change management - 北美华人人力资源协会

change management

The top 5 HR trends today – and HR's guide to what's next SAP SuccessFactors 每年都会深入研究全球 HR 趋势，以帮助企业制定更有效的人才战略。2025 年，他们分析了来自 40 家全球权威媒体的 254 项预测，归纳出 5 大核心“元趋势”，展现 HR 在企业中的双重角色：既是变革的“指挥者”，也是政策落地的“引航者”。 1️⃣ 重新连接员工：由于经济压力、决策争议和信任危机，员工体验恶化，57% 的员工认为如果公司不采取措施，他们的倦怠问题不会改善。HR 需关注心理契约，增强员工信任。 2️⃣ AI 从炒作走向实际价值： AI 进入大规模落地阶段，企业需明确 ROI 并平衡员工和领导者对 AI 价值的不同预期。46% 的员工认为 AI 省下的时间属于自己，而非公司。 3️⃣ 技能转型的平衡策略：由于 AI 发展迅猛，企业技能鸿沟加剧。除了关注技能，薪酬激励成为推动学习的重要因素，54% 的员工表示，如果公司实施基于技能的薪酬体系，他们会更愿意学习新技能。 4️⃣ DEI&B 的分歧：企业对多元化、公平性和包容性（DEI&B）态度不一，26% 的员工认为公司对 DEI&B 关注过多，而 33% 认为关注太少。HR 需明确 DEI&B 战略，以促进长期文化变革。 5️⃣ 混合办公的未来：组织已基本确定办公模式，2025 年将验证其成效。54% 的员工愿意牺牲部分薪酬，以换取更大的工作灵活性。这些趋势展现了 HR 在塑造未来工作模式中的关键作用，企业需借助创新技术和数据驱动的洞察来优化人力资源管理。 Each year, the HR Research Scientists at SAP SuccessFactors conduct research to understand the top HR and workforce trends facing organizations and share our perspective on what HR teams should consider as they look to help their companies address these trends. This year we aggregated and synthesized data from 40 global and regional reputable business press sources that put forward 254 individual trends and predictions grounded in their own research and data. We then conducted a content analysis of the trends sample to derive the five key themes, or “meta-trends.” While our annual report always includes some pointed commentary and critique about each trend based on our expertise in psychology, new this year is calling upon our own body of original applied research to incorporate datapoints and insights, resulting in a more evidence-based point of view. This year’s trends are in different stages of maturity and on different trajectories; therefore, the role that HR needs to play to help businesses tackle and capitalize on these trends is different. We’ve organized the trends into two sections aligned to the dual role HR will play in addressing them. First, HR will need to act as a Conductor, leading the orchestration of a strategy and associated change management across the business to realize the opportunities these trends offer: Trend #1: Reconnecting the disconnected employee: Contentious decisions, macroeconomic and sociopolitical stressors, and breached trust with leadership has led to employee stress and burnout – and consequently, a crisis of disconnect and counterproductivity. In the year ahead: Leaders must ruthlessly prioritize fulfilling their end of the “psychological contract” by meeting employees’ basic needs. People managers will be seen as a lifeline for employees drowning in disconnect. STAT: 57% of employees feel unless their companies make some serious changes, their burnout will not get better. Trend #2: Moving from AI hype to AI impact:Organizations are shifting from AI pilot projects to enterprise-wide rollouts, demanding proof of clear value and ROI. In the year ahead: Organizations will home in on their key value drivers for AI, revealing their true priorities. The body of research on the ROI of AI will be built this year. Organizations will find friction between leaders’ and employees’ goals for using AI. STAT: 46% of employees feel that the time that they save by using AI tools at work belongs to them, not their organization. Trend #3: Striking a balance to steer skills forward: Organizations continue to face pervasive skills gaps, in part due to rapid AI advancements. A more balanced approach is needed to see tangible progress in skills-based transformations this year. In the year ahead: “Skills-based” will no longer be the only goal. Pay will prove itself the missing piece of the upskilling puzzle. The human vs. technical skill debate will move from or to and. STAT: 54% of employees would be more motivated to learn new skills if their company instituted skills-based pay. Second, HR will need to act as a Navigator, leading the organization through precarious waters and circumventing obstacles to put policies into practice for the betterment of all stakeholders: Trend #4: Divesting or doubling down on diversity, equity, inclusions, and belonging (DEI&B): Some organizations remain committed to DEI&B goals, continuing to ask “How are we going to do this?” Others plan to divest, instead now asking “Are we going to do this?” In the year ahead: Some will shy away from DEI&B goals, but these approaches will vary. Taking a stand on DEI&B will change company cultures in the long term, but it’s not clear exactly how. STAT: 26% of employees say companies focus too much on DEI&B, 41% of employees say companies focus an appropriate amount on DEI&B, and 33% of employees say companies focus too little on DEI&B. Trend #5: Plugging into or pulling the plug on hybrid work: Now that organizations have determined their position on where their employees will work, it’s time to see if they achieve the outcomes they intended. In the year ahead: Those businesses choosing the return-to-office path will see whether their bets paid off this year. Those choosing the hybrid or remote path will take it a step further, integrating autonomy as a core value in other aspects of work design. STAT: 54% of employees would consider being paid less if they could have more flexibility in where and when they work. Read the report to see what’s now and what’s next for each trend, along with some fast facts that uplevel the nerdiness of this year’s trends report. We also include a section on how SAP SuccessFactors solutions can help organizations address the 2025 HR trends.

