• Innovation
    美国联邦贸易委员会（FTC）FTC 宣布全国范围内禁止竞业协议，详细请看 美国联邦贸易委员会（FTC）于2024年4月23日发布最终规定，全国范围内禁止非竞争协议。此举旨在通过保护工人更换工作的自由来促进竞争，增加创新，并推动经济增长。根据FTC的预测，新业务的形成将每年增加2.7%，预计每年将新增超过8500家新企业。此外，预计工人的平均收入将增加524美元，未来十年内医疗费用预计将减少高达1940亿美元。同时，预计该规定还将在未来十年内每年新增17000至29000项专利。 详情以英文版为准： FTC Announces Rule Banning Noncompetes FTC’s final rule will generate over 8,500 new businesses each year, raise worker wages, lower health care costs, and boost innovation Today, the Federal Trade Commission issued a final rule to promote competition by banning noncompetes nationwide, protecting the fundamental freedom of workers to change jobs, increasing innovation, and fostering new business formation. “Noncompete clauses keep wages low, suppress new ideas, and rob the American economy of dynamism, including from the more than 8,500 new startups that would be created a year once noncompetes are banned,” said FTC Chair Lina M. Khan. “The FTC’s final rule to ban noncompetes will ensure Americans have the freedom to pursue a new job, start a new business, or bring a new idea to market.” The FTC estimates that the final rule banning noncompetes will lead to new business formation growing by 2.7% per year, resulting in more than 8,500 additional new businesses created each year. The final rule is expected to result in higher earnings for workers, with estimated earnings increasing for the average worker by an additional $524 per year, and it is expected to lower health care costs by up to $194 billion over the next decade. In addition, the final rule is expected to help drive innovation, leading to an estimated average increase of 17,000 to 29,000 more patents each year for the next 10 years under the final rule. Noncompetes are a widespread and often exploitative practice imposing contractual conditions that prevent workers from taking a new job or starting a new business. Noncompetes often force workers to either stay in a job they want to leave or bear other significant harms and costs, such as being forced to switch to a lower-paying field, being forced to relocate, being forced to leave the workforce altogether, or being forced to defend against expensive litigation. An estimated 30 million workers—nearly one in five Americans—are subject to a noncompete. Under the FTC’s new rule, existing noncompetes for the vast majority of workers will no longer be enforceable after the rule’s effective date. Existing noncompetes for senior executives - who represent less than 0.75% of workers - can remain in force under the FTC’s final rule, but employers are banned from entering into or attempting to enforce any new noncompetes, even if they involve senior executives. Employers will be required to provide notice to workers other than senior executives who are bound by an existing noncompete that they will not be enforcing any noncompetes against them. In January 2023, the FTC issued a proposed rule which was subject to a 90-day public comment period. The FTC received more than 26,000 comments on the proposed rule, with over 25,000 comments in support of the FTC’s proposed ban on noncompetes. The comments informed the FTC’s final rulemaking process, with the FTC carefully reviewing each comment and making changes to the proposed rule in response to the public’s feedback. In the final rule, the Commission has determined that it is an unfair method of competition, and therefore a violation of Section 5 of the FTC Act, for employers to enter into noncompetes with workers and to enforce certain noncompetes. The Commission found that noncompetes tend to negatively affect competitive conditions in labor markets by inhibiting efficient matching between workers and employers. The Commission also found that noncompetes tend to negatively affect competitive conditions in product and service markets, inhibiting new business formation and innovation. There is also evidence that noncompetes lead to increased market concentration and higher prices for consumers. Alternatives to Noncompetes The Commission found that employers have several alternatives to noncompetes that still enable firms to protect their investments without having to enforce a noncompete. Trade secret laws and non-disclosure agreements (NDAs) both provide employers with well-established means to protect proprietary and other sensitive information. Researchers estimate that over 95% of workers with a noncompete already have an NDA. The Commission also finds that instead of using noncompetes to lock in workers, employers that wish to retain employees can compete on the merits for the worker’s labor services by improving wages and working conditions. Changes from the NPRM Under the final rule, existing noncompetes for senior executives can remain in force. Employers, however, are prohibited from entering into or enforcing new noncompetes with senior executives. The final rule defines senior executives as workers earning more than $151,164 annually and who are in policy-making positions. Additionally, the Commission has eliminated a provision in the proposed rule that would have required employers to legally modify existing noncompetes by formally rescinding them. That change will help to streamline compliance. Instead, under the final rule, employers will simply have to provide notice to workers bound to an existing noncompete that the noncompete agreement will not be enforced against them in the future. To aid employers’ compliance with this requirement, the Commission has included model language in the final rule that employers can use to communicate to workers. The Commission vote to approve the issuance of the final rule was 3-2 with Commissioners Melissa Holyoak and Andrew N. Ferguson voting no. Commissioners’ written statements will follow at a later date. The final rule will become effective 120 days after publication in the Federal Register. Once the rule is effective, market participants can report information about a suspected violation of the rule to the Bureau of Competition by emailing noncompete@ftc.gov. The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the FTC on social media, read consumer alerts and the business blog, and sign up to get the latest FTC news and alerts.  
    Innovation
    2024年04月23日
  • Innovation
    101 real-world gen AI use cases from the world's leading organizations 在过去一年半的时间里，生成式人工智能（AI）在企业领域的应用迅速发展。Google Cloud的Next活动中展示了超过300家组织如何利用AI推动企业转型。这些企业已经从简单的问答助手，发展到能够进行预测和采取行动的AI代理，进一步扩展其业务功能和提升效率。 具体来说，AI代理在以下几个关键领域表现出显著的效益：首先是客户服务，AI能够帮助企业更好地理解和满足客户需求；其次是员工赋能，通过自动化日常任务和优化工作流程，AI提升了工作效率；在创意构思和生产领域，AI助力企业快速生成创新的解决方案；数据分析方面，AI通过高效处理和解析大数据，支持决策制定；在编码创建方面，AI简化了开发流程，提高了代码质量；最后在网络安全领域，AI加强了数据保护和风险管理。 这些应用不仅提高了生产力和操作效率，还极大地改善了客户体验和企业的创新能力。AI的多模态能力，即在文本、语音、视频等多种通信模式中的应用，使其能够更全面地满足不同行业的需求。通过这些先进的技术，企业正在开创一个智能、高效和互联的新时代。 我们一起来看看，是否有参考？ Since generative AI first captured the world’s attention a year and a half ago, there’s been a vigorous discussion about what, exactly, the new technology is best used for. While we all enjoyed those early funny chats and witty limericks, we’ve quickly discovered that many of the biggest AI opportunities are clearly in the enterprise. Our customers and partners at Google Cloud have found real potential for creating new processes, efficiencies, and innovations with generative AI. For proof, look no further than the 300-plus organizations who are featured at this week’s Next event in Las Vegas. In a matter of months, organizations like these have gone from AI helping answer questions, to AI making predictions, to generative AI agents. What makes AI agents unique is that they can take actions to achieve specific goals, whether that’s guiding a shopper to the perfect pair of shoes, helping an employee looking for the right health benefits, or supporting nursing staff with smoother patient hand-offs during shifts changes. In our work with customers, we keep hearing that their teams are increasingly focused on improving productivity, automating processes and modernizing the customer experience. These aims are now being achieved through the AI agents they’re developing in six key areas: customer service; employee empowerment; creative ideation and production; data analysis; code creation; and cybersecurity. These special capabilities are made possible in large part by the new multimodal capacity of generative AI and AI foundation models, which allow agents to handle tasks across a range of communications modes, including text, voice, video, audio, code, and more. With human support, agents can converse, reason, learn, and make decisions. The hundreds of customers who joined us at Next ‘24 to showcase and discuss early versions of their AI agents and gen-AI solutions have come to rely on Google Cloud technologies that include our AI infrastructure, Gemini models, Vertex AI platform, Google Workspace, and Google Distributed Cloud. We were also joined by more than 100 partners supporting the creation of AI agents and AI solutions, which you can read about in detail.Here’s a snapshot of how 101 of these industry leaders are putting AI into production today, creating real-world use cases that will transform tomorrow. Similar to great sales and service people, customer agents are able to listen carefully, understand your needs, and recommend the right products and services. They work seamlessly across channels including the web, mobile, and point of sale, and can be integrated into product experiences with voice and video. ADT is building a customer agent to help its millions of customers select, order, and set up their home security. Alaska Airlines is developing a personalized travel search experience using advanced AI techniques, creating hyper-personalized recommendations that engage customers early and foster loyalty through AI-generated content. Best Buy is using Gemini to launch a generative AI-powered virtual assistant this summer that can troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more; in-store and digital customer-service associates are also gaining gen-AI tools to better serve customers anywhere they need help. The Central Texas Regional Mobility Authority is using Vertex AI to modernize transportation operations for a smoother, more efficient journey. Etsy uses Vertex AI training to optimize their search recommendations and ads models, delivering better listing suggestions to buyers and helping sellers grow their businesses. Golden State Warriors are using AI to improve the fan experience content in their Chase Center app. IHG Hotels & Resorts is building a generative AI-powered chatbot to help guests easily plan their next vacation directly in the IHG One Rewards mobile app. ING Bank aims to offer a superior customer experience and has developed a gen-AI chatbot for workers to enhance self-service capabilities and improve answer quality on customer queries. Magalu, one of Brazil’s largest retailers, has put customer service at the center of its AI strategy, including using Vertex AI to create “Lu’s Brain” to power an interactive conversational agent for Lu, Magalu's popular brand persona (the 3D bot has more than 14 million followers between TikTok and Instagram). Mercedes Benz will infuse e-commerce capabilities into its online storefront with a gen AI-powered smart sales assistant. Mercedes also plans to expand its use of Google Cloud AI in its call