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
机器学习
2024年02月18日
机器学习
How Will AI Impact People Analytics in 2024 and Beyond(Podcast)2024年，人员分析将面临一个转折点。这个转折点集中在AIML上，以及它如何为组织创造优势，以及 HR 中的大量活动和工作，HR内部的数据科学分析团队有一个独特的位置可以研究。
将人员分析从洞察转变为影响需要什么？人员分析和人力资源专业人员如何影响企业领导者？人工智能和机器学习对现在和未来的人员分析有何影响？
这些只是我们本周在数字人力资源领导者播客从寒假回归时讨论的三个主题。与我一起参加会议的还有三位嘉宾，他们都亲自启发了我，还有无数其他人，他们在人员分析领域拥有超过40年的经验：
Dawn Klinghoffer，Microsoft人员分析全球主管和 Insight222 的董事会顾问，他在过去20年中一直在 Microsoft 建立和领导人员分析功能。
杰里米·夏皮罗（Jeremy Shapiro），默克公司（Merck & Co）劳动力分析全球主管，纽约战略人力资源分析会议小组的联合召集人。
Thomas Hedegaard Rasmussen，壳牌组织发展和学习副总裁，曾在澳大利亚国民银行、壳牌和马士基建立并领导人员分析职能。
您可以通过单击下面的图片或访问播客网站来收听。
在对话中，我们探讨了如何通过人员分析来推动业务价值，重点介绍了 Thomas 最近与 Mike Ulrich 和 Dave Ulrich 合著的论文（将人员分析从洞察力转移到影响力）中的发现，以及 Insight222的2023 年人员分析趋势报告中确定的领先公司的八个特征。
如果您能够访问企业中的战略对话，则更容易受到它们的影响。而且，如果你有高层领导的支持，以试图一次做太多事情为代价来追求那些相对较少的高价值项目，那么确定优先级也更容易。
Dawn、Jeremy、Thomas 和我还讨论了：
人员分析的三个“I”：洞察力、影响力和影响力
确定人员分析工作的优先级并将其与业务需求保持一致的技术
如何在人员分析和财务之
间建立成功的关系
人工智能和机器学习如何支持人员分析工作
人工智能将如何在未来12个月和几年内改变工作世界。
我希望人工智能能够真正帮助人力资源部门充满活力，并有能力去做有意义的工作，消除今天人力资源部门发生的许多任务的苦差事。
资源
以下是本集讨论的一些材料的链接：
托马斯与迈克·乌尔里希（Mike Ulrich）和戴夫·乌尔里希（Dave Ulrich）共同发表的论文：将人员分析从洞察力转移到影响
Insight222 的 2023 年人员分析趋势研究：投资交付——人员分析的新模型
Dawn 关于蓬勃发展的文章：为什么 Microsoft 衡量员工的发展，而不是敬业度
杰里米与汤姆·达文波特（Tom Davenport）和珍妮·哈里斯（Jeanne Harris）合著的开创性文章《哈佛商业评论》：在人才分析上竞争
Source Linkedin
机器学习
2024年01月31日
机器学习
Hiring Trends 2024: For Tech And Digital Global Employers
ANWESHA ROY 8 MINUTE READ
The hiring landscape has gone through a lot of fluctuations in the last two years. The United States and the European Union (EU) fell into recession, triggering widespread panic amongst tech and digital companies. Businesses had to lay off a large chunk of their workforce as a cost-cutting measure, some even freezing hiring temporarily.
Fast-paced digital agencies and startups understood that they needed a flexible hiring approach to adapt to these circumstances. They realized that hiring remote talents from offshore locations like LatAm, East EU, East Asia, and India was a viable way to grow their workforce. Recruiters soon realized that they needed to prioritize both skills and cultural adaptability while looking for remote talents. Hiring platforms emerged as the helping hand in this matter, with their comprehensive solutions geared to deliver a fast and reliable hiring experience.
In this blog, we will discuss these developments and other hiring trends for 2024, and the job roles that will grow in the near future.
