• 观点
    How Generative AI Adds Value to the Future of Work 这篇Upwork的文章深入探讨了生成式人工智能(AI)在重新塑造工作价值方面的变革力量,强调了自动化和创新不仅改变了工作岗位,还在各个行业提高了生产力和创造力。文章着重讨论了对劳动力市场的细微影响,强调了技能发展和道德考虑的重要性,并对人工智能与人类合作的未来提供了前瞻性的视角。 Authors:  Dr. Ted Liu, Carina Deng, Dr. Kelly Monahan Generative AI’s impact on work: lessons from previous technology advancements In this study, we provide a comprehensive analysis of the initial impact of generative AI (artificial intelligence) on the Upwork marketplace for independent talent. Evidence from previous technological innovations suggests that AI will have a dual impact: (1) the displacement effect, where job or task loss is initially more noticeable as technologies automate tasks, and (2) the reinstatement effect, where new jobs and tasks increase earnings over time as a result of the new technology. Take for example the entry of robotics within the manufacturing industry. When robotic arms were installed along assembly lines, they displaced some of the tasks that humans used to do. This was pronounced in tasks that were routine and easy to automate. However, new tasks were then needed with the introduction of robotics, such as programming the robots, analyzing data, building predictive models, and maintaining the physical robots. The effects of new technologies often counterbalance each other over time, giving way to many new jobs and tasks that weren’t possible or needed before. The manufacturing industry is now projected to have more jobs available as technologies continue to advance, including Internet of Things (IoT), augmented reality, and AI, which transform the way work is completed. The issue now at hand is ensuring enough skilled workers are able to work alongside these new technologies. While this dynamic of displacement and reinstatement generally takes years to materialize, as noted above in the manufacturing example, the effects of generative AI may be taking place already on Upwork. For the platform as a whole, we observe that generative AI has increased the total number of job posts and the average spend per new contract created. In terms of work categories, generative AI has reduced demand in writing and translation, particularly in low-value work, while enhancing earnings in high-value work across all groups. In particular, work that relies on this new technology like Data Science and Analytics are reaping the benefits. The report highlights the importance of task complexity and the skill-biased nature of AI's impact. Skills-biased technology change is to be expected as the introduction of new technologies generally favors highly skilled workers. We observe this on our platform as high-skill freelancers in high-value work are benefiting more, while those in low-value work face challenges, underscoring the need for skilling and educational programs to empower freelancers to adapt and transition in this evolving work landscape. Understanding the lifecycle of work on Upwork and the impact of gen AI Generative AI has a growing presence in how people do their work, especially since the public release of ChatGPT in 2022. While there’s been extensive discussion about the challenges and opportunities of generative AI, there is limited evidence of such impact based on transaction data in the broader labor market. In this study, we use Upwork’s platform data to estimate the short-term effects of generative AI on freelance outcomes specifically. The advantage of the Upwork platform is that it is in itself a complete marketplace for independent talent, as we observe the full life cycle of work: job posts, matching, work execution, performance reviews, and payment. Few other instances exist where a closed-system work market can be studied and observed. Thus, the results of this study offer insights into not only the online freelance market, but also the broader labor market. How technological progress disrupts the labor market is not a new topic. Acemoglu and Restrepo (2019) argue that earning gain arises from new tasks created by technological progress, which they term the “reinstatement effect,” even if the automation of certain tasks may have a displacement effect in the labor market initially. What this means is that there may be a dynamic effect going on: the displacement effect (e.g., work loss) may be more noticeable in the beginning of a new technology entry, but as new jobs and tasks are being created, the reinstatement effect (e.g., rates increase, new work) will begin to prevail. In the broader labor market, such dynamics will likely take years to materialize. But in a liquid and active independent work marketplace like Upwork, it’s possible that we’re already observing this transition happening. Existing studies such as this provides a useful conceptual framework to think about the potential impact of generative AI. It’s likely that in the short term, the replacement of generative AI will continue to be more visible, not just at Upwork, but also in the broader labor market. Over time and across work categories, however, generative AI will likely spur new tasks and jobs, leading to the reinstatement effect becoming stronger and increasing rates for those occupations with new tasks and a higher degree of task complexity. We’ve already seen evidence of new demand as a result of gen AI on our Upwork platform, with brand new skill categories like AI content creator and prompt engineer emerging in late 2022 and early 2023. We test this hypothesis of both work displacement and reinstatement, and provide insights into how generative AI affects work outcomes. Impact of generative AI on work To understand the short-term impact of generative AI on the Upwork freelance market, we