警惕升级：英国招聘诈骗案激增，全球招聘诈骗都不少
英国伦敦警察局的最新数据显示，向行动欺诈部门报告招聘诈骗案件的人数增加了超过八倍，过去一年通过招聘诈骗短信和WhatsApp信息盗取的金额从2万英镑跃升至近100万英镑。然而，伦敦警察局临时指挥官奥利弗·肖表示，这可能只是“冰山一角”，因为此类欺诈行为“极其被低报”。
招聘诈骗涉及犯罪分子以额外工作或收入的承诺吸引受害者，然后骗取他们的银行详情或控制他们的手机来盗取钱款。
18岁的贝拉·贝特顿（Bella Betterton）来自德文郡，她上了招聘诈骗的当，被骗走了3000英镑。诈骗分子首先通过WhatsApp信息和电话与她联系。诈骗分子通过电话进行了一次贝拉以为是真实的面试，面试内容是关于远程工作，包括使用他们的钱购买和评估产品。犯罪分子通过数十条信息和电话与贝拉沟通，直到他们在她的手机上安装了她怀疑是恶意软件的东西，从而进行了四笔大额的信用卡支付，支付给了一个不明的加密货币交易所。诈骗分子可能会要求支付少量的预付款，他们声称这些款项将在受害者的第一份工资中报销，用于支付正当的费用——如DBS检查、安全检查和小型设备。
金斯顿大学的犯罪学家、也是欺诈者使用的语言和短语专家的伊丽莎白·卡特博士说，招聘诈骗是一种高数量、多阶段的犯罪。“这些短信只会对一部分人有意义……但这是一个数量游戏。犯罪分子只需要少数人回应，受害者就会自行筛选。
“欺诈者会让受害者经历几个阶段，这些阶段是你通常会期望一个人力资源部门会问的——姓名、地址、出生日期、银行详情。
“所有这些信息本身就是有价值的数据，所以即使这个案件没有变成欺诈，这些数据也是有价值的，可以在暗网上出售。”
许多人员招聘公司已经加入了Jobsaware计划，这是执法机关和英国政府为应对这一问题而成立的特别工作组的一部分。该计划是在大流行期间招聘诈骗案件增加后推出的。到目前为止，近500家英国招聘公司向其候选人推广JobsAware，每天有超过50万个在线职位广告展示JobsAware的标志。
The rise in recruitment scams in the UK, as reported by the City of London Police, is a concerning trend that highlights the evolving nature of online fraud. The statistics indicate a significant increase in these scams, with an eightfold rise in reports to Action Fraud and the amount of money stolen jumping from £20,000 to nearly £1 million in just one year. This dramatic escalation underscores the seriousness and growing sophistication of these scams.
Recruitment scams typically involve criminals offering fake job opportunities to lure individuals. They use the promise of work or extra income to deceive victims into providing bank details or gaining access to their phones, often leading to substantial financial losses. The case of 18-year-old Bella Betterton from Devon is a poignant example. She was deceived through WhatsApp messages and phone calls, believing she was participating in a legitimate job interview. The scammers groomed her with consistent communication before presumably installing malware on her phone, which resulted in significant financial loss.
The complexity of these scams is highlighted by Dr. Elisabeth Carter, a criminologist at Kingston University. She points out that recruitment scams are high-volume, multi-stage crimes, employing specific language and tactics to manipulate victims. By mimicking the processes of legitimate HR departments, fraudsters collect valuable personal data, which can be used for further fraud or sold on the dark web.
The response to this growing threat includes initiatives like the Jobsaware scheme, a collaboration between law enforcement, the UK government, and staffing firms. This program aims to combat recruitment scams, especially following their increase during the pandemic. Nearly 500 UK recruiters are participating in promoting JobsAware to their candidates, and more than half a million online job adverts display the JobsAware logo daily, demonstrating the scale of efforts to raise awareness and protect job seekers.
This situation serves as a stark reminder of the need for vigilance in the digital age, especially when seeking employment opportunities online. Individuals must be cautious and verify the legitimacy of job offers and recruiters. Additionally, this highlights the importance of ongoing efforts by authorities, organizations, and the public in combating such fraudulent activities.
2024年的HRTech：GenAI、分析和技能技术In 2024, the field of Human Resources is experiencing a transformative shift with the integration of cutting-edge technologies such as Generative AI (GenAI), advanced analytics, and skills technology. This article by Dave Zielinski, featured on SHRM Online, delves into the evolving landscape of HR, highlighting the significant impact of these technologies on enhancing the employee experience, improving regulatory compliance, and revolutionizing talent management. Industry analysts and thought leaders share insights on the growing importance of GenAI in HR processes, the challenges of maintaining employee experience in cost-cutting scenarios, and the potential of predictive analytics in optimizing workforce planning.