centers and is using Vertex AI and Gemini to personalize marketing campaigns. Oppo/OnePlus is incorporating Gemini models and Google Cloud AI into their phones to deliver innovative customer experiences, including news and audio recording summaries, AI toolbox, and more. Samsung is deploying Gemini Pro and Imagen 2 to their Galaxy S24 smartphones so users can take advantage of amazing features like text summarization, organization, and magical image editing. The Minnesota Division of Driver and Vehicle Services helps non-English speakers get licenses and other services with two-way real-time translation. Pepperdine University has students and faculty who speak many languages, and with Gemini in Google Meet, they can benefit from real-time translated captioning and notes. Sutherland, a leading digital transformation company, is focused on bringing together human expertise and AI, including boosting its client-facing teams by automatically surfacing suggested responses and automating insights in real time. Target uses Google Cloud to power AI solutions on the Target app and Target.com, including personalized Target Circle offers and Starbucks at Drive Up, their curbside pickup solution. Tokopedia, an Indonesian ecommerce leader, is using Vertex AI to improve data quality, increasing unique products being sold by 5%. US News saw a double-digit impact in key metrics like click-through rate, time spent on page, and traffic volume to its pages after implementing Vertex AI Search. IntesaSanpaolo, Macquarie Bank, and Scotiabank are exploring the potential of gen AI to transform the way we live, work, bank, and invest — particularly how the new technology can boost productivity and operational efficiency in banking. Watch the session to learn more. Employee agents help workers be more productive and collaborate better together. These agents can streamline processes, manage repetitive tasks, answer employee questions, as well as edit and translate critical communications. Avery Dennison empowered their employees with generative AI to enable secure, flexible, and borderless collaboration for enhanced productivity to drive growth. Bank of New York Mellon built a virtual assistant to help employees find relevant information and answers to their questions. Bayer is building a radiology platform that will assist radiologists with data analysis, intelligent search, and to create documents that meet healthcare requirements needed for regulatory approval. The bioscience company is also harnessing BigQuery and Vertex AI to develop additional digital medical solutions and drugs more efficiently. Bristol Myers Squibb is transforming its document processes for clinical trials using Vertex AI and Google Workspace. Now, documentation that took scientists weeks now gets to a first draft in minutes. BenchSci develops generative AI solutions empowering scientists to understand complex connections in biological research, saving them time and financial resources and ultimately bringing new medicine to patients faster. Cintas is using Vertex AI Search to develop an internal knowledge center for customer service and sales teams to easily find key information. Covered California, the state’s healthcare marketplace, is using Document AI to help improve the consumer and employee experience by automating parts of the documentation and verification process when residents apply for coverage. Dasa, the largest medical diagnostics company in Brazil, is helping physicians detect relevant findings in test results more quickly. DaVita leverages DocAI and Healthcare NLP to transform kidney care, including analyzing medical records, uncovering critical patient insights, and reducing errors. AI enables physicians to focus on personalized care, resulting in significant improvements in healthcare delivery. Discover Financial helps their 10,000 contact center representatives to search and synthesize information across detailed policies and procedures during calls. HCA Healthcare is testing Cati, a virtual AI caregiver assistant that helps to ensure continuity of care when one caregiver shift ends and another begins. They are also using gen AI to improve workflows on time-consuming tasks, such as clinical documentation, so physicians and nurses can focus more on patient care. The Home Depot has built an application called Sidekick, which helps store associates manage inventory and keep shelves stocked; notably, vision models help associates prioritize which actions to take. Los Angeles Rams are utilizing AI across the board from content analysis to player scouting. McDonald’s will leverage data, AI, and edge technologies across its thousands of restaurants to implement innovation faster and to enhance employee and customer experiences. Pennymac, a leading US-based national mortgage lender, is using Gemini across several teams including HR, where Gemini in Docs, Sheets, Slides and Gmail is helping them accelerate recruiting, hiring, and new employee onboarding. Robert Bosch, the world's largest automotive supplier, revolutionizes marketing through gen AI-powered solutions, streamlining processes, optimizing resource allocation, and maximizing efficiency across 100+ decentralized departments. Symphony, the communications platform for the financial services industry, uses Vertex AI to help finance and trading teams collaborate across multiple asset classes. Uber is using AI agents to help employees be more productive, save time, and be even more effective at work. For customer service representatives, they’ve launched new tools that summarize communications with users and can even surface context from previous interactions, so front-line staff can be more helpful and effective The U.S. Dept. of Veterans Affairs is using AI at the edge to improve cancer detection for service members and veterans. The Augmented Reality Microscope (ARM) is deployed at remote military treatment facilities around the world. The prototype device is helping pathologists find cancer faster and with better accuracy. The U.S. Patent and Trademark Office has improved the quality and efficiency of their patent and trademark examination process by implementing AI-driven technologies. Verizon is using generative AI to help teams in network operations and customer experience get the answers they need faster. Victoria’s Secret is testing AI-powered agents to help their in-store associates find information about product availability, inventory, and fitting and sizing tips, so they can better tailor recommendations to customers. Vodafone uses Vertex AI to search and understand specific commercial terms and conditions across more than 10,000 contracts with more than 800 communications operators. WellSky is integrating Google Cloud's healthcare and Vertex AI capabilities to reduce the time spent completing documentation outside work hours. Woolworths, the leading retailer in Australia, boosts employees’ confidence in communications with “Help me write” across Google Workspace products for more than 10,000 administrative employees. It’s also using Gemini to create next-generation promotions, as well as for quickly assisting customer service reps in summarizing all previous customer interactions in real time. Box, Typeface, Glean, CitiBank, and Securiti AI discuss developing AI-powered apps across the enterprise, with measurable returns on investment for marketing, financial services, and HR use cases. Highmark Health and Freenome join Bristol Myers Squibb to explore how AI can improve efficiency and innovation across care delivery, drug discovery, clinical trial planning, and bringing medicines to market. Creative agents can expand your organization with the best design and production skills, working across images, slides, and exploring concepts with workers. Many organizations are building agents for their marketing teams, audio and video production teams, and all the creative people that can use a hand. With creative agents, anyone can become a designer, artist, or producer. Belk ECommerce is using generative AI to craft better product descriptions, a necessary yet time-consuming task for digital retails that has often been done manually. Canva is using Vertex AI to power its Magic Design for Video, helping users skip tedious editing steps while creating shareable and engaging videos in a matter of seconds. Carrefour used Vertex AI to deploy Carrefour Marketing Studio in just five weeks — an innovative solution to streamline the creation of dynamic campaigns across various social networks. In just a few clicks, marketers can build ultra-personalized campaigns to deliver customers advertising that they care about. Major League Baseball continues to innovate its Statcast platform, so teams, broadcasters, and fans have access to live in-game insights. Paramount currently relies on manual processes to create the essential metadata and video summaries used across its Paramount+ platform for showcasing content and creating personalized experiences for viewers. VertexAI Text Bison is now helping to streamline this process. Procter & Gamble used Imagen to develop an internal gen AI platform to accelerate the creation of photo-realistic images and creative assets, giving marketing teams more time to focus on high-level planning and delivering superior experiences for its consumers. WPP will integrate Google Cloud’s gen AI capabilities into its intelligent marketing operating system, called WPP Open, which empowers its people and clients to deliver new levels of personalization, creativity, and efficiency. This includes the use of Gemini 1.5 Pro models to supercharge both the accuracy and speed of content performance predictions. Data agents are like having knowledgeable data analysts and researchers at your fingertips. They can help answer questions about internal and external sources, synthesize research, develop new models — and, best of all, help find the questions we haven’t even thought to ask yet, and then help get the answers. AI21 Labs offers a BigQuery integration called Contextual Answers that allows users to query data conversationally and get high-quality answers quickly Anthropic has partnered with Google Cloud to offer its family of Claude 3 models on Vertex AI — providing organizations with more model options for intelligence, speed, cost-efficiency, and vision for enterprise use cases. The Asteroid Institute is using AI to discover hidden asteroids in existing astronomical data. This is a major focus for astronomers researching the evolution of the Solar System, investors and businesses hoping to fly missions to asteroids, and for all of us who want to prevent future large asteroid impacts on Earth. Contextual is working with Google Cloud to offer enterprises fully customizable, trustworthy, privacy-aware AI grounded in internal knowledge bases. Cox 2M, the commercial IoT division of Cox Communications, is able to make smarter, faster business decisions using AI-powered analytics. Essential AI, a developer of enterprise AI solutions, is using Google Cloud’s AI-optimized TPU v5p accelerator chips to train its own AI models. Generali Italia, Italy's largest insurance provider, used Vertex AI to build a model evaluation pipeline that helps ML teams quickly evaluate performance and deploy models. Globo, one of Brazil’s