The best HR & People Analytics articles of January 20242024年对HR专业人士来说是充满挑战和机遇的一年。经济不确定性、地缘政治紧张和技术进步是主要的挑战。文章强调了生产力的重要性，以及来自PwC、麦肯锡和埃森哲的洞察。利物浦经理朱尔根·克洛普的离职案例展示了领导力和文化的重要性。文章还强调了人力资源分析的重要性，提供了来自领先公司的见解。
2024年的HR趋势和预测涵盖了人工智能的影响和向基于技能的组织的转变。工作场所的心理安全、多样性、平等、包容和归属感仍然是重要议题。这篇文章为HR专业人士提供了全面的指导，帮助他们在未来一年中导航复杂性。
2024 is set to be a momentous year. With economic uncertainty, rising geopolitical conflict, and rapid advances in technology, it is also set to be a stormy 12 months for the world, for organisations, and for HR professionals too.
Perhaps this explains the slew of insightful resources in January, which has made compiling this month’s collection as challenging as it has been enjoyable. One of the key focuses has been on ‘productivity’, and I’ve brought together a number of resources on this topic. There are also new studies from the likes of PwC, McKinsey, Glassdoor, Accenture, and Deloitte as well as articles featuring practitioners from companies including Spotify, Microsoft, Ericsson, Lloyds Banking Group, and Standard Chartered. There’s lots to enjoy and learn from.
Join me for a webinar on February 21 to discover how Leading Companies shift People Analytics from insight to impact
Are you an HR or People Analytics Leader seeking to transform your organisation’s People Analytics from mere insights to impactful business outcomes? If so, I invite you to join me for a webinar that Insight222 is hosting on February 21. Naomi Verghese and I will walk through the findings from the Insight222 People Analytics Trends research, unveiling the distinctive characteristics of ABCD Teams that propel organisations to new heights. Naomi and I will be joined by Alan Susi, VP and Global Head of Organisational Analytics and People Insights at S&P Global. Alan will share insights into how S&P Global successfully elevated their approach to people analytics, turning data into tangible business outcomes. You can register for the webinar here – or by clicking the image below.
Jürgen Klopp – a study in leadership, culture, and analytics
As a fervent supporter, I’m still processing the totally unexpected news that Jürgen Klopp will be leaving his post as the manager of Liverpool at the end of the current football season. In his press conference on taking the reins at Anfield in October 2015, Klopp stated his goal was to turn Liverpool from “doubters to believers.” He has done this with some aplomb amassing a haul of seven trophies (to date) including the Champions League in 2019 and then, the following year, the Holy Grail of Liverpool’s first league title in 30 years.
But Klopp is more than a brilliant football manager. He is the epitome of an empathetic leader. His emotional intelligence and natural humility not only endears Klopp to his players, but to supporters too for whom he is adored. The reaction to the news reduced many Liverpool supporters to tears. I’m still hoping – probably forlornly - that like Alex Ferguson in 2002, Klopp will change his mind and stay.
In the likely event that he does depart, I’m sure that multiple studies will be made on Klopp’s time at Anfield, and that his leadership skills, use of data and analytics, and ability to build an inclusive winning culture will be deservedly celebrated. YNWA.
Looking for a new role in people analytics or HR tech?
Before we get to this month’s collection of resources, 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 500 roles.
Looking for a people analytics event to attend in 2024?
Richard Rosenow has also been busy compiling a study of People Analytics Conferences to attend in 2024 with the data collected from practitioners themselves. Society for Industrial and Organizational Psychology (SIOP), People Analytics World and the Wharton People Analytics Conference all come out well as does the Insight222 Global Executive Retreat. Thanks to Richard for putting this together.
Share the love!
Enjoy reading the collection of resources for January 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 December’s compendium (including those in the Comments below).
If you enjoy a weekly dose of curated learning (and the Digital HR Leaders podcast), the Insight222 newsletter: Digital HR Leaders newsletter is published every Tuesday – subscribe here.
THE QUEST FOR PRODUCTIVITY
MCKINSEY - 2024 and beyond: Will it be economic stagnation or the advent of productivity-driven abundance? | PwC - 27th Annual Global CEO Survey: Thriving in an age of continuous reinvention | JOSH BERSIN - HR Predictions for 2024: The Global Search For Productivity | ERIK BRYNJOLFSSON - How AI Will Transform Productivity | BEN WABER AND NATHANAEL J. FAST - Is GenAI’s Impact on Productivity Overblown?
When I talk with CHROs and People Analytics Leaders at the companies we work with at Insight222, one of the words I’m hearing most at the moment is ‘productivity’. Continuing economic and geopolitical uncertainty, the promise of AI, and challenging talent demographics are all fuelling the demand for productivity from CEOs. Here are five resources that can be filed under the ‘productivity’ umbrella: (1) McKinsey’s Ezra Greenberg, Asutosh Padhi, and Sven Smit present a model for businesses to capture the three-sided productivity opportunity (see FIG 1). (2) Amongst a ton of takeaways, the standout theme from the annual PwC CEO survey is that the vast majority of participating companies are already taking some steps towards reinvention, while CEOs believe that 40% of their work is wasted productivity (see FIG 2). (3) Josh Bersin draws from the PwC survey in his 2024 predictions, where he outlines The Productivity Advantage where “If you can help your company move faster (productivity implies speed, not only profit), you can reinvent faster than your competition.” (4) Stanford professor Erik Brynjolfsson offers leaders an overview of how AI will transform productivity. (5) Finally, Ben Waber and Nathanael Fast’s absorbing essay in Harvard Business Review cautions leaders on leaning into the hype on GAI’s supposed positive impact on productivity too heavily. The authors break down two of the key challenges with LLMs: a) their persistent ability to produce convincing falsities and b) the likely long-term negative effects of using LLMs on employees and internal processes.
FIG 1: The three-side productivity opportunity (Source: McKinsey)
FIG 2: CEOs estimate administrative inefficiency at 40% (Source: PwC)
GERGELY OROSZ AND ABI NODA - Measuring Developer Productivity: Real-World Examples
Continuing the productivity theme, this is an invaluable resource by Gergely Orosz and Abi Noda in The Pragmatic Engineer newsletter. It provides detail on developer productivity metrics at 17 tech companies including Google, Microsoft, Spotify, and Uber (see summary in FIG 3).
FIG 3: Developer productivity metrics at 17 tech companies (Source: Pragmatic Engineer)
2024 HR TRENDS AND PREDICTIONS
JASMINE PANAYIDES - Nine Ways to Put HR Trends and Predictions into Practice in 2024
There has been a flood of articles advising what the key HR trends, predictions, and opportunities for 2024 are, but how are HR professionals supposed to make sense of these? In her article for the myHRfuture blog, Jasmine Panayides provides actionable tips on how HR professionals can apply the trends, predictions and opportunities to their work, and their organisations so they can deliver value to the company and the workforce. Jasmine also helpfully summarises the trends/predictions from a variety of sources into one table (see FIG 4), including from: Visier Inc., Gartner, Bernard Marr, UNLEASH, Mercer, and Culture Amp as well as my own 12 Opportunities for HR in 2024 article.
FIG 4: Analysis of HR Trends and Predictions for 2024 (Source: myHRfuture)
KATARINA BERG - HR Trends for 2024 | GARTNER - 9 Future of Work Trends for 2024 | GLASSDOOR – 2024 Workforce Trends | HUNG LEE - Forecasting 2024 in Recruitment Part 1, Part 2, Part 3, and Part 4 | KEVIN WHEELER - What Does 2024 Hold in Store for Us? | STACIA GARR AND DANI JOHNSON – 2024 Mega Trends and how people leaders should respond (Webinar)
The deluge of commentators offering their HR trends and opportunities continued in January. As such, it is a challenge to sort the wheat from the chaff but in addition to those I highlighted in this compendium in December, and in Jasmine’s article above, I recommend diving into the following: (1) Spotify’s chief people officer, Katarina Berg, highlights ten trends with the common theme being each trend is a bridge, connecting the past with the future, and HR professionals are the architects crafting these vital links – including “Staying Human in the Age of AI – The Humanity Bridge”. (2) Gartner’s Jordan Turner and Emily Rose McRae highlight nine future of work trends for the year ahead (see FIG 5). (3) Aaron Terrazas and Daniel Zhao identify eight workforce trends based on Glassdoor’s data on workplace satisfaction, culture, and conversations. (4) Hung Lee is at the cutting edge of recruiting and HR tech, so his four-part series on recruiting in 2024 is definitely worth checking out – two examples include: “Multi-generational replaces neurodiversity as DEIB hot topic” and “Capital Allocation Shifts from Sourcing & Engagement to Assessment & Verification Tech”. (5) Futurist Kevin Wheeler offers seven insights and predictions together with his self-assessed certainty rating including “Generative AI will dominate, and every product will attempt to incorporate AI. 90% certainty” and “More firms will embrace a four-day workweek 50% certainty”. (6) Finally, I strongly recommend viewing the 2024 Mega Trends webinar hosted by Stacia Sherman Garr and Dani Johnson for RedThread Research, which breaks down the key macro factors impacting the world of work and how HR can respond.
