推荐：The best HR & People Analytics articles of March 2024024年3月，David Green带领我们深入了解了人力资源和人力分析的最新趋势。在欧洲和美国的几场关键活动中，他强调了人力分析在提升员工体验、AI在工作场所的角色、以及四天工作周趋势的增长中的转型作用。此外，Culture Amp对Orgnostic的收购和在Culture First Leaders Forum上的见解，突出了培养适宜的组织文化对于未来工作的战略重要性。Green的观点强调了HR需要采用数据驱动策略，以实现有效的劳动力规划、技能发展和组织增长。
2024 HR TRENDS AND PREDICTIONS
KATE BRAVERY, JOANA SILVA, AND JENS PETERSON - Workforce 2.0: Unlocking human potential in a machine-augmented world – Mercer Global Talent Trends 2024
The world of work is in full metamorphosis, forever changed by the seismic shifts of the past few years and accelerated by the imminent human-machine teaming revolution. Just as organizations were settling into a new normal — with a focus on hybrid working, comprehensive health and well-being, digitalization, and upskilling — Generative AI (Gen AI) burst onto the scene.
Those are the opening words from the Mercer Global Talent Trends 2024 report, which has recently been published. As ever, the study, which is based on a survey of more than 12,000 executives, HR leaders, employees, and investors, and is authored by Kate Bravery Joana Silva and Jens Peterson is an absolute must-read. The study highlights a disconnect between what HR is prioritising for the 2024 people agenda and the initiatives that executives believe will have the most impact on business growth (see FIG 1). The study highlights four priorities that firms that outpace their competitors are focusing on: (1) Driving human-centric productivity. (2) Anchoring to trust and equity. (3) Boosting the corporate immune system. (4) Cultivating a digital-first culture. My tip to enjoy the study: find a couple of hours, make yourself a cup of tea and have a pen and paper to hand.
FIG 1: HR priorities for the 2024 people agenda (Source: Mercer Global Talent Trends 2024)
FIG 2: Drivers and drainers of employee productivity(Source: Mercer Global Talent Trends 2024)
HYBRID, GENERATIVE AI AND THE FUTURE OF WORK
BRIAN ELLIOTT - Return-to-Office Mandates: How to Lose Your Best Performers
There is mounting evidence that mandates don’t improve financial performance. Instead, they damage employee engagement and increase attrition, especially among high-performing employees and particularly those with caregiving responsibilities.
That’s according to Brian Elliott in his latest column in MIT Sloan Management Review, which highlights that the workers most likely to be turned off by return-to-office mandates are the company’s highest performers. Elliott highlights the link between factors such as pressure from investors and the CEO echo chamber with RTO pronouncements, as well as how only one in three executives believe that RTO has had even a slight impact on productivity. He recommends instead focusing on productivity rather than physical presence (see FIG 3) and how this can inspire a boom loop in engagement as opposed to a doom loop in trust. Finally, Elliott presents findings from the Future Forum and i4CP, highlighting the negative impact of RTO mandates, before offering guidance on how to build an outcomes-driven organisation: “The bottom line is that when trust is balanced with accountability, people and organizations will thrive.”
FIG 3: Focus on Productivity, Not Physical Presence (Sources: Future Forum, Centre for Transformative Work Design, and Slack)
AARON DE SMET, SANDRA DURTH, BRYAN HANCOCK, MARINO MUGAYAR-BALDOCCHI, AND ANGELIKA REICH - The human side of generative AI: Creating a path to productivity
As teams start using gen AI to help free up their capacity, the middle manager’s job will evolve to managing both people and the use of this technology to enhance their output.
A fascinating new study from McKinsey, which provides analysis on workers who are at the forefront of gen AI usage (which as FIG 4 shows is dominated by those in non-technical roles) and dives into the job factors and skills these workers say they need. The authors emphasise how firms can enhance productivity by crafting jobs that put people before tech – rather than the other way around. They conclude that companies that set a people-centric talent strategy will give themselves a competitive edge as more workers and jobs are affected by the changes gen AI brings. The article is rich with data and powerful visualisations – kudos to the authors: Aaron De Smet Sandra Durth Bryan Hancock Marino Mugayar-Baldocchi and Angelika Reich ).
FIG 4: Workers who use generative AI as part of their jobs comprise a much larger group than those who hold traditionally technical roles (Source: McKinsey)
PETER CAPPELLI, PRASANNA (SONNY) TAMBE, AND VALERY YAKUBOVICH - Will Large Language Models Really Change How Work Is Done?
LLMs are much more complicated to use effectively in an organizational context than is typically acknowledged, and they have yet to demonstrate that they can satisfactorily perform all of the tasks that knowledge workers execute in any given job.
In their article, Peter Cappelli Prasanna Tambe and Valery Yakubovich look at the use and challenges of integrating Large Language Models (LLMs) in organisations, and present practical recommendations on how to work with LLMs successfully. The five challenges outlined in the article are: (1) The Knowledge Capture Problem. (2) The Output Verification Problem. (3) The Output Adjudication Problem. (4) The Cost-Benefit Problem. (5) The Job Transformation Problem – How will LLMs work with workers? Guidance includes developing and circulating standards for the use of LLMs in organisations, establishing a central office to produce important LLM output, and providing training to users.
NICK BLOOM – Why WFH is a win-win-win | WFH research update (March 2024)
Nick Bloom’s recent post on LinkedIn highlighting his research on why remote working is a win for firms (due to increased productivity of $20,000 a year for each remote day a week), employees, and society is extremely compelling. I also recommend reading Nick’s latest monthly data for March, which includes numerous insights such as that workers in their 50s and 60s are fully onsite more often than younger workers. For more from Nick, please tune in to his discussion with me on the Digital HR Leaders podcast: Unmasking Common Myths Around Remote Work.
FIG 5: Workers in their 50s and 60s are fully onsite more often than younger workers (Source: WFH Research)
PEOPLE ANALYTICS
PIETRO MAZZOLENI - Transforming HR: How IBM measures the success of its people data platform investments
For those of you who haven’t already subscribed to Pietro Mazzoleni’s People Data Platform newsletter, where he unpacks insights from transforming IBM's internal data platform for people analytics, I highly recommend you do. In this edition, Pietro walks through the three tiers of Key Performance Indicators (KPIs) IBM uses to evaluate investments in Workforce 360, its people data platform (see FIG 6). Watch out for an upcoming episode of the Digital HR Leaders podcast, where I discuss with CHRO Nickle LaMoreaux how IBM is augmenting HR programs with AI. The episode will air from April 9.
FIG 6: Three tiers of KPIs to evaluate investments in a people data platform (Source: Pietro Mazzoleni)
NAOMI VERGHESE - Influencing C-Suite and Board Decisions with People Analytics Insights
Naomi Verghese shares key learnings from the recent Peer Meeting for member companies of the Insight222 People Analytics Program, hosted by HSBC in their global headquarters in London. The Peer Meeting, which was attended by over 60 people analytics leaders and practitioners from more than 40 companies focused on two of the key findings from the Insight222 People Analytics Trends study for 2023: influencing senior stakeholders and measuring value. In her article, Naomi covers four topics: (1) how to implement a people analytics operating model that drives business outcomes (based on insights shared at the Peer Meeting by Rob Etheridge and Bec Aoude). (2) how to use AI to democratise insights from people data, using an example of work Andrew Elston has led at HSBC. (3) how Microsoft’s employee listening ecosystem (see FIG 7) helped the firm identify the moments that matter for in-person collaboration (insights from a session led by Dawn Klinghoffer), and (4) how to influence the board of directors, with insights from Justine Thompson. If you would like to learn more about our People Analytics Program, contact us today.
FIG 7: Microsoft’s employee listening ecosystem (Source: Dawn Klinghoffer, Microsoft)
BRENT DYKES - The Future Of Data Storytelling Is Augmented, Not Automated
Brent Dykes continues his rich vein of writing with an article exploring whether AI tools should be used to automate data storytelling. He provides reasons why data storytelling can’t or shouldn’t be automated including for reasons of oversimplification, transparency and trust, and the fact that storytelling is essentially a human skill. Instead, Brent advocates that the path forward should be augmented data storytelling, and lays out a powerful illustration of how this would work (see FIG 8)
The most powerful person in the world is the storyteller. The storyteller sets the vision, values, and agenda of an entire generation that is to come.