largest media networks, is using Service Extensions and Media CDN to fight piracy during live events by blocking pirated streams in real time. Hugging Face is collaborating with Google across open science, open source, cloud, and hardware to enable companies to build their own AI with the latest open models from Hugging Face and Google Cloud hardware and software. Kakao Brain, part of Korean technology company Kakao Group, has built a large-scale AI language model that is the largest Korean language-specific LLM in the market, with 66 billion parameters. They’ve also developed a text-to-image generator called Karlo. Mayo Clinic has given thousands of its scientific researchers access to 50 petabytes worth of clinical data through Vertex AI search, accelerating information retrieval across multiple languages. McLaren Racing is using Google AI to get up-to-the-millisecond insights during races and training to gain a competitive edge. Mercado Libre is testing BigQuery and Looker to optimize capacity planning and reservations with delivery carriers and airlines to fulfill shipments faster. Mistral AI will use Google Cloud's AI-optimized infrastructure, to further test, build, and scale up its LLMs, all while benefiting from Google Cloud's security and privacy standards. MSCI uses machine learning with Vertex AI, BigQuery and Cloud Run to enrich its datasets to help our clients gain insight into around 1 million asset locations to help manage climate-related risks. NewsCorp is using Vertex AI to help search data across 30,000 sources and 2.5 billion news articles updated daily. Orange operates in 26 countries where local data must be kept in each country. They are using AI on Google Distributed Cloud to improve network performance and deliver super-responsive translation capabilities. Spotify leveraged Dataflow for large-scale generation of ML podcast previews, and they plan to keep pushing the boundaries of what’s possible with data engineering and data science to build better experiences for their customers and creators. UPS is building a digital twin of its entire distribution network, so both workers and customers can see where their packages are at any time. Workday is using natural language processing in Vertex Search and Conversation to make data insights more accessible for technical and non-technical users alike. Woven — Toyota's investment in the future of mobility — is partnering with Google to leverage vast amounts of data and AI to enable autonomous driving, supported by thousands of ML workloads on Google Cloud’s AI Hypercomputer. This has resulted in resulting in 50% total-cost-of-ownership savings to support automated driving. Broward County, Florida, and Southern California Edison are using geospatial capabilities and AI to improve infrastructure planning and monitoring, generate new insights, and create regional resilience for communities facing climate challenges today and tomorrow. Kinaxis and Dematic are building data-driven supply chains to address logistics use cases including scenario modeling, planning, operations management, and automation. NOAA and USAID are among the U.S. government agencies using Google Cloud AI to unlock critical data insights to streamline operations and improve mission outcomes — all with an emphasis on responsible AI. Watch the session to learn more. Code agents are helping developers and product teams to design, create, and operate applications faster and better, and to ramp up on new languages and code bases. Many organizations are already seeing double-digit gains in productivity, leading to faster deployment and cleaner, clearer code. Capgemini has been using Code Assist to improve software engineering productivity, quality, security, and developer experience, with early results showing workload gains for coding and more stable code quality. Commerzbank is enhancing developer efficiency through Code Assist's robust security and compliance features. Quantiphi saw developer productivity gains of more than 30% during their Code Assist pilot. Replit developers will get access to Google Cloud infrastructure, services, and foundation models via Ghostwriter, Replit's software development AI, while Google Cloud and Workspace developers will get access to Replit’s collaborative code editing platform. Seattle Children's hospital is using AI to boost data engineering productivity and accelerate development. Turing is customizing Gemini Code Assist on their private codebase, empowering their developers with highly personalized and contextually relevant coding suggestions that have increased productivity around 30 percent and made day-to-day coding more enjoyable. Wayfair piloted Code Assist, and those developers with the code agent were able to set up their environments 55 percent faster than before, there was a 48 percent increase in code performance during unit testing, and 60 percent of developers reported that they were able to focus on more satisfying work. Security agents assist security operations by radically increasing the speed of investigations, automating monitoring and response for greater vigilance and compliance controls. They can also help guard data and models from cyberattacks, such as malicious prompt injection. BBVA uses AI in Google SecOps to detect, investigate, and respond to security threats with more accuracy, speed, and scale. The platform now surfaces critical security data in seconds, when it previously took minutes or even hours, and delivers highly automated responses. Behavox is using Google Cloud technology and LLMs to provide industry leading regulatory compliance and front office solutions for financial institutions globally. Charles Schwab has integrated their own intelligence into the AI-powered Google SecOps, so analysts can better prioritize work and respond to threats. Fiserv’s security operations engineers create detections and playbooks with much less effort, while analysts get answers more quickly. Grupo Boticário, one of the largest beauty retail and cosmetics companies in Brazil, employs real-time security models to prevent fraud and to detect and respond to issues. Palo Alto Networks’ Cortex XSIAM, the AI-driven security operations platform, is built on more than a decade of expertise in machine-learning models and the most comprehensive, rich, and diverse data store in the industry. Backed by Google's advanced cloud infrastructure and advanced AI services, including BigQuery and Gemini models, the combination delivers global scale and near real-time protection across all cybersecurity offerings. Pfizer can now aggregate cybersecurity data sources, cutting analysis times from days to seconds. To find even more customers using our AI tools to build agents and solutions for their most important enterprise projects, visit the Google Cloud customer hub and watch the Next ‘24
    Innovation
    2024年04月12日
  • Innovation
    美国领先企业联合成立了一个联盟，应对人工智能对技术岗位劳动力的影响 由思科（Cisco）牵头，埃森哲（Accenture）、谷歌（Google）、国际商业机器公司（IBM）和微软（Microsoft）等主要行业参与者参与的人工智能 ICT 劳动力联盟（AI-Enabled ICT Workforce Consortium）AI-Enabled Information and Communication Technology (ICT) Workforce Consortium 旨在评估和减轻人工智能对技术工作的影响。该联盟旨在确定受人工智能进步影响的岗位所需的关键技能，为再培训和提高技能提供途径。该倡议借鉴了私营部门、顾问和政府的合作见解，为人工智能环境下的劳动力做好准备，强调了全球合作促进包容性技术未来的必要性。 人工智能 ICT 劳动力联盟致力于提供实际可行的洞见，发掘重新培训和提升技能的新机遇 思科牵头成立的AI赋能信息通信技术（ICT）工作力联盟，包括埃森哲、Eightfold、谷歌、IBM、Indeed、英特尔、微软和SAP等行业领导者的加入。该联盟将评估人工智能对科技岗位的影响，并为最可能受到AI影响的职业确定技能发展途径。 联盟的成立得到了美国-欧盟贸易与技术委员会人才成长工作组的推动，思科主席兼CEO Chuck Robbins在该工作组的参与，以及美国商务部的建议，起到了催化剂的作用。 顾问团包括美国劳工联盟-产业组织联合会、CHAIN5、美国通信工人联合会、DIGITALEUROPE、欧洲职业培训协会、可汗学院和SMEUnited等。 比利时鲁汶，2024年4月4日-- 思科（纳斯达克代码：CSCO）和另外八家行业领先公司包括埃森哲、Eightfold、谷歌、IBM、Indeed、英特尔、微软和SAP，以及六位顾问今天宣布，成立了致力于提升和重新培训最可能受到AI影响岗位的AI赋能ICT工作力联盟。该联盟受到美国-欧盟贸易与技术委员会人才成长工作组的启发，旨在探究AI对ICT岗位的影响，帮助工作者发现并参与相关培训计划，同时连接企业和具备相应技能、准备就绪的工作者。 作为私营部门的合作平台，联盟正评估AI如何改变工作岗位及所需技能，让工作者取得成功。首阶段工作成果将总结为一份提供给企业领导者和工作者实际建议的报告。未来几个月将公布更多详情。研究结果旨在为那些寻求为员工重新培训和提升技能的雇主提供实用的洞见和建议。 联盟成员涵盖了在AI前沿创新的企业，他们深知AI对劳动力市场的当前和未来影响。各成员企业已分别记录了AI带来的机遇与挑战。通过合作，这些组织能够汇聚见解，推荐行动计划，并在其广泛的影响领域内实施这些发现。 “人工智能正加速全球劳动力市场的变革，为私营部门提供了一个强大机会，帮助工作者重新培训和提升技能，以迎接未来，”思科执行副总裁兼首席人事、政策与目标官Francine Katsoudas表示。“我们新成立的AI赋能工作力联盟的任务是向组织提供关于AI对劳动力影响的知识，并装备工作者以相关技能。我们期待吸引更多利益相关方——包括政府、非政府组织和学术界——一同迈出确保AI革命惠及每个人的重要一步。” 联盟的工作受到了美国-欧盟贸易与技术委员会人才成长工作组的启发，思科主席兼CEO Chuck Robbins领导其技能培训工作流程的指导，以及美国商务部的建议。美国总统拜登、欧盟委员会主席冯德莱恩和欧洲理事会主席米歇尔于2021年6月成立了TTC，目的是通过合作和民主方法在贸易、技术和安全领域推进美国和欧盟的竞争力和繁荣。 “在美国商务部，我们致力于推动先进技术的发展，并深化与全球伙伴和盟友之间的贸易与投资关系。这项工作正帮助我们建立一个强大且具竞争力的经济体，由能够获得高质量、高薪、可维持家庭生活的未来工作的才华横溢的劳动力所推动。我们明白，经济安全与国家安全紧密相连。这就是我为何感到自豪地看到人才成长工作组的努力以及AI赋能ICT工作力联盟的成立，”美国商务部长Gina Raimondo表示。“我感激联盟成员加入这一努力，共同面对AI快速发展所带来的新型劳动力需求。这项工作将为这些工作的具体技能需求提供前所未有的见解。我希望这个联盟仅是一个开始，并且私营部门将其视为一个行动呼吁，确保我们的劳动力能够享受到AI带来的好处。” AI赋能ICT工作力联盟的工作解决了对具备AI各方面技能训练的熟练劳动力的紧迫需求。联盟将利用其成员和顾问的力量，推荐和扩大包容性的重新培训和提升技能培训计划，以惠及多方利益相关者——学生、职业转换者、当前的IT工作者、雇主和教育者——大规模提升工作者以适应AI时代。 在其首阶段工作中，联盟将评估AI对56个ICT岗位角色的影响，并为受影响岗位提供培训建议。这些岗位角色根据Indeed Hiring Lab的数据，包括在2023年2月至2024年期间在美国和五个ICT劳动力最多的欧洲国家（法国、德国、意大利、西班牙和荷兰）获得最高岗位发布量的前45个ICT职位的80%。这些国家的ICT部门共计拥有1000万名ICT工作者，占据了行业的重要份额。 联盟成员普遍认识到，随着AI在商业的所有方面的加速融合，及时集结力量，建立一个包容性、能提供维持家庭生活机会的劳动力市场的重要性。联盟成员承诺，在将越来越多地整合人工智能技术的职业领域，开发工作者路径。为此，联盟成员设定了具有远见的目标，并通过技能发展和培训计划，在未来十年内对全球超过9500万人产生积极影响。联盟成员的目标包括： 思科承诺到2032年为2500万人提供网络安全和数字技能培训。 IBM将在2030年前为3000万人提供数字技能培训，包括200万人的AI技能。 英特尔计划到2030年为超过3000万人提供当前和未来工作的AI技能。 微软承诺到2025年为来自弱势社区的1000万人提供需求旺盛的数字技能培训和认证，为他们在数字经济中提供工作和生计机会。 SAP计划到2025年为全球200万人提供提升技能培训。 谷歌最近宣布投入2500万欧元，支持全欧洲人民的AI培训和技能提升。 埃森哲 “帮助组织识别技能差距并进行大规模快速培训是埃森哲的重点任务，这个联盟汇集了一系列致力于在我们社区中发展尖端技术、数据和AI技能的行业合作伙伴。在各个行业中，为与AI协同工作的人员进行重新培训至关重要。那些在技术投资中与学习投资同等重视的组织，不仅创造了职业发展路径，还能在市场中占据领先地位。” - 埃森哲首席领导力与人力资源官Ellyn Shook Eightfold “工作的动态和本质正在以前所未有的速度演变。Eightfold通过深入分析最受欢迎的职位，了解重新培训和提升技能的需求。通过其人才智能平台，我们为商业领袖提供了迅速适应不断变化的商业环境的能力。我们为能够为组织预备未来工作做出贡献而感到自豪。” - Eightfold AI首席执行官兼联合创始人Ashutosh Garg 谷歌 “谷歌坚信，技术创造的机遇应真正面向所有人。我们自豪地加入AI赋能工作力联盟，进一步推动我们使AI技能培训普及化的工作。我们致力于跨领域合作，确保不同背景的工作者都能有效利用AI，为面向未来的职位做好准备，获得新机会，在经济中茁壮成长。” - 谷歌成长计划创始人Lisa Gevelber IBM “IBM自豪地加入这个及时的企业主导倡议，通过汇集我们的共同专业知识和资源，为AI时代的劳动力做好准备。作为行业领袖，我们共同的责任是发展可信赖的技术，并为所有背景和经验水平的工作者提供学习新技能和提升现有技能的机会，以应对AI采纳改变工作方式并创造新职位的挑战。” - IBM欧洲中东非洲人力资源副总裁Gian Luigi Cattaneo Indeed “Indeed的使命是帮助人们找到工作。我们的研究表明，Indeed上今天发布的几乎每个职位，从卡车司机到医生到软件工程师，都将面临不同程度的受到基于GenAI的变革的影响。我们期待为工作力联盟的重要工作做出贡献。那些授权其员工学习新技能并获得与不断发展的AI工具的实践经验的公司，将加深他们的专业团队，提高员工留存率并扩大其合格候选人库。” - Indeed AI创新部门负责人Hannah Calhoon 英特尔 “作为全球AI创新的领导者，英特尔自豪地加入ICT工作力联盟，继续我们的努力，为所有人塑造一个包容和公平的技术未来。作为联盟的一员，我们将与行业领袖合作，分享最佳实践，创造可访问的学习机会，并与各方利益相关者协作，确保工作者掌握了迎接明天的技术技能。” - 微软人力资源法律副总裁兼副总法律顾问Amy Pannoni SAP “SAP自豪地加入这一努力，帮助为未来的工作准备我们的劳动力，并确保AI在企业和职位中的应用是相关的、可靠的、负责任的。面对我们不断变化的世界的复杂性，AI有潜力重塑行业、革新解决问题的方式，并释放前所未有的人类潜能，使我们能够构建一个更智能、更高效和更包容的劳动力。多年来，SAP支持了许多技能发展计划，我们期待作为联盟的一部分推动更多的学习机会、创新和积极变化。” - SAP副总裁兼全球开发学习负责人Nicole Helmer 关于思科 思科（纳斯达克代码：CSCO）是全球技术领袖，通过帮助我们的客户重新构想他们的应用、支持混合工作模式、保障企业安全、改造基础设施，并实现可持续发展目标，连接一切，让任何事情成为可能。在新闻室了解更多信息，并在X上关注我们@Cisco。 思科和思科标志是思科及/或其在美国和其他国家的关联公司的商标或注册商标。思科的商标列表可在www.cisco.com/go/trademarks查看。提到的第三方商标属于其各自所有者。使用“合作伙伴”一词并不意味着思科与任何其他公司之间存在合伙关系。 来源：思科公司   LEUVEN, Belgium, April 4, 2024 - Cisco (NASDAQ: CSCO) and a group of eight leading companies including Accenture, Eightfold, Google, IBM, Indeed, Intel, Microsoft and SAP as well as six advisors today announced the launch of the AI-Enabled Information and Communication Technology (ICT) Workforce Consortium focused on upskilling and reskilling roles most likely to be impacted by AI. The Consortium is catalyzed by the work of the U.S.