capitalize on a natural experiment arising from the public release of ChatGPT in November 2022. Because this release was largely an unanticipated event to the general public, we’re able to estimate the causal impact of generative AI. The essential idea behind this natural experiment is that we want to compare the work groups affected by AI with the counterfactual in which they are not. To implement this, we use a statistical and machine-learning method called synthetic control. Synthetic control allows us to see the impact that an intervention, in this case, the introduction of gen AI, has on a group over time by comparing it to a group with similar characteristics not exposed to the intervention. The advantage of this approach is that it allows us to construct reasonably credible comparison groups and observe the effect over time. The units of analysis we use are work groups on the Upwork platform; we analyze variables such as contract number and freelancer earnings. Instead of narrowly focusing on a single category like writing, we extend the analysis to all the major work groups on Upwork. Moreover, we conduct additional analysis of the more granular clusters within each major group. The synthetic control method allows for flexibility in constructing counterfactuals at different levels of granularity. The advantage of our comprehensive approach is that we offer a balanced view of the impact of generative AI across the freelance market. Generative AI’s short-term impact on job posts and freelancer earnings Looking at the platform as a whole, we observe that generative AI has increased the total number of job posts by 2.4%, indicating the overall increased demand from clients. Moreover, as shown in Figure 1, for every new job contract, there is an increase of 1.3% in terms of freelancer earnings per contract, suggesting a higher value of contracts. Figure 1 Effect of Generative AI on Freelancer Earning per Contract The Upwork platform has three broad sectors: 1. Technological and digital solutions (tech solutions); 2. Creative & outreach; 3. Business operations and consulting. We have observed both positive and negative effects within each of the sectors, but two patterns are worth noting: The reinstatement effect of generative AI seems to be driving growth in freelance earnings in sectors related to tech solutions and business operations. In contrast, within the creative sector, while sales and marketing earnings have grown because of AI, categories such as writing and translation seem disproportionately affected more by the replacement effect. This is to be expected due to the nature of tasks within these categories of work, where large language models are now able to efficiently process and generate text at scale. Generative AI has propelled growth in high-value work across the sectors and may have depressed growth in low-value work. This supports a skills-biased technology change argument, which we’ve observed throughout modern work history. More specifically and within tech solutions, data science & analytics is a clear winner, with over 8% of growth in freelance earnings attributed to generative AI. This makes sense as the reinstatement effect is at work; new work and tasks such as prompt engineering have been created and popularized because of generative AI. Simultaneously, while tools such as ChatGPT automate certain scripting tasks (therefore leading to a replacement effect), it mainly results in productivity enhancements for freelancers and potentially leads to them charging higher rates and enjoying higher overall earnings per task. In terms of contracts related to business operations, we observe that accounting, administrative support, and legal services all experience gains in freelance earnings due to generative AI, ranging from 6% to 7%. In this sector, customer service is the only group that has experienced reduced earnings (-4%). The reduced earnings result for customer service contracts is an example of the aggregate earnings outcomes of AI, related to the study by Brynjolfsson et al (2023), who find that generative AI helps reduce case resolution time at service centers. A potential outcome of this cut in resolution time is that service centers will need fewer workers, as more tasks can be completed by a person working alongside AI. At the same time, the reinstatement effect has not materialized yet because there are no new tasks being demanded in such settings. This may be an instance where work transformation has not yet been fully realized, with AI enabling faster work rather than reinventing a way of working that leads to new types of tasks. A contrasting case is the transformation that happened with bank tellers when ATMs were introduced. While the introduction of these new technologies resulted in predictions of obsolete roles in banks, something different happened over time. Banks were able to increase efficiency as a result of ATMs and were able to scale and open more branches than before, thereby creating more jobs. In addition, the transactional role of a bank teller became focused on greater interpersonal skills and customer relationship tasks. When taken together, the overall gains in such business operations work on Upwork are an encouraging sign. These positions tend to require relatively intensive interpersonal communication, and it seems the short-term effects of generative AI have helped increase the value of these contracts, similar to what we saw in the banking industry when ATMs were introduced. As of now, the replacement effect of AI seems more noticeable in creative and outreach work. The exception is sales and marketing contracts, which have experienced a 