FIG 8: Data storytelling comparisons: Humans vs. AI (Source: Brent Dykes)
HALLIE BREGMAN – Where should People Analytics sit in an Organisation? Part 1 & Part 2 | WILLIS JENSEN – Can Data Cleaning be Automated? | COLE NAPPER - Universal Models & People Analytics | ALEXANDER LOCHER - How to harness the value of people data and operational HR insights | ANGELA LE MATHON, STACIA GARR, AND DANI JOHNSON - Generating Value from People Data
In recent editions of the Data Driven HR Monthly, I’ve been featuring a collection of articles by current and recent people analytics leaders. These act as a spur and inspiration to the field. Five are highlighted here. (1) If you don’t already follow Hallie Bregman, PhD on LinkedIn, you really should. Hallie regularly publishes thoughtful and insightful posts on topics important to the field. The two I’ve included here look at the pros and cons of situating people analytics in or outside HR. (2) Willis Jensen analyses whether AI will reduce the amount of data cleaning undertaken by people analysts given that much of this work involves judgement without hard, fast or consistent rules. (3) Cole Napper, who I’m looking forward to co-chairing People Analytics World with in London in April – also with Michael M. Moon, PhD – explains how many of the models we use in people analytics are borrowed from other disciplines. (4) Alexander S. Locher highlights some of the current trends in people analytics (see FIG 9) and offers guidance on how to harness value from your people data. (5) Angela LE MATHON, VP People Data and Analytics, shares how GSK generates value with their people data, how they’re using AI to gather information, and how skills verification ties in with Stacia Sherman Garr and Dani Johnson of RedThread Research.
FIG 9: Current trends in people analytics (Source: Alexander Locher, EY)
THE EVOLUTION OF HR, LEARNING, AND DATA DRIVEN CULTURE
JO IWASAKI, KAREN EDELMAN, AND YASMINE CHAHED - Time to rethink talent in the boardroom
Just over a third of board and c-level executives believe their workforce related discussions are adequate to meet their organisation’s needs. That’s the standout finding from a new global survey by Jo Iwasaki Karen Edelman and Dr Yasmine Chahed for Deloitte of 500 board members and C-suite executives in more than 50 countries on corporate governance and talent. The three top insights from the study were: (1) Many boards could be focusing more on talent-related issues. (2) Most organisations are just starting to think about their AI strategies. (3) Amplifying the talent experience will require boards to adopt a broader perspective.
FIG 10: Workplace related topics that are top board priorities (Source: Deloitte)
DAVE ULRICH - Pre-flections on GenAI and HR: Where to Go and How to Get There
GenAI will help shape HR’s future by offering both information symmetry to synthesize and optimize the past and present and information asymmetry to create and guide the future.
Dave Ulrich offers some initial reflections on what the journey could look like for applying GenAI to HR work, as well as some possible actions to drive progress (see example in FIG 11 for ‘Talent’). Dave also highlights four important considerations to manage the risk and realise the opportunity of GenAI in HR. (1) Who should champion, sponsor, participate in, and be accountable for this journey? (2) What individual skills and organisation capabilities will be required to make GenAI in HR happen? (3) What will be the regulatory and legal policies and risks associated with the effort? (4) What metrics of value-added GenAI for HR will be most useful and tracked?
FIG 11: Examples of GenAI/HR initiatives in the Talent domain (Source: Dave Ulrich)
HEIN KNAPPEN - How HR Adds to Enterprise Value
Hein J.M. Knaapen, a former chief people officer himself, shares his perspectives on the crucial role HR plays in driving business value, and offers practical advice to CHROs on how to make this a reality. Hein highlights the four people priorities that connect to value: (1) Performance management, (2) Succession management, (3) Leadership development, and (4) Capability building, providing guidance on each.
Value creation should be the focus. Nothing else. And only four people priorities connect to value: performance management, succession management, leadership development and capability building.
WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS
STEFAN HIERL - Identifying the AI Potential in Your Organization: A Strategic Approach
Leveraging Generative AI to assess the AI Potential in a workforce helps businesses go beyond just talking about how AI might change jobs.
As Stefan Hierl astutely observes in his excellent article, in the rush to jump on the AI bandwagon, many companies fall into the trap of overlooking a critical preliminary step: conducting a systematic evaluation of where AI can deliver transformative value. In his article, Stefan outlines a five-step approach to quantify the potential of AI to support organisations identify opportunities for automating and augmenting work activities. The five steps (see FIG 12), which Stefan outlines in detail are: (1) Decomposing roles by breaking down each role into its main activities and respective time shares. (2) AI potential assessment – estimating the potential of AI at the activity level. (3) Expert validation – cross-verifying the generative AI findings with domain experts. (4) Identify high-value areas – creating an overview where AI can significantly enhance workforce productivity (see example in FIG 13). (5) Use case development – exploring specific AI applications to capitalise on identified potential.
FIG 12: Five steps to perform an activity based AI potential assessment (Source: Stefan Hierl)
FIG 13: AI potential by role – example (Source: Stefan Hierl)
MATT SIGELMAN, JOSEPH FULLER, AND ALEX MARTIN - Skills-Based Hiring: The Long Road from Pronouncements to Practice
For all its fanfare, the increased opportunity promised by Skills- Based Hiring was borne out in not even 1 in 700 hires last year (2023).
This is one of the standout findings from a new study by Matt Sigelman and Alex Martin of The Burning Glass Institute and Joseph Fuller from Harvard Business School. Their analysis reveals three categories of firms, who have publicly stated they have removed degree requirements in hiring, based on their actual hiring outcomes: (1) Skills-based hiring leaders (e.g. Cigna) – who have increased their share of non-degree hires in the roles analysed by nearly 20%. (2) In name only (e.g. Bank of America) – 45% of firms studied have made the shift in name only with no meaningful difference in actual skills-based hiring. (3) Backsliders e.g. Uber) – 20% of the firms analysed had made short-term gains by dropping degree requirements, but the change doesn’t stick. The report also highlights the roles best positioned for skills-based hiring (see FIG 14).
FIG 14: The roles best positioned for skills-based hiring (Source: Sigelman et al)
JORDAN PETTMAN - How to Accelerate the Impact of Strategic Workforce Planning (SWP) through the Organisation Strategy Ecosystem
Jordan Pettman, one of my colleagues at Insight222, knows a thing or two about workforce planning. In his recent article for myHRfuture, Jordan explores how strategic design can be brought to life through an integrated ecosystem (see FIG 15) encompassing four components: (1) Organisation strategy, (2) Operating model, (3) Organisation design and strategic workforce planning, and (4) Organisation effectiveness.
FIG 15: The Organisation Strategy Ecosystem (Source: Jordan Pettman, Insight222)
EMPLOYEE LISTENING, EMPLOYEE EXPERIENCE, AND EMPLOYEE WELLBEING
JACQUELINE BRASSEY, LARS HARTENSTEIN, BARBARA JEFFERY, AND PATRICK SIMON – Working nine to thrive
One of the few positives to emerge through and since the pandemic has been a stronger focus on employee health and wellbeing. According to new research by Jacqui Brassey, PhD, MA, MAfN (née Schouten) Lars Hartenstein Barbara Jeffery and Dr. Patrick Simon, on behalf of the McKinsey Health Institute, improving employee health and wellbeing doesn’t just benefit workers and organisations, it could generate between $3.7 to $11.7 trillion in global economic value (see FIG 16). Their article focuses on six drivers of health that employers can influence - social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep – and provides guidance on how organisations can move the dial on each.
FIG 16: Improving global employee health and wellbeing could create up to $11.7 trillion in economic value (Source: McKinsey Health Institute)
LEADERSHIP, CULTURE AND LEARNING
LINKEDIN LEARNING – Workplace Learning Report 2024: L&D powers the AI future
As AI reshapes how people learn, work, and chart their careers, L&D sits at the center of organizational agility, delivering business innovation and critical skills.
