• talent intelligence
    The best HR & People Analytics articles of July 2025 HR如何在AI时代掌握主动？David Green发布的7月《Data Driven HR Monthly》汇集全球顶尖报告与实践，聚焦“技能+任务”新范式、AI对员工体验与倦怠的双面影响，以及CHRO在企业AI战略中的领导地位。BCG数据显示，印度AI使用率达92%，但全球员工对AI培训满意度仅36%。Upwork报告揭示：高效AI用户更易疲惫离职。McKinsey与Gartner呼吁HR重构组织模型与人才规划体系。本期还探讨神经多元、NASA人才图谱与“Vibe Coding”等创新实践。 I always enjoy spending time in India, so I was delighted to arrive in Delhi yesterday ahead of People Matters Tech HR later this week. I’ll be delivering the opening keynote on how HR leaders can ace the next curve of change as well as leading a workshop on the science of better decisions. I’m looking forward to catching up with fellow speakers such as Jason Averbook (tip: subscribe to his Now to Next blog, if you don’t already), Pushkaraj Bidwai, Mukesh Jain, and Shefali Raias well as immersing myself in what is happening in the Indian HR tech scene. In this month’s edition of the Data Driven HR Monthly, which comes against the backdrop of CEOs flexing on the impact of AI on jobs, I’ve included new research from BCG and Upwork on AI at work, and the role of HR. Marc Effron is spot on here with his assessment that CHROs need to be leading the strategic conversation with the executive team on their desire to reduce costs through job reduction enabled by AI: “CHROs can lead this conversation through organization, operating model and job design, where we should be experts.” I expect plenty of discussion at Tech HR on this topic as well as the wider impact of AI on work, the workforce, and the workplace. One of the messages, I’ll look to get across in my keynote is: AI guides, but humans decide. We must prioritise the ‘H’ in HR. This edition of the Data Driven HR Monthly is sponsored by our friends at TechWolf Skills, Tasks, and Workforce Intelligence: Navigating the AI Transformation This month’s edition highlights an important conversation from the TechWolf Podcast, recorded live in New York, featuring Marc Steven Ramos, global learning leader with 25+ years’ global transformation experience with Google, Microsoft, Accenture, Novartis, Oracle, and Cornerstone, and Jeroen Van Hautte ?, CTO & Co-Founder of TechWolf. The discussion explores how task-based intelligence complements skills data to create a complete view of workforce capabilities, empowering organizations to navigate one of the largest business transformations in history: the AI-driven redefinition of work. Skills without context can be ambiguous. Tasks ground them in real work, and that’s where change, productivity, and AI come together — Marc Ramos Why This Matters Now: The pace of change in the workforce is unprecedented. Leading enterprises are already recognizing that workforce intelligence - the ability to understand, predict, and act on how work is changing in real time - is no longer optional. From skills to skills + tasks + jobs: Combining these data points allows organizations to connect individual capabilities to tangible outputs and outcomes. AI as a catalyst: AI is accelerating job evolution, making real-time visibility into tasks and skills essential for workforce planning and redeployment. Strategic urgency for boards: Workforce automation isn’t a distant trend — it is reshaping workforces today, creating pressure on executives to act on reskilling, redeployment, and workforce design at speed. To really understand a skill, you need to understand the context in which it’s applied — the tasks. And that’s where AI can add transformative clarity — Jeroen Van Hautte For HR leaders, this is an opportunity to lead. With skills and tasks as the foundation, HR is uniquely positioned to drive cultural alignment, manage change, and deliver on the board-level mandate to prepare workforces for the AI era. Listen to the Episode: ?️ Marc Ramos & Jeroen Van Hautte on Tasks, Skills & the Future of Work (TechWolf website summary) To sponsor an edition of the Data Driven HR Monthly, and share your brand with more than 145,000 Data Driven HR Monthly subscribers, send an email to dgreen@zandel.org. JULY ROAD REPORT Until flying to Delhi yesterday, as mentioned above for Tech HR India later this week, July had been a light month of travel other than a short trip to Switzerland to run an AI workshop with the HR leadership team of one of the companies that are part of the Insight222 People Analytics Program. For those interested, one of my speaking engagements from earlier this year, at the Wharton People Analytics Conference, is now available to view (see below). In the talk, I explore the critical role of data democratisation and adoption in driving workforce insights, enhancing decision-making, and scaling HR’s strategic impact. I also share best practices from our work and research at Insight222 for making people analytics accessible to leaders and employees alike, the challenges of adoption, and the key investments required to unlock the full potential of workforce data. Enjoy! Share the love! Enjoy reading the collection of resources for July and, if you do, please share some data driven HR love with your colleagues and networks. Thanks to the many of you who liked, shared and/or commented on June’s compendium. If you enjoy a weekly dose of curated learning (and the Digital HR Leaders podcast), the Insight222 newsletter: Digital HR Leaders newsletter is usually published every other Tuesday – subscribe here – and read the latest edition. HYBRID, GENERATIVE AI AND THE FUTURE OF WORK BCG - AI at Work: Momentum Builds, but Gaps Remain | JOHN BRAZIER AND NICK SOUTH - BCG’s AI at Work 2025 report: Four takeaways for HR leaders Companies are realizing that merely introducing AI tools into existing ways of working isn’t enough to unlock their full potential. The real magic happens—and value generated —when businesses go further and reshape their workflows end-to-end. BCG’s annual AI at work global survey of employees is packed full of insights and guidance for business and HR leaders looking to maximise value, adoption and employee experience with AI. The key takeaways include: (1) AI is now part of our daily work lives: 72% of respondents are regular AI users (although adoption amongst frontline employees has stalled at 51%). (2) Investment in training, leadership support and access to the right tools can break this ceiling: Yet only 36% of employees are satisfied with their AI training. (3) The Global South is showing higher adoption of AI. India leads the pack with 92% of regular users compared to the US (64%), UK (68%) and Japan (51%). (4) The next frontier: from adoption to value with end-to-end redesign. One-half of respondents say their company is starting to reshape processes. These companies also invest more in their people – and it pays off (see FIG 1). (5) AI agents are not widely deployed. Only 13% see agents integrated into broader workflows (see FIG 2). Kudos to the authors: Vinciane Beauchene, Sylvain Duranton, Nipun Kalra, and David Martin. For HR leaders, I also recommend reading John Brazier’s interview with BCG’s Nick South about the implications of the report’s findings for HR on the UNLEASH blog. FIG 1: The relationship between workflow redesign due to AI and investment in people (Source: BCG) FIG 2: Use of AI agents (Source: BCG) GABBY BURLACU AND KELLY MONAHAN - From Tools to Teammates: Navigating the New Human-AI Relationship Full time employees getting the most done with AI are also the most burned out, disengaged, and disconnected from their teams. In their study for the Upwork Research Institute, Gabriela (Gabby) Burlacu and Kelly Monahan, Ph.D. identify a crucial message for the future of work: while AI is undeniably boosting productivity – with a reported 40% jump for many workers – it's also creating a human paradox. Alarmingly, top AI performers are experiencing high burnout (88%) and are twice as likely to leave, often feeling disconnected from strategy and even trusting AI more than human colleagues (see FIG 3 and 4). The report offers three urgent calls to action for business leaders: (1) Redesign work for human-centered, AI-empowered talent and workflows, prioritising autonomy, trust and psychological safety. (2) Cultivate flexible and resilient talent ecosystems, combining full-time employees, freelancers, and AI capabilities to create agile, resilient, and high-performing teams. (3) Redefine AI strategies to focus on the end-to-end human experience, including new roles, norms, and governance. For HR leaders, these findings are a wake-up call. We must prioritise the relational side of AI, ensuring human connection, well-being, and purpose are augmented, not eroded. It's about preventing burnout in our most productive AI users, fostering alignment, and learning from agile models like freelancers to build a truly sustainable human-AI collaborative future. FIG 3: The human cost of AI productivity (Source: The Upwork Research Institute) FIG 4: The rise of human-like relationships with AI (Source: The Upwork Research Institute) COBUS GREYLING - Do AI Agents Substitute Human Workers — Or Enable Humans To Succeed In New Ways? | L. ELISA CELIS, LINGXIAO HUANG, AND NISHEETH K. VISHNOI - A Mathematical Framework for AI-Human Integration in Work AI Agents are good at tasks not jobs… In his article, Cobus Greyling provides an insightful and accessible analysis of a new study by Elisa Celis, Lingxiao Huang, and Nisheeth Vishnoi, which presents a mathematical framework that models jobs, workers, and worker-job fit, and introduces a novel decomposition of skills into decision-level and action-level subskills to reflect the complementary strengths of humans and GenAI. Greyling’s incisive analysis offers a helpful perspective for HR leaders navigating the future of work. His core message is clear: AI agents are fantastic at tasks, not entire jobs. They're not just substitutes, but powerful amplifiers of human capability, especially for less experienced workers, effectively compressing productivity gaps and fostering extraordinary collaboration. Here are four key learnings for HR: (1) Agentic AI Augments Human Potential: AI agents boost efficiency and performance, particularly for junior talent, by handling structured tasks and freeing humans for higher-order work. (2) Redefine Skills & Development: While AI takes on the mundane, HR must strategically ensure continuous skill development, focusing on uniquely human capabilities like judgment, creativity, and complex problem-solving. (3) Design for Human-AI Synergy: Organisational design must pivot to foster premium collaborations between humans and AI. It's about combining complementary strengths to achieve outcomes greater than the sum of the parts. (4) HR Leads Strategic Integration: Our role in HR is pivotal. We must orchestrate the strategic integration of agentic AI, balancing its efficiency gains with the imperative to preserve and nurture human ingenuity, driving both innovation and connection. FIG 5: Al for work: skill difficulty continuum (Source: Cobus Greyling) PEOPLE ANALYTICS KETAKI SODHI AND COLE NAPPER - Who Needs a “Human in the Loop” When AI Gives Itself Feedback Ketaki Sodhi, PhD, Program Owner for Agentic Listening and Analytics at Microsoft, and Cole Napper provide a fascinating perspective on the "human in the loop" concept for Generative AI, provocatively asking: which human, and how? This isn't just a technical question; it's where I/O Psychology and People Analytics come into their own. The article frames AI "evals"— the systems for assessing whether AI outputs are useful, accurate or aligned —as essentially performance management for Large Language Models. Just as we've wrestled with defining "good" in complex human knowledge work for decades, we now face the same challenge in building AI systems. In a world of infinite " " answers, AI evals demand the same nuance we apply to human systems: competency models, multi-rater input, calibration, and context. One of the key takeaways from Ketaki and Cole is that true success lies not in chasing perfect answers from AI, but in designing smart, human-informed systems. These are the systems that can discern between good, better, and what genuinely drives impact for your organisation. For people analytics leaders and I/O psychologists, this is a clarion call to leverage their deep expertise in human performance to shape the very fabric of our AI-driven future. FIG 6: Source – Ketaki Sodhi BEN BERRY - The future is built by everyone: What is Vibe Coding and why should People Analytics teams adopt it | ROSARIO GERMINO - From People Analytics to People Economics and Impact | ADRIAN PEREZ – GitLab People Analytics Team Handbook | DOMINIK TOMICEVIC - Can NASA’s People Graph and LLMs Revolutionize Workforce Planning? | MORGAN DEPENBUSCH - How to let color do the storytelling In each edition of the Data Driven HR Monthly, I feature a collection of articles by current and recent people analytics leaders. These are intended to act as a spur and inspiration to the field. Five are highlighted in this month’s edition: (1) In a particularly insightful piece, Ben Berry examines whether vibe coding, a product management practice of using AI tools to rapidly build functional prototypes to help turn rough ideas into working concepts, should be adopted in people analytics. (2) In her thoughtful article, Rosario Germino argues that to elevate people decisions to the same level of strategic investment as product or finance, we need a new way of thinking—and a new kind of function – People Economics and Impact, which she then breaks down into the why (see FIG 7 on the multi-dimensional aspect of informed decision making), what and how. (3) In a recent post, Adrian M. Pérez provides open source access to GitHub’s People Analytics Team Handbook, a rich resources covering areas such as (i) data governance framework, (ii) tools and methodologies, (iii) survey administration, and (iv) Tableau dashboard strategies. (4) Dominik Tomicevic provides a compelling account of how NASA’s People Graph is supporting a range of priorities from upskilling to workforce planning – with insights from the NASA team of David Meza, Madison Ostermann and Katharine Knott, MBA: “Knowledge graphs offer flexibility, since you don’t need a full schema upfront. We began with known relationships and expanded as we uncovered more insights in the data.” (5) In an edition of her excellent Trending Up newsletter, Morgan Depenbusch, PhD offers some compelling guidance on the use of colour in data visualisation and storytelling. FIG 7: Informed decisions are multi-dimensional. Financial logic makes them investable (Source: Rosario Germino) THE EVOLUTION OF HR, LEARNING, AND DATA DRIVEN CULTURE MCKINSEY - HR Monitor 2025 The gap is widening between what is needed from an efficient, effective HR function and what most organizations currently offer McKinsey's HR Monitor 2025 benchmark study of workforce and HR trends across Europe, delivers a sharp analysis of the critical shifts shaping the HR profession, emphasising that the next 12-24 months are decisive for the function. The report identifies five key trends: (1) Workforce planning is not approached strategically enough – see FIG 8 - (“…with rapid changes driven by gen AI and shifting skill needs, workforce planning must move beyond short-term staffing forecasts to include a longer-term view