6.5% increase in freelance earnings. There is no significant impact yet observed on design. For writing and translation, however, generative AI seems to have reduced earnings by 8% and 10% respectively. However, as we will discover, task complexity has a moderating effect on this. High-value work benefit from generative AI, upskilling needed for low-value work Having discussed the overall impact of generative AI across categories, we now decompose the impact by values. The reason we’re looking at the dimension of work value is that there may be a positive correlation between contract value and skill complexity. Moreover, skill complexity may also be positively correlated with skill levels. Essentially, by evaluating the impact of AI by different contract values, we can get at the question of AI's impact by skill levels. This objective is further underscored by a discrepancy that sometimes exists in the broader labor markets – a skills gap between demand and supply. It simply takes time for upskilling to take place, so it’s typical for demand to exceed supply until a more balanced skilled labor market takes place. It is worth noting, however, freelancers on the Upwork platform seem more likely than non-freelancers to acquire new skills such as generative AI. For simplicity, let’s assume that the value of contracts is a good proxy for the level of skill required to complete them. We’d then assume that high-skill freelancers typically do high-value work, and low-skill freelancers do low-value work. In other words, our goal is also to understand whether the impact of generative AI is skills-biased and follows a similar pattern from what we’ve seen in the past with new technology disruptions. Note that we’re focusing on the top and bottom tails of the distribution of contract values, because such groups (rather than median or mean) might be most susceptible to displacement and/or reinstatement effects, therefore of primary concern. We define high-value (HV) work as those with $1,000 or more earnings per contract. For the remaining contracts, we focus on a subset of work as low-value (LV) work ($251-500 earnings). Figure 2 shows the impact of AI by work value, across groups on Upwork. As we discussed before, writing and translation work has experienced some reduction in earnings overall. However, if we look further into the effect of contract value, we see that the reduction is largely coming from the reduced earnings from low-value work. At the same time, for these two types, generative AI has induced substantial growth in high-value earnings – the effect for translation is as high as 7%. We believe the positive effect on translation high-value earning is driven by more posts and contracts created. In the tech solutions sector, the growth in HV earnings in data science and web development is also particularly noticeable, ranging from 6% to 9%. Within the business solutions sector, administrative support is the clear winner. There are two takeaways from this analysis by work value. First, while we’re looking at a sample of all the contracts on the platform, it’s possible that the decline of LV work is more than made up for by the growth of HV work in the majority of the groups. In other words, except for select work groups, the equilibrium results for the Upwork freelance market overall seem to be net positive gains from generative AI. Second, if we assume that freelancers with high skills (or a high degree of skill complexity) tend to complete such HV work (and low-skill freelancers do LV work), we observe that the impact of generative AI may be biased against low-skill freelancers. This is an important result: In the current discussion of whether generative AI is skill-based, there exists limited evidence based on realized gains and actual work market transactions. We are one of the first to provide market-transaction-based evidence to illustrate this potentially skill-biased impact. Finally, additional internal Upwork analysis finds that independent talent engaged in AI-related work earn 40% more on the Upwork marketplace than their counterparts engaged in non-AI-related work. This suggests there may be additional overlap between high-skill work and AI-related work, which can further reinforce the earning potential of freelancers in this group. Figure 2 Case study: 3D content work To illustrate the impact of generative AI in more depth, we have conducted a case study of Engineering & Architecture work within the tech solutions sector. The reason is that we want to illustrate the potentially overlooked aspects of AI impact, compared with the examples of data science and writing contracts. This progress in generative AI has the potential to reshape work in traditional areas like design in manufacturing and architecture, which rely heavily on computer-aided design (CAD) objects, and newer sectors such as gaming and virtual reality, exemplified by NVIDIA's Omniverse. Based on activities on the Upwork platform, we see that there is consistent growth of job posts and client spending in this category, with up to 12% of gross service value growth year over year in 2023 Q3, and over 11% in job posts during the same period. Moreover, applying the synthetic control method, we show a causal relationship between gen AI advancements and the growth in job posts and earnings per contract. More specifically, there is a significant increase in overall earnings because of AI, an average 11.5% increase. Additionally, as shown by Figure 3, the positive effect also applies to earning per contract. This indicates a positive impact on freelancer productivity and quality of work, due to the fact that we’re measuring the income for every