接受SHRM Online采访的人力资源行业分析师、从业者和思想领袖表示，今年，人力资源职能部门将采用生成式人工智能 (GenAI)，投资于提升员工体验的技术，并采用强大的预测分析和技能技术。
人力资源领导者将转向技术，这些技术不仅可以提高法规遵从性，还可以帮助其组织做出更好、更快的人才决策并重新定义工作方式。
有远见的公司将继续投资 EX
一些分析师预测，随着高管将注意力转向降低成本和提高效率，远离包容性、公平和多样性等问题，员工体验 (EX) 将在 2024 年出现“衰退”；灵活的工作安排；和员工心理健康。员工的工作选择将减少，雇主将收回一些影响力。
不过，尽管许多组织可能会在 2024 年减少或冻结 EX 支出，但专家对此类举措的后果提出警告。
JP Gownder 是 Forrester 的副总裁兼首席分析师。他在博文中写道，根据 Forrester 研究，66% 的技术决策者表示，他们将在 2024 年增加对 EX 或人力资源技术的投资，其中许多投资将旨在提高效率，而不是 EX 结果。
但逆流而上的领导者将在 2024 年获得实实在在的好处。
“通过开发成熟的 EX 计划，您的组织可以提高生产力、降低人员流失率并提高创造力，”Gownder 写道。
其他专家认为，足智多谋的人力资源领导者会在预算紧张的情况下找到投资 EX 的方法。
管理咨询公司光辉国际 (Korn Ferry) 首席人力资源官 (CHRO) 业务的高级客户合伙人丹·卡普兰 (Dan Kaplan) 表示：“人力资源部门将被迫在低迷的市场中保持参与度，甚至在成本削减和削减的整个过程中也不例外。” “这将是一场艰难的舞蹈，但最好的人力资源领导者会找到办法做到这一点。”
光辉国际 (Korn Ferry) 专门负责人力资源问题的高级客户合伙人胡安·巴勃罗·冈萨雷斯 (Juan Pablo Gonzalez) 表示，组织对 EX 的承诺在 2024 年不会减弱，但 EX 看起来会非常不同。
“EX 的本质可能会变得更加个性化，同时也会变得不那么个性化，”冈萨雷斯说。“例如，通过使用 Microsoft Office Copilot、Workday 和 Salesforce 等大型软件平台中已有的人工智能功能，雇主和员工已经改变了他们的 EX。正在发生的情况是，员工与技术的互动越来越多地取代了与人的互动，但与技术的互动已经变得更加适合员工的特定需求和情况。”
亚特兰大人力资源咨询公司 IA 的创始人兼管理负责人 Mark Stelzner 表示，虽然由于组织面临控制盈利的挑战，预算将在 2024 年重新分配，但良好的 EX 相关技术投资将继续为公司带来红利。
“我认为投资 EX 实际上会提高效率并降低成本，”Stelzner 说。“到 2024 年，我们可能会看到组织不断转向‘流程主导、技术支持’的理念。端到端流程的优化通常会导致诸如消除现有技术债务以及统一工具和技术等决策，以减少员工的困惑并优化个性化，从而减少集成良好的接触点。”
Gartner 专门研究人力资源技术的副总裁分析师 John Kostoulas 表示，做出更具战略性的采购决策和改善现有技术生态系统的治理是改善 EX 的两个关键。Gartner 最近的研究发现，60% 的人力资源领导者认为他们当前的技术阻碍而不是改善了员工体验。
Nucleus Research 专门负责员工体验的研究经理 Evelyn McMullen 表示，仅仅为了提高效率而不是 EX 结果而设计的技术投资可能被证明是短视的。她指出，改进的 EX 通常会带来更好的绩效并降低与营业额相关的成本。
麦克马伦说：“考虑到劳动力市场和求职者优势的不断波动，减少 EX 预算的风险尤其大。” “当控制权不可避免地回到求职者手中时，保留 EX 投资的组织将能够更好地捕获和留住最优秀的人才。”
GenAI 从实验转向加速采用
到 2024 年，通过更多地采用该技术，人力资源职能将从涉足 GenAI 转向更深的领域。
随着领导者制定更严格的 GenAI 治理计划以及使用该技术的风险开始降低，人力资源和招聘部门将越来越多地使用其 HRIS 平台中已有的 GenAI 工具来编写职位描述和面试指南、创建敬业度调查、开发培训课程、分析数据，并制定政策。
世界大型企业联合会 2023 年底对首席人力资源官的调查发现，61% 的首席人力资源官计划在 2024 年投资人工智能以简化人力资源流程。
分析师 Eser Rizaoglu 表示：“许多人力资源领导者的 GenAI 之旅仍处于起步阶段，但要么通过现有的人力资源技术提供商获得 GenAI 功能，要么到 2024 年中期购买新的 GenAI 工具。” Gartner 的人力资源研究和咨询实践。
Rizaoglu 表示，许多人力资源技术供应商仍在努力弄清楚如何充分利用 GenAI 的功能，同时平衡保护数据、确保有效治理和考虑道德因素的需求。他表示：“在实现这种精细的平衡之前，GenAI 能力在人力资源领域的大规模扩散将面临挑战。”
Stelzner 表示，虽然去年 GenAI 带来了兴奋并刺激了人力资源领域的实验，但“冷酷的现实”是许多组织仍然没有准备好全力投入。
“到 2024 年，GenAI 采用率的任何增长都可能是渐进式的，包括更好地利用聊天机器人、增强员工沟通的个性化、更加关注人才招聘领域的可能性以及系统升级和实施测试的自动化。”他说。
埃森哲进行的研究发现，GenAI 有潜力改变组织 40% 的工作时间。“这并不意味着 40% 的工作岗位将会消失，而是反映了工作方式的转变，”负责该公司人力资源转型和交付实践的埃森哲董事总经理迈克尔·本亚明 (Michael Benyamin) 表示。“技术将取代一些任务，让员工在工作中变得更有生产力、更具创造力和效率。人工智能是人类能力的倍增器。”
随着 GenAI 开始增强或转变更多的工作角色，人力资源和学习领导者将需要创建敏捷的学习计划，以重新培训员工使用快速发展的 GenAI 工具的技能。许多工人几乎没有接受过如何使用该技术的培训。
Salesforce 于 2023 年进行的一项调查发现，62% 的员工表示他们缺乏有效、安全使用 GenAI 的技能。波士顿咨询集团的另一项研究发现，尽管该技术有望从根本上重塑他们的工作方式，但只有 14% 的一线员工接受过与人工智能相关的技能提升。
Benyamin 表示，随着 GenAI 在工作场所变得越来越普遍，人力资源部门必须帮助制定负责任和道德的人工智能使用政策，并制定培训计划来解决偏见、歧视、数据保护和适当数据使用等问题。
更加关注变革管理，提高新人力资源软件的采用率
专家认为，许多人力资源领导者将寻求通过采用变革管理策略来提高 2024 年技术投资的回报，例如确保员工使用新采用的技术解决方案。
人力资源面临的一项持续挑战是管理云技术供应商源源不断的更新和新功能，导致许多人力资源软件即服务 (SaaS) 许可证闲置。位于加利福尼亚州帕洛阿尔托的 SaaS 智能平台 Productiv 于 2023 年进行的一项研究发现，组织中 53% 的 SaaS 许可证总体未使用。
位于阿拉巴马州亨茨维尔的人力资源咨询和研究公司 Lighthouse Research 的首席研究官本·尤班克斯 (Ben Eubanks) 表示，许多组织低估了如何确保员工在新的人力资源平台和应用程序推出后定期使用它们。
“人力资源和人才技术不是‘按下开关就可以开始’类型的解决方案，”尤班克斯说。“但许多雇主仍然这么认为，并低估了采用该技术所需的行为改变。”
重新思考员工敬业度调查
更多的人力资源和执行团队将重新考虑如何创建敬业度调查以及分发调查的频率，以减少“调查疲劳”。
ServiceNow 高级副总裁兼员工工作流程产品总经理 Gretchen Alarcon 表示，随着组织继续努力寻找“秘方”，让员工在 2024 年更频繁地重返办公室，人力资源领导者将需要使用更有意义的方法测量工具。
她说：“组织将利用员工的声音调查和反馈来分析在办公室花费的时间与员工情绪和生产力的关系。” “这将使领导者能够根据数据而不是假设做出决策，这样他们就可以根据员工的需求、行为和提高生产力的因素来调整重返办公室 [RTO] 策略。”
从改进的技能技术中获益
转向基于技能的招聘和晋升策略的人力资源和招聘领导者将受益于技术的发展，例如使用人工智能和机器学习自动创建、组织和更新员工技能数据库的技能本体，从而显着减少体力工作量人力资源部要求。
下一代本体论和其他新兴技能技术可以使人力资源领导者更轻松地识别组织中的技能差距，然后相应地调整招聘或学习和发展计划。虽然市场上没有真正的端到端技能技术解决方案，但许多人力资源领导者正在将人工智能驱动的点解决方案结合在一起，以创建有效的技能数据库和评估工具。
“到 2024 年，随着组织采用技能智能技术，他们将开始认识到，这不是拥有最大的技能数据库，而是一个不断更新的丰富且互联的技能数据库，”Alarcon 说。她补充说，此类数据库使公司能够了解人才缺口是否是由于缺乏合适的人才或缺乏技能造成的，以及他们是否需要为未来培养、购买或借用人才。
预测分析工具变得更加强大
人力资源从业者和分析师认为，人力资源部门将受益于日益强大的预测分析工具，这些工具将改善劳动力规划和数据驱动的决策。
光辉国际 (Korn Ferry) 的冈萨雷斯 (Gonzalez) 表示：“凭借更大的数据集和改进的算法，人力资源部门应该能够采取一些措施，例如缓和过去几年的招聘盛衰周期。” 例如，冈萨雷斯表示，雇主不会雇佣数千名员工，然后在六个月后解雇其中一半，而是能够更好地预测在合理的时间内他们需要的员工数量和类型。他说：“然后他们可以雇用和培养一支更稳定的员工队伍，以造福所有组织利益相关者。”
Stelzner 认为，许多人力资源部门由于没有充分发挥数据分析的潜力而错失了机会。他说，如果未能投资分析人力资源数据所需的工具和技能，可能会导致洞察力缺失，并阻碍人力资源战略与更广泛的业务目标保持一致的能力。
“从历史上看，人力资源部门也一直在努力解决数据的准确性问题，”斯特尔兹纳说。“这会影响该职能部门依靠报告和数据分析来通知和支持其决策的能力。更糟糕的是，企业的其他部门已经接受过培训，预计人力资源系统会提供有问题的数据，因此在数据清理、报告和分析方面还有很多工作要做，以重新获得整个企业的可信度。”
Dave Zielinski 是 Skiwood Communications 的负责人，这是一家位于明尼阿波利斯的商业写作和编辑公司。
作者：Dave Zielinski
观点
2024年01月09日
观点
2023 Recap: A Turbulent Year with Significant M&A Changes in the HR Technology Market
Our annual reporting on M&A in the HR technology space is one of our most popular pieces of content year after year (see our 2022 version here). We look at some of the many announcements that happen over the course of the year, flag some key ones, and identify any big trends that seem to appear across the landscape.