and future-scenario planning”). (2) Talent acquisition is becoming more complex: with only 56% offer acceptance rates, 18% of new hires leaving during their probationary period and the overall hiring success rate in Europe standing at a lowly 46%, a more strategic and coordinated approach to attracting and hiring talent is required. (3) Employee development continues to be highly fragmented (“To prepare the workforce for future challenges, organizations must connect performance management, learning and development, and talent development in one cohesive strategy”). (4) Employee experience is essential—and underdeveloped (“A more tailored, data-driven approach to the employee experience is needed to build motivation and long-term commitment to employers”). (5) Gen AI and shared-services centres could boost efficiency and effectiveness (“HR departments must modernize their operating models by expanding SSC adoption and using automation and gen AI to increase speed, scalability, and strategic impact”). For Chief People Officers, the message is clear: You must align HR strategy directly with business priorities, strengthen your HR operating model, and aggressively build digital and AI skills within HR. This is about laying the foundation for a modern, AI-enabled HR function that is both deeply people-centric and laser-focused on organizational performance. Kudos to the authors: Julian Kirchherr, Vincent Bérubé, Charlotte Seiler, Dr. Kira Alexandra Rupietta, Kristina Stoerk, Nina-Marlene Senst, and Simon Gallot Lavallée. ...with rapid changes driven by gen AI and shifting skill needs, workforce planning must move beyond short-term staffing forecasts to include a longer-term view and future-scenario planning FIG 8: Engagement in workforce planning (Source: McKinsey) FIG 9: Predicted impact of gen AI on HR department (Source: McKinsey) ESER RIZAOGLU AND STEPHANIE CLEMENT - How CHROs Can Prepare Their Function and the Enterprise for AI Transformation CHROs play a key role in safely using AI at scale to deliver business outcomes. Recent research by Eser Rizaoglu and Stephanie Clement for Gartner provides a helpful roadmap for CHROs steering their organisations through AI transformation, by focusing on HR's pivotal role in shaping the future of work. The report highlights three key actions for CHROs to enable their organisation's AI approach: (1) Assist in delivering business outcomes using AI: Leverage GenAI for HR productivity first, then expand to drive enterprise-wide improvements with a broader AI portfolio. (2) Manage behavioural outcomes of AI: Cultivate a culture of innovation, build human-centred change management plans, and introduce new HR roles to foster human-machine partnerships. (3) Enable workforce readiness for AI: Implement AI literacy programs for all (see FIG 10), while targeting upskilling efforts on segments most impacted, building empathy, and tracking readiness indicators. For CHROs in Steady-AI-Pace organisations, the focus is on foundational AI literacy and policy. Those at an Accelerated-AI-Pace must deepen this by targeting high-impact workforce segments and deploying AI champions to drive effective, human-centric change. FIG 10: AI Literacy Program Roadmap (Source: Gartner) DAVE ULRICH - Navigating Eight Paradoxes of AI for HR When algorithms combine with human empathy, judgement, and creativity, sustained progress occurs. In his article, Dave Ulrich highlights eight paradoxes on the AI for HR agenda that he believes business and HR leaders need to navigate to move up the s-curve and waves of HR impact (see FIG 11) to deliver more value. As Dave explains: “Navigating (not just managing) paradox means highlighting and working through opposing ideas—each of which is valid—that combine to create more value.” The eight paradoxes identified in the article are: (1) AI and AI: Artificial Intelligence * Authentic Intimacy. (2) Remove jobs and redefine work. (3) Bottom line efficiency and top line growth. (4) Distribute and concentrate power. (5) Lower and increase risk. (6) Expand perspective and reduce cognition. (7) Provide answers and explore questions. (8) Isolate and connect. FIG 11: Five stages of AI for HR evolution (Source: Dave Ulrich) EMPLOYEE LISTENING, EMPLOYEE EXPERIENCE, AND EMPLOYEE WELLBEING JARED WEINTRAUB - A day in the life of a GenAI-enabled workforce Deloitte forecasts that 25 percent of companies currently using GenAI will launch agentic pilots this year, rising to 50 percent by 2027 Jared Weintraub, PhD, SPHR's article for Deloitte paints a tantalising picture of a 'Gen-AI enabled workforce,' showcasing how AI agents are already transforming our daily work. Through a fictional Fortune500 company, Jared brings to life three key personas: (1) New Hire (Riley): Experiences personalised onboarding, with AI agents helping her navigate culture and quickly excel in her role. (2) VP (Taylor): Sees optimised leadership workflows, receiving instant summaries, personalised action items, and even real-time feedback on calls. (3) CEO (Angelina): Gains powerful support for strategic decision-making, with AI agents providing real-time insights and even coaching for high-stakes events like public town halls. These examples demonstrate AI's profound potential not to replace workers, but to fundamentally enhance human potential, leading to a significantly improved employee experience where individuals, teams, and organisations can thrive and perform at their absolute best. Thanks to Brian Heger for highlighting in his excellent Talent Edge Weekly. WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS SCOTT REIDA AND KRISTIN SABOE - Applying the Rule of 72 to Workforce Skill Obsolescence and Productivity Degradation Amazon's Scott Reida and Google's Kristin Saboe, Ph.D. introduce a powerful financial concept to HR: the "Rule of 72." Traditionally, it's a shortcut to estimate how long an investment takes to double, by dividing 72 by its annual growth rate. They ingeniously flip this, applying it to skill evolution: by dividing 72 by a role's weighted average 3-year Compound Annual Growth Rate (CAGR) of its skills, one estimates the "years to obsolescence" if no upskilling occurs. This provides critical directional clarity on how fast job competencies are shifting. Their framework, illustrated in FIG 12, categorises skills into four key zones: (1) Emerging (low adoption, high growth, representing the cutting edge). (2) Table Stakes (widely adopted, foundational must-haves with steady growth). (3) On the Cusp (moderate adoption, sustained expansion, offering long-term value). (4) Sunset (declining demand, requiring intentional upskilling). This enables smarter workforce planning. HR can now target training budgets where skill erosion is rapid, shifting from reactive to proactive strategies. It transforms talent into a dynamic portfolio , informing sharper hiring and career development in our accelerating world. FIG 12: Categorising skills into four key zones (Source – Scott Reida and Kristin Saboe) McKINSEY - The new rules for getting your operating model redesign right When people feel invested in and supported, they are more likely to embrace change, contribute meaningfully, and sustain the behaviors that drive long-term impact. New research from McKinsey updating their nine golden rules for operating model redesign, which finds that five original (evergreen) rules have stood the test of time while four new (evolved) rules have emerged (see FIG 13). The study identifies a key finding: redesign success jumps from 59 percent when using all nine original rules to 97 percent when using all nine in the refreshed set. The article also presents four broad redesign themes for leaders to focus on: (1) Create alignment among leaders and decision-makers, grounded in strategy. (2) Invest deeply in rewiring workflows. (3) Make significant investments in people. (4) Create a performance-oriented culture for durable impact. For Chief People Officers, the key takeaway is that they need to become the architects of dynamic, human-centric operating models. Their focus shifts from traditional talent management to proactively designing how work gets done, emphasising skills and capabilities over static roles. CPOs should also lead on ethical AI integration, foster a culture of continuous learning, and empower leaders. This creates a workforce built for perpetual reinvention, driving sustained value in an increasingly uncertain world. Kudos to the authors: Brooke Weddle, J.R. Maxwell, Tristan Allen, Deepak Mahadevan, Elizabeth Mygatt, and Olli Salo. FIG 13: The refreshed golden rules of organisational redesign (Source: McKinsey) LEADERSHIP, CULTURE, AND LEARNING JEFF WETZLER - The Right Way to Prepare for a High-Stakes Conversation Curiosity increases your ability to process new information and respond creatively to complex problems. It activates the brain’s learning and reward centers, increasing your capacity for insight and creative problem-solving. In his recent HBR article, Jeff Wetzler introduces a helpful concept for leaders: The Curiosity Check (see FIG 14). This diagnostic is designed to fundamentally shift your mindset from defensive certainty to productive curiosity, and so improve your effectiveness in high-stakes discussions and boost your influence. It’s all about unlocking crucial, often hidden, insights. Wetzler outlines three actionable steps: (1) Choose Curiosity Over Certainty: Actively ask yourself "What am I missing?" challenging your assumptions. (2) Make It Safe to Speak Up: Create an environment where people feel secure sharing their true thoughts, proving safety through action, not just words. (3) Pose Quality Questions: Shift from shallow or leading questions to open-ended, neutral, and deeper inquiries that encourage genuine reflection. Wetzler brings this to life with examples, highlighting how leaders often miss critical information when they assume team alignment, never probing for the "unspoken thoughts" that hold the real insights. This approach empowers you to tap into wisdom you might otherwise completely overlook. Thanks to Amy Edmondson for highlighting. FIG 14: The Curiosity Curve (Source: Jeff Wetzler) MCKINSEY RESEARCH AND INNOVATION LEARNING LAB – Reimagined: Development for the Future of Work – Evolving Trends in L&D Article | Full report Leaders must prepare for a future defined by radical candor regarding the impacts of AI on work and the workforce. The 2025 McKinsey Learning Perspective spotlights three interconnected themes crucial for people development in a rapidly changing world: (1) Fluid Development Ecosystems: Organisations must design work to be inherently developmental, shifting from rigid structures to dynamic, data-driven ecosystems. This means de-siloing HR functions and embedding learning into daily work, making growth continuous and seamless. The goal is to make daily challenges catalysts for growth, supported by real-time data and foresight. (2) Responsible AI Adoption: This defining moment demands leaders preserve employee trust by showing AI will help them thrive, not just automate work. It's about fostering powerful human-AI collaboration, offloading repetitive tasks to AI to unlock human creativity and higher-order skills. Responsible adoption hinges on equipping employees with uniquely human capabilities like critical thinking and judgment. (3) Resilient and Adaptable Individuals and Organisations: Thriving organisations anticipate challenges, adapt, and grow, building structural and cultural foundations for resilience. This involves unlocking the potential of diverse, multigenerational workforces, supporting recuperation to prevent burnout, and enabling organisational resilience through sustainable workflows. It means seeing resilience as a shared, cultivated capability, not just an individual trait. Read the article by Heather Stefanski, Benjamin Hall, Jake Gittleson, and Jessica Glazer, and then dive into the full report, which also includes contributions from the likes of Sandra Durth. DIVERSITY, EQUITY, INCLUSION AND BELONGING ROBERT D. AUSTIN, NEIL BARNETT, CHLOE R. CAMERON, HIREN SHUKLA, THORKIL SONNE, AND JOSE VELASCO - How Neuroinclusion Builds Organizational Capabilities Leaders should consider neuro-inclusion as a strategic capability-building opportunity rather than a diversity initiative In a rapidly evolving world, neuro-inclusion is emerging as a critical organisational capability, as highlighted by Robert Austin, Neil Barnett, Chloe Cameron, Hiren Shukla, Thorkil Sonne, and Jose Velasco in the MIT Sloan Management Review. This isn't merely a diversity initiative; it's a strategic imperative that unlocks competitive advantage by leveraging the rich, natural variation in human cognition. By intentionally designing processes for neurodistinct individuals, organisations can profoundly improve: (1) Hiring, by tapping into overlooked talent pools with unique skills (as seen with SAP attracting highly credentialed candidates often missed by traditional interviews); (2) Innovation, through diverse perspectives that spark novel solutions (Microsoft's Teams ‘Blur’ feature emerged from a neurodistinct engineer's insights); and ultimately, (3) Culture, by fostering a more adaptive and truly inclusive environment for everyone. As the article reveals, EY, Microsoft, and SAP are prime examples of organisations already reaping these benefits, demonstrating that embracing neurodiversity enhances collective intelligence and drives superior business outcomes. FRANK DOBBIN AND ALEXANDRA KALEV - Achieve DEI Goals Without DEI Programs Many management innovations designed to improve performance actually boost workforce diversity as well, without inviting the backlash of formal DEI programs. Frank Dobbin and Alexandra Kalev, in their recent HBR article, challenge the traditional view of DEI. They argue that as formal DEI programs face headwinds, HR leaders can still drive significant diversity, equity, and inclusion by focusing on high-performance management techniques that naturally foster inclusion and improve business outcomes, all without the ‘DEI program’ label. They highlight five powerful techniques and provide examples of how these have been implemented by companies: (1) Referral programs: Companies like Oracle use these effectively, often boosting representation organically. (2) Skills upgrading: Walmart exemplifies this, investing in employee upskilling that broadens opportunities for diverse talent (see FIG 15). (3) Mentoring programs: IBM has long leveraged robust mentoring to support career progression across all groups. (4) Scheduling flexibility and stability: Gap demonstrates how providing predictable yet flexible schedules empowers diverse workforces. (5) Performance-based retention: Amazon uses data-driven approaches to identify and retain top performers, inherently benefiting those who excel regardless of background (also see FIG 15). This approach embeds DEI within the fabric of how we manage and develop our people, making it an undeniable component of business success. It’s about doing good by doing well. FIG 15: Walmart and Amazon’s changing workforces (Source: Dobbin and Kalev) HR TECH VOICES Much of the innovation in the field continues to be driven by the vendor and analyst community, and I’ve picked out a few resources