Machine Learning - 北美华人人力资源协会

Machine Learning

The 10 golden rules for establishing a people analytics practice 十大黄金法则：战略适配性：确保人力资源分析项目与组织的战略目标对齐，以实现最大的价值和影响。持续的员工倾听：通过整合员工和业务的声音，优先处理正确的战略人力资源问题。证据基础的HR服务整合：将所有基于证据的HR服务整合到一个功能中，提升人力资源分析的交付速度和质量。清晰的人力资源分析操作模型：建立一个目标操作模型，明确客户、可交付服务、服务水平和交付时间。数据隐私合规性：遵守数据隐私法规，同时考虑数据分析在文化和业务连续性方面的影响。数据驱动决策的HR能力提升：通过提升HR社区的数据和洞察力使用，将业务机会转化为分析服务。管理HR数据：建立集中的企业级数据基础设施，改善数据的组合、共享和分析能力。产品设计和思维：确保人力资源分析服务的用户设计友好，易于导航，并激励用户在决策中使用数据。实验与最小可行产品：通过实验和最小可行产品，逐步评估和改进解决方案，避免大规模实施失败。利用人工智能的潜力：构建和实施基于机器学习的AI功能，确保模型的性能和有效性，同时控制数据偏见和合法性。这些法则展示了通过系统方法创建并采纳人力资源分析实践的重要性，强调了以数据和证据为基础支持人力资源功能的必要性。 It is time for an update on my previous posts on the 10 golden rules of people analytics, simply because so much has happened since then. For example, continuous employee listening, artificial intelligence (AI in HR), agile HR, employee experience, strategic workforce management, and hybrid working are just a few emerging topics in recent years listed in Gartner's hype cycle for HR transformation (2023). In the last year, I have spoken to many people working in different organisations on establishing people analytics as an accepted practice. I have also joined some great conferences (HRcoreLAB, PAW London & Amsterdam) where I learned from excellent speakers. I also (re)engaged with some excellent people analytics and workforce management vendors, such as Crunchr, Visier, eQ8, AIHR, One Model, Mindthriven, and Agentnoon. Finally, I also enjoyed having multiple elevating discussions with some thought leaders who influenced my thinking (e.g., David Green, Rob Briner, Jonathan Ferrar, Dave Millner, Sjoerd van den Heuvel, Ian O'Keefe, Brydie Lear, Jaap Veldkamp, RJ Milnor, and Nick Kennedy). These encounters and my ongoing PhD research on adopting people analytics resulted in a treasure trove of new ideas and knowledge that confirmed my experience and beliefs that it is all about creating an embraced people analytics practice using a systemic approach in supporting HR in becoming more evidence-based. So, like I said, it's time for an update. I hope you enjoy and appreciate the post, and I invite you to engage and react in the comments or send me a direct message. Create a strong strategy FIT. It is obvious but not a common practice that your people analytics portfolio needs to align or fit with your strategic organisational goals. A strong strategic FIT ensures you execute people analytics projects with the most value and impact on your organisation. It is, therefore, important to integrate the decision-making on where to play in people analytics with your periodic HR prioritisation process. Strategic workforce management and continuous employee listening are pivotal in prioritising the right strategic workforce issues The bigger picture is that two people analytics-related HR interventions, strategic workforce management and continuous employee listening, are pivotal in prioritising the right strategic workforce issues. By blending the insights from these HR interventions, you ensure you are prioritising based on the voice of the business and the voice of the employee. See also my previous post on strategic workforce management. Because people analytics is at the core of these HR interventions and provides many additional strategic insights, I argue we need a new HR operating model where the people analytics practice is positioned at the centre of HR. I argue that we need a new HR operating model where the people analytics practice is positioned at the centre of HR Grow and integrate evidence-based HR services. Based on my experience and research, I strongly advise integrating all evidence-based HR services into one function. See also my previous post on establishing a people analytics practice. This integration will enhance the speed and quality of your people analytics delivery, make you a trusted analytical strategic advisor, and make you a more attractive employer for top people analytics talent. All other people analytics function setups seem like compromises. With evidence-based HR services, I refer to activities such as reporting, advanced analytics, survey management, continuous employee listening, organisational design and strategic workforce management. It is hardly ever that a strategic question is answered by only one of these services. In most cases, you will need to combine survey management (i.e., collecting new data), perform advanced analytics (i.e., build a predictive model), and share the outcomes in a dashboard (i.e., reporting) or build new system functionality based on the models (e.g., vacancy recommendation). You will need to combine various people analytics services to provide real strategic value Create a clear people analytics operating model. Because the people analytics practice is maturing, it deserves a clear target operating model. In a target operating model, you clarify to the organisation whom you consider your clients, what services or solutions you can deliver, what service levels your clients can expect, and when and how you will deliver the solution. Being transparent about your target operating model will build trust and legitimacy in your organisation. Inspired by the work of Insight222, a people analytics target operating model consists of a demand engine (understanding and prioritising demand), a solution engine (e.g., data management, building models, designing surveys), and a delivery engine (e.g., dashboards, advisory with story-telling, bringing models to production), ideally covering all the evidence-based HR services mentioned under rule 2 in this post. Additionally, more practices are applying agile principles to increase time-to-delivery and are using some form of release management to balance capacity. Built trust and legitimacy Compliance with data privacy regulations has been an important topic since the early days of people analytics ten years ago. Even before the GDPR era, organisations did well to understand when personal data could be collected, used, or shared. Legislation such as GDPR offers guidance and more structure to organisations on how to deal with data privacy issues. Being fully compliant is not where responsible data handling ends However, being fully compliant is not where responsible data handling ends. Simply because you can, according to data privacy regulations, doesn't mean you should. There are also contextual and ethical elements to take into account. For example, being able and regulatory-wise allowed to build an internal sourcing model matching internal employees with specific skills with internal vacancies doesn't mean you should. From a cultural or business continuity perspective, creating internal mobility may not be beneficial or desired in specific areas of your organisation. Assessing the implications of using data analytics in a broader context than just regulations will also enhance the needed trust and legitimacy. Upskill HR in data-driven decision-making Having a mature people analytics practice that delivers high-quality, evidence-based HR services is not enough to ensure value creation for your organisation. Suppose your organisation, including your HR community, struggles to translate business opportunities into analytical services or finds it hard to use data and insights on a daily basis in their decision-making. In that case, upskilling is a necessary intervention. HR upskilling in data-driven decision-making is a necessity in growing towards a truly evidence-based HR culture Creating awareness of the