unit of work produced. This suggests that gen AI is not just a facilitator of efficiency but also enhances the quality of output. ‍Figure 3 Effect of Generative AI on Freelancer Earning per Contract in EngineeringIn a traditional workflow to create 3D objects without generative AI, freelancers would spend extensive time and effort to design the topology, geometry, and textures of the objects. But with generative AI, they can do so through text prompts to train models and generate 3D content. For example, this blog by NVIDIA’s Omniverse team showcases how ChatGPT can interface with traditional 3D creation tools. Thus, the positive trajectory of generative AI in 3D content generation we see is driven by several factors. AI significantly reduces job execution time, allowing for higher productivity. It facilitates the replication and scaling of 3D objects, leading to economies of scale. Moreover, freelancers can now concentrate more on the creative aspects of 3D content, as AI automates time-consuming and tedious tasks. This shift has not led to a decrease in rates due to the replacement effect. In fact, this shift of workflow may create new tasks and work. We will likely see a new type of occupation in which technology and humanities disciplines converge. For instance, a freelancer trained in art history now has the tools to recreate a 3D rendering of Japan in the Edo period, without the need to conduct heavy coding. In other words, the reinstatement effect of AI will elevate the overall quality and value proposition of the work, and ultimately enable higher earning gains. This paradigm shift underscores generative AI's role in not just transforming work processes but also in creating new economic dynamics within the 3D content market. Fortunately, it seems many freelancers on Upwork are ready to reap the benefits: 3D-related skills, such as 3D modeling, rendering, and design, are listed among the top five skills of freelancer profiles as well as in job posts. A dynamic interplay: task complexity, skills, and gen AI Focusing on the Upwork marketplace for independent talent, we study the impact of generative AI by using the public release of ChatGPT as a natural experiment. The results suggest a dynamic interplay of replacement and reinstatement effects; we argue that this dynamic is influenced by task complexity, suggesting a skill-biased impact of gen AI. Analysis across Upwork's work sectors shows varied effects: growth in freelance earnings in tech solutions and business operations, but a mixed impact in the creative sector. Specifically, high-value work in data science and business operations see significant earnings growth, while creative contracts like writing and translation experience a decrease in earnings, particularly in lower-value tasks. Using the case study of 3D content creation, we show that generative AI can significantly enhance productivity and quality of work, leading to economic gains and a shift toward higher-value tasks, despite initial concerns of displacement. Acemoglu and Restrepo (2019) argue that the slowdown of earning growth in the United States the past three decades can partly be explained by new technologies’ replacement effect overpowering the reinstatement effect. But with generative AI, we’re at a point of completely redefining what human tasks mean, and there may be ample opportunities to create new tasks and work. It's evident that while high-value types of work are being created, freelancers engaged in low-value tasks may face negative impact, possibly due to a lack of skills needed to capitalize on AI benefits. This situation underscores the necessity of supporting freelancers not only in elevating their marketability within their current domains but also in transitioning to other work categories. To ensure as many people as possible benefit, there’s an imperative need to provide educational resources for them to gain the technical skills, and more importantly skills of adaptability to reinvent their work. This helps minimize the chance of missed opportunities by limiting skills mismatch between talent and new demands created by new technologies. Upwork has played a significant role here by linking freelancers to resources such as Upwork Academy’s AI Education Library and Education Marketplace, thereby equipping them with the necessary tools and knowledge to adapt and thrive in an AI-present job market. This approach can help bridge the gap between low- and high-value work opportunities, ensuring a more equitable distribution of the advantages brought about by generative AI. Methodology To estimate the causal impact of generative AI, we take a synthetic control approach in the spirit of Abadie, Diamond, and Hainmueller (2010). The synthetic control method allows us to construct a weighted combination of comparison units from available data to create a counterfactual scenario, simulating what would have happened in the absence of the intervention. We use this quasi-experimental method due to the infeasibility of conducting a controlled large-scale experiment. Additionally, we use Lasso regularization to credibly construct the donor pool that serves the basis of the counterfactuals and minimize the chance of overfitting the data. Moreover, we supplement the analysis by scoring whether a sub-occupation is impacted or unaffected by generative AI. The scoring utilizes specific criteria: 1. Whether a certain share of job posts are tagged as AI contracts by the Upwork platform; 2. AI occupational exposure score, based on a study by Felten, Raj, and Seamans (2023), to tag these sub-occupations. We also use data smoothing techniques through three-month moving averages. We