Hiring Trends That Will Define 2024
Adaptable hiring strategies will help tackle the talent shortage
Remote hiring for remote positions is here to stay
Skill-based hiring will gain more prominence
India’s rising talent pool to meet global needs
Talent expectations from global employers are changing
Organizations will look for culture-fit talents
Emergence of hiring platforms
Artificial Intelligence (AI) and Machine Learning (ML) will play a crucial role in optimizing the hiring process
1. Adaptable hiring strategies will help tackle the talent shortage
By 2030, the global tech talent shortage will rise to 85.2 million, leading to a massive loss in revenue. Global employers will be more careful and strategic when hiring in 2024. The demand-supply gap of skilled tech and digital talents is growing every year, which means startups have to work harder to onboard the best talents. They will also look to hire remote talents from offshore locations to upscale as per their budget and resources.
2. Remote hiring for remote positions is here to stay
In 2023, tech and digital startups have to deal with the growing tech talent shortage amidst a precarious global economic scenario. Remote hiring is the most viable solution for these organizations, as they can easily access skilled and cost-effective talents across the globe, with a faster hiring process. Even companies following on-site or hybrid workstyles are hiring certain roles remotely, due to its benefits. contract hiring – uncertain economic conditions are compelling companies to hire full-time long-term contractual employees for flexibility and scalability. Global employers are also open to long-term contractual engagements for full-time employees, to ensure flexibility and scalability.
The number of startups hiring remotely has grown from 900 in 2019, 2,500 in 2020, and 14,000+ in 2022. With a growing number of talents preferring remote workstyle, companies will be able to retain their top talents by setting up distributed teams instead of strictly adhering to local hiring.
3. Skill-based hiring will gain more prominence
92.5% of companies have seen a reduction in their mis-hire rate when implementing skills-based hiring, with 44% reporting a decrease of more than 25%. Going ahead, the qualification of a candidate will be defined by their hard and soft skills, and not just their education and work experience. Technical skills, problem-solving abilities, leadership, adaptability, and more will be closely evaluated by companies. A study shows that hiring for skills is five times more predictive of job performance than hiring for work experience.
To drive this initiative, startups will rely on vetting tools and integrate them within their hiring process. The assessment will be tailor-made for tech and digital roles to aid in finding the most suitable talent. Furthermore, startups have to drop degree requirements from job descriptions and become more specific about the capabilities they are looking for.
4. India’s rising talent pool to meet global needs
Contrary to the talent crisis across the globe, India is generating tech and digital talents consistently in large numbers. Their tech talent pool has grown by 120% in the last five years, with two million STEM graduates every year. The country also has a surplus of 2.5 million digital talents, presenting a great opportunity for global employers.
The average salaries of Indian talents is lower than that of US, EU, and AUS talents, which means global companies can hire equally or better-skilled professionals at a lesser cost.
India also has a wide network of talents specializing in emerging technologies. The number of Indian AI experts on LinkedIn has grown by 14x in the last seven years, the 5th fastest growth after Singapore, Finland, Ireland, and Canada.
These reasons have helped India become the most preferred talent-sourcing hub in the world.
5. Talent expectations from global employers are changing
The global labor market is very tight and the talents have an upper hand in deciding their next employer. To remain competitive, startups have to reexamine their hiring strategies and cater to what the top talents are looking for.
A study reveals that top Indian remote talents want better pay, good work-life balance, and prospects of career growth while choosing an employer. Before hiring from India, global employers have to prepare an offer that fulfills the expectations of these talents.
6. Organizations will look for culture-fit talents
Technical proficiency makes a candidate qualified for the job role, but a cultural fitment aligns makes them the perfect addition to the organization. Both large-scale companies and startups need talents who take initiative, have a positive attitude, and handle situations in a non-confrontational manner. Such skills will uphold the work environment and promote a healthy culture. An org-culture fit talent will be more engaged and satisfied with their job than just a skilled professional.
Finding and hiring culture-fit professionals also impacts the retention rates, as a study shows that 73% of talents have left a job due to poor cultural fitment.
7. Emergence of hiring platforms
According to a 2022 survey by Upwork, 50% of businesses outsource at least some of their work. Of those businesses that outsource, 38% use hiring platforms to find freelancers and contractors. Another report reveals that 48% of companies are planning to increase their use of hiring platforms for offshoring in the next two years.
