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    LinkedIn发现,内部流动正在蓬勃发展,但对于低级别员工来说却并非如此 文章讨论了公司内部流动性的增长趋势,强调其对提高员工保留率和参与度的好处。LinkedIn的最新研究显示,自2021年以来,内部职位变动增加了30%,主要在中层及以上员工中。报告强调需要通过提供可见性、支持和发展机会来创建一个包容的流动文化。此外,文章还提到了内部招聘的广泛好处,如节省成本和增强公司文化。成功的内部流动技能包括协作、适应性和包容性领导。 内部职位转换 —— 当一名员工在同一家公司内部转到一个新职位 —— 正在显著增长,自2021年以来增长了30%,根据LinkedIn在2月22日发布的结果。 增长的一个重要原因是,LinkedIn的高级内容经理Greg Lewis在一篇博客中指出,内部职位转换是一种未被充分利用的补充空缺职位的方法,同时也是一个强大的工具,用于增加员工保留率并保持员工的积极参与。然而,这种转换似乎主要局限于中级员工及以上级别:比起普通员工,管理层及更高级别的员工进行内部职位转换的可能性要高出两倍。 人力资源专家可以通过“创建更加包容和平等的内部职位转换文化”来帮助缩小这一差距,Lewis提出。这包括为内部职位空缺提供更多可见性和支持,鼓励跨功能合作和指导,寻找和培养内部转移者倾向于发展的技能,如多样性与包容性(diversity and inclusion)、情感智力(emotional intelligence)和变革管理(change management)。 根据人才获取公司Symphony Talent的二月份报告,近半数的人力资源专业人员表示,建立人才管道是他们2024年的首要目标。内部招聘可以成为这一管道的一部分,带来如节约成本和增加员工留存等积极结果,其他研究也已表明这一点。 过去几年这种做法有所起伏,2020年疫情期间达到高峰,The Josh Bersin Company之前的研究揭示了这一点。那时,公司利用现有员工填补劳动力缺口,并发现内部招聘有助于提高公司文化、提升员工保留率、降低成本和缩短招聘时间。 据LinkedIn称,内部人员流动率在2021年有所下降,但在2022年开始回升,并持续到次年。 正确的策略能使每个人受益,早期的LinkedIn研究显示。职业发展机会被员工视为留在公司的顶级原因之一,一位LinkedIn高管表示。那些提供个性化职业发展并帮助员工建立技能的组织,其内部职位转换率比缺乏培训的公司高出15%。 在这份报告中,LinkedIn比较了成员在开始新职位前12个月加到他们个人资料中的技能。结果显示,与离开公司的同事相比,内部转移者更有可能发展特定技能。 例如,内部转移者发展多样性与包容性技能的可能性几乎高出50%;发展情感智力技能的可能性高出27%;发展变革管理技能的可能性高出21%。其他显著的技能包括利益相关者参与(超过14%)和敏捷项目管理(12%)。 “最能预示内部职位转换者的技能主要围绕合作、包容和适应性 —— 能够与同事建立联系、让每个人感受到包容,并在组织层面推动变革,”LinkedIn表示。
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    2024年03月02日
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    Workday收购HiredScore的意义,这可能颠覆人力资源科技领域 Workday计划收购HiredScore,这是人力资源技术领域的一次重大变革。HiredScore是一家领先的基于AI的招聘匹配工具提供商,此举将大大增强Workday在人才智能和招聘方面的能力。这次收购预计将整合HiredScore的专长到Workday的系统中,显著改善其应聘者追踪系统(ATS)、技能云和整体人才智能产品。此战略性收购可能会重塑人力资源软件市场,迫使其他供应商加速他们的AI计划,可能激发一轮新的收购热潮。 以下是原文: This week Workday announced intent to acquire HiredScore, a leading provider of AI-based matching tools for recruiting (called “talent orchestration”). While it wasn’t discussed much in the earnings call, this deal is a big positive for Workday and could have many implications for the HR Tech market. Let me explain. (I have not been briefed by Workday yet, so more information will come as I learn more.) Right now there is a massive marketplace war for high-powered AI-based recruiting tools (estimated at $30.1 billion). Historically dominated by applicant tracking systems (ATS), this market provides essential technology to help every company grow. The ATS market, which is more than 25 years old, has been rapidly transformed with high-powered AI tools that help with candidate matching, search, skills inference, and sourcing. And now that AI tools are readily available, these systems are becoming big data platforms loaded with billions of employee profiles, running complex AI models to help match people to jobs, projects, and gigs. Most ATS vendors (including Workday) have slowly extended into this space through matching. The original idea of a resume parser (software that reads a resume and scores it against a job description) has evolved into complex text analysis and AI-powered inference technology, forcing ATS vendors to invest. As the ATS vendors enhance their AI capabilities, a parallel universe of AI-first Talent Intelligence vendors emerged. These vendors, like Eightfold, Gloat, Beamery, Phenom, Seekout, Skyhive, Retrain, and Techwolf are building skills-centric big data platforms to match people to jobs, gigs, and mentors. These