-EU Trade and Technology Council's (TTC) Talent for Growth Task Force, with the goal of exploring AI's impact on ICT job roles, enabling workers to find and access relevant training programs, and connecting businesses to skilled and job-ready workers. Working as a private sector collaborative, the Consortium is evaluating how AI is changing the jobs and skills workers need to be successful. The first phase of work will culminate in a report with actionable insights for business leaders and workers. Further details will be shared in the coming months. Findings will be intended to offer practical insights and recommendations to employers that seek ways to reskill and upskill their workers in preparation for AI-enabled environments. Consortium members represent a cross section of companies innovating on the cutting edge of AI that also understand the current and impending impact of AI on the workforce. Individually, Consortium members have documented opportunities and challenges presented by AI. The collaborative effort enables their organizations to coalesce insights, recommend action plans, and activate findings within their respective broad spheres of influence. "AI is accelerating the pace of change for the global workforce, presenting a powerful opportunity for the private sector to help upskill and reskill workers for the future," said Francine Katsoudas, Executive Vice President and Chief People, Policy & Purpose Officer, Cisco. "The mission of our newly unveiled AI-Enabled Workforce Consortium is to provide organizations with knowledge about the impact of AI on the workforce and equip workers with relevant skills. We look forward to engaging other stakeholders—including governments, NGOs, and the academic community—as we take this important first step toward ensuring that the AI revolution leaves no one behind." The Consortium's work is inspired by the TTC's Talent for Growth Task Force and Cisco Chair and CEO Chuck Robbins' leadership of its skills training workstream, and input from the U.S. Department of Commerce. The TTC was established in June 2021 by U.S. President Biden, European Commission President von der Leyen, and European Council President Michel to promote U.S. and EU competitiveness and prosperity through cooperation and democratic approaches to trade, technology, and security. "At the U.S. Department of Commerce, we're focused on fueling advanced technology and deepening trade and investment relationships with partners and allies around the world. This work is helping us build a strong and competitive economy, propelled by a talented workforce that's enabling workers to get into the good quality, high-paying, family-sustaining jobs of the future. We recognize that economic security and national security are inextricably linked. That's why I'm proud to see the efforts of the Talent for Growth Task Force continue with the creation of the AI-Enabled ICT Workforce Consortium," said U.S. Secretary of Commerce Gina Raimondo. "I am grateful to the consortium members for joining in this effort to confront the new workforce needs that are arising in the wake of AI's rapid development. This work will help provide unprecedented insight on the specific skill needs for these jobs. I hope that this Consortium is just the beginning, and that the private sector sees this as a call to action to ensure our workforces can reap the benefits of AI." The AI-Enabled ICT Workforce Consortium's efforts address a business critical and growing need for a proficient workforce that is trained in various aspects of AI, including the skills to implement AI applications across business processes. The Consortium will leverage its members and advisors to recommend and amplify reskilling and upskilling training programs that are inclusive and can benefit multiple stakeholders – students, career changers, current IT workers, employers, and educators – in order to skill workers at scale to engage in the AI era. In its first phase of work, the Consortium will evaluate the impact of AI on 56 ICT job roles and provide training recommendations for impacted jobs. These job roles include 80% of the top 45 ICT job titles garnering the highest volume of job postings for the period February 2023-2024 in the United States and five of the largest European countries by ICT workforce numbers (France, Germany, Italy, Spain, and the Netherlands) according to Indeed Hiring Lab. Collectively, these countries account for a significant segment of the ICT sector, with a combined total of 10 million ICT workers. Consortium members universally recognize the urgency and importance of their combined efforts with the acceleration of AI in all facets of business and the need to build an inclusive workforce with family-sustaining opportunities. Consortium members commit to developing worker pathways particularly in job sectors that will increasingly integrate artificial intelligence technology. To that end, Consortium members have established forward thinking goals with skills development and training programs to positively impact over 95 million individuals around the world over the next 10 years. Consortium member goals include: Cisco to train 25 million people with cybersecurity and digital skills by 2032. IBM to skill 30 million individuals by 2030 in digital skills, including 2 million in AI. Intel to empower more than 30 million people with AI skills for current and future jobs by 2030. Microsoft to train and certify 10 million people from underserved communities with in-demand digital skills for jobs and livelihood opportunities in the digital economy by 2025. SAP to upskill two million people worldwide by 2025. Google has recently announced EUR 25 million in funding to support AI training and skills for people across Europe. Accenture "Helping organizations identify skills gaps and train people at speed and scale is a major priority for Accenture, and this consortium brings together an impressive ecosystem of industry partners committed to growing leading-edge technology, data and AI skills within our communities. Reskilling people to work with AI is paramount in every industry. Organizations that invest as much in learning as they do in the technology not only create career pathways, they are well positioned to lead in the market." - Ellyn Shook, chief leadership & human resources officer, Accenture Eightfold "The dynamics of work and the very essence of work are evolving at an unprecedented pace. Eightfold examines the most sought-after job roles, delving into the needs for reskilling and upskilling. Through its Talent Intelligence Platform, it empowers business leaders to adapt swiftly to the changing business environment. We take pride in contributing to the creation of a knowledgeable and responsible resource that assists organizations in preparing for the future of work." - Ashutosh Garg, CEO and Co-Founder, Eightfold AI Google "Google believes the opportunities created by technology should truly be available to everyone. We're proud to join the AI-Enabled Workforce Consortium, which will advance our work to make AI skills training universally accessible. We're committed to collaborating across sectors to ensure workers of all backgrounds can use AI effectively and develop the skills needed to prepare for future-focused jobs, qualify for new opportunities, and thrive in the economy." - Lisa Gevelber, Founder, Grow with Google IBM "IBM is proud to join this timely business-led initiative, which brings together our shared expertise and resources to prepare the workforce for the AI era. Our collective responsibility as industry leaders is to develop trustworthy technologies and help provide workers—from all backgrounds and experience levels—access to opportunities to reskill and upskill as AI adoption changes ways of working and creates new jobs." - Gian Luigi Cattaneo, Vice President, Human Resources, IBM EMEA Indeed "Indeed's mission is to help people get jobs. Our research shows that virtually every job posted on Indeed today, from truck driver to physician to software engineer, will face some level of exposure to GenAI-driven change. We look forward to contributing to the Workforce Consortium's important work. The companies who empower their employees to learn new skills and gain on-the-job experience with evolving AI tools will deepen their bench of experts, boost retention and expand their pool of qualified candidates." - Hannah Calhoon, Head of AI Innovation at Indeed Intel "At Intel, our purpose is to create world-changing technology that improves the lives of every person on the planet, and we believe bringing AI everywhere is key for businesses and society to flourish. To do so, we must provide access to AI skills for everyone. Intel is committed to expanding digital readiness by collaborating with 30 countries, empowering 30,000 institutions, and training 30 million people for current and future jobs by 2030. Working alongside industry leaders as part of this AI-enabled ICT workforce consortium will help upskill and reskill the workforce for the digital economy ahead." – Christy Pambianchi, Executive Vice President and Chief People Officer at Intel Corporation Microsoft "As a global leader in AI innovation, Microsoft is proud to join the ICT Workforce Consortium and continue our efforts to shape an inclusive and equitable technology future for all. As a member of the consortium, we will work with industry leaders to share best practices, create accessible learning opportunities, and collaborate with stakeholders to ensure that workers are equipped with the technology skills of tomorrow," - Amy Pannoni, Vice President and Deputy General Counsel, HR Legal at Microsoft SAP "SAP is proud to join this effort to help prepare our workforce for the jobs of the future and ensure AI is relevant, reliable, and responsible across businesses and roles. As we navigate the complexities of our ever-evolving world, AI has the potential to reshape industries, revolutionize problem-solving, and unlock unprecedented levels of human potential, enabling us to create a more intelligent, efficient, and inclusive workforce. Over the years, SAP has supported many skills building programs, and we look forward to driving additional learning opportunities, innovation, and positive change as part of the consortium." - Nicole Helmer, Vice President & Global Head of Development Learning at SAP About Cisco Cisco (NASDAQ: CSCO) is the worldwide technology leader that securely connects everything to make anything possible. Our purpose is to power an inclusive future for all by helping our customers reimagine their applications, power hybrid work, secure their enterprise, transform their infrastructure, and meet their sustainability goals. Discover more on The Newsroom and follow us on X at @Cisco. Cisco and the Cisco logo are trademarks or registered trademarks of Cisco and/or its affiliates in the U.S. and other countries. A listing of Cisco's trademarks can be found at www.cisco.com/go/trademarks. Third-party trademarks mentioned are the property of their respective owners. The use of the word partner does not imply a partnership relationship between Cisco and any other company. SOURCE Cisco Systems, Inc.