2023 was no different than recent years. It’s a perennial “trend” from the “experts” in the space that “we will see more consolidation.” That statement is about as safe as saying that summer in Texas will be hot this year. Shocker.
What’s most exciting for us at Lighthouse is that many of these companies that are succeeding and shaking up the industry are also winners in the HR Tech Awards program (now accepting 2024 submissions), a clear indication that the judges in our program see significant value in what these firms are bringing to the market for their clients.
If you’re an employer looking for the right technology so support your organization, don’t hesitate to reach out for our insights.
Overall HR Tech M&A Trends and Insights
A considerable amount of consolidation in the services space, which is a bit tangential to this analysis of HR technology but significant for the larger market.
For instance, Arthur Gallagher & Co. acquired Buck in the benefits consulting and administration services space. We’re starting to see some of these benefits companies using their data in creative ways to identify health trends, provide analytics back to employers on their workforce, etc.
WilsonHCG picked up Personify in the recruitment process outsourcing industry. Our 2023 RPO research is some of the most compelling in the world when it comes to RPO buyer insights and behaviors.
Engage PEO acquired Zamp. Relatively small in the bigger scheme of things, but we’re planning some PEO research in 2024 to explore how PEO is evolving so we’re watching the space closely.
Also seeing some interesting crossover as services companies buy technology firms to scale and differentiate their services as well as technology companies buying service providers to reach more clients and bring more data into their solutions. AI-based solutions require a lot of data to train the models, and if it’s structured properly, services companies are sitting on a ton of data that can be a competitive advantage.
In the past month we’ve met with two different companies that started as services firms and built a technology that could take their intellectual property and scale it to more customers via a platform. The challenge with that is that services companies make money when they touch customer accounts and support them, but product/technology companies make money when they don’t have to touch customer accounts. It’s a difficult transition to make.
Let’s take a look at some of the announcements over the last year.
Key 2023 HR Technology Mergers and Acquisitions
Talent Acquisition and Hiring
Radancy, a global leader in recruiting with its Radancy Talent Cloud, acquired Brazen, a hiring event and communication platform and Ascendify, an enterprise-focused recruiting CRM.
Lightcast, a labor market insights company created by the merger of Emsi and Burning Glass, acquired Gazelle, a provider of B2B intelligence.
LiveHire, an HR Tech Award-winning hiring platform, acquired Arrived Workforce Connections to support growth in candidate reach and direct sourcing.
Clovers acquired Talvista to bring two inclusive hiring solutions under one roof.
Fama, an innovator in social media background screening and HR Tech Award-winning company, acquired Social Intelligence.
Spark Hire, a video interviewing solution, acquired Chally, an assessment solution. Video-only screening providers are trailing off in favor of video + assessment solutions that can provide a more full (and unbiased) picture of what candidates are about. Spark Hire also merged with/acquired Comeet, an ATS solution.
Veritone (Pandologic AI-driven programmatic recruiting solution) acquired Broadbean, a legacy recruiting technology provider. Intrigued by the depth and breadth of data this might offer to train the Veritone AI solutions under the hood.
Appcast, a leader in programmatic recruiting technology, acquired Bayard Advertising.
Hirevue acquired Modern Hire, an HR Tech Award-winning provider of video interview and hiring assessment solutions for enterprise employers.
Fountain, a high-volume hiring and onboarding solution, acquired Clevy.
Talent Management and Employee Experience
Engagedly, an HR Tech Award-winning company, acquired theEMPLOYEEapp for enabling client communications with frontline workers.
Neobrain, a global skills insights and intelligence provider, acquired Flashbrand to establish a US presence and bring its popular technology to North America.
Mitratech, an HR Tech Award-winning company and leader in employer compliance solutions, acquired Trakstar (talent acquisition and development) and Circa (DEI and OFCCP compliance).
