Autonomous Corporate Learning Platforms: Arriving Now, Powered by AIJosh Bersin 的文章通过人工智能驱动的自主平台介绍了企业学习的变革浪潮，标志着从传统学习系统到动态、个性化学习体验的重大转变。他重点介绍了 Sana、Docebo、Uplimit 和 Arist 等供应商的出现，它们利用人工智能动态生成和个性化内容，满足了企业培训不断变化的需求。Bersin 讨论了跟上多样化学习需求所面临的挑战，以及人工智能解决方案如何提供可扩展的高效方法来管理知识和提高学习效果，并预测了人工智能将从根本上改变教学设计和内容交付的未来。推荐给大家：
Thanks to Generative AI, we’re about to see the biggest revolution in corporate learning since the invention of the internet. And this new world, which will bring together personalization, knowledge management, and a delightful user experience, is long overdue.
I’ve been working in the corporate learning market since 1998, when the term “e-learning” was invented. And every innovation since that time has been an attempt to make training easier to build, easier to consume, and more personalized. Many of the innovations were well intentioned, but often they didn’t work as planned.
First came role based learning, then competency-driven training and career-driven programs. These worked great, but they couldn’t adapt fast enough. So people resorted to short video, YouTube-style platforms, and then user-authored content. We then added mobile tools, highly collaborative systems, MOOCs, and more recently Learning Experience Platforms. Now everyone is focused on skills-based training, and we’re trying to take all our content and organize it around a skills taxonomy.
Well I’m here to tell you all this is about to change. While none of these important innovations will go away, a new breed of AI-powered dynamic content systems is going to change everything. And as a long student of this space, I’d like to explain why. And in this conversation I will discuss four new vendors, each of which prove my point (Sana, Docebo, Uplimit, and Arist).
The Dynamic Content Problem: Instructional Design By Machine
Let’s start with the problem. Companies have thousands of topics, professional skills, technical skills, and business strategies to teach. Employees need to learn about tools, business strategies, how to do their job, and how to manage others. And every company’s corpus of knowledge is different.
Rolls Royce, a company now starting to use Galileo, has 120 years of engineering, technology, and manufacturing expertise embedded in its products, documentation, support systems, and people. How can the company possibly impart this expertise into new engineers? It’s a daunting problem.
Every company has this issue. When I worked at Exxon we had hundreds of manuals explaining how to design pumps, pressure vessels, and various refinery systems. Shell built a massive simulation to teach production engineers how to understand geology and drilling. Starbucks has to teach each barista how to make thousands of drinks. And even Uber drivers have to learn how to use their app, take care of customers, and stay safe. (They use Arist for this.)
All these challenges are fun to think about. Instructional designers and training managers create fascinating training programs that range from in-class sessions to long courses, simulations, job aids, and podcasts. But as hard as they try and as creative as they are, the “content problem” keeps growing.
Right now, for example, everyone is freaked out about AI skills, human-centered leadership, sustainability strategies, and cloud-based offerings. I’ve never seen a sales organization that does quite enough training, and you can multiply that by 100 when you think about customer service, repair operations, manufacturing, and internal operations.
While I always loved working with instructional designers earlier in my career, their work takes time and effort. Every special course, video, assessment, and learning path takes time and money to build. And once it’s built we want it to be “adaptive” to the learner. Many tools have tried to build adaptive learning (from Axonify to Cisco’s “reusable learning objects“) but the scale and utility of these innovations is limited.
What if we use AI and machine learning to simply build content on the fly? And let employees simply ask questions to find and create the learning experience they want? Well thanks to innovations from the vendors I mentioned above, this kind of personalized experience is available today. (Listen to my conversation with Joel Hellermark from Sana to hear more.)
What Is An Autonomous Learning Platform?
The best analogy I’ve come up with is the “five levels of autonomous driving.” We’re going from “no automation” to “driver assist” to “conditional automation” to “fully automated.” Let me suggest this is precisely what’s happening in corporate training.
If you look at the pace of AI announcements coming (custom GPTs, image and video generation, integrated search), you can see that this reality has now arrived.
How Does This Really Work
Now that I’ve had more than a year to tinker with AI and talk with dozens of vendors, the path is becoming clear. The new generation of learning platforms (and yes, this will eventually replace your LMS), can do many things we need:
First, they can dynamically index and injest content into an LLM, creating an “expert” or “tutor” to answer questions. Galileo, for example, now speaks in my own personal voice and can answer almost any question in HR I typically get in person. And it gives references, examples, and suggests follow-up questions. Companies can take courses, documents, and work rules and simply add them to the corpus.