change management
2025年03月07日
change management

超级员工的崛起 -The Rise of the Superworker: Delivering On The Promise Of AI 《超级员工的崛起》研究报告揭示了AI如何深刻改变工作场所与工作方式。随着AI技术融入工作流程，传统工作模型被重新定义，AI正助力“超级员工”以创新的方式提升生产力和创造力。报告指出，企业若想在AI时代中保持竞争力，必须重新设计工作与组织模式。首先，需要通过AI实现任务自动化并提高工作效率；其次，推动工作流程的整合，利用智能代理提升整体生产力；最后，培养员工适应变化的能力，推动动态化的工作环境。 AI并不是简单地取代工作，而是通过赋能实现员工能力的跃升。例如，一些企业利用AI快速生成培训计划，将原本需要数月的工作缩短为数天。报告也强调，随着AI成为“同事”，全新岗位将随之出现，如知识库维护员、AI数据隐私与伦理管理者等。为了迎接这一变革，报告提出了五大关键战略：重新设计工作与组织模式，构建动态人才模型，调整薪酬与绩效体系，加强以人为本的领导力，以及加速系统性HR®的转型。只有将技术与人的因素完美结合，企业才能成功实现AI转型。报告强调，AI的核心并非技术，而是通过创新推动人与组织的共同成长。1月28日的发布会将深入剖析这些趋势与战略。 We’re excited to launch our groundbreaking research “The Rise of the Superworker,” a deep dive into the impact of AI on the future of work. As our hallmark research for the year, it defines the roadmap for leadership, technology, and HR. (Register for the launch webinar on January 28.) The Workforce and Workplace Environment We are entering a year of political change, economic disruption, and changing labor markets. As I discussed recently (The Tumultuous Year Ahead), the world is experiencing talent shortages in front-line and blue collar work (US unemployment remains at 4.1%) while white-collar employment is softening. CEOs are investing in AI in a quest for productivity and workers are asking to be retrained. And many core values (diversity and inclusion, pay equity, remote work) remain challenging. Companies believe that AI will transform their business, so investment in technology is exploding. Yet as history tells us, this “trillian dollar AI-based re-engineering” effort is about people, not technology. As the research points out, the AI revolution, as exciting as it feels, is all about redesigning the way we get things done. And that lands in the laps of HR: how we redesign, reskill, and redeploy people in a world of highly intelligent systems. Understanding The Superworker and The Superworker Company Let’s start with the basics. Companies are filled with business processes, tools, and job models designed around traditional people-centric work. Every job function, from sales to marketing to manufacturing, has been designed around the old-fashioned job families of the past. In other words, we’ve run our companies as “people machines.” We design a set of jobs and job families, then hire, train, and promote people to grow. This model creates a sprawling company filled with skills challenges, people wanting promotion, and fragility as the business goes through change. The digital revolution, which defines the last 27 years of transformation, did speed things up. It automated many processes and opened up the ideas of self-service, e-commerce, and direct consumer transactions. But it didn’t fundamentally change how companies are organized: rather it accelerated the processes we had. Suddenly, with AI everything is different. As the most intelligent and data hungry technology ever, AI stands to integrate and redefine every business process and “superpower” every employee. And this shift, toward copilots, agents, digital twins, and intelligent platforms, forces us to rethink how we’re organized, what we do, and what we define as a “job.” We are building a company of Superworkers. What exactly is a “Superworker?” A Superworker is an individual who uses AI to dramatically enhance their productivity, performance, and creativity. As routine work gets automated, AI has the potential to empower everyone, eliminating some roles while empowering many others. A “Superworker company” is an organization that embraces this transformation, building a culture of adaptability where people reinvent themselves. Our new Dynamic Organization research shows that such change-ready companies outperform their peers by six-times. Just as Superman Clark Kent learned to channel his powers, we must learn to harness AI for individual and team performance. This means not just automating existing tasks, but rethinking how work gets done, empowering people to do more, and creating opportunities for growth. The Historical Perspective: From Automation to Autonomy We’ve seen waves of automation before, but this time it’s different. In the past we used machines to automate the work of craftsmen and tradespeople. A welder, farmer, or shoemaker had his or her expertise built into a machine so their craft could scale at low cost. The expert didn’t go away, rather he or she helped design the machine. AI does the same for white collar work. Writers, analysts, marketers, and sales people are now superpowered, leveraging their skills to drive scale. AI will not replace these special individuals: it empowers them to scale and expand their impact. But in the case of AI we go further: it doesn’t just automate tasks; it becomes a co-worker itself: listening, learning, reasoning, and acting. So new and better jobs are created, designing, training, and managing the AI. And the shift to Superworker happens everywhere: from the retail clerk to the nursing supervisor to the senior executive. The New Corporate Imperative: Redesign Work and Jobs This transformation won’t happen without effort. Today, as AI systems still mature, our challenge is not implementing AI, but redesigning jobs, and business processes around AI. And that’s why success with AI is a people problem, not a technology one. And if you don’t get this right, your AI transformation will lag. Academic studies show that 45% of change management programs fail, and 72% of the reason is “people resistance.” So consider this: For each dollar spent on machine learning technology, companies may need to spend nine dollars on intangible human capital,” Erik Brynjolfsson wrote in 2022, citing research by him and others. Consider the four stage model below, where we look at “current jobs” vs “re-engineered jobs” on the horizontal, and level of output on the vertical. AI transformation begins with