FIG 5: 9 Future of Work Trends for 2024 (Source: Gartner)
GREG NEWMAN - 10 important topics that HR will likely ignore in 2024
Greg Newman takes an alternative, wry and contrarian approach by focusing his list of “predictions” on ten things most HR teams will continue to ignore in 2024. My favourite three are: (1) speaking the language of the business, (2) focusing AI conversations on ethics before technology, and (3) learning that good data is required to realise the dreams of AI and analytics.
By aligning HR language with business terminology, we can more effectively demonstrate the value of our initiatives in a way that resonates with business stakeholders.
GENERATIVE AI AND THE FUTURE OF WORK
ELLYN SHOOK AND PAUL DAUGHERTY - Work, workforce, workers: Reinvented in the age of generative AI
A new study from Accenture, co-authored by Ellyn Shook and Paul Daugherty, on how generative AI is impacting work, provides guidance on how leaders can: “Set and guide a vision to reinvent work, reshape the workforce and prepare workers for a generative AI world, while building a resilient culture to navigate continuous waves of change.” The report reveals a trust gap between workers and leaders on key elements related to GAI’s impact on work, the workforce, and workers. The authors also highlight four accelerators for leaders to navigate the journey ahead: (1) Lead and learn in new ways, (2) Reinvent work, (3) Reshape the workforce (see example in FIG 6), and (4) Prepare workers.
FIG 6: Illustrative example of how work and roles can be reallocated in a GAI future (Source: Accenture)
ROGER W. HOERL AND THOMAS C. REDMAN - What Managers Should Ask About AI Models and Data Sets
The decision on whether to deploy AI models within an organisation ultimately lies with business leaders who may not be qualified to identify risks and weaknesses related to AI models and data sets. In their article, Roger Hoerl and Tom Redman provide (1) A framework (see FIG 7) designed to equip leaders with context and based on their concept of the right data. (2) A set of six questions for leaders to ask their AI model developers before and during modelling work and deployment. (3) Guidance for leaders on how to assess AI model developers’ answers to those six questions.
FIG 7: The Right Data Framework (Source: Roger W. Hoerl and Thomas C. Redman)
PEOPLE ANALYTICS
STEVE HATFIELD, SUE CANTRELL, AND BRAD KREIT - Beyond the quick fix: How workforce data can drive deeper organizational problem-solving
The premise of this thoughtful article by Steve Hatfield, Susan Cantrell, and Brad Kreit is that without the right context, even simple measurements can undermine efforts to convert people data into value. They then explore several examples – in the workforce, in the workplace, and in the work – where organisations might be limiting their analysis to the surface level and how deeper analysis can reveal systemic issues that lead to opportunities for transformation. Guidance on three actions leaders can take to help ensure they are not missing important context in their data analysis are provided: (1) Bring data from different domains and sources together for analysis. (2) Make sure you’re measuring what you should—not just what you can. (3) Identify potential biases in data collection algorithms.
If organizations want to move beyond quick fixes and use work and workforce data to drive deeper—and often more challenging—problem-solving, it is important that they look at the data in context.
NAOMI VERGHESE - How to Measure the Value of People Analytics
My Insight222 colleague Naomi Verghese digs how to measure the commercial value of people analytics, highlighting a powerful case study from Jaesun HA and LG Electronics. Naomi provides detail on four key areas where people analytics adds value (business performance, workforce experiences, driving an analytics culture and societal benefit) as well as providing data on the characteristics of companies that ARE creating commercial value from people analytics (see FIG 8).
FIG 8: Characteristics of people analytics that disclosed and measured commercial value of people analytics solutions (Source: Insight222 People Analytics Trends, 2023)
ANDRÉS GARCIA AYALA - 5 Change Drivers Impacting People Analytics & How To Thrive In Them | WILLIS JENSEN - Attrition versus Retention: Which Should I Use? | KEITH McNULTY – Regression Modeling in People Analytics: Survival Analysis | LYDIA WU - The Market Sucks and You are Looking for a Job, Now What? | SEBASTIAN SZACHNOWSKI - 16 HR Metrics for IT | ERIN FLEMING AND NICK JESTEADT - People Analytics Perspectives from the Fringe: Current Priorities and a View on Optimized Teams in 2024
January saw a slew of articles from current and recent people analytics leaders, which typically act as a spur and inspiration for the field. Six are highlighted here: (1) Andrés García Ayala highlights some of the key change drivers impacting people analytics and ways to incorporate them into our work. (2) Willis Jensen builds on the recent primer on attrition metrics by Ben Teusch that I highlighted in December’s edition. He explains why we should be using attrition and retention as separate terms that lead to distinct metrics with different objectives (see also FIG 9). (3) Keith McNulty provides another indispensable practical guide for people analysts with a step-by-step tutorial to conducting survival analysis in R. (4) The prolific Lydia Wu turns her attention to providing some handy guidance for those looking for their next people analytics / HR tech role. (5) Sebastian Szachnowski provides a useful breakdown of 16 HR metrics for technology companies. (6) Last but definitely not least, Erin Fleming and Nick Jesteadt provide insights from their survey of fellow people analytics practitioners. Insights include a) 41% of respondents (n=49) operate as a one-person people analytics team, and ii) the main current focus areas of work include employee turnover, cultural engagement, return to office, and restructuring.
FIG 9: When to use Attrition and Retention (Source: Willis Jensen)
MAX BLUMBERG - The Big List of GPTs to Revolutionize Your People Processes | JOHANNES SUNDLO - GenAI for People Analytics
Two articles addressing the opportunity for generative AI in the people space. (1) Max Blumberg (JA) ?? sets out 93 potential ways to upgrade your People Processes with AI and GPTs across four categories – workforce planning and strategy, recruitment, learning and development, and employee wellbeing. (2) Johannes Sundlo provides examples of companies using GAI in their people analytics work to support analyses on engagement data, skills, and tailoring training recommendations.
GPTs are an amazing tool for scenario planning, forecasting future workforce needs, identifying talent gaps, and developing integrated talent strategies.
THE EVOLUTION OF HR AND DATA DRIVEN CULTURE
DAVE ULRICH, NORM SMALLWOOD, AND JOE GROCHOWSKI - Why and How to Move HR to an Outside-In Approach
When asked the question, “What is the biggest challenge in your job today?” HR professionals will typically provide answers such as: “Build a skills-based organisation” or “Help our employees have a better experience”. As Dave Ulrich, Norm Smallwood, and Joe Grochowski write, these answers would be far more powerful when a “so that” is applied e.g. “Help employees have a better experience so that customer experience improves.” The article demonstrates that greater value is created with an outside-in approach that starts with the needs of external stakeholders (customers, investors, community) and then figuring out the implications inside the company for meeting those needs. Dave, Norm, and Joe also present their Human Capability Framework and a tool that provides an assessment of an organisation’s outside-in performance (see FIG 10).
FIG 10: Human capability from the outside-in - diagnostic questions (Source: Dave Ulrich et al)
WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS
AMY WEBB - Bringing True Strategic Foresight Back to Business
In her article for Harvard Business Review, Amy Webb defines strategic foresight as “a disciplined and systematic approach to identify where to play, how to win in the future, and how to ensure organizational resiliency in the face of unforeseen disruption.” Her article also advocates for the integration of strategic foresight as a core competency in every organisation, regardless of size. Moreover, Amy provides guidance on how to operationalise strategic foresight by unveiling a ten-step process. Read alongside another article authored by Amy for HBR: How to Do Strategic Planning Like a Futurist, which includes Amy’s Futurist’s Framework for Strategic Planning (see FIG 11).