Second, these systems can dynamically create courses, videos, quizzes, and simulations. Arist’s tool builds world-class instructional pathways from documents (try our free online course on Predictions 2024 for example) and probably eliminates 80% of the design time. Docebo Shape can take sales presentations and build an instructional simulation automatically, enabling sales people to practice and rehearse.
Third, they can give employees interactive tutors and coaches to learn. Uplimit’s new system, which is designed for technical training, automatically gives you an LLM-powered coach to step you through exercises, and it learns who you are and what kind of questions you need help with. No need to “find the instructor” when you get stuck.
Fourth, they can personalize content precisely for you. Sana’s platform, which Joel describes here, can not only dynamically generate content but by understanding your behavior, can actually give you a personalized version of any course you choose to take.
These systems are truly spectacular. The first time you see one it’s kind of shocking, but once you understand how they work you see a whole new world ahead.
Where Is This Going
While the market is young, I see four huge opportunities ahead.
First, companies can now take millions of hours of legacy content and “republish it” in a better form. All those old SCORM or video-based courses, exercises, and simulations can turn into intelligent tutors and knowledge management systems for employees. This won’t be a simple task but I guarantee it’s going to happen. Why would I want to ramble around in the LMS (or even LinkedIn Learning) to find the video, or information I need? I”d just like to ask a system like Galileo to answer a question, and let the platform answer the question and take me to the page or word in the video to watch.
Second, we can liberate instructional design. While there will always be a need for great designers, we can now democratize this process, enabling sales operations people, and other “non-designers” to build content and courses faster. Projects like video authoring and video journalism (which we do a lot in our academy) can be greatly accelerated. And soon we’ll have “generated VR” as well.
Third, we can finally integrate live learning with self-directed study. Every live event can be recorded and indexed in the LLM. A two hour webinar now becomes a discoverable learning object, and every minute of explanation can be found and used for learning. Our corpus, for example, includes hundreds of hours of in-depth interviews and case studies with HR leaders. All this information can be brought to life with a simple question.
Fourth, we can really simplify compliance training, operations training, product usage, and customer support. How many training programs are designed to teach someone “what not to do” or “how to avoid breaking something” or “how to assemble or operate” some machine? I’d suggest its millions of hours – and all this can now be embedded in AI, offered via chat (or voice), and turned loose on employees to help them quickly learn how to do their jobs.
Vendors Watch Out
This shift is about as disruptive as Tesla has been to the big three automakers. Old LMS and LXP systems are going to look clunkier than ever. Mobile learning won’t be a specialized space like it has been. And most of the ERP-delivered training systems are going to have to change.
Sana and Uplimit, for example, are both AI-architected systems. These platforms are not “LMSs with Gen AI added,” they are AI at the core. They’re likely to disrupt many traditional systems including Workday Learning, SuccessFactors, Cornerstone, and others.
Consider the content providers. Large players like LinkedIn Learning, Skillsoft, Coursera, and Udemy have the opportunity to rethink their entire strategy, and either put Gen AI on top of their solution or possibly start with a fresh approach. Smaller providers like us (and thousands of others) can take their corpus of knowledge and quickly make it come to life. (There will be a massive market of AI tools to help with this.)
I’m not saying this is easy. If you talk with vendors like Sana, Docebo, Arist, and Uplimit, you see that their AI platforms have to be highly tuned and optimized for the right user experience. This is not as simple as “dumping content into ChatGPT,” believe me.
But the writing is on the wall, Autonomous Learning is coming fast.
As someone who has lived in the L&D market for 25 years, I see this era as the most exciting, high-value time in two decades. I suggest you jump in and learn, we’ll be here to help you along the way.
About These Vendors
Sana (Sana Labs) is a Sweden-based AI company that focuses on transforming how organizations learn and access knowledge. The company provides an AI-based platform to help people manage information at work and use that data as a resource for e-learning within the organization. Sana Labs’ platform combines knowledge management, enterprise search, and e-learning to work together, allowing for the automatic organization of data across different apps used within an organization.
Docebo is a software as a service company that specializes in learning management systems (LMS). It was founded in 2005 and is known for its Docebo Learn LMS and other tools, including Docebo Shape, its AI development system. The company has integrated learning-specific artificial intelligence algorithms into its platform, powered by a combination of machine learning, deep learning, and natural language processing. The company went public in 2019 and is listed on the Toronto Stock Exchange and the Nasdaq Global Select Market.