Aligning learning programs to business goals emerges as the top L&D focus area for 2024 in LinkedIn Learning’s annual report on the L&D field, which is based on analysis of LinkedIn behavioural data and focus interviews with L&D professionals around the globe. The report is structured into three chapters: (1) The State of L&D (the study finds that a strong learning culture derives retention, mobility, and promotion. – see FIG 17), (2) Skills agility (the study finds that only 33% of organisations have internal mobility programs), and (3) How L&D succeeds) with priorities #1 and #2 being to lean into analytics and build the right metrics – see FIG 18). The report features contributions from the likes of: Amanda Nolen (who asks: “What if Chief Learning Officers become Chief Skills Officers”), Chris Louie Geraldine Murphy Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Alexandra Halem Ekpedeme "Pamay" M. Bassey Shruti Bharadwaj and Dani Johnson.
FIG 17: Business outcomes and learning culture (Source: LinkedIn Learning)
FIG 18: How L&D tracks business impact (Source: LinkedIn Learning)
AYSE KARAEVLI AND SERDEN ÖZCAN - Make Better Allies of Your Workforce
When the board takes the recommendations of employee advisory groups seriously and incorporates them into decisions, employees become more empowered, and their perspectives become embedded into their company’s long-term objectives.
In their article for MIT Sloan Management Review, Ayse Karaevli and Serden Ozcan present findings from their interviews with board directors, CEOs, CFOs, and employee representatives to understand how to manage conflict and engage workers. From their analysis, Ayse and Serden identified three strategies effective leaders use to include employees (see FIG 19): (1) Identify mutual goals and interests, (2) Foster inclusive decision processes, and (3) Give employees strategic responsibilities. The article then describes each of these in detail with examples from the likes of ThyssenKrupp, Allianz, Siemens, and Bayer before highlighting the importance of employee advisory groups, engagement with board members and the role of committees and task forces to imbue governance and participation.
FIG 19: Three Strategies to Avert Workforce Controversies (Source: Ayse Karaevli and Serden Özcan)
DIVERSITY, EQUITY, INCLUSION, AND BELONGING
SUNDIATU DIXON-FYLE, MASSIMO GIORDANO, TANIA HOLT, TUNDE OLANREWAJU, DARA OLUFON, AND SANDRA SANCIER-SULTAN - Ethnocultural minorities in Europe: A potential triple win
Greater inclusion of ethnocultural minorities could fill talent gaps and spur company growth, increase economic empowerment of these groups, and generate benefits for the economy and broader society.
Despite the anti-immigration policies of many current European governments (that includes you, Rishi Sunak), stagnant economies, tight labour markets, and shrinking working populations mean that immigration is key to unlocking economic growth. In their superb analysis for McKinsey, Sundiatu Dixon-Fyle Massimo Giordano Tania Zulu Holt Tunde Olanrewaju Dara Olufon and Sandra Sancier-Sultan provide data insights on what they classify as ethnocultural minorities in Europe, and their (mostly challenging) experiences. The authors also provide guidance for companies on ethnocultural minority employee inclusion across five dimensions (see FIG 20).
FIG 20: Companies can consider ethnocultural minority employee inclusion across five dimensions (Source: McKinsey)
HR TECH VOICES
Much of the innovation in the field continues to be driven by the vendor community, and I’ve picked out a few resources from March that I recommend readers delve into:
ANDREA DERLER, PETER BAMBERGER, MANDA WINLAW, AND CUTHBERT CHOW - When New Hires Get Paid More, Top Performers Resign First - To attract talent to the organisation, employers often pay new hires more than they pay equivalent workers in the same role. Analysis by the Visier Inc. team of Andrea Derler, Ph.D. Peter Bamberger Manda Winlaw and Cuthbert Chow shows that in these times of increasing pay transparency, this strategy risks your high-performers resigning.
ANDREW PITTS AND CHAD MITCHELL - Exploring a few largely untapped sources of data for passive Organizational Network Analysis – This article by Andrew Pitts and Chad Mitchell of Polinode looks at a number of data sources that are typically overlooked for ONA including: 360 reviews, peer to peer recognition tools, opportunity marketplaces, and talent intelligence data.
FRANCISCO MARIN - Key Considerations for Defining the Scope of an ONA Pilot – Francisco Marin of Cognitive Talent Solutions provides a helpful guide to defining the scope of an ONA pilot including tips on clarifying the objective, data privacy and securing executive sponsorship.
HAKKI OZDENOREN AND JOHN BOUDREAU – Is the Future of Work Lost in Translation – John Boudreau joins forces with Hakki Ozdenoren of Revelio Labs to conduct analysis on resumes and jobs mentioning the ‘future of work’, with HR featuring prominently (see FIG 21).
FIG 21: A diverse set of roles contribute to the Future of Work (Source: Revelio Labs)
PODCASTS OF THE MONTH
In another month of high-quality podcasts, I’ve selected five gems for your aural pleasure: (you can also check out the latest episodes of the Digital HR Leaders Podcast – see ‘From My Desk’ below):
JAMIL ZAKI, BRYAN HANCOCK, BROOKE WEDDLE, AND LUCIA RAHILLY - It’s cool to be kind: The value of empathy at work – In this episode of McKinsey Talks Talent, Jamil Zaki (author of The War for Kindness) joins Bryan Hancock Brooke Weddle and Lucia Rahilly to make the case for investing in empathic behaviour—for reasons including higher productivity, a stronger workplace culture, and better organisational health—as well as to discuss how to go about cultivating kindness at work.
CAL NEWPORT AND ADAM GRANT – How to be productive without burning out – Cal Newport discusses insights from his new book, Slow Productivity: The Lost Art of Accomplishment Without Burnout, with Adam Grant on WorkLife. They dig into the data on productivity, debate the benefits and drawbacks of doing fewer things (and spending less time on email and social media), and discuss individual habits and organisational practices for preventing burnout and promoting worthwhile work.
JOSH BERSIN - Why “Talent Density” Is So Critical In Business Today – Fresh from discussing his Dynamic Organizations research at Gloat Live, Josh Bersin discusses why ‘Talent Density’ is becoming one of the key strategies for growth.
DONNA MORRIS AND LARS SCHMIDT - Inside Walmart’s Bold Strategy to Transform Retail Work – Walmart’s chief people officer, Donna Morris, joins Lars Schmidt on his Redefining Work podcast to discuss how Walmart is not just navigating but leading the revolution in workplace technology—with people firmly at its core. This was an especially insightful listen as only two days before I had seen Marty Autrey speaking at the Wharton People Analytics conference on how Walmart provides data-based nudges to its store managers to help them drive business outcomes and enhance employee experience.
RYAN HAMMOND, COLE NAPPER AND SCOTT HINES - Turnover Prediction, ML Ethics, & The HiQ Story – Ryan Hammond shares the epic story of HiQ Labs with Directionally Correct hosts Cole Napper and Scott Hines, PhD, as well as insights from his practitioner and academic backgrounds including how to ethically use internal and external data to do turnover prediction.
VIDEO OF THE MONTH
TANUJ KAPILASHRAMI, MICHAEL FRACCARO, TAMLA OATES-FARNEY, AND DAVID GREEN – CHRO Panel: Delivering against the transformation imperative
March’s Video of the Month proved to be a highlight for me as it features me moderating the CHRO Panel at the recent Gloat Live event in New York. The panel was comprised of Tanuj Kapilashrami Michael Fraccaro and Tamla Oates-Forney, and featured discussion on the increasingly pivotal role of the CHRO in business transformation, lessons learnt and successes from transitioning to a skills-based organisation, and how technology can enable a culture of inclusivity and opportunity.
BOOKS OF THE MONTH
With a lot of travelling back and forth from the US in March, I found time to dig into two new books, which I recommend to readers of this newsletter:
MARC SOKOL AND BEVERLY TARULLI – Strategic Workforce Planning: Best Practices and Emerging Directions
Strategic workforce planning – the process of looking forward, assessing how to compete and win in your chosen market or business arena, and linking those insights to your existing and potential future workforce – is core to any institution that aspires to sustain itself over time.
Those are the opening words of Marc Sokol and Beverly Tarulli, Ph.D., the editors of an indispensable new volume of SIOP’s Professional Practice Series. It provides an overview of SWP, covering best practices, methodologies and new directions in the field as well as featuring contributions and case studies from a stellar list of contributors. These include: Sheri Feinzig Alexis Fink Adam Gibson Brian Heger Adam McKinnon, PhD. Kanella Salapatas and Dave Ulrich. Grab yourself a copy!