systems do much more than rate matches: they identify skills, find adjacent skills, match people to careers, find mentors, and more. They are essentially open big-data AI platforms built on vector databases that can be used for many enterprise apps (job architecture design, skills planning, internal mobility, pay equity analysis, etc.). In many ways they represent the future of HR Tech. (Read our Talent Intelligence Primer for more.) As the Talent Intelligence vendors grow, they start to deliver “HCM-threatening” platforms that impinge on the HCM “System of Record” idea. If you have all your employees, candidates, alumni, and prospects in Eightfold, Phenom, Seekout, or Gloat, for example, Workday or SAP look like a tactical payroll and workflow management system. (ServiceNow also understands this, and is building talent intelligence into its workflow platform.) Up until now the big HCM vendors like Workday, Oracle, and SAP have struggled to build these new systems, largely because their original architectures were not AI-based. So they’ve attracted customers with offerings like the Workday Skills Cloud or SAP Opportunity Marketplace that aren’t fully completed yet. We have talked with dozens of Workday Skills Cloud customers, for example, and they see it as an important “skills system of record,” but its real AI matching and inference capabilities have been limited. Along comes HiredScore, a well respected AI-based matching system with 150 employees and 40+ seasoned AI engineers in Israel. These folks are experts at candidate matching (quite a complex problem), and they’ve built a very innovative “orchestration” system to help line managers coordinate activities with HR business partners and recruiters (more on this later). While I’m sure they’ll continue to build out HiredScore, they can also contribute to Workday’s overall talent intelligence offering, improving the entire system – including the Skills Cloud, Workday Learning, Workday’s Talent Marketplace. As large as the recruiting software market is, the market for internal career tools, talent mobility, skills inference, and corporate learning is five times bigger. This acquisition gives Workday a shot in the arm to accelerate its entire AI platform strategy. (As the Identified acquisition did back in 2014.  Identified was the roots of the Workday Skills Cloud.) Market Implications Of This Move This move could change the market for HR software in a few significant ways. First, Workday Recruiting customers will be thrilled. Workday’s ATS now benefits from a first class matching and candidate scoring solution. This helps Workday compete with the bigger ATS players and gives Workday a new revenue source as they sell HiredScore to the existing 4,000+ Workday ATS customers. (Similar to the Peakon acquisition in Employee Experience.) And the talent orchestration features (kind of like a “staffing copilot”) gives Workday a very unique feature set. Second, this forces Workday’s talent intelligence partners to step up their game. Remember when Apple acquired Dark Sky, the most compelling micro-weather app on the market? Once they integrated it into Apple’s other apps, the market for third party weather apps went away. Workday could limit its partner network to avoid letting HiredScore competitors into the ecosystem. Third, this forces HCM vendors to accelerate their AI. Since HiredScore is such