FIG 11: A Futurist’s Framework for Strategic Planning (Source: Amy Webb)
WORLD ECONOMIC FORUM AND PwC - Putting Skills First: Opportunities for Building Efficient and Equitable Labour Markets
As the introduction to this compelling collaboration between the World Economic Forum and PwC begins: “Skills and talent shortages are critical challenges facing societies and economies today. The absence of relevant skills impedes business growth, hinders economic prosperity, and inhibits individuals from realizing their full potential.” The report identifies five specific opportunities for intervention where the gains from skills-first solutions are most likely for employers and workers alike (see ‘Skills-first Framework’ in FIG 12). Additionally, the report also showcases 13 Skills First “Lighthouses”, including IBM, Siemens, Standard Chartered and Sanofi. It concludes by offering key takeaways regarding six success factors in implementing skills-first approaches including (1) Sponsorship from leadership, (2) Alignment with business needs, and (3) Data and evaluation for iteration. (Authors: Genesis Elhussein, Mark Rayner, Aarushi Singhania, Saadia Zahidi, Peter Brown MBE, Miral Mir, and Bhushan Sethi).
A cultural shift to skills-first approaches needs both sponsorship from executives and governance from human-resources professionals
FIG 12: Skills-first Framework (Source: World Economic Forum
PETER SHEPPARD - Learning from our Skills Journey | BEN AUTY - What are the new skills people will need for the future of work? | TANUJ KAPILASHRAMI - How Standard Chartered is Unlocking the Power of Skills in the Workplace
Many of the organisations we work with at Insight222 have embarked on the road to becoming a skills-based organisation. It is not an easy journey, so it is helpful to learn from other companies who are treading this path. Three of these are Ericsson, Lloyds Banking Group, and Standard Chartered. (1) In his article, Peter Sheppard shares learnings from Ericsson’s skills journey including a) it’s not jobs or skills; it’s skills and jobs, b) it’s a whole organisation activity, c) Less is more with skills, and d) Data drives value. (2) Ben Auty shares insights as to why Lloyds Banking Group is developing a learning culture to build the workforce of the future at the bank, the main skills they are focusing on, and the central role the recently established Reskilling Team is playing. (3) Tanuj Kapilashrami shares how Standard Chartered catalysed their work on skills by identifying adjacencies between ‘sunset’ and ‘sunrise’ roles.
We looked at skills adjacencies between ‘sunset’ jobs and ‘sunrise’ jobs: so, what are the jobs that are going to go away? What are the skills that help employees get reskilled into some of these sunrise jobs? We ran five proofs of concept, we showed some real redeployment opportunities and started making the skills narrative real.
EMPLOYEE LISTENING, EMPLOYEE EXPERIENCE, AND EMPLOYEE WELLBEING
JENNIFER E. SIGLER WITH STEPHANIE DENINO - So Many Stakeholders, So Little Time: State of EX 2023-2024
The fifth annual State of EX study authored by Jennifer E. Sigler, PhD on behalf of The EXchange, Inc, TI PEOPLE and FOUNT Global, Inc. is a treasure chest of insights on the fast-evolving practice of employee experience. It highlights the top four priorities for EX as: (1) Redesigning experiences, (2) Getting broader buy-in for EX work across the organisation, (3) Building an EX roadmap for the organisation, and (4) Getting more / better data. One other standout finding from the study suggests that senior leaders are increasingly focused on EX with a majority of respondents (63%) saying their organisation’s senior leaders view EX as equal to or even more important than other corporate priorities. This bodes well for the future of EX. Thanks to Stephanie Denino and Volker Jacobs for highlighting the study.
FIG 13: EX Team Priorities YOY Change (Source: The EXchange, TI People and FOUNT Global, Inc)
LEADERSHIP AND CULTURE
NADJIA YOUSIF, ASHLEY DARTNELL, GRETCHEN MAY, AND ELIZABETH KNARR - Psychological Safety Levels the Playing Field for Employees | PETER CAPPELLI AND LIAT ELDOR - Can Workplaces Have Too Much Psychological Safety?
Two perspectives on psychological safety in the workplace. In the first article, Nadjia Yousif, Ashley Dartnell, Gretchen May, and Elizabeth Knarr present the findings of Boston Consulting Group (BCG) research, which finds how psychological safety benefits inclusion, reduces attrition in diverse groups and effectively acts as an equaliser - enabling diverse and disadvantaged employee groups to achieve the same levels of workplace satisfaction as their more advantaged colleagues. The study also highlights the direct relationship between empathetic leadership and feelings of psychological safety in the workforce, giving leaders a clear directive to be empathetic and thereby engender psychological safety. The second article by Peter Cappelli and Liat Eldor presents research that found that when you move from average to high levels of psychological safety, performance in routine jobs actually declined.
FIG 14: Psychological safety has an outsize impact on retention for diversity groups (Source: BCG)
RASMUS HOUGAARD, JACQUELINE CARTER, AND ROB STEMBRIDGE - The Best Leaders Can’t Be Replaced by AI
While there are some areas where AI is already surpassing or will surpass human capabilities, there are several it cannot replace. Based on their research into employees’ comfort with AI in management, as well as their decades of research on the qualities of effective leadership, Rasmus Hougaard, Jacqueline Carter, and Robert Stembridge identify the promise (and perils) of AI-enabled management (see FIG 15), as well as the three uniquely human capabilities leaders need to focus on honing, especially as AI begins to figure more in management: (1) awareness, (2) compassion, and (3) wisdom. For more from Rasmus, I recommend listening to his podcast discussion with me: How To Be a More Compassionate Leader.
Leaders who deepen their ability to lead with humanity will win at attracting, retaining, developing, and motivating top talent.
FIG 15: AI versus Human: A matric of leadership activities (Source: Potential Project)
DIVERSITY, EQUITY, INCLUSION, AND BELONGING
JULIE COFFMAN, ALEX NOETHER, BIANCA BAX, CASSY REICHERT, AND KRYSTLE JIANG - The Business of Belonging: Why making everyone feel included is smart strategy
Revealing data from a Bain survey of 6,000+ employees across four countries, which finds employees who have seen their companies intentionally invest in inclusion since 2020 are three times more likely to feel fully included than employees who have not seen such investment from their employers. Other findings include (1) Combining diversity and inclusion maximises a company’s capacity (by 4x) to innovate, and (2) Employees with inclusive leadership are 9x more likely to feel fully included at work (see FIG 16). (Authors: Julie Coffman, Alex Noether, Bianca Bax, Cassy Reichert, and Krystle Jiang).
FIG 16: Employees with inclusive leadership are 9x more likely to feel fully included at work (Source: Bain)
SHUJAAT AHMAD - DEIB Is At A Crossroads—It’s Time for Bold Action and Clear Metrics
Given recent developments it’s reasonable to say that Diversity, Equity, Inclusion, and Belonging (DEIB) is at an existential crossroads. As Shujaat Ahmad writes in his excellent article for Round: “Boards, leadership teams, and investors hold the power to set the tone, shape the policies, and allocate the resources to support DEIB initiatives: for DEIB to work effectively, they must shift from well-intentioned wordsmiths to committed drivers that hold the organization accountable for outcomes and positive change.” Shujaat then unveils his blueprint to help leaders assess progress and drive meaningful change, clarifying the ‘why’ before diving into the ‘how’ covering measuring what matters and interventions (see FIG 17). For more from Shujaat, I recommend visiting Belong and Lead.
FIG 17: Source – Shujaat Ahmad
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 January that I recommend readers delve into:
ERNEST NG - If the Pitch is Too Smooth, It Probably Is: Why AI in HR is Difficult – Part 2 of an insightful essay from Ernest Ng, PhD of HiredScore (see also Part 1 on disclosures here) where he cuts through the hype to assess how we should be implementing AI in HR.
LOUJAINA ABDELWAHED - A Tale of Two Cultures - In One Company - Loujaina Abdelwahed, PhD from Revelio Labs highlights the growing disparity between junior and senior employees (see FIG 18) and identifies the factors causing this malaise. Thanks to Ben Zweig for highlighting.