    Innovation
    2024年04月05日
  • Innovation
    Josh Bersin谈How To Create Talent Density 如何打造人才密度 Josh Bersin发表文章谈到：在过去几年里，我注意到大公司的表现开始不如小公司。我们现在看到苹果和谷歌都出现了这种情况，而微软应对这一挑战也有相当长的一段时间了。 随着公司的发展，帮助我们推动组织绩效的一个重要理念就是人才密度。这篇文章讨论了人才密度的概念，即公司中技能、能力和表现的质量和密度。强调传统的员工绩效评估模型已导致平庸。建议采用人才密度方法，包括招聘增加或乘数效应的人才，基于帕累托分布管理绩效，以及专注于赋权、反馈和领导力。文章强调，为了创新和市场竞争力，尤其在AI和技术进步的背景下，维持高人才密度的重要性。 In this (long) article, I want to talk about a new concept called Talent density. And as I pondered the concept I think it represents one of the more important topics in management. So I hope you find it as interesting as I do. First of all, the concept of talent density, pioneered by Netflix by the way, is simple. Talent Density is the quality and density of skills, capabilities and performance you have in your company. So, if you have a company that is 100% high performers, you’re very dense. If you have a company that’s 20% high performers, you’re not very dense. It’s easy to understand, but hard to implement, because it gets to the point of how we define performance, how we select people to hire, how we decide who’s going to get promoted, how we decide who’s going to work on what project and how we’re going to distribute pay. So before I explain talent density, let’s talk about the basic beliefs most companies have. Most organizations believe that they’re operating with a normal distribution or bell curve of performance. I don’t know why that statistical model has been applied to organizations, but it has become almost a standard policy. (Academics have proven it false, as I explain below.) Using the bell curve, we identify the “mean” or average performance, and then categorize performance into five levels. Number ones are two standard deviations to the right and number fives are two standard deviations to the left. The people operating at level one get a big raise, the people operating at level two get medium raise, the people operating at level three get an average raise, the people operating at level four get a below average raise and the people operating at level five probably need to leave. Lots of politics in the process, but that’s typically how it works. As I describe in The Myth of The Bell Curve, these performance and pay strategies have been used for decades. And at scale they create a mediocrity-centered organization, because the statistics limit the quantity and value of 1’s. If you’re operating at 1 level and you get a 2, you’ll quit. If you’re operating at 3 level, you’re probably going to coast. You get my drift. And since the bulk of the company is rated 2 or 3, most of the managers are in the middle. As the saying goes, A managers hire A people, B managers hire C people. So over time, if not constantly tuned, we end up with an organization that is almost destined to be medium in performance. Now I’m not saying every company goes through this process, but if you look at the productivity per employee in large organizations it’s almost always below that of smaller organizations. Why? Because as organizations grow, the talent density declines. (Netflix, as an example, example, generates almost $3M of revenue per employee, twice that of Google and 10X that of Disney. And they are the only profitable streaming company, with fewer than 20,000 employees and a $240 billion market cap.) The traditional model was fine in the industrial age when we had a surplus of talent, jobs were clearly defined, and most employees were measure by the “number of widgets they produced.” In those days we could swap out a “low performer” for a “high performer” because there were lots of people in the job market. We don’t live in that world anymore. The world we now live in has sub 4% unemployment, a constant shortage of key skills, and a growing shortage of labor. And thanks to automation and AI, the revenue or value per person has skyrocketed, almost an order of magnitude higher than it was 30 years ago. So we need a better way to think about performance in a world where companies with fewer people can outperform those who get too big. Look at how Salesforce, Google, Apple, who are essentially creative companies, have slowed their ability to innovate as they get bigger. Look at how OpenAI, who is a tiny company, is outperforming Google and Microsoft. Today most businesses outperform through innovation, time to market, customer intimacy, or IP – not through scale or “harder work.” How do we maintain a high level of talent density when we’re growing the company and hiring lots of people? Netflix wrote the book on this, so let me give you the story. First, the hiring process should focus on talent density, not butts in seats. Rather than hire someone to “fill a role” we look for someone who is additive or multiplicative to the entire team. Hire someone that challenges the status quo and brings new ideas, skills, and ideas beyond the “job” as defined. Netflix values courage, innovation, selflessness, inclusion, and teamwork, for example. These are not statements about “doing your job as defined.” Netflix’s idea is that each incremental hire should make everybody else in the company and everybody else in the team produce at a higher level. Now this is a threatening thing for an insecure manager because most managers don’t want to hire somebody that could take their job away. But that’s why we have this problem. Second, we need to manage or create some type of performance management process that is built around the Pareto distribution (also called the Power Law) and not the normal distribution. In the Pareto distribution or the power law, we have a small number of people who generate an outsized level of performance, you can call it the 80/20 rule or the 90/10 rule. (20% of the people do 80% of the work) Studies have shown that companies and many populations work this way, and it makes sense. Think about athletes, where a small number of super athletes are 2-3 better than their peers. The same thing is true in music, science, and entertainment. It’s also true in sales and many business disciplines. Research conducted in 2011 and 2012 by Ernest O’Boyle Jr. and Herman Aguinis (633,263 researchers, entertainers, politicians, and athletes in a total of 198 samples). found that performance in 94 percent of these groups did not follow a normal distribution. Rather these groups fall into what is called a “Power Law” distribution. In every population of human beings there are a few people who just have God-given gifts to outperform in the job, and they just naturally seem to be far better than everyone else. Bill Gates once told the company that there were of the three engineers that he felt made the company of Microsoft. And I’ve heard this in many other companies, where one software engineer and the right role can do the work of 10 other people. Now, this is not to say that everybody will fall into one level of the Pareto distribution. At a given point in time in your career, you may be in the 80% and over time, as you learn and grow and find the things that you’re naturally good at, you’ll end up in the 20%. But in a given company this is a dynamic that’s constantly taking place. And that’s what Netflix is doing – constantly working on talent density. What does this mean for performance management? It means that in order to care for a population like this, we have to hire differently, avoid the bell curve, and pay high performers well. Not just a little more than everybody else, a lot more. And that’s what happens in sports and entertainment, so why not in business. If you look at companies like Google, Microsoft, and others, there are individuals in those companies that make two to three times more than their peers. And as long as these decisions are made based on performance, people are fine with it. What obviously does not work is when person making all the money is the person who’s the best politician, best looking, or most popular. And that leads me to item three: In the Netflix culture there’s a massive amount of empowerment, 360 feedback, candor and honesty. You’ve probably read the Netflix culture manifesto: it’s all about the need for people to be honest, to speak truth, to give each other feedback, and to focus on judgement, courage, and accountability. Netflix only recently added job levels: they didn’t have job levels for many years. Giving people feedback is a challenge