from July that I recommend readers delve into: LISA K. SIMON - How Much Is a Skill Worth? In her article, Lisa K. Simon, Chief Economist at Revelio Labs, presents the findings of a new paper, she co-authored with David Dorn, Ludger Woessmann, Moritz Seebacher and Florian Schoner, which finds that the number and type of skills workers report are strong predictors of how much they earn: “In fact, differences in skills predict earnings better than differences in education or past experience. Workers who list more skills tend to be in better-paid jobs. On average, each additional skill listed on a resume is associated with 0.67 percentage points higher earnings.” Another finding is that not all skills are valued equally, with occupation-specific and managerial skills providing the largest boost to income, while a higher prevalence of general skills is associated with lower earnings (see FIG 16). Thanks to Seth Hollander, MBA for highlighting the article and paper. Workers who list more skills tend to be in better-paid jobs. On average, each additional skill listed on a resume is associated with 0.67 percentage points higher earnings. FIG 16: Only having general skills on a resume is associated with lower earnings (Source: Revelio Labs) WARDEN AI - State of AI Bias in Talent Acquisition This is an excellent new report from Jeffrey Pole and the team at Warden AI, which provides a comprehensive and data-driven review of AI bias, compliance and responsible AI practices in talent acquisition – the area of HR, which perhaps has the most significant adoption of AI. With a foreword by Kyle Lagunas, and contributions from the likes of Hung Lee (see quote below) and Sarah Smart, Sultan Murad Saidov and Trent Cotton, key findings include: (1) 75% of HR leaders say bias is a top concern when adopting AI. (2) 15% of AI systems fail to meet fairness metrics for one or more demographic group. (3) AI scores 0.94 vs 0.67 for humans, outperforming on average across fairness metrics (see FIG 17). (4) AI is up to 45% more fair than humans for women and racial minority candidates. Congrats too to Jeff and the team for raising $1.6m in a recent funding round. We are right to worry about AI bias, but we should not forget that the baseline, human only judgment, is far from bias-free - Hung Lee FIG 17: AI outperforms humans across fairness metrics (Source - Warden AI, State of AI Bias in Talent Acquisition) COLE NAPPER - From HR Skills…to HR Jobs When new trends emerge at work, they are likely to first appear as skills. As skills evolve, they consolidate into job titles and full occupations. The prolific Cole Napper highlights Lightcast data to paint a compelling analysis on the journey of people analytics, workforce planning and talent intelligence from trends to skills to jobs: “When new trends emerge at work, they are likely to first appear as skills. As skills evolve, they consolidate into job titles and full occupations.” In the article, Cole presents data visualisations and analysis on how job postings mentioning each of the three skills fluctuated over time, how this translated into job titles, and the wage premium (see FIG 18) that these three categories have on HR salaries in general (on the theme of people strategy and analytics salaries, read this post by Pallavi Narang) Look out for Cole’s book, People Analytics: Using data-driven HR and Gen AI as a business asset, which is available for pre-order now ahead of being published on August 26. FIG 18: Median salaries in HR areas (Source: Lightcast) PODCASTS OF THE MONTH In another month of high-quality podcasts, I’ve selected four gems for your aural pleasure: (you can also check out the latest episodes of the Digital HR Leaders Podcast – see ‘From My Desk’ below): PETER FASOLO - Leading with impact as a chief human resources officer – In this must-listen episode of Capital H, Peter Fasolo, Ph.D., former chief human resources officer at Johnson & Johnson, joins host Kyle Forrest to discuss the power of systems thinking, board collaboration, aligning your people agenda with enterprise strategy, and more. ANGELA LE MATHON - AI-Native HR Operating Model & AI Agents for Skills/Tasks – The brilliant Angela LE MATHON joins Cole Napper to discuss how AI is transforming the work that people analytics does and how the function operates as well as envisioning a new AI-native operating model for HR. SVENJA GUDELL, BROOKE WEDDLE, AND BRYAN HANCOCK - What the labor market isn’t telling you—yet – Svenja Gudell, chief economist at Indeed, joins Brooke Weddle, Bryan Hancock, and host Lucia Rahilly, on an episode of McKinsey Talks Talent to help leaders make sense of the current collision of labour market trends: generative AI, agentic AI, an aging workforce, shifting priorities, and more. BEN WEIN – How Bristol-Myers Squibb used skills data to solve a life-or-death talent shortage – Ben Wein, Director of Workforce Skills Enablement at Bristol Myers Squibb, joins Julius Schelstraete ? on The TechWolf Podcast to share how BMS is becoming a skills-based organisation—starting with a business-critical talent shortage in cell therapy manufacturing. Ben explains how BMS uses skills data to drive faster hiring, smarter workforce planning, and ultimately, patient impact. VIDEO OF THE MONTH DJ PATIL - Data, Decisions, and the Future of Work: How AI and Curiosity Are Redefining Careers Many of the videos of the talks at the recent Wharton People Analytics Conference are now available on the Wharton School YouTube channel, including my talk on How Top Companies Scale People Analytics Adoption. There are some wonderful talks from the likes of Amy Edmondson, Ravin Jesuthasan, CFA, FRSA, Ben Waber, Karalee Close, Guru Sethupathy and Michael Fraccaro, but perhaps my favourite session of the two days was former US Chief Data Scientist DJ Patil’s fireside chat with Eric Bradlow on how firms can harness data science to navigate the future of work. They explore the evolving relationship between AI and human collaboration, the promises and pitfalls of algorithmic management, and how leaders can build ethical, resilient, and high-performing organizations in an increasingly data-driven world. BOOKS OF THE MONTH Given it’s the summer in Europe and North America, here are two books to read while you are getting some well-earned relaxation time: PETER HINSSEN – The Uncertainty Principle - Peter Hinssen's The Uncertainty Principle, his fifth book, is a vital read for HR leaders. It argues we're in a "Never Normal" world, where constant change is inevitable. Hinssen transforms uncertainty from a threat to an opportunity, urging us to move faster and think bigger. For HR, this means embracing ambiguity, leading cultural shifts, leveraging people data, and redefining talent and leadership for relentless evolution. It's about equipping our people to thrive and transform every challenge into a strategic advantage. For a preview of the book, I recommend Peter’s recent discussion with me on the Digital HR Leaders podcast: Uncertainty as an Opportunity: HR's role in Shaping the Future. JENNY DEARBORN AND KELLY RIDER - The Insight-Driven Leader: How High-Performing Companies are Using Analytics to Unlock Business Value - Jenny Dearborn, MBA and Kelly Rider's The Insight-Driven Leader is an inspirational guide to unlocking serious business value through people analytics. This book shows how to transform raw data into powerful workforce insights, solving critical challenges and driving success. You'll learn: (1) How to move beyond traditional rear-view HR metrics to actionable insights. (2) Real-life case studies from leading organisations, as well as cautionary tales. (3) Recommendations for becoming an insights-driven organization using workforce analytics. The book is a must-read for leaders aiming to align data with strategy and build a truly insight-driven culture. FROM MY DESK July saw four new episodes of the Digital HR Leaders podcast – all sponsored by our friends at Mercer (thanks IŞIL ÇAYIRLI KETENCI): ANSHUL SHEOPURI - How People Analytics is Powering Business Strategy - Anshul Sheopuri, Executive Vice President of People Operations & Insights at Mastercard, joins me for a conversation on how to embed analytics into enterprise-wide decision-making at scale. Thanks to Sasha Houlihan for organising. PETER HINSSEN - Uncertainty as an Opportunity: HR's role in Shaping the Future – As highlighted in the Books of the Month above, Peter Hinssen joined me to discuss what it really takes for HR to embrace uncertainty and lead in this era of the ‘Never Normal.’ RAVIN JESUTHASAN AND BRIAN FISHER - The Skills Revolution: Your Playbook for Workforce Agility – Ravin Jesuthasan, CFA, FRSA and Brian Fisher join me to explore why skills-based workforce planning has surged to the top of the HR agenda - and what leading companies are doing to turn intent into action. AMY BAXENDALE - How Arcadis Built a Skills-Powered Organisation – Amy Baxendale , Global Future of Workforce Director at Arcadis, provides a detailed guide on the journey the company has embarked to become a skills-powered organisation. The episode includes discussion on the business case, securing sponsorship, setting up governance, the partnership with Mercer and Eightfold, and the early benefits: We are early in the journey, but we are seeing some promising signs of progress. Our time to hire is trending downwards - that has a direct commercial impact for the business. We've also actually been able to calculate the financial impact of work that's being completed through gigs and show the actual impact on EBITDA LOOKING FOR A NEW ROLE IN PEOPLE ANALYTICS OR HR TECH? I’d like to highlight once again the wonderful resource created by Richard Rosenow and the One Model team of open roles in people analytics and HR technology, which now numbers over 525 roles with half of these being new. THANK YOU To HR magazine and Charissa King for including me again in their annual HR Most Influential list as one of the ten most influential practitioners The Talent Games for including the Digital HR Leaders podcast at #6 in its 27 Best Leadership Podcasts for HR Leaders. Steve Sands for including my work as part of his Human Resource Management Analytics night class at the National College of Ireland. A huge thank you to the following people who either shared the June edition of Data Driven HR Monthly and/or posted about the Digital HR Leaders podcast, conferences or other content. It's much appreciated: Emmanuel Duncan, Rob Baker, FCIPD, MAPP, Richard Hall, Robert Rogowski, Catherine de la Poer, Caroline Lambe, Jeremy Sholl, Narelle Burke, Edan Halili, Francesca Caroleo (SHRM-SCP, ICF-ACC), Uwe Gohr, Joseph Frank, PhD CCP GWCCM, Randeep Kaur, Aaron Chasan, Danial Singh Kang, Jorge-Luis Gonzalez, Anisha Moosaأنيشا موسى?????, Carlos Lopes, Danielle Farrell, MA, CSM, Kris Saling, Hiroyuki MIYAI, Ph.D., Yukiko Hosomi, Dr. Christoph Spöck, Joachim Rotzinger, Kevin Le Vaillant, Seung Won Yoon, Alexis Fink, Timo Tischer, Dr. Tobias Bartholomé, Jose Luis Chavez Vasquez, Meg Bear, Abhinav Tiwari, Esther Abraas, Gareth Flynn, Elizabeth Musso, Jana Glogowski, Maarten van Beek, K Nair, Joonghak Lee, Sameer Tahir, Robert Allen, Volker Jacobs, Bilal Laouah, Florent Maire, Oliver Kasper, Jaap Veldkamp, Patrick Coolen, Jeff Wellstead, Jean-Francois (Jeff) BOUBANGA MIGOLET, Dan George, Shujaat Ahmad, Alexandra Nawrat, People Edge Consulting Ltd., Andrew Spence, Roshaunda Green, MBA, CDSP, Phenom Certified Recruiter ?, Austin Brockert, MBA, Dan Riley, Sanja Licina, Ph.D., Anna A. Tavis, PhD, Stela Lupushor, Jeremy Shapiro, David Simmonds FCIPD, Catriona Lindsay, Aravind Warrier, Michael Arena, Greg Pryor, Isabella Cheshire, Amardeep Singh, MBA, Aline Costa, Anis Alexandros El Namparaoui, Adam Treitler, Helder Figueiredo, Sebastian Knepper, Sebastian Kolberg, Lewis Garrad, Kerry Ghize, Preetha Ghatak Mukharjee, Jacob Nielsen, Pete Jaworski, Søren Kold, Prabhakar Pandey, Avani Solanki Prabhakar, Ian Grant FCIPD, Erik Samdahl, Max Blumberg, Sergey Puchka, Romy Hobson, Bettina Dietsche, Hernan Chiosso, CSPO, SPHR ?, Paola Alfaro Alpízar, Sergio Garcia Mora, Hanadi El Sayyed, David van Lochem, Maria Nolazco Masson, David McLean, Clara W Estanqueiro, Shonna Waters, PhD, Kevin Martin, Kathi Enderes, Serena H. Huang, Ph.D., Smadar Tadmor, Tobias W. Goers ツ, Dr. Denise Turley AI.Impact.Equity, Stella Ioannidou, Apeksha Awaji, Evan Franz, MBA, L N Divya Mudundi, Ross Sparkman, Salman Farooq, Megan Reitz, Todd Tauber, Heather Muir, AJ Herrmann, Priyanka Mehrotra, Oliver Auty, Priya Subrahmanyan, Naotake Momiyama, Bill Banham, Matthew Yerbury, Prachi Agasti, Robin Haag, Fabian Stokes, MBA, SWP, Monika Manova, Barry Swales, Dean Carter, Ian OKeefe, Ying Li, Alexandre Monin, Mike Zarrilli, Natasha Fearon, Pedro Pereira, David Balls (FCIPD), Naomi Verghese, Geetanjali Gamel, Frankie Close, Warren Howlett, Stephanie Murphy, Ph.D., John Gunawan, Jesse Clark, MBA, Caitie Jacobson Mikulis, Meghan M. Biro, Dan Trares, Kouros Behzad, Kathleen Kruse, Nick Lynn, Mariana Allain Carrasqueira, Marina Pearce, PhD, Dawn Klinghoffer, Raquel Mitie Harano, Delia Majarín, Deborah M. Weiss, Courtney McMahon, Nirit Peled-Muntz, Hanne Hoberg, Adam McKinnon, PhD., Don Dela Paz, Matt Elk, Sophia Houziaux, Danielle Bushen, Nabil Dewsi, Sai Bon Timmy Cheung 張世邦, Dolapo (Dolly) Oyenuga Agnes Garaba, Wouter Minten, Olly Britnell, Nick Hudgell, Roxanne Laczo, PhD, Claire Masson, Daisy Grewal, Ph.D., Laura Cole, Brian Elliott, Erin Eatough, PhD Henrik Håkansson Gabe Horwitz Russell Klosk (智能虎) The final note this month is a sad one - rest in peace Diogo Jota and André Silva. ABOUT THE AUTHOR David Green ?? is a globally respected author, speaker, conference chair, and executive consultant on people analytics, data-driven HR and the future of work. As Managing Partner and Executive Director at Insight222, he has overall responsibility for the delivery of the Insight222 People Analytics Program, which supports the advancement of people analytics in over 100 global organisations. Prior to co-founding Insight222, David accumulated over 20 years experience in the human resources and people analytics fields, including as Global Director of People Analytics Solutions at IBM. As such, David has extensive experience in helping organisations increase value, impact and focus from the wise and ethical use of people analytics. David also hosts the Digital HR Leaders Podcast and is an instructor for Insight222's myHRfuture Academy. His book, co-authored with Jonathan Ferrar, Excellence in People Analytics: How to use Workforce Data to Create Business Value was published in the summer of 2021. MEET ME AT THESE EVENTS I'll be speaking about people analytics, the future of work, and data driven HR at a number of upcoming events in 2025: July 31 - August 1 - People Matters TechHR India 2025, Delhi August 13-16 - GCHRA Africa, Accra, Ghana (I will join virtually) September 25 - Visier Outsmart Local London, London October 7-9 - Insight222 Global Executive Retreat, Atlanta (exclusive to the people analytics leader in member companies of the Insight222 People Analytics Program®) October 15-16 - People Analytics World, New York October 21-22 - UNLEASH World, Paris November 12-13 - HR Forum 2025, Oslo More events will be added as they are confirmed.