a well-respected product (every client we talk with adores it), it will become part of Workday demos and sales proposals quickly. Workday’s HCM competitors will start scratching around to find a similarly mature AI vendor to acquire. And that could kick off another round of acquisitions, similar to the frenzy that took place in the mid 2010s. Finally, there’s one more scenario, and I give this good odds. Not to be outdone by Workday, the Talent Intelligence vendors may just expand their ATS capability and decide to go “full stack.” I wouldn’t be surprised to see this happen. Why Is AI-Based Candidate Matching So Important Why is this technology so important? Well if you’ve ever tried to recruit on Indeed or LinkedIn, you know why. The quality and reliability of “candidate matching technology” is a lynchpin of a talent platform. Just as Google Search crushed Yahoo, Excite, and Inktomi, a powerful next-gen matching tool adds an enormous amount of value. Not only does it speed talent acquisition, it fuels all the internal mobility, career portals, skills, and eventually learning and pay systems. Why do I say this?  A “match” is a sophisticated problem. Unlike a Google search which looks at text and traffic, when you search for a person to fill a role you have to think about dozens of complex relationships. What are this person’s skills and capabilities? What are their credentials or certifications? Who else are they connected with? How likely will they fit into the job, role, and company? What is the impact of their industry experience? What tools and technologies do they understand? And it gets much more complex. The Heidrick Navigator platform (built on Eightfold), uses AI to assess functional skills for management and leadership, identifies a person’s “ability to drive results,” and more. This important application of AI powers many of the most important decisions we make in business. That’s why the Talent Intelligence space is growing so fast. As of this week there are more than 1,800 Director or VPs of “Talent Intelligence” in LinkedIn, and that number is up almost six-fold from one year ago. Can Workday take the lead in this emerging space?  It’s impossible to tell at this point, but the horses have left the gate and the race is on. This deal sets the players in the right lanes and feels like the earthquake to shake things up.  
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    2024年03月01日
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    滴滴出行选用NICE,以提供基于实时 AI 的个性化服务 NICE has partnered with DiDi Global to enhance customer and employee experiences through its cloud-based Workforce Management (WFM) and Employee Engagement Manager (EEM) solutions. This collaboration aims to streamline DiDi's global contact center operations, improving operational efficiency and customer satisfaction with AI-driven forecasting and scheduling. The implementation of NICE's solutions facilitates real-time management and self-scheduling for agents, boosting employee engagement and operational efficiency. DiDi's choice of NICE highlights the importance of advanced, flexible technology in supporting the dynamic needs of modern, app-based transportation services. 领先的移动出行平台通过利用 NICE 的客户体验 AI 技术,使其员工能够提供轻松且高效的客户服务体验 新泽西州霍博肯-NICE (纳斯达克: NICE) 今日宣布,滴滴出行已经选用了 NICE 劳动力管理 (WFM) 和员工参与管理 (EEM) 作为其云端创新技术的一部分。滴滴现在可以全面预测、规划和管理其全球客户联系中心的运作;同时提升运营效率和员工的参与度,并确保客服代表能够在首次通话中解决问题。