because it’s uncomfortable. So this has to to start at the top and it has to be done in a developmental, honest way. This does not mean people should threaten or disparage each other, but we all need to know that at the end of a project or the end of the meeting it’s okay for somebody to tell us “here’s what was great about that and here’s what wasn’t great about it.” One of the most important institutions in the world, the US military lives, eats and dies by this process. If you’re in the military and you mess something up, you can guarantee that somebody’s going to tell you about it, and you’re going to get some help making sure you don’t do it again. We don’t have life or death situations in companies, but we can certainly use this kind of discipline. The fourth thing that matters in talent density is leadership and goal setting. One of the things that really gets in the way of a high performing company is too many individual goals, too many siloed projects and responsibilities and people not seeing the big picture. If your goal setting and performance management process is 100% based on individual performance you are sub-optimizing your company. Not only does this work against teamwork, but there really isn’t a single thing in a company that anybody can do alone. So our performance management research continuously shows that people should be rewarded for both their achievements as well as that of the team. (Here’s the research to explain.) Why is talent density important right now? Let me mention a few reasons. First, we’re entering a period of low unemployment so every hire is going to be challenging. And thanks to AI, companies are going to be able to operate with smaller teams. What better time to think about how to “trim down” your company so it’s performing at its best? Second, the transformations from AI are going to require a lot of flexibility and learning agility in your company. You want a highly focused, well aligned team to help make that happen. And while AI will help every company improve, your ability to leverage AI quickly will turn into a competitive advantage (think back about how web and digital and e-commerce did the same). (I firmly believe the companies with the most ingenious applications of AI will disrupt their competitors. I’m still amazed at Whole Food’s hand recognition checkout process: I can see self-service coffee, groceries, and other retail and hospitality coming.) Third, the post-industrial business world is going to start to devalue huge, lumbering organizations. Many big companies just need a lot of people, but as Southwest Airlines taught us long ago, it’s the small team that performs well. So if you can’t break your company into small high-performing teams, your talent density will suffer. When the book is written on Apple’s $10 Billion car, I bet one problem was the size and scale of the team. We’ll see soon enough. By the way, I still recommend everyone read “The Mythical Man-Month,” which to me is the bible of organizing around small teams. What if you’re a healthcare provider, retailer, manufacturer, hospitality company? Does talent density apply to you? Absolutely! Go into a Costco and see how happy and engaged the employees are. Then go into a poorly run retailer and you’ll feel the difference. In my book Irresistible I give examples of companies who embrace what I call “the unquenchable power of the human spirit.” Nobody wants to feel like they’re underperforming. With the right focus on accountability and growth we can help everyone out-perform their expectations. Now is a time rethink how our organizations work. Not only should we promote and reward the hyper-performers, the Pareto rule and Talent Density thinking encourage us to help mid-level performers learn, grow, and transform themselves into superstars. Let’s throw away the old ideas of bell curve, forced distribution, and simplistic performance management. Companies that push for everlasting high performance are energizing places to work, they deliver outstanding products and services, and they’re great investments for stakeholders.   AI中文翻译： 在这篇篇幅较长的文章中，我想探讨一个被称为“人才密度”的新概念。思考此概念时，我认为它是管理领域中极其重要的议题之一。希望您能像我一样发现其趣味性。 首先，Netflix首创的“人才密度”概念其实很简单。 人才密度指的是公司内部技能、能力和表现的质量与密集程度。 换句话说，如果你的公司全是高绩效人才，那么你的“人才密度”就很高。如果只有20%是高绩效人才，那么你的“人才密度”就不高。这个概念虽然容易理解，但实际执行起来却颇具挑战，因为它涉及到我们如何定义绩效、招聘员工的标准、晋升决策、项目分配以及薪酬分配。 在详细解释“人才密度”之前，让我们先看看大多数公司的基本信念。许多组织相信，他们的员工表现遵循一个正态分布或钟形曲线。这个统计模型为何被广泛应用于组织之中，我并不清楚，但它几乎已成为标准做法。（实际上，如我下文将解释的，学术研究已证明这一模型是错误的。） 采用钟形曲线，我们确定平均表现（即“平均线”），然后将员工的表现划分为五个等级。表现最好的被归为一级，标准为右偏两个标准差；表现最差的被归为五级，左偏两个标准差。 一级表现者获得大幅度加薪，二级表现者获得中等加薪，三级表现者获得平均水平的加薪，四级表现者加薪低于平均，五级表现者可能就需要离开公司了。虽然这个过程充满了政治操作，但这就是它通常的运作方式。 正如我在《钟形曲线的神话》中所述，这些关于绩效和薪酬的策略已经使用了数十年。而且，当这些策略在大规模下实施时，它们会造成以平庸为中心的组织文化，因为这种统计方法限制了顶尖人才的数量和价值。如果你是一级表现者却被评为二级，你很可能就会选择离职。如果你是三级表现者，你可能就会选择安于现状。你应该明白我的意思了。而且，由于大部分员工的评级为二级或三级，大多数管理者也就处于中等水平。 常言道，A级的管理者招聘A级人才，B级的管理者则招聘C级人才。因此，如果不持续进行优化调整，组织最终几乎注定会变得中庸。 我并不是说每家公司都会经历这一过程，但如果你查看大型组织的员工生产率，通常都低于小型组织的生产率。为什么呢？因为随着组织规模的扩大，“人才密度”往往会下降。（以Netflix为例，其每名员工创造的收入几乎为300万美元，是Google的两倍，是迪士尼的十倍。他们是唯一盈利的流媒体公司，员工不足20,000人，市值2400亿美元。） 在工业时代，人才供过于求，工作职责明确，大多数员工的表现以“生产的产品数量”来衡量。那个时候，低绩效者可以轻松地被高绩效者替换，因为劳动市场上有大量的人才可供选择。 但我们不再生活在那个时代了。在我们现在的世界里，失业率低于4%，关键技能持续短缺，劳动力整体也日益减少。而且，得益于自动化和AI技术，每位员工创造的收入或价值比30年前高出了几个数量级。 因此，在一个人员更少的公司可以超越体量更大的公司的世界中，我们需要一种更好的绩效思考方式。看看Salesforce、Google、Apple这些本质上依靠创新的公司，随着规模扩大，它们的创新能力如何变缓。再看看OpenAI，尽管是一个小公司，却在超越Google和Microsoft。 如今，大多数企业通过创新、市场响应速度、客户亲密度或知识产权而非规模或“更加努力的工作”来实现超越。 在我们不断发展公司并招聘大量人员的同时，我们如何保持高水平的“人才密度”？Netflix在此领域有着开创性的工作，让我来分享一下他们的故事。 首先，招聘过程应专注于提高“人才密度”，而不是仅仅为了填补空缺。我们寻找的不是简单地“填补一个角色”的人，而是能够为整个团队带来正面或倍增效果的人才。我们寻找的是那些能够挑战现状、带来新观点和技能，并超出传统“工作定义”的人。例如，Netflix重视勇气、创新、无私、包容和团队合作等价值观，并不仅仅是“完成既定工作”。 Netflix的理念是，每一次新增的招聘都应该使公司内每个人和团队的每个成员的生产力得到提升。这对于那些缺乏安全感的管理者来说可能是个挑战，因为大多数管理者并不希望招聘可能会取代他们的人。但正是这种思维方式导致了我们当前的问题。 其次，我们需要建立或改进一种围绕帕累托分布（也称作幂律分布）而非正态分布的绩效管理流程。在帕累托分布或幂律分布中，少数人贡献了超出常规的绩效水平，这可以称作80/20规则或90/10规则。（即20%的人完成了80%的工作） 研究显示，许多公司和人群实际上都是以这种方式运作的，这是合理的。想想那些在体育、音乐、科学和娱乐领域表现出色的人，其中少数顶尖人才的表现是同龄人的两到三倍。销售和许多商业领域也是如此。 2011年和2012年由Ernest O’Boyle Jr.和Herman Aguinis进行的研究（涵盖了633,263名研究人员、艺术家、政治家和运动员，共198个样本）发现，这94%的群体的表现并不遵循正态分布，而是呈现所谓的“幂律分布”。 在每个人群中，总有少数人因为天赋异禀，在工作中表现出色，自然而然地比其他人优秀得多。 比尔·盖茨曾经对微软说过，他认为公司中的三名工程师是公司的基石。我也在许多其他公司听到过类似的故事，其中一位软件工程师在合适的位置上可以完成其他十人的工作量。 这并不意味着每个人都将被归入帕累托分布的某一层级。在你职业生涯的某个阶段，你可能处于80%的群体中，但随着你不断学习、成长并找到自己真正擅长的领域，你最终可能进入20%的群体。但在任何一个公司，这种动态都在不断发生。这就是Netflix一直在努力提升“人才密度”的原因。 这对绩效管理意味着什么？这意味着，为了照顾这样一个群体，我们必须采取不同的招聘方式，避免使用钟形曲线，并且为高绩效者提供丰厚的薪酬。这不仅仅是支付比其他人稍微多一点的薪水，而是要多得多。这在体育和娱乐领域已经是常态，那么为什么不可以应用到商业领域呢？ 如果你观察Google、Microsoft等公司，你会发现，这些公司中的个别人物赚取的收入是他们同事的两到三倍。只要这些决策基于绩效，大家通常都能接受它。 当然，不起作用的情况是，赚取高薪的是那些最擅长政治、外表最出众或最受欢迎的人。 这就引出了第三点：在Netflix的文化中，存在着大量的授权、360度反馈、直率和诚实。您可能已经读过Netflix的文化宣言，它强调人们需要诚实、坦诚、互相提供反馈，并专注于判断力、勇气和责任感。直到最近，Netflix才引入了职级制度——在很多年里，他们根本没有职级制度。 提供反馈是挑战性的，因为这会使人感到不适。因此，这个过程必须从高层开始，并以一种促进发展、诚实的方式进行。这并不意味着人们应互相威胁或贬低，但我们都需要明白，在项目结束或会议结束时，对方告诉我们“这是成功之处，这是失败之处”是完全可以接受的。 美国军队是世界上最重要的机构之一，它依靠这种过程生存、发展和克服困难。如果你在军队犯错，你可以确信会有人告诉你，并且你会得到帮助以确保你不会再犯同样的错误。虽然公司里没有生死攸关的情况，但我们完全可以借鉴这种纪律性。 在“人才密度”中很重要的第四点是领导力和目标设定。阻碍高绩效公司发展的一个常见问题是过多的个人目标、孤立的项目和职责，以及员工无法看到整体大局。 如果你的目标设定和绩效管理过程完全基于个人表现，那么你就在削弱你的公司。这不仅阻碍了团队合作，而且实际上没有什么是公司内任何人能够独立完成的。因此，我们的绩效管理研究不断表明，人们应该同时因其个人成就和团队成就而获得奖励。（这是相关的研究。） 为什么“人才密度”在当前尤为重要？我来列举几个原因。 首先，我们正处于一个失业率低的时期，因此每次招聘都将是一个挑战。而且，随着AI技术的帮助，公司将能够以更小的团队运作。在这样一个时刻，有什么比考虑如何“精简”你的公司、使其发挥最佳表现更合适的时机呢？ 其次，随着AI的变革，你的公司将需要极大的灵活性和学习适应能力。你需要一个高度专注、良好协调的团队来实现这一目标。而且，尽管AI将帮助每个公司提高效率，但你快速应用AI的能力将变成一个竞争优势（回想一下网站、数字化和电子商务如何实现了同样的事情）。 （我坚信，那些能够巧妙应用AI的公司将会颠覆它们的竞争对手。我对Whole Foods的手掌识别结账过程仍感到惊讶：我预见到自助服务咖啡、杂货及其他零售和酒店业务的出现。） 第三，后工业时代的商业世界将开始贬低庞大、笨重的组织。许多大公司只是需要大量员工，但正如西南航空所示，小团队的表现通常更好。因此，如果你无法将你的公司划分为小型高效团队，你的“人才密度”将受到影响。 当有关Apple的100亿美元汽车项目的书籍编写时，我敢打赌问题之一将是团队的规模和规模。我们很快就会发现。顺便说一下，我还是推荐每个人阅读《神话般的人月》，对我而言，这本书是关于围绕小团队进行组织的经典之作。 如果你是医疗服务提供者、零售商、制造商或酒店业者，“人才密度”是否适用于你？当然适用！走进一家Costco，看看员工是多么的开心和投入。然后走进一个管理混乱的零售商，你就能感受到区别。 在我的书《不可抗拒》中，我列出了那些拥抱我所称之为“人类精神不可磨灭力量”的公司的例子。没有人愿意觉得自己表现不佳。通过适当的关注责任和成长，我们可以帮助每个人超越他们自己的期望。 现在是时候重新考虑我们的组织如何运作了。我们不仅应该提升和奖励顶尖表现者，帕累托法则和“人才密度”思维还鼓励我们帮助中等表现者学习、成长，并将自己转变为明星。 让我们抛弃旧的钟形曲线、强制分配和简单化绩效管理的想法。不断追求高绩效的公司是充满活力的工作场所，他们提供卓越的产品和服务，并且对投资者而言是极好的投资。
    Innovation
    2024年03月10日
  • Innovation
