• Skill Development
    美国劳工部发布职场人工智能使用原则,保护员工权益(附录原文) 今天5月16日,美国劳工部发布了一套针对人工智能(AI)在职场使用的原则,旨在为雇主提供指导,确保人工智能技术的开发和使用以员工为核心,提升所有员工的工作质量和生活质量。代理劳工部长朱莉·苏在声明中指出:“员工必须是我们国家AI技术发展和使用方法的核心。这些原则反映了拜登-哈里斯政府的信念,人工智能不仅要遵守现有法律,还要提升所有员工的工作和生活质量。” 根据劳工部发布的内容,这些AI原则包括: 以员工赋权为中心:员工及其代表,特别是来自弱势群体的代表,应被告知并有真正的发言权参与AI系统的设计、开发、测试、培训、使用和监督。这确保了AI技术在整个生命周期中考虑到员工的需求和反馈。 道德开发AI:AI系统应以保护员工为目标设计、开发和培训。这意味着在开发AI时,需要优先考虑员工的安全、健康和福祉,防止技术对员工造成不利影响。 建立AI治理和人工监督:组织应有明确的治理体系、程序、人工监督和评估流程,确保AI系统在职场中的使用符合伦理规范,并有适当的监督机制来防止误用。 确保AI使用的透明度:雇主应对员工和求职者透明地展示其使用的AI系统。这包括向员工说明AI系统的功能、目的以及其在工作中的具体应用,增强员工的信任感。 保护劳动和就业权利:AI系统不应违反或破坏员工的组织权、健康和安全权、工资和工时权以及反歧视和反报复保护。这确保了员工在AI技术的应用下,其基本劳动权益不受侵害。 使用AI来支持员工:AI系统应协助、补充和支持员工,并改善工作质量。这意味着AI应被用来提升员工的工作效率和舒适度,而不是取代员工或增加其工作负担。 支持受AI影响的员工:雇主应在与AI相关的工作转换期间支持或提升员工的技能。这包括提供培训和职业发展机会,帮助员工适应新的工作环境和技术要求。 确保负责任地使用员工数据:AI系统收集、使用或创建的员工数据应限于合法商业目的,并被负责地保护和处理。这确保了员工数据的隐私和安全,防止数据滥用。 这些原则是根据拜登总统发布的《安全、可靠和可信赖的人工智能开发和使用行政命令》制定的,旨在为开发者和雇主提供路线图,确保员工在AI技术带来的新机遇中受益,同时避免潜在的危害。 拜登政府强调,这些原则不仅适用于特定行业,而是应在各个领域广泛应用。原则不是详尽的列表,而是一个指导框架,供企业根据自身情况进行定制,并在员工参与下实施最佳实践。通过这种方式,拜登政府希望能在确保AI技术推动创新和机会的同时,保护员工的权益,避免技术可能带来的负面影响。 这套原则发布后,您认为它会对贵公司的AI技术使用和员工权益保护产生怎样的影响? 英文如下: Department of Labor's Artificial Intelligence and Worker Well-being: Principles for Developers and Employers Since taking office, President Biden, Vice President Harris, and the entire Biden-Harris Administration have moved with urgency to harness AI's potential to spur innovation, advance opportunity, and transform the nature of many jobs and industries, while also protecting workers from the risk that they might not share in these gains. As part of this commitment, the AI Executive Order directed the Department of Labor to create Principles for Developers and Employers when using AI in the workplace. These Principles will create a roadmap for developers and employers on how to harness AI technologies for their businesses while ensuring workers benefit from new opportunities created by AI and are protected from its potential harms. The precise scope and nature of how AI will change the workplace remains uncertain. AI can positively augment work by replacing and automating repetitive tasks or assisting with routine decisions, which may reduce the burden on workers and allow them to better perform other responsibilities. Consequently, the introduction of AI-augmented work will create demand for workers to gain new skills and training to learn how to use AI in their day-to-day work. AI will also continue creating new jobs, including those focused on the development, deployment, and human oversight of AI. But AI-augmented work also poses risks if workers no longer have autonomy and direction over their work or their job quality declines. The risks of AI for workers are greater if it undermines workers' rights, embeds bias and discrimination in decision-making processes, or makes consequential workplace decisions without transparency, human oversight and review. There are also risks that workers will be displaced entirely from their jobs by AI. In recent years, unions and employers have come together to collectively bargain new agreements setting sensible, worker-protective guardrails around the use of AI and automated systems in the workplace. In order to provide AI developers and employers across the country with a shared set of guidelines, the Department of Labor developed "Artificial Intelligence and Worker Well-being: Principles for Developers and Employers" as directed by President Biden's Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, with input from workers, unions, researchers, academics, employers, and developers, among others, and