Uplimit is an online learning platform that offers live group courses taught by top experts in the fields of AI, data, engineering, product, and business. The platform is known for its AI-powered teaching assistant and personalized learning approach, which includes real-time feedback, tailored learning plans, and support for learners. Uplimit’s courses cover technical and leadership topics and are designed to help individuals and organizations acquire the skills needed for the future.
Arist is a company that provides a text message learning platform, allowing Fortune 500 companies, governments, and nonprofits to rapidly teach and train employees entirely via text message. The platform is designed to deliver research-backed learning and nudges directly in messaging tools, making learning accessible and effective. Arist’s approach is inspired by Stanford research and aims to create hyper-engaging courses in minutes and enroll learners in seconds via SMS and WhatsApp, without the need for a laptop, LMS, or internet. The company has been recognized for its innovative and science-backed approach to microlearning and training delivery.
BY JOSHBERSIN
2024年人力资源预测：全球追求生产力In this fast-evolving era, all companies and individuals are seeking for change and efficiency. We can see the core of productivity in the new year: AI. Let's have a look at details on the Josh Bersin Predictions for 2024.
在过去的二十年里，我一直在写关于人力资源预测的文章，但今年不同。我看到这一年打破了范式，改变了商业中的每一个角色。不仅人工智能会改变每家公司和每一项工作，而且公司将开始不懈地寻求生产力。
想想我们的过去。2008年金融危机后，世界开始了加速增长的零利率时期。公司增加了收入，雇用了员工，并看着他们的股价上涨。招聘继续以狂热的速度进行，导致2019年底失业率创下3.5%的历史新低。
随之而来的是大流行，在六个月内，一切都停滞不前。2020年4月，失业率飙升至15%，公司将人们送回家，我们重新设计了我们的产品、服务和经济，以应对远程工作、混合工作制和对心理健康的关注。
一旦经济再次启动（由于美国的财政刺激），公司又回到了旧的招聘周期。但随着利率上升和需求不足，我们看到裁员一再发生，在过去的18个月里，我们看到了招聘、裁员，然后再次招聘以复苏经济。
为什么会出现跷跷板效应？
首席执行官和首席财务官正处于我们所说的“工业时代”——招聘以增长经济，然后在事情好转时裁员。
今天，当我们进入2024年时，一切都不同了。我们必须“囤积人才”，投资于生产力，并重新开发和重新部署人员以实现增长。
我们生活在一个失业率为 3.8% 的世界，几乎每个职位都存在劳动力短缺，劳动力权力日益增强，员工需求不断涌现：对加薪、灵活性、自主权和福利的要求。每年有超过20%的美国员工换工作（每月2.3%），其中近一半的变化是进入新行业。
为什么这是“新常态”？