Simpplr, an internal communication and work hub, acquired Socrates.ai, one of the industry’s most compelling intelligent chatbot solutions for employee experience and navigation, to increase the ease of which employees find, access, and act on information.
Edenred acquired Reward Gateway, a rewards and recognition provider.
Perceptyx acquired Humu, the “intelligent nudges” company made famous by its founder, former Google HR leader Lazlo Bock. Intrigued to see this functionality in the Perceptyx ecosystem.
Peoplelogic, an HR Tech Award-winning firm, picked up Plai to enhance its features across performance management and the overall employee experience.
Core HR/HCM/Compensation
Salary.com, an HR Tech Award-winning company, acquired CompXL to scale its enterprise compensation management functionality across merit increases, bonus allocations, and other rewards.
Paycor, an HR Tech Award-winning SMB HR, payroll, and talent solution, acquired Verb for microlearning support.
ADP, a leader in payroll and HR solutions, acquired Sora, a low-code workflow automation tool.
Deltek, the global leader in payroll and finance solutions for government contractors, acquired Replicon, a workforce management system.
When I Work, an HR Tech Award-winning company for its workforce management solution, acquired Lean Financial to incorporate earned wage access into the solution.
UKG acquired Immedis, a global payroll solution, to enable more seamless payroll in countries around the world.
Learning and Talent Development
This year we’re unveiling our new Learning Tech Awards program to focus more deeply on the sophisticated and robust technologies supporting talent development, employee growth, and skills intelligence across the industry. If you operate in this space, you won’t want to miss it.
Docebo, a leader in the global LMS market, acquired Edugo.ai to increase its AI capabilities.
Go1, a leader in global learning content, acquired Blinkist and Anders Pink.
LMS365 acquired performance management solution Weekly10.
The Bottom Line
In spite of the continuing challenges from an economic perspective, 2024 is poised to have some interesting activity. Election years are always a bit unpredictable, and many new providers are emerging to tackle today’s most pressing talent and workforce challenges. Stay on top of the latest by following our ongoing research and insights across the HR technology market.
Curious what we do at Lighthouse?
We work with employers by providing research and advisory services around 1) the complicated HR technology landscape, 2) the talent trends and practices that matter most to the modern workforce, and 3) executive presentations to internal teams on how the market is changing in their industry or demographic.
We work with solution providers and vendors that want to sell more product and serve more customers. We use a combination of advisory, industry insights, market intelligence, and custom research to support our partners.
HR Tech Awards opens for submissions on January 3rd: learn about benefits of participation
Ben Eubanks
Ben Eubanks is the Chief Research Officer at Lighthouse Research & Advisory. He is an author, speaker, and researcher with a passion for telling stories and making complex topics easy to understand.
His latest book Talent Scarcity answers the question every business leader has asked in recent years: “Where are all the people, and how do we get them back to work?” It shares practical and strategic recruiting and retention ideas and case studies for every employer.
His first book, Artificial Intelligence for HR, is the world’s most-cited resource on AI applications for hiring, development, and employee experience.
Ben has more than 10 years of experience both as an HR/recruiting executive as well as a researcher on workplace topics. His work is practical, relevant, and valued by practitioners from F100 firms to SMB organizations across the globe.
He has spoken to tens of thousands of HR professionals across the globe and enjoys sharing about technology, talent practices, and more. His speaking credits include the SHRM Annual Conference, Seminarium International, PeopleMatters Dubai and India, and over 100 other notable events.
Contact Ben
观点
2024年01月05日
观点
Josh Bersin:2024: The Year That Changes Business Forever (Podcast)The podcast "2024: The Year That Changes Business Forever" by Josh Bersin explores anticipated transformations in business by 2024. It highlights the impact of AI, labor shortages, and evolving organizational structures. The podcast delves into the 2023 economic performance, changes in employee engagement, and the necessity for businesses to adapt strategically. It emphasizes a shift towards dynamic, flatter organizations and the critical role of systemic HR practices in shaping future business landscapes.
Josh Bersin探讨了2024年企业预期的转型。这些转型由AI的应用、劳动力短缺和组织结构的变化驱动。播客讨论了2023年的经济表现、员工参与度的变化以及企业为应对未来挑战所需的适应策略。它强调了向动态、扁平化组织的转变和系统性人力资源实践在塑造未来商业环境中的重要作用。
In this podcast I recap 2023 and discuss the big stories for 2024, and to me this year is a tipping point that changes business forever. Why do I say this? Because we’re entering a world of labor shortages, redesign of our companies, and business transformation driven by AI. We’ll look back on 2024 and realize it was a very pivotal year.
(Note: In mid-January we’re going to be publishing our detailed predictions report. This article is an edited transcript of this week’s podcast, so it reads like a conversation.)
Podcast Begins:
Interestingly, the entire year 2023 people were worried about a recession and it didn’t happen. In fact, economically and financially, we had a very strong year. Inflation in the United States and around the world went down. We did have to suffer rising interest rates, and that was a shock, but it was long overdue.
I really think the problem we experienced is we had low interest rates for far too long, encouraging speculative investment. Now that the economy is more rational, consumer demand is high, the business environment is solid, and the stock market is performing well. The Nasdaq is almost at an all time high, the seven super stocks did extremely well: the big tech companies, the big retailers, the oil companies, many of the consumer luxury goods companies did extremely well. And the only companies that didn’t do well were the companies that couldn’t make it through the transformation that’s going on.
On the cultural front we had the Supreme Court overturning affirmative action in education, which led to a political backlash on diversity and inclusion. The woke mind virus by Elon Musk and similar discussions further pushed back DEI programs, which has made chief diversity officers life difficult. We’re living through two wars, which have been very significant for many companies. I know a lot of you have closed down operations in Russia, and anybody doing business in Israel is having a tough time. And we’ve had this continuous period where every piece of data about employee engagement shows that employees are burned out, tired, stressed. They feel that they’re overworked.
Despite this employee sentiment, wages went up by over 5% and people who changed jobs saw raise wages of 8% or more. The unemployment rate is very low so there are a lot of jobs. You could ask yourself, why are people stressed?
I think it’s a continued overhang of the pandemic: the remote work challenges, the complexities and inconsistencies in hybrid work. And something else: the younger part of the workforce, those who are going to be living a lot longer than people who are baby boomers, are basically saying I don’t really want to kill myself just to get ahead. I want to have a life. I want to quietly quit. If my company don’t take care of me, I’m going to work my wage, meaning I’m going to work as hard as I’m paid, no more than that. And that mentality has created an environment for the four-day work week, which I think is coming quicker than you realize. And unions, which are politically in favor, are rising at an all time increase in about 25, 30 years.