through public listening sessions. APPLYING THE PRINCIPLES The following Principles apply to the development and deployment of AI systems in the workplace, and should be considered during the whole lifecycle of AI – from design to development, testing, training, deployment and use, oversight, and auditing. The Principles are applicable to all sectors and intended to be mutually reinforcing, though not all Principles will apply to the same extent in every industry or workplace. The Principles are not intended to be an exhaustive list but instead a guiding framework for businesses. AI developers and employers should review and customize the best practices based on their own context and with input from workers. The Department's AI Principles for Developers and Employers include: [North Star] Centering Worker Empowerment: Workers and their representatives, especially those from underserved communities, should be informed of and have genuine input in the design, development, testing, training, use, and oversight of AI systems for use in the workplace. Ethically Developing AI: AI systems should be designed, developed, and trained in a way that protects workers. Establishing AI Governance and Human Oversight: Organizations should have clear governance systems, procedures, human oversight, and evaluation processes for AI systems for use in the workplace. Ensuring Transparency in AI Use: Employers should be transparent with workers and job seekers about the AI systems that are being used in the workplace. Protecting Labor and Employment Rights: AI systems should not violate or undermine workers' right to organize, health and safety rights, wage and hour rights, and anti-discrimination and anti-retaliation protections. Using AI to Enable Workers: AI systems should assist, complement, and enable workers, and improve job quality. Supporting Workers Impacted by AI: Employers should support or upskill workers during job transitions related to AI. Ensuring Responsible Use of Worker Data: Workers' data collected, used, or created by AI systems should be limited in scope and location, used only to support legitimate business aims, and protected and handled responsibly.
    Skill Development
    2024年05月16日
  • Skill Development
    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.
    Skill Development
    2024年02月23日
  • Skill Development
    HR Predictions for 2024: The Global Search For Productivity 2024年的HR预测强调了生产力和AI在商业和雇佣实践中的关键作用。这篇文章讨论了公司在动态的经济条件和不断变化的劳动力市场背景下,如何适应他们的人才管理和招聘策略。强调了员工赋权的增加,劳动力市场的变化,以及技能发展的重要性。文章还探讨了劳动力囤积、混合工作模式和员工激活等关键概念。此外,还涉及领导力挑战、薪酬公平、DEI计划,以及可能的四天工作周。 一起来看Josh Bersin 带来新得见解 For the last two decades I’ve written about HR predictions, but this year is different. I see a year of shattering paradigms, changing every role in business. Not only will AI change every company and every job, but companies will embark on a relentless search for productivity. Think about where we have been. Following the 2008 financial crisis the world embarked on a zero-interest rate period of accelerating growth. Companies grew revenues, hired people, and watched their stock prices go up. Hiring continued at a fevered pace, leading to a record-breaking low unemployment rate of 3.5% at the end of 2019. Along came the pandemic, and within six months everything ground to a halt. Unemployment shot up to 15% in April of 2020, companies sent people home, and we re-engineered our products, services, and economy to deal with remote work, hybrid work arrangements, and a focus on mental health. Once the economy started up again (thanks to fiscal stimulus in the US), companies went back to the old