assistance, then moves to augmentation, then to work replacement and then to autonomy. The level of performance improvement goes up exponentially. This process of rethinking business processes takes time. When electricity was invented companies replaced horse-driven machines with motors. Decades later engineers realized we could redesign the entire manufacturing process by integrating the entire supply chain. The same will happen again. We may start by automating emails and data access, but over time we build “digital twins” and configurable agents to manage entire projects and business processes. One of our clients built an entire platform that can interview stakeholders, import documentation, build training programs, and publish training and certification programs by AI. Humans are still needed, but now they’re the “super-curators” and “craftsmen” perfecting the product. New programs that took 3-6 months can be generated in a few days. This kind of redesign is now being used for claims analysis, sales enablement, RFP generation, and workplace design. (Our report 100 Use Cases For Galileo explains dozens of such solutions available now for HR.) The Work Redesign Challenge How do we get there? Business and HR teams work together, following these stages. Improve efficiency at current job: Use AI to make existing work more efficient: same job as before, new tools to make it easier. Examples include an office worker using MS Copilot. Automate tasks to increase scale: An engineer uses AI to write code. A marketer builds videos and campaigns automatically. An HR manager rapidly builds job descriptions or analyzes performance. Integrate processes to improve productivity: Agents now handle multiple connected steps. A retail clerk automatically checks out customers; a nurse uses a machine to monitor dozens of patients and make diagnostics; an HR manager builds learning programs in minutes. Leverage autonomy for more: The AI manages multi-step processes (customer service, candidate communications, recruiting, campaign design) and the people “manage” the digital employee. This creates four types of Superworker: An Example: The HR Business Partner Consider the role of HR Business Partner (HRBP), a complex job that’s constantly changing. An HR business partner (HRBP) equipped with AI like Galileo™ can automatically analyze turnover, productivity, individual performance, and leadership potential. The AI HR Agent can help compare job candidates against multiple requirements. Analysis, coaching, and hiring speed goes up, and the HRBP is now a Superworker. Then the transformation continues. What if we give the AI to managers. Do we need the HRBP at all? (IBM has made this step.) Yes, now the HRBP manages the AI. Just as Wayze may drive you automatically, someone behind the scenes is monitoring your trip to help you when things go wrong. This “Superworker” job is the upgraded role of the HRBP. AI As A Job Creation Technology Many new jobs will be created. Who maintains the knowledge base that feeds the AI? Who ensures data privacy and security? Who handles the ethical issues that arise? Who monitors the AI to make sure it’s trained well? And once these multi-step digital employees exist, who will manage them? These are new Superworker jobs. Five Imperatives for 2025 How do we make this transition a success? Here are five key imperatives detailed in our study: Redesign Work, Jobs, and Organizational Models: Focus on the customer, how success is measured, then apply AI. This is what we call “productivity-based job design”. Deconstruct work into activities, evaluate AI solutions, and determine the human role alongside AI, using the models above. Create a Dynamic Talent Model: The traditional “prehire to retire” model is becoming obsolete. We need a more dynamic approach where people move across roles and projects. Prioritize internal mobility and foster a culture of growth. Focus on “doing more with what we have” by upgrading the productivity of our existing workforce. Focus on building “talent density“. Rethink Pay, Rewards, and Performance: Move from traditional pay models to “systemic rewards,” based on role, skills, and output. New roles may warrant higher pay, not lower. (Lightcast sees a $45,000 premium for workers with AI skills.) Refine Leadership and Culture: Focus on human-centered leadership: this is a time of change. Ensure leaders understand AI, foster innovation, and focus on productivity, not headcount. Start co-design projects in every functional areas. Get line employees involved in transformation efforts. Accelerate the Shift to Systemic HR®: HR must operate in a consulting role. Integrate HR silos, develop a change-enablement team. Experiment with AI tools in HR and train the HR team about AI. Let me give you an example. One of our large clients, a healthcare company, created a “transformation enablement” team in HR that does co-design workshops throughout the business, helping with process redesign, role design, job changes and pay and rewards changes. They built a set of tools and methodologies which are well established. HR professionals rotate into this team for education. Every HR function should set up “AI transformation teams” like this. AI isn’t here to replace us; it’s here to empower us. How To Get The Research and Learn The Rise of the Superworker predictions report is available to all users of Galileo™, The Josh Bersin Academy, or Corporate Members. (A Galileo Pro membership is only $39 per month, and JBA membership is $49 per month.) If you want to learn more and follow our ongoing case studies, briefs, and AI tools, download the Rise of the Superworker Overview today. You will be registered for regular updates. And please register for our launch webinar on January 28 where I will detail this entire story. The Superworker era has arrived, join us in the journey!