Betta作为全球最大的 WFM 客户群之一的支持者,在实施过程中与 NICE 价值实现服务携手合作,负责执行集成,并在多国提供咨询、培训和支持服务。 滴滴出行寻求一种能够满足其核心业务、功能及技术需求,并能够随公司成长而扩展的劳动力管理解决方案。NICE WFM 结合了 AI 技术与灵活性,能够满足跨多个大洲、具有特定区域特色的运营需求,这不仅成本效益高,而且精确度高,确保维持最佳的服务水平。通过精准预测,确保在合适的时间有合适技能的代理人,从而大幅提升客户满意度。 通过引入 NICE EEM,可以实时解决人员配置需求,使得客服代理能够自我调节工作时间表,从而增强员工参与度和工作满意度。此外,利用智能日内自动调整功能,能够主动地进行调整,预防问题的发生。 滴滴出行国际客户体验执行总监 Caio Poli 表示:“基于多个考量因素,NICE 显然是我们的首选。我们寻找的是一个顶尖的云端劳动力管理解决方案,能够使我们的全球运营在保证运营效率和员工参与度的同时,提供卓越的客户体验。NICE 的智能日内自动化功能给我们留下了深刻印象,我们的选择是基于 AI 驱动的策略以及云技术的速度和灵活性。” NICE 美洲总裁 Yaron Hertz 表示:“随着滴滴持续全球扩张,NICE 很高兴有机会为这家数字时代最具创新和活力的应用型运输公司之一提供服务。我们相信,通过采用 NICE 的 AI 驱动预测和机器学习来进行最适合的调度安排,对于联系中心和员工而言,这将有助于推动滴滴的未来发展。” 关于滴滴出行公司 滴滴出行公司是一个领先的移动技术平台,它在亚太地区、拉丁美洲及其他全球市场提供一系列基于应用的服务,包括网约车、叫车服务、代驾以及其他共享出行方式,还涵盖某些能源和车辆服务、食品配送和城市内部货运服务。滴滴为车主、司机和配送伙伴提供灵活的工作和收入机会,致力于与政策制定者、出租车行业、汽车行业及社区合作,利用 AI 技术和本地化智能交通创新解决全球的交通、环境和就业挑战。滴滴力图为未来城市构建一个安全、包容和可持续的交通与本地服务生态系统,以创造更好的生活体验和更大的社会价值。更多信息,请访问:www.didiglobal.com 关于 NICE 借助 NICE (纳斯达克: NICE),全球各地不同规模的组织现在可以更容易地创造卓越的客户体验,同时满足关键的业务指标。作为世界领先的云原生客户体验平台 CXone 的提供者,NICE 是 AI 驱动自助服务和代理辅助客户体验软件领域的全球领导者,服务范围超出了传统的联系中心。超过 25,000 个组织在超过 150 个国家,包括 85 家以上的财富 100 强公司,都选择与 NICE 合作,以改造并提升每一次客户互动。www.nice.com 商标说明:NICE 和 NICE 标志是 NICE Ltd. 的商标或注册商标。所有其他标志属于它们各自的所有者。NICE 商标的完整列表,请访问:www.nice.com/nice-trademarks。
    头条
    2024年02月27日
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    Is DEI Going to Die in 2024? Josh Bersin 的文章讨论了 2024 年多元化、公平与包容(DEI)项目所面临的重大挑战和批评,特别强调了 "反觉醒 "评论家的攻击和克劳迪娜-盖伊(Claudine Gay)从哈佛辞职的事件。报告探讨了多元包容计划在当前的文化战争中扮演的角色、人们对它的看法以及法律挑战对多元包容计划招聘和投资的影响。尽管存在这些挑战,贝尔辛还是强调了发展型企业的实际商业利益,展示了成功的战略以及将发展型企业融入业务而不仅仅是人力资源的重要性。他认为,应将重点转向在所有业务部门嵌入包容、公平薪酬和开放讨论的原则,并指出,未来的企业发展指数至关重要,但需要适应和领导层的承诺才能茁壮成长。 Is DEI Going to Die in 2024? By Josh Bersin For anyone working in Diversity, Equity, and Inclusion (DEI), it is safe to say that it has been a tough start to 2024. For a while now, there has been a concerted attack on DEI programs, with ‘anti-woke’ commentators and public figures querying their value, worth, and even existence. Those attacks increased enormously in 2024 with the resignation of Claudine Gay from Harvard. While the call to resign was supposedly related to plagiarism, one can’t help but feel that her position as a leading DEI advocate also fuelled the demand. It means that DEI has come under increased and sustained fire, and despite the many benefits provided by a good DEO program – to both employer and employee – there is a feeling that 2024 could be the year that DEI fades away. How likely is this to happen, and what would the impact be if it did? DEI and the culture wars Anyone living and working in the US (or most other countries worldwide) over the past few years will have likely heard of the culture wars. Brought on by declining trust in institutions, growing inequalities, and the proliferation of technology, the culture wars involve opposing social groups seeking to impose their ideologies. All manner of things has been caught up in this, from what’s on the curriculum at schools to taking a knee at sporting events and from definitions of what constitutes a woman to allegations of tokenism in the workplace. DEI has played an unwitting but central part in the culture wars. There’s a perception that DEI programs are ‘woke’ and prioritize ethnicity and gender over achievement and ability. In August of 2023, an attorney filed (and won) a lawsuit against a VC firm that gives grants