SALVATORE V. FALLETTA – Creepy Analytics: Avoid Crossing the Line and Establish Ethical HR Analytics for Smarter Workforce Decisions
In Creepy Analytics, Dr. Salvatore Falletta provides a thoughtful approach to HR Analytics that is both evidence-based and ethical – ensuring that organisations get the insights they need while respecting employee privacy. The book is built around the author’s seven-step HR Analytics Cycle (see FIG 22) and is well-researched. Thanks to Salvatore too for referencing Excellence in People Analytics several times, particularly in relation to the guidance Jonathan Ferrar and I offer around governance and the development of an ethics charter. As Alec Levenson opines in his endorsement of the book: “Falletta has done a masterful job addressing some of the most important ethical issues for workforce analytics.”
FIG 22: The HR Analytics Cycle (Salvatore V. Falletta)
RESEARCH REPORT OF THE MONTH
MAX BLUMBERG, ALEC LEVENSON, AND DAVE MILLNER - A Strategically Aligned HR Operating Model
In their recently published paper, three eminent and progressive thinkers in our field – Max Blumberg (JA) ?? Alec Levenson and Dave Millner – set out a pivot in how HR is structured and works in order to more closely align the function to the capabilities required for successful strategy execution. They present a new HR structure (see FIG 23) designed around four key pillars, before describing each pillar in detail and providing some diagnostic steps to implement this new operating model.
FIG 23: A new HR structure (Source: Blumberg, Levenson, and Millner)
FROM MY DESK
March saw four episodes from Series 37 of the Digital HR Leaders podcast, sponsored by our friends at Culture Amp - thank you to Ellisa Packer and Jodie Evans, a round-up of series 36 and a guest appearance by yours truly on the Future Work/Life podcast:
DAVID GREEN AND OLLIE HENDERSON - Driving growth in people and businesses using data – In a role reversal, it was my turn in the hotseat as I joined Ollie Henderson on his Future Work/Life podcast to talk people analytics, talent marketplaces, AI, hybrid work models and the future skills required by HR professionals.
DORIE CLARK - How to Embrace Long-Term Thinking in HR Leadership – Dorie Clark and I discuss how to pivot to long-term thinking, how to prioritise effectively, and why embracing failure can drive innovation and creativity.
DIDIER ELZINGA - How to Prove the ROI of a Positive Company Culture – Didier Elzinga joins me to discuss ways of engaging the board on culture topics, the relationship between a healthy culture and business performance, and how to demonstrate the ROI of culture and engagement initiatives.
ROB BRINER - What is Evidence Based HR and Why is it Important? – Rob Briner shares the principles of evidence-based HR, how it differs from people analytics, and offers recommendations to chief people officers on how they can incorporate EBHR into their work.
LOUISE MILLAR AND OLIVIA EDWARDS - Actionable People Analytics Strategies to Influence Senior Leadership – In a powerful example of people analytics in practice at a SME, Louise Millar and Olivia Edwards share insights from the people analytics journey at Chetwood.
DAVID GREEN – How will AI transform the role of HR? – A round-up of series 36 of the Digital HR Leaders podcast, with insights from episodes featuring Dawn Klinghoffer Jeremy Shapiro Thomas Hedegaard Rasmussen Serena H. Huang, Ph.D. Luke Farrugia Kaz Hassan Eric Siegel and Bernard Marr.
THANK YOU
Thomas Kohler for including the February edition of Data Driven HR in his round-up of HR resources.
Reb Rebele for referencing me in his post about the Wharton People Analytics Conference – you were missed, Reb.
Olimpiusz Papiez for providing a great set of takeaways on the Digital HR Leaders podcast episode with Dawn Klinghoffer, Jeremy Shapiro, and Thomas Rasmussen on People Analytics, AI and ML.
Peter Johnson for including me in his list of HR thought leaders.
Mokkup.ai for including my article on How Will AI Impact People Analytics in 2024 and Beyond? in their collection of Top 14 reads for Data Professionals.
Thinkers360 for including me in their list of the Top 50 B2B Thought Leaders, Analysts & Influencers You Should Work With In 2024 (EMEA)
Joveo for including me in their list of Top 9 Twitter Influencers Every Talent Acquisition Specialist Should Follow
To the following people who sharing the February edition of Data Driven HR Monthly. It's much appreciated: Allison Ardianto Eakkasit Toratana Jillian Meade David Balls (FCIPD) Kingsley Taylor Military Veterans of LinkedIn Robin Carlin Amy C. Lewis, PhD Russ Fatum Kouros Behzad Emily Klein Madison Clary Robert Rogowski Phillip M. Randall, PhD, CPG Gord Johnston MA, BHJ, BA, CHRP ANDRES CAMPOVERDE Aravind Warrier Francisca Solano Beneitez Satya Prakash Pandey Malgorzata (GOSIA) LANGLOIS Dr. Zohaib Azhar (PhD-HR) Jane Datta David McLean John Lawson Alice Damonte Martha Curioni Vipul M. Mali ↗️ Jens Keuter Phil Inskip Andrew Smith MBA Ekta Vyas Ph.D Oswaldo Machado Bill Brown Barry Marshall Paola Carranco Murthy Nibhanipudi VS Jaana Saramies ? Robert Houghton Aysegul Tigli Indre Radzeviciute Radha Jeevan Melissa Hopper Fritz Tina Peeters, PhD Morten Hartvig Berg Pedro Pereira Gavin Wiseman
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Insight222 Peer Meetings, like this event in London, are a core component of the Insight222 People Analytics Program®. They allow participants to learn, network and co-create solutions together with the purpose of ultimately growing the business value that people analytics can deliver to their organisations. If you would like to learn more, contact us today.
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 90 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.
Valoir 报告显示 HR 尚未准备好迎接 AI，你呢？
研究显示，人力资源管理领导者面临的主要问题包括缺少 AI 相关的专业知识以及面临的风险和合规性问题。
弗吉尼亚州阿灵顿--Valoir 发布的一项全球新报告显示，尽管 AI 驱动的自动化似乎无法避免，但人力资源部门似乎并未做好准备。这项涵盖超过150位人力资源执行官的调查揭示了利用 AI 的巨大机会，但同时也显示出在制定政策、实施实践和进行培训方面普遍存在不足，以便安全有效地将 AI 技术应用于人力资源管理。
“虽然许多机构开始采用生成式 AI，但很少有组织建立必要的政策、准则和保障措施。作为员工数据的保护者和公司政策的制定者，人力资源领导者需要在 AI 的政策和培训方面走在前列，不仅为自己的团队，也为广大员工群体做好准备。”
以下内容需要特别注意： “AI 正在快速融入人力资源管理领域，特别是在招聘、人才发展和劳动力管理等方面。然而，引入 AI 也伴随着诸如数据泄露、误解、偏见和不当内容等风险，”Valoir 的首席执行官 Rebecca Wettemann 表示。“面对这些挑战并采取措施减少风险的人力资源部门，可以显著提升其从 AI 中获得的益处。”
人力资源的自动化与战略转型潜力
报告指出，有35%的人力资源部门员工的日常工作非常适合自动化处理。在所有人力资源管理活动中，招聘环节最有潜力应用 AI 技术，并且已成为采纳率最高的领域，近四分之一的组织已经开始利用 AI 支持的招聘流程。人才发展、劳动力管理以及培训和发展同样被视为 AI 自动化的关键领域。
生成式 AI 正在加速人力资源部门的生产力提升及风险增加
尽管到2023年中旬，超过三分之四的人力资源领域工作者已经尝试使用过某种形式的生成式 AI，但仅有16%的组织制定了关于使用生成式 AI 的具体政策。而且，真正关于其伦理使用的政策数量更是寥寥无几。人力资源领导者认为，缺乏 AI 相关技能和专业知识是采纳 AI 的最大障碍，但只有14%的组织制定了有效的 AI 使用培训政策。这些政策对于确保所有员工都能充分利用 AI 带来的好处并最小化风险是至关重要的。
“尽管生成式 AI 正被广泛采纳，但几乎没有哪些组织建立了必要的政策、准则和保护措施。作为员工数据的守护者和公司政策的制定者，人力资源领导者必须在 AI 政策和培训方面先行一步，这不仅是为了他们自己的团队，也是为了整个员工群体的利益，”Wettemann 表示。
报告的关键知识点：
Integration Challenges: HR faces challenges in managing AI use due to lack of policies, practices, and training.