change management
2025年01月16日
change management

Why it’s time for HR Business Partners 2.0 文章中强调了人力资源商业伙伴（HRBP）从通才到战略顾问的演变。最初旨在将人力资源战略与商业目标对齐，HRBP经常被日常运营任务分散注意力。Kathi Enderes 主张通过加强培训、指导和系统性的人力资源方法来复兴这一角色，这种方法整合了商业咨询能力。她引用了TomTom和乐高集团的例子，这些公司已成功地将其HRBP角色转变为更具战略性、数据驱动和有效促进业务增长和创新的角色。文章指出，只有11%的公司完全整合了这种模式，但见证了更高的增长和创新。 Kathi Enderes的观点强调了在当今由AI驱动的市场中，将HRBP转变为战略顾问不仅是一种改变，更是一种必需。 Global Industry Analyst Kathi Enderes, SVP of Research at The Josh Bersin Company, sees the need to clear the dust off a 30-year great idea of HRBPs. Expert Insight HRBPs are a crucial part of the success of the HR functions, and organizations as a whole. However, as Kathi Enders, SVP of Research at The Josh Bersin Company, shares in this exclusive OpEd, they need to move from being a jack of all trades to becoming a business savvy consultant. Here's how to achieve this! Thirty years ago, HR embraced a groundbreaking concept: the HR Business Partner (HRBP). The idea was that these professionals would collaborate closely with business leaders and line managers to align people strategies with the organization’s broader business objectives. This remains a crucial concept and a contribution that organizations desperately need. The problem is that somewhere along the way, we lost sight of the strategic part of the equation. As a result, we’ve ended up misusing resources and devolving the role of the HRBP into a much more tactical, and less globally impactful, function. In fact, the HRBP role is the most critical, yet the most misunderstood, of all HR jobs. But by refreshing and modernizing the original concept and investing in HRBP capabilities, we can revitalize the role and get it back to its even more strategic purpose. How we got here, and where we have to go next We introduced HRBPs when we transitioned to the tiered HR service delivery model in the 1990s. Originally, the HRBP was envisioned as a crucial connector between the various HR Centers of Excellence (COE) and the business. But before too long, a lot of operational tasks were loaded onto their plates by business managers who needed immediate assistance with less strategic, day-to-day issues—think, “I need to hire someone but don’t know how to submit the requisition in the system,” or “I need to transfer someone: can you help me with that?” When this happens frequently, the HRBP unintentionally becomes more of an HR workflow admin assistant. While this helps solve short-term issues, it detracts from the original strategic intent of the role. Consequently, many HRBPs end up not working “at top of license”—acting more like HR generalists than the specialized, strategic partners they could be. To get things on track and empower HRBPs to grow into the strategic role you hired them for (and what they came on board to do), look to: accept and encourage them to become business consultants, not just advisors or general admins, and support them in developing strong relationships with business leaders and the rest of HR build the level of HR business partner capabilities they need to do that organize their roles in new ways, and communicate clearly how you expect them to operate and contribute. Leading the development of this critical in-house resource It’s important to emphasize that all three elements noted above are crucial to the success of HRBPs – and they are interconnected. Implementing just one recommendation won’t achieve the desired outcomes. Equally importantly, this isn’t about increasing headcount costs; it’s about enhancing the training and utilization of the people you already have. Indeed, in some organizations, there are significant numbers of HRBPs; myself and The Josh Bersin Company have worked with organizations where there are 200 or more in place. So, the mission of the CHRO is to develop them, help them build the right relationships across the business, give them the support they need, and consciously organize them for success. For capability development, some of that investment will go towards formal learning programs. However, a significant portion will also be dedicated to facilitating mentorships and fostering connections. This approach works best by consciously placing HRBPs in project roles where they can expand their knowledge and gain valuable exposure. How to move to next-gen HRBP ground-level support A Systemic HR approach, a concept The Josh Bersin Company introduced to the market last year, can be the driver of transformation here. Why? Because by its very definition, Systemic HR transforms HR from a siloed service provider into an integrated, consultative function that tackles a company’s most pressing business challenges. By doing so, the HRBP evolves from an HR ‘jack of all trades’ to a highly-skilled, data- and technology-savvy business consultant. According to our research, only 11% of companies operate a truly Systemic HR function, so there is huge opportunity here – and these organizations have much higher company growth, delight their customers, innovate more, and create a great place to work. Next-generation HRBPs can accelerate the journey towards Systemic HR and drive successful business outcomes. However, to achieve this, you must be prepared to both pose and find answers to questions such as: What are my new-style HRBPs’ specific accountabilities? What does success look like? How will our newly-energized and skilled-up HRBPs interact with managers and leaders? Evidence from front-rank organizations, like TomTom, a geolocation technology company that specializes in mapping, navigation, and real-time traffic information services, suggests a move to a more integrated, fully data-driven, Systemic HR framework can deliver significant benefits. In its case, TomTom has strategically restructured its HRBP team, moving away from a traditional, rigid HR model to a more fluid, team-based approach. Its HRBPs are now organized into cross-functional teams that operate with flat hierarchies, allowing for quicker decision-making and more responsive HR practices. Its HRBPs also now sit on the HR strategy and strategic business partnering team, which also includes HR strategy, people analytics and insights, HR portfolio management, and organizational development. Working across this group, collaborating with the business, and supporting the highest-priority initiatives makes the HR function much more impactful. Through this organizational model, TomTom ensures that its HRBPs are well-equipped to support the organization’s dynamic needs, driving effectiveness and efficiency. Achieving ‘Master Builder’ HRBP capability TomTom is not the only one looking at a new way to utilize HRBPs. Famous Danish toy leader The LEGO Group has taken a proactive approach to building HRBP capabilities. Specifically, it implemented a series of initiatives aimed at enhancing business acumen, leadership skills, and understanding of complex organizational dynamics. This includes specialized training programs to equip HRBPs with skills in change management, organization design, and coaching and developing leaders. This new approach to the HRBP also centers on supporting their participation in cross-functional projects so as to develop a deeper understanding of its multiple business units and achieve a truly holistic view of the organization. Doing so broadens their perspective and enhances their ability to contribute to strategic discussions and initiatives. This is an approach many other organizations can and should explore, as it’s a great way to develop full-stack HRBP capabilities. In summary, HRBPs are incredibly important to organizational success, but along the way, we lost sight of how to maximize their potential fully. As businesses accelerate under the influence of AI and other factors, this oversight becomes a luxury we cannot afford. Therefore, the CHRO must prioritize developing HRBPs to enable their business to outperform competitors, nurture talent, and cultivate the innovation-driven organization necessary to thrive and endure. 原文来自：https://www.unleash.ai/strategy-and-leadership/why-its-time-for-hr-business-partners-2-0/