    talent intelligence
    2025年07月27日
  • talent intelligence
    员工体验平台的演进：推动 AI 转型的关键引擎 Josh Bersin 公司发布新研究指出：员工体验平台（EXP）正在成为企业 AI 转型的关键基础设施。EXP 不再只是HR工具，而是推动组织学习、透明沟通和员工赋能的核心平台。研究提出五大战略：以人为本、自下而上、持续学习、透明沟通和实时优化。案例包括 Microsoft 的 HR AI 转型、ASOS 的 AI 自动化、Clifford Chance 的法律文书 AI 起草。EXP 赋能组织实现敏捷变革和AI落地。 AI 正在快速改变职场——不仅是技术，更是组织文化与工作方式的深刻变革。 人工智能（AI）的广泛应用为生产力、效率和业务增长带来了前所未有的机遇。然而，AI 转型并不仅仅意味着“部署新技术”，它实际上深刻地重塑了员工体验，影响着组织文化、团队协作方式与工作流程。 在这一转型过程中，员工体验平台（Employee Experience Platform，简称 EXP） 正逐渐从传统的 HR 工具，演进为推动企业成功实施 AI 的关键引擎。EXP 不再只是一个用于请假或查政策的门户，而是集成沟通、学习、协作、数据与自动化的智能化平台，帮助组织推动 AI 采纳、提升员工准备度，并确保 AI 真正带来业务价值。 员工体验平台的演进 EXP 的初始功能主要是处理事务性流程，如请假申请、薪资查询等。但如今，随着 AI 技术的发展，EXP 已演变为智能化的交互中心，集成以下核心功能： 跨系统的员工沟通与协作 提供关于 AI 使用和员工情绪的实时洞察 支持个性化的学习与技能建设 自动化重复任务，让员工专注于更有价值的工作 同时，得益于 AI Agent 的融入，如今的 EXP 变得更易使用，员工可通过自然语言与系统交互，实现跨系统流程操作，无需再进入多个事务性系统。 因此，EXP 不再是“可有可无”的系统，而是 企业 AI 成功转型的关键基础设施。 企业 AI 转型案例 我们调研了三家具有代表性的公司，探讨他们在 AI 转型中如何借助 EXP 实现落地与成效： 1. ASOS（线上时尚零售） 部署 Microsoft Copilot 与 Microsoft Viva 赋能多业务部门 用 AI 驱动 HR 案例处理工具，提升服务效率 通过自助服务门户精简事务流程 用自定义 AI bot 自动完成可持续认证流程 成果：员工生产力提升、参与度增强、AI 无缝落地 2. Microsoft（打造 AI 驱动的 HR 部门） 通过 Viva 学习模块开展 AI 培训 自助 HR 工具增强员工支持体验 实时分析 AI 使用情况，持续优化策略 成果：HR 效率显著提升，数千名 HR 领导参与 AI 社群 3. Clifford Chance（国际律所） 用 AI 起草法律文件，为律师提供初稿 借助 AI 语言工具跨越法律语境差异 利用 AI 管理法律知识，快速找出相关案例 成果：文书效率提升、知识共享加速、决策更精准 AI 转型的敏捷性要求 与传统变革不同，AI 推广不是一次性事件，而是一个 持续试验、迭代和适应的过程。因此，企业需具备“变革敏捷性”（Change Agility），用灵活的机制推动员工学习和组织协同。 借助 EXP 实现 AI 成功的五大战略 我们总结出五个成功企业在 AI 转型过程中普遍遵循的策略，而 EXP 是支撑这些策略实施的核心平台： 1. 以人为本与目标导向（Focus on People and Purpose） AI 的导入需与组织使命、价值观和员工需求保持一致。EXP 可确保所有 AI 工具围绕员工体验设计，提升参与度、工作效率和福祉。 ? 案例：Microsoft HR 借助 Viva Amplify 定制 AI 推广内容，让 HR 团队及时获取战略沟通信息，确保 AI 项目与业务目标保持一致。 2. 采用自下而上的迭代方法（Bottom-Up, Iterative Approach） AI 转型不能靠高层指令推动，而应依赖一线员工的反馈与试验。EXP 通过实时反馈与学习机制，让员工在实际工作中试用、迭代与优化 AI 工具。 ? 案例：ASOS 借助 Viva 社区功能发起“Work Smarter”活动，员工可在平台上公开交流 AI 使用案例，形成知识共享文化。 3. 鼓励透明沟通与试验精神（Transparent Communication and Experimentation） 员工需要明确知道 AI 工具的使用场景、目的与风险，才能建立信任并积极参与。EXP 提供结构化、公开的试验机制，确保过程透明。 ? 案例：Clifford Chance 在 Microsoft Viva 中嵌入 AI 工作流程，员工可以实时测试 AI 辅助起草功能，同时了解其运行逻辑。 4. 推动持续学习与技能建设（Continuous Learning and Skill-Building） 员工必须掌握 AI 基本素养，才能有效融入 AI 工具。EXP 提供基于角色定制的学习路径，支持技能升级与长期成长。 ? 案例：Clifford Chance 借助 Viva Learning 培训员工 prompt 工程、AI 素养与数据分析技能，为 AI 工具的使用打下基础。 5. 实现实时度量与持续优化（Real-Time Measurement and Improvement） 与传统 IT 项目不同，AI 推广必须持续监测并快速调整策略。EXP 提供实时分析能力，帮助企业追踪员工情绪、生产力与 AI 使用情况。 ? 案例：Microsoft HR 借助 Viva Insights 实时追踪 AI 使用频率、员工负荷减轻情况与情绪变化，以便动态调整 AI 战略。 HR 在 AI 转型中的新角色 在 AI 重构工作的过程中，HR 部门不再只是支持者，而是： 主导员工技能升级与再培训 协助重塑岗位定义与工作流程 在 HR、IT 与业务之间架起 AI 战略桥梁 落实负责任 AI 政策，确保 AI 应用符合伦理与企业文化 HR 将在未来的 AI 时代中扮演 “战略引导者 + 管理变革催化者” 的核心角色。 行动建议与未来展望 企业若想在 AI 转型中取得成功，应当： ✅ 采用“变革敏捷”思维，持续学习、实时迭代 ✅ 建立 AI 驱动的员工体验平台，支持流程与文化融合 ✅ 打破 HR、IT、业务之间的壁垒，实现跨部门协同 ✅ 实施实时度量机制，根据反馈不断优化 AI 战略 EXP 已成为企业迈入 AI 未来的基础设施。 AI 将持续重塑职场，但决定 AI 成败的关键并非技术本身，而是组织是否能让员工真正拥抱 AI、用好 AI。 EXP 不再只是一个 HR 工具，而是打造学习型组织、推动信任建设和灵活变革的“中枢神经系统”。企业若想在 AI 驱动的时代中保持竞争力，就必须把员工体验放在战略核心位置。 作者：Kathi Enderes | 全球研究与行业分析高级副总裁 | Josh Bersin Company
    talent intelligence
    2025年07月19日
  • talent intelligence
    Josh Bersin: Understanding the Path to CHRO Josh Bersin 最新研究揭示 CHRO 成长轨迹与未来挑战!近年来，首席人力资源官（CHRO） 这一角色正在经历前所未有的变革。最新发布的 《Understanding the Path to CHRO》 报告(点击可以下载报告，同时附录在文章后)，基于对 20,000 多名 CHRO 的数据分析，深入研究了 CHRO 的成长路径、核心能力及全球 HR 领导者如何适应企业需求的变化。 该研究揭示了HR 从传统行政职能向战略核心的转型趋势，同时发现： 75% 的 CHRO 来自外部招聘，内部继任计划严重不足。 CHRO 逐步迈入 C-suite，13% 进入企业最高薪酬前五名，相比 30 年前增长 26 倍。 四类 CHRO 发展路径浮出水面：职业型 CHRO（Career CHRO）、企业型 CHRO（Company CHRO）、业务型 CHRO（Business CHRO）、运营型 CHRO（Operations CHRO）。 具备国际化经验的 CHRO 绩效更高，75% 的高绩效 CHRO 曾在海外工作。 政治学、经济学背景的 CHRO 更具影响力，而 HR 专业背景反而在高绩效公司中占比最低。 从这些数据来看，CHRO 角色不再是简单的人才管理者，而是企业变革的推动者、业务战略的支持者、AI 与科技革新的领导者。那么，中国的 HR 领导者如何才能成长为具备全球视野的 CHRO？本文将从CHRO 角色的转型趋势、职业路径、核心能力模型及中国 HR 的成长路径四个方面展开分析。 报告下载地址：https://www.hrtechchina.com/Resources/59250FA4-A800-58D9-5CE6-76E4DBC4F82A.html ? CHRO 的转型趋势：从 HR 负责人到企业变革领导者 传统 HR 主要聚焦于招聘、薪酬管理、劳动合规等事务性工作，过去常被视为“后勤支持”部门。然而，随着 全球劳动力市场变化、AI 赋能 HR、企业运营模式调整，CHRO 的角色发生了深刻变化： 1️⃣ CHRO 从 HR 服务交付者转变为业务战略伙伴过去 HR 被认为是支持职能，而今天，CHRO 需要直接参与企业战略决策，关注人才如何驱动业务增长。例如，疫情后全球远程办公兴起，CHRO 需要设计全新的组织架构、推动员工体验升级、调整绩效激励模式，以适应新的工作模式。 2️⃣ AI 与数字化重塑 HR 角色AI 和 HR Tech（人力资源科技）正在改变 HR 的运作方式。CHRO 不仅需要理解 AI 招聘、数据驱动绩效管理、智能学习平台，还要在组织中推动这些技术的应用。例如，采用 AI 进行人才画像分析、通过自动化面试减少招聘成本、利用数据分析优化员工保留率。 3️⃣ 全球化人才流动与多元化管理企业越来越依赖国际市场，CHRO 需要具备 跨文化管理、远程团队领导、国际雇佣合规 的能力。报告发现，在高绩效公司中，75% 的 CHRO 具备国际工作经验，这说明全球视野已成为 HR 领导者不可或缺的竞争力。 ? 四类 CHRO 发展路径：你属于哪一类？ 研究报告将 CHRO 的职业路径划分为 四种主要类型，每种路径各有优势和挑战： 1️⃣ 职业型 CHRO（Career CHRO）——最常见的路径 通过在不同公司担任 HR 领导职务不断晋升，占比 73%。 优势：具备跨行业 HR 经验，能从外部引入最佳实践，拥有更广阔的专业网络。 挑战：对新公司的文化和业务理解较浅，缺乏长期稳定的 C-suite 关系。 2️⃣ 企业型 CHRO（Company CHRO）——公司内部晋升 在同一公司内部从 HR 经理逐步晋升为 CHRO，占比 17%。 优势：深谙企业文化和业务流程，与内部管理层关系紧密。 挑战：缺乏外部视角，可能难以推动 HR 变革和创新。 3️⃣ 业务型 CHRO（Business CHRO）——来自业务部门 从 销售、运营、市场等业务部门 转型进入 HR，占比 8%。 优势：更能理解业务需求，与 C-suite 关系更紧密，推动 HR 战略落地能力强。 挑战：缺乏 HR 专业知识，需要依赖强大的 HR 团队支持。 4️⃣ 运营型 CHRO（Operations CHRO）——来自行政管理 从 财务、法务、风控、合规等行政职能 转型进入 HR，占比 2%。 优势：擅长数据分析、预算管理、企业治理。 挑战：缺乏人才管理经验，对 HR 战略落地理解较弱。 ? 在北美工作的华人 HR 领导者如何突破瓶颈？ 在北美职场，华人 HR 面临 文化适应、晋升壁垒、C-suite 话语权较弱 等挑战。如何突破天花板，成长为 CHRO？ ? 1. 强化本土商业思维，提升 C-suite 话语权 深入了解北美商业环境、企业运营模式、行业趋势。 参与跨部门会议，与 CEO、CFO 直接对话，培养以业务为核心的人才战略思维。 ? 2. 培养影响力，突破“华人 HR 只擅长执行”的刻板印象 多发声，多展示成果：在公司内外分享 HR 变革案例，塑造领导者形象。 主导 HR 变革项目，例如推动 AI 赋能招聘、优化薪酬激励机制，提升 HR 价值感。 ? 3. 争取国际轮岗机会，提升全球 HR 领导力 申请企业的跨国 HR 轮岗项目，拓展跨文化管理经验。 参与 国际 HR 论坛、北美 HR 高管社群，建立全球视野和人脉资源。 ? 4. 选择适合的 CHRO 职业路径 喜欢跨行业挑战？选择 职业型 CHRO 路线。 想深耕企业文化？适合 企业型 CHRO 发展路径。 具备销售、运营经验？向 业务型 CHRO 方向发展。 ? 5. 强化数据与 AI 能力，掌握 HR 科技趋势 学习 数据分析、AI 招聘、人才预测建模，让 HR 决策更具数据支撑。 掌握 HR Tech 生态系统，推动数字化 HR 变革，提高 HR 部门的战略价值。 ? 结论：北美华人 HR 需要突破自我，成为“全球 CHRO” 要成为北美企业的 CHRO，仅靠 HR 知识远远不够，商业思维、数据能力、影响力、全球化经验都是必备技能。北美华人 HR 需要打破行业天花板，成为推动企业变革、掌控未来人才战略的全球化 CHRO 领导者！
    talent intelligence
    2025年02月17日
  • talent intelligence
    How To Make Productivity Soar: Four Stages of AI Transformation We’ve been doing a lot of advisory work on skills and job design and now that AI tools have arrived, we’re reinventing work faster than ever. So let me give you some thoughts on this process, and you can also learn more from my recent podcast. As you know, there are many types of AI business tools: Copilots, Assistants, Agents, Talent Intelligence Systems, and embedded applications. Each of these products are built on an AI-first foundation and they layer on domain expertise, use-case analysis, and iterative design to build smarter and smarter systems. Self-driving cars started as voice assistants, automatic braking, and lane warnings. Now they keep you in the lane and slow your car when the speed limit changes. And soon enough they’ll be driving for us, so we can sit in the back seat and read a book. Our HR Assistant Galileo started as a research and problem solving tool, and it’s rapidly becoming an AI coach, benchmarking tool, recruiting, and change management system. So all these tools go from simple use-cases to deeper applications and autonomy over time. As the tools get smarter and more domain focused we are going to have to rethink our jobs and business processes. And unlike ERP, where we essentially trained people to “adopt” the system, now a lot of the groundbreaking applications come from the bottom up. Individuals will discover capabilities for AI and then apply them in increasingly innovative ways. And over time, as they get smarter, our jobs move more to “supervisors” and “trainers” of AI, not just consumers. For example if our self-driving car took a bumpy route, we may “retrain it” to take a longer but smoother road. As I discuss in the podcast, I believe there are four stages of adoption today. And we’re in the middle of doing all four at the same time. Level 1: Make existing work easier. (Same job, better tools.) This is where we click on the Microsoft Copilot or Zoom or Teams and the system analyzes a meeting, summarizes emails, or writes a document with our help. We do our jobs the same way we did before, but we now have a “super-productivity” tool to make it easier. These “add-on” use cases are emerging everywhere, and they already feel like a commodity. In most cases employees see 10-15% or more improvements here, but life isn’t that much different. And sometimes the tool slows us down (Copilot doesn’t create slides well at all yet) and may actually get in the way. But we can expect this mode to continue and most of us figure this out on our own. Level 2: Major steps eliminated, but the job is the same. (Same job, tools eliminate work.) At level 2 we automated a lot. Software engineers now use copilots to develop 70% of their code, so they’re spending more time testing and prompting the AI. Their individual coding skills may atrophy, but they can now work on more architectural issues. The “job” of software engineer may still be the same, but the output is far greater. So we’re making the same pay, doing the same work, but using highly automated tools. This includes scenarios like chip designers, software engineers, supermarket checkout clerks, nurse scheduling jobs, and even recruiting assistants. Paradox customers, for example, virtually eliminate “scheduling assistants” for recruiting. At this level companies can see 50-75% productivity improvement, and free time to focus on quality management, customer service, and ongoing improvements to the tools. Level 3: Re-engineered work, partnered with agents. (New job, redesigned process, agents automate work.) At level 3 we go further: we re-engineer the process and the work. Imagine how McDonald’s replaced its counter workers with a kiosk, eliminating the “may I take you order please?” role. This took some major design effort but resulted in a whole new set of roles, workflow, and management structure in the restaurants. The “cost per burger” went down, and the customer experience is almost as good (not quite). Here we need to be careful because sometimes the “self-service, AI-enabled” experience doesn’t work. A good example is the supermarket self-checkout. It rarely works well and usually takes longer than standing in line. But it will get better, and the resulting experience is faster throughput, more data (the self-service agent might offer you a discount since it knows your buying history), and far superior employee roles. In level 3 the employees are still involved, and we are more or less “working with the machine,” aiding and supporting the process. Level 4: Autonomous intelligent agents, people training and managing the AI. (New job, redesigned process, people “manage” the agents.) At level 4 we go even further. Imagine an AI recruiter (Paradox does this) that could email a hiring manager and his team, gain feedback and requirements on a job and role, consolidate input, and create a total description. This Agent could then review this job against the company culture and pay policies, compare the job against similar jobs in the external market, and tweak the level, job title, and description to be competitive. And then it could start sourcing, and give the hiring manager and human recruiter a set of candidates ranked by various criteria. That process, which takes dozens of steps for a recruiter, could be fully automated and vastly improved. The Agent could even look at prior hires and get even smarter about who to source based on the success of other candidates. Now the human job is to “train” and “monitor” and “manage” this AI Agent, who has effectively become a digital employee. (Note: Salesforce is doing a terrific job of building this out for sales and service.) The Rise of the SuperWorker Our thesis is that AI is not a “job-replacement” technology, it’s a “SuperWorker empowerment” technology. In other words, most of these scenarios result in higher value jobs, higher pay, and value creation (not cost reduction) in the business. This is happening fast. We’re in the middle of a big study in this area and I’ll be explaining this more in our upcoming 2025 Predictions report. The upside of all this will be new and higher paying jobs, faster response to business change, but a lot of IT, design, and data management to do. But based on our research, this is coming soon.