various analytical opportunities, developing critical thinking, creating an inquisitive mindset, identifying success metrics for HR interventions and policies, evaluating these metrics, and understanding the power of innovative data services, such as generative AI, is essential. When upskilling, be sure to recognise the different HR roles and their needs and preferences. For example, your HR business partners will likely want to develop their skills in identifying strategic workforce metrics and strategic workforce management. However, your COE lead (i.e., HR domain leads) wants to develop their ability to collect and understand internal clients' feedback and improve their HR services (e.g., recruitment, learning programs, leadership development). So, diversify your learning approach to make it more effective. Manage your HR data There is enormous value in integrating your HR and business data in a structured matter. Integrated enterprise-wide data allows you to combine, improve, share, and analyse data more efficiently. More organisations are using data warehouse and data lake principles to create this central enterprise-wide data infrastructure based on, for example, Microsoft Azure or Amazon Web Services technology. A mature people analytics team is best equipped to create an HR data strategy and manage the corresponding data pipeline. HR would do well to improve its capability to manage the data pipeline by hiring data engineers. It is an interesting discussion about where to position this data management capability and related skill set. The first thought is to position this capability close to the HR systems and infrastructure function. This setup might work perfectly. However, based on your HR context and maturity, I argue that the people analytics practice is a good and sometimes better alternative. Mature people analytics teams are likely more able to think about data management and creating data products and services built with machine learning models. Traditional HR systems and infrastructure teams may tend to focus too much on the efficiency of the HR infrastructure (e.g., straight-through processing, rationalising the HR tech landscape). Excel in product design and thinking Successful people analytics or evidence-based HR services excel in product design. Whether built with PowerBI or vendor-led BI platforms (e.g., Crunchr, Visier, One Model), dashboards must be user-friendly, easy to navigate, and motivate users to work with data in their decision-making. The same applies to functionality based on machine learning models, such as chatbots, learning assistants, or vacancy recommendations. The user design, the functionality provided, and the flawless and timely delivery all contribute to maximising the usage of these analytical services and, ultimately, decision-making. Strong product design and thinking requires product owners to have a marketing mindset As important as the product design is product thinking by the product owner. A product owner for, e.g., recruitment or leadership programs, should be constantly interested in hearing what internal clients think about their products. This behaviour requires product owners to have a marketing mindset. As part of a larger continuous listening program, an internal client feedback mechanism should provide the necessary information to improve your products and services continuously. A product owner should be curious about questions like: Are your internal clients satisfied? Should we tailor the products for different user types? What functionality can we improve or add? Allow yourself to experiment When a solution looks good and makes sense based on your analytics, management tends to go for an immediate big-bang implementation. However, don't be afraid to experiment and learn before rolling out your solution to all possible users. Starting with a minimum viable product (i.e., MVP) allows you to evaluate your product among a select group of users early in the development process. Based on feedback, you can enhance your product incrementally (i.e., agile) manner. It also enables you, when valuable, to compare treatment groups with non-treatment groups. These types of experiments (i.e., difference-in-difference comparisons) help you to evaluate the effect the new product intends to have. People analytics services can support this incremental approach, testing a minimal viable product (MVP) and obtaining feedback to provide additional insights that may avoid a big implementation failure of your new products. Embrace the potential of AI in HR Today, artificial intelligence (AI) is predominantly based on machine learning (ML). These AI-ML models provide powerful functionality such as vacancy and learning recommendations, chatbots, and virtual career or work schedule assistants. There is no need to fear these applications, but having a deeper understanding of them is necessary. However, implementing these types of functionality without checking and validating them is risky and, therefore, unwise. A mature people analytics practice allows you to build your own machine-learning-based AI functionality A mature people analytics practice allows you to create and build these AI functionalities internally. You can also buy AI functionality by implementing a vendor tool, but please ensure you do not end up with a new vendor for each AI functionality you desire. If you choose to buy AI functionality, the people analytics team should act as a gatekeeper. Internally built machine learning models are subject to checks and balances. And rightfully so. However, the same should apply to ML-based AI functionality from external providers. The people analytics team should check the performance and validity of the model and control for biases in the data and legal and ethical justification. The people analytics leader can make the difference If you are the people analytics leader within your organisation, it might be daunting or reassuring to hear that you can make the difference between failure and success. You bring the people analytics practice alive by reaching out to stakeholders, developing your team, understanding your clients, learning from external experts, and building a road map to analytical maturity. A successful people analytics practice starts with the right people analytics leader As a people analytics leader, you should excel in business acumen, influencing skills, strategic thinking, critical and analytical thinking, understanding the HR system landscape, understanding the possibilities of analytical services, project management, and, last but not least, people management (as all leaders should). The result of having all these capabilities is that a people analytics leader, together with the people analytics team, becomes a trusted advisor to senior management, understands the most pressing issues within an organisation, can effectively manage the HR data pipeline, and can build new analytical services to enhance decision-making and ultimately drive organisational performance and employee well-being. I hope you enjoyed my update on the 10 golden rules for establishing people analytics practice. If you enjoyed the post, please hit ? or feel invited to engage and react in the comments. Send me a direct message if you want to schedule a virtual meeting to exchange thoughts one-on-one. Thanks to Jaap Veldkamp for reviewing. 作者：Patrick Coolen https://www.linkedin.com/pulse/10-golden-rules-establishing-people-analytics-practice-patrick-coolen-85use/