change management
2024年08月31日
change management

Josh Bersin: 随着经济放缓，关注未来的技能：改变的能力本周，我们看到美国失业率“上升”到 4.3%，经济学家开始呼吁降低利率。对于那些每天与公司和领导者交流的人来说，我会说我们正经历一个正常的经济周期。上一次重大衰退（不包括疫情，因为那不是需求放缓）是在2008年和2009年。这意味着我们已经有近16年没有经历严重的经济周期了，几乎是通常周期的两倍。虽然疫情确实让公司放慢了脚步，但我们迅速恢复了。所以从失业率来看，它大致是这样的。在经历了50年的失业率变化后，目前的失业率比五十年前降低了12%，这让我得出结论，我们正生活在“长期劳动力短缺”中。同时，美国的GDP在此期间增长了1500%。虽然我们目前的GDP增长可能有所放缓（我认为这是由消费者价格使我们耗尽了支出引起的），但实际上我们只是看到从“工业化、高劳动密集型企业”向所谓的“后工业化”公司的长期转型，这些公司往往需要更少的“工人”和更多高技能员工。（阅读我们的后工业时代研究。）仍然有大量的小时工工作：护理、医疗、交通、建筑、零售、娱乐、能源和许多其他行业依赖各种类型的“劳动”工人。这些工作随着时间的推移变得越来越自动化，导致工资提高和技能升级，但美国仍有约63%的工人没有大学学位，其中大多数人找到了工作。虽然每个人似乎对英特尔、UKG, Intuit或其他“与AI相关”的裁员感到有些恐慌，但美国经济的反应良好。我知道许多公司正在试验AI和其他技术，每个公司都担心失去有价值的人才，因为劳动力市场依然竞争激烈。是的，一些公司会进行裁员。通常这是由糟糕的领导、糟糕的规划或只是对投资者的本能反应引起的。最终，随着出生率保持在低水平，我们仍将面临劳动力短缺，人的价值将继续上升（正如我过去指出的，裁员并非不可避免）。在过去的三周里，我与欧洲超过20家大公司会面，每家公司都在投资于员工发展、技能再培训、内部流动性和提高生产力的项目。在欧洲，裁员既困难又昂贵，因此公司感受到劳动力短缺的压力，他们仍在投资员工。至于消费者需求开始下降，我们正面临一个“长期结束”阶段，这是由高价格的延续引起的。消费者对过去五年的高价格感到厌倦，而在此之前，我们经历了近十年的零利率时期，房价和大多数资本品价格持续上涨。现在这两个因素都结束了，我们只是回到了更正常的经济状态。换句话说，如果你因为“可以”而提高价格，最终你将付出代价，当消费者反抗时。如果你停止投资于员工，他们会“悄然离职”或另寻他处。这些是我认为的“正常商业经济”，我认为我们正看到这种正常性的发生。作为一个动态组织运营当然，最大的“趋势”是各行业的数字化和AI革命。汽车制造商被“虚假”引导进入电动车领域，发现混合动力发动机、数字相机和电子产品以及新的购车方式非常具有破坏性。出版商正在找出如何应对AI平台，这次他们保留了自己的知识产权并协商了许可协议。能源公司正在慢慢转向新来源，其他公司都在找出如何实现数字化、AI赋能，并进一步简化我们生产和销售的产品。这都是商业的“激动人心的工作”，一切都与成为一个动态组织有关。我们的研究指出，以动态方式运营完全是关于人。经济，通常以周期性变化（通常由过度兴奋和随之而来的疲惫引起）为特征，只是需要应对的事情。对于我这样经历了许多这种高峰和低谷的人来说，当事情不再上升并且我们看到一些冷却期时，我总是感到有点“解脱”。是的，股市可能会暴跌。它总会在某个时候发生。但那实际上是“众包”效应，通常与我们的公司无关。如果你照顾好你的客户，投资未来，迅速学习AI和所有新技术，你将顺利过渡。正如许多HR领导本月与我谈到的，你的成功很大程度上取决于人。未来的技能很明确：推动变革的能力今天我与一群我们每隔几周就交流的HR领导进行了一次有趣的会议。每一位CHRO和其他领导都告诉我们，他们正在投资于员工的“变革管理”和“业务转型”技能。这意味着什么？这意味着这样。虽然我们都希望公司有更多的工程师、制造专家、科学家和销售与营销专家，但我们最需要的“技能”是“推动变革的能力”。这种特定的技能非常复杂，需要时间来学习，并且在当前尤为重要。这引出了我的最后一点。如果你是图凡·厄金比尔吉，劳斯莱斯的首席执行官，你正处于业务转型的过程中，旨在推动工程效率和卓越，你不仅要担心工程师。你要担心那些能够推动、领导、激励和创造变革的人。我相信，这些就是大家常谈的“未来技能”。如果你作为一个专业人士、经理或领导者真正知道如何“推动和执行变革”，这些经济周期在你的职业生涯中只是“一个小波折”。在与HR领导交谈超过30年并在许多周期中经营我们自己的业务后，我敦促你“不要过于担心”这些大的经济数据。我们正生活在一个每个公司中每个人的经济价值飙升的时期。投资于你的员工和自己，随着经济的变化，你会做得很好。 https://joshbersin.com/2024/08/as-the-economy-changes-focus-on-the-real-skills-of-the-future/