to black entrepreneurs. Similar suits have been filed against firms with diversity hiring programs, scholarships, and internships. The resignation of Claudine Gay has reinvigorated the commentary around DEI programs. Josh Hammer, a conservative talk show host and writer, wrote on the social media platform X that taking down Dr. Gay was a “huge scalp” in the “fight for civilizational sanity. ” It was described as “a crushing loss to DEI, wokeism, antisemitism & university elitism,” by conservative commentator Liz Wheeler, and the “beginning of the end for DEI in America’s institutions,” by the conservative activist Christopher Rufo, who had helped publicize the plagiarism allegations against Claudine Gay. When something is as consistently criticized and devalued as DEI programs have been, a toll is inevitably taken. That is certainly indicated by the latest hiring data for DEI professionals. According to data from labor market analytics company Lightcast, hiring for DEI positions in the US is down by 48% year over year, in the middle of an economic boom. Clearly, DEI investments are under attack. And when you look at companies doing layoffs, DEI jobs are frequently high on the list of jobs to cut. I even heard a recent podcast with four well-known venture capitalists – three agreed that “doing away with DEI programs” was top on their list. The value of DEI Given this criticism of DEI programs, one could be forgiven for thinking such programs carry no value to HR and the wider business. Yet many companies invest in DEI programs, and the value is high in almost every case I come across. Our Elevating Equity research in 2022 and 2023 found companies focus on diversity and inclusion for very pragmatic reasons, including: An inclusive hiring strategy broadens and deepens the recruiting pool. An inclusive leadership strategy drives a deeper leadership pipeline. An inclusive management approach helps attract diverse customers and markets. An inclusive board drives growth and market leadership. (proven statistically) An inclusive supply chain program improves sustainability of the supply chain. An inclusive culture creates growth, retention, and engagement in the employee base. Organizations are not prioritizing DEI programs because they are woken or as a box-ticking exercise. They do so because DEI provides real and tangible business benefits. Workday, one of the most admired HR technology companies in the market, has pioneered DEI internally and through its products, and the company has outgrown and outperformed its competitors for years. Their product VIBE, an analytics system designed for this purpose, shows intersectionality, and helps companies set targets and find inequities in leadership, hiring, pay, and career development. But some law firms have posited that these types of programs are illegal – is there a case to answer? DEI legality In response, it’s important to consider the massive and complex pay equity problem. Until the last few years, most companies had no problem paying people in very idiosyncratic ways. The Josh Bersin Company looked at leadership, succession, and pay programs worldwide last year and found that there are massive variations in pay with no clear statistical correlation in most larger companies. This problem is called “pay equity,” and when you look at pay vs. gender, age, race, nationality, and other non-performance