Early Adoption vs. Preparedness: While HR has been an early adopter of AI, most organizations still lack the proper frameworks for safe and effective AI adoption.
Rapid Product Release: Post-Chat GPT announcement, HR software vendors have rapidly released generative AI products with varying capabilities.
AI’s Double-Edged Sword: AI offers great benefits but also poses risks of "accidents" due to immature technology, inadequate policies, and lack of training.
AI Experimentation and Automation Opportunity: Over three-quarters of HR workers have experimented with generative AI. 35% of HR tasks could potentially be automated by AI.
Current AI Utilization: The main opportunities for HR benefits from AI are in recruiting, learning and development, and talent management, with recruiting leading in AI adoption.
Adoption Barriers: Main hurdles include lack of AI expertise (28%), fear of compliance and risk (23%), and lack of resources (21%).
Policy and Training Deficiencies: Only 16% of organizations have policies on generative AI use, and less than 16% have training policies for AI usage.
Risk Areas in AI: Data compromises, AI hallucinations, bias and toxicity, and recommendation bias are identified as primary risks.
Future Plans for AI: Over 50% of organizations plan to apply AI in recruiting, talent management, and training within the next 24 months.
Least Likely AI Adoption: Benefits management has the lowest likelihood of current or future AI adoption due to data sensitivity concerns.
AI Skills and Expertise: The significant gap in AI skills and expertise impacts the adoption and effective use of AI in HR.
HR’s Role in AI Adoption: HR needs to develop policies, provide training, and ensure ethical AI use aligning with organizational principles.
Recommendations for HR: Suggestions include experimenting with generative AI, developing ethical AI usage policies, creating role-specific AI training, and identifying employee groups at risk from AI automation.
AI
2024年03月12日
AI
Josh Bersin谈How To Create Talent Density 如何打造人才密度
Josh Bersin发表文章谈到：在过去几年里，我注意到大公司的表现开始不如小公司。我们现在看到苹果和谷歌都出现了这种情况，而微软应对这一挑战也有相当长的一段时间了。 随着公司的发展，帮助我们推动组织绩效的一个重要理念就是人才密度。这篇文章讨论了人才密度的概念，即公司中技能、能力和表现的质量和密度。强调传统的员工绩效评估模型已导致平庸。建议采用人才密度方法，包括招聘增加或乘数效应的人才，基于帕累托分布管理绩效，以及专注于赋权、反馈和领导力。文章强调，为了创新和市场竞争力，尤其在AI和技术进步的背景下，维持高人才密度的重要性。
In this (long) article, I want to talk about a new concept called Talent density. And as I pondered the concept I think it represents one of the more important topics in management. So I hope you find it as interesting as I do.
First of all, the concept of talent density, pioneered by Netflix by the way, is simple.
Talent Density is the quality and density of skills, capabilities and performance you have in your company.
So, if you have a company that is 100% high performers, you’re very dense. If you have a company that’s 20% high performers, you’re not very dense. It’s easy to understand, but hard to implement, because it gets to the point of how we define performance, how we select people to hire, how we decide who’s going to get promoted, how we decide who’s going to work on what project and how we’re going to distribute pay.
So before I explain talent density, let’s talk about the basic beliefs most companies have. Most organizations believe that they’re operating with a normal distribution or bell curve of performance. I don’t know why that statistical model has been applied to organizations, but it has become almost a standard policy. (Academics have proven it false, as I explain below.)
Using the bell curve, we identify the “mean” or average performance, and then categorize performance into five levels. Number ones are two standard deviations to the right and number fives are two standard deviations to the left.
The people operating at level one get a big raise, the people operating at level two get medium raise, the people operating at level three get an average raise, the people operating at level four get a below average raise and the people operating at level five probably need to leave. Lots of politics in the process, but that’s typically how it works.
As I describe in The Myth of The Bell Curve, these performance and pay strategies have been used for decades. And at scale they create a mediocrity-centered organization, because the statistics limit the quantity and value of 1’s. If you’re operating at 1 level and you get a 2, you’ll quit. If you’re operating at 3 level, you’re probably going to coast. You get my drift. And since the bulk of the company is rated 2 or 3, most of the managers are in the middle.
As the saying goes, A managers hire A people, B managers hire C people. So over time, if not constantly tuned, we end up with an organization that is almost destined to be medium in performance.
Now I’m not saying every company goes through this process, but if you look at the productivity per employee in large organizations it’s almost always below that of smaller organizations. Why? Because as organizations grow, the talent density declines. (Netflix, as an example, example, generates almost $3M of revenue per employee, twice that of Google and 10X that of Disney. And they are the only profitable streaming company, with fewer than 20,000 employees and a $240 billion market cap.)
The traditional model was fine in the industrial age when we had a surplus of talent, jobs were clearly defined, and most employees were measure by the “number of widgets they produced.” In those days we could swap out a “low performer” for a “high performer” because there were lots of people in the job market.
We don’t live in that world anymore. The world we now live in has sub 4% unemployment, a constant shortage of key skills, and a growing shortage of labor. And thanks to automation and AI, the revenue or value per person has skyrocketed, almost an order of magnitude higher than it was 30 years ago.
So we need a better way to think about performance in a world where companies with fewer people can outperform those who get too big. Look at how Salesforce, Google, Apple, who are essentially creative companies, have slowed their ability to innovate as they get bigger. Look at how OpenAI, who is a tiny company, is outperforming Google and Microsoft.
Today most businesses outperform through innovation, time to market, customer intimacy, or IP – not through scale or “harder work.”
How do we maintain a high level of talent density when we’re growing the company and hiring lots of people? Netflix wrote the book on this, so let me give you the story.
First, the hiring process should focus on talent density, not butts in seats. Rather than hire someone to “fill a role” we look for someone who is additive or multiplicative to the entire team. Hire someone that challenges the status quo and brings new ideas, skills, and ideas beyond the “job” as defined. Netflix values courage, innovation, selflessness, inclusion, and teamwork, for example. These are not statements about “doing your job as defined.”
Netflix’s idea is that each incremental hire should make everybody else in the company and everybody else in the team produce at a higher level. Now this is a threatening thing for an insecure manager because most managers don’t want to hire somebody that could take their job away. But that’s why we have this problem.
Second, we need to manage or create some type of performance management process that is built around the Pareto distribution (also called the Power Law) and not the normal distribution. In the Pareto distribution or the power law, we have a small number of people who generate an outsized level of performance, you can call it the 80/20 rule or the 90/10 rule. (20% of the people do 80% of the work)
Studies have shown that companies and many populations work this way, and it makes sense. Think about athletes, where a small number of super athletes are 2-3 better than their peers. The same thing is true in music, science, and entertainment. It’s also true in sales and many business disciplines.
Research conducted in 2011 and 2012 by Ernest O’Boyle Jr. and Herman Aguinis (633,263 researchers, entertainers, politicians, and athletes in a total of 198 samples). found that performance in 94 percent of these groups did not follow a normal distribution. Rather these groups fall into what is called a “Power Law” distribution.
In every population of human beings there are a few people who just have God-given gifts to outperform in the job, and they just naturally seem to be far better than everyone else.
Bill Gates once told the company that there were of the three engineers that he felt made the company of Microsoft. And I’ve heard this in many other companies, where one software engineer and the right role can do the work of 10 other people.
Now, this is not to say that everybody will fall into one level of the Pareto distribution. At a given point in time in your career, you may be in the 80% and over time, as you learn and grow and find the things that you’re naturally good at, you’ll end up in the 20%. But in a given company this is a dynamic that’s constantly taking place. And that’s what Netflix is doing – constantly working on talent density.
What does this mean for performance management? It means that in order to care for a population like this, we have to hire differently, avoid the bell curve, and pay high performers well. Not just a little more than everybody else, a lot more. And that’s what happens in sports and entertainment, so why not in business.
If you look at companies like Google, Microsoft, and others, there are individuals in those companies that make two to three times more than their peers. And as long as these decisions are made based on performance, people are fine with it.