    SHRM:Most American Workers Experience Incivility in the Workplace; Divisive Dialogue Undermines Inclusion and Employee Wellbeing 2024年SHRM文明研究指出，工作场所不文明行为日益普遍，近三分之二的员工在过去一个月内经历或目睹了此类行为。研究强调维护文明的重要性，将不文明的工作环境与员工不满和更高的离职率联系起来。不文明行为阻碍了员工真实自我表达和福祉，导致员工过滤他们的话语并犹豫不决地分享诚实的想法。研究确定了工作场所观察到的五大不文明行为，并鼓励组织通过参与文明对话来促进文明。 SHRM Launches "1 Million Civil Conversations" Initiative to Propel Workplace Civility ALEXANDRIA, Va.SHRM, the trusted authority on all things work, today announced its "1 Million Civil Conversations" initiative, aimed at fostering inclusive and respectful workplace cultures that allow people and business to thrive. A reported two-thirds of U.S. workers experienced or witnessed incivility in their workplace over the past month, underscoring the critical need to foster spaces of respect and understanding. SHRM believes everyone can play a role in transforming workplaces to be more civil, one conversation at a time. With two major elections on the horizon in 2024 (United States and India), the world will likely see heightened tensions and polarizing viewpoints. In addition to challenging people worldwide to engage in 1 million civil conversations this year, SHRM is equipping employers with the necessary research, resources, and guidance to empower their workforce with the skills and tools to foster civil dialogue in their workplaces. SHRM 2024 research shows the disturbing trend of incivility in today's workplaces, ultimately impacting workplace wellbeing and employee retention: Two-thirds of U.S. workers (66%) experienced or witnessed incivility in the workplace over the past month. Workers who rate their workplace as uncivil are three times more likely to express job dissatisfaction (28%); and more than twice as likely to consider leaving their job in the next year (38%). Thirty three percent of U.S. workers expect workplace conflict to increase over the next 12 months. Johnny C. Taylor, Jr., SHRM-SCP, SHRM President and CEO, emphasizes the significance of individual contributions to building a truly inclusive workplace culture. "If we want to build a world of work that works for all, we need more than corporate objectives. Civility is inclusion in action and must be carried out by the people in their daily interactions," he affirms. “Looking forward, the future of work hinges on collaboration, ideation, and innovation, with civility serving as the indispensable catalyst for bridging discord and empowering workforce synergy. SHRM is encouraging organizations and individuals to be catalysts for civility by starting 1 million civil conversations.” Throughout 2024, SHRM will be engaging people across the country through experiential pop-up events and will be measuring civility by the upcoming Civility Index, a periodic pulse survey designed to gauge the prevailing levels of civility in the workplace and society. Learn more about how SHRM is creating more civil workplaces and how you can join the conversation at https://www.shrm.org/topics-tools/topics/civility SHRM is a member-driven catalyst for creating better workplaces where people and businesses thrive together. As the trusted authority on all things work, SHRM is the foremost expert, researcher, advocate, and thought leader on issues and innovations impacting today’s evolving workplaces. With nearly 340,000 members in 180 countries, SHRM touches the lives of more than 362 million workers and their families globally. Discover more at SHRM.org. [caption id="attachment_1678" align="alignnone" width="1056"] "1 Million Civil Conversations"[/caption]
    Innovation
    2024年03月08日
  • Innovation
    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.
    Innovation
    2024年02月23日
  • Innovation
    HR领导者可以从泰勒·斯威夫特身上学到什么 人力资源领导层可以从音乐家泰勒·斯威夫特身上学到一两件事，特别是在了解她如何建立和发展自己的品牌、形象和声誉时。 “我/我们希望因什么而出名？” 渗透到 Swift 所做的每一项行动和企业决策中，这是每个企业领导者都应该考虑的事情。这种方法使她能够突破界限并探索新的音乐风格，同时从青少年乡村歌手无缝过渡到全球流行歌手和女商人，并在 2023 年收入近 20 亿美元。 人力资源领导层可以采取泰勒·斯威夫特方法的 3 种方式 1. 让关键利益相关者感受到自己的声音被倾听 通过精心设计，想想斯威夫特强有力的、相关的信息如何让她的年轻女孩目标受众感到更“被倾听”、更有希望、更强大和更自信。这些特质进一步强化了她的品牌——在当今负面头条新闻和坏演员不断出现的情况下，这些特质不容低估或掉以轻心。 斯威夫特表现出了高管风范，同时也被塑造成一个极其积极、表现出色的人，她总是在场并建立自己的人际网络。她似乎会仔细倾听别人的意见，周围都是与她合作的高素质顾问，然后执行她想做的事情，或者更准确地说，她需要做的事情。在你的角色中，你有可以依靠的盟友和顾问吗？ 作为人力资源领导者，你的“观众”并不完全是一个充满尖叫粉丝的体育场，也不是数百名参与流行歌星音乐和营销机器运作的人。但您的员工应该像 Swifties 一样感受到“被倾听”。 作为人力资源领导者，您希望您的团队和员工感到更有希望、更强大、更自信。从您的角度来看，您可以在公司中做些什么来做到这一点？   2.尝试新事物 斯威夫特是一位果断、打破常规的领导者，她能迅速尝试新模式或业务行动，使她的品牌和内容更容易获得，创造多样化的收入来源，并对其知识产权提供更大的控制权。每个人都在推销商品，但斯威夫特用她的粉丝认可的独特“商品”重塑了游戏，这推动了更多的品牌亲和力和社区——这个庞然大物每年的销售额超过 2 亿美元。 在人力资源方面，我们有很好的机会效仿 Swift 的做法，尝试新的模型和方法来支持团队。不要陷入“我们一直都是这样做的”的陷阱。与您的员工保持联系并倾听。失去员工并替换他们是一个非常昂贵的提议。倾听是为了理解，而不仅仅是回应。 斯威夫特又是一位开箱即用的领导天才，她重新录制自己的专辑，获得了对母带录音的控制权，同时巩固了未来的所有权，再次震撼了整个行业。在这里，斯威夫特巧妙地展示了她令人印象深刻的解决问题的方法，其完全原创，但与其他顶级艺术家（例如失去了对音乐的控制/权利的保罗·麦卡特尼）相比，更令人印象深刻。 人力资源领导者有很多机会以不同的方式思考来解决公司的问题：士气低落、人员流动、生产力、培训差距和招聘挑战。你可以在哪里拓展你的思维，接受新的想法和创造性的方法来解决熟悉的问题？ 事后看来，将“Swifties”商标注册是另一个明显的举动，但这只是强大而多样化的商标组合的一部分。还是无法参加演出？没问题：斯威夫特继续创作最好的音乐会电影：《泰勒·斯威夫特：时代巡回演唱会》，目前是音乐会和纪录片历史上票房最高的电影，全球票房超过 2.616 亿美元。和足球明星约会？利用社交媒体和高调的电视露面来扩展品牌并控制信息！ 从领导的角度来看，她做这样的事情看起来很不错，而且在做这件事的过程中，她总是表现出良好的关怀行为。作为人力资源领导者，我们可以开发不同的方式与员工沟通，并设计不同形式的活动以将人们聚集在一起。 3. 以同理心领导 除了为企业持续和多元化的发展制定路线之外，斯威夫特还以同理心领导。例如，当里约热内卢的一名球迷因高温死亡时，她推迟了音乐会，并立即在 Instagram 上发布了自己在体育场更衣室里“悲痛欲绝”的消息。 也许更令人印象深刻的是，巡演组织者——斯威夫特品牌的延伸——承担了责任并道歉，同时令人震惊地承认他们本可以采取更多措施来确保音乐会观众的安全。斯威夫特还与家人会面，并邀请他们作为她最后一场里约演出的嘉宾。 斯威夫特富有同理心的领导风格也体现在她关心和投资的事业上。她的慈善事业有一种方法：将金钱和时间投入到有意义的事业上，例如向家乡图书馆捐赠书籍、帮助粉丝偿还学生贷款、将歌曲收益捐赠给纽约市的一所学校，为性侵犯受害者而战，支持路易斯安那州洪水和纳什维尔龙卷风影响的人们，回馈食品银行和宠物救援组织等等。 斯威夫特还投资了 Toms Shoes 和 Bombay Socks 等无私的公司，这是她的粉丝所拥护的另一件事。Z世代特别喜欢与那些强烈表明他们愿意回馈共同利益的品牌建立联系。 人力资源部门可以在指导公司清楚地传达他们支持的事业和慈善活动方面发挥关键作用。Z 世代在加入组织时正在寻找这一点。 有人可能会说 Swift 建立的不仅仅是一家企业；更是一家企业。她在这个历来由男性主导的行业中发起了一场运动。人们购买这些品牌是因为他们想要积极的联想。斯威夫特反复证明，她拥有一种体现积极性并吸引各个年龄段人士的企业文化。她是一个如此积极的榜样，以至于父母可能愿意为孩子支付每张 1,000 美元的门票来见她。 杰出的人力资源领导者知道如何分享他们的愿景，并了解如何让人们参与他们正在迈向的任何新目标。斯威夫特如此提高了标准，以至于像其他标志性商业领袖一样，她面临着“我如何才能让人们保持兴奋？”的挑战。我很高兴看到她如何应对未来的挑战，因为我知道我们都可以从泰勒·斯威夫特的表演中学到一两点关于人力资源领导力的知识。 作者：温迪·汉森 Wendy Hanson 是 New Level Work 的联合创始人兼首席文化和社区官，负责监督所有项目，并负责招聘、管理和培养全球充满活力的高管教练和协调员社区。作为一名认证高管教练已有二十多年的经验，她曾与各种规模的公司合作，为各个行业的最高管理层领导者和业务团队提供培训
    Innovation
    2024年02月06日
  • Innovation
    OpenAI革新发布：ChatGPT团队版与GPT商店 OpenAI最近推出了两项重大创新：ChatGPT团队版和GPT商店。这些进展不仅展示了OpenAI在人工智能领域的深厚实力，而且标志着AI技术在商业和创新应用领域的新篇章。 首先，让我们深入了解ChatGPT团队版。这是一个专为团队合作设计的产品，旨在提高团队工作效率和协作。ChatGPT团队版在传统ChatGPT的基础上增加了许多新功能和服务，包括： 高级模型访问权：用户可以使用如GPT-4这样的高级模型，这些模型具有更长的上下文窗口，能够处理更复杂的对话和数据分析。 工具支持：团队版提供DALL·E 3、GPT-4 with Vision、浏览功能和高级数据分析工具，以及更高的消息上限。 数据安全与隐私：OpenAI保证不会在用户的业务数据或对话上进行模型训练，其模型也不会从用户的使用中学习，确保数据安全和隐私。 安全的工作空间：提供了一个专门的、安全的协作空间，方便团队成员之间的沟通和协作。 自定义GPT创建和共享：用户可以创建和共享定制版的GPT，以适应特定的工作流程和需求，且无需编码。 管理控制台：提供了一个便于团队和工作空间管理的管理员控制台。 新功能和改进的早期访问：用户可以优先体验OpenAI的新功能和改进。 ChatGPT团队版的定价为每个用户每月25美元（按年计费）或每个用户每月30美元（按月计费）。这一产品不仅提供了强大的AI工具和服务，而且通过其高级功能和定制选项，为各种团队和业务提供了前所未有的灵活性和高效性。 哈佛商学院的一项研究表明，使用GPT-4的波士顿咨询集团员工在完成任务时，速度比未使用AI的同事快25%，工作质量提高了40%。这一发现凸显了ChatGPT团队版在提高团队效率和工作质量方面的巨大潜力。 除了ChatGPT团队版，OpenAI还推出了GPT商店。这是一个探索和使用定制版ChatGPT的平台，旨在促进AI技术的广泛应用和创新。GPT商店汇集了来自OpenAI合作伙伴和社区开发的各种GPT应用，覆盖艺术创作、学术研究等多个领域。这个商店不仅是产品展示和购买的平台，还是一个促进创新和技术交流的社区。 在GPT商店中，用户可以发现各种有趣和有用的GPT应用，这些应用不仅展示了AI技术的多样性，也体现了OpenAI在推动AI技术广泛应用方面的决心。通过这个平台，OpenAI旨在建立一个充满活力的社区，鼓励更多的人参与到AI技术的创新和应用中来。 OpenAI的这两项新产品发布，不仅彰显了公司在人工智能领域的领先地位，更体现了其推动AI技术广泛应用和社会发展的愿景。随着AI技术的不断进步和普及，我们可以预见，OpenAI将继续在人工智能领域扮演重要角色，推动技术和社会的共同发展。 总的来说，ChatGPT团队版和GPT商店的推出是OpenAI在其使命——“通过友好AI推动所有人的福祉”——上的重要一步。通过这些产品，OpenAI不仅提高了团队协作和创新的可能性，而且为广泛的用户群体提供了更加丰富和多样的AI体验。随着这些产品在市场上的推广和应用，我们期待看到AI技术在更多领域的积极变革。
    Innovation
    2024年01月10日
  • Innovation