    talent intelligence
    2024年12月01日
  • talent intelligence
    Cornerstone Galaxy: Acquisition Of SkyHive Could Pay Off Cornerstone在人力资源技术领域长期以来一直是学习管理系统（LMS）的领导者。公司最近推出了Galaxy，这是一个集成了人工智能的全新人才管理平台。这一重大进展是在一系列收购之后实现的，尤其是最近收购了SkyHive，显著增强了公司的数据处理能力。Galaxy平台通过提供全面的技能发展、绩效管理和员工晋升系统，为HR技术空间树立了新标准。 Galaxy区别于市场上其他基于技能的或智能平台，例如Eightfold主要从人才获取开始，而Gloat着眼于人才流动性。Galaxy则从另一个角度出发，即员工发展，这是由Cornerstone在学习与发展（L&D）领域深厚的背景所支撑的。Galaxy系统内置了完整的用户界面，能够推断技能，让员工标记和评估自己的技能，帮助员工找到并完成各种学习形式，管理合规性和认证程序，通过任务、评估或管理辅导提升技能。 通过整合性能管理、发展计划、继任计划，以及招聘过程，Galaxy使公司能够通过绩效管理推动技能发展。在收购SkyHive之前，Cornerstone试图仅使用其LMS信息的数据集来实现这一目标，但这些数据并不足以构建完整的人工智能语料库。通过这次收购，Cornerstone获得了一个完整的劳动力市场数据系统、一个公司中立的职位架构以及大量行业技能，使Galaxy能够与其他主要的人才智能和人才市场供应商直接竞争。 Cornerstone spent the last decade acquiring LMS and talent software companies, all in a goal to build an integrated skills platform. Finally, after years of hard work and integration, the company introduces Galaxy, an advanced offering in the world of AI-powered HR systems. Before I explain Galaxy, the history is important. Founded in 1999, Cornerstone started as an e-learning platform company (CyberU). The company established a foothold in the emerging LMS market and grew through strong marketing, sales, and product innovation. Since then the company has gone public, reached a $5.2 billion valuation, and was then acquired by a private equity firm (Aug. 2021, three years ago). The new management team continued to acquire companies (EdCast, SumTotal, Talespin, and most recently SkyHive) and has now stitched these systems together into a unified platform called Galaxy. Galaxy, as I show below, is a skills-powered integrated talent management platform, built around the core of learning management. And this is what makes it unique. The other talent intelligence or skills-based platforms started elsewhere. Eightfold started in talent acquisition; Gloat started in talent mobility; SeekOut started in recruiting; Beamery started in CRM; and players like Retrain.ai and NeoBrain started in more vertical domains. Each of these companies use large-scale profile data to infer skills, give companies tools to find and match candidates, and eventually to deliver learning. Cornerstone, with deep background in L&D, is coming at this from another direction: employee development. The Galaxy system, which is built into a complete user interface, infers skills, lets employees tag and assess their skills, helps employees find and complete many forms of learning, manage compliance and certification programs, and advance skills through gigs, assignments, assessments, or management coaching. And since Cornerstone is an integrated talent suite, the system lets companies drive skills through performance management, development planning, succession planning, and also recruiting. Before the acquisition of SkyHive, Cornerstone was trying to do this with its own data set of LMS information. This data, which includes billions of learning records, was simply not sufficient to build out the entire AI corpus. By acquiring SkyHive, Cornerstone gained an entire labor market system of data, a company-neutral job architecture, and lots of industry skills. This brings Galaxy into direct competition with the other major talent intelligence and talent marketplace vendors. I have not yet talked with Galaxy customers, but the user experience is integrated and shows the sophistication of thinking under the covers. Remember that Cornerstone acquired Evolv, Clustree, and EdCast before acquiring SkyHive, so the team has been building AI capabilities and use-cases for several years. And now that Cornerstone has a VR platform for learning, more use-cases are coming. While I don’t know Cornerstone’s revenues, the leadership team assures me that the company is growing and the profitability is high. This means the company has long-term sustainability and despite its many acquisitions, is likely to evolve to “Oracle-like” status. (Oracle has acquired hundreds of companies over the years and now looks at M&A as one of its core strengths). Here’s the major play in the market. With 7,000+ customers, Cornerstone has many customers shopping for new tools. If Galaxy is as solid as it looked in the demos, some percentage of these buyers could upgrade to Galaxy and avoid the purchase of Gloat, Eightfold, or another LMS. While we cannot be sure where Galaxy will play, for companies that want to deploy a skills architecture across all talent practices, it looks like a solid option. Cornerstone Vision: Cornerstone User Experience Cornerstone Career and Talent Marketplace Cornerstone Performance Management Skills in Goal Management Why Cornerstone Still Matters Cornerstone has a massive customer base. The users of Cornerstone, Saba, SumTotal, Lumesse, and Halogen include many of the world’s largest companies and thousands of mid-market organizations as well. These organizations have invested billions of dollars into learning infrastructure, content, and user portals to reach employees. If Cornerstone Galaxy delivers on its promise, the company can help many of these organizations avoid buying lots of standalone new tools. And given Cornerstone’s size, the company could become, as I mentioned above, the “Oracle” of the space. And note, by the way, that a recent survey by HR.com found that the top rated HR tech issue to address is L&D infrastructure, so this issue is on everyone’s mind. While the market is highly competitive and there are many skills-based tools in the market, Cornerstone’s focus on L&D is unique. None of the other major LMS vendors have the skills infrastructure of Cornerstone today. If your skills strategy is focused on building skills, Galaxy may be the answer. More to come as we talk with more Galaxy customers. Additional Information  
    talent intelligence
    2024年09月03日
  • talent intelligence
    Josh Bersin: When Will The Trillions Invested In AI Pay Off? Sooner Than You Think. 近年来，生成式人工智能（GenAI）的投资已达数万亿美元，但围绕其回报问题的争论不断升级。一些分析师，如麻省理工学院教授达隆·阿西莫格鲁（Daron Acemoglu）和纽约大学心理学与神经科学教授加里·马库斯（Gary Marcus），对AI的经济影响持悲观态度，认为其对美国生产力和GDP增长的推动作用有限，甚至可能导致市场崩溃。相反，另一派如高盛的全球经济学家则乐观地认为，AI有望在未来十年内大幅提高生产力。然而，文章指出，生成式AI的真正价值在于其特定领域的应用。例如，Paradox和Galileo等HR技术平台通过高度专业化的解决方案，显著提升了招聘和人才管理的效率。最终，文章强调，AI行业仍处于早期阶段，成功的关键在于找到具有专注性和精确性的创新解决方案。 In the last few weeks there has been a lot of concern that Gen AI is a “bubble” and companies may never see the return on the $Trillion being spent on infrastructure. Let me cite four analyst’s opinions. Will Today’s Massive AI Investments Pay Off? MIT professor Daron Acemoglu estimates that over the next ten years AI will impact less than 5% of all tasks, concluding that AI will only increase US productivity by .5% and GDP growth by .9% over the next decade. As he puts it, the impact of AI is not “a law of nature.” On a similar vein, Gary Marcus, professor emeritus of psychology and neural science at New York University, believes Gen AI is soon to collapse, and the trillions spent will largely result in a loss of privacy, increase in cyber terror, and a lack of differentiation between providers. The result: a market with low profits and big losses. Goldman Sachs Head of Equity Research Jim Covello is similarly pessimistic, arguing simply that the $1 Trillion spent on AI is focused on tech that cannot truly automate complex tasks, and that vendors’ over-focus on “human-like features” will miss the boat in delivering business productivity.  (He studies stocks, not the economy.) And Goldman Sachs Global Economist, who is a fan, estimates that AI could automate 25% of work tasks and raise US productivity by 9T and GDP by 6.1% over the next decade. He follows the traditional business meme that “AI changes everything” for the better. What’s going on? Quite simply this new technology is very expensive to build, so we’re all unsure where the payoffs will be. Buyers Are Looking For A Return Soon If we discount the work going on at Google, Meta, Perplexity, and Microsoft to build AI-based search businesses, which make money on advertising (Zuckerberg essentially just said that in a few years AI will guarantee your ad spend pays off), corporate IT managers are asking questions. An article in Business Insider pointed to a large Pharma company that cancelled their Microsoft Copilot licenses because the tool was not adding any significant value (Chevron’s CIO was quoted similarly in The Information). Another quoted a Chief Marketing Officer who stated Google Gemini’s email marketing tool and the new AI-powered ad-buying tool performed worse than the human workers it was intended to replace (or support). Given that these tools almost double the “price per user” for the productivity suites, I think it’s fair that CIOs, CMOs, to expect them to pay for themselves fairly quickly. What’s Going On?  The Big Wins Will Be Domain Specific As with all new technologies that enter the market quickly, “the blush on the rose” is over. We’ve been dazzled by the power of ChatGPT and now we’re searching for real solutions to problems. And unlike the internet, where research was funded by the government, there’s going to be a lag (and some risk) between the trillions we spend and the trillions we save. Given that ChatGPT is less than two years old and OpenAI has morphed from a research company into a product company, it’s easy to see what’s happening. Every vendor and tool provider is narrowing its AI “strategy” and not just pasting little AI “stars” on their websites, looking for useful things to do. And this process may take a few years. In the world of HR, I think we can all agree that a “push the button job description generator” is a bit of a commodity. However if the AI analyzes the job title, identifies the skills needed through a large skills engine, and tunes the job description by company size, industry, and role, then it’s a fantastic solution.  (Galileo does this, as does SeekOut, SAP, and some other vendors.) The more “specific” and “narrow” the AI is, the more useful it becomes. Generic LLMs that aren’t highly trained, optimized, and tuned to your company, business, and job are simply not going to command high prices. So while we all thought ChatGPT was Nirvana, we’re now figuring out that highly specialized solutions are the answer. Let me give you some examples. The first is the platform built by Paradox, a pioneering company that started work on AI-based recruiting agents in 2016. Paradox, now valued at around $2 Billion, delivers an end-to-end recruitment platform that automates the entire process of candidate marketing, candidate experience, assessment, selection, interview scheduling, hiring, and onboarding. Most people believe its a “Chatbot” but in reality it’s an AI-powered end-to-end system that radically simplifies and speeds the recruitment process in a groundbreaking way. Companies like 7-11, FedEx, GM, and others see massive improvements in operational efficiency and both candidates, managers, and recruiter adore it. It took Paradox eight years to build this level of integrated solution. The second is our platform Galileo. Galileo, which is now licensed by more than 10,000 HR professionals, is a highly tuned AI agent specifically designed to help HR professionals (leaders, business partners, consultants, recruiters, and other roles) do the “complex work” HR professionals do. It’s not a generic LLM: it’s a highly specialized solution designed specifically for HR professionals, and we’ve added specialized content partners and are building special integrations with other HR platforms. Our clients tell us it’s saving them 1-2 hours a day. The third is the platform HiredScore, that was recently acquired by Workday. Founded in 2012, the HiredScore team built tools to help identify “fit” between individuals and jobs, and tuned its AI to be highly explainable, unbiased, and very easy to use. It took Athena Karp and the team a few years to nail down the use-cases and user interface but now HiredScore is considered one of the most powerful recruitment “orchestration” tools in the market, and is also used for internal hiring and many other applications. Every customer I talk with tells me it’s essential and saves them months of manual, error-prone effort. The fourth is the platform Eightfold, which was invented in 2016 as a way to build “Google-scale” matching between job seekers and jobs. Through many years of engineering, product management, and ongoing sales process the company has become the leader in a new space called “Talent Intelligence,” now a billion dollar rapid-growing category. The company is about ten years old and now has some of the world’s largest companies building their hiring, career management, and talent management processes using AI. Companies like EY, Bayer, and Chevron now use it for all their strategic talent programs. Each of these vendors, including others like Gloat, Sana, Arist, Lightcast, Draup, Uplimit, Firstup, and hundreds of others have patiently taken the power of Generative AI and applied it with laser precision to their solutions. Each of these companies is different, and as we work with them we see lightning bolts of innovation: not in AI itself, but in finding new ways to solve problems and do what I call “crawling up the value curve.” This is the path for AI in the coming years. As with all new technologies, the “trough of disappointment” is always followed by the “bowling pin” of hitting the nail on the head. Innovators, entrepreneurs, and startup founders are the ones who will take GenAI and apply it in unique ways to solve problems. And soon enough, “AI-powered” will be a phrase we barely even need to say. The Best Solutions Will Be Narrow Not Wide GenAI solutions require a large “platform” of data, infrastructure, and software. That alone is not where the value resides. Rather, the big productivity advantages come after years of effort, focusing the data sets and working with customers to find the features, UI designs, and data sets that add enormous value. And we are still in the early stages. If you want to learn more about HR Technology and AI, join me at the HR Technology Conference on September 24-25 in Vegas, or at Unleash in Paris in October 16-17. While I can’t predict who will win the core AI platform game (Microsoft, OpenAI, Google, Meta, Amazon will fight it out), I can predicts this: Generative AI will deliver massive improvements in business productivity. You just have to shop around a bit and wait for just the right solutions to arrive.