Machine Learning
2024年04月15日
Machine Learning

首位人工智能软件工程师 Devin诞生，会改变职场？ Devin是由Cognition开发的第一个完全自主的人工智能软件工程师，标志着人工智能和软件开发行业的一个重大飞跃（点击这里访问视频）。 Devin通过独立解决GitHub问题和通过工程面试，证明了其执行专业工程任务的能力。这一革命性的AI正在改变技术与人类合作的动态，影响人力资源策略、人才获取以及自由职业和合同工作的未来。对于人力资源专业人士来说，像Devin这样的AI的崛起需要重新评估招聘实践并将AI整合到劳动力中，确保它补充而不是替代人类专业知识。 Devin的成功标志着劳动力动态的转变，强调人力资源在适应技术进步和AI开发的道德考虑方面的不断发展的角色。在人工智能与软件开发的前沿领域，我们迎来了一个划时代的里程碑——全球首个完全自动化的AI软件工程师Devin的问世。由Cognition——一个专注于技术中的推理与规划的应用AI实验室所创造，Devin设定了全新的软件工程标准。 Devin之所以与众不同，在于它在软件开发过程中无需人工干预就能独立操作和解决问题的卓越能力。Devin不仅在SWE-Bench编码基准测试中独立解决了13.86%的GitHub开源项目问题，还成功通过了顶尖AI公司的实际工程面试，并在Upwork上完成了真实的工作任务，证明了其符合甚至超越专业工程标准的能力。 Devin的引入，不仅是技术实力的展现，更代表了技术与人类协作关系中的一次范式转变。Devin配备了完整的开发者工具集，并具有独特的学习适应能力，能够在软件开发生命周期内无缝工作，从修复bug到开发应用程序，再到微调机器学习模型，无所不能。 Devin的出现对人力资源专业人士和企业团队来说，意味着超出软件工程本身的深远影响。AI技术的融入劳动力市场，为HR部门带来新的机遇与挑战。AI能够自主完成面试并执行传统由人完成的工作，迫使人力资源部门需要重新评估招聘和管理的标准策略。此外，Devin在Upwork等平台的成功案例，展示了自由职业和合约工作的新趋势，影响了公司对项目人员配置和远程工作政策的看法。对于人力资源部门来说，适应这一变化意味着将AI协作视为人才的补充，促进AI与人类共同创新的环境。然而，引入AI工程师如Devin也带来了劳动力发展和AI伦理使用方面的重要讨论。人力资源专业人士在这些变革中将扮演关键角色，确保像Devin这样的AI进步加强而不是取代人类专长，并维持AI开发和部署的伦理标准。随着Devin进入早期接入阶段，Cognition邀请工程师和企业团队体验与AI软件工程师合作的潜力。这不仅是科技行业的一大步，也是人力资源专业人士重新思考并塑造未来工作方式的号召。总结而言，Devin的发布标志着软件工程和劳动力动态领域的重大转折点。随着AI技术的不断进步，人类与AI的协作提供了创新和效率的新途径。Devin从一个概念到一个运行中的AI工程师的发展，不仅展示了AI技术的快速发展，还突出了人力资源在技术驱动世界中日益变化的角色。

Machine Learning
2024年03月13日
Machine Learning

LinkedIn发现，内部流动正在蓬勃发展，但对于低级别员工来说却并非如此文章讨论了公司内部流动性的增长趋势，强调其对提高员工保留率和参与度的好处。LinkedIn的最新研究显示，自2021年以来，内部职位变动增加了30%，主要在中层及以上员工中。报告强调需要通过提供可见性、支持和发展机会来创建一个包容的流动文化。此外，文章还提到了内部招聘的广泛好处，如节省成本和增强公司文化。成功的内部流动技能包括协作、适应性和包容性领导。内部职位转换 —— 当一名员工在同一家公司内部转到一个新职位 —— 正在显著增长，自2021年以来增长了30%，根据LinkedIn在2月22日发布的结果。增长的一个重要原因是，LinkedIn的高级内容经理Greg Lewis在一篇博客中指出，内部职位转换是一种未被充分利用的补充空缺职位的方法，同时也是一个强大的工具，用于增加员工保留率并保持员工的积极参与。然而，这种转换似乎主要局限于中级员工及以上级别：比起普通员工，管理层及更高级别的员工进行内部职位转换的可能性要高出两倍。人力资源专家可以通过“创建更加包容和平等的内部职位转换文化”来帮助缩小这一差距，Lewis提出。这包括为内部职位空缺提供更多可见性和支持，鼓励跨功能合作和指导，寻找和培养内部转移者倾向于发展的技能，如多样性与包容性（diversity and inclusion）、情感智力（emotional intelligence）和变革管理（change management）。根据人才获取公司Symphony Talent的二月份报告，近半数的人力资源专业人员表示，建立人才管道是他们2024年的首要目标。内部招聘可以成为这一管道的一部分，带来如节约成本和增加员工留存等积极结果，其他研究也已表明这一点。过去几年这种做法有所起伏，2020年疫情期间达到高峰，The Josh Bersin Company之前的研究揭示了这一点。那时，公司利用现有员工填补劳动力缺口，并发现内部招聘有助于提高公司文化、提升员工保留率、降低成本和缩短招聘时间。据LinkedIn称，内部人员流动率在2021年有所下降，但在2022年开始回升，并持续到次年。正确的策略能使每个人受益，早期的LinkedIn研究显示。职业发展机会被员工视为留在公司的顶级原因之一，一位LinkedIn高管表示。那些提供个性化职业发展并帮助员工建立技能的组织，其内部职位转换率比缺乏培训的公司高出15%。在这份报告中，LinkedIn比较了成员在开始新职位前12个月加到他们个人资料中的技能。结果显示，与离开公司的同事相比，内部转移者更有可能发展特定技能。例如，内部转移者发展多样性与包容性技能的可能性几乎高出50%；发展情感智力技能的可能性高出27%；发展变革管理技能的可能性高出21%。其他显著的技能包括利益相关者参与（超过14%）和敏捷项目管理（12%）。 “最能预示内部职位转换者的技能主要围绕合作、包容和适应性 —— 能够与同事建立联系、让每个人感受到包容，并在组织层面推动变革，”LinkedIn表示。