cycle of hiring. But as interest rates rose and demand fell short we saw layoffs repeat, and over the last 18 months we’ve seen hiring, layoffs, and then hiring again to recover. Why the seesaw effect? CEOs and CFOs are operating in what we call the “Industrial Age” – hire to grow, then lay people off when things slow down. Well today, as we enter 2024, all that is different. We have to “hoard our talent,” invest in productivity, and redevelop and redeploy people for growth. We live in a world of 3.8% unemployment rate, labor shortages in almost every role, an increasingly empowered workforce, and a steady drumbeat of employee demands: demands for pay raises, flexibility, autonomy, and benefits. More than 20% of all US employees change jobs each year (2.3% per month), and almost half these changes are into new industries. Why is this the “new normal?” There are several reasons. First, as we discuss in our Global Workforce Intelligence research, industries are overlapping. Every company is a digital company; every company wants to build recurring revenue streams; and soon every company will run on AI. Careers that used to stay within an industry are morphing into “skills-based careers,” enabling people to jump around more easily than ever before. Second, employees (particularly young ones) feel empowered to act as they wish. They may quietly quit, “work their wage,” or take time out to change careers. They see a long runway in their lives (people live much longer than they did in the 1970s and 1980s) so they don’t mind leaving your company to go elsewhere. Third, the fertility rate continues to drop and labor shortages will increase. Japan, China, Germany, and the UK all have shrinking workforce populations. And in the next decade or so, most other developed economies will as well. Fourth, labor unions are on the rise. Thanks to a new philosophy in Washington, we’ve seen labor activity at Google, Amazon, Starbucks, GM, Ford, Stellantis, Kaiser, Disney, Netflix, and others. While union participation is less than 11% of the US workforce, it’s much higher in Europe and this trend is up. What does all this mean? There are many implications. First, companies will be even more focused on building a high-retention model for work (some call it “labor hoarding.”) This means improving pay equity, continuing hybrid work models, investing in human-centered leadership, and giving people opportunities for new careers inside the company. This is why talent marketplaces, skills-based development, and learning in the flow of work are so important. Second, CEOs have to understand the needs, desires, and demands of workers. As the latest Edelman study shows, career growth now tops the list, along with the desire for empowerment, impact, and trust. A new theme we call “employee activation” is here: listening to the workforce and delegating decisions about their work to their managers, teams, and leaders. Third, the traditional “hire to grow” model will not always work. In this post-industrial age we have to operate systemically, looking at internal development, job redesign, experience, and hiring together. This brings together the silo’d domains of recruiting, rewards and pay, learning & development, and org design. (Read our Systemic HR research for more.) What does “business performance” really mean? If you’re a CEO you want revenue growth, market share, profitability, and sustainability. If you can’t grow by hiring (and employees keep “activating” in odd ways), what choice do you have? It’s pretty simple: you automate and focus on productivity. Why do I see this as the big topic in 