FIG 18: The growing disparity in sentiment between junior and senior employees (Source: Revelio Labs)
JEREMIE BRECHEISEN - Where Employees Think Companies’ DEIB Efforts Are Failing – Jeremie K Brecheisen presents findings from Gallup that reveals a disconnect between how well employees and HR leaders believe their organisations are doing when it comes to diversity, equity, inclusion, and belonging: 84% of CHROs say their organisations are increasing investment in DEIB, while only 31% of employees say their organisation is committed to improving racial justice or equity in their workplace (see FIG 19). The article then outlines ten needs employees say are not being met and then offers strategies to help organisations address the disconnect.
FIG 19: How employees and HR leaders differ on perceptions of DEIB progress (Source: Gallup)
FRANCISCO MARIN - Navigating the ONA Landscape: Trends and Challenges for 2024 - Another good read from Cognitive Talent Solutions, as Francisco Marin explores the key trends and challenges shaping the ONA space in 2024.
IAN WHITE - The three C’s of effective performance management – Ian White, CEO at ChartHop, presents the three C’s of performance management — continuous, contextual and cultural — designed to help companies understand their employees more holistically.
CHRISTINA JANZER - The surprising connection between after-hours work and decreased productivity – Christina Janzer presents findings from Slack’s Workforce Index, which identifies findings on how to structure the workday to maximise employee productivity, well-being and satisfaction – including the connection between after hours work and decreased productivity.
FIG 20: Source – Slack
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):
AMY EDMONDSON AND LAURIE RUETTIMANN – Right Kind of Failure – Amy Edmondson joins Laurie Ruettimann on the brilliantly named Punk Rock HR to explore the essential role of failure in our professional and personal growth.
STACIA GARR, COLE NAPPER, AND SCOTT HINES - People Analytics & HR Tech Research by Industry Analysts – Stacia Sherman Garr, one of the industry’s top analysts, joins Cole Napper and Scott Hines, PhD on the Directionally Correct podcast to discuss the research Stacia and her team at RedThread Research do in the people analytics and HR technology space.
RICHARD ROSENOW, MADDIE GRANT, AND SANJA LICINA - How to Build an Integrated Framework for Workforce Listening – In an episode of the Empowering Workplaces podcast, Richard Rosenow joins hosts Maddie Grant and Sanja Licina, Ph.D. to talk about The Three Channels of Workforce Information: conversations (“what people say”), surveys (“what people say they do”) and systems (“what people do”) as a way to build a comprehensive understanding of your workforce.
McKINSEY - The shape of talent in 2023 and 2024 - In this episode of McKinsey Talks Talent, Bryan Hancock, Brooke Weddle and host Lucia Rahilly highlight the trends that shaped last year’s talent landscape—and those poised to ‘redefine its contours’ yet again in 2024.
MATTHEW BIDWELL AND DAN LONEY – Forecasting 2024 Workplace Trends – Wharton Professor and convenor of the Wharton People Analytics Conference, Matthew Bidwell, joins host of the Wharton Business Daily Dan Loney to look at the year ahead in the workplace.
VIDEO OF THE MONTH
CHRIS LOUIE, TOMAS CHAMORRO-PREMUZIC, TERRI HORTON, AND LINDSEY SHINTANI - Power a dynamic workforce by embracing AI
An enlightening panel discussion from the recent LinkedIn Talent Connect where Chris Louie, Dr Tomas Chamorro-Premuzic, Terri Horton, EdD, MBA, MA, SHRM-CP, PHR, and Lindsey Shintani discuss how AI is changing learning and career paths. They provide guidance on how to overcome AI anxiety and empower impactful futures.
BOOK OF THE MONTH
KEVIN WHEELER AND BAS VAN DE HATERD – Talent Acquisition Excellence
An excellent new book published by Kogan Page and authored by Kevin Wheeler and Bas van de Haterd (He/His/Him). It provides an insightful and detailed analysis of how technologies such as artificial intelligence and machine learning in combination with analytics can improve talent acquisition and recruitment.
RESEARCH REPORT OF THE MONTH
YUYE DING AND MARK (SHUAI) MA - Return-to-Office Mandates
A huge thank you to Nick Bloom for bringing my attention to this paper from Yuye Ding and Mark Ma, which studied the impact of 137 Return to Office mandates on the performance of S&P500 firms from 2020-2023. The key findings, as summarised by Nick, are illuminating: (1) RTO mandates are more likely in firms with poor recent stock performance, and in those with powerful male CEOs. (2) Glassdoor data finds RTO mandates significantly reduce employee ratings for job satisfaction, work-life balance, and senior management. (3) There is no significant impact of RTO mandates on either firm profitability or firm stock-returns.
FIG 21: Distribution of firms’ RTO mandates (Source: Yuye Ding and Mark Ma)
FROM MY DESK
January saw the first three episodes of Series 36 of the Digital HR Leaders podcast, sponsored by our friends at ScreenCloud. Thank you to Luke Farrugia.
DAVID GREEN - The best 60 HR & People Analytics articles of 2023 Part 1 | Part 2 – My tenth annual collection of HR and people analytics resources is spread across two articles and ten themes. Part 1 covers i) the future of work and people strategy, ii) workplace design and strategy, iii) AI and the world of work, iv) people analytics, and v) employee experience, listening and wellbeing. Part 2 covers: vi) the evolution of HR, HR operating models and the CHRO, vii) building a data driven culture in HR, viii) workforce planning, skills, and talent marketplace, ix) leadership and culture, and x) diversity, equity, inclusion and belonging.
THOMAS RASMUSSEN, DAWN KLINGHOFFER, AND JEREMY SHAPIRO - HR in 2024: The Impact of People Analytics, AI & ML – In a special episode of the Digital HR Leaders podcast to kick off 2024, I was joined by Thomas Rasmussen, Dawn Klinghoffer, and Jeremy Shapiro to discuss the outlook for HR and people analytics in the coming 12 months.
SERENA HUANG - How to Enhance Your Career in People Analytics - Serena H. Huang, Ph.D., who has led people analytics functions at companies including GE, PayPal and Kraft Heinz, joins me to discuss the common career paths observed in the people analytics field and how they have evolved over the years.
KAZ HASSAN AND LUKE FARUGGIA - How to Bridge the Gap Between Customer and Employee Experience - What can HR learn from marketing's journey in using data, analytics and technology to understand and personalise the customer experience? How can we leverage these insights in HR to boost our employee experience initiatives? Kaz Hassan and Luke Faruggia join me to discuss these topics and more.
THANK YOU
Finally, this month I’d like to thank:
Recruit CRM for nominating me as ‘The People Analytics Pioneer’ in their list of 50 Recruitment Influencers to Follow in 2024
Likewise, a huge thank you to 365Talents for including me as one of the Top 50 HR Influencers to Follow in 2024
Similarly, thanks to HRCap, Inc. for including me in their list of 10 HR Influencers who Provide Remarkable Insights
The Social Craft (here) and The Talent Games (here) for also including me in their lists of HR and HR Tech leaders to follow.
HRDConnect for quoting me in their article Data Literacy: A must-have for HR professionals in 2024.
Gianni Giacomelli for including the Data Driven HR monthly in his list of seven must-read newsletters.
HR Geckos for including Excellence in People Analytics as a book recommendation in their HR Bytes Newsletter for January 2024.
Sebastian Szachnowski for including Excellence in People Analytics in his list of books to get better at people analytics.
Leapsome for including the Digital HR Leaders podcast as one of its Top 10 HR Podcasts for 2024.
Similarly, Alexandre Darbois for also including the Digital HR Leaders podcast as one of his 5 HR Podcasts.
Melissa Meredith for using my 12 Opportunities for HR in 2024 article to highlight the importance of the HR-Finance partnership in building a thriving company.
Bill Brown for also highlighting my 12 Opportunities for HR in 2024 article in his Eleven Trends Transforming the Future of Work in 2024.
Mirro.io for including me as a contributor in their list of 15 HR Trends for 2024.
Dhanesh K for including as one of his 10 Top HR Leaders to Follow.
Lanteria HR for recommending me as one of their HR Experts to Follow in 2024.
Semos Cloud for including my 12 Opportunities for HR in 2024 as part of their round-up of HR insights.
Thomas Kohler for including my Best HR and People Analytics Articles of 2023 in their collection of HR resources to read.
Thinkers360 for including me in their Top Voices EMEA 2023.
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.