Machine Learning
2024年03月02日
Machine Learning

滴滴出行选用NICE，以提供基于实时 AI 的个性化服务 NICE has partnered with DiDi Global to enhance customer and employee experiences through its cloud-based Workforce Management (WFM) and Employee Engagement Manager (EEM) solutions. This collaboration aims to streamline DiDi's global contact center operations, improving operational efficiency and customer satisfaction with AI-driven forecasting and scheduling. The implementation of NICE's solutions facilitates real-time management and self-scheduling for agents, boosting employee engagement and operational efficiency. DiDi's choice of NICE highlights the importance of advanced, flexible technology in supporting the dynamic needs of modern, app-based transportation services. 领先的移动出行平台通过利用 NICE 的客户体验 AI 技术，使其员工能够提供轻松且高效的客户服务体验新泽西州霍博肯-NICE (纳斯达克: NICE) 今日宣布，滴滴出行已经选用了 NICE 劳动力管理 (WFM) 和员工参与管理 (EEM) 作为其云端创新技术的一部分。滴滴现在可以全面预测、规划和管理其全球客户联系中心的运作；同时提升运营效率和员工的参与度，并确保客服代表能够在首次通话中解决问题。Betta作为全球最大的 WFM 客户群之一的支持者，在实施过程中与 NICE 价值实现服务携手合作，负责执行集成，并在多国提供咨询、培训和支持服务。滴滴出行寻求一种能够满足其核心业务、功能及技术需求，并能够随公司成长而扩展的劳动力管理解决方案。NICE WFM 结合了 AI 技术与灵活性，能够满足跨多个大洲、具有特定区域特色的运营需求，这不仅成本效益高，而且精确度高，确保维持最佳的服务水平。通过精准预测，确保在合适的时间有合适技能的代理人，从而大幅提升客户满意度。通过引入 NICE EEM，可以实时解决人员配置需求，使得客服代理能够自我调节工作时间表，从而增强员工参与度和工作满意度。此外，利用智能日内自动调整功能，能够主动地进行调整，预防问题的发生。滴滴出行国际客户体验执行总监 Caio Poli 表示：“基于多个考量因素，NICE 显然是我们的首选。我们寻找的是一个顶尖的云端劳动力管理解决方案，能够使我们的全球运营在保证运营效率和员工参与度的同时，提供卓越的客户体验。NICE 的智能日内自动化功能给我们留下了深刻印象，我们的选择是基于 AI 驱动的策略以及云技术的速度和灵活性。” NICE 美洲总裁 Yaron Hertz 表示：“随着滴滴持续全球扩张，NICE 很高兴有机会为这家数字时代最具创新和活力的应用型运输公司之一提供服务。我们相信，通过采用 NICE 的 AI 驱动预测和机器学习来进行最适合的调度安排，对于联系中心和员工而言，这将有助于推动滴滴的未来发展。” 关于滴滴出行公司滴滴出行公司是一个领先的移动技术平台，它在亚太地区、拉丁美洲及其他全球市场提供一系列基于应用的服务，包括网约车、叫车服务、代驾以及其他共享出行方式，还涵盖某些能源和车辆服务、食品配送和城市内部货运服务。滴滴为车主、司机和配送伙伴提供灵活的工作和收入机会，致力于与政策制定者、出租车行业、汽车行业及社区合作，利用 AI 技术和本地化智能交通创新解决全球的交通、环境和就业挑战。滴滴力图为未来城市构建一个安全、包容和可持续的交通与本地服务生态系统，以创造更好的生活体验和更大的社会价值。更多信息，请访问：www.didiglobal.com 关于 NICE 借助 NICE (纳斯达克: NICE)，全球各地不同规模的组织现在可以更容易地创造卓越的客户体验，同时满足关键的业务指标。作为世界领先的云原生客户体验平台 CXone 的提供者，NICE 是 AI 驱动自助服务和代理辅助客户体验软件领域的全球领导者，服务范围超出了传统的联系中心。超过 25,000 个组织在超过 150 个国家，包括 85 家以上的财富 100 强公司，都选择与 NICE 合作，以改造并提升每一次客户互动。www.nice.com 商标说明：NICE 和 NICE 标志是 NICE Ltd. 的商标或注册商标。所有其他标志属于它们各自的所有者。NICE 商标的完整列表，请访问：www.nice.com/nice-trademarks。