analyzed data collected on our platform from 2021 through Q3 2023. We specifically look at freelancer data across all 12 work categories on the platform for high-value contracts, defined as those with a contract of at least $1,000, and low-value contracts, consisting of those between $251 and under $500. The main advantage of our approach is that it is a robust yet flexible way to identify the causal effects on not only the Upwork freelance market but also specific work categories. Additionally, we control for macroeconomic or aggregate shocks such as U.S. monetary policy in the pre-treatment period. However, we acknowledge the potential biases in identifying which sub-occupations are influenced by generative AI and the effects of external factors in the post-treatment period. About the Upwork Research Institute The Upwork Research Institute is committed to studying the fundamental shifts in the workforce and providing business leaders with the tools and insights they need to navigate the here and now while preparing their organization for the future. Using our proprietary platform data, global survey research, partnerships, and academic collaborations, we produce evidence-based insights to create the blueprint for the new way of work. About Ted Liu Dr. Ted Liu is Research Manager at Upwork, where he focuses on how work and skills evolve in relation to technological progress such as artificial intelligence. He received his PhD in economics from the University of California, Santa Cruz. About Carina Deng Carian Deng is the Lead Analyst in Strategic Analytics at Upwork, where she specializes in uncovering data insights through advanced statistical methodologies. She holds a Master's degree in Data Science from George Washington University. About Kelly Monahan Dr. Kelly Monahan is Managing Director of the Upwork Research Institute, leading our future of work research program. Her research has been recognized and published in both applied and academic journals, including MIT Sloan Management Review and the Journal of Strategic Management.
    观点
    2024年02月23日
  • 观点
    Indeed:生成式人工智能的技能能够带来近 50% 的薪资增长 Indeed的最新报告显示,掌握生成式人工智能(AI)技能的技术工作者平均薪资可达174,727美元,比没有这些技能的竞争者高出47%。随着2023年的职场波动让位给2024年的稳定,企业恢复延期的项目并推进AI实施,对技术人才的需求日益增长。数据科学家、机器学习工程师和软件工程师等角色尤为抢手。报告强调了AI技能在竞争激烈的就业市场中的价值,并指出市场上对AI相关技能的短缺。尽管对提升技能和学习AI技能的兴趣浓厚,但仅有不到四分之一的开发者表示其雇主提供了升级技能或学习AI技能的时间。 根据周三发布的 Indeed 报告,与不具备生成式人工智能技能的求职者相比,进入市场的求职者的平均薪资提高了47%。该公司在其平台上审查了职位发布的薪资数据。 根据该公司的分析,能够胜任生成式人工智能的技术人员的平均薪资预计高达174,727 美元。 生成式人工智能与其他关键技能一起为求职者带来高薪,包括深度学习、计算机视觉以及特定软件语言和框架(如Rust 或 PyTorch )的知识。 在技术行业,一个新的趋势正在改变就业市场的面貌——掌握生成式人工智能(AI)技能的工作者,其平均薪资相较于其他技术工作者高出将近50%。根据Indeed最新发布的报告,这类技术人才的平均薪资可达174,727美元,显示出市场对于此类技能的极高需求。 随着2023年的职场不确定性逐步平息,2024年迎来了更多的稳定与项目复苏,尤其是在AI实施方面。数据科学家、机器学习工程师及软件工程师等角色变得极其抢手,他们掌握的技能成为了获得高薪的关键。 报告指出,AI技术领域的半数最高薪技能都与AI直接相关,强调了AI技能在激烈的就业市场中的价值。此外,就业市场对于AI相关技能的渴求与可用人才之间存在明显差距,这一点从几乎400,000个活跃的技术职位空缺和对于数据科学家等专业人才的需求中可以看出。 然而,尽管对于提升技能和学习AI技能的需求日益增长,少于四分之一的开发者表示他们的雇主提供了学习或提升这些技能的时间。这揭示了一个问题,即尽管技术行业对于AI技能的需求日益增长,但在培养这些技能方面,企业和教育机构还有很长的路要走。 Indeed的报告不仅仅是一个薪酬调查,它也是对于技术行业未来走向的一个预示。生成式AI技能的价值在不断上升,对于那些希望在职业生涯中获得成功的技术专业人士来说,现在是最好的时机去掌握这些未来技能。 在这个由技术驱动的时代,生成式AI不仅仅是一个工具或者一个概念,它代表了未来的方向和无限的可能性。对于技术工作者而言,掌握这些技能不仅能够带来薪酬上的优势,更能在竞争激烈的就业市场中脱颖而出,成为真正的行业新贵。
    观点
    2024年02月22日
  • 观点
    People Analytics:Introducing the Visier Path, a Proven Route to Business Impact Visier Path是一种策略框架,旨在指导组织通过人员分析的旅程。它汇集了十多年的专业知识和数千个成功部署的精华,为HR和业务领导者提供了一个清晰、易于访问的“地图”。该框架将人员分析活动和HR实践与CHRO的战略优先事项对齐,提供了一个灵活、经过验证的途径,以实现显著的业务影响。通过先锋类比,它强调了从原始数据到可操作见解的演变,确保组织可以根据特定目标和策略定制他们的旅程。推荐阅读这篇文章: The Visier Path is the framework we use to guide our cuhttps://www.visier.com/path/stomers to solve the people challenges they care about most. It’s a distillation of our deep people analytics expertise spanning thousands of successful deployments over a decade.  Here, VP of People Analytics, Ian Cook, explains what the Visier Path is, how it can help you, and why we’re sharing this “uniquely Visier” concept with the world.  Pioneering in people analytics Traveling through the Rockies on North America's West Coast is a stunning journey. The land is vast and varied. Driving through high peaks and dense forests, one cannot help but ponder the fates and fortunes of the pioneers who first attempted to travel from east to west. Their journey, likely slow and difficult, laid the foundation for current-day road journeys. We cruise effortlessly along a smooth, well-engineered, and clearly marked highway. Following the path created by others, we can be certain of a speedy trip to our destination. People analytics was once a new concept that had to be pioneered. Back in the late 90’s, academic institutions started to pursue the work that we now call people analytics. They saw the promise of studying the “digital records” of employees to look for trends that could benefit the whole organization. Flash forward to today: people analytics is now a well-established discipline, with commonly accepted approaches and a detailed understanding of how to build a successful practice. Visier played a pioneering role in making this happen. We were the first purpose-built analytics platform for people data, and we’ve supported thousands of organizations as they founded and scaled their PA teams. As an organization, we’ve gathered a lot of knowledge about how to get from A to B in people analytics. Now, our goal is to smooth and speed the people analytics journey for you. Introducing the Visier Path When you choose Visier, you get access to a vast amount of knowledge not available anywhere else. The Visier Path distills the wisdom from over a decade of experience into one straightforward “map” that’s accessible to HR and