SEE 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 2024:
Feb 21 - Discover how Leading Companies shift People Analytics from insight to impact (Webinar)
Feb 28 - People Analytics World 2024: Exploring the Potential of Analytics and AI in Employee Experience (Zurich)
March 4-6 - Gloat Live! (New York)
March 14-15 - Wharton People Analytics Conference (Philadelphia)
April 24-25 - People Analytics World (London)
May 7-9 - UNLEASH America (Las Vegas)
September 24-26 - Insight222 Global Executive Retreat (Colorado, US) - exclusively for member organisations of the Insight222 People Analytics Program
October 16-17 - UNLEASH World (Paris)
More events will be added as they are confirmed.
How Generative AI Adds Value to the Future of Work
这篇Upwork的文章深入探讨了生成式人工智能（AI）在重新塑造工作价值方面的变革力量，强调了自动化和创新不仅改变了工作岗位，还在各个行业提高了生产力和创造力。文章着重讨论了对劳动力市场的细微影响，强调了技能发展和道德考虑的重要性，并对人工智能与人类合作的未来提供了前瞻性的视角。
Authors: Dr. Ted Liu, Carina Deng, Dr. Kelly Monahan
Generative AI’s impact on work: lessons from previous technology advancements
In this study, we provide a comprehensive analysis of the initial impact of generative AI (artificial intelligence) on the Upwork marketplace for independent talent. Evidence from previous technological innovations suggests that AI will have a dual impact: (1) the displacement effect, where job or task loss is initially more noticeable as technologies automate tasks, and (2) the reinstatement effect, where new jobs and tasks increase earnings over time as a result of the new technology. Take for example the entry of robotics within the manufacturing industry. When robotic arms were installed along assembly lines, they displaced some of the tasks that humans used to do. This was pronounced in tasks that were routine and easy to automate. However, new tasks were then needed with the introduction of robotics, such as programming the robots, analyzing data, building predictive models, and maintaining the physical robots. The effects of new technologies often counterbalance each other over time, giving way to many new jobs and tasks that weren’t possible or needed before. The manufacturing industry is now projected to have more jobs available as technologies continue to advance, including Internet of Things (IoT), augmented reality, and AI, which transform the way work is completed. The issue now at hand is ensuring enough skilled workers are able to work alongside these new technologies.
While this dynamic of displacement and reinstatement generally takes years to materialize, as noted above in the manufacturing example, the effects of generative AI may be taking place already on Upwork. For the platform as a whole, we observe that generative AI has increased the total number of job posts and the average spend per new contract created. In terms of work categories, generative AI has reduced demand in writing and translation, particularly in low-value work, while enhancing earnings in high-value work across all groups. In particular, work that relies on this new technology like Data Science and Analytics are reaping the benefits. The report highlights the importance of task complexity and the skill-biased nature of AI's impact. Skills-biased technology change is to be expected as the introduction of new technologies generally favors highly skilled workers. We observe this on our platform as high-skill freelancers in high-value work are benefiting more, while those in low-value work face challenges, underscoring the need for skilling and educational programs to empower freelancers to adapt and transition in this evolving work landscape.
Understanding the lifecycle of work on Upwork and the impact of gen AI
Generative AI has a growing presence in how people do their work, especially since the public release of ChatGPT in 2022. While there’s been extensive discussion about the challenges and opportunities of generative AI, there is limited evidence of such impact based on transaction data in the broader labor market. In this study, we use Upwork’s platform data to estimate the short-term effects of generative AI on freelance outcomes specifically. The advantage of the Upwork platform is that it is in itself a complete marketplace for independent talent, as we observe the full life cycle of work: job posts, matching, work execution, performance reviews, and payment. Few other instances exist where a closed-system work market can be studied and observed. Thus, the results of this study offer insights into not only the online freelance market, but also the broader labor market.
How technological progress disrupts the labor market is not a new topic. Acemoglu and Restrepo (2019) argue that earning gain arises from new tasks created by technological progress, which they term the “reinstatement effect,” even if the automation of certain tasks may have a displacement effect in the labor market initially. What this means is that there may be a dynamic effect going on: the displacement effect (e.g., work loss) may be more noticeable in the beginning of a new technology entry, but as new jobs and tasks are being created, the reinstatement effect (e.g., rates increase, new work) will begin to prevail. In the broader labor market, such dynamics will likely take years to materialize. But in a liquid and active independent work marketplace like Upwork, it’s possible that we’re already observing this transition happening.
Existing studies such as this provides a useful conceptual framework to think about the potential impact of generative AI. It’s likely that in the short term, the replacement of generative AI will continue to be more visible, not just at Upwork, but also in the broader labor market. Over time and across work categories, however, generative AI will likely spur new tasks and jobs, leading to the reinstatement effect becoming stronger and increasing rates for those occupations with new tasks and a higher degree of task complexity. We’ve already seen evidence of new demand as a result of gen AI on our Upwork platform, with brand new skill categories like AI content creator and prompt engineer emerging in late 2022 and early 2023. We test this hypothesis of both work displacement and reinstatement, and provide insights into how generative AI affects work outcomes.
Impact of generative AI on work
To understand the short-term impact of generative AI on the Upwork freelance market, we capitalize on a natural experiment arising from the public release of ChatGPT in November 2022. Because this release was largely an unanticipated event to the general public, we’re able to estimate the causal impact of generative AI. The essential idea behind this natural experiment is that we want to compare the work groups affected by AI with the counterfactual in which they are not. To implement this, we use a statistical and machine-learning method called synthetic control. Synthetic control allows us to see the impact that an intervention, in this case, the introduction of gen AI, has on a group over time by comparing it to a group with similar characteristics not exposed to the intervention. The advantage of this approach is that it allows us to construct reasonably credible comparison groups and observe the effect over time.
The units of analysis we use are work groups on the Upwork platform; we analyze variables such as contract number and freelancer earnings. Instead of narrowly focusing on a single category like writing, we extend the analysis to all the major work groups on Upwork. Moreover, we conduct additional analysis of the more granular clusters within each major group. The synthetic control method allows for flexibility in constructing counterfactuals at different levels of granularity. The advantage of our comprehensive approach is that we offer a balanced view of the impact of generative AI across the freelance market.
Generative AI’s short-term impact on job posts and freelancer earnings
Looking at the platform as a whole, we observe that generative AI has increased the total number of job posts by 2.4%, indicating the overall increased demand from clients. Moreover, as shown in Figure 1, for every new job contract, there is an increase of 1.3% in terms of freelancer earnings per contract, suggesting a higher value of contracts.
Figure 1 Effect of Generative AI on Freelancer Earning per Contract
The Upwork platform has three broad sectors: 1. Technological and digital solutions (tech solutions); 2. Creative & outreach; 3. Business operations and consulting. We have observed both positive and negative effects within each of the sectors, but two patterns are worth noting:
The reinstatement effect of generative AI seems to be driving growth in freelance earnings in sectors related to tech solutions and business operations. In contrast, within the creative sector, while sales and marketing earnings have grown because of AI, categories such as writing and translation seem disproportionately affected more by the replacement effect. This is to be expected due to the nature of tasks within these categories of work, where large language models are now able to efficiently process and generate text at scale.
Generative AI has propelled growth in high-value work across the sectors and may have depressed growth in low-value work. This supports a skills-biased technology change argument, which we’ve observed throughout modern work history.
More specifically and within tech solutions, data science & analytics is a clear winner, with over 8% of growth in freelance earnings attributed to generative AI. This makes sense as the reinstatement effect is at work; new work and tasks such as prompt engineering have been created and popularized because of generative AI. Simultaneously, while tools such as ChatGPT automate certain scripting tasks (therefore leading to a replacement effect), it mainly results in productivity enhancements for freelancers and potentially leads to them charging higher rates and enjoying higher overall earnings per task.
In terms of contracts related to business operations, we observe that accounting, administrative support, and legal services all experience gains in freelance earnings due to generative AI, ranging from 6% to 7%. In this sector, customer service is the only group that has experienced reduced earnings (-4%). The reduced earnings result for customer service contracts is an example of the aggregate earnings outcomes of AI, related to the study by Brynjolfsson et al (2023), who find that generative AI helps reduce case resolution time at service centers.