Inflation and the need to raise wages to attract talent leads to pay equity problems. This domain is more complex than you think. You can read about it in our research and in 2024 it belongs on your list. 2024 will also see enormous demand for career reinvention, career development, growth programs, coaching, mentorship, allyship and support amongst the younger part of the workforce. And that means that if you’re in retail, healthcare, hospitality, or one of the other industries that hires younger people you have to accommodate this tremendous demand for benefits. These are things that became very clear in 2023.
But let’s talk about the elephant in the room: the biggest thing that happened in 2023 was AI.
AI has transformed the conversations we have about everything from media to publishing to HR technology to recruiting to employee development to employee experience. As you probably know, I’m very high on AI. I think it’s going to have a huge transformational effect on our companies, our jobs, our careers, and our personal lives. AI will improve our health, our ability to learn, the way we consume news (note that the NYT just sued OpenAI and Microsoft for copyright infringement). Almost every part of our life will be transformed by AI.
I know from our conversations that most of you are trying to understand it and see where it fits. And many of you have been told by your CEO, “we need an AI strategy for the company as well as in HR.” And the AI strategy in HR is one thing, but the bigger topic is the rest of the company. So HR is going to have to be a part of this transformation: the new roles, jobs, rewards, and skills we need.
This year I’m very excited that we introduced Galileo™, which about 500 or so of you have been using. We’re going to launch the corporate version for everybody in the corporate membership in February, so corporate members stay tuned (or join). Galileo brings AI to HR in an easy-to-use, safe, and high-value way, so it will help you get your strategy together. It’s basically ready to go. Then later in the year we’ll launch a version to the JBA community and more. AI, despite all the fear-mongering, is already a very positive technology.
Where are we going next? Well as the title of this article states, I think this is the year that changes business forever. And I’m not trying to be hyperbolic, I really see a tipping point. Let me give you the story.
For about a decade I’ve been writing about the flattening of organizations, breaking down of hierarchies, creating what I used to call the networked organization. And this is now mainstream and we’ve decided to call it the Dynamic Organization.
And what we mean by this, as you read about in the Dynamic Organization research or in the Post-Industrial Age study, is that the functional hierarchies of jobs, careers, organizations and companies are being broken down for really good reasons.
The reason we have functional hierarchies, job levels and siloed business functions is because they’re patterned after the industrial age when companies made money by selling products and services at scale. The automobile industry, the oil and gas industry, the manufacturing industries, the CPG industries, even the pharmaceutical companies are essentially building things, bringing them to market, launching them, selling them, and distributing them in a linear chain. And that “scalable industrial business model” is how we designed our organizations.
So we built large organizations for R&D, large organizations for product management and product design and packaging, large organizations for marketing, large organizations for sales, large organizations for business development and distribution, supply chain, and so on (including Finance and HR). And all these ten or fifteen business functions had their own hierarchies. So you, as an employee, worked your way up those hierarchies. When I graduated from college in 1978 as an engineer, I went into one of those hierarchies.
For each employee you were an engineer, a salesperson, a marketing manager, or whatever and you worked your way up the pyramid. And at some point in your career you crossed over and did other things, but that was fairly unusual. That wasn’t really the career path. You worked about 35-40 years in that profession and then you retired.
And a lot of companies had another construct: management and labor. Management decided “what to do” and labor “did it.”
And all of these designs helped us build most of the HR practices we use today, including hiring, pay, performance management, succession, career management, goal setting, leadership development, and on and on. Today, if you look at how the most valued companies in the world, they don’t operate this way any more. Why? Because it slows them down like molasses. If you have to traverse a functional hierarchy to come up with a new idea it takes months or years to create something new.
Today value is created through innovation, time to market, closeness to customers, and unique and high-value offerings. The “hierarchy” wasn’t designed for this at all.
Here are a few dogmas to consider. We used to think that all new ideas come out of R&D. That’s crazy. Of course R&D is important, but some of the most innovative companies in the world don’t even have R&D departments, they have product teams. The Research Department at Microsoft didn’t even invent AI, the company had to partner with OpenAI, a company that has less than a thousand employees.
Here’s another one to consider. Deloitte consultants used to talk about “innovation at the edge,” otherwise known as “skunk works.” We used to advise clients to “separate the new ideas from the scale business” so they new ideas don’t get crushed or ignored. Well today all the new ideas come from the operating businesses, and we iterate in a real-time way. So there’s another industrial organization structure that just no longer applies.
So what we’ve been going through in the dynamic organization, and we’ve studied this in detail, is that we’ve got to design our companies to be flatter. We’ve got to simplify the job titles and descriptions so people can move around. We have to organize people into cross functional teams, we have to motivate and train people to work across the functional silos. We have to build agile working groups, we have to redo performance management around teams and projects, not around individual goals and cascading goals. We need to build pay equity into the system so you’re paid fairly regardless of where you started.
Let’s talk about pay. One of the problems with the hierarchy is you get a raise every year based on your performance appraisal. And after a few years your pay may have been quite a bit different than somebody sitting next to you simply because of your appraisals. But you may not be delivering any more than them. That wasn’t fair.
If you came into the company with a background in marketing, you made less money than somebody who came into the company with a background in engineering. But five years later you might be doing the same stuff but making different amounts of money. And then there’s gender bias, age bias, and other non-performance factors. In a “skills meritocracy,” as we call it, pay equity has to get fixed.
We’ve got to have developmental careers and talent marketplaces and open job opportunities and mentoring for people. And these people practices are the facilitation of becoming more dynamic. And the problem of not being dynamic is what happened at Salesforce, Meta, and other tech companies last year. Salesforce hired thousands of salespeople during the last upcycle after the pandemic, and then a year later laid most of them off. Meta did the same thing. Google’s probably next.
These companies, operating in the industrial mindset, thought that the only way to grow is to hire more salespeople, more engineers, or more marketing folks. But the quantity of people in one of these business functions doesn’t necessarily drive growth and profitability. What matters is how they work together and what they do, not how many of them there are.
This old idea that we’re going to grow the company by hiring, hiring, hiring is gone. It doesn’t work anymore. It’s still a part of the growth part of the company, you’re always hiring to replace people, to bring new skills, et cetera, and to bring new perspectives. But in a dynamic organization, a lot of the growth comes from within. People grow too.
Even the word growth mindset has become overused. We need to have an organizational growth mindset so that we can grow as an organization. A great example of this is Intel. Intel lost their way in the manufacturing of semiconductors and also in the R&D. Now they’re reinventing themselves internally and their stock is skyrocketing. They didn’t hire some guru to tell them what to do, they know what to do. They just need to get around to doing it.