factors, most companies find problems. Is this a “DEI” program? When we looked at pay equity in detail last year, we found that only 5% of companies have embarked on a strategic equity analysis. While most companies do their best to keep pay consistent with performance, these studies always find problems. Would it be considered illegal to analyze pay by race or nationality and then fix the disparities? The future of DEI DEI is undoubtedly a complex issue, and many organizations will be uncertain about the best course of action. Despite the current wave of criticism, there has been vast investment in DEI strategy over recent years, and business leaders are highly unlikely to let that fade away. Despite the anti-woke movement, political debates, and the inability of Harvard, Penn, and other universities to speak clearly on these topics, businesses will not stop. Affirmative Action was not created to discriminate; it was designed to reduce discrimination. At the University of California, where Affirmative Action was halted in 1995, studies found that earnings among African American STEM graduates decreased significantly. So, one could argue that they were making a real difference. DEI will not die – it is far too important for that to happen. However, it’s time to do away with the “DEI police” in HR and focus on embedding the principles of inclusion, fair pay, and open-minded discussions across all business units. Senior leaders must take ownership of this issue. In the early 2000s, companies hired Chief Digital Officers to drive digital technology implementation, ideas, and strategies. As digital tools became commonplace, the role went away. We may be entering a period where the Chief Diversity Officer has a new role: putting the company on a track to embrace inclusion and diversity in every business area and spending less time pushing the agenda from a central group. In every interview we conduct on this topic, we see overwhelming positive stories from various DEI strategies. Each successful company frames DEI as a business rather than an HR strategy. While HR-centric DEI investments are shrinking, it’s more like them migrating into the business where they belong. 中文翻译如下,仅供参考: 2024年,多样性、公平与包容(DEI)将走向消亡吗?作者:Josh Bersin 对于那些致力于多样性、公平与包容(DEI)领域的人士来说,2024年的开端无疑充满挑战。近期,DEI项目遭到了前所未有的集中攻击,包括一些“反觉醒”评论员和公众人物对其价值、意义乃至存在的质疑。 特别是随着Claudine Gay从哈佛大学的辞职,这种攻击愈发激烈。尽管她的辞职表面上与剽窃事件有关,但不难察觉,她作为DEI领域的领军人物,这一身份似乎也是辞职呼声高涨的一个重要因素。 这意味着,DEI正面临着前所未有的挑战。尽管高效的DEI项目能够为雇主和雇员带来众多益处,但人们仍担忧2024年可能成为DEI逐渐淡出视野的一年。这种情况发生的可能性有多大?如果真的发生,又会产生何种影响? DEI与文化战争 近年来,无论是在美国还是全球其他大多数国家,你可能都会听说过“文化战争”。这场战争源于对机构的信任下降、不平等现象的加剧以及技术的广泛传播,涉及到试图强加自己意识形态的社会对立群体。 从学校课程内容、体育赛事中的下跪行为,到对“女性”定义的争议、以及工作场所中的代表性指控等,无一不被卷入这场文化战争。而DEI,在这场战争中虽不愿意却占据了核心位置。 人们普遍认为DEI项目倾向于“觉醒”,过分强调种族和性别因素,而忽视了成就和能力。2023年8月,一位律师成功对一家支持黑人创业者的风险投资公司提起诉讼。类似的诉讼也针对那些实施多样性招聘、奖学金和实习计划的公司提起。 Claudine Gay的辞职再次引发了对DEI项目的广泛讨论。保守派脱口秀主持人和作家Josh Hammer在社交媒体平台X上表示,击败Gay博士是“为文明理智而战的一大胜利”。保守派评论员Liz Wheeler称之为“对DEI、觉醒主义、反犹太主义及大学精英主义的沉重打击”,而保守派活动家Christopher Rufo则称这是“DEI在美国机构中走向终结的开始”。 如此一致的批评和贬低无疑对DEI项目造成了重创。根据劳动力市场分析公司Lightcast的数据显示,尽管经济蓬勃发展,但美国DEI相关职位的招聘量同比下降了48%。显然,DEI正面临严峻挑战。 当提到公司裁员时,DEI相关职位往往是裁减名单上的重点。我最近听到一个播客,四位知名风险投资家中有三位认为“取消DEI项目”是他们的首要任务。 DEI的价值 面对如此批评,人们或许会误以为DEI项目对人力资源和更广泛的商业活动没有任何价值。然而,实际上,许多公司对DEI项目的投资极具价值,几乎每个案例都能证明这一点。 我们在2022年和2023年的《提升公平研究》中发现,公司出于实际原因关注多样性和包容性,这包括: 包容性招聘策略扩大了招聘范围。 包容性领导力策略深化了领导力储备。 包容性管理方式吸引了多元化的客户和市场。 