    talent intelligence
    2024年08月10日
  • talent intelligence
    Josh Bersin:AI最终会连通整个企业界吗？问问ServiceNow，他们的回答是肯定的。 本周，ServiceNow在其年度用户大会上迎来了超过22,000名IT和技术专业人士，同时公布了几项对其战略的重大增强。ServiceNow的目标是通过一个全新的工作流软件层整合企业资源，使之能够实现全面连接，进而让员工、顾客以及企业领导通过一个统一的界面访问信息和系统。 历史上，ServiceNow主要通过一系列案例管理和服务交付工具来实现这一目标，这些工具主要服务于IT部门。这个平台极大地帮助IT服务团队自动化和优化了他们日常面临的各种IT请求和问题处理。但随着公司的发展及其野心的扩大，尤其是在Bill McDermott这位我所见过的最杰出领导者的带领下，ServiceNow的发展已经远远超出了原有范畴。 在员工服务领域，ServiceNow即将推出一系列雄心勃勃的产品。首先，原名为HR的员工工作流服务现已扩展到包括工作场所管理、员工入职、调查问卷以及技能分析工具等，还包括从Hitch收购的人才智能平台、内部职位调动与招聘等功能。公司正在开发专门的招聘工作流程，以帮助企业简化面试安排、候选人管理以及内外部候选人关系协调等复杂问题。这个看似小众的领域实际上正变得越来越重要，因为越来越多的公司开始重视内部职位流动和动态。 但在我本周参加这次会议时，我注意到ServiceNow的战略叙述发生了显著变化。Bill McDermott花了近一个小时重申他之前的讲话，但这次他将重点从工作流转移到了人工智能。他基本上将原本的工作流引擎战略替换为了“商业转型的AI平台”，显然，他们不再仅仅关注“数字化转型”。 ServiceNow的商业转型AI平台 他们强调的一个非常重要的信息是，AI将成为企业变革的技术，这与数字化变革的影响相似。 几个月前我们还在讨论数字化转型，现在，这已成为过去式：未来的变革将借助AI实现。没错，从某种意义上来说，每种技术都能成为业务变革的工具，但AI带来的变革程度将远超过互联网本身。这一点我稍后将进一步解释。 ServiceNow将其视为一个集成了异构系统的集合，中心是一个AI引擎。会议上的一个关键案例是一家面包店，因应不同地点的天气变化而调整各种烘焙产品的生产。中心的AI系统能够感知这些变化，预测接下来的行动，并指导工厂调整生产策略，从而满足市场需求，提升销售额。这是一个非常吸引人的案例。 ServiceNow的AI架构 有趣的是，像SAP在20世纪80年代推广集成供应链管理，Qualtrics推广客户体验管理时讲的故事，似乎都在用不同的方法解决相似的问题。Bill McDermott凭借丰富的经验，将这一叙事应用于ServiceNow，展现了他作为CEO的卓越远见和沟通能力。 那么，AI真的能成为未来的企业协调系统吗，将各种遗留平台联系起来，提供解决方案吗？ 我确信这将成为现实。虽然这不会一夜之间实现，但最终会发生。这种变革同样适用于我们的消费者生活。 我们日常工作中的效率之所以低下，往往是因为在寻找信息、处理文档、查询库存等活动中分心。如果能直接通过AI查询并获取答案，无疑会极大提升工作效率。（正如我尝试让Siri帮忙却发现它做不到的那样。） 正如我在本周的播客中讨论的，通过语音或文本提出问题比在Workday、Oracle、SAP等系统中四处查找要简单得多。我们曾努力使这些系统更加用户友好，现在，生成式AI的出现将为这些努力带来新的变革。 ServiceNow之所以能够很好地定位自己，有两个主要原因。 首先，他们长期以来一直在开发工作流工具，很多系统都设计得非常用户友好，即便是普通用户也能轻松使用。这就是为什么有这么多IT开发者选择使用ServiceNow的原因。（我在这周的大会上亲眼见证了这一点。） 其次，他们对这些后端系统的工作原理非常熟悉。ServiceNow的工程和产品团队对SAP、Oracle、Workday等系统进行的工作流程和交易处理有深入了解。 我与HR产品团队有过深入的交流，他们向我展示了如何将员工交易集成到ServiceNow平台中，以便构建生成式AI解决方案，与公司内部的系统进行交互。这一过程需要时间，也不会十全十美，但他们已经在正确的道路上迈出了坚实的步伐。这也引出了一个问题：我们是否应该将所有企业系统整合到这种新的界面下。 ServiceNow的HR产品 然而，这种架构也有潜在的缺点。 首先，成本较高。你现在不仅要购买ServiceNow这一复杂的企业平台，还要维护你现有的复杂企业平台。Oracle、SAP、Workday、Salesforce和Hubspot等都在尝试提供类似服务。你已经为他们的建设付出了巨大的成本，现在又要为ServiceNow付费。ServiceNow的产品不便宜，这从他们的收入就可以看出。 其次，这可能会造成系统的脆弱性。ServiceNow的工作流本身可能会因为业务规则的嵌入而变得过时，你必须管理许多小型应用程序，这些应用程序位于你的其他应用程序之上。当这些应用程序更新或被替换时（很多软件公司被收购后，其产品线会发生变化），作为客户的你需要重新配置在ServiceNow中所做的设置。 这让我回想起我在IBM的日子，当时我在主机世界工作，我们的应用程序都是定制开发的，我们有大量的COBOL程序员在构建这些系统。我曾与数据处理部门合作，探讨他们是如何在公司内部整合所有这些系统的。现在，你可能需要在ServiceNow基础设施中构建类似的工具。 如果我现在能与Bill McDermott面对面交谈，我相信他会说：“这是一种更有效的运营方式，我们ServiceNow将为你自动化这一切。”我相信这将随着时间的推移逐渐成为现实。但这也引出了一个问题：你希望将多少组织功能和经验整合进ServiceNow，而不是选择其他核心系统？ 每当我们需要构建新功能时，我们都必须决定将其部署在何处。我们是应该将职业管理平台部署在Workday、Eightfold、Degreed还是Cornerstone？你的决定将决定你依赖于哪个供应商及其发展路线图。 考虑到ServiceNow已经取得了巨大的成功，许多公司都在说：“既然我们已经为此付出了代价，那就让我们继续吧。” 生成式AI能否实现这一承诺？例如，在天气变暖时，它能否提前告诉你需要烘焙更多的面包圈？ 根据我们通过Galileo®获得的AI经验，我认为答案是肯定的。 这项技术的重要性堪比电力的发明。虽然这听起来有些夸张，但我是认真的。我从未见过如此自我发展、强大并且发展迅速的技术。我们正处于生成式AI进入市场的初期阶段。 在未来几年内，生成式AI很可能会改善这些连接，开发代码，并优化这些端到端的“单一视窗系统”。Google正在消费者领域推进此类工作，Microsoft则在办公生产力领域努力。如果ServiceNow能成为企业级应用中的佼佼者，那将不足为奇。
    talent intelligence
    2024年05月10日
  • talent intelligence
    是时候重塑人才招聘了 -Research Shows It’s Time To Reinvent Talent Acquisition Josh Bersin 的文章 "研究表明，是时候重塑人才招聘了 "强调了人才招聘亟需进行的变革。由于只有 32% 的人力资源高管参与战略规划，而且许多人觉得自己只是个接单员，因此这篇文章呼吁进行战略改革。在劳动力短缺和急需技能型招聘的情况下，目前削减成本和减少招聘力度的方法与对技能型专业人才日益增长的需求相矛盾。文章敦促企业将人才招聘作为一项重要的战略职能，利用现代技术并将其与学习和发展相结合，以提高效率并关注内部人才流动。 原文如下： This week we published a disappointing research study, Talent Acquisition at a Crossroads. The study, conducted in partnership with AMS, points out that talent acquisition leaders (this is a senior position) are largely left out of their company’s strategic planning process and many feel they operate as “order takers.” In today’s world of labor and skills shortages, this is a wakeup call for change. Here’s the data: Among these 130+ HR executives only 32% are involved in any form of strategic workforce planning, 42% believe their company has no workforce plan at all, and 46% say “they’re running around to keep up.” And when layoffs do occur, often the recruiters go first. (Witness Tesla this week.) All this is happening in a world where 58% of companies feel skills shortages are significantly impacting their business plans, more than three-quarters believe they must transform their talent practices to grow, and “skills-based hiring” is a top priority yet difficult to implement. Here’s the paradox: companies are cutting their talent acquisition spending at the same time CEOs feel that skills shortages are getting worse. What’s going on? Talent Acquisition Needs A Reinvention Let’s just face it: recruiting as a business function has to change. Once considered the “staffing department,” where companies posted jobs and scanned resumes, talent acquisition has become highly strategic operation. What skills do we need? How do we find people who will fit our culture? What internal candidates should fill our key positions? Who are the right leaders for us to hire? Unfortunately, almost 80% of talent acquisition functions are quite tactical. PwC’s CEO survey found that CEOs rate “hiring” as the third most bureaucratic process in their companies, tied with “too many emails” and “too many meetings” as a time-wasting process. And that explains why two-thirds of TA leaders are being asked to cut costs. I had a conversation last week with a former TA leader for one of the Big Three automakers. He told me that in the fervor to hire staff for EV engineering he was asked to hire “any engineer he could find, regardless of skill,” because the company was in such a hurry. No time for skills assessment, competitive planning, or even location analysis. Just “go out there and hire engineers.” We have been studying the auto industry as part of our GWI study and found that important EV roles (reliability engineer or power plant engineer, for example), are quite specialized and hard to find. Strategic recruiting departments need to understand these roles and source these individuals carefully. Just hiring engineering grads from a local community college is not going to move this needle. (Consider the data by Draup on what these roles are. Talent Acquisition teams with talent intelligence skills can pinpoint who to hire.) And it gets worse. In our Dynamic Organization research we found that high performing companies focus heavily on internal hiring, talent intelligence tools to find hidden talent, and continuous internal development to fill skills gaps. We can’t simply throw job requisitions over to the recruiting function any more: the people we need may be buried inside the company. This week Tesla announced a layoff of 10% of their workforce. Was their time to balance and redeploy talent internally? Absolutely not. According to my sources every business unit had to let 10% go, and and many of the people being fired were talent acquisition leaders, the very people who help with these issues. We talk with many HR executives and there is an enlightened group. Companies that understand this issue (about one in eight) have elevated Talent Acquisition to a strategic function, they merge or integrate TA with L&D, and they redefine their recruiters as “talent advisors.” Mastercard, as a leader, just renamed their recruiters as “Career Coaches,” demonstrating their role in helping people find the right jobs. Despite the onslaught of AI, this role is becoming even more human-centric. High-powered recruiting teams source internal candidates, understand company culture, and have a deep knowledge of jobs, roles, and organizational dynamics. When well supported and trained, these professionals are strategic advisors, not just “recruiters.” And companies that understand this often outsource or automate much of the administration in recruiting. Technology plays a major role in this reinvention. Most large companies have dozens of legacy systems, many of which make the candidate experience difficult. When organizations focus on modernizing and streamlining their technology, talent acquisition can become 10-100X more efficient. This, in turn, gives recruiters and talent advisors the time to search for the right skills, carefully select the best candidates, and focus on internal hiring and development as a strategy. Technology Is Here But Not The Entire Answer Of all the HR technology markets, recruiting is the most innovative of all. New AI-powered systems like HiredScore (just acquired by Workday), Paradox (leader in conversational AI), Eightfold, Gloat, Draup, and Lightcast (pioneers in talent intelligence), and many others can reduce time to hire from months to weeks and weeks to days. But none of this technology works if the Talent Acquisition team is left on an island. In the last year I have met with more than 50 heads of talent acquisition and once the door is closed and we talk honestly, they always tell me the same thing. “We are not treated as a strategic function, we are being asked to cut costs, and we are constantly running from fire to fire to keep executives happy.” This type of “service-delivery” focus simply will not work in the new economy. What should companies do? As part of our Systemic HR initiative, we help companies evolve their TA Function to operate in a more strategic way. Organizations like Bayer, Verizon, and many others have elevated the role of recruiter to talent advisor, they’re building skills in talent intelligence, and they’re integrating the recruiting function with L&D, career management, and employee engagement. I’ve always felt that recruiting is the most important things HR professionals do. If we can’t get the “right” people into the company, no amount of management can recover. But what does “right” mean? And how can we source, locate, and attract these particular people? This is a highly strategic operation, and one that must integrate with internal mobility, culture, and employee experience. I encourage you to read our Systemic HR research, join our Academy, or reach out to us or AMS for advice. In this new era of talent and skills shortages, we simply cannot run recruiting in this tactical way any longer.
