work anymore. It’s still a part of the growth part of the company, you’re always hiring to replace people, to bring new skills, et cetera, and to bring new perspectives. But in a dynamic organization, a lot of the growth comes from within. People grow too. Even the word growth mindset has become overused. We need to have an organizational growth mindset so that we can grow as an organization. A great example of this is Intel. Intel lost their way in the manufacturing of semiconductors and also in the R&D. Now they’re reinventing themselves internally and their stock is skyrocketing. They didn’t hire some guru to tell them what to do, they know what to do. They just need to get around to doing it. Google has more AI engineers than OpenAI, Anthropic, and all the other little guys put together, but they didn’t execute well. Now they’re executing better. They brought their AI teams together into cross-functional groups and they’re sharing IP from YouTube with other business areas. I bet they stomp many of the others in AI once they get it going. That’s part of being a dynamic organization. You as HR people know better than anybody how dysfunctional it is when there are multiple groups in the company doing competing things and they’re not working together because they don’t know about each other, or they don’t talk to each other. There’s no cross fertilization or they’re protecting their turf. All of these are the things that get in the way of being a dynamic organization. And the reason it’s relevant in the next year is this has taken hold. Things like talent marketplaces and career pathways and skills-based organizations, skills based hiring, skills based pay, skills based careers, skills based development, et cetera…  these are not just HR fads, they’re solutions to this big shift: making companies more dynamic. Despite their value in the past, hierarchical stove-piped companies don’t operate very well anymore. Now this isn’t an A-B switch type of thing. This is an evolution, but it’s taking place very quickly. And the reason we came up with this concept of Systemic HR is we in HR have to do the same thing. The HR function itself operates in silos. We’ve got the recruiting group, the DEI group, the Comp group, the L&D group, the business partners, the group that does compliance, the group that worries about wellbeing. We’ve got somebody over here is doing an EX project, somebody over there is doing a data management project, a people analytics group. Okay. Those are all great functional areas that belong in HR. But if they’re not working together on the problems that the company has, and I mean the big problems, growth, profitability, productivity, M&A, etc., then who cares? Then you’re at level one or level two in systemic HR. We built the Systemic HR initiative around business problems. And that’s how we came up with the new HR operating model (read more details here or view the video overview). I think Systemic HR will be a very big deal for 2024, and there are many reasons. Not only are we living in a labor shortage but there’s another accelerant, and that is AI. For those of you that have used Galileo, and I hope you all get a chance to use it this year, it’s absolutely unbelievable how AI can pull together information, data, text from many sources in the company and make sense of what your company is doing. You know as well as I do, if you’ve worked in sales, if you’ve worked in marketing, if you worked in finance, these are siloed groups. Few companies have a truly integrated data management system for all of their customer data match to their sales, data match to their revenue, data match to their marketing.  Customer data platforms are a idea, but it doesn’t really happen very often, and it takes tens to hundreds of millions of dollars and many, many systems to do that. Well, AI does this almost automatically. So when you pull together a tool like Galileo, and you use our research as part of the corpus, and you add data about employee turnover, for example, in your company, or pay variations, you’ll see the relationship between pay and turnover just by asking a question. You don’t have to go spend months doing an analysis and trying to figure out if the analysis is any good. And that’s happening all over the company in sales and customer service and R&D and marketing – everywhere. So this more integrated, dynamic organization is happening before your eyes. In 2024, this is the context for almost everything we’re going to be working on now. The other context is the labor market, which is going to be very tough. You’ve read about from us and others about how tight the labor market is now. Unemployment in the United States is 3.8%, and it’s not going to get much better. Even if we do have a recession, which is questionable, there aren’t enough people to hire. The fertility rate is low, and even if every company gives employees fertility benefits and they all have babies, it will take twenty years for these people to go to work. So all of the developed countries: US, UK, Canada, Germany, Japan, the Nordics, China, Russia, the fertility rate has been low for a long time. The World Bank sees working population shrinking within ten years in almost every developed economy. Since hiring is going to get harder and we’ll see fewer and fewer working people, companies have to be much more integrated in hiring. And we all have to look the Four R’s: Recruit, Retain, Reskill, Redesign. This puts HR in