Google has more AI engineers than OpenAI, Anthropic, and all the other little guys put together, but they didn’t execute well. Now they’re executing better. They brought their AI teams together into cross-functional groups and they’re sharing IP from YouTube with other business areas. I bet they stomp many of the others in AI once they get it going. That’s part of being a dynamic organization.
You as HR people know better than anybody how dysfunctional it is when there are multiple groups in the company doing competing things and they’re not working together because they don’t know about each other, or they don’t talk to each other. There’s no cross fertilization or they’re protecting their turf. All of these are the things that get in the way of being a dynamic organization.
And the reason it’s relevant in the next year is this has taken hold. Things like talent marketplaces and career pathways and skills-based organizations, skills based hiring, skills based pay, skills based careers, skills based development, et cetera… these are not just HR fads, they’re solutions to this big shift: making companies more dynamic. Despite their value in the past, hierarchical stove-piped companies don’t operate very well anymore.
Now this isn’t an A-B switch type of thing. This is an evolution, but it’s taking place very quickly. And the reason we came up with this concept of Systemic HR is we in HR have to do the same thing. The HR function itself operates in silos. We’ve got the recruiting group, the DEI group, the Comp group, the L&D group, the business partners, the group that does compliance, the group that worries about wellbeing. We’ve got somebody over here is doing an EX project, somebody over there is doing a data management project, a people analytics group.
Okay. Those are all great functional areas that belong in HR. But if they’re not working together on the problems that the company has, and I mean the big problems, growth, profitability, productivity, M&A, etc., then who cares? Then you’re at level one or level two in systemic HR. We built the Systemic HR initiative around business problems. And that’s how we came up with the new HR operating model (read more details here or view the video overview).
I think Systemic HR will be a very big deal for 2024, and there are many reasons. Not only are we living in a labor shortage but there’s another accelerant, and that is AI. For those of you that have used Galileo, and I hope you all get a chance to use it this year, it’s absolutely unbelievable how AI can pull together information, data, text from many sources in the company and make sense of what your company is doing.
You know as well as I do, if you’ve worked in sales, if you’ve worked in marketing, if you worked in finance, these are siloed groups. Few companies have a truly integrated data management system for all of their customer data match to their sales, data match to their revenue, data match to their marketing. Customer data platforms are a idea, but it doesn’t really happen very often, and it takes tens to hundreds of millions of dollars and many, many systems to do that. Well, AI does this almost automatically.
So when you pull together a tool like Galileo, and you use our research as part of the corpus, and you add data about employee turnover, for example, in your company, or pay variations, you’ll see the relationship between pay and turnover just by asking a question. You don’t have to go spend months doing an analysis and trying to figure out if the analysis is any good. And that’s happening all over the company in sales and customer service and R&D and marketing – everywhere.
So this more integrated, dynamic organization is happening before your eyes. In 2024, this is the context for almost everything we’re going to be working on now.
The other context is the labor market, which is going to be very tough. You’ve read about from us and others about how tight the labor market is now. Unemployment in the United States is 3.8%, and it’s not going to get much better. Even if we do have a recession, which is questionable, there aren’t enough people to hire. The fertility rate is low, and even if every company gives employees fertility benefits and they all have babies, it will take twenty years for these people to go to work. So all of the developed countries: US, UK, Canada, Germany, Japan, the Nordics, China, Russia, the fertility rate has been low for a long time. The World Bank sees working population shrinking within ten years in almost every developed economy.
Since hiring is going to get harder and we’ll see fewer and fewer working people, companies have to be much more integrated in hiring. And we all have to look the Four R’s: Recruit, Retain, Reskill, Redesign. This puts HR in the middle of a lot of job redesign, career reinvention, and a serious look at developing skills, not hiring skills, and using the tools we have as hr professionals to help the organization improve productivity without just hiring and hiring and hiring.
I measure the success of companies by two things. One is their endurance: how well have they fared over ups and downs? The second is their revenue per employee. Companies with low revenues per employee tend to be poorly managed companies relative to their peers. Of course there’s a lot of industry differences.
When we went through our GWI industry work: healthcare, consumer goods, pharma, banking, we could see the high performing companies were very efficient on a headcount basis. And we found out these companies are actually implementing Systemic HR practices.
The other driver that we’re living in a service economy. Interestingly enough, in the United States, more than 70% of our GDP is now services. So the people you have, the humans in your company, are the product. And if you’re not getting good output per dollar of revenue per human, you’re not running the company very well.
And this leads to many management topics.
How are we going to build early and mid-level leaders?
How can we rethink what employees really need? The topics of employee engagement and employee experience are really 25 to 30 years old. They need a massive update.
How are we going to implement AI in L&D and replace a lot of these old systems that everybody kind of hates, but we’re stuck with?
What’s going on with the ERP vendors and what role will they play as we replace our HR tech with AI powered systems?
How will we implement scalable talent intelligence? In a world of labor shortages talent intelligence becomes even more important, whether you think of it for sourcing and recruiting or an internal mobility or just a strategic planning initiative.
How do we all get comfortable with AI?
And then there’s this issue of Systemic HR and developing your team, your function, your operating model to be more adaptive and more dynamic.
So I look back on 2023 I feel it was one of the most fascinating and fun and enriching years that I’ve had. I am always amazed and impressed and energized by you, by you guys who were out there on the firing lines, dealing with these complex issues and companies with old technologies and all sorts of changes going on and how you’re adapting. I continue to be more impressed and more excited about the HR profession every year. I think a lot of people who aren’t in HR think we do a lot of compliance and administration stuff and we fire people. That is the tiniest part of what we do.
2024 is going to be an important year. You as an HR professional are going to have to learn a lot of things. You’re going to learn about Systemic HR issues, you’re going to learn about AI, and you’re going to learn to be a consultant.
There’s no question in my mind that over the next decade or two dynamic organization management is going to become a bigger and bigger issue – how we manage people and companies. And I don’t mean manage like supervise, I mean develop, move, retain, pay, et cetera, culture, all of those things.
I leave 2023 very energized about what’s to come with AI. And if you’re afraid of AI, just take a deep breath and relax. It’s not going to bite you. There’s nothing evil here. It’s a data driven system. If you don’t have your data act together, you’re not going to get a lot of good value out of AI.
I talked to Donna Morris at Walmart last week; I talked to Nickle LaMoreaux at IBM; and I talked with the senior HR leaders at Microsoft. They’re all seeing huge returns on investment from the early implementations, and seeing hundreds of use cases. We’re going to have a lot of new tools and lots of vendor shakeout. (Check out what SAP is up to and where Workday is going.)