2024? For three big reasons. First, CEOs care about it. The 2024 PwC CEO survey found that CEO’s believe 40% of the work in their company is wasted productivity. As shocking as that sounds, it rings true to me:  too many emails, too many meetings, messy hiring process, bureaucratic performance management, and more. (HR owns some of these problems.) Second, AI enables it. AI is designed to improve white-collar productivity. (Most automation in the past helped blue or gray collar workers.) Generative AI lets us find information more quickly, understand trends and outliers, train ourselves and learn, and clean up the mess of documents, workflows, portals, and back office compliance and administration systems we carry around like burdens. Third, we’re going to need it. How will you grow when it’s so hard to find people? Time to hire went up by almost 20% last year and the job market is getting even tougher. Can you compete with Google or OpenAI for tech skills? Internal development, retooling, and automation projects are the answer. And with Generative AI, the opportunities are everywhere. What does all this mean for HR? Well as I describe in the HR Predictions, we have a lot of issues to address. We have to accelerate our shift to a dynamic job and organization structure. We have to get focused and pragmatic about skills. We have to rethink “employee experience” and deal with what we call “employee activation.” And we are going to have to modernize our HR Tech, our recruiting, and our L&D systems to leverage AI and make these systems more useful. Our HR teams will be AI-powered too. As our Galileo™ customers already tell us, a well-architected “expert assistant” can revolutionize how HR people work. We can become “full-stack” HR professionals, find data about our teams in seconds instead of weeks, and share HR, leadership, and management practices with line leaders in seconds. (Galileo is being used as a management coach in some of the world’s largest companies.) There are some other changes as well. As the company gets focused on “growth through productivity,” we have to think about the 4-day week, how we institutionalize hybrid work, and how we connect and support remote workers in a far more effective way. We have to refocus on leadership development, spend more time and money on first line managers, and continue to invest in culture and inclusion. We have to simplify and rethink performance management, and we have to solve the vexing problem of pay-equity. And there’s more. DEI programs have to get embedded in the business (the days of the HR DEI Police are over). We have to clean up our employee data so our AI and talent intelligence systems are accurate and trustworthy. And we have to shift our thinking from “supporting the business” to “being a valued consultant” and productizing our HR offerings, as our Systemic HR research points out. All this is detailed in our new 40-page report “HR Predictions for 2024,” launching this week, including a series of Action Plans to help you think through all these issues. And let me remind you of a big idea. Productivity is why HR departments exist. Everything we do, from hiring to coaching to development to org design, is only successful if it helps the company grow. As experts in turnover, engagement, skills, and leadership, we in HR have make people and the organization productive every day. 2024 is a year to focus on this higher mission. One final thing: taking care of yourself. The report has 15 detailed predictions, each with a series of action steps to consider. The last one is really for you: focus on the skills and leadership of HR. We, as stewards of the people-processes, have to focus on our own capabilities. 