David Green : The best HR & People Analytics articles of April 2024
My highlight for April, and indeed of the year so far, was the People Analytics Worldconference in London. I first chaired the event in 2014, and over the last decade People Analytics World has established itself as the go-to event in Europe for the field.
The 2024 edition was sold out with close to 400 people attending across the two days (4x compared to 2014!). I had the privilege of co-chairing, along with Cole Napper and Michael M. Moon, PhD and also delivering the opening keynote on how leading companies deliver value with people analytics, based on our research at Insight222.
You can find the slides I shared in the keynote below. These include the results of three polls I ran with attendees at People Analytics World on (1) the current state of people analytics in their organisation, (2) the financial impact of people analytics in the last 12 months, and (3) the data literacy of HR professionals. Additionally, you can also access the Insight222 research I shared here.
The conference only ended a few days ago, but already many of the attendees have shared some of their key takeaways and learnings. Do check out the ones from Patrick Coolen (here), Giovanna Constant (here), Sebastian Knepper (here), Mariana Rossi Campos (here), Fatma Hedeya (here), Pietro Mazzoleni (here), Maria Manso Garcia (here), Ekkehard Ernst (here), Marcela Mury (here) and Jaejin Lee (here).
Finally on People Analytics World, congratulations to Barry Swales and the Tucana team for organising such a successful event, thank you to all those who attended the Insight222 dinner, visited the Insight222 stand and who took the time to interact with me over the two days. Lastly, thank you to all of the brilliant speakers and panellists in the Plenary sessions and Strategy track that I moderated: Richard Rosenow Ian Cook Sue Lam Rob Briner Peter Cheese Aizhan Tursunbayeva, PhD, GRP Abigail Gilbert Alexis Saussinan Michael Cox Gemma McNair David Shontz Amit Mohindra Clare Moncrieff Jo Thackray Lucie Vottova Andrew Elston Rob Etheridge Isabel Naidoo James Fenlon and Ekkehard Ernst.
People Analytics World 2024 | London
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Enjoy reading the collection of resources for April 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 March’s compendium (including those in the Thank You section 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.
HYBRID, GENERATIVE AI AND THE FUTURE OF WORK
DIANE GHERSON AND LYNDA GRATTON - Highly Skilled Professionals Want Your Work But Not Your Job
Without question, there has been a huge shift. Many of the individuals we’re looking to attract—in technology, data sciences, machine learning, blockchain, and the internet of things—have a different mindset now. They want more-flexible working arrangements.
This quote from Peter Fasolo, Ph.D. chief human resources officer at Johnson & Johnson, perfectly captures the challenge that Diane Gherson and Lynda Grattonhighlight in their article for Harvard Business Review: more and more workers want to work as freelancers. As the article highlights, Gartner predict that independent workers will make up 35% to 40% of the global workforce by 2025. Moreover, one-third earn more than $150,000 per year, and just over half were providing knowledge services—such as computer programming, marketing, IT, and business consulting. Integrating and managing what this ‘blended workforce’ will be one of the main managerial challenges in the years ahead. Based on their interviews with executives at leading companies that are experimenting with how best to bring freelancers into their organisations, Diane and Lynda set out some guidance and highlight emerging management practices that forward-looking companies are embracing. These include: (1) Helping freelancers understand and embrace company culture. (2) Following rigorous practices to retain institutional knowledge. (3) Adopting a ‘sponsor’ mindset to guide freelancers’ performance. (4) Leveraging digital workflows and building trust to manage changes in project needs.
FIG 1: The Emerging Blended Workforce (Source: Diane Gherson and Lynda Gratton)
LEILA HOTEIT, ANTON STEPANENKO, PAVEL LUKSHA, SAGAR GOEL, AND LEONID GORENBURG - The Next 50 Years of Work
Contrary to popular fears that the future will offer