Machine Learning
2024年02月27日
Machine Learning

Autonomous Corporate Learning Platforms: Arriving Now, Powered by AI Josh Bersin 的文章通过人工智能驱动的自主平台介绍了企业学习的变革浪潮，标志着从传统学习系统到动态、个性化学习体验的重大转变。他重点介绍了 Sana、Docebo、Uplimit 和 Arist 等供应商的出现，它们利用人工智能动态生成和个性化内容，满足了企业培训不断变化的需求。Bersin 讨论了跟上多样化学习需求所面临的挑战，以及人工智能解决方案如何提供可扩展的高效方法来管理知识和提高学习效果，并预测了人工智能将从根本上改变教学设计和内容交付的未来。推荐给大家： Thanks to Generative AI, we’re about to see the biggest revolution in corporate learning since the invention of the internet. And this new world, which will bring together personalization, knowledge management, and a delightful user experience, is long overdue. I’ve been working in the corporate learning market since 1998, when the term “e-learning” was invented. And every innovation since that time has been an attempt to make training easier to build, easier to consume, and more personalized. Many of the innovations were well intentioned, but often they didn’t work as planned. First came role based learning, then competency-driven training and career-driven programs. These worked great, but they couldn’t adapt fast enough. So people resorted to short video, YouTube-style platforms, and then user-authored content. We then added mobile tools, highly collaborative systems, MOOCs, and more recently Learning Experience Platforms. Now everyone is focused on skills-based training, and we’re trying to take all our content and organize it around a skills taxonomy. Well I’m here to tell you all this is about to change. While none of these important innovations will go away, a new breed of AI-powered dynamic content systems is going to change everything. And as a long student of this space, I’d like to explain why. And in this conversation I will discuss four new vendors, each of which prove my point (Sana, Docebo, Uplimit, and Arist). The Dynamic Content Problem: Instructional Design By Machine Let’s start with the problem. Companies have thousands of topics, professional skills, technical skills, and business strategies to teach. Employees need to learn about tools, business strategies, how to do their job, and how to manage others. And every company’s corpus of knowledge is different. Rolls Royce, a company now starting to use Galileo, has 120 years of engineering, technology, and manufacturing expertise embedded in its products, documentation, support systems, and people. How can the company possibly impart this expertise into new engineers? It’s a daunting problem. Every company has this issue. When I worked at Exxon we had hundreds of manuals explaining how to design pumps, pressure vessels, and various refinery systems. Shell built a massive simulation to teach production engineers how to understand geology and drilling. Starbucks has to teach each barista how to make thousands of drinks. And even Uber drivers have to learn how to use their app, take care of customers, and stay safe. (They use Arist for this.) All these challenges are fun to think about. Instructional designers and training managers create fascinating training programs that range from in-class sessions to long courses, simulations, job aids, and podcasts. But as hard as they try and as creative as they are, the “content problem” keeps growing. Right now, for example, everyone is freaked out about AI skills, human-centered leadership, sustainability strategies, and cloud-based offerings. I’ve never seen a sales organization that does quite enough training, and you can multiply that by 100 when you think about customer service, repair operations, manufacturing, and internal operations. While I always loved working with instructional designers earlier in my career, their work takes time and effort. Every special course, video, assessment, and learning path takes time and money to build. And once it’s built we want it to be “adaptive” to the learner. Many tools have tried to build adaptive learning (from Axonify to Cisco’s “reusable learning objects“) but the scale and utility of these innovations is limited. What if we use AI and machine learning to simply build content on the fly? And let employees simply ask questions to find and create the learning experience they want? Well thanks to innovations from the vendors I mentioned above, this kind of personalized experience is available today. (Listen to my conversation with Joel Hellermark from Sana to hear more.) What Is An Autonomous Learning Platform? The best analogy I’ve come up with is the “five levels of autonomous driving.” We’re going from “no automation” to “driver assist” to “conditional automation” to “fully automated.” Let me suggest this is precisely what’s happening in corporate training. If you look at the pace of AI announcements coming (custom GPTs, image and video generation, integrated search), you can see that this reality has now arrived. How Does This Really Work Now that I’ve had more than a year to tinker with AI and talk with dozens of vendors, the path is becoming clear. The new generation of learning platforms (and yes, this will eventually replace your LMS), can do many things we need: First, they can dynamically index and injest content into an LLM, creating an “expert” or “tutor” to answer questions. Galileo, for example, now speaks in my own personal voice and can answer almost any question in HR I typically get in person. And it gives references, examples, and suggests follow-up questions. Companies can take courses, documents, and work rules and simply add them to the corpus. Second, these systems can dynamically create courses, videos, quizzes, and simulations. Arist’s tool builds world-class instructional pathways from documents (try our free online course on Predictions 2024 for example) and probably eliminates 80% of the design time. Docebo Shape can take sales presentations and build an instructional simulation automatically, enabling sales people to practice and rehearse. Third, they can give employees interactive tutors and coaches to learn. Uplimit’s new system, which is designed for technical training, automatically gives you an LLM-powered coach to step you through exercises, and it learns who you are and what kind of questions you need help with. No need to “find the instructor” when you get stuck. Fourth, they can personalize content precisely for you. Sana’s platform, which Joel describes here, can not only dynamically generate content but by understanding your behavior, can actually give you a personalized version of any course you choose to take. These systems are truly spectacular. The first time you see one it’s kind of shocking, but once you understand how they work you see a whole new world ahead. Where Is This Going While the market is young, I see four huge opportunities ahead. First, companies can now take millions of hours of legacy content and “republish it” in a better form. All those old SCORM or video-based courses, exercises, and simulations can turn into intelligent tutors and knowledge management systems for employees. This won’t be a simple task but I guarantee it’s going to happen. Why would I want to ramble around in the LMS (or even LinkedIn Learning) to find the video, or information I need? I”d just like to ask a system like Galileo to answer a question, and let the platform answer the question and take me to the page or word in the video to watch. Second, we can liberate instructional design. While there will always be a need for great designers, we can now democratize this process, enabling sales operations people, and other “non-designers” to build content and courses faster. Projects like video authoring and video journalism (which we do a lot in our academy) can be greatly accelerated. And soon we’ll have “generated VR” as well. Third, we can finally integrate live learning with self-directed study. Every live event can be recorded and indexed in the LLM. A two hour webinar now becomes a discoverable learning object, and every minute of explanation can be found and used for learning. Our corpus, for example, includes hundreds of hours of in-depth interviews and case studies with HR leaders. All this information can be brought to life with a simple question. Fourth, we can really simplify compliance training, operations training, product usage, and customer support. How many training programs are designed to teach someone “what not to do” or “how to avoid breaking something” or “how to assemble or operate” some machine? I’d suggest its millions of hours – and all this can now be embedded in AI, offered via chat (or voice), and turned loose on employees to help them quickly learn how to do their jobs. Vendors Watch Out This shift is about as disruptive as Tesla has been to the big three automakers. Old LMS and LXP systems are going to look clunkier than ever. Mobile learning won’t be a specialized space like it has been. And most of the ERP-delivered training systems are going to have to change. Sana and Uplimit, for example, are both AI-architected systems. These platforms are not “LMSs with Gen AI added,” they are AI at the core. They’re likely to disrupt many traditional systems including Workday Learning, SuccessFactors, Cornerstone, and others. Consider the content providers. Large players like LinkedIn Learning, Skillsoft, Coursera, and Udemy have the opportunity to rethink their entire strategy, and either put Gen AI on top of their solution or possibly start with a fresh approach. Smaller providers like us (and thousands of others) can take their corpus of knowledge and quickly make it come to life. (There will be a massive market of AI tools to help with this.) I’m not saying this is easy. If you talk with vendors like Sana, Docebo, Arist, and Uplimit, you see that their AI platforms have to be highly tuned and optimized for the right user experience. This is not as simple as “dumping content into ChatGPT,” believe me. But the writing is on the wall, Autonomous Learning is coming fast. As someone who has lived in the L&D market for 25 years, I see this era as the most exciting, high-value time in two decades. I suggest you jump in and learn, we’ll be here to help you along the way. About These Vendors Sana (Sana Labs) is a Sweden-based AI company that focuses on transforming how organizations learn and access knowledge. The company provides an AI-based platform to help people manage information at work and use that data as a resource for e-learning within the organization. Sana Labs’ platform combines knowledge management, enterprise search, and e-learning to work together, allowing for the automatic organization of data across different apps used within an organization. Docebo is a software as a service company that specializes in learning management systems (LMS). It was founded in 2005 and is known for its Docebo Learn LMS and other tools, including Docebo Shape, its AI development system. The company has integrated learning-specific artificial intelligence algorithms into its platform, powered by a combination of machine learning, deep learning, and natural language processing. The company went public in 2019 and is listed on the Toronto Stock Exchange and the Nasdaq Global Select Market. Uplimit is an online learning platform that offers live group courses taught by top experts in the fields of AI, data, engineering, product, and business. The platform is known for its AI-powered teaching assistant and personalized learning approach, which includes real-time feedback, tailored learning plans, and support for learners. Uplimit’s courses cover technical and leadership topics and are designed to help individuals and organizations acquire the skills needed for the future. Arist is a company that provides a text message learning platform, allowing Fortune 500 companies, governments, and nonprofits to rapidly teach and train employees entirely via text message. The platform is designed to deliver research-backed learning and nudges directly in messaging tools, making learning accessible and effective. Arist’s approach is inspired by Stanford research and aims to create hyper-engaging courses in minutes and enroll learners in seconds via SMS and WhatsApp, without the need for a laptop, LMS, or internet. The company has been recognized for its innovative and science-backed approach to microlearning and training delivery. BY JOSHBERSIN