What obviously does not work is when person making all the money is the person who’s the best politician, best looking, or most popular.
And that leads me to item three: In the Netflix culture there’s a massive amount of empowerment, 360 feedback, candor and honesty. You’ve probably read the Netflix culture manifesto: it’s all about the need for people to be honest, to speak truth, to give each other feedback, and to focus on judgement, courage, and accountability. Netflix only recently added job levels: they didn’t have job levels for many years.
Giving people feedback is a challenge because it’s uncomfortable. So this has to to start at the top and it has to be done in a developmental, honest way. This does not mean people should threaten or disparage each other, but we all need to know that at the end of a project or the end of the meeting it’s okay for somebody to tell us “here’s what was great about that and here’s what wasn’t great about it.”
One of the most important institutions in the world, the US military lives, eats and dies by this process. If you’re in the military and you mess something up, you can guarantee that somebody’s going to tell you about it, and you’re going to get some help making sure you don’t do it again. We don’t have life or death situations in companies, but we can certainly use this kind of discipline.
The fourth thing that matters in talent density is leadership and goal setting. One of the things that really gets in the way of a high performing company is too many individual goals, too many siloed projects and responsibilities and people not seeing the big picture.
If your goal setting and performance management process is 100% based on individual performance you are sub-optimizing your company. Not only does this work against teamwork, but there really isn’t a single thing in a company that anybody can do alone. So our performance management research continuously shows that people should be rewarded for both their achievements as well as that of the team. (Here’s the research to explain.)
Why is talent density important right now? Let me mention a few reasons.
First, we’re entering a period of low unemployment so every hire is going to be challenging. And thanks to AI, companies are going to be able to operate with smaller teams. What better time to think about how to “trim down” your company so it’s performing at its best?
Second, the transformations from AI are going to require a lot of flexibility and learning agility in your company. You want a highly focused, well aligned team to help make that happen. And while AI will help every company improve, your ability to leverage AI quickly will turn into a competitive advantage (think back about how web and digital and e-commerce did the same).
(I firmly believe the companies with the most ingenious applications of AI will disrupt their competitors. I’m still amazed at Whole Food’s hand recognition checkout process: I can see self-service coffee, groceries, and other retail and hospitality coming.)
Third, the post-industrial business world is going to start to devalue huge, lumbering organizations. Many big companies just need a lot of people, but as Southwest Airlines taught us long ago, it’s the small team that performs well. So if you can’t break your company into small high-performing teams, your talent density will suffer.
When the book is written on Apple’s $10 Billion car, I bet one problem was the size and scale of the team. We’ll see soon enough. By the way, I still recommend everyone read “The Mythical Man-Month,” which to me is the bible of organizing around small teams.
What if you’re a healthcare provider, retailer, manufacturer, hospitality company? Does talent density apply to you? Absolutely! Go into a Costco and see how happy and engaged the employees are. Then go into a poorly run retailer and you’ll feel the difference.
In my book Irresistible I give examples of companies who embrace what I call “the unquenchable power of the human spirit.” Nobody wants to feel like they’re underperforming. With the right focus on accountability and growth we can help everyone out-perform their expectations.
Now is a time rethink how our organizations work. Not only should we promote and reward the hyper-performers, the Pareto rule and Talent Density thinking encourage us to help mid-level performers learn, grow, and transform themselves into superstars.
Let’s throw away the old ideas of bell curve, forced distribution, and simplistic performance management. Companies that push for everlasting high performance are energizing places to work, they deliver outstanding products and services, and they’re great investments for stakeholders.