business leaders alike. Consider The Visier Path a guide that outlines exactly which people analytics activities and HR business practices are required to achieve a CHRO’s key strategic priorities. Guiding you toward the right business impact How, exactly, does the Visier Path help meet a CHRO’s objectives? It’s all about linking data and action. Too often, people analytics is dressed up in confusing language and seen as either an esoteric internal research project or an IT project focused on wrangling data. We believe people analytics teams should model their work in the same way as FP&A teams support the CFO. That is to say: they must provide ongoing information about the performance and behavior of the workforce so that leaders can make the decisions that will have the right impact. The Visier Path breaks down the steps needed for an organization to move from raw data to the right analysis, to tangible action that supports a business goal. The quality of that linkage—from data through to impact—is what separates the best people analytics teams from the rest. Every organization’s journey is different Every organization has different goals and strategies. That’s why we designed the Visier Path to be completely flexible depending on what’s important to you. While every company will start with the fundamentals (shown in yellow), the rest of your journey along the Path is completely bespoke. The Visier Path, your proven route to people analytics success. Click to enlarge. The Visier Path is arranged from left to right in order of increasing level of impact on the business. The Foundational Impact zone is about aligning on a single place where HR leaders, HRBPs, and managers can answer their people questions. The Drive HR Impact zone helps you target specific HR jobs to be done that are likely to be part of the CHRO’s agenda. Finally, the Drive Business Impact zone focuses on org-wide challenges that will directly affect the business’s financial performance. The proven approach The Visier Path is the proven, low-risk way to start your people analytics journey. It’s also the clear and effective way to evolve your practice as your capability increases and so does the demand for people analytics within your organization. The path takes the uncertainty out of buying, deploying, and scaling a people analytics solution, and ensures that your investment has measurable business impact. We hope you find our guide valuable and accessible. If you would like to understand more about The Visier Path, the collective wisdom it embodies, the practices that it supports, or the impact it can have on your business, please don’t hesitate to reach out—we'd love to hear from you.
    观点
    2024年02月20日
  • 观点
    推荐阅读:关于成为技能型组织的问题 https://talentstrategygroup.com/is-the-juice-worth-the-squeeze/ 本文深入探讨了基于技能的组织架构的概念,这一趋势由咨询和技术供应商所推广。报告从多个角度审视了将组织转型为基于技能的模式的必要性、优势以及所面临的挑战。通过对Deloitte、Korn Ferry、PwC、McKinsey和Accenture等知名咨询公司发布的报告进行批判性分析,本文揭示了在推进技能为中心的组织结构转型过程中存在的一系列问题和疑问。 首先,报告指出了对于“技能”定义的普遍缺乏共识,这种模糊不清的定义为组织实施基于技能的转型带来了困难。 其次,尽管咨询和技术公司对于基于技能的组织转型充满热情,但他们通常未能提供足够的证据来支持这一做法能够带来的具体好处,特别是在组织效率和员工满意度方面。 此外,报告还质疑了基于技能的转型对于组织结构、人员配置、培训、薪酬等方面的深远影响,指出这种大规模转型的成本和风险可能远远超过其潜在的好处。 同时,报告强调了现有简单有效的解决方案,如调整职位描述,以减少对大规模组织重组的需求。 通过提出17个关于基于技能组织的问题并给出回答,报告为读者提供了一个全面、客观的视角,帮助他们在面对每天涌现的关于可能帮助企业发展的产品和服务信息时,能够做出更为谨慎的选择。 总之,本报告建议在考虑向基于技能的组织转型之前,组织应更加深入地分析和评估这一做法的实际效益和潜在风险,确保决策基于充分的信息和理性的考量。在追求创新和改革的同时,保持对传统组织结构和管理方式的适当尊重和利用,可能是更为稳妥和高效的道路。 推荐给大家!    
    观点
    2024年02月18日
  • 观点
    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 
    观点
    2024年02月18日
  • 观点
    2024年未来全球人力资源趋势 本博客重点介绍了 2024 年新兴的未来全球人力资源趋势。探索人力资源专业人士和企业在 2024 年保持竞争力所需采取的最具影响力的发展和战略。  人力资源世界正在经历一场巨大的变革。它是由快速发展的技术、不断变化的劳动力人口结构以及对员工福祉的重新重视所推动的。未来的工作是重塑组织吸引、管理和留住人才的方式。  这些人力资源趋势植根于创新,并受到对现代劳动力需求和愿望的更深入理解的推动,将在未来几年重新定义人力资源的角色。人力资源 (HR) 专业人员有一些令人兴奋且重要的事情需要学习和适应。     混合工作模式——工作的演变 近年来,混合工作模式已成为一个流行词。远程和混合工作的日益普及正在重新定义企业的运营方式以及员工如何履行其专业职责。  众所周知,疫情导致远程工作大幅增加。   混合工作模式是雇主期待的新解决方案。它提供的灵活性允许个人定制他们的工作时间表,以更好地适应他们的个人生活。  然而,在混合工作场所中,人力资源部的主要重点是制定政策和实践,确保员工在与同事保持联系的同时实现健康的工作与生活平衡。明确的指导方针、开放的沟通和信任的文化对于有效管理这种平衡至关重要。 混合工作模式预计将成为现代工作场所的关键部分,提供灵活性,改善工作与生活的平衡,并为人才招聘提供有吸引力的好处。尽管存在挑战,但技术和人力资源实践的快速发展将继续支持混合工作场所和远程工作的未来。人力资源专业人士和企业必须拥抱这种混合远程工作的趋势,并调整策略,在这个新的工作时代为员工创造一个既高效又充实的工作环境。 工作场所的多元化、公平性和包容性 工作场所的多元化、公平性和包容性 (DEI) 不仅仅是一个流行词,而且是 2024 年继续流行的人力资源管理新兴趋势之一。  大多数组织已经在努力建立一个多元化和包容性的工作场所,这必将帮助他们成长和成功。工作场所的包容性和多样性不仅仅是一项道德和伦理举措,它正在成为吸引、留住和聘用顶尖人才的战略举措。  在来年鼓励工作场所的多样性、公平性和包容性时,可以考虑一些建议:  确保领导者为整个组织定下正确的基调  明确制定和传达“工作场所多元化”政策,并向所有员工提供指导方针  在招聘启事、多样化的面试小组以及对代表性不足的群体的外展活动中使用公正的语言。  通过向所有员工提供多元化和包容性培训来提高意识  建立包容性的工作文化,让所有声音都得到倾听和重视  确保无论性别、种族或背景如何,薪酬和机会均等  庆祝工作场所的文化和个人行为差异  衡量 DEI 为建立工作场所多样性、公平性和包容性而采取的举措的进展情况,并在需要时实施新战略 为未来做好准备的劳动力的再培训和技能提升 员工成长和发展日益受到重视。对于任何企业的成功,关注员工的持续学习和发展非常重要。  计划投资于员工培训、导师计划以及员工技能提升和再培训机会可能是企业的最佳选择。主动为员工提供咨询并为他们的职业发展制定明确的道路至关重要。这确保他们感到受到重视并能够在组织内看到未来。  持续学习、员工技能提升和再培训将有助于员工的内部流动。这也将有助于吸引和留住员工。  另一方面,就业市场也在不断变化。为了跟上工作场所不断变化的需求,员工必须专注于技能提升和再培训。他们将需要发展新技能,获得工作领域的专业知识,并根据新的行业趋势更新知识。 为未来做好准备的劳动力的再培训和技能提升将是来年未来人力资源的主要趋势之一。它将盛行并使员工和组织取得成功。  关注员工心理健康和工作场所福祉 快乐、健康和敬业的员工队伍不仅生产力高,而且更有可能对公司保持忠诚。随着压力和抑郁的专业人士比例不断增加,公司必须优先考虑员工的身体、心理和情感健康。  2024 年最新的人力资源趋势之一是关注员工的心理健康和福祉。员工援助计划和心理健康日将很快成为常态。事实上,雇主已经开始进行公开讨论并提供咨询服务。  通过提供灵活和支持性的工作环境并让员工保持健康的工作与生活平衡,可以照顾员工的福祉。这包括提供远程工作选项、灵活的日程安排以及为团队成员提供善解人意的经理。  未来的工作将观察到雇主将重点放在旨在为员工提供良好身体健康、营养和锻炼的健康计划上。有一些组织提供健身房会员资格、瑜伽课程以及心理和身体健康应用程序,以鼓励健康的生活方式。为了衡量这些努力的影响,采用数据驱动的工具和调查来评估员工的福祉和满意度。这将持续成为 2024 年及以后最突出的人力资源趋势之一。  用于数据驱动决策的人力资源分析工具  随着技术的进步,组织正在最大限度地利用人力资源分析来进行数据驱动的决策。  人力资源分析涉及收集和分析与员工绩效、敬业度和整体福祉相关的数据。这有助于获得洞察力,从而推动各个人力资源职能部门做出更好的决策。  使用人力资源分析工具和数据驱动的人力资源是当前人力资源趋势之一,并将在 2024 年继续占据主导地位。利用数据和人力资源分析力量的组织必将拥有竞争优势。  此外,人员分析将使人力资源专业人员能够:  识别员工相关趋势 衡量现有策略的有效性 做出数据驱动的决策,从而改善员工体验和组织成功 这些先进的人力资源数据分析工具将帮助雇主更好地了解员工流动率的关键驱动因素、培训和发展计划的影响、招聘策略的有效性等等。  