change management
2024年08月03日
change management

2024年组织中人力资源部门的21个关键角色-来自AIHR 组织中人力资源部门的21个关键角色，分为“关键角色”、“合规角色”和“新兴角色”三个部分，如下所示：关键角色吸引候选人：开发和执行策略以吸引合适的候选人。选择候选人：从众多申请者中挑选出最适合的候选人。内部和外部招聘：内部晋升和外部招聘的管理。绩效评估：对员工的工作表现进行评估。薪酬：设计和实施薪酬策略。员工福利管理：设计和管理员工福利计划。学习与发展：确保员工技能与组织需求保持一致。合规角色晋升：晋升机制的设计与实施。问题解决小组：创建和管理解决问题的小组。全面质量管理（TQM）：实施全面质量管理以提高服务或产品质量。信息共享：确保重要信息能够及时传达给所有员工。组织发展：通过战略性的人力资源管理提升组织效能。调查管理：管理各种员工调查，收集反馈以改进工作环境。合规管理：确保公司遵守所有相关法律和规章制度。商业合作伙伴：HR作为管理层的战略合作伙伴，提供人力资源解决方案。新兴角色数据与分析管理：使用数据分析来支持决策过程。人力资源技术管理：管理和优化HR相关的技术和系统。变更管理：领导和管理组织变更。员工体验：设计和改进员工的整体工作体验。多元化、公平、包容和归属感（DEIB）：推广和实施多元化和包容性策略。公关：管理公司的公共形象和应对公关危机。原文来自：https://www.aihr.com/blog/human-resources-roles/ Attracting candidates, Selecting candidates, Hiring from within and from outside, Performance appraisals, Compensation, Employee benefit management, Learning & development, Promotions, Problem-solving groups, Total quality management (TQM), Information sharing, Organizational development, Survey management, Compliance management, Business partnering, Data & analytics management, HR technology management, Change management, Employee experience, DEIB, PR 吸引候选人、选择候选人、内部和外部招聘、绩效评估、薪酬、员工福利管理、学习与发展、晋升、问题解决小组、全面质量管理 (TQM)、信息共享、组织发展、调查管理、合规管理、业务合作、数据与分析管理、人力资源技术管理、变革管理、员工体验、DEIB、公共关系

change management
2024年05月12日

change management

Happy Easter!祝大家复活节快乐！ Happy Easter to all our dear friends at NACSHR! As we step into this beautiful season of renewal and hope, accompanied by the delightful presence of chocolate bunnies and colorful eggs, we're sending a heartfelt bouquet of wishes your way. May this Easter not only fill your homes with laughter and warmth but also ignite your professional spaces with innovative ideas and vibrant growth. Let's allow the rejuvenating spirit of Easter to inspire us to hatch groundbreaking strategies and nurture our aspirations. This Easter, let's cherish the incredible power of connection and the beauty of our community. It's the perfect occasion to strengthen our bonds and celebrate the diverse tapestry of talents and perspectives that each of us contributes. To all our HR friends, let's take this moment to deepen our dedication to creating workplaces where every individual feels valued, supported, and empowered to flourish. Wishing you an Easter that overflows with joy and presents new opportunities to make meaningful impacts. As we indulge in Easter treats and gather with our loved ones, let us also gaze ahead with hope and anticipation for what the future unfolds. May the essence of Easter inspire us to approach challenges with bravery and embrace opportunities with open arms. Here's to a magnificent Easter, abundant in peace, prosperity, and progress for everyone. May it rejuvenate our spirits and rekindle our enthusiasm for making every workplace a beacon of positivity and growth. Happy Easter, dear friends at NACSHR! May this season bring everlasting happiness and success to you and your families. 亲爱的NACSHR的朋友们，祝大家复活节快乐！当我们迈入这个充满更新与希望的美好季节，同时享受着巧克力兔和彩蛋带来的乐趣，我们向你们发送最真挚的祝福。愿这个复活节不仅让你们的家充满笑声和温暖，也在你们的职场中点燃创新的思维和活力的增长。让复活节的复兴精神激励我们孵化出创新的策略，培养我们的志向。在这个复活节，让我们珍视连接的力量和我们社群的美好。这是完美的时刻来加强我们的联系，庆祝我们每个人贡献的多样化才能和视角。对于我们所有HR的朋友们，让这成为一个深化我们致力于创造每个人都感到被重视、支持和有力量成长的工作环境的时刻。祝愿你们的复活节充满欢乐，开启新的机会，让我们做出有意义的影响。当我们享受复活节的美食并与我们所爱的人聚集时，让我们也满怀希望和期待地看向未来。愿复活节的精神鼓励我们勇敢面对挑战，开放心扉迎接机会。祝大家有一个辉煌的复活节，充满和平、繁荣和进步。愿它重新点燃我们的精神，重新激发我们为让每一个工作场所成为积极成长的灯塔的热情。亲爱的NACSHR的朋友们，复活节快乐！愿这个季节为你们及你们的家人带来永恒的幸福和成功。

change management
2024年03月31日
change management

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

change management
2024年01月09日
change management

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.