A potential outcome of this cut in resolution time is that service centers will need fewer workers, as more tasks can be completed by a person working alongside AI. At the same time, the reinstatement effect has not materialized yet because there are no new tasks being demanded in such settings. This may be an instance where work transformation has not yet been fully realized, with AI enabling faster work rather than reinventing a way of working that leads to new types of tasks. A contrasting case is the transformation that happened with bank tellers when ATMs were introduced. While the introduction of these new technologies resulted in predictions of obsolete roles in banks, something different happened over time. Banks were able to increase efficiency as a result of ATMs and were able to scale and open more branches than before, thereby creating more jobs. In addition, the transactional role of a bank teller became focused on greater interpersonal skills and customer relationship tasks.
When taken together, the overall gains in such business operations work on Upwork are an encouraging sign. These positions tend to require relatively intensive interpersonal communication, and it seems the short-term effects of generative AI have helped increase the value of these contracts, similar to what we saw in the banking industry when ATMs were introduced.
As of now, the replacement effect of AI seems more noticeable in creative and outreach work. The exception is sales and marketing contracts, which have experienced a 6.5% increase in freelance earnings. There is no significant impact yet observed on design. For writing and translation, however, generative AI seems to have reduced earnings by 8% and 10% respectively. However, as we will discover, task complexity has a moderating effect on this.
High-value work benefit from generative AI, upskilling needed for low-value work
Having discussed the overall impact of generative AI across categories, we now decompose the impact by values. The reason we’re looking at the dimension of work value is that there may be a positive correlation between contract value and skill complexity. Moreover, skill complexity may also be positively correlated with skill levels. Essentially, by evaluating the impact of AI by different contract values, we can get at the question of AI's impact by skill levels. This objective is further underscored by a discrepancy that sometimes exists in the broader labor markets – a skills gap between demand and supply. It simply takes time for upskilling to take place, so it’s typical for demand to exceed supply until a more balanced skilled labor market takes place. It is worth noting, however, freelancers on the Upwork platform seem more likely than non-freelancers to acquire new skills such as generative AI.
For simplicity, let’s assume that the value of contracts is a good proxy for the level of skill required to complete them. We’d then assume that high-skill freelancers typically do high-value work, and low-skill freelancers do low-value work. In other words, our goal is also to understand whether the impact of generative AI is skills-biased and follows a similar pattern from what we’ve seen in the past with new technology disruptions. Note that we’re focusing on the top and bottom tails of the distribution of contract values, because such groups (rather than median or mean) might be most susceptible to displacement and/or reinstatement effects, therefore of primary concern. We define high-value (HV) work as those with $1,000 or more earnings per contract. For the remaining contracts, we focus on a subset of work as low-value (LV) work ($251-500 earnings).
Figure 2 shows the impact of AI by work value, across groups on Upwork. As we discussed before, writing and translation work has experienced some reduction in earnings overall. However, if we look further into the effect of contract value, we see that the reduction is largely coming from the reduced earnings from low-value work. At the same time, for these two types, generative AI has induced substantial growth in high-value earnings – the effect for translation is as high as 7%. We believe the positive effect on translation high-value earning is driven by more posts and contracts created. In the tech solutions sector, the growth in HV earnings in data science and web development is also particularly noticeable, ranging from 6% to 9%. Within the business solutions sector, administrative support is the clear winner.
There are two takeaways from this analysis by work value. First, while we’re looking at a sample of all the contracts on the platform, it’s possible that the decline of LV work is more than made up for by the growth of HV work in the majority of the groups. In other words, except for select work groups, the equilibrium results for the Upwork freelance market overall seem to be net positive gains from generative AI. Second, if we assume that freelancers with high skills (or a high degree of skill complexity) tend to complete such HV work (and low-skill freelancers do LV work), we observe that the impact of generative AI may be biased against low-skill freelancers. This is an important result: In the current discussion of whether generative AI is skill-based, there exists limited evidence based on realized gains and actual work market transactions. We are one of the first to provide market-transaction-based evidence to illustrate this potentially skill-biased impact. Finally, additional internal Upwork analysis finds that independent talent engaged in AI-related work earn 40% more on the Upwork marketplace than their counterparts engaged in non-AI-related work. This suggests there may be additional overlap between high-skill work and AI-related work, which can further reinforce the earning potential of freelancers in this group.
Figure 2
Case study: 3D content work
To illustrate the impact of generative AI in more depth, we have conducted a case study of Engineering & Architecture work within the tech solutions sector. The reason is that we want to illustrate the potentially overlooked aspects of AI impact, compared with the examples of data science and writing contracts. This progress in generative AI has the potential to reshape work in traditional areas like design in manufacturing and architecture, which rely heavily on computer-aided design (CAD) objects, and newer sectors such as gaming and virtual reality, exemplified by NVIDIA's Omniverse.
Based on activities on the Upwork platform, we see that there is consistent growth of job posts and client spending in this category, with up to 12% of gross service value growth year over year in 2023 Q3, and over 11% in job posts during the same period. Moreover, applying the synthetic control method, we show a causal relationship between gen AI advancements and the growth in job posts and earnings per contract. More specifically, there is a significant increase in overall earnings because of AI, an average 11.5% increase. Additionally, as shown by Figure 3, the positive effect also applies to earning per contract. This indicates a positive impact on freelancer productivity and quality of work, due to the fact that we’re measuring the income for every unit of work produced. This suggests that gen AI is not just a facilitator of efficiency but also enhances the quality of output.
Figure 3 Effect of Generative AI on Freelancer Earning per Contract in EngineeringIn a traditional workflow to create 3D objects without generative AI, freelancers would spend extensive time and effort to design the topology, geometry, and textures of the objects. But with generative AI, they can do so through text prompts to train models and generate 3D content. For example, this blog by NVIDIA’s Omniverse team showcases how ChatGPT can interface with traditional 3D creation tools.
Thus, the positive trajectory of generative AI in 3D content generation we see is driven by several factors. AI significantly reduces job execution time, allowing for higher productivity. It facilitates the replication and scaling of 3D objects, leading to economies of scale. Moreover, freelancers can now concentrate more on the creative aspects of 3D content, as AI automates time-consuming and tedious tasks.
This shift has not led to a decrease in rates due to the replacement effect. In fact, this shift of workflow may create new tasks and work. We will likely see a new type of occupation in which technology and humanities disciplines converge. For instance, a freelancer trained in art history now has the tools to recreate a 3D rendering of Japan in the Edo period, without the need to conduct heavy coding. In other words, the reinstatement effect of AI will elevate the overall quality and value proposition of the work, and ultimately enable higher earning gains. This paradigm shift underscores generative AI's role in not just transforming work processes but also in creating new economic dynamics within the 3D content market. Fortunately, it seems many freelancers on Upwork are ready to reap the benefits: 3D-related skills, such as 3D modeling, rendering, and design, are listed among the top five skills of freelancer profiles as well as in job posts.
A dynamic interplay: task complexity, skills, and gen AI
Focusing on the Upwork marketplace for independent talent, we study the impact of generative AI by using the public release of ChatGPT as a natural experiment. The results suggest a dynamic interplay of replacement and reinstatement effects; we argue that this dynamic is influenced by task complexity, suggesting a skill-biased impact of gen AI. Analysis across Upwork's work sectors shows varied effects: growth in freelance earnings in tech solutions and business operations, but a mixed impact in the creative sector. Specifically, high-value work in data science and business operations see significant earnings growth, while creative contracts like writing and translation experience a decrease in earnings, particularly in lower-value tasks. Using the case study of 3D content creation, we show that generative AI can significantly enhance productivity and quality of work, leading to economic gains and a shift toward higher-value tasks, despite initial concerns of displacement.
Acemoglu and Restrepo (2019) argue that the slowdown of earning growth in the United States the past three decades can partly be explained by new technologies’ replacement effect overpowering the reinstatement effect. But with generative AI, we’re at a point of completely redefining what human tasks mean, and there may be ample opportunities to create new tasks and work. It's evident that while high-value types of work are being created, freelancers engaged in low-value tasks may face negative impact, possibly due to a lack of skills needed to capitalize on AI benefits. This situation underscores the necessity of supporting freelancers not only in elevating their marketability within their current domains but also in transitioning to other work categories.
To ensure as many people as possible benefit, there’s an imperative need to provide educational resources for them to gain the technical skills, and more importantly skills of adaptability to reinvent their work. This helps minimize the chance of missed opportunities by limiting skills mismatch between talent and new demands created by new technologies. Upwork has played a significant role here by linking freelancers to resources such as Upwork Academy’s AI Education Library and Education Marketplace, thereby equipping them with the necessary tools and knowledge to adapt and thrive in an AI-present job market. This approach can help bridge the gap between low- and high-value work opportunities, ensuring a more equitable distribution of the advantages brought about by generative AI.