AI中文翻译：
在这篇篇幅较长的文章中，我想探讨一个被称为“人才密度”的新概念。思考此概念时，我认为它是管理领域中极其重要的议题之一。希望您能像我一样发现其趣味性。
首先，Netflix首创的“人才密度”概念其实很简单。
人才密度指的是公司内部技能、能力和表现的质量与密集程度。
换句话说，如果你的公司全是高绩效人才，那么你的“人才密度”就很高。如果只有20%是高绩效人才，那么你的“人才密度”就不高。这个概念虽然容易理解，但实际执行起来却颇具挑战，因为它涉及到我们如何定义绩效、招聘员工的标准、晋升决策、项目分配以及薪酬分配。
在详细解释“人才密度”之前，让我们先看看大多数公司的基本信念。许多组织相信，他们的员工表现遵循一个正态分布或钟形曲线。这个统计模型为何被广泛应用于组织之中，我并不清楚，但它几乎已成为标准做法。（实际上，如我下文将解释的，学术研究已证明这一模型是错误的。）
采用钟形曲线，我们确定平均表现（即“平均线”），然后将员工的表现划分为五个等级。表现最好的被归为一级，标准为右偏两个标准差；表现最差的被归为五级，左偏两个标准差。
一级表现者获得大幅度加薪，二级表现者获得中等加薪，三级表现者获得平均水平的加薪，四级表现者加薪低于平均，五级表现者可能就需要离开公司了。虽然这个过程充满了政治操作，但这就是它通常的运作方式。
正如我在《钟形曲线的神话》中所述，这些关于绩效和薪酬的策略已经使用了数十年。而且，当这些策略在大规模下实施时，它们会造成以平庸为中心的组织文化，因为这种统计方法限制了顶尖人才的数量和价值。如果你是一级表现者却被评为二级，你很可能就会选择离职。如果你是三级表现者，你可能就会选择安于现状。你应该明白我的意思了。而且，由于大部分员工的评级为二级或三级，大多数管理者也就处于中等水平。
常言道，A级的管理者招聘A级人才，B级的管理者则招聘C级人才。因此，如果不持续进行优化调整，组织最终几乎注定会变得中庸。
我并不是说每家公司都会经历这一过程，但如果你查看大型组织的员工生产率，通常都低于小型组织的生产率。为什么呢？因为随着组织规模的扩大，“人才密度”往往会下降。（以Netflix为例，其每名员工创造的收入几乎为300万美元，是Google的两倍，是迪士尼的十倍。他们是唯一盈利的流媒体公司，员工不足20,000人，市值2400亿美元。）
在工业时代，人才供过于求，工作职责明确，大多数员工的表现以“生产的产品数量”来衡量。那个时候，低绩效者可以轻松地被高绩效者替换，因为劳动市场上有大量的人才可供选择。
但我们不再生活在那个时代了。在我们现在的世界里，失业率低于4%，关键技能持续短缺，劳动力整体也日益减少。而且，得益于自动化和AI技术，每位员工创造的收入或价值比30年前高出了几个数量级。
因此，在一个人员更少的公司可以超越体量更大的公司的世界中，我们需要一种更好的绩效思考方式。看看Salesforce、Google、Apple这些本质上依靠创新的公司，随着规模扩大，它们的创新能力如何变缓。再看看OpenAI，尽管是一个小公司，却在超越Google和Microsoft。
如今，大多数企业通过创新、市场响应速度、客户亲密度或知识产权而非规模或“更加努力的工作”来实现超越。
在我们不断发展公司并招聘大量人员的同时，我们如何保持高水平的“人才密度”？Netflix在此领域有着开创性的工作，让我来分享一下他们的故事。
首先，招聘过程应专注于提高“人才密度”，而不是仅仅为了填补空缺。我们寻找的不是简单地“填补一个角色”的人，而是能够为整个团队带来正面或倍增效果的人才。我们寻找的是那些能够挑战现状、带来新观点和技能，并超出传统“工作定义”的人。例如，Netflix重视勇气、创新、无私、包容和团队合作等价值观，并不仅仅是“完成既定工作”。
Netflix的理念是，每一次新增的招聘都应该使公司内每个人和团队的每个成员的生产力得到提升。这对于那些缺乏安全感的管理者来说可能是个挑战，因为大多数管理者并不希望招聘可能会取代他们的人。但正是这种思维方式导致了我们当前的问题。
其次，我们需要建立或改进一种围绕帕累托分布（也称作幂律分布）而非正态分布的绩效管理流程。在帕累托分布或幂律分布中，少数人贡献了超出常规的绩效水平，这可以称作80/20规则或90/10规则。（即20%的人完成了80%的工作）
研究显示，许多公司和人群实际上都是以这种方式运作的，这是合理的。想想那些在体育、音乐、科学和娱乐领域表现出色的人，其中少数顶尖人才的表现是同龄人的两到三倍。销售和许多商业领域也是如此。
2011年和2012年由Ernest O’Boyle Jr.和Herman Aguinis进行的研究（涵盖了633,263名研究人员、艺术家、政治家和运动员，共198个样本）发现，这94%的群体的表现并不遵循正态分布，而是呈现所谓的“幂律分布”。
在每个人群中，总有少数人因为天赋异禀，在工作中表现出色，自然而然地比其他人优秀得多。
比尔·盖茨曾经对微软说过，他认为公司中的三名工程师是公司的基石。我也在许多其他公司听到过类似的故事，其中一位软件工程师在合适的位置上可以完成其他十人的工作量。
这并不意味着每个人都将被归入帕累托分布的某一层级。在你职业生涯的某个阶段，你可能处于80%的群体中，但随着你不断学习、成长并找到自己真正擅长的领域，你最终可能进入20%的群体。但在任何一个公司，这种动态都在不断发生。这就是Netflix一直在努力提升“人才密度”的原因。
这对绩效管理意味着什么？这意味着，为了照顾这样一个群体，我们必须采取不同的招聘方式，避免使用钟形曲线，并且为高绩效者提供丰厚的薪酬。这不仅仅是支付比其他人稍微多一点的薪水，而是要多得多。这在体育和娱乐领域已经是常态，那么为什么不可以应用到商业领域呢？
如果你观察Google、Microsoft等公司，你会发现，这些公司中的个别人物赚取的收入是他们同事的两到三倍。只要这些决策基于绩效，大家通常都能接受它。
当然，不起作用的情况是，赚取高薪的是那些最擅长政治、外表最出众或最受欢迎的人。
这就引出了第三点：在Netflix的文化中，存在着大量的授权、360度反馈、直率和诚实。您可能已经读过Netflix的文化宣言，它强调人们需要诚实、坦诚、互相提供反馈，并专注于判断力、勇气和责任感。直到最近，Netflix才引入了职级制度——在很多年里，他们根本没有职级制度。
提供反馈是挑战性的，因为这会使人感到不适。因此，这个过程必须从高层开始，并以一种促进发展、诚实的方式进行。这并不意味着人们应互相威胁或贬低，但我们都需要明白，在项目结束或会议结束时，对方告诉我们“这是成功之处，这是失败之处”是完全可以接受的。
美国军队是世界上最重要的机构之一，它依靠这种过程生存、发展和克服困难。如果你在军队犯错，你可以确信会有人告诉你，并且你会得到帮助以确保你不会再犯同样的错误。虽然公司里没有生死攸关的情况，但我们完全可以借鉴这种纪律性。
在“人才密度”中很重要的第四点是领导力和目标设定。阻碍高绩效公司发展的一个常见问题是过多的个人目标、孤立的项目和职责，以及员工无法看到整体大局。
如果你的目标设定和绩效管理过程完全基于个人表现，那么你就在削弱你的公司。这不仅阻碍了团队合作，而且实际上没有什么是公司内任何人能够独立完成的。因此，我们的绩效管理研究不断表明，人们应该同时因其个人成就和团队成就而获得奖励。（这是相关的研究。）
为什么“人才密度”在当前尤为重要？我来列举几个原因。
首先，我们正处于一个失业率低的时期，因此每次招聘都将是一个挑战。而且，随着AI技术的帮助，公司将能够以更小的团队运作。在这样一个时刻，有什么比考虑如何“精简”你的公司、使其发挥最佳表现更合适的时机呢？
其次，随着AI的变革，你的公司将需要极大的灵活性和学习适应能力。你需要一个高度专注、良好协调的团队来实现这一目标。而且，尽管AI将帮助每个公司提高效率，但你快速应用AI的能力将变成一个竞争优势（回想一下网站、数字化和电子商务如何实现了同样的事情）。
（我坚信，那些能够巧妙应用AI的公司将会颠覆它们的竞争对手。我对Whole Foods的手掌识别结账过程仍感到惊讶：我预见到自助服务咖啡、杂货及其他零售和酒店业务的出现。）
第三，后工业时代的商业世界将开始贬低庞大、笨重的组织。许多大公司只是需要大量员工，但正如西南航空所示，小团队的表现通常更好。因此，如果你无法将你的公司划分为小型高效团队，你的“人才密度”将受到影响。
当有关Apple的100亿美元汽车项目的书籍编写时，我敢打赌问题之一将是团队的规模和规模。我们很快就会发现。顺便说一下，我还是推荐每个人阅读《神话般的人月》，对我而言，这本书是关于围绕小团队进行组织的经典之作。
如果你是医疗服务提供者、零售商、制造商或酒店业者，“人才密度”是否适用于你？当然适用！走进一家Costco，看看员工是多么的开心和投入。然后走进一个管理混乱的零售商，你就能感受到区别。
在我的书《不可抗拒》中，我列出了那些拥抱我所称之为“人类精神不可磨灭力量”的公司的例子。没有人愿意觉得自己表现不佳。通过适当的关注责任和成长，我们可以帮助每个人超越他们自己的期望。
现在是时候重新考虑我们的组织如何运作了。我们不仅应该提升和奖励顶尖表现者，帕累托法则和“人才密度”思维还鼓励我们帮助中等表现者学习、成长，并将自己转变为明星。
让我们抛弃旧的钟形曲线、强制分配和简单化绩效管理的想法。不断追求高绩效的公司是充满活力的工作场所，他们提供卓越的产品和服务，并且对投资者而言是极好的投资。
AI
2024年03月10日
AI
根据美世 2024 年全球人才趋势研究，高管认为人工智能是提高生产力的关键，但大多数员工尚未做好转型的准备Mercer's 2024 Global Talent Trends Study unveils critical insights from over 12,000 global leaders and employees, highlighting the increasing importance of AI in productivity, discrepancies between executive and HR perceptions, the necessity of human-centric work design, and the growing challenges in trust, diversity, and resilience within the workforce. The study emphasizes the urgency of adapting talent strategies to foster greater agility and employee well-being amidst technological advances and shifting workforce dynamics.