Stay tuned for our big Predictions report coming out in mid January. That report is my chance to give you some deep perspectives on where I think things are going, recap things that have happened over the last couple of years, and give you some perspectives for the year ahead.
As always we would be more than happy to walk through these things with your team.
I hope you have a really nice holiday season and you take a deep breath.
The world is never perfect. It’s never been perfect. It wasn’t perfect in the past. It won’t be perfect in the future.
But the environment you live in and the environment that you create can be enriching, enjoyable, productive, and healthy, and fun if you decide. And I think we all have the opportunity to make those decisions.
It has been a pleasure and an honor for me to serve and work with you this last year, and I’m really looking forward to an amazing 2024 together.
–END OF PODCAST–
Irresistible: The Seven Secrets of the World’s Most Enduring, Employee-Focused Organizations
观点
2023年12月30日
观点
Sam Altman的17条“希望早点知道的建议”,帮你更好得做2024年规划Sam Altman 给你17条“希望早点知道的建议”
即将踏入2024年，Sam Altman更新博客，写下17条“希望早点知道的建议”，希望对正在做2024年规划的人们有帮助。
1.乐观、执着、自信、原始的动力和人际关系是事情开始的关键。
2.有凝聚力的团队，冷静和紧迫的合理搭配，以及非凡的投入是成事的关键。长期的方向目标是稀缺的；无需过分担心短期内其他人的看法，随着时间的推移，这会变得更容易。
3.对于团队而言，完成一项真正重要的艰巨任务，比起做一些并不那么重要的简单工作要更有意义；大胆的想法能够激发斗志。
4.激励机制的效果有如超能力，在设定时需经过慎重考虑。
5.把你的资源集中在少数有着高度信念的目标上，实际上可以剔除掉的东西多于你的想象。
6.沟通要清晰简洁。
7.每当你看到官僚主义和废话时，就与之斗争，也要让其他人参与斗争。不要让组织架构妨碍人们高效地协同工作。
8.结果才是最重要的；好过程不是坏结果的遮羞布。
9.花更多的时间在招聘上。在高潜力、成长快的人身上冒险。除了智力之外，除了智力外，还要寻找他们实际完成任务的证据。
10.超级明星实际比表面更有价值；但评价员工时，需要考虑他们对组织整体绩效的真正影响。
11.迅速迭代能可以弥补许多不足；通常情况下，如果你能迅速调整，即使犯下错误也无所谓。计划应以十年为周期，执行则应以周来衡量。
12.不要违背商业上的基本规律。
13.灵感易逝，人生苦短。不行动是一种既隐而又致命的风险。 14.规模往往具有令人惊讶的涌现特性。
15.借助复合增长的力量；尤其是，你会想要创建一个随着规模扩大而能够获得增长优势的企业。
16.站起来继续前行。
17.与优秀的人共事是人生中最美好的部分之一。
What I Wish Someone Had Told Me
Optimism, obsession, self-belief, raw horsepower and personal connections are how things get started.
Cohesive teams, the right combination of calmness and urgency, and unreasonable commitment are how things get finished. Long-term orientation is in short supply; try not to worry about what people think in the short term, which will get easier over time.
It is easier for a team to do a hard thing that really matters than to do an easy thing that doesn’t really matter; audacious ideas motivate people.
Incentives are superpowers; set them carefully.
Concentrate your resources on a small number of high-conviction bets; this is easy to say but evidently hard to do. You can delete more stuff than you think.
Communicate clearly and concisely.
Fight bullshit and bureaucracy every time you see it and get other people to fight it too. Do not let the org chart get in the way of people working productively together.
Outcomes are what count; don’t let good process excuse bad results.
Spend more time recruiting. Take risks on high-potential people with a fast rate of improvement. Look for evidence of getting stuff done in addition to intelligence.
Superstars are even more valuable than they seem, but you have to evaluate people on their net impact on the performance of the organization.
Fast iteration can make up for a lot; it’s usually ok to be wrong if you iterate quickly. Plans should be measured in decades, execution should be measured in weeks.
Don’t fight the business equivalent of the laws of physics.
Inspiration is perishable and life goes by fast. Inaction is a particularly insidious type of risk.
Scale often has surprising emergent properties.
Compounding exponentials are magic. In particular, you really want to build a business that gets a compounding advantage with scale.
Get back up and keep going.
Working with great people is one of the best parts of life.
https://blog.samaltman.com/what-i-wish-someone-had-told-me
Josh Bersin人工智能实施越来越像传统IT项目Josh Bersin的文章《人工智能实施越来越像传统IT项目》提出了五个主要发现：
数据管理：强调数据质量、治理和架构在AI项目中的重要性，类似于IT项目。
安全和访问管理：突出AI实施中强大的安全措施和访问控制的重要性。
工程和监控：讨论了持续工程支持和监控的需求，类似于IT基础设施管理。
供应商管理：指出了AI项目中彻底的供应商评估和选择的重要性。
变更管理和培训：强调了有效变更管理和培训的必要性，这对AI和IT项目都至关重要。
原文如下，我们一起来看看：
As we learn more and more about corporate implementations of AI, I’m struck by how they feel more like traditional IT projects every day.
Yes, Generative AI systems have many special characteristics: they’re intelligent, we need to train them, and they have radical and transformational impact on users. And the back-end processing is expensive.
But despite the talk about advanced models and life-like behavior, these projects have traditional aspects. I’ve talked with more than a dozen large companies about their various AI strategies and I want to encourage buyers to think about the basics.
Finding 1: Corporate AI projects are all about the data.
Unlike the implementation of a new ERP system, payroll system, recruiting, or learning platform, an AI platform is completely data dependent. Regardless of the product you’re buying (an intelligent agent like Galileo™, an intelligent recruiting system like Eightfold, or an AI-enabling platform to provide sales productivity), success depends on your data strategy. If your enterprise data is a mess, the AI won’t suddenly make sense of it.
This week I read a story about Microsoft’s Copilot promoting election lies and conspiracy theories. While I can’t tell how widespread this may be, it simply points out that “you own the data quality, training, and data security” of your AI systems.
Walmart’s My Assistant AI for employees already proved itself to be 2-3x more accurate at handling employee inquiries about benefits, for example. But in order to do this the company took advantage of an amazing IT architecture that brings all employee information into a single profile, a mobile experience with years of development, and a strong architecture for global security.
One of our clients, a large defense contractor, is exploring the use of AI to revolutionize its massive knowledge management environment. While we know that Gen AI can add tremendous value here, the big question is “what data should we load” and how do we segment the data so the right people access the right information? They’re now working on that project.