Machine Learning
2024年02月18日

Machine Learning

2024年的HRTech：GenAI、分析和技能技术 In 2024, the field of Human Resources is experiencing a transformative shift with the integration of cutting-edge technologies such as Generative AI (GenAI), advanced analytics, and skills technology. This article by Dave Zielinski, featured on SHRM Online, delves into the evolving landscape of HR, highlighting the significant impact of these technologies on enhancing the employee experience, improving regulatory compliance, and revolutionizing talent management. Industry analysts and thought leaders share insights on the growing importance of GenAI in HR processes, the challenges of maintaining employee experience in cost-cutting scenarios, and the potential of predictive analytics in optimizing workforce planning. 接受SHRM Online采访的人力资源行业分析师、从业者和思想领袖表示，今年，人力资源职能部门将采用生成式人工智能 (GenAI)，投资于提升员工体验的技术，并采用强大的预测分析和技能技术。人力资源领导者将转向技术，这些技术不仅可以提高法规遵从性，还可以帮助其组织做出更好、更快的人才决策并重新定义工作方式。有远见的公司将继续投资 EX 一些分析师预测，随着高管将注意力转向降低成本和提高效率，远离包容性、公平和多样性等问题，员工体验 (EX) 将在 2024 年出现“衰退”；灵活的工作安排；和员工心理健康。员工的工作选择将减少，雇主将收回一些影响力。不过，尽管许多组织可能会在 2024 年减少或冻结 EX 支出，但专家对此类举措的后果提出警告。 JP Gownder 是 Forrester 的副总裁兼首席分析师。他在博文中写道，根据 Forrester 研究，66% 的技术决策者表示，他们将在 2024 年增加对 EX 或人力资源技术的投资，其中许多投资将旨在提高效率，而不是 EX 结果。但逆流而上的领导者将在 2024 年获得实实在在的好处。 “通过开发成熟的 EX 计划，您的组织可以提高生产力、降低人员流失率并提高创造力，”Gownder 写道。其他专家认为，足智多谋的人力资源领导者会在预算紧张的情况下找到投资 EX 的方法。管理咨询公司光辉国际 (Korn Ferry) 首席人力资源官 (CHRO) 业务的高级客户合伙人丹·卡普兰 (Dan Kaplan) 表示：“人力资源部门将被迫在低迷的市场中保持参与度，甚至在成本削减和削减的整个过程中也不例外。” “这将是一场艰难的舞蹈，但最好的人力资源领导者会找到办法做到这一点。” 光辉国际 (Korn Ferry) 专门负责人力资源问题的高级客户合伙人胡安·巴勃罗·冈萨雷斯 (Juan Pablo Gonzalez) 表示，组织对 EX 的承诺在 2024 年不会减弱，但 EX 看起来会非常不同。 “EX 的本质可能会变得更加个性化，同时也会变得不那么个性化，”冈萨雷斯说。“例如，通过使用 Microsoft Office Copilot、Workday 和 Salesforce 等大型软件平台中已有的人工智能功能，雇主和员工已经改变了他们的 EX。正在发生的情况是，员工与技术的互动越来越多地取代了与人的互动，但与技术的互动已经变得更加适合员工的特定需求和情况。” 亚特兰大人力资源咨询公司 IA 的创始人兼管理负责人 Mark Stelzner 表示，虽然由于组织面临控制盈利的挑战，预算将在 2024 年重新分配，但良好的 EX 相关技术投资将继续为公司带来红利。 “我认为投资 EX 实际上会提高效率并降低成本，”Stelzner 说。“到 2024 年，我们可能会看到组织不断转向‘流程主导、技术支持’的理念。端到端流程的优化通常会导致诸如消除现有技术债务以及统一工具和技术等决策，以减少员工的困惑并优化个性化，从而减少集成良好的接触点。” Gartner 专门研究人力资源技术的副总裁分析师 John Kostoulas 表示，做出更具战略性的采购决策和改善现有技术生态系统的治理是改善 EX 的两个关键。Gartner 最近的研究发现，60% 的人力资源领导者认为他们当前的技术阻碍而不是改善了员工体验。 Nucleus Research 专门负责员工体验的研究经理 Evelyn McMullen 表示，仅仅为了提高效率而不是 EX 结果而设计的技术投资可能被证明是短视的。她指出，改进的 EX 通常会带来更好的绩效并降低与营业额相关的成本。麦克马伦说：“考虑到劳动力市场和求职者优势的不断波动，减少 EX 预算的风险尤其大。” “当控制权不可避免地回到求职者手中时，保留 EX 投资的组织将能够更好地捕获和留住最优秀的人才。” GenAI 从实验转向加速采用到 2024 年，通过更多地采用该技术，人力资源职能将从涉足 GenAI 转向更深的领域。随着领导者制定更严格的 GenAI 治理计划以及使用该技术的风险开始降低，人力资源和招聘部门将越来越多地使用其 HRIS 平台中已有的 GenAI 工具来编写职位描述和面试指南、创建敬业度调查、开发培训课程、分析数据，并制定政策。