有几个原因。首先，正如我们在全球劳动力情报研究中所讨论的那样，行业是重叠的。每家公司都是数字化公司；每家公司都希望建立经常性收入来源；很快，每家公司都将使用人工智能。过去停留在行业内的职业正在转变为“基于技能的职业”，让人们比以往任何时候都更容易跳槽。
其次，员工（尤其是年轻员工）感到有权按照自己的意愿行事。他们可能会悄悄地辞职，“做兼职”，或者抽出时间转行。他们看到自己的生活很长（人们的寿命比 1970 年代和 1980 年代长得多），所以他们不介意离开你的公司去其他地方。
第三，生育率持续下降，劳动力短缺加剧。日本、中国、德国和英国的劳动力人口都在萎缩。在未来十年左右的时间里，大多数其他发达经济体也将如此。
第四，工会正在崛起。由于华盛顿的新理念，我们看到了谷歌、亚马逊、星巴克、GM、福特、Stellantis、凯撒、迪士尼、Netflix等公司的劳工活动。虽然工会参与率不到美国劳动力的11%，但在欧洲要高得多，而且这一趋势正在上升。
这一切意味着什么？
这有很多影响。
首先，公司将更加专注于建立高保留率的工作模式（有人称之为“劳动力囤积”）。这意味着改善薪酬公平，继续混合工作模式，投资于以人为本的领导力，并为员工提供在公司内部从事新职业的机会。这就是为什么人才市场、基于技能的发展和工作流程中的学习如此重要的原因。
其次，CEO必须了解员工的需求、愿望和要求。正如爱德曼的最新研究表明的那样，职业发展现在位居榜首，同时对授权、影响力和信任的渴望也排在首位。我们称之为“员工激活”的新主题：倾听员工的意见，并将有关他们工作的决定委托给他们的经理、团队和领导者。
第三，传统的“雇佣成长”模式并不总是奏效。在这个后工业时代，我们必须系统地运作，将内部发展、工作再设计、经验和招聘放在一起。这汇集了招聘、奖励和薪酬、学习与发展以及组织设计等独立领域。（阅读我们的系统性人力资源研究了解更多信息。）
“业务绩效”的真正含义是什么？
如果你是首席执行官，你希望增长收入、增加市场份额、提高盈利能力和可持续性。如果你不能通过招聘来成长（而员工不断以奇怪的方式“激活”），你还有什么选择？这很简单：您可以自动化生产并专注于生产力。
虽然这张图表令人印象深刻，但它给每个CEO都引出了一个问题：我们在这张图表上的位置是什么？我们的运营速度是否与同行一样快、一样高效？
我认为这导致了一种我称之为“生产力优势”的策略。如果你能帮助你的公司更快地发展（生产力意味着速度，而不仅仅是利润），你就可以比你的竞争对手更快地进行重塑。这才是真正让CEO们夜不能寐的原因。
考虑一下普华永道最新的CEO调查数据。今年，我们必须比以往任何时候都更快地重塑我们的公司。到2024年，45%的CEO（去年为39%）认为他们的业务在十年内将无法生存。
生产力优势
为什么生产力如此重要？有四个原因。
首先，CEO们关心它。
2024 年普华永道 CEO 调查发现，CEO 认为公司 40%的工作浪费了生产力。
尽管这听起来令人震惊，但对我来说却是真实的：太多的电子邮件、太多的会议、混乱的招聘流程、官僚主义的绩效管理等等。（HR 就有其中一些问题。）
其次，AI让人生产力优势成为可能。
人工智能的应用旨在提高白领的生产力。（过去大多数自动化都有助于蓝领或灰领工人。）生成式 AI 让我们能够更快地查找信息，了解趋势和异常值，训练自己和学习，并清理我们随身携带的文档、工作流程、门户以及后台合规和管理混乱的系统。
第三，公司的发展需要AI。
当很难找招聘到人时，你将如何成长？去年，招聘时间增加了近20%，就业市场变得更加艰难。你能在技术技能上与谷歌或OpenAI竞争吗？
内部开发、重组和自动化项目就是答案。有了生成式人工智能，机会无处不在。
第四，生产力推动重塑。
如果你考虑重塑你公司（新产品、利用人工智能、进入新市场等）的需求，最大的障碍是惯性。为什么诺基亚和黑莓的手机业务输给了苹果？因为这些公司“又胖又快乐”。在这个人才和技能短缺的时代，这是灾难的根源。
普华永道（PwC）估计，“效率低下”产生了对GDP10万亿美元的税收，相当于全球GDP的7%。这种税收阻碍了您的公司转型。每当我们简化、减少会议并更好地定义决策权时，我们都会加快并实现变革。
这一切对人力资源意味着什么？
正如我在《人力资源预测》中所描述的那样，我们有很多问题需要解决。
我们必须加快向动态工作和组织结构的转变。我们必须专注于和务实地对待技能。我们必须重新思考“员工体验”，并处理我们所说的“员工激活”。我们将不得不对我们的人力资源技术、招聘和L&D系统进行现代化改造，以利用人工智能并使这些系统更加有用。
我们的人力资源团队也将由人工智能驱动。正如我们的Galileo™客户告诉我们的那样，一个架构良好的“专家助理”可以彻底改变人力资源人员的工作方式。我们可以成为“全栈”人力资源专业人员，在几秒钟而不是几周内找到有关我们团队的数据，几秒钟与一线领导分享人力资源、领导力和管理实践。（Galileo被一些世界上最大的公司用作管理教练。）