the middle of a lot of job redesign, career reinvention, and a serious look at developing skills, not hiring skills, and using the tools we have as hr professionals to help the organization improve productivity without just hiring and hiring and hiring. I measure the success of companies by two things. One is their endurance: how well have they fared over ups and downs? The second is their revenue per employee. Companies with low revenues per employee tend to be poorly managed companies relative to their peers. Of course there’s a lot of industry differences. When we went through our GWI industry work: healthcare, consumer goods, pharma, banking, we could see the high performing companies were very efficient on a headcount basis. And we found out these companies are actually implementing Systemic HR practices. The other driver that we’re living in a service economy. Interestingly enough, in the United States, more than 70% of our GDP is now services. So the people you have, the humans in your company, are the product. And if you’re not getting good output per dollar of revenue per human, you’re not running the company very well. And this leads to many management topics. How are we going to build early and mid-level leaders? How can we rethink what employees really need? The topics of employee engagement and employee experience are really 25 to 30 years old. They need a massive update. How are we going to implement AI in L&D and replace a lot of these old systems that everybody kind of hates, but we’re stuck with? What’s going on with the ERP vendors and what role will they play as we replace our HR tech with AI powered systems? How will we implement scalable talent intelligence? In a world of labor shortages talent intelligence becomes even more important, whether you think of it for sourcing and recruiting or an internal mobility or just a strategic planning initiative. How do we all get comfortable with AI? And then there’s this issue of Systemic HR and developing your team, your function, your operating model to be more adaptive and more dynamic. So I look back on 2023 I feel it was one of the most fascinating and fun and enriching years that I’ve had. I am always amazed and impressed and energized by you, by you guys who were out there on the firing lines, dealing with these complex issues and companies with old technologies and all sorts of changes going on and how you’re adapting. I continue to be more impressed and more excited about the HR profession every year. I think a lot of people who aren’t in HR think we do a lot of compliance and administration stuff and we fire people. That is the tiniest part of what we do. 2024 is going to be an important year. You as an HR professional are going to have to learn a lot of things. You’re going to learn about Systemic HR issues, you’re going to learn about AI, and you’re going to learn to be a consultant. There’s no question in my mind that over the next decade or two dynamic organization management is going to become a bigger and bigger issue – how we manage people and companies. And I don’t mean manage like supervise, I mean develop, move, retain, pay, et cetera, culture, all of those things. I leave 2023 very energized about what’s to come with AI. And if you’re afraid of AI, just take a deep breath and relax. It’s not going to bite you. There’s nothing evil here. It’s a data driven system. If you don’t have your data act together, you’re not going to get a lot of good value out of AI. I talked to Donna Morris at Walmart last week; I talked to Nickle LaMoreaux at IBM; and I talked with the senior HR leaders at Microsoft. They’re all seeing huge returns on investment from the early implementations, and seeing hundreds of use cases. We’re going to have a lot of new tools and lots of vendor shakeout. (Check out what SAP is up to and where Workday is going.) Stay tuned for our big Predictions report coming out in mid January. That report is my chance to give you some deep perspectives on where I think things are going, recap things that have happened over the last couple of years, and give you some perspectives for the year ahead. As always we would be more than happy to walk through these things with your team. I hope you have a really nice holiday season and you take a deep breath. The world is never perfect. It’s never been perfect. It wasn’t perfect in the past. It won’t be perfect in the future. But the environment you live in and the environment that you create can be enriching, enjoyable, productive, and healthy, and fun if you decide. And I think we all have the opportunity to make those decisions. It has been a pleasure and an honor for me to serve and work with you this last year, and I’m really looking forward to an amazing 2024 together. –END OF PODCAST– Irresistible: The Seven Secrets of the World’s Most Enduring, Employee-Focused Organizations  
    Josh Bersin
    2023年12月30日
  • Josh Bersin