世界大型企业联合会 2023 年底对首席人力资源官的调查发现，61% 的首席人力资源官计划在 2024 年投资人工智能以简化人力资源流程。分析师 Eser Rizaoglu 表示：“许多人力资源领导者的 GenAI 之旅仍处于起步阶段，但要么通过现有的人力资源技术提供商获得 GenAI 功能，要么到 2024 年中期购买新的 GenAI 工具。” Gartner 的人力资源研究和咨询实践。 Rizaoglu 表示，许多人力资源技术供应商仍在努力弄清楚如何充分利用 GenAI 的功能，同时平衡保护数据、确保有效治理和考虑道德因素的需求。他表示：“在实现这种精细的平衡之前，GenAI 能力在人力资源领域的大规模扩散将面临挑战。” Stelzner 表示，虽然去年 GenAI 带来了兴奋并刺激了人力资源领域的实验，但“冷酷的现实”是许多组织仍然没有准备好全力投入。 “到 2024 年，GenAI 采用率的任何增长都可能是渐进式的，包括更好地利用聊天机器人、增强员工沟通的个性化、更加关注人才招聘领域的可能性以及系统升级和实施测试的自动化。”他说。埃森哲进行的研究发现，GenAI 有潜力改变组织 40% 的工作时间。“这并不意味着 40% 的工作岗位将会消失，而是反映了工作方式的转变，”负责该公司人力资源转型和交付实践的埃森哲董事总经理迈克尔·本亚明 (Michael Benyamin) 表示。“技术将取代一些任务，让员工在工作中变得更有生产力、更具创造力和效率。人工智能是人类能力的倍增器。” 随着 GenAI 开始增强或转变更多的工作角色，人力资源和学习领导者将需要创建敏捷的学习计划，以重新培训员工使用快速发展的 GenAI 工具的技能。许多工人几乎没有接受过如何使用该技术的培训。 Salesforce 于 2023 年进行的一项调查发现，62% 的员工表示他们缺乏有效、安全使用 GenAI 的技能。波士顿咨询集团的另一项研究发现，尽管该技术有望从根本上重塑他们的工作方式，但只有 14% 的一线员工接受过与人工智能相关的技能提升。 Benyamin 表示，随着 GenAI 在工作场所变得越来越普遍，人力资源部门必须帮助制定负责任和道德的人工智能使用政策，并制定培训计划来解决偏见、歧视、数据保护和适当数据使用等问题。更加关注变革管理，提高新人力资源软件的采用率专家认为，许多人力资源领导者将寻求通过采用变革管理策略来提高 2024 年技术投资的回报，例如确保员工使用新采用的技术解决方案。人力资源面临的一项持续挑战是管理云技术供应商源源不断的更新和新功能，导致许多人力资源软件即服务 (SaaS) 许可证闲置。位于加利福尼亚州帕洛阿尔托的 SaaS 智能平台 Productiv 于 2023 年进行的一项研究发现，组织中 53% 的 SaaS 许可证总体未使用。位于阿拉巴马州亨茨维尔的人力资源咨询和研究公司 Lighthouse Research 的首席研究官本·尤班克斯 (Ben Eubanks) 表示，许多组织低估了如何确保员工在新的人力资源平台和应用程序推出后定期使用它们。 “人力资源和人才技术不是‘按下开关就可以开始’类型的解决方案，”尤班克斯说。“但许多雇主仍然这么认为，并低估了采用该技术所需的行为改变。” 重新思考员工敬业度调查更多的人力资源和执行团队将重新考虑如何创建敬业度调查以及分发调查的频率，以减少“调查疲劳”。 ServiceNow 高级副总裁兼员工工作流程产品总经理 Gretchen Alarcon 表示，随着组织继续努力寻找“秘方”，让员工在 2024 年更频繁地重返办公室，人力资源领导者将需要使用更有意义的方法测量工具。她说：“组织将利用员工的声音调查和反馈来分析在办公室花费的时间与员工情绪和生产力的关系。” “这将使领导者能够根据数据而不是假设做出决策，这样他们就可以根据员工的需求、行为和提高生产力的因素来调整重返办公室 [RTO] 策略。” 从改进的技能技术中获益转向基于技能的招聘和晋升策略的人力资源和招聘领导者将受益于技术的发展，例如使用人工智能和机器学习自动创建、组织和更新员工技能数据库的技能本体，从而显着减少体力工作量人力资源部要求。下一代本体论和其他新兴技能技术可以使人力资源领导者更轻松地识别组织中的技能差距，然后相应地调整招聘或学习和发展计划。虽然市场上没有真正的端到端技能技术解决方案，但许多人力资源领导者正在将人工智能驱动的点解决方案结合在一起，以创建有效的技能数据库和评估工具。 “到 2024 年，随着组织采用技能智能技术，他们将开始认识到，这不是拥有最大的技能数据库，而是一个不断更新的丰富且互联的技能数据库，”Alarcon 说。她补充说，此类数据库使公司能够了解人才缺口是否是由于缺乏合适的人才或缺乏技能造成的，以及他们是否需要为未来培养、购买或借用人才。预测分析工具变得更加强大人力资源从业者和分析师认为，人力资源部门将受益于日益强大的预测分析工具，这些工具将改善劳动力规划和数据驱动的决策。光辉国际 (Korn Ferry) 的冈萨雷斯 (Gonzalez) 表示：“凭借更大的数据集和改进的算法，人力资源部门应该能够采取一些措施，例如缓和过去几年的招聘盛衰周期。” 例如，冈萨雷斯表示，雇主不会雇佣数千名员工，然后在六个月后解雇其中一半，而是能够更好地预测在合理的时间内他们需要的员工数量和类型。他说：“然后他们可以雇用和培养一支更稳定的员工队伍，以造福所有组织利益相关者。” Stelzner 认为，许多人力资源部门由于没有充分发挥数据分析的潜力而错失了机会。他说，如果未能投资分析人力资源数据所需的工具和技能，可能会导致洞察力缺失，并阻碍人力资源战略与更广泛的业务目标保持一致的能力。 “从历史上看，人力资源部门也一直在努力解决数据的准确性问题，”斯特尔兹纳说。“这会影响该职能部门依靠报告和数据分析来通知和支持其决策的能力。更糟糕的是，企业的其他部门已经接受过培训，预计人力资源系统会提供有问题的数据，因此在数据清理、报告和分析方面还有很多工作要做，以重新获得整个企业的可信度。” Dave Zielinski 是 Skiwood Communications 的负责人，这是一家位于明尼阿波利斯的商业写作和编辑公司。作者：Dave Zielinski