2024 will be a year to grow, learn, and work as a team. If we deal with these 15 issues well, we’ll help our companies thrive in the year ahead. Details on the Josh Bersin Predictions The predictions study is our most widely-read report each year. It includes a detailed summary of all our research and discusses fifteen essential issues for CEOs, CHROs, and HR professionals. It will be available in the following forms: Webinar and launch on January 24: Register Here (replays will be available) Infographic with details: Available on January 24. Microlearning course on Predictions: Available on January 24. Detailed Report and Action Guide: Available to Corporate Members and Josh Bersin Academy Members (JBA).  (Note you can join the JBA for $495 per year and that includes our entire academy of tools, resources, certificate courses, and SuperClasses in HR.)
    Skill Development
    2024年01月19日
  • Skill Development
    人工智能正在以比我预期更快的速度改变企业学习AI Is Transforming Corporate Learning Even Faster Than I Expected 在《AI正在比我预想的更快地改变企业学习AI Is Transforming Corporate Learning Even Faster Than I Expected》这一文中,Josh Bersin强调了AI对企业学习和发展(L&D)领域的革命性影响。L&D市场价值高达3400亿美元,涵盖了从员工入职到操作程序等一系列活动。传统模型正在随着像Galileo™这样的生成性AI技术的发展而演变,这改变了内容的创建、个性化和传递方式。本文探讨了AI在L&D中的主要用例,包括内容生成、个性化学习体验、技能发展,以及用AI驱动的知识工具替代传统培训。举例包括Arist的AI内容创作、Uplimit的个性化AI辅导,以及沃尔玛实施AI进行即时培训。这种转型是深刻的,呈现了一个AI不仅增强而且重新定义L&D策略的未来。 在受人工智能影响的所有领域中,最大的变革也许发生在企业学习中。经过一年的实验,现在很明显人工智能将彻底改变这个领域。 让我们讨论一下 L&D 到底是什么。企业培训无处不在,这就是为什么它是一个价值 3400 亿美元的市场。工作中发生的一切(从入职到填写费用账户再到复杂的操作程序)在某种程度上都需要培训。即使在经济衰退期间,企业在 L&D 上的支出仍稳定在人均 1200-1500 美元。 然而,正如研发专业人士所知,这个问题非常复杂。有数百种培训平台、工具、内容库和方法。我估计 L&D 技术空间的规模超过 140 亿美元,这甚至不包括搜索引擎、知识管理工具以及 Zoom、Teams 和 Webex 等平台等系统。多年来,我们经历了许多演变:电子学习、混合学习、微型学习,以及现在的工作流程中的学习。 生成式人工智能即将永远改变这一切。 考虑一下我们面临的问题。企业培训并不是真正的教学,而是创造一个学习的环境。传统的教学设计以教师为主导,以过程为中心,但在工作中常常表现不佳。人们通过多种方式学习,通常没有老师,他们寻找参考资料,复制别人正在做的事情,并依靠经理、同事和专家的帮助。因此,必须扩展传统的教学设计模型,以帮助人们学习他们需要的东西。 输入生成人工智能,这是一种旨在合成信息的技术。像Galileo™这样的生成式人工智能工具 可以以传统教学设计师无法做到的方式理解、整合、重组和传递来自大型语料库的信息。这种人工智能驱动的学习方法不仅效率更高,而且效果更好,能够在工作流程中进行学习。 早期,在工作流程中学习意味着搜索信息并希望找到相关的东西。这个过程非常耗时,而且常常没有结果。生成式人工智能通过其神经网络的魔力,现在已经准备好解决这些问题,就像 L&D 的瑞士军刀一样。 这是一个简单的例子。我问Galileo™(该公司经过 25 年的研究和案例研究提供支持):“我该如何应对总是迟到的员工?请给我一个叙述来帮助我?” 它没有带我去参加管理课程或给我看一堆视频,而是简单地回答了问题。这种类型的互动是企业学习的大部分内容。 让我总结一下人工智能在学习与发展中的四个主要用例: 生成内容:人工智能可以大大减少内容创建所涉及的时间和复杂性。例如,移动学习工具Arist拥有AI生成功能Sidekick,可以将综合的操作信息转化为一系列的教学活动。这个过程可能需要几周甚至几个月的时间,现在可以在几天甚至几小时内完成。 我们在Josh Bersin 学院使用 Arist ,我们的新移动课程现在几乎每月都会推出。Sana、Docebo Shape和以用户为中心的学习平台 360 Learning 等其他工具也同样令人兴奋。 个性化学习者体验:人工智能可以帮助根据个人需求定制学习路径,改进根据工作角色分配学习路径的传统模型。人工智能可以理解内容的细节,并使用该信息来个性化学习体验。这种方法比杂乱的学习体验平台(LXP)有效得多,因为LXP通常无法真正理解内容的细节。 Uplimit是一家致力于构建人工智能平台来帮助教授人工智能的初创公司,它正在使用其Cobot和其他工具为学习人工智能的技术专业人员提供个性化的指导和技巧。Cornerstone 的新 AI 结构按技能推荐课程,Sana 平台将 Galileo 等工具与学习连接起来,SuccessFactors 中的新 AI 功能还为用户提供了基于角色和活动的精选学习视图。 识别和发展技能:人工智能可以帮助识别内容中的技能并推断个人的技能。这有助于提供正确的培训并确定其有效性。虽然许多公司正在研究高级技能分类策略,但真正的价值在于可以通过人工智能识别和开发的细粒度、特定领域的技能。 人才情报领域的先驱者Eightfold、Gloat和SeekOut可以推断员工技能并立即推荐学习解决方案。实际上,我们正在使用这项技术来推出我们的人力资源职业导航器,该导航器将于明年初推出。 用知识工具取代培训:人工智能在学习与发展中最具颠覆性的用例也许是完全取代某些类型培训的潜力。人工智能可以创建提供信息和解决问题的智能代理或聊天机器人,从而可能消除对某些类型培训的需求。这种方法不仅效率更高,而且效果更好,因为它可以在个人需要时为他们提供所需的信息。 沃尔玛今天正在实施这一举措,我们的新平台 Galileo 正在帮助万事达卡和劳斯莱斯等公司在无需培训的情况下按需查找人力资源信息和政策信息。LinkedIn Learning 正在向 Gen AI 搜索开放其软技能内容,很快 Microsoft Copilot 将通过 Viva Learning 找到培训。 这里潜力巨大 在我作为分析师的这些年里,我从未见过一种技术具有如此大的潜力。人工智能将彻底改变 L&D 格局,重塑我们的工作方式,以便 L&D 专业人员可以花时间为企业提供咨询。 L&D 专业人员应该做什么?花一些时间来了解这项技术,或者参加Josh Bersin 学院的一些新的人工智能课程以了解更多信息。 随着我们继续推出像伽利略这样的工具,我知道你们每个人都会对未来的机会感到惊讶。L&D 的未来已经到来,而这一切都由人工智能驱动。
    Skill Development
    2023年12月13日