    Josh Bersin人工智能实施越来越像传统IT项目 Josh Bersin的文章《人工智能实施越来越像传统IT项目》提出了五个主要发现： 数据管理：强调数据质量、治理和架构在AI项目中的重要性，类似于IT项目。 安全和访问管理：突出AI实施中强大的安全措施和访问控制的重要性。 工程和监控：讨论了持续工程支持和监控的需求，类似于IT基础设施管理。 供应商管理：指出了AI项目中彻底的供应商评估和选择的重要性。 变更管理和培训：强调了有效变更管理和培训的必要性，这对AI和IT项目都至关重要。 原文如下，我们一起来看看： As we learn more and more about corporate implementations of AI, I’m struck by how they feel more like traditional IT projects every day. Yes, Generative AI systems have many special characteristics: they’re intelligent, we need to train them, and they have radical and transformational impact on users. And the back-end processing is expensive. But despite the talk about advanced models and life-like behavior, these projects have traditional aspects. I’ve talked with more than a dozen large companies about their various AI strategies and I want to encourage buyers to think about the basics. Finding 1: Corporate AI projects are all about the data. Unlike the implementation of a new ERP system, payroll system, recruiting, or learning platform, an AI platform is completely data dependent. Regardless of the product you’re buying (an intelligent agent like Galileo™, an intelligent recruiting system like Eightfold, or an AI-enabling platform to provide sales productivity), success depends on your data strategy. If your enterprise data is a mess, the AI won’t suddenly make sense of it. This week I read a story about Microsoft’s Copilot promoting election lies and conspiracy theories. While I can’t tell how widespread this may be, it simply points out that “you own the data quality, training, and data security” of your AI systems. Walmart’s My Assistant AI for employees already proved itself to be 2-3x more accurate at handling employee inquiries about benefits, for example. But in order to do this the company took advantage of an amazing IT architecture that brings all employee information into a single profile, a mobile experience with years of development, and a strong architecture for global security. One of our clients, a large defense contractor, is exploring the use of AI to revolutionize its massive knowledge management environment. While we know that Gen AI can add tremendous value here, the big question is “what data should we load” and how do we segment the data so the right people access the right information? They’re now working on that project. During our design of Galileo we spent almost a year combing through the information we’ve amassed for 25 years to build a corpus that delivers meaningful answers. Luckily we had been focused on data management from the beginning, but if we didn’t have a solid data architecture (with consistent metadata and information types), the project would have been difficult. So core to these projects is a data management team who understands data sources, metadata, and data integration tools. And once the new AI system is working, we have to train it, update it, and remove bias and errors on a regular basis. Finding 2: Corporate AI projects need heavy focus on security and access management. Let’s suppose you find a tool, platform, or application that delivers a groundbreaking solution to your employees. It could be a sales automation system, an AI-powered recruiting system, or an AI application to help call center agents handle problems. Who gets access to what? How do you “layer” the corpus to make sure the right people see what they need? This kind of exercise is the same thing we did at IBM in the 1980s, when we implemented this complex but critically important system called RACF. I hate to promote my age, but RACF designers thought through these issues of data security and access management many years ago. AI systems need a similar set of tools, and since the LLM has a tendency to “consolidate and aggregate” everything into the model, we may need multiple models for different users. In the case of HR, if build a talent intelligence database using Eightfold, Seekout, or Gloat which includes job titles, skills, levels, and details about credentials and job history, and then we decide to add “salary” …  oops.. well all of a sudden we have a data privacy problem. I just finished an in-depth discussion with SAP-SuccessFactors going through the AI architecture, and what you see is a set of “mini AI apps” developed to operate in Joule (SAP’s copilot) for various use cases. SAP has spent years building workflows, access patterns, and various levels of user security. They designed the system to handle confidential data securely. Remember also that tools like ChatGPT, which access the internet, can possibly import or leak data in a harmful way. And users may accidentally use the Gen AI tools to create unacceptable content, dangerous communications, and invoke other “jailbreak” behaviors. In your talent intelligence strategy, how will you manage payroll data and other private information? If the LLM uses this data for analysis we have to make sure that only appropriate users can see it. Finding 3: Corporate AI projects need focus on “prompt engineering” and system monitoring. In a typical IT project we spend a lot of time on the user experience. We design portals, screens, mobile apps, and experiences with the help of UI designers, artists, and craftsmen. But in Gen AI systems we want the user to “tell us what they’re looking for.” How do we train or support the user in prompting the system well? If you’ve ever tried to use a support chatbot from a company like Paypal you know how difficult this can be. I spent weeks trying to get Paypal’s bot to tell me how to shut down my account, but it never came close to giving me the right answer. (Eventually I figured it out, even though I still get invoices from a contractor who has since deceased!) We have to think about these issues. In our case, we’ve built a “prompt library” and series of workflows to help HR professionals get the most out of Galileo to make the system easy to use. And vendors like Paradox, Visier (Vee), and SAP are building sophisticated workflows that let users ask a simple