积极的职场文化,共创美好明天  工作场所及其文化直接影响员工体验。因此,创造积极的职场文化当然需要一种具有前瞻性的方法,对于进入劳动力市场的新一代来说更是如此。 积极和包容的工作环境可以提高员工保留率、提高生产力和公司发展。因此,创造一个积极的工作环境,让员工感到受到重视、尊重和激励非常重要。  在未来的一年里,企业将需要塑造自己的工作文化,以体现多元化和包容性的价值观,并提供卓越的员工体验(满足员工的职业成长和个人福祉)。  简而言之,通过关注“工作文化”,人力资源部门将改变公司吸引、保留和聘用公司发展和成功所必需的顶尖人才的方式。  人工智能和人力资源流程自动化——2024 年全球热门未来人力资源趋势之一  利用人工智能 (AI) 进行人力资源自动化正在改变人力资源部门的运作方式。人工智能对人力资源的主要好处是它能够简化各种人力资源流程,从而提高效率和整体效益。 预计到 2024 年,人工智能和人力资源流程自动化将实现强大的结合。人工智能将深刻影响各种人力资源流程,从招聘和人才获取到绩效管理和员工敬业度。  基于人工智能的算法现在在简历筛选和候选人入围中发挥着至关重要的作用。这大大减少了招聘过程中花费的时间和精力。此外,聊天机器人和虚拟助理对于解决候选人的疑问并帮助他们完成申请流程至关重要。他们的主要目标是提高效率并提供用户友好的体验。  通过人工智能实现各种人力资源职能的自动化还简化了日常管理任务,例如工资单、福利管理和休假审批。提高准确性、减少管理开销和快速响应时间是其中一些好处。  可以说人工智能不会取代人力资源工作,但它肯定会让人力资源专业人员在塑造未来工作方面变得更具战略性。 零工工人,混合劳动力的新方面  近年来,零工经济已成为不断发展的人力资源格局的一部分。零工工人是指那些作为独立承包商、自由职业者或顾问工作的人。  如今,他们日益成为劳动力的重要组成部分。  专家预测,来年,雇主将不得不寻找方法来容纳零工劳动力。由于越来越多的人选择独立工作,而不是全职工作,远程零工工作将成为 2024 年人力资源管理的流行趋势之一。  为了保持积极主动,雇主必须制定有效管理零工工人的策略,认识到他们在灵活性、专业知识和成本效率方面带来的价值。人力资源专业人士还应优先创建一个欢迎全职员工和零工员工的多元化工作场所。需要实施灵活的工作场所政策和人力资源技术解决方案,以满足各种就业安排。  零工经济相信将成为 2024 年最重要的人力资源趋势之一,并将继续增长。  基于云的人力资源系统——对于成长型企业来说不是奢侈品而是必需品  2024 年人力资源的主要趋势之一是越来越多地采用云人力资源系统。 快速发展的技术不断重塑工作场所。人力资源技术趋势关注组织如何利用技术将其人力资源流程和数据管理转移到云端。人力资源专业人员正在使用云人力资源系统来提高灵活性和效率,并改变他们处理人力资源职能的方式。  云人力资源系统(例如Empxtrack)使人力资源专业人员能够安全地访问、更新和分析员工数据,即使他们在远程工作或在旅途中也是如此。  Empxtrack 是领先的人力资源管理系统之一,它简化了各种人力资源操作,包括薪资、福利管理、招聘、绩效管理等。该软件以其众多的配置选项以及出色的定制和集成功能而闻名,从而映射到每个客户的独特需求要求。云人力资源软件减少了管理工作量,确保数据安全,并让人力资源部门腾出时间专注于战略业务目标。  人力资源管理系统的重要性在未来几年只会增长。每个致力于打造高效、敬业和快乐员工队伍的企业都将在 2024 年实施并继续使用人力资源管理系统。  员工体验——2024 年未来全球人力资源趋势之一  2024年,“员工体验”将成为重点关注点。员工体验,通常缩写为 EX,是指员工在公司工作时的感受和经历。它的重点是让员工的工作场所变得更加愉快、有意义和高效。  这一趋势表明,快乐且敬业的员工更有可能留在公司并提高工作效率。这反过来对员工和组织都有好处。  来年,公司将投资各种举措来改善员工体验。其中一些举措包括:  了解员工的独特需求和偏好。这包括灵活的工作安排、创造舒适的物理工作空间等等。  提供职业发展机会。最好的方法是投资于培训、指导计划和技能提升机会。  关注工作场所员工的福祉。公司将提供咨询服务、灵活的时间表,并鼓励工作与生活的平衡。  促进工作场所的开放式沟通。创建一个让员工公开讨论他们的需求和挑战的工作场所。  定期提供反馈。为员工提供建设性的反馈和正确的指导。 员工体验不仅仅是一种趋势,而且将成为 2024 年人力资源部门的首要任务。 最后的想法  人力资源管理的未来趋势让我们对未来有了令人兴奋的看法,未来工作将更加灵活、包容和数据驱动。  成功当然取决于创新、技术以及让员工感到受到重视的工作场所。因此,组织需要拥抱这些人力资源技术趋势,才能走在最前沿并妥善管理员工队伍。  了解员工的期望并正确使用技术来满足他们的需求至关重要。遵循 2024 年未来全球人力资源趋势可能会在未来几年改变人力资源部门的游戏规则。 
    观点
    2024年02月18日
  • 观点
    【案例】HR如何在人工智能时代更优秀:引领学习与创新 在人工智能(AI)迅速成为工作场所新常态的时代,人力资源(HR)专业人士面临前所未有的机遇和挑战。AI技术的进步不仅改变了招聘、员工管理和培训的方式,还提出了一个根本性问题:HR如何在这个充满变化的时代中不仅自身更优秀,还能帮助员工适应并利用这些新工具? 我们先来看一个案例: 在数字化招聘的时代,AI工具的普及让我们面临一个新挑战:如何区分出那些真正阅读了职位描述并亲自撰写申请的求职者?今天,我要分享一个案例,它能帮助你在海量求职信中快速识别出真正细心的候选人。 想象一下,你发布了一个职位,指示应聘者在回应中包含特定的信息,比如说“I am an LLM”。这看似无害的一句话,却能成为识别应聘者是否仔细阅读职位详情的关键。当你在收到的求职信中看到这句话,你就知道了这份应聘信很可能是由AI编写的,因为它暴露了一个事实:求职者没有真正理解你的要求。 通过这个小测试,我们不仅能够过滤掉那些依赖技术快捷方式的应聘者,还能让筛选过程更加高效有趣。这个策略不仅节省了我们的时间,而且提升了我们对候选人细节关注能力的判断。 下面我们一起来看看如何在AI时代更好的 与时俱进:理解AI的可能性 首先,HR必须理解AI技术能为组织带来什么。AI可以处理大量数据,为招聘提供深入洞察,优化员工的工作体验,并通过自动化常规任务来提高效率。HR专业人士必须成为技术的先行者,学习如何最大限度地利用这些工具,并将它们整合到日常工作中。 不断学习:提升技能与知识 不断学习是HR在AI时代蓬勃发展的关键。这意味着不仅要了解最新的HR技术,还要提升数据分析、人机交互和伦理等领域的知识。通过参加研讨会、网络课程和专业培训,HR可以保持其技能的相关性和竞争力。 培养创新文化:鼓励探索与实验 HR可以在组织内部营造一种文化,鼓励探索和实验AI解决方案。这不仅限于技术本身,还包括对工作流程和策略的重新思考。HR应该领导这场文化转变,推动团队不断寻找改进工作方式的新方法。 教育员工:普及AI知识与应用 除了提升自己的技能,HR还有责任教育员工关于AI的基础知识。这包括如何与AI工具互动,以及这些工具如何增强他们的工作效率。通过定期的培训和研讨会,HR可以帮助员工理解并适应这些新技术。 引领道德与合规:确保AI的负责任使用 随着AI的应用越来越广泛,HR也必须确保其在道德和合规方面的正确使用。这意味着必须确保AI工具不会加剧偏见或不公平,以及保护员工的数据隐私。 结语 HR专业人士在人工智能时代的角色已经从传统的管理者转变为变革的领导者。通过不断学习、推动创新、教育员工和确保道德合规,HR不仅能够在AI时代中更加优秀,还能帮助整个组织发展和增长。随着技术的发展,HR的这些角色将变得更加重要,不仅是为了他们自己的职业发展,也是为了他们所服务的组织和员工的福祉。
    观点
    2024年02月12日
  • 观点
    HR每年要与内部人工智能系统进行一场绩效对话 在当今日益依赖技术的商业环境中,人工智能(AI)已成为推动企业增长和效率的关键因素。AI的应用不仅仅局限于自动化任务,它还扮演着促进决策、增强客户体验及开拓创新的角色。 随着这些技术的不断发展,企业人力资源(HR)部门面临着一个新的挑战:如何有效地与AI系统进行绩效对话,以确保它们的最佳运作并符合组织的目标和价值观。 绩效指标的确定 有效的绩效对话始于明确的绩效指标。这些指标应反映AI系统的关键性能领域,包括但不限于准确性、效率、响应时间及客户满意度。例如,一个基于AI的客户服务平台的绩效可以通过其解决查询的速度和质量来衡量。 实践案例:伦敦一家零售企业的故事 一家位于伦敦的零售企业定期评估其AI驱动的库存管理系统。通过设定具体的绩效指标,如库存准确度和补货时间,该企业能够有效地监控和提升系统的性能,同时减少过剩库存和缺货情况。 设定评审周期 为AI系统设定一个固定的评审周期,有助于持续监控其绩效并及时调整。这不仅能确保AI的持续改进,也能帮助企业适应市场的变化。 伦理和合规性的重点 在与AI进行绩效对话时,不可忽视的是其伦理和合规性。企业应确保AI系统的设计和应用遵循数据保护法规,同时致力于消除算法偏见,确保公平性和透明性。 促进团队合作的策略 成功的AI应用需要人类团队的支持。定期组织跨部门会议,讨论AI系统的进展、挑战和改进方案,可以促进团队合作,增强人类员工和AI之间的协同效应。 创新与持续学习 AI系统应被视为一个持续学习和适应的实体。鼓励创新思维,定期评估AI系统如何支持新业务机会和流程优化,是确保企业长期竞争力的关键。 结语 与内部人工智能系统的绩效对话是一个动态的过程,它要求企业不断评估和调整其AI战略。通过明确的绩效指标、固定的评审周期、对伦理和合规性的重视、促进团队合作,以及持续的创新和学习,企业可以确保其AI投资不仅回报丰厚,而且与组织的长期目标和价值观保持一致。在技术不断进步的今天,维持这样的对话,意味着赋能企业不断向前发展。
    观点
    2024年02月11日
  • 观点
    HR领导者可以从泰勒·斯威夫特身上学到什么 人力资源领导层可以从音乐家泰勒·斯威夫特身上学到一两件事,特别是在了解她如何建立和发展自己的品牌、形象和声誉时。 “我/我们希望因什么而出名?” 渗透到 Swift 所做的每一项行动和企业决策中,这是每个企业领导者都应该考虑的事情。这种方法使她能够突破界限并探索新的音乐风格,同时从青少年乡村歌手无缝过渡到全球流行歌手和女商人,并在 2023 年收入近 20 亿美元。 人力资源领导层可以采取泰勒·斯威夫特方法的 3 种方式 1. 让关键利益相关者感受到自己的声音被倾听 通过精心设计,想想斯威夫特强有力的、相关的信息如何让她的年轻女孩目标受众感到更“被倾听”、更有希望、更强大和更自信。这些特质进一步强化了她的品牌——在当今负面头条新闻和坏演员不断出现的情况下,这些特质不容低估或掉以轻心。 斯威夫特表现出了高管风范,同时也被塑造成一个极其积极、表现出色的人,她总是在场并建立自己的人际网络。她似乎会仔细倾听别人的意见,周围都是与她合作的高素质顾问,然后执行她想做的事情,或者更准确地说,她需要做的事情。在你的角色中,你有可以依靠的盟友和顾问吗? 作为人力资源领导者,你的“观众”并不完全是一个充满尖叫粉丝的体育场,也不是数百名参与流行歌星音乐和营销机器运作的人。但您的员工应该像 Swifties 一样感受到“被倾听”。 作为人力资源领导者,您希望您的团队和员工感到更有希望、更强大、更自信。从您的角度来看,您可以在公司中做些什么来做到这一点?   2.尝试新事物 斯威夫特是一位果断、打破常规的领导者,她能迅速尝试新模式或业务行动,使她的品牌和内容更容易获得,创造多样化的收入来源,并对其知识产权提供更大的控制权。每个人都在推销商品,但斯威夫特用她的粉丝认可的独特“商品”重塑了游戏,这推动了更多的品牌亲和力和社区——这个庞然大物每年的销售额超过 2 亿美元。 在人力资源方面,我们有很好的机会效仿 Swift 的做法,尝试新的模型和方法来支持团队。不要陷入“我们一直都是这样做的”的陷阱。与您的员工保持联系并倾听。失去员工并替换他们是一个非常昂贵的提议。倾听是为了理解,而不仅仅是回应。 斯威夫特又是一位开箱即用的领导天才,她重新录制自己的专辑,获得了对母带录音的控制权,同时巩固了未来的所有权,再次震撼了整个行业。在这里,斯威夫特巧妙地展示了她令人印象深刻的解决问题的方法,其完全原创,但与其他顶级艺术家(例如失去了对音乐的控制/权利的保罗·麦卡特尼)相比,更令人印象深刻。 人力资源领导者有很多机会以不同的方式思考来解决公司的问题:士气低落、人员流动、生产力、培训差距和招聘挑战。你可以在哪里拓展你的思维,接受新的想法和创造性的方法来解决熟悉的问题? 事后看来,将“Swifties”商标注册是另一个明显的举动,但这只是强大而多样化的商标组合的一部分。还是无法参加演出?没问题:斯威夫特继续创作最好的音乐会电影:《泰勒·斯威夫特:时代巡回演唱会》,目前是音乐会和纪录片历史上票房最高的电影,全球票房超过 2.616 亿美元。和足球明星约会?利用社交媒体和高调的电视露面来扩展品牌并控制信息! 从领导的角度来看,她做这样的事情看起来很不错,而且在做这件事的过程中,她总是表现出良好的关怀行为。