change management
2023年12月17日
change management

视频：Leading Through Transformation The Future of HR in the AI Era Leading Through Transformation The Future of HR in the AI Era Jiajia Chen Senior Group Product Manager Nvidia 点击访问：https://www.youtube.com/watch?v=toiy_sBDXHs 以下为演讲稿翻译整理，仅供参考：引领变革：人工智能时代人力资源的未来欢迎大家，我很高兴有机会讨论一个自2022年底以来成为焦点的话题。随着chat的广泛成功，许多人开始思考一个问题：我还会有工作吗？对于一些父母来说，这个问题可能会有所不同：我的孩子将来会有工作吗？在深入这个问题之前，让我简单介绍一下自己。我早期的职业生涯涉及多个商业领域，包括人力资源，后来我专注于人工智能产品管理。我拥有几个学位，包括法律学位、MBA学位、经济学科学学位和软件工程学位。我曾在Nidia管理人工智能基础设施产品组合几年。去年晚些时候，我转移到另一个名为Nidia Omniverse的产品组，这是一个数字孪生平台工业元宇宙。我们的企业客户可以使用Omniverse来创建数字孪生工业元宇宙，通过利用模拟和生成性人工智能以及与大型生态系统合作。通过这些经历，我对人工智能和人力资源有了深刻的理解。在这次演讲中，我希望能提供一个框架，帮助大家思考如何在人工智能时代领导转型，如何保持相关性并比人工智能发展得更快。人工智能并不是一个新概念。让我们快速回顾一下人工智能发展的简史，为今天的对话奠定基础。人工智能领域诞生于1950年代。1950年，艾伦·图灵提出了模仿人类智能的通用机器的概念。1956年，人工智能这一术语被创造出来。在1970年代和1980年代，人工智能最初的乐观预期开始减弱，因为进展没有达到高期望，人工智能研究的资金减少，领域经历了被称为人工智能冬天的时期。在人工智能冬天期间，研究人员专注于发展专家系统，这是基于规则的系统，旨在模仿人类专家在特定领域的知识和决策能力。这种方法在实际应用中取得了一些进展，例如医学诊断和工业自动化。1980年代，人工智能的焦点转向了机器学习和神经网络。机器学习算法允许计算机在没有明确编程的情况下从数据中学习，并做出预测或决策。受人类大脑结构启发的神经网络引起了关注，并被应用于各种任务，包括图像和语音识别。得益于大量数据的可用性和计算能力的进步，人工智能经历了复兴。Nidia的贡献是关键的。 2022年11月推出的ChatGPT标志着人工智能的关键时刻。生成性人工智能正在推动机器创造的边界。人工智能越来越多地融入各种应用和行业，正在金融、医疗保健、网络安全等领域发挥作用，转变行业并创造新的机会。你们中有多少人尝试过ChatGPT？你们喜欢它的哪些功能？是否用它来草拟电子邮件、创建培训材料，或者提出棘手的问题，试图愚弄chat GPT，证明你的人类智能更高级？人工智能预计将在各个维度对工作场所产生重大变化。以下是人工智能可能带来的九个变化。首先，提高生产力：人工智能是否会提高生产力和经济增长？许多人这样预期，但也有很多人告诉你，到目前为止，这种生成性人工智能趋势并没有大幅提高生产力，除了提供一些有趣的玩具。你们中的一些人可能听说过“生产力悖论”，这是1970年代和1980年代在美国发生的现象。我的预测是，人工智能不会发生这种情况。人工智能可以更快地传播，且所需的资本投资更少。这是因为人工智能在短期内的应用主要是软件革命，所需的大部分基础设施，如计算设备、网络和云服务，已经到位。你现在可以通过手机立即使用chat GPT和迅速增长的类似软件。其次，收入不平等：人工智能是否会带来自动化的奢华时代，还是只会加剧现有的不平等？美国国家经济研究局发布的一份报告称，自1980年以来，美国工资变化的50%到70%可以归因于蓝领工人被自动化取代或降级导致的工资下降。人工智能、机器人技术和新的复杂技术导致财富高度集中。直到最近，受过大学教育的白领专业人士基本上没有受到低教育工人的命运。拥有研究生学位的人看到他们的薪水上涨，而低教育工人的薪水显著下降。这一问题将加剧，低技能的白领工人也将受到影响。第三，劳动力技能提升和风险转移：随着某些任务的自动化，人工智能需要专注于提升和重新技能化劳动力。员工需要获得新的技能和知识，以适应不断变化的工作要求，并有效地与人工智能系统协作。有关这一主题的研究很多，不同研究的数据也有所不同。彭博社的研究显示，由于人工智能对工作的影响，全球将有超过1.2亿工人在未来三年内需要重新培训。据信，由于人工智能相关部署，中国将有超过5000万工人需要重新培训。美国将需要重新培训1150万人，以适应劳动力市场的需求。巴西、日本和德国的数百万工人也将需要帮助应对人工智能、机器人技术及相关技术带来的变化。根据麦肯锡的一项研究，由于快速自动化的采用，多达3.75亿工人可能需要转换职业类别。第四，重新定义工作角色：人工智能有潜力重塑工作角色并创造新的角色。一些任务和工作可能会完全自动化，导致某些领域的工作流失。然而，人工智能也为创造涉及管理和协作人工智能系统、分析人工智能生成的内容、开发和维护人工智能技术的新角色创造了机会。