Machine Learning
2024年01月09日
Machine Learning

Exploring the Top 10 HR Tech Trends of 2024 The HR field is undergoing significant changes in 2024, with technology playing a pivotal role. Key trends include the use of AI and ML in talent acquisition, a shift to skills-based hiring, and the integration of remote and hybrid work models. Emphasis on diversity, equity, and inclusion (DEI) is growing, alongside the exploration of the Metaverse for virtual HR practices. Data analytics is crucial for informed decision-making, and there's a focus on optimizing user experience and supporting employee well-being and mental health. Enhancing the candidate experience and ensuring data security and compliance are also critical. 2024年人力资源领域正在经历重大变革，技术发挥着关键作用。主要趋势包括在人才招聘中使用人工智能和机器学习，向基于技能的招聘模式转变，以及远程和混合工作模式的整合。多元化、平等和包容性（DEI）的重视日益增加，同时探索元宇宙在虚拟人力资源实践中的应用。数据分析对于做出明智的决策至关重要，优化用户体验和支持员工福祉和心理健康也同样重要。加强候选人体验和确保数据安全与合规性也是关键。 Human Resources is continuously evolving, and in 2024, it is set to undergo a remarkable transformation. With the integration of cutting-edge technology and innovative approaches, HR departments are better equipped than ever to attract, retain, and manage talent effectively. In this blog, we'll delve into the top 10 HR tech trends of 2024, offering a glimpse of how these trends shape the future of HR. Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of HR tech trends in 2024. These technologies are revolutionizing talent acquisition by streamlining the recruitment process. AI-driven algorithms can assess resumes, conduct initial candidate screenings, and predict a candidate's suitability based on historical data. ML algorithms analyze patterns within employee data to provide insights into performance, helping HR departments make more informed decisions about promotions and job placements. Skills-based Hiring Skills-based hiring, which prioritizes a candidate's specific skills and abilities, will significantly impact companies in 2024. This approach will help companies swiftly adapt to the changing job landscape and technological advancements. In 2024, skills-based hiring will: Improve Recruitment Efficiency: Companies will find it easier to match candidates directly to job requirements, reducing time and resources spent sifting through resumes and interviews. Reduce Skill Gaps: With skills-based hiring, companies can address skill gaps more effectively and invest in training and upskilling for existing employees to meet the organization's needs. Increase Employee Productivity: Hiring individuals with the right skills results in quicker onboarding and increased employee productivity, driving business performance. Remote and Hybrid Work: Remote work has swiftly transformed the modern workplace and is set to become a permanent fixture in 2024. HR professionals are tasked with managing the challenges of overseeing remote teams, encouraging collaboration, and maintaining employee engagement within virtual environments. Moreover, the growing prominence of hybrid work models demands strategic initiatives to enhance productivity and work-life balance for employees, regardless of their location within or outside the office. Diversity, Equity, and Inclusion (DEI): Inclusion and diversity will remain at the forefront of HR agendas in 2024. Organizations will strive to create inclusive cultures where employees from all backgrounds feel valued, respected, and empowered. HR professionals will focus on building diverse talent pipelines, implementing unconscious bias training, and developing inclusive leadership practices. Addressing pay equity and ensuring equal opportunities at all levels will also be prioritized. Focus on Metaverse in HR The Metaverse is poised to redefine HR practices. This revolutionary technology seamlessly integrates virtual meetings, interviews, onboarding, and learning experiences. By creating lifelike virtual environments, HR professionals can host dynamic meetings, conduct immersive interviews, and foster engaging discussions among remote teams. Recognizing its vast potential, organizations embrace the Metaverse to reshape traditional HR processes and enhance collaboration across distributed teams. The Metaverse empowers HR to transcend geographical boundaries, ushering in a new era of impactful virtual interactions that elevate employee experiences and boost organizational productivity. Harnessing Data for Informed Decision-Making This trend revolves around leveraging advanced data analytics tools to collect, process, and interpret vast amounts of data within the HR domain. By doing so, HR professionals can gain valuable insights into various aspects of workforce management, including employee engagement, performance metrics, talent acquisition, and workforce planning. This trend is driven by the recognition that data is critical to making strategic and informed decisions. HR departments increasingly adopt predictive analytics to foresee trends, identify potential challenges, and devise proactive solutions. Through data-driven decision-making, organizations can optimize their HR strategies, streamline operations, and enhance overall workforce effectiveness. Optimize the User Experience As HR tech evolves, the user experience is optimized for HR professionals and employees. This trend is about making the technology more user-friendly and intuitive. User-friendly interfaces, simplified navigation, and customized dashboards make it easier for HR personnel to access and utilize HR tools, ultimately improving efficiency and reducing the learning curve. Employee Well-being and Mental Health Support HR technology trends are placing a spotlight on employee well-being and mental health. Innovative tools and applications are designed to monitor and support employee well-being, offering resources to help individuals manage stress and achieve a healthy work-life balance. This emerging trend underscores the recognition of the significance of comprehensive employee care. Emphasizing Candidate Experience Enhancement Even with resource limitations in 2024, CHROs are committed to maintaining their teams' focus on essential tasks. Companies recognize the imperative need to continually enhance the candidate experience, fortify their employment brand, and expedite their recruitment processes to remain competitive in attracting top talent. Among the myriad HR trends discussed, refining the candidate experience remains an enduring challenge for TA teams. Data Security and Compliance Data security and compliance are paramount with the growing use of HR tech. HR departments are increasingly implementing data protection measures to safeguard sensitive employee information and adhere to the ever-evolving global data protection regulations. Conclusion As we step into 2024, HR tech trends are shaping the future of human resources management. These trends, from artificial intelligence and machine learning to a strong focus on employee experience, enhance how organizations attract, retain, and manage talent. By staying abreast of these top 10 HR tech trends, businesses can position themselves to succeed in an ever-changing world of work. Embracing these technologies will streamline HR processes and create a more engaged, diverse, and resilient workforce. by Navjot Kaur

Machine Learning
2023年11月17日
Machine Learning

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.

Machine Learning
2023年10月24日

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