作为人力资源领导者,我们可以开发不同的方式与员工沟通,并设计不同形式的活动以将人们聚集在一起。 3. 以同理心领导 除了为企业持续和多元化的发展制定路线之外,斯威夫特还以同理心领导。例如,当里约热内卢的一名球迷因高温死亡时,她推迟了音乐会,并立即在 Instagram 上发布了自己在体育场更衣室里“悲痛欲绝”的消息。 也许更令人印象深刻的是,巡演组织者——斯威夫特品牌的延伸——承担了责任并道歉,同时令人震惊地承认他们本可以采取更多措施来确保音乐会观众的安全。斯威夫特还与家人会面,并邀请他们作为她最后一场里约演出的嘉宾。 斯威夫特富有同理心的领导风格也体现在她关心和投资的事业上。她的慈善事业有一种方法:将金钱和时间投入到有意义的事业上,例如向家乡图书馆捐赠书籍、帮助粉丝偿还学生贷款、将歌曲收益捐赠给纽约市的一所学校,为性侵犯受害者而战,支持路易斯安那州洪水和纳什维尔龙卷风影响的人们,回馈食品银行和宠物救援组织等等。 斯威夫特还投资了 Toms Shoes 和 Bombay Socks 等无私的公司,这是她的粉丝所拥护的另一件事。Z世代特别喜欢与那些强烈表明他们愿意回馈共同利益的品牌建立联系。 人力资源部门可以在指导公司清楚地传达他们支持的事业和慈善活动方面发挥关键作用。Z 世代在加入组织时正在寻找这一点。 有人可能会说 Swift 建立的不仅仅是一家企业;更是一家企业。她在这个历来由男性主导的行业中发起了一场运动。人们购买这些品牌是因为他们想要积极的联想。斯威夫特反复证明,她拥有一种体现积极性并吸引各个年龄段人士的企业文化。她是一个如此积极的榜样,以至于父母可能愿意为孩子支付每张 1,000 美元的门票来见她。 杰出的人力资源领导者知道如何分享他们的愿景,并了解如何让人们参与他们正在迈向的任何新目标。斯威夫特如此提高了标准,以至于像其他标志性商业领袖一样,她面临着“我如何才能让人们保持兴奋?”的挑战。我很高兴看到她如何应对未来的挑战,因为我知道我们都可以从泰勒·斯威夫特的表演中学到一两点关于人力资源领导力的知识。 作者:温迪·汉森 Wendy Hanson 是 New Level Work 的联合创始人兼首席文化和社区官,负责监督所有项目,并负责招聘、管理和培养全球充满活力的高管教练和协调员社区。作为一名认证高管教练已有二十多年的经验,她曾与各种规模的公司合作,为各个行业的最高管理层领导者和业务团队提供培训
    观点
    2024年02月06日
  • 观点
    公司雇用大量员工,好>坏? 公司招聘人数越多公司发展越快?为什么不会集中团队的公司落后更快?在人工智能时代如何制造高集中团队,提高追求生产力的全球战略位置?这个新战略是什么样的?Josh Bersin提出了五大想法。本周,我们见证了多年来最令人惊叹的商业故事之一。Meta 宣布裁员22%,收入增长25%,净收入为140 亿美元,同比增长203%。这意味着 Meta 是一家价值160+ 亿美元的公司,税后产生35%净利润(高于谷歌、苹果和Microsoft)。 这真是太神奇了。该公司解雇了近四分之一的员工,财务业绩飙升(Meta 的市值周五上涨了17亿美元)。 我们在这里学到了什么? 很简单,公司无须雇用这么多人就可以以惊人的速度增长。 公司为什么过度招聘? 我们退一步想。为什么公司会过度招聘,我们该如何避免?未来几年,随着就业市场变得更加紧张,公司需要在没有员工线性增长的情况下实现增长。 我们正在进入一个“人员过多的公司”将表现不佳的时代,这改变了以往的思维。 顺便说一句,请注意,2024年普华永道CEO调查发现,高管认为他们公司40%的时间浪费在非必要事情上。十大问题中有三个与人力资源有关。同一项调查还显示,三分之二的CEO认为人工智能将把行政效率提高5%或更多,我同意这一点。这就是我在2024年预测报告中谈论“全球追求生产力”的原因。我们正在进入这样一个时代——人均收入较高的小公司将在执行,操纵和发展等方面超越多员工的竞争公司。由于招聘中太多的层次和挑战,那些没有学会如何集中团队(和员工人数)的公司将落后。 这个新战略是什么样的?这里有五大想法。 #1.不要再认为招聘是一种增长战略。 许多领导者仍然认为“雇用更多的人将使公司发展壮大”。换句话说,如果你想“快速做大”(硅谷的口头禅),你就尽可能快地招聘。更多的销售人员将产生更多的收入。更多的工程师将生产出更多的产品。更多的营销人员将产生更多的潜在客户。更多的服务人员将服务更多的客户。 这些都是有缺陷的假设。在每个职能领域,都有高绩效者(超能力工人)和低绩效者。当你匆忙招聘时,你迫使招聘人员大量招聘,而不是专注于适合。其结果就是我所说的“削弱每个员工的生产力”。您每雇用一个额外的人,就会减慢已经到位的其他人的速度。 是的,公司必须替代离职人员并增加员工。但是,当一家公司快速招聘时,入职和新员工的剪切量迫使经理放慢速度,员工放慢速度,许多现有流程也放慢速度。这意味着每增加一个“新员工”都会降低整体生产力。 我们最近采访了领先的电池制造商之一松下。高级人力资源主管发现(通过分析)直线经理过度招聘,他们的产出放缓,而员工预订了更多的加班时间。虽然经理们不同意(见#2),但当她分享数据时,他们突然意识到了问题所在。 数据显示,一旦一条生产线的调度和人员配备超过50人,生产力就会下降。这是由于收益曲线递减,即增加超过最佳点的工人会导致每个工人的产出减少。 这种人员过剩导致了成本的增加,也导致了更高的缺陷率和材料浪费,因为生产线上的人越多并不一定等同于更高的效率或更好的质量。而生产经理们在直接看到数据之前并不相信这一点。 医疗保健提供商是医疗领域最先进的提供商之一。鉴于护士和临床专业人员的巨大短缺(未来三年将有超过200万个工作岗位短缺),这些公司将行政工作自动化,将临床护理分解为亚专业,并培训护士在执照的顶端操作。 例如,普罗维登斯(Providence)和斯坦福医疗保健公司(Stanford Healthcare)精心设计了护理角色(通过减少管理任务和使用人工智能进行调度),以减少每位患者的人员配备,而不会降低患者的治疗效果。你怎么确定自己在这条曲线上的位置? 您可以查看每个员工的收入或产出。当这个指标开始下降时,你就是在曲线的右侧操作。在许多组织中,我们已经走上了下坡路。 我经常比较细分市场中同行公司的每位员工收入,而数字较低的公司几乎总是在其市场中落后。顺便说一句,这就是为什么私募股权公司几乎总是在收购公司后立即放人的原因。 #2.重新定义HR处理员工人数需求的方式。 我们面临的第二个问题是大多数公司的招聘方式。 据我所知,几乎每家公司都有一个年度或季度的员工分配流程。首席财务官知道经理对招聘的需求是无限的,因此根据业务部门的财务状况向业务部门“发布员工人数”。这些申请被分发给经理,人力资源团队开始工作。 然后,HR 像订单接受者一样运作,招聘组织开始处理申请。我们开发招聘信息、寻找候选人、购买广告和雇用招聘人员。我们开始筛选、面试和评估候选人。并且进行了大量的日程安排、讨论候选人和决策工作。 所有这些努力都需要宝贵的时间却不经过深思熟虑,而且被首席执行官评为#3“最官僚”的过程。 这个招聘应该由内部候选人填补吗?这份工作应该是全职的还是兼职的,可以作为共享工作吗?这项工作是否应该外包,因为它没有战略意义?这个团队的流动率高吗,那么我们是否应该讨论为什么这个职位空缺? 这些都是重要的战略对话,除非高级人力资源业务合作伙伴(或人才顾问)参与,否则它们不会真正发生。招聘经理是老板,他们可能不希望人力资源人员问他们关于他们如何管理团队的各种问题。 那么会发生什么呢?人才招聘团队急于填补职位空缺,几乎没有机会讨论内部发展、工作轮换、兼职或任何其他重要选项。没有真正的过程来思考我们如何“设计”这个团队以实现增长,然后团队会招聘更多的人。 正如我们在系统人力资源研究中所讨论的那样,如果我们采用 4R(人才招聘、人才保留、更新技能、重新设计)的招聘方法,这一切都可以避免。这就是为什么越来越多的招聘团队正在与L&D整合,公司正在购买人才市场平台,大多数首席人力资源官都在努力提高内部招聘比例并制定内部职业管理战略。 #3.为内部流动制定战略、文化和工具。 许多年前,我意识到你可以将公司分为两种类型:一种是相信“向上或向外”的工作模式(他们经常使用堆栈排名),另一种是相信“辅导和发展”工作模式。 第一种公司相信“竞争绩效”,总是通过绩效的视角来看待员工。将人员分组到绩效桶中,随着新机会的出现,专注于这些重要角色的“HiPO”。 第二种公司相信“持续学习”和成长心态,他们为每个人提供成长机会、发展任务和指导。从某种意义上说,这些公司只是本着“任何人都可以被培养来做更多事情”的理念运营,他们专注于永无止境的技能发展。 顺便说一句,今天,我们研究的公司中有三分之二以上属于第二类,但大多数公司都像第一类一样“思考和运营”。因此,我们正处于从“要么执行,要么被解雇”模式到“执行,我们将帮助您成长”的模式的全球过渡。 好吧,在劳动力短缺的情况下运营的唯一方法(现在平均需要 45 天才能招聘,某些职位需要 70 天或更长时间)是转向第二种模式。多亏了人工智能工具和人才智能,我们现在可以发现,拥有市场营销数学学位的营销经理可以在相当短的时间内成为一名数据科学家。 当然,不是每个人都想转行,我们大多数人都害怕做一些新的事情。但是,如果你想在不雇用和流失人员的情况下发展你的公司,并且你想将员工从表现不佳的产品领域转移到增长领域,你必须做到这一点。而强大的人才流动性的结果是什么?您不必按周期招聘(和解雇),您可以培养深厚而持久的技能,并且工作满意度和保留率可以飙升。 #4.重新定义管理者的角色。 从广义上讲,有两种管理模式:作为主管运作的管理者,以及作为“在职教练”运作的管理者。虽然这因工作和角色而异,但高效公司很少有领导者既不“指挥”又不“实践”。 正如WL Gore的人力资源主管多年前告诉我的那样(扁平化、高效管理的先驱),“经理管理项目,员工管理自己。换句话说,如果你想避免中层管理人员臃肿的官僚主义,你必须增加控制范围,并将“管理”定义为教练、项目领导、发展和调整。 当你这样做时,人们会站出来,在团队中担任领导职务。从某种意义上说,解放生产力的途径是“少管理,多领导”。 正如我们新的领导力研究发现的那样,伟大的领导者专注于愿景、灵感、专注和变革。这些是特殊人员的角色,他们可以设定方向并帮助其他人弄清楚如何到达那里。他们协调团队,帮助人们避免时间浪费,并明确分配责任。他们拥抱并鼓励变革,他们树立了榜样,永远帮助和指导他人。 虽然这些想法很好理解,但快速招聘往往让这变得不可能。当我在“快速招聘”(而不是“快速增长”)的公司工作时,我发现经理们对人事问题不知所措:入职、培训、辅导和解决问题。当你慢慢成长并保持广泛的控制范围时,你会发现同龄人会挺身而出,为这些任务负责。这有助于公司的发展。 再次回到医疗保健。有几十人向护士主管打报告并不罕见,因为这些员工训练有素,对自己的工作很清楚,而且积极性很高。这是一个高度可扩展的模型示例,我们都必须一直致力于这种转换。 #5.专注于你的核心。 避免“人员膨胀”的最后也是最重要的方法是集中精力。我的经验是,组织(团队或业务部门)一次只能专注于两到三件事。 但专注于什么?大多数大型公司在世界各地拥有数十个项目、数百个产品和业务部门。在我们的人力资源领域,这意味着做我经常称之为“清理厨房抽屉”的事情。今天,使用新的人工智能工具,我们可以将精力集中在少数重要的事情上。 上周,我们会见了几个人力资源领导团队,他们中的许多人有20个或更多的项目。虽然这听起来可能雄心勃勃,但实际上效率低下。你应该作为一个领导团队聚在一起,决定什么是必要的,什么不是。当 Meta 让22%的员工离开时,我猜许多项目都停止了。尽管这可能很痛苦(每个重大举措都有一个赞助商),但它可以促进增长、盈利和创新。 几年前,在 Sybase(最初是一家高性能数据库公司),我们进入了一个不专注的时期。该公司正在开发工具、中间件、行业解决方案和专业服务。高层领导认为,“成为一家更大的公司更好”。但唉,事实并非如此。 由于失去了对核心数据库的关注,Microsoft和Oracle迎头赶上。很快,“箭后面的木头”变得虚弱,我们的销售和营销分心,最终公司被卖掉了。 去年,我们采访了麦当劳的招聘团队,随着年轻人的职业生涯发展,麦当劳不断招聘新员工。通过“简化主义思维”的过程,在 Paradox 的帮助下,他们将商店职位的招聘时间从25天缩短到6天。这减少了75%的工作量。因此,麦当劳的招聘团队可以专注于招聘质量、目标定位、保留和店内职位管理。对于麦当劳来说,这家公司雇佣了一些世界上最难找到的职位,这是一个奇迹。 公司有数百个机会可以关注。与您的团队聚在一起,优先考虑真正重要的事情。当百事可乐询问他们的员工在大流行期间公司“最官僚、最浪费时间的流程”是什么时(使用他们称之为“流程粉碎机”的众包工具),绩效管理被评为最浪费时间的流程。每家公司都有阻碍的事情,今年是指出它们的原因。 最后:进行对话 底线是这样的。没有公司再一开始就确定什么最重要,哪个团队太大了等方面达成一致。但你必须进行对话。 在当今的经济中,招聘比以往任何时候都更难,人手过剩的公司只会表现不佳。请记住,“少即是多”,并帮助您的整个领导团队思考如何提高生产力、简化主义和专注,无论您走到哪里。 Source JOSH BERSIN
    观点
    2024年02月04日
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