    Don’t Be A Copy-Cat: People Analytics as the Antidote to HR Strategy Copy-Cats This article is written to discuss: why copying the HR practices that everyone else uses doesn’t lead to the positive outcomes you assume it will. DISCLAIMER: If you like the HR strategy at your organization, you can probably stop reading now… If not, feel free to keep reading. Context Childhood wisdom: No one likes a copy-cat. We all remember being children once. Kids are known to tease each other from time to time. One common reason to be teased when you were a child was being called a “copy-cat”. It didn’t feel good, often because we knew that if we were labeled a copy-cat, it was likely true. We were copying someone else. It felt bereft, unoriginal, and commonplace. We knew we were capable of being more, but we had settled for less. We were better than that. HR strategy can be better than that too. Fast forward to the present, in HR being a copy-cat is all the rage. A priestly caste of HR influencers, HR tech consultants, FAANG companies, and sometimes even academics determine what is considered ‘en vogue’ as an HR strategy. Then, early adopter HR departments fall in-line; followed by the early majority and late majority after a few HR monkeys get “shot into space” without injury. The laggards may never arrive because they are still trying to move away from using paper files stuffed in filing cabinets, but nonetheless, being a copy-cat all the sudden became cool. Why be original when you could be doing what everyone else is doing? Perhaps, this is why Forrester is forecasting an EX winter coming soon… In the African savanna, large numbers of herd animals, such as wildebeest, zebra, and gazelles, travel in packs. Why do they do this? Because there is safety in numbers. A zebra with a single imperfection or mark is easily identified and pulled from the pack by predators. Is the same true for HR? Are we safer in a pack? Is there wisdom in being a copy-cat? Would anything different make us stand out and therefore be put in danger? I think not. I think the opposite is true, in fact. If you do all the same things as your competitors, how can you expect to get different results? Does this HR strategy sound familiar? “We’re going to try to hire the best talent, but only pay at the 50th percentile.” “We’re a performance-driven organization, but we’re going to do performance reviews once a year on a 5-point rating scale, and we’ve got to implement a pay-for-performance incentive structure.” “Our HR operating model is to use HR Business Partners, Centers of Excellence, and Shared Services.” “We’re going to copy what Google did 10 years ago, or what GE did in the 80s.” “We’re going to make data-driven decisions. I know! Let’s create another HR dashboard.” If your organization wants to be radically better, it’s going to have to try some things that are radically different. Did anyone see Coinbase’s recent blog on Talent Density? I’m not saying I agree with the changes to their HR strategy, but at least they are trying to differentiate their HR strategy to be something different. They are getting into the game, for better or worse. Source What To Do, What To Do? HR strategy should be composed of elements that are as unique to your business as your business strategy is unique to your business. It’s really as simple as that. HR Strategy is upstream of people analytics. A vanilla, copy-cat HR strategy is going to lead to vanilla, copy-cat people analytics. In my opinion, people analytics doesn’t spend enough of its resources trying to familiarize itself, influence, and control HR strategy. People analytics should speak in the social currency of the organization. We should embed ourselves and influence key decision making, and have a seat at the table by speaking in the language of the business. There is social capital to be had, and the more I learn, the more I realize the necessity of this alternative currency. We should drive strategy. With generative AI disrupting the value that human capital brings to organizations, who are the organizations that are going to be the innovators of tomorrow? Who are the organizations who will get the message early? Who will treat the need for differentiation with the existential demand that it dictates? Who will survive? Source “Best Practices” I’m tired of the term ‘best practices’. I’m at a point in my career where I bristle when I hear someone say it. Perhaps it's one of the reasons why some people hate HR. Organizational research is important, but best practices are a road to mediocrity. No one ever got fired for going with IBM, and no one ever got fired for using best practices… Until the whole firm loses to its competition, and everyone gets fired. Read it again, and think about that. It’s a short-term vs long-term thinking dilemma. Obviously, balance the two, but make sure to think with the long-term in mind. What if instead of copy-catting, you: A/B tested your HR strategy against those of other firms Used opposition research to understand your competitors HR strategy better, so you can do something different Implemented evidence-based practices on commoditized work, but experimented with firm-specific practices in the most strategically-relevant work Focused on first-principles thinking as to how firm value is derived by its talent Choose function over fad, when it comes to HR strategy Rebuild HR strategy like the Oaklands As (and the Houston Astros) tore down and rebuilt their teams based on talent derived from data. Embed data, measurement, accountability, and the “improvement feedback loop” into every single workstream that HR engages. Henry Ford once said “if you always do what you’ve always done, you always get what you’ve always got.” HR could be convicted of being mediocre. Average is over (or maybe even above average is over?). Differentiation is king. Strategic neglect (i.e., neglecting things that don’t add value) is also a valuable tool. Where do we need to be world class? Where can we be average? Answer those questions, then execute. Source Rebuilding HR Around Data & Measurement In most HR functions, data is only used to validate, not to guide. No one thinks for themselves. Mimicry and mimesis abound. People analytics is a competitive advantage for firms who use it properly. People analytics is the future of HR. Proclamations such as this have been made consistently in the past (e.g., HR is over, remote work is the future, there is no need for management, human tasks at work will be automated with AI, etc.), but this one is for real. Firms who are not embedding data into the way they do business, evaluating what they do with data, and projecting the future with data are going to be irrelevant. In the future, even in the age of generative AI, there is only one currency, and that is truth. Truth can only be derived as data put into practice. Classical test theory states that all measurement is “truth-plus-error”, with error being any deviation between measurement and the truth. Some stakeholders believe that to mean that truth can never be attained because error will always exist. Practically, this is a misinterpretation. Organizations that can manifest the best data with the least error will be the closest to truth, and therein lies the root of competitive advantage via data. People analytics is not inherently useful. Data is not inherently useful. Only accurate data, with analysis and cogent results, derived into a form that facilitates timely and accurate decision making, and that is put into action, is useful. And across the aggregate of thousands, if not millions of small decisions made leads one organization to prevailing over another. May the odds ever be in your favor. Source Moving Forward “Traditional HR” has been on the way out for decades. This article is for HR people who believe in challenging the status quo. Deep down they know there is a better way; a way forward for their organization. To not outsource their originality to others. To not be a copy-cat. Let’s focus on what the pathway forward looks like with a new highest principle – no longer “what is everyone else doing?” – but with data and measurement at the center. This article is for the HR professional who knows that HR can be smarter, faster, and better at their organization, and they are bound to make it happen. Join the movement. Don’t be a copy-cat. Let’s see how high we can fly together. PS - I’m thinking of writing a book on this topic. If you’re a publisher and you are interested in this topic, or others I’ve written about before, please contact me directly. Special shout out: Thanks to Brad Harris & Pat Downes for our previous conversations on this topic. I hope you like this article. If so, I have a few more articles coming out soon. Stay tuned. If you are interested in learning more directly from me, please connect with me on LinkedIn. Cole’s recent articles What’s Old is New: The Quest for Excellence in Workforce Planning A Historian, Demographer, & Data Scientist Walk Into a Bar… The Phoenix & The Dragon Why Buy When You Could Rent: SEC’s Push for Human Capital Disclosure Elephant Hunting: Weighing Human vs. Algorithmic Input to Decision Making For access to all of Cole’s previous articles, go here. By: Cole Napper 原文来自：https://directionallycorrectnews.substack.com/p/dont-be-a-copy-cat-people-analytics
    Innovation
    2023年11月30日
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