例子包括美国政府试图将制造业带回美国。许多人认为，像第二次世界大战后一样，将创造数百万高薪的蓝领工人工作。然而，这最有可能不会发生，因为在美国建造的新工厂几乎不会雇用许多人类工人。一切都将通过机器人或管理系统自动化。第五，增强决策制定：人工智能系统可以分析大量数据，检测模式，并生成支持决策过程的洞察。这可以使员工和管理者获得更准确、更及时的信息，使各种职能（如运营、市场营销、财务、人力资源）的决策更加明智。2019年哈佛商业评论提出了一个概念，称为人工智能驱动的决策，与数据驱动的决策相比，它允许我们克服作为人类处理器的固有局限性，如低效和认知偏见，因为你可以指派机器来处理大量数据，让我们人类应用判断力、文化价值观和情境来选择决策选项。第六，人工智能与人类的协作：人工智能技术使得人与智能系统之间的协作成为可能。这种协作可能涉及利用人工智能在数据分析、模式识别和预测方面的优势，而人类则提供批判性思维、创造力、同理心和复杂问题解决技能。如果能够有效地实现人与人工智能系统的协作，可以带来改进的成果和创新。的确，许多公司已经使用人工智能自动化流程，但到目前为止，证据表明，那些旨在取代员工的部署只会带来短期的生产力提升。在一项涉及1500家公司的基本研究中发现，当人类和机器一起工作时，公司取得了最显著的绩效提升。第七，增强智能：人工智能可以通过补充和增强人类能力来增强人类智能。它可以协助人们执行诸如信息检索、数据分析和问题解决等任务。人工智能支持的虚拟助手和机器人可以为人们提供即时支持和指导，提高他们的效率和效果。第八，伦理考虑：人工智能在工作场所的整合引发了与隐私、安全、公平、透明度和问责制相关的伦理考虑。组织需要建立伦理框架和指南来确保人工智能系统的合理和可信赖的开发和部署。第九，监控和评估AI实施。这个变化涉及到持续监控人工智能在工作场所的影响，并从员工那里收集反馈，以识别改进领域。定期的评估和反馈循环将有助于完善人工智能的实施和使用，确保其在增进工作效率、创新和其他方面的应用是有效和恰当的。（以上为AI补充，仅供参考）目前，我们已经详细讨论了人工智能在工作场所创造的变化，以及人力资源应该如何应对这些变化。现在，让我分享这张早先在一次HR会议上使用的幻灯片。2016年，我在一个名为“HR新模型”的会议上发表了演讲。现在，让我们看看这个模型。一个典型的组织结构包括首席执行官、人力资源业务伙伴、共享服务和一个运营部门，支持管理者和员工群体。公司是否能用这个模型应对人工智能在工作场所带来的变化？我们是否需要一个不同的模型？在回答这个问题之前，让我们看看应对每种类型变化需要发生什么。在这张幻灯片上，我展示了我简单的颜色编码技术。我简单地将所有类型的能力和技能分类并用不同颜色高亮显示。现在我们可以看到几个主要类别和一些零散项目。让我们稍微深入一些颜色分类的挑战。首先，以蓝色突出显示的助理挑战和两个工作场所的变化。HR可以评估利用人工智能的技能和能力要求，为员工提供必要的资源，使他们能够理解和利用人工智能技术，以及如何通过人工智能来增强他们的工作。这包括关于人工智能概念、数据分析、自动化工具和人工智能支持决策的培训。HR可以培养持续学习的文化。其次，以绿色突出显示的变革管理和沟通，在四个不同的工作场所变化中出现。HR可以积极地向员工传达人工智能实施的目的和好处，以提高生产力和效率。HR可以协助经理和员工分析工作并重新设计工作流程，以利用人工智能技术。这涉及识别可以自动化或由人工智能增强的任务和活动，简化工作流程，消除冗余或低价值测试，并确定人类和人工智能如何合作以优化生产力和效率。第三，以热粉色突出显示的职业发展和内部流动性，在三个不同的工作场所变化中出现。HR可以进行技能评估，以确定组织内现有技能，并确定需要解决的AI相关角色的差距。这包括识别与人工智能技术合作所需的技术技能，如机器学习，以及有效沟通、批判性思维和问题解决所必需的软技能。最后，以灰蓝色突出显示的伦理指导和治理，在三个不同的工作场所变化中出现。HR可以与法律、合规团队等相关利益相关者协作，为人工智能变革建立治理框架。那些仍以黑色显示的功能在未来几年将看到更多的自动化和置换，投资较少，因为这些能力在人工智能转型中的相关性较低。为了跟上甚至领导人工智能趋势及其对工作场所的影响，HR可以采取几个积极的步骤。以下是我们可以考虑的一些关键行动：持续学习，HR专业人士可以深入了解人工智能技术、应用和影响；识别人力资源中的人工智能用例，HR可以探索各种可以增强其功能和简化流程的人工智能应用，例如自动化日常行政任务、改进候选人筛选和选拔流程，以及提供个性化的学习和发展机会；评估组织的人工智能准备情况，HR可以评估组织当前的基础设施、技术能力和文化，以确定其采用人工智能的准备情况；通信和透明度，人工智能实施期间的沟通和透明度对于缓解对工作安全的担忧、澄清人工智能采用的好处以及确保员工理解人工智能技术将如何增强而非取代他们的工作至关重要；监控和评估人工智能实施，HR可以持续监控人工智能对工作场所的影响，并从员工那里收集反馈，以识别改进领域。定期的评估和反馈循环将有助于完善人工智能实施。

change management
2023年07月02日

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