Hiring platforms offer a number of advantages to businesses, including access to a large pool of skilled and experienced freelancers and contractors, the ability to scale their workforce up or down as needed, and cost savings on labor costs. They also help in vetting candidates to find the right technical and cultural fit, helping in making an informed hiring decision. With their end-to-end solutions, hiring platforms help both fast-paced businesses and enterprises in upscaling confidently within a short period of time.
8. Artificial Intelligence (AI) and Machine Learning (ML) will play a crucial role in optimizing the hiring process
44% of recruiters find AI useful in shortening the hiring cycle, which is the main priority, 32% found it a good way to cut down overhead costs, and 24% found it helpful in identifying the right talents.
Studies suggest that it takes 29 to 66 days to fill tech-based vacancies, which is a very long hiring cycle for startups. In a fast-paced environment with constant deadlines, open roles must be filled as quickly as possible. As time is of the essence, startups are beginning to leverage Artificial Intelligence (AI) and Machine Learning (ML) in their hiring process.
By reducing the time to hire, small-scale startups are also able to cut down overhead and operational costs. In fact, AI/ML have helped companies in North America cut down their costs by 40%, in Europe by 36%, and in the APAC region by 25%.
Application Tracking System (ATS) is also being used by startups to ensure a seamless hiring process. The ATS is useful in organizing applications, managing communications, and tracking the status of candidature. 99% of Fortune 500 Global companies are using ATS for an elevated hiring experience and short cycle, so why shouldn’t startups? After all, it oversees all the tedious processes in hiring, so managers can focus their energy on decision-making and other important tasks.
Region-wise Job Roles Which Will Grow In Demand in 2024
United States
Europe
Australia
According to a survey by NASSCOM, future skills demand is expected to grow to 3.5-3.7 million by 2024, rising from the present 1.2-1.3 million currently employed by the industry. Building on that, here are a few predicted jobs that will be in demand in the next few years, sorted region-wise.
United States
The United States is leading the world in next-gen technology, which reflects in their plans to hire more cloud engineers, machine learning engineers, data scientists, and salesforce developers. The digital sector is also growing at an average of 8.5% CAGR, and the startups are looking to hire more web developers, ad specialists, UI/UX designers, and digital marketing managers.
Europe
European tech startups will focus increasingly on their core services and hire more front-end developers, DevOps engineers, and blockchain developers. Similarly, digital companies will look for PHP developers, web developers, and digital marketing managers. SaaS-based startups in the EU will focus on building next-gen products and user privacy, which is why they will hire more product managers, customer success managers, and security engineers.
Australia
Despite fears of recession, Australian tech startups are focussing on resilient hiring to support their services. They will look to onboard more back-end developers, database administrators, and systems engineers. In the digital sector, SEO specialists, web analytics specialists, and digital sales representatives will be in demand. SaaS-based startups in the country will focus on better customer service by hiring account executives, customer success managers, and e-commerce managers.
Jobs created by AI to look out for in 2024
Prompt Engineer
Prompt engineers are experts in designing and developing AI-generated text prompts for improving the AI prompt generation process for several applications. They use data analysis and programming skills to deliver an elevated user experience in tech and SaaS products.
AI Trainer
AI trainers are responsible for teaching AI systems how to think and interact with users. They work with the development team to ensure the chatbots and virtual assistants respond to customer queries and resolve them effectively. These experts have a strong background in data science, natural language processing (NLP), and machine learning.
AI Auditor
AI auditors evaluate the safety, legality, and ethics of AI systems so they can be put to good use. They review codes, conduct data analysis, and test the systems to ensure the system does not produce biased or discriminatory responses.
Machine Managers
Machine managers oversee the AI-operated hardware and systems, and ensure everything is intact for peak performance. They are responsible for the efficient operation and minimum downtime of AI tools, making them indispensable for the tech sector.
Final Thoughts
The secret to success in talent acquisition is to identify the trends, adapt your strategy, and prepare for the future. It is important to constantly monitor the ever-changing hiring landscape to build a productive workforce for the long run.
As we enter 2024, the major focus for global employers will be on identifying the best candidates for the role and leveraging digital tools for a smarter process. Digital agencies also have to offer what talents seek in their employer in order to improve their chance of hiring the best candidates.
By aligning these hiring trends in advance, global recruiters like digital agencies, IT services companies, and SaaS-based tech companies can stay ahead of the curve and hire methodically.
机器学习
机器学习
机器学习
机器学习
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