Methodology
To estimate the causal impact of generative AI, we take a synthetic control approach in the spirit of Abadie, Diamond, and Hainmueller (2010). The synthetic control method allows us to construct a weighted combination of comparison units from available data to create a counterfactual scenario, simulating what would have happened in the absence of the intervention. We use this quasi-experimental method due to the infeasibility of conducting a controlled large-scale experiment. Additionally, we use Lasso regularization to credibly construct the donor pool that serves the basis of the counterfactuals and minimize the chance of overfitting the data.
Moreover, we supplement the analysis by scoring whether a sub-occupation is impacted or unaffected by generative AI. The scoring utilizes specific criteria: 1. Whether a certain share of job posts are tagged as AI contracts by the Upwork platform; 2. AI occupational exposure score, based on a study by Felten, Raj, and Seamans (2023), to tag these sub-occupations. We also use data smoothing techniques through three-month moving averages. We analyzed data collected on our platform from 2021 through Q3 2023. We specifically look at freelancer data across all 12 work categories on the platform for high-value contracts, defined as those with a contract of at least $1,000, and low-value contracts, consisting of those between $251 and under $500.
The main advantage of our approach is that it is a robust yet flexible way to identify the causal effects on not only the Upwork freelance market but also specific work categories. Additionally, we control for macroeconomic or aggregate shocks such as U.S. monetary policy in the pre-treatment period. However, we acknowledge the potential biases in identifying which sub-occupations are influenced by generative AI and the effects of external factors in the post-treatment period.
About the Upwork Research Institute
The Upwork Research Institute is committed to studying the fundamental shifts in the workforce and providing business leaders with the tools and insights they need to navigate the here and now while preparing their organization for the future. Using our proprietary platform data, global survey research, partnerships, and academic collaborations, we produce evidence-based insights to create the blueprint for the new way of work.
About Ted Liu
Dr. Ted Liu is Research Manager at Upwork, where he focuses on how work and skills evolve in relation to technological progress such as artificial intelligence. He received his PhD in economics from the University of California, Santa Cruz.
About Carina Deng
Carian Deng is the Lead Analyst in Strategic Analytics at Upwork, where she specializes in uncovering data insights through advanced statistical methodologies. She holds a Master's degree in Data Science from George Washington University.
About Kelly Monahan
Dr. Kelly Monahan is Managing Director of the Upwork Research Institute, leading our future of work research program. Her research has been recognized and published in both applied and academic journals, including MIT Sloan Management Review and the Journal of Strategic Management.
People Analytics:Introducing the Visier Path, a Proven Route to Business ImpactVisier Path是一种策略框架，旨在指导组织通过人员分析的旅程。它汇集了十多年的专业知识和数千个成功部署的精华，为HR和业务领导者提供了一个清晰、易于访问的“地图”。该框架将人员分析活动和HR实践与CHRO的战略优先事项对齐，提供了一个灵活、经过验证的途径，以实现显著的业务影响。通过先锋类比，它强调了从原始数据到可操作见解的演变，确保组织可以根据特定目标和策略定制他们的旅程。推荐阅读这篇文章：
The Visier Path is the framework we use to guide our cuhttps://www.visier.com/path/stomers to solve the people challenges they care about most. It’s a distillation of our deep people analytics expertise spanning thousands of successful deployments over a decade.
Here, VP of People Analytics, Ian Cook, explains what the Visier Path is, how it can help you, and why we’re sharing this “uniquely Visier” concept with the world.
Pioneering in people analytics
Traveling through the Rockies on North America's West Coast is a stunning journey. The land is vast and varied. Driving through high peaks and dense forests, one cannot help but ponder the fates and fortunes of the pioneers who first attempted to travel from east to west.
Their journey, likely slow and difficult, laid the foundation for current-day road journeys. We cruise effortlessly along a smooth, well-engineered, and clearly marked highway. Following the path created by others, we can be certain of a speedy trip to our destination.
People analytics was once a new concept that had to be pioneered. Back in the late 90’s, academic institutions started to pursue the work that we now call people analytics. They saw the promise of studying the “digital records” of employees to look for trends that could benefit the whole organization.
Flash forward to today: people analytics is now a well-established discipline, with commonly accepted approaches and a detailed understanding of how to build a successful practice.
Visier played a pioneering role in making this happen. We were the first purpose-built analytics platform for people data, and we’ve supported thousands of organizations as they founded and scaled their PA teams. As an organization, we’ve gathered a lot of knowledge about how to get from A to B in people analytics. Now, our goal is to smooth and speed the people analytics journey for you.
Introducing the Visier Path
When you choose Visier, you get access to a vast amount of knowledge not available anywhere else. The Visier Path distills the wisdom from over a decade of experience into one straightforward “map” that’s accessible to HR and business leaders alike.
Consider The Visier Path a guide that outlines exactly which people analytics activities and HR business practices are required to achieve a CHRO’s key strategic priorities.
Guiding you toward the right business impact
How, exactly, does the Visier Path help meet a CHRO’s objectives? It’s all about linking data and action.
Too often, people analytics is dressed up in confusing language and seen as either an esoteric internal research project or an IT project focused on wrangling data. We believe people analytics teams should model their work in the same way as FP&A teams support the CFO. That is to say: they must provide ongoing information about the performance and behavior of the workforce so that leaders can make the decisions that will have the right impact.
The Visier Path breaks down the steps needed for an organization to move from raw data to the right analysis, to tangible action that supports a business goal. The quality of that linkage—from data through to impact—is what separates the best people analytics teams from the rest.
Every organization’s journey is different
Every organization has different goals and strategies. That’s why we designed the Visier Path to be completely flexible depending on what’s important to you. While every company will start with the fundamentals (shown in yellow), the rest of your journey along the Path is completely bespoke.
The Visier Path, your proven route to people analytics success. Click to enlarge.
The Visier Path is arranged from left to right in order of increasing level of impact on the business. The Foundational Impact zone is about aligning on a single place where HR leaders, HRBPs, and managers can answer their people questions. The Drive HR Impact zone helps you target specific HR jobs to be done that are likely to be part of the CHRO’s agenda. Finally, the Drive Business Impact zone focuses on org-wide challenges that will directly affect the business’s financial performance.
The proven approach
The Visier Path is the proven, low-risk way to start your people analytics journey. It’s also the clear and effective way to evolve your practice as your capability increases and so does the demand for people analytics within your organization. The path takes the uncertainty out of buying, deploying, and scaling a people analytics solution, and ensures that your investment has measurable business impact.
We hope you find our guide valuable and accessible. If you would like to understand more about The Visier Path, the collective wisdom it embodies, the practices that it supports, or the impact it can have on your business, please don’t hesitate to reach out—we'd love to hear from you.
Autonomous Corporate Learning Platforms: Arriving Now, Powered by AIJosh Bersin 的文章通过人工智能驱动的自主平台介绍了企业学习的变革浪潮，标志着从传统学习系统到动态、个性化学习体验的重大转变。他重点介绍了 Sana、Docebo、Uplimit 和 Arist 等供应商的出现，它们利用人工智能动态生成和个性化内容，满足了企业培训不断变化的需求。Bersin 讨论了跟上多样化学习需求所面临的挑战，以及人工智能解决方案如何提供可扩展的高效方法来管理知识和提高学习效果，并预测了人工智能将从根本上改变教学设计和内容交付的未来。推荐给大家：
Thanks to Generative AI, we’re about to see the biggest revolution in corporate learning since the invention of the internet. And this new world, which will bring together personalization, knowledge management, and a delightful user experience, is long overdue.
I’ve been working in the corporate learning market since 1998, when the term “e-learning” was invented. And every innovation since that time has been an attempt to make training easier to build, easier to consume, and more personalized. Many of the innovations were well intentioned, but often they didn’t work as planned.
First came role based learning, then competency-driven training and career-driven programs. These worked great, but they couldn’t adapt fast enough. So people resorted to short video, YouTube-style platforms, and then user-authored content. We then added mobile tools, highly collaborative systems, MOOCs, and more recently Learning Experience Platforms. Now everyone is focused on skills-based training, and we’re trying to take all our content and organize it around a skills taxonomy.