    talent intelligence
    2024年04月24日
  • talent intelligence
    Josh Bersin 谈Workday的创新论坛：Why I’m Bullish On Workday Again: The Innovation Summit 本周Workday创新峰会揭示了公司由产品主导向市场主导的战略转型。Workday一直以云技术为核心，自主开发了面向对象的数据系统和全球安全架构。然而，随着市场的演进和竞争的加剧，公司在新任CEO Carl Eschenbach的领导下，开始转向市场导向的商业模式。 在这次转型中，Workday开始拓宽其业务模型，更加开放地与合作伙伴合作。公司不再限制API的使用和合作伙伴的接入，而是致力于构建一个像苹果iPhone那样的开放平台，允许更多的行业应用集成到其系统中。这一策略旨在提供更加灵活和综合的企业解决方案，以适应不同行业的需求。 同时，Workday也大力投入到人工智能技术的开发中，推出了基于企业自有数据的微型机器学习模型（micro-LLMs），并在全球范围内调整这些模型以满足本地客户的需求。此外，Workday正在将其人才智能市场向外扩展，通过与多个行业解决方案提供商的合作，强化其在健康护理和金融等领域的业务。 AI技术的应用不仅仅限于技术层面的改进，Workday还通过这些技术优化了用户体验，使得各种任务的完成变得更加便捷。例如，在Workday平台上，用户可以看到AI图标，通过点击即可获得智能辅助完成工作。 在人才管理方面，Workday引入了许多新功能，如智能工作架构中心（Intelligent Job Architecture Hub）以及加强的Workday人才市场，这些都是为了帮助企业简化和改进职位描述和技能需求。 此外，Workday的领导层也展现出了更开放和实用的态度，这对公司未来的发展是一个积极的信号。总的来说，Workday的这一系列战略调整，旨在更好地适应快速变化的市场需求，提高公司的竞争力和市场份额。 Josh Bersin 写了这篇文章，强烈推荐给大家了解下：以下是中英文的供参考   This week I attended the Workday Innovation Summit and there’s a lot to discuss. Having just celebrated its 19th birthday, the company is embarking on a major transformation . And it’s not just product innovation that’s happening, the company is greatly expanding its business model. Workday Has Been A Product Led Company Much of Workday’s success goes back to its focus on being “born for the cloud.” Rather than build business apps in a typical database-centric architecture, Workday developed its own object-oriented data system, integrated workflow system, and global security architecture from scratch. Nobody knew the cloud would be so big nor that we’d have “superscalers” like Google, Microsoft, and Amazon as platforms. Nor could we predict the advent of global data governance, AI, or data and apps distributed across thousands of servers. Well Workday, led by Aneel Bhusri, pulled this off. And not only did they sell architecture, they sold “the Power of One.” In Workday, unlike other ERP business systems, all the applications were designed to work together. No acquisitions, no integrations, no open systems: just a beautifully designed, easy-to-use, scalable enterprise application. (I noted that it reminded me of the i-Phone at the time: beautiful, easy to use, and closed.) This “beautiful walled garden” served Workday well. While Oracle, SAP, and other vendors struggled to redesign their client-server apps and acquire missing pieces, Workday grew like wildfire and is now a global ERP vendor with more than $7.3 Billion in recurring revenue, 10,000+ enterprise and mid-market customers, and a brand known for trust, customer focus, and quality. And all this happened with a founding team that was largely still in place. Last year Workday’s co-founder and CEO Aneel Bhusri decided it was time to step back and the company brought in Carl Eschenbach to be CEO. And now things are starting to change. The company is becoming a “markets-led” business. The “product-led” focus for Workday was both good and bad. Workday was not easy to integrate, there were few APIs for developers, and the company limited its partners. As part of its mission to be pure, Workday prevented many vendors from “partnering” and forced integrators to pay large fees and certify dedicated teams. This “scarcity” strategy created high demand and high prices, and customers actually appreciated it. All was good, until things started to change. Today, with many competing vendors at all levels of the ERP stack, Workday is becoming more pragmatic. And as I’ll explain below, they’re changing their message from “The Power of One” to “Workday is a Platform.” Workday Is Becoming A Markets-Led Company The HCM and Financials market is complex. There are dozens of sub-markets, application areas, and industry solutions to address. An HR system designed for a large hospital system is unlikely to need the same features as a system for a global insurance company. So Workday started to realize its system, while integrated and highly functional, couldn’t keep up. And within HR itself there are hundreds of vendors who sell recruiting tools, career systems, learning platforms, engagement tools, mobile apps, benefits, and data analytics systems. And each of these sub-markets are being transformed by AI. (Our upcoming research on Talent Intelligence, for example, will show you how fragmented this is.) Workday was having a hard time keeping up. The company embarked on a series of acquisitions (Platfora, Mediacore, Adaptive Insights, VNDLY, Peakon, HiredScore, and others). This forced the product teams to focus on user interface and architectural integration, somewhat slowing the feature expansion. And many partners who wanted to integrate with Workday (which customers demand) were ignored. Well under Carl’s leadership, all this is changing. Workday is now fully open to partners, ISV’s, resellers, and industry solutions. Almost 25% of the entire Innovation Summit was focused on Workday’s open partner strategy. And the big message was this: Workday is not a “system,” it’s a “platform.” What does this mean? It means that if you buy Workday you’re buying a platform like the i-Phone. It works amazingly well, it’s safe, and will sport a family of industry apps to help you build a total solution. This worked for Apple and Salesforce and it’s likely to work well for Workday. SAP has a similar offering, but its level of integration is far more complex. This lets Workday move deeply into new domains and sub markets. (Workday highlighted its new integrations with Shiftwizard in healthcare, Auditoria and Kyriba in finance, and many others. These are not just ISV relationships: Workday is reselling these products. But there’s much more. Workday Unveils Its AI Strategy At last year’s event Workday really waffled about AI. They gave us a lot of arm waving discussions of “Workday AI” but it didn’t make a lot of sense. Well they’ve figured it out, so let me briefly explain. Enterprises don’t want AI for its own sake and they definitely don’t want crowdsourced data which creates legal risk. They want AI solutions that work on their own data. Well Workday has now embarked on a wide variety of AI features, each delivered through its own “micro-LLMs” trained on a company’s own data. (Very similar to how we implement Galileo, our AI HR expert assistant.) And for larger AI capabilities they use a global LLM with local weights and biases for each client. (This is similar to how the Microsoft Copilot works.) So your enterprise data trains your “version” of Workday without sharing any data with others. In some cases (the Skills Cloud, for example), customers can opt to share data anonymously. This lets Workday build a “global skills database” which everyone can share. Vendors like Eightfold, Lightcast, and Draup do this at a massive scale (far beyond what Workday does today), so Workday is now moving into this “talent intelligence” market. (Lightcast is now a Workday Skills Cloud partner.) Many of these features are simple (rewriting a job description or matching invoices to purchase orders) but powerful. All over Workday you now see a little AI icon to help you complete a task. In fact Workday has already re-engineered about 280 different tasks and is working on around 2,000 in total. Customers constantly tell me Workday is difficult to use, and it’s largely just because the system is quite complex. These AI-enhanced experiences are slowly going to make the system more and more “I-Phone like.” Many New Talent Features Now that the product teams have a strong underlying architecture, they’re going crazy with features. Workday is introducing a new “Intelligent Job Architecture Hub,” for example, to help companies simplify and improve job names, levels, descriptions, and skills. (It also shows trending skills in the external market.) Everyone is going to use this. The Workday Talent Marketplace, which is not widely used yet, is being enhanced through HiredScore: employees will get Teams or Slack messages recommending jobs. This is an example of “orchestration,” a new buzz-word among AI systems. (Imagine AI booking your trip including hotels, air, car, etc.) The Workday Manager Hub now shows managers detailed employee engagement data (Peakon has more than 18 billion responses now) and will gives managers “Conversation Starters” to help them start performance coaching, all based on feedback from other employees. There is a major focus on contingent, gig, and contract workers. For the first time I believe Workday can handle most professional services businesses (including pricing projects based on staff pricing), healthcare and retail (AI-powered scheduling and shift management), and many deskless worker needs. It turns out that healthcare and retail are two of Workday’s biggest industries, so these talent-constrained industries are now a good market. Let me talk briefly about HiredScore. This company built an in-line “talent orchestration system” that uses AI to show recruiters who is most suited for a job, explain why it made its decisions, and use this data to find and source internal candidates automatically. While this type of technology is widely used in systems like Eightfold, Beamery, Phenom, and others, the HiredScore system is workflow-oriented. Recruiters love it and it greatly improves hiring speed, quality, and internal mobility. And by the way, despite lots of complaints from users, Workday Recruiting is starting to dominate the ATS market. With more than 4,000 customers it’s becoming a more “safe buy” as companies rationalize their old ATS systems. As David Somers (head of product) put it, HiredScore is the acquisition that “keeps on giving.” In other words the AI team at HiredScore is now going to work with Workday’s Skills Cloud team to evolve and improve that system. The Skills Cloud, while beautifully visioned and named, has had limited success. With HiredScore’s help (and the leadership of Athena Karp, founder and CEO), this system will get more attention. (That includes more content partnerships and a broader set of tools.) This means Workday’s recruiting system (which is one of the most critical business systems in today’s talent shortages) is now highly coupled with the internal mobility and job architecture system, something customers desperately want. I still believe systems like Eightfold and Gloat are far more advanced, but Workday is catching up. Management Culture And Trust And then there’s the biggest issue of all: Workday’s leadership. I spent some time chatting with Carl Eschenbach and he has a very different persona than Aneel Bhusri. Carl clearly wants Workday to go after new markets: new geographies (EMEA, Asia, Japan), new industries (healthcare, pharma, retail), the mid-market segment, and channel partners. Workday is now actively searching for resellers, mid-market integrators, and ISVs to round out the solution. As always, the leadership team at Workday is highly aligned and much more pragmatic. Many times I would attend a Workday event and feel a slight sense of arrogance at the top. As with all successful software companies, it’s easy to think you’re always right when things are going well. I believe this has changed. I actually found Workday to be humble, attentive to new issues, and open-minded to new ideas, new partners, and self-inspection. This, to me, is a bullish sign. And from top to bottom the company is focused on trust, AI safety, and customer service. One more thing I want to point out: the “Workday as a Platform” idea. The company now realizes that this highly proprietary, business-optimized system can no longer be sold as a beautifully walled garden. The company is building a massive set of easy to use development tools, expanded APIs, and programs to attract software developers, partners, and integrators. Now, when customers ass for functionality Workday can look for a partner to resell or embed. The company is losing its “if we didn’t build it we don’t trust it” mentality. I also believe this leadership team really likes each other. As many of you know, team culture is massively important in the tech space. Things change so fast and there are so many competitors the company has to stay aligned. I sense everyone really understands what’s going on. Growth Potential Will Workday accelerate its growth above its respectable 17% per year? Well the company has challenges. Many of its legacy clients have found a plethora of advanced tools around Workday and I know large companies that are switching back to SAP. And despite all the new features, Workday is an older, complicated, rigid system. That all said, I think the company is managing its transformation well. Let’s watch to see how all this plays out.   