包容性董事会推动了市场增长和领导地位(这一点已通过统计数据得到证明)。 包容性供应链项目提升了供应链的可持续性。 包容性文化促进了员工的增长、留存和参与。 组织之所以优先考虑DEI项目,并非仅仅因为“觉醒”,或者作为勾选式行动。他们这样做是因为DEI确实带来了实际和有形的商业利益。例如,Workday这样的HR技术公司在市场上备受尊敬,它不仅在内部推广DEI,在其产品中也体现了这一点,多年来一直超越竞争对手的增长和表现。它们的产品VIBE,一个专门设计的分析系统,展示了交叉性,帮助公司设定目标,找出领导力、招聘、薪酬和职业发展中的不平等。 然而,一些律所提出这类计划可能违法——这是否成立呢? DEI的合法性 面对这一问题,我们不得不考虑到复杂且广泛的薪酬公平问题。直到最近几年,大多数公司在个性化支付薪酬方面并未遇到太大问题。Josh Bersin Company去年对全球的领导力、继承计划和薪酬计划进行了研究,发现在许多大公司中,薪酬存在巨大差异,且大多没有明显的统计相关性。 这个问题被称作“薪酬公平”。当涉及到性别、年龄、种族、国籍等非绩效因素时,大多数公司都存在问题。那么,分析基于种族或国籍的薪酬差异并加以解决,这会被认为是非法的吗? DEI的未来 DEI无疑是一个复杂的议题,许多组织对于采取何种措施感到不确定。尽管面临当前的批评浪潮,但近年来对DEI策略的巨大投资表明,商业领袖们不太可能让这一切付诸东流。 尽管存在反觉醒运动、政治辩论,以及哈佛、宾夕法尼亚大学等教育机构在这些议题上的模糊立场,但商界不会因此而停滞不前。平权行动的初衷不是为了歧视,而是为了减少歧视。例如,在加州大学,自从1995年停止实施平权行动以来,研究发现非洲裔美国人STEM专业毕业生的收入显著下降。因此,可以说这些措施确实产生了积极的影响。 DEI不会消亡——它对此太重要了。然而,现在是时候取消人力资源部门中的“DEI警察”,转而专注于在所有业务单元中嵌入包容性、公平薪酬和开放性讨论的原则。高级领导层必须对这一议题负起责任来。 回顾21世纪初,许多公司聘请首席数字官来推动数字技术的实施、创意和战略。随着数字工具成为常态,这一角色逐渐消失。我们可能正处于一个新的时期,首席多样性官的角色也在发生变化:不再是从中心团队推动议程,而是引导公司在每一个业务领域都拥抱包容性和多样性。 通过我们在这个话题上的每次采访,我们都能看到各种DEI策略的积极故事。每个成功的公司都将DEI视为一项业务策略,而非仅仅是人力资源策略。虽然以HR为中心的DEI投资正在减少,但这更像是它们向业务领域的转移,这正是它们应有的归属。  
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    2024年02月23日
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    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.
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    2024年02月23日
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    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。
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    2024年02月22日
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    Google Workspace推出Gemini:开启AI增强生产力的新篇章 Google Workspace推出了为各种规模的组织设计的Gemini Business计划,以及一个全新的、具有企业级数据保护的独立Gemini聊天体验。Gemini Business计划每用户每月20美元起,提供包括文档和邮件中的写作帮助、表格中的增强智能填充和幻灯片中的图像生成等功能。Gemini Enterprise计划则以每用户每月30美元提供更多使用量和额外的AI驱动会会议的AI功能,如闭幕字幕的翻译和会议记录。 此外,Gemini还提供了一个独立的企业级聊天体验,通过使用最大且最有能力的1.0 Ultra模型,确保了企业级的数据保护,不用于广告目的或改进生成机器学习技术,不被人工审查或与其他用户或组织共享。这些更新旨在提高工作效率和团队合作,同时保障用户数据的安全和隐私。 2024年2月21日 — Google Workspace引入了Gemini Business和Gemini Enterprise,这标志着在其套件内整合人工智能的重大进步。由Aparna Pappu(副总裁兼总经理)领衔的这一举措旨在满足组织多样化的需求,用AI增强日常操作。 向前迈出的革命性一步 本月,Google宣布Duet AI转变为Google Workspace的Gemini,提供了对先进AI模型的访问。此次升级将Gemini集成到广泛使用的Workspace应用中,旨在简化从个人事件规划到复杂商业战略制定等任务。 用AI赋能企业 以每用户每月20美元(需年度承诺)的竞争价格推出的Gemini Business,旨在为所有规模的组织普及生成式AI技术的使用。它提供了如Docs和Gmail中的“帮我写”,Sheets中的“增强智能填充”以及Slides中的图像生成等功能,目的是提高生产力和创造力。 以每用户每月30美元的价格,Gemini Enterprise扩展了这些功能,并增加了AI驱动会议的附加特性,包括实时翻译100多种语言以及即将推出的会议记录功能。现有的Duet AI客户将自动过渡到这个增强计划。 交互的新维度 一个突出的特点是与Gemini的新独立聊天体验,利用1.0 Ultra模型进行更深入、更有洞察力的互动。这个平台承诺提供企业级数据保护,确保通信的隐私和安全。 展望未来 Google Workspace不仅在增强当前的商业和企业产品,还在探索扩展到教育领域。这一举措反映了Google利用AI提高各类用户群体的效率和创新的承诺。 Gemini for Workspace代表了企业、教育机构和个人利用AI实现更大生产力和创造力的关键发展。随着Google Workspace的持续演进,Gemini的整合预示着一个技术和人类智慧无缝融合的未来。
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    2024年02月21日
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    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!    
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    2024年02月18日
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    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 
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    2024年02月18日
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    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。 
    头条
    2024年02月18日
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