question (“what candidates are at stage 3 of the pipeline”) and get a well formatted answer. If you ask a recruiting bot something like “who are the top candidates for this position” and plug it into the ATS, will it give you a good answer? I’m not sure, to be honest – so the vendors (or you) have to train it and build workflows to predict what users will ask. This means we’ll be monitoring these systems, looking at interactions that don’t work, and constantly tuning them to get better. A few years ago I interviewed the VP of Digital Transformation at DBS (Digital Bank of Singapore), one of the most sophisticated digital banks in the world. He told me they built an entire team to watch every click on the website so they could constantly move buttons, simplify interfaces, and make information easier to find. We’re going to need to do the same thing with AI, since we can’t really predict what questions people will ask. Finding 4: Vendors will need to be vetted. The next “traditional IT” topic is going to be the vetting of vendors. If I were a large bank or insurance company and I was looking at advanced AI systems, I would scrutinize the vendor’s reputation and experience in detail. Just because a firm like OpenAI has built a great LLM doesn’t mean that they, as a vendor, are capable of meeting your needs. Does the vendor have the resources, expertise, and enterprise feature set you require? I recently talked with a large enterprise in the middle east who has major facilities in Saudi Arabia, Dubai, and other countries in the region. They do not and will not let user information, queries, or generated data leave their jurisdiction. Does the vendor you select have the ability to handle this requirement? Small AI vendors will struggle with these issues, leading IT to do risk assessment in a new way. There are also consultants popping up who specialize in “bias detection” or testing of AI systems. Large companies can do this themselves, but I expect that over time there will be consulting firms who help you evaluate the accuracy and quality of these systems. If the system is trained on your data, how well have you tested it? In many cases the vendor-provided AI uses data from the outside world: what data is it using and how safe is it for your application? Finding 5: Change management, training, and organization design are critical. Finally, as with all technology projects, we have to think about change management and communication. What is this system designed to do? How will it impact your job? What should you do if the answers are not clear or correct? All these issues are important. There’s a need for user training. Our experience shows that users adopt these systems quickly, but they may not understand how to ask a question or how to interpret an answer. You may need to create prompt libraries (like Galileo), or interactive conversation journeys. And then offer support so users can resolve answers which are wrong, unclear, or inconsistent. And most importantly of all, there’s the issue of roles and org design. Suppose we offer an intelligent system to let sales people quickly find answers to product questions, pricing, and customer history. What is the new role of sales ops? Do we have staff to update and maintain the quality of the data? Should we reorganize our sales team as a result? We’ve already discovered that Galileo really breaks down barriers within HR, for example, showing business partners or HR leaders how to handle issues that may be in another person’s domain. These are wonderful outcomes which should encourage leaders to rethink how the roles are defined. In our company, as we use AI for our research, I see our research team operating at a higher level. People are sharing information, analyzing cross-domain information more quickly, and taking advantage of interviews and external data at high speed. They’re writing articles more quickly and can now translate material into multiple languages. Our member support and advisory team, who often rely on analysts for expertise, are quickly becoming consultants. And as we release Galileo to clients, the level of questions and inquiries will become more sophisticated. This process will happen in every sales organization, customer service organization, engineering team, finance, and HR team. Imagine the “new questions” people will ask. Bottom Line: Corporate AI Systems Become IT Projects At the end of the day the AI technology revolution will require lots of traditional IT practices. While AI applications are groundbreaking powerful, the implementation issues are more traditional than you think. I will never forget the failed implementation of Siebel during my days at Sybase. The company was enamored with the platform, bought, and forced us to use it. Yet the company never told us why they bought it, explained how to use it, or built workflows and job roles to embed it into the company. In only a year Sybase dumped the system after the sales organization simply rejected it. Nobody wants an outcome like that with something as important as AI. As you learn and become more enamored with the power of AI, I encourage you to think about the other tech projects you’ve worked on. It’s time to move beyond the hype and excitement and think about real-world success.
    Josh Bersin
    2023年12月17日
  • Josh Bersin
    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。
    Josh Bersin
    2023年12月13日
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