还有一些其他变化。随着公司专注于“通过生产力实现增长”，我们必须考虑每周 4 天的工作制，我们如何将混合工作制度化，以及如何以更有效的方式连接和支持远程工作者。我们必须重新关注领导力发展，在一线经理身上花费更多的时间和金钱，并继续投资于文化和包容性。我们必须简化和重新思考绩效管理，我们必须解决令人头疼的薪酬公平问题。
还有更多。
DEI 计划必须嵌入到业务中（人力资源 DEI 警察的时代已经结束）。我们必须清理我们的员工数据，以便我们的人工智能和人才情报系统准确且值得信赖。正如我们的系统性人力资源研究所指出的那样，我们必须将思维从“支持业务”转变为“成为有价值的顾问”，并将我们的人力资源服务产品化。
所有这些都在我们本周发布的40页新报告“2024 年人力资源预测”中进行了详细说明，其中包括一系列行动计划，以帮助您思考所有这些问题。
让我提醒你一个大观念。生产力是人力资源部门存在的原因。
我们所做的一切，从招聘到辅导，从开发到组织设计，只有在帮助公司成长的情况下才能成功。作为人员流动、敬业度、技能和领导力方面的专家，我们人力资源部门每天都在提高员工和组织的生产力。2024年是专注于这一更高使命的一年。
最后一件事：照顾好自己。
该报告有15个详细的预测，每个预测都有一系列需要考虑的行动步骤。最后一个真正适合你：专注于人力资源的技能和领导力。作为流程的管理者，我们必须专注于我们自己的能力。2024年将是成长、学习和团队合作的一年。如果我们处理好这15个问题，我们将帮助我们的公司在未来一年蓬勃发展。
Josh Bersin预测的详细信息
预测研究是我们每年阅读量最大的报告。它包括我们所有研究的详细摘要，并讨论了首席执行官、首席人力资源官和人力资源专业人士的15个基本问题。它将以以下形式提供：
包含详细信息的信息图。（点击这里)
Source JOSH BERSIN
What Issues are Top of the Mind for HR Leaders Heading into 2024?根据康奈尔大学工业劳动关系学院高级人力资源研究中心的一份调查报告，“转型和演变”这一广泛而重要的话题最近受到人力资源领导者的关注，该问题被确定为2024年企业的最紧迫问题。
“考虑到公司一直面临的所有颠覆，无论是在业务方面，还是在地缘政治问题的更广泛环境中，看到转型成为今年的首要目标，我并不感到惊讶，”康奈尔大学战略人力资源教授兼该中心主任布拉德贝尔说。
根据上周发布的调查，超过三分之二（67%）的人力资源领导者认为转型和演变（包括人力资源转型、文化演变和混合工作演变）是首要问题。而2023年，转型与演进排名第三，只有大约45%的受访者认为是首要问题。
调查显示，由于地缘政治力量和劳动力变化导致的业务中断正在加剧人们对转型和演变的担忧。Bell 说，人力资源领导者特别关注人力资源内部的转型，例如保持公司的敏捷性、提高效率和优化运营。他指出，中东的冲突和乌克兰的持续战争限制了这些地区的员工流动，另外，总体上减缓了一些人力资源转型工作。他补充说，对组织治理问题的高度关注，包括股东对高管薪酬的发言权，也在缓和人力资源转型，因为这种努力可能会限制招聘工作。
此外，Bell 表示，调查参与者报告说，快速的组织文化变化使员工难以建立联系并发展共同的目标，尤其是在当今分散的工作环境中。作为回应，人力资源领导者经常更新他们的混合工作模式，这可能会损害包容性或其他相关目标，从而阻碍文化发展。
HR 优先事项如何变化
排名前五的问题分别是人才管理、技术、员工体验以及领导力和继任计划。
Bell说，技术是今年进入前五名的新事物，这主要是由于人力资源部门对人工智能的兴趣。在前几年，该主题被嵌入到其他类别中，例如数字员工体验。去年排名第四的总奖励从榜单上掉了下来。
“每年，似乎都会有一个新话题出现在前 5 名名单上，”贝尔说。他说，2023 年，在高通胀和寻求为员工提供经济救济的组织推动下，总薪酬是增加的。但今年，通胀正在放缓，对经济衰退的担忧正在缓解，这可能会减少雇主对这一领域的担忧。
DEI 和福祉仍然是人力资源的优先事项吗？
Bell 说，尽管他们没有进入前五名，但 DEI 和福祉仍然是人力资源领导者最关心的问题之一。与去年一样，他们在 2024 年分别排名第六和第七。
Bell 说：“人力资源主管谈到希望保持他们迄今为止在 DEI 方面取得的进展，甚至希望将这些努力提升到一个新的水平。“例如，他们不仅考虑多样性和包容性，还考虑我们如何推动公平和各种人才实践。”
然而，他指出，一些公司正在撤回他们的 DEI 努力。这些行动反映在最高法院去年对平权行动作出裁决后，削减 DEI 预算和裁员 DEI 官员。
他说，同样，雇主对幸福感的关注也在减弱。
“大流行后，人们对员工的健康和福祉非常关注，”贝尔说。“我认为它已经有所消退。我不认为它像我们在调查中看到的其他一些主题那样受到同等程度的关注。
Source Human Resource Executive
观点
观点
观点
观点
观点
观点
观点
观点
观点
观点
扫一扫
添加客服