美世今天发布了2024年全球人才趋势研究。该研究借鉴了全球 12,000 多名高管、人力资源主管、员工和投资者的见解，揭示了雇主为在这个新时代蓬勃发展而采取的行动。
“今年的调查结果突显了工作中的惊人转变，”美世总裁帕特·汤姆林森 (Pat Tomlinson) 表示。“他们指出，高管层和人力资源部门对于 2024 年业务发展的看法存在显着分歧，而且员工对于技术影响的看法也存在滞后。随着我们迎来人机团队的时代，组织需要将人置于转型的核心。”
生成式人工智能 (AI) 被视为提高生产力的关键
生成式人工智能能力的快速增长引发了人们对劳动力生产力提升的希望，40% 的高管预测人工智能将带来超过 30% 的收益。然而，五分之三 (58%) 的人认为科技进步的速度超过了公司对员工进行再培训的速度，不到一半 (47%) 的人认为他们可以通过当前的人才模式满足今年的需求。
“通过人工智能提高生产力是高管们最关心的问题，但答案不仅仅在于技术。提高员工生产力需要有意识的、以人为本的工作设计。”美世全球人才咨询主管兼该研究的作者 Kate Bravery 说道。“领先的公司认识到人工智能只是其中的一部分。他们正在从整体的角度来解决生产力下降的问题，并通过新的人机协作模式提供更大的敏捷性。”
寻找通向未来工作的可持续道路面临着挑战。四分之三 (74%) 的高管担心他们的人才的转变能力，不到三分之一 (28%) 的人力资源领导者非常有信心他们能够使人机团队取得成功。提高敏捷性的关键是采用技能驱动的人才模型，这是高增长公司已经掌握的。
员工信任度全面下降
2023 年，对雇主的信任度从 2022 年的历史最高水平下降，这是一个危险信号，因为研究表明信任对员工的精力、蓬勃发展感和留下来的意愿产生重大影响。那些相信雇主会为他们和社会做正确事情的人，表示自己正在蓬勃发展、具有强烈的使命感、归属感和被重视感的可能性是其他人的两倍。
近一半的员工表示，他们希望为一个令他们感到自豪的组织工作，一些公司的回应是优先考虑可持续发展工作和“良好工作”原则。鉴于公平薪酬（34%）和发展机会（28%）是员工今年留下来的主要驱动力，雇主有动力在未来一年在薪酬公平、透明度和公平获得职业机会方面取得更快进展。
在全球范围内，员工都清楚，归属感有助于他们成长，但只有 39% 的人力资源领导者表示，女性和少数族裔在其组织的领导团队中拥有良好的代表，只有 18% 的人表示，最近的多元化、公平性和包容性努力提高了员工保留率关键多元化群体。四分之三的员工 (76%) 目睹过年龄歧视。由于这些挑战加上持续的技能短缺，更多地关注包容性和满足员工的需求将有助于所有员工蓬勃发展。
未来几年，韧性将至关重要
最近在风险缓解方面的投资已获得回报，64% 的高管表示他们的业务能够承受不可预见的挑战，而两年前这一比例为 40%。通货膨胀等近期担忧严重影响高管的三年计划，但网络和气候等长期风险可能没有得到应有的必要关注。
建立个人韧性与企业韧性同样重要，五分之四 (82%) 的员工担心自己今年会精疲力竭。为员工福祉重新设计工作对于缓解这一风险至关重要，51% 的高增长公司（2023 年收入增长 10% 或以上）已经这样做了，而低增长同行中只有 39% 这样做了。
员工体验是重中之重
超过一半的高管 (58%) 担心他们的公司在激励员工采用新技术方面做得不够，三分之二 (67%) 的人力资源领导者也担心他们在没有改变工作方式的情况下实施了新技术解决方案。员工体验是今年HR的首要任务；这是一个值得关注的问题，因为蓬勃发展的员工表示雇主设计的工作体验能够发挥他们的最佳水平的可能性是普通员工的 2.6 倍。
人力资源部门在改善所有人的工作方面发挥着关键作用，但人力资源部门越来越有必要与风险和数字化领导者合作，以按要求的速度引入必要的变革。为了满足组织和员工的期望，96% 的公司计划今年对人力资源职能进行一些重新设计，重点是跨部门交付和领先的数字化工作方式。
投资者重视敬业的员工队伍
今年，美世首次收集资产管理公司关于组织的人才战略如何影响其投资决策的意见。近十分之九 (89%) 的人将员工敬业度视为公司绩效的关键驱动力，84% 的人认为“流失和燃烧”方法会损害商业价值。投资者还表示，营造信任和公平的氛围是未来五年建立真正、可持续价值的最重要因素。
单击此处了解更多信息并下载今年的研究。
关于美世 2024 年全球人才趋势研究
美世全球人才趋势目前已进入第九个年头，汇集了来自 17 个地区和 16 个行业的 12,200 多名高管、人力资源领导者、员工和投资者的见解，该研究重点介绍了当今领先组织为确保人员长期可持续发展所采取的措施。在此过程中走得更远的组织在四个领域取得了长足的进步。(1) 他们认识到，以人为本的生产力需要关注工作的演变以及工作人员的技能和动机。(2) 他们认识到信任是真正的工作对话，通过透明度和公平的工作实践得到加强。(3) 随着风险变得更加关联且难以预测，他们认识到，提高风险意识和缓解水平对于建立一支准备就绪、有复原力的员工队伍至关重要。(4) 他们承认，随着工作变得越来越复杂，简化、吸引和激励员工走向数字化的未来至关重要。
关于美世
美世坚信，可以通过重新定义工作世界、重塑退休和投资成果以及释放真正的健康和福祉来建设更光明的未来。美世在 43 个国家/地区拥有约 25,000 名员工，公司业务遍及 130 多个国家/地区。美世是Marsh McLennan （纽约证券交易所股票代码：MMC）旗下的企业，Marsh McLennan 是风险、战略和人才领域全球领先的专业服务公司，拥有超过 85,000 名同事，年收入达 230 亿美元。通过其市场领先的业务（包括达信、Guy Carpenter和奥纬咨询），达信帮助客户应对日益动态和复杂的环境。
AI
2024年03月07日
AI
滴滴出行选用NICE，以提供基于实时 AI 的个性化服务NICE has partnered with DiDi Global to enhance customer and employee experiences through its cloud-based Workforce Management (WFM) and Employee Engagement Manager (EEM) solutions. This collaboration aims to streamline DiDi's global contact center operations, improving operational efficiency and customer satisfaction with AI-driven forecasting and scheduling. The implementation of NICE's solutions facilitates real-time management and self-scheduling for agents, boosting employee engagement and operational efficiency. DiDi's choice of NICE highlights the importance of advanced, flexible technology in supporting the dynamic needs of modern, app-based transportation services.
领先的移动出行平台通过利用 NICE 的客户体验 AI 技术，使其员工能够提供轻松且高效的客户服务体验
新泽西州霍博肯-NICE (纳斯达克: NICE) 今日宣布，滴滴出行已经选用了 NICE 劳动力管理 (WFM) 和员工参与管理 (EEM) 作为其云端创新技术的一部分。滴滴现在可以全面预测、规划和管理其全球客户联系中心的运作；同时提升运营效率和员工的参与度，并确保客服代表能够在首次通话中解决问题。Betta作为全球最大的 WFM 客户群之一的支持者，在实施过程中与 NICE 价值实现服务携手合作，负责执行集成，并在多国提供咨询、培训和支持服务。
滴滴出行寻求一种能够满足其核心业务、功能及技术需求，并能够随公司成长而扩展的劳动力管理解决方案。NICE WFM 结合了 AI 技术与灵活性，能够满足跨多个大洲、具有特定区域特色的运营需求，这不仅成本效益高，而且精确度高，确保维持最佳的服务水平。通过精准预测，确保在合适的时间有合适技能的代理人，从而大幅提升客户满意度。
通过引入 NICE EEM，可以实时解决人员配置需求，使得客服代理能够自我调节工作时间表，从而增强员工参与度和工作满意度。此外，利用智能日内自动调整功能，能够主动地进行调整，预防问题的发生。
滴滴出行国际客户体验执行总监 Caio Poli 表示：“基于多个考量因素，NICE 显然是我们的首选。我们寻找的是一个顶尖的云端劳动力管理解决方案，能够使我们的全球运营在保证运营效率和员工参与度的同时，提供卓越的客户体验。NICE 的智能日内自动化功能给我们留下了深刻印象，我们的选择是基于 AI 驱动的策略以及云技术的速度和灵活性。”
NICE 美洲总裁 Yaron Hertz 表示：“随着滴滴持续全球扩张，NICE 很高兴有机会为这家数字时代最具创新和活力的应用型运输公司之一提供服务。我们相信，通过采用 NICE 的 AI 驱动预测和机器学习来进行最适合的调度安排，对于联系中心和员工而言，这将有助于推动滴滴的未来发展。”
关于滴滴出行公司
滴滴出行公司是一个领先的移动技术平台，它在亚太地区、拉丁美洲及其他全球市场提供一系列基于应用的服务，包括网约车、叫车服务、代驾以及其他共享出行方式，还涵盖某些能源和车辆服务、食品配送和城市内部货运服务。滴滴为车主、司机和配送伙伴提供灵活的工作和收入机会，致力于与政策制定者、出租车行业、汽车行业及社区合作，利用 AI 技术和本地化智能交通创新解决全球的交通、环境和就业挑战。滴滴力图为未来城市构建一个安全、包容和可持续的交通与本地服务生态系统，以创造更好的生活体验和更大的社会价值。更多信息，请访问：www.didiglobal.com
关于 NICE
借助 NICE (纳斯达克: NICE)，全球各地不同规模的组织现在可以更容易地创造卓越的客户体验，同时满足关键的业务指标。作为世界领先的云原生客户体验平台 CXone 的提供者，NICE 是 AI 驱动自助服务和代理辅助客户体验软件领域的全球领导者，服务范围超出了传统的联系中心。超过 25,000 个组织在超过 150 个国家，包括 85 家以上的财富 100 强公司，都选择与 NICE 合作，以改造并提升每一次客户互动。www.nice.com
商标说明：NICE 和 NICE 标志是 NICE Ltd. 的商标或注册商标。所有其他标志属于它们各自的所有者。NICE 商标的完整列表，请访问：www.nice.com/nice-trademarks。
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