During our design of Galileo we spent almost a year combing through the information we’ve amassed for 25 years to build a corpus that delivers meaningful answers. Luckily we had been focused on data management from the beginning, but if we didn’t have a solid data architecture (with consistent metadata and information types), the project would have been difficult.
So core to these projects is a data management team who understands data sources, metadata, and data integration tools. And once the new AI system is working, we have to train it, update it, and remove bias and errors on a regular basis.
Finding 2: Corporate AI projects need heavy focus on security and access management.
Let’s suppose you find a tool, platform, or application that delivers a groundbreaking solution to your employees. It could be a sales automation system, an AI-powered recruiting system, or an AI application to help call center agents handle problems.
Who gets access to what? How do you “layer” the corpus to make sure the right people see what they need? This kind of exercise is the same thing we did at IBM in the 1980s, when we implemented this complex but critically important system called RACF. I hate to promote my age, but RACF designers thought through these issues of data security and access management many years ago.
AI systems need a similar set of tools, and since the LLM has a tendency to “consolidate and aggregate” everything into the model, we may need multiple models for different users.
In the case of HR, if build a talent intelligence database using Eightfold, Seekout, or Gloat which includes job titles, skills, levels, and details about credentials and job history, and then we decide to add “salary” … oops.. well all of a sudden we have a data privacy problem.
I just finished an in-depth discussion with SAP-SuccessFactors going through the AI architecture, and what you see is a set of “mini AI apps” developed to operate in Joule (SAP’s copilot) for various use cases. SAP has spent years building workflows, access patterns, and various levels of user security. They designed the system to handle confidential data securely.
Remember also that tools like ChatGPT, which access the internet, can possibly import or leak data in a harmful way. And users may accidentally use the Gen AI tools to create unacceptable content, dangerous communications, and invoke other “jailbreak” behaviors.
In your talent intelligence strategy, how will you manage payroll data and other private information? If the LLM uses this data for analysis we have to make sure that only appropriate users can see it.
Finding 3: Corporate AI projects need focus on “prompt engineering” and system monitoring.
In a typical IT project we spend a lot of time on the user experience. We design portals, screens, mobile apps, and experiences with the help of UI designers, artists, and craftsmen. But in Gen AI systems we want the user to “tell us what they’re looking for.” How do we train or support the user in prompting the system well?
If you’ve ever tried to use a support chatbot from a company like Paypal you know how difficult this can be. I spent weeks trying to get Paypal’s bot to tell me how to shut down my account, but it never came close to giving me the right answer. (Eventually I figured it out, even though I still get invoices from a contractor who has since deceased!)
We have to think about these issues. In our case, we’ve built a “prompt library” and series of workflows to help HR professionals get the most out of Galileo to make the system easy to use. And vendors like Paradox, Visier (Vee), and SAP are building sophisticated workflows that let users ask a simple question (“what candidates are at stage 3 of the pipeline”) and get a well formatted answer.
If you ask a recruiting bot something like “who are the top candidates for this position” and plug it into the ATS, will it give you a good answer? I’m not sure, to be honest – so the vendors (or you) have to train it and build workflows to predict what users will ask.
This means we’ll be monitoring these systems, looking at interactions that don’t work, and constantly tuning them to get better.
A few years ago I interviewed the VP of Digital Transformation at DBS (Digital Bank of Singapore), one of the most sophisticated digital banks in the world. He told me they built an entire team to watch every click on the website so they could constantly move buttons, simplify interfaces, and make information easier to find. We’re going to need to do the same thing with AI, since we can’t really predict what questions people will ask.
Finding 4: Vendors will need to be vetted.
The next “traditional IT” topic is going to be the vetting of vendors. If I were a large bank or insurance company and I was looking at advanced AI systems, I would scrutinize the vendor’s reputation and experience in detail. Just because a firm like OpenAI has built a great LLM doesn’t mean that they, as a vendor, are capable of meeting your needs.
Does the vendor have the resources, expertise, and enterprise feature set you require? I recently talked with a large enterprise in the middle east who has major facilities in Saudi Arabia, Dubai, and other countries in the region. They do not and will not let user information, queries, or generated data leave their jurisdiction. Does the vendor you select have the ability to handle this requirement? Small AI vendors will struggle with these issues, leading IT to do risk assessment in a new way.
There are also consultants popping up who specialize in “bias detection” or testing of AI systems. Large companies can do this themselves, but I expect that over time there will be consulting firms who help you evaluate the accuracy and quality of these systems. If the system is trained on your data, how well have you tested it? In many cases the vendor-provided AI uses data from the outside world: what data is it using and how safe is it for your application?
Finding 5: Change management, training, and organization design are critical.
Finally, as with all technology projects, we have to think about change management and communication. What is this system designed to do? How will it impact your job? What should you do if the answers are not clear or correct? All these issues are important.
There’s a need for user training. Our experience shows that users adopt these systems quickly, but they may not understand how to ask a question or how to interpret an answer. You may need to create prompt libraries (like Galileo), or interactive conversation journeys. And then offer support so users can resolve answers which are wrong, unclear, or inconsistent.
And most importantly of all, there’s the issue of roles and org design. Suppose we offer an intelligent system to let sales people quickly find answers to product questions, pricing, and customer history. What is the new role of sales ops? Do we have staff to update and maintain the quality of the data? Should we reorganize our sales team as a result?
We’ve already discovered that Galileo really breaks down barriers within HR, for example, showing business partners or HR leaders how to handle issues that may be in another person’s domain. These are wonderful outcomes which should encourage leaders to rethink how the roles are defined.
In our company, as we use AI for our research, I see our research team operating at a higher level. People are sharing information, analyzing cross-domain information more quickly, and taking advantage of interviews and external data at high speed. They’re writing articles more quickly and can now translate material into multiple languages.
Our member support and advisory team, who often rely on analysts for expertise, are quickly becoming consultants. And as we release Galileo to clients, the level of questions and inquiries will become more sophisticated.
This process will happen in every sales organization, customer service organization, engineering team, finance, and HR team. Imagine the “new questions” people will ask.
Bottom Line: Corporate AI Systems Become IT Projects
At the end of the day the AI technology revolution will require lots of traditional IT practices. While AI applications are groundbreaking powerful, the implementation issues are more traditional than you think.
I will never forget the failed implementation of Siebel during my days at Sybase. The company was enamored with the platform, bought, and forced us to use it. Yet the company never told us why they bought it, explained how to use it, or built workflows and job roles to embed it into the company. In only a year Sybase dumped the system after the sales organization simply rejected it. Nobody wants an outcome like that with something as important as AI.
As you learn and become more enamored with the power of AI, I encourage you to think about the other tech projects you’ve worked on. It’s time to move beyond the hype and excitement and think about real-world success.
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