本周我参加了Workday创新峰会，有很多内容值得讨论。在刚刚庆祝了其成立19周年之际，该公司正在进行重大转型。而且，不仅仅是产品创新在进行，公司的业务模式也在大幅扩展。 Workday一直是一家以产品为导向的公司 Workday的成功很大程度上归功于其专注于“为云而生”。Workday没有采用典型的以数据库为中心的架构来构建商业应用程序，而是从零开始开发了自己的面向对象的数据系统、集成的工作流系统和全球安全架构。没有人知道云计算会如此重要，也没有人预料到我们会有像Google、Microsoft和Amazon这样的“超级计算”平台。我们也无法预测全球数据治理、AI或者跨数千服务器分布的数据和应用程序的出现。 在Aneel Bhusri的领导下，Workday做到了这一点。他们不仅销售架构，还销售了“一体化的力量”。在Workday中，不同于其他ERP商业系统，所有应用程序都被设计为可以协同工作。没有收购，没有集成，没有开放系统：只有一个设计精美、易于使用、可扩展的企业应用程序。（我注意到这让我想起了当时的iPhone：美观、易用且封闭。） 这个“美丽的围墙花园”为Workday服务良好。而Oracle、SAP和其他供应商在重新设计其客户端-服务器应用程序和获取缺失部分时挣扎，Workday却如野火般成长，现在已成为一家全球ERP供应商，拥有超过73亿美元的经常性收入、超过10,000个企业和中端市场客户，以及以信任、客户关注和质量而闻名的品牌。而且，这一切都是在创始团队基本上仍在位的情况下发生的。 去年，Workday的联合创始人兼CEO Aneel Bhusri认为是时候退居幕后了，公司聘请了Carl Eschenbach担任CEO。现在，事情开始改变。该公司正在成为一家“以市场为导向”的企业。 Workday的“以产品为导向”的重点既有好处也有坏处。Workday不容易集成，开发者可用的API很少，公司也限制了其合作伙伴。作为其保持纯净使命的一部分，Workday阻止了许多供应商的“合作”，并迫使集成商支付高额费用并认证专门团队。这种“稀缺”策略创造了高需求和高价格，而客户实际上对此感到满意。 一切都很好，直到情况开始改变。如今，随着ERP堆栈各层面的竞争供应商越来越多，Workday正在变得更加务实。正如我将在下文中解释的那样，他们正在将信息从“一体化的力量”变为“Workday是一个平台”。 Workday正在成为一家以市场为导向的公司 人力资源管理(HCM)和财务市场非常复杂。有数十个子市场、应用领域和行业解决方案需要解决。一个为大型医院系统设计的HR系统不太可能需要与为全球保险公司设计的系统相同的功能。因此，Workday开始意识到，尽管其系统集成且功能强大，但它无法跟上。 而且，在HR本身，有数百家供应商销售招聘工具、职业系统、学习平台、参与工具、移动应用程序、福利和数据分析系统。每一个子市场都在被AI转型。（例如，我们即将发布的关于人才智能的研究将向您展示这是多么的碎片化。） Workday很难跟上。该公司开始了一系列收购（Platfora、Mediacore、Adaptive Insights、VNDLY、Peakon、HiredScore等）。这迫使产品团队专注于用户界面和架构集成，从而在某种程度上减缓了功能扩展。许多希望与Workday集成的合作伙伴（客户需求）被忽视了。 在Carl的领导下，所有这些都在改变。Workday现在对合作伙伴、独立软件供应商、经销商和行业解决方案完全开放。整个创新峰会将近25%的时间专注于Workday的开放合作伙伴策略。而且重要的信息是：Workday不是一个“系统”，它是一个“平台”。 这是什么意思？这意味着如果您购买Workday，您就是在购买一个像iPhone那样的平台。它运行非常好，安全，并将配备一系列行业应用程序以帮助您构建完整解决方案。这对Apple和Salesforce有效，对Workday来说可能也会很有效。SAP也有类似的产品，但其集成程度要复杂得多。这让Workday能够深入新的领域和子市场。（Workday突出显示了其在医疗保健领域与Shiftwizard、在财务领域与Auditoria和Kyriba的新集成等。这些不仅仅是独立软件供应商关系：Workday正在转售这些产品。 但还有更多。 Workday公开其AI战略 在去年的活动中，Workday对AI真的犹豫不决。他们给了我们很多关于“Workday AI”的手势讨论，但这并没有太多意义。好吧，他们已经想通了，让我简单解释一下。 企业并不是因为AI本身而想要AI，他们绝对不想要可能产生法律风险的众包数据。他们想要的是可以在自己的数据上运行的AI解决方案。 现在，Workday已经开始了各种AI功能，每个功能都通过其自己的“微型大语言模型”交付，这些模型是在公司自己的数据上训练的。（这与我们实现的Galileo，我们的AI HR专家助手非常相似。）对于更大的AI功能，他们使用一个全球大语言模型，为每个客户本地调整权重和偏差。（这与Microsoft Copilot的工作方式类似。）因此，您的企业数据训练您的“版本”的Workday，而不与其他人共享任何数据。 在某些情况下（例如技能云），客户可以选择匿名分享数据。这让Workday能够构建一个“全球技能数据库”，每个人都可以分享。像Eightfold、Lightcast和Draup这样的供应商在大规模（远超Workday目前的做法）上做到了这一点，所以Workday现在正在进入这个“人才智能”市场。（Lightcast现在是Workday技能云的合作伙伴。） 这些功能中的许多都很简单（重写工作描述或将发票与采购订单匹配），但功能强大。在Workday的各个地方，您现在都可以看到一个小AI图标，帮助您完成任务。事实上，Workday已经重新设计了大约280个不同的任务，并且正在处理大约2,000个总任务。 客户不断告诉我Workday很难使用，这主要是因为系统相当复杂。这些通过AI增强的体验将逐渐使系统越来越像“iPhone”。 许多新的人才功能 现在产品团队拥有了强大的底层架构，他们正疯狂地推出功能。例如，Workday正在推出一个新的“智能工作架构中心”，以帮助公司简化并改进工作名称、级别、描述和技能。（它还显示外部市场中的趋势技能。）每个人都将使用这个。 Workday人才市场尚未广泛使用，现在正在通过HiredScore进行增强：员工将通过Teams或Slack消息获得推荐工作。这是“编排”的一个例子，这是AI系统中的一个新的流行词。（想象一下AI预订您的旅行，包括酒店、飞机、汽车等。） Workday经理中心现在向经理们显示详细的员工参与数据（Peakon现在有超过180亿的反馈）并将给经理提供“对话开始器”，以帮助他们开始绩效辅导，所有这些都基于其他员工的反馈。 还有一个主要关注点是临时工、零工和合同工。我相信Workday首次可以处理大多数专业服务业务（包括基于员工定价的定价项目）、医疗保健和零售（AI驱动的排班和班次管理），以及许多无固定工作场所的工人的需求。事实证明，医疗保健和零售是Workday的两个最大行业，所以这些人才匮乏的行业现在是一个好市场。 让我简要谈谈HiredScore。这家公司建立了一个内嵌的“人才编排系统”，使用AI向招聘人员展示最适合某个职位的人员，解释为什么会做出这样的决定，并使用这些数据来找到并自动获取内部候选人。虽然这种技术在Eightfold、Beamery、Phenom等系统中广泛使用，但HiredScore系统是以工作流为导向的。招聘人员非常喜欢它，它极大地提高了招聘的速度、质量和内部流动性。 顺便说一句，尽管用户有很多抱怨，Workday招聘正在开始主导ATS市场。现在已有超过4,000个客户，随着公司对旧ATS系统进行合理化，它正在成为一个更“安全的购买”。 正如产品负责人David Somers所说，HiredScore是一笔“源源不断的收益”。换句话说，HiredScore的AI团队现在将与Workday的技能云团队合作，以发展和改进该系统。技能云虽然构想得很美，名字很漂亮，但成功有限。在HiredScore的帮助下（以及创始人兼CEO Athena Karp的领导下），这个系统将得到更多关注。（这包括更多的内容合作伙伴和一套更广泛的工具。） 这意味着Workday的招聘系统（这是当今人才短缺中最关键的商业系统之一）现在与内部流动性和工作架构系统高度耦合，这正是客户迫切需要的。我仍然认为像Eightfold和Gloat这样的系统更先进，但Workday正在迎头赶上。 管理文化和信任 然后是最大的问题之一：Workday的领导层。我花了一些时间与Carl Eschenbach聊天，他与Aneel Bhusri的个性非常不同。Carl明确希望Workday进军新市场：新地理区域（EMEA、亚洲、日本）、新行业（医疗保健、制药、零售）、中端市场细分市场和渠道合作伙伴。Workday现在正在积极寻找经销商、中端市场集成商和独立软件供应商来完善解决方案。 一如既往，Workday的领导团队高度一致，更加务实。很多时候，我参加Workday的活动，都能感受到顶层有些自负。就像所有成功的软件公司一样，当事情进展顺利时，很容易认为自己总是对的。 我认为这已经改变了。我实际上发现Workday很谦虚，对新问题很关注，对新想法、新合作伙伴和自我检查持开放态度。对我来说，这是一个看涨的信号。而且从上到下，公司都专注于信任、AI安全和客户服务。 我还想指出一件事：关于“Workday作为一个平台”的想法。该公司现在意识到，这种高度专有的、业务优化的系统不再能作为一个美丽的围墙花园来销售。公司正在构建一套大型的易于使用的开发工具、扩展的API和吸引软件开发者、合作伙伴和集成商的计划。现在，当客户询问功能时，Workday可以寻找一个合作伙伴来转售或嵌入。公司正在失去“如果我们没有构建它，我们就不信任它”的心态。 我还相信这个领导团队真的很喜欢彼此。正如你们许多人所知，团队文化在科技领域非常重要。事情变化如此之快，竞争对手如此之多，公司必须保持一致。我感觉每个人都真正理解发生了什么。 增长潜力 Workday能否将其每年17%的尊重增长率加速？好吧，公司面临挑战。它的许多老客户发现在Workday周围有大量的先进工具，我知道一些大公司正在回归SAP。尽管所有这些新功能，Workday仍然是一个较老、复杂、僵化的系统。 话虽如此，我认为公司正在很好地管理其转型。让我们拭目以待，看看这一切将如何发展。
    talent intelligence
    2024年04月19日
  • talent intelligence
    Josh Bersin:3400亿美元的企业学习的市场将迎来巨大变革 作者：Josh Bersin  本文探讨了企业学习行业的演变，特别是人工智能如何引领这一行业的巨变。企业每年在员工培训和发展上的开支超过3400亿美元，从传统的课堂培训到在线学习，再到以技能为中心的学习，行业一直在不断发展。现在，人工智能预计将彻底改变公司的学习管理系统（LMS）和学习体验平台（LXP），通过个性化和动态生成内容来提高学习效率和效果。文章强调了适应这种变化的重要性，以及AI在企业培训和人才发展中的潜力。 企业在员工培训和发展上的年支出超过3400亿美元，平均每名员工每年花费超过1500美元。这笔巨额开支支撑着一个全球产业，涉及数百家内容和技术公司，现正站在重新定义的风口浪尖。请允许我详细解释这一过程。 从电子学习到集体学习再到自主学习的演变 20世纪90年代末，随着互联网的崛起，以传统教室授课为主的培训产业发生了翻天覆地的变化。企业和内容提供者纷纷开发“电子学习”课程，试图在线复制面对面教学的体验。那是一个充满创新的时期，虽然今天看来有些过时，但它孕育了像Skillsoft（并购了众多竞争对手）、Cornerstone（同样并购了众多竞争对手）以及一大批传统的学习管理系统（LMS，例如Plateau、SumTotal、Learn.com、Pathlore等）公司，这些公司最终都被并购。 如今，LMS市场的规模已超过200亿美元，这一切几乎都是在线培训推动的结果。虽然这些系统可能看起来笨重，但它们对全球每家公司的交易和记录保持都至关重要。 当公司争相购买LMS系统——这是一个投资者非常关注的热门市场时，他们发现一个庞大的课程目录并不实用。因此，他们开始构建一套特征，我称之为“以人才为驱动的学习”，包括基于能力的学习、与职业角色一致的课程和职业发展路径系统。这些特征被添加到LMS中，使得这些系统不仅仅是教育工具，更像是“人力资源系统”，从而促使供应商扩展到更多的人才管理功能。 早期的开拓者Saba和Cornerstone开始推出绩效管理工具。回顾起来，这些尝试可能看起来有些简单，但当时它们代表了一个重大突破。突然之间，公司不再单独购买LMS系统，而是选择购买包含多个功能的“人才管理套件”，这迫使专注于LMS的供应商开始涉足招聘、目标管理乃至薪酬管理。他们可能没有意识到，放弃核心业务最终会导致他们被市场颠覆。 随着Facebook（2004年）、YouTube（2005年）和Twitter（2006年）的相继出现，内容世界发生了巨变。视频、文章和专家意见变得触手可及，那些笨重、以课程目录为导向的LMS系统显得格外难以使用。因此，随着公司寻求新的解决方案，原本投入巨资于人才管理的LMS市场开始显露老态。学习体验平台（LXP）市场随着Pathgather（2010年）、Degreed（2012年）、EdCast（2013年）的诞生而兴起，企业转向这一新兴领域投资。（更多历史，请参阅《从电子学习到集体学习》。） 2010年代初，整个行业的理念是尝试模仿Google，打造一个既具有Twitter式动态性又拥有YouTube式丰富内容的企业学习系统。传统的LMS和人才管理系统逐渐过时，供应商在缓慢的增长中寻求出路，最终合并为几家大型玩家。 随后，微学习的概念兴起。iPhone成为了视频播放平台（2008年），Instagram（2010年）、Snapchat（2011年）及后来的TikTok（2015年）向我们展示了短视频和“微学习”可以是多么的有趣。过长的两小时在线课程变得不受欢迎，因此LXP供应商开始扩展自己的产品线。随着公司将越来越多的内容投入到LXP中，我们意识到需要一种方法来寻找、精准定位并个性化所有这些学习材料。 此变化自然引发了内容市场的爆发。LinkedIn、Coursera、Udemy、OpenSesame、Go1等供应商决定开拓这个领域，推动了新材料的狂热消费。自那以后，内容市场继续繁荣发展，尽管仍然主要由小型玩家主导，但被更大的聚合商所整合，这些聚合商销售并分发多种品牌。 （顺便提一下，Workday在2016年收购了视频公司Mediacore，以抓住这波趋势。由于缺少核心LMS功能，他们花费数年时间将其发展成为一个完整的LMS。） 进入技能的世界。 你可能不会相信，但“技能记录系统”的概念最初出现在LXP领域，供应商如Degreed和EdCast建立了一个搜索术语数据库，并用“技能”一词标记内容。在消费者市场，我们能接收到成百上千的信号来推荐广告，但LXP供应商只有少数工程师，因此他们的“技能分类”相对简单。这个概念迅速走红，公司开始专注于构建基于“技能”的培训，随后是招聘和人才战略。 同时，L&D领域正处于创造性混乱之中。出现了如360 Learning、Fuse Universal、Kineo等数百家内容创作和分享系统的供应商，旨在帮助公司创作、分享视频内容，并按角色、技能或职能进行组织。这些并非严格意义上的LMS系统，但它们位于LMS前端，使员工能够轻松创建和消费动态内容。 这一时期，从2018年至今，成为L&D领域的热潮。市场充斥着各式各样的视频内容工具，同时像STRIVR和Talespin这样的先锋公司开始为虚拟现实（VR）构建工具和内容系统。自创内容平台、视频平台和VR平台正在满足重要需求，而LMS市场则变得更加固定、枯燥和无趣。（Talespin最近被Cornerstone收购。） 顺带一提，我仍然认为“能力学院平台”市场具有巨大潜力（这类平台提供综合的专业能力和小组学习功能，例如我们的Josh Bersin Academy）。Docebo、Learn-In、Nomadic、NovoEd和Intrepid等供应商仍在增长，但随着时间推移，这些系统可能被整合进人才市场。这一领域一直是行业的一个亮点。（想了解更多，请阅读《能力学院：L&D的未来方向》。） 作为分析师，我得诚实说，过去几年对我来说有些单调。我们帮助了数百家公司决定该选择哪种L&D系统，但通常我们发现这些组织有太多平台，内容分散杂乱，缺乏一致性的数据处理，以及在这一领域的过度投资。因此，这个静态期代表了过去3到5年的趋势，是企业整理过去十年购买历史的好机会。 世界突然再次发生变化。技能分类的理念迅速蔓延，同时新兴的人才智能系统，如Eightfold、Gloat、Fuel50等纷纷涌现。这些新兴系统使公司能够按技能寻找人才、根据技能推荐职位和机会，并按技能动态规划职业路径，再次与L&D领域发生碰撞，促使我们将所有内容“整合”进这些新平台中。（更多信息，请阅读《人才智能入门》。） 本周我刚与我最喜爱的L&D专家之一通话（他即将在我们的会议上演讲），他向我展示了他所在的大型制药公司如何将其LMS、LXP和人才市场融合成一个无缝、端到端的体系。他可能略微超前于当前趋势，但这正是事物发展的方向。 然而，故事还在继绀。又一场变革已经到来，这一次的影响力与YouTube、Instagram或iPhone相媲美，甚至更大。没错，就是AI。 AI，如许多人所预料，将彻底颠覆这个行业。正如我们在电子学习和人才管理时代所见证的那样，这意味着供应商生态将彻底改变。 AI如何改变一切 让我不夸大其词地告诉你。在这30年的故事中，有一点始终未变：企业培训关注的核心始终是内容。是的，我们希望内容更简短、更快速、能在手机上查看——但如果内容本身没有实用价值，不切实际，不易于消费，它就无法发挥作用。你们中有多少人为了得到学分而快速点击通过那些以页面为基础的合规课程，但实际上几乎没有注意内容？这正是我们面临的挑战。所有这些向视频、微学习、大规模开放在线课程（MOOCs）以及其他形式的转变，都是为了解决这个问题的尝试。 比如，假设企业学习系统能识别你是谁，你只需提出一个问题，它就能生成答案、一系列资源和一组动态学习对象供你消费。有时候，你可能只需快速获取答案即可。其他时候，你可能会深入研究内容。还有时，你可能会浏览整个课程，并花时间学习所需的知识。 假设这一切都是完全个性化的。这意味着你不会看到一个“标准课程”，而是根据你当前知识水平定制的特殊课程。 这就是AI即将带给我们的。而且，这已经在今天开始发生了。 不仅生成式AI能够回答问题和吸收内容（例如，Galileo™已经容纳了我们25年以上的每一项研究，包括视频、播客和文章），它还能生成视频、测试、测验甚至整个课程。它可以作为技术课程的教学助手，也可以作为领导力项目的教练或导师，并且能够进行语言转换。 AI能够根据你的身份动态生成内容，这意味着什么？ 那么，LMS市场、LXP市场、VR学习市场以及所有内容提供商将如何呢？在未来几年，我们将见证一场巨大的行业洗牌。 供应商正在采取的行动 虽然我无法确切知道每个L&D供应商正在做什么，但可以肯定，变化正在迅速进行中。 Docebo Shape能够从文档中生成高效的互动式培训材料（Arist也能做到这点）。Uplimit构建了一个完整的L&D平台，采用AI智能体和课程中自动生成的内容。我们的合作伙伴Sana不仅能自动生成内容，还围绕AI核心建立了一个完整的LMS系统。Cornerstone通过收购Talespin，能够动态创建角色模拟和几乎可以无限配置的场景。快速增长的“精确技能”供应商Growthspace，可以根据1100种具体的商业技能，为你匹配一个“技能教练”，与你的具体目标对齐。 LMS市场不会消失，但正如人才智能系统正在逐渐取代应聘追踪系统（ATS）和人力资源管理系统（HRMS）一样，AI驱动的内容平台将逐步侵蚀LMS市场。我的制药公司朋友希望他的LXP能成为他们的“动态内容系统”，但坦白说，我不确定LXP供应商是否已经准备好迎接这个挑战。许多供应商，从LinkedIn到Microsoft，将不得不重新考虑他们如何成为“动态学习”系统，以及他们希望在其中扮演什么角色。 正如所有技术转变一样，通常情况下，从头开始构建的系统会超越旧有系统。对于Cornerstone或Docebo这样拥有数千客户的公司来说，当新技术出现时，他们不能简单地“替换”他们已经建立的系统。因此，新兴的AI驱动学习系统可能会由新的供应商推出，并随着这些公司的发展，开始取代和竞争现有的系统。 尽管看上去简单，学习技术实际上非常复杂。Workday几乎花了十年时间从Mediacore发展到一个相对健全的LMS，并且他们才刚刚开始尝试AI。因此，不要期望你现有的供应商能够一夜之间彻底改变。 但有一件事我可以确定：颠覆即将来临。就像Plateau、Saba和SumTotal在2000年代初期时“市场上最热门的供应商”一样，它们很快就成为了过时系统和收购目标，当市场变化时同样的情况也可能发生在今天。新兴供应商如Sana、Growthspace、Uplimit、Docebo、LMS365等将崭露头角。 尽管风险资本家通常对这个市场持谨慎态度，但往往是那些拥有最佳管理团队的公司最终胜出。大型供应商如LTG、Cornerstone和Skillsoft拥有充足的资金，因此随着市场的发展，任何事情都有可能发生。但对我来说，一件事是明确的：前方是一个巨大的增长周期。 AI的机会是真实的，而且极为巨大 想象一下我们公司中的遗留内容量。全球必然存在价值超过一万亿美元的  合规培训、销售培训、运营培训、安全培训和领导力发展内容。如果AI能够在大规模上“重新利用”和“再创造”这些内容，我们将看到这个巨大的市场向新系统转变，最终实现知识管理和学习的完美结合。 我来举一个简单的例子。我们的一位Galileo客户是一家拥有百年历史的大型航空航天公司，他们在工程、产品设计、航空和国防技术方面有着丰富的积累。他们构建了喷气引擎、导弹、核潜艇以及各种系统。对于一名新工程师，他们需要超过三年的时间来完成“入职培训”，因为需要掌握大量的知识产权、设计专长和系统操作。他们的资深工程师们都在逐渐退休！ 他们在我们的帮助下，开始了一个以AI为中心的试点项目，把多年累积的内容放到一个新平台中，供年轻工程师使用。我相信，这将带来翻天覆地的变化。Galileo将协助处理管理层面的问题，而一个类似的AI助手将帮助工程师学习、寻找文档、观看视频并参加相关课程。传统的LMS和HRMS工具可能不会在这一过程中发挥重要作用。 考虑一下你的公司。你们囤积了多少内容、专业知识和旧有的培训资料？AI可以“释放”这些资源给你的员工，使其以前所未有的方式变得可用。这是一个激动人心的新时代，充满了即将到来的变革。
    talent intelligence
    2024年03月21日
    • 12
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