Well I’m here to tell you all this is about to change. While none of these important innovations will go away, a new breed of AI-powered dynamic content systems is going to change everything. And as a long student of this space, I’d like to explain why. And in this conversation I will discuss four new vendors, each of which prove my point (Sana, Docebo, Uplimit, and Arist).
The Dynamic Content Problem: Instructional Design By Machine
Let’s start with the problem. Companies have thousands of topics, professional skills, technical skills, and business strategies to teach. Employees need to learn about tools, business strategies, how to do their job, and how to manage others. And every company’s corpus of knowledge is different.
Rolls Royce, a company now starting to use Galileo, has 120 years of engineering, technology, and manufacturing expertise embedded in its products, documentation, support systems, and people. How can the company possibly impart this expertise into new engineers? It’s a daunting problem.
Every company has this issue. When I worked at Exxon we had hundreds of manuals explaining how to design pumps, pressure vessels, and various refinery systems. Shell built a massive simulation to teach production engineers how to understand geology and drilling. Starbucks has to teach each barista how to make thousands of drinks. And even Uber drivers have to learn how to use their app, take care of customers, and stay safe. (They use Arist for this.)
All these challenges are fun to think about. Instructional designers and training managers create fascinating training programs that range from in-class sessions to long courses, simulations, job aids, and podcasts. But as hard as they try and as creative as they are, the “content problem” keeps growing.
Right now, for example, everyone is freaked out about AI skills, human-centered leadership, sustainability strategies, and cloud-based offerings. I’ve never seen a sales organization that does quite enough training, and you can multiply that by 100 when you think about customer service, repair operations, manufacturing, and internal operations.
While I always loved working with instructional designers earlier in my career, their work takes time and effort. Every special course, video, assessment, and learning path takes time and money to build. And once it’s built we want it to be “adaptive” to the learner. Many tools have tried to build adaptive learning (from Axonify to Cisco’s “reusable learning objects“) but the scale and utility of these innovations is limited.
What if we use AI and machine learning to simply build content on the fly? And let employees simply ask questions to find and create the learning experience they want? Well thanks to innovations from the vendors I mentioned above, this kind of personalized experience is available today. (Listen to my conversation with Joel Hellermark from Sana to hear more.)
What Is An Autonomous Learning Platform?
The best analogy I’ve come up with is the “five levels of autonomous driving.” We’re going from “no automation” to “driver assist” to “conditional automation” to “fully automated.” Let me suggest this is precisely what’s happening in corporate training.
If you look at the pace of AI announcements coming (custom GPTs, image and video generation, integrated search), you can see that this reality has now arrived.
How Does This Really Work
Now that I’ve had more than a year to tinker with AI and talk with dozens of vendors, the path is becoming clear. The new generation of learning platforms (and yes, this will eventually replace your LMS), can do many things we need:
First, they can dynamically index and injest content into an LLM, creating an “expert” or “tutor” to answer questions. Galileo, for example, now speaks in my own personal voice and can answer almost any question in HR I typically get in person. And it gives references, examples, and suggests follow-up questions. Companies can take courses, documents, and work rules and simply add them to the corpus.
Second, these systems can dynamically create courses, videos, quizzes, and simulations. Arist’s tool builds world-class instructional pathways from documents (try our free online course on Predictions 2024 for example) and probably eliminates 80% of the design time. Docebo Shape can take sales presentations and build an instructional simulation automatically, enabling sales people to practice and rehearse.
Third, they can give employees interactive tutors and coaches to learn. Uplimit’s new system, which is designed for technical training, automatically gives you an LLM-powered coach to step you through exercises, and it learns who you are and what kind of questions you need help with. No need to “find the instructor” when you get stuck.
Fourth, they can personalize content precisely for you. Sana’s platform, which Joel describes here, can not only dynamically generate content but by understanding your behavior, can actually give you a personalized version of any course you choose to take.
These systems are truly spectacular. The first time you see one it’s kind of shocking, but once you understand how they work you see a whole new world ahead.
Where Is This Going
While the market is young, I see four huge opportunities ahead.
First, companies can now take millions of hours of legacy content and “republish it” in a better form. All those old SCORM or video-based courses, exercises, and simulations can turn into intelligent tutors and knowledge management systems for employees. This won’t be a simple task but I guarantee it’s going to happen. Why would I want to ramble around in the LMS (or even LinkedIn Learning) to find the video, or information I need? I”d just like to ask a system like Galileo to answer a question, and let the platform answer the question and take me to the page or word in the video to watch.
Second, we can liberate instructional design. While there will always be a need for great designers, we can now democratize this process, enabling sales operations people, and other “non-designers” to build content and courses faster. Projects like video authoring and video journalism (which we do a lot in our academy) can be greatly accelerated. And soon we’ll have “generated VR” as well.
Third, we can finally integrate live learning with self-directed study. Every live event can be recorded and indexed in the LLM. A two hour webinar now becomes a discoverable learning object, and every minute of explanation can be found and used for learning. Our corpus, for example, includes hundreds of hours of in-depth interviews and case studies with HR leaders. All this information can be brought to life with a simple question.
Fourth, we can really simplify compliance training, operations training, product usage, and customer support. How many training programs are designed to teach someone “what not to do” or “how to avoid breaking something” or “how to assemble or operate” some machine? I’d suggest its millions of hours – and all this can now be embedded in AI, offered via chat (or voice), and turned loose on employees to help them quickly learn how to do their jobs.
Vendors Watch Out
This shift is about as disruptive as Tesla has been to the big three automakers. Old LMS and LXP systems are going to look clunkier than ever. Mobile learning won’t be a specialized space like it has been. And most of the ERP-delivered training systems are going to have to change.
Sana and Uplimit, for example, are both AI-architected systems. These platforms are not “LMSs with Gen AI added,” they are AI at the core. They’re likely to disrupt many traditional systems including Workday Learning, SuccessFactors, Cornerstone, and others.
Consider the content providers. Large players like LinkedIn Learning, Skillsoft, Coursera, and Udemy have the opportunity to rethink their entire strategy, and either put Gen AI on top of their solution or possibly start with a fresh approach. Smaller providers like us (and thousands of others) can take their corpus of knowledge and quickly make it come to life. (There will be a massive market of AI tools to help with this.)
I’m not saying this is easy. If you talk with vendors like Sana, Docebo, Arist, and Uplimit, you see that their AI platforms have to be highly tuned and optimized for the right user experience. This is not as simple as “dumping content into ChatGPT,” believe me.
But the writing is on the wall, Autonomous Learning is coming fast.
As someone who has lived in the L&D market for 25 years, I see this era as the most exciting, high-value time in two decades. I suggest you jump in and learn, we’ll be here to help you along the way.
About These Vendors
Sana (Sana Labs) is a Sweden-based AI company that focuses on transforming how organizations learn and access knowledge. The company provides an AI-based platform to help people manage information at work and use that data as a resource for e-learning within the organization. Sana Labs’ platform combines knowledge management, enterprise search, and e-learning to work together, allowing for the automatic organization of data across different apps used within an organization.
Docebo is a software as a service company that specializes in learning management systems (LMS). It was founded in 2005 and is known for its Docebo Learn LMS and other tools, including Docebo Shape, its AI development system. The company has integrated learning-specific artificial intelligence algorithms into its platform, powered by a combination of machine learning, deep learning, and natural language processing. The company went public in 2019 and is listed on the Toronto Stock Exchange and the Nasdaq Global Select Market.
Uplimit is an online learning platform that offers live group courses taught by top experts in the fields of AI, data, engineering, product, and business. The platform is known for its AI-powered teaching assistant and personalized learning approach, which includes real-time feedback, tailored learning plans, and support for learners. Uplimit’s courses cover technical and leadership topics and are designed to help individuals and organizations acquire the skills needed for the future.
Arist is a company that provides a text message learning platform, allowing Fortune 500 companies, governments, and nonprofits to rapidly teach and train employees entirely via text message. The platform is designed to deliver research-backed learning and nudges directly in messaging tools, making learning accessible and effective. Arist’s approach is inspired by Stanford research and aims to create hyper-engaging courses in minutes and enroll learners in seconds via SMS and WhatsApp, without the need for a laptop, LMS, or internet. The company has been recognized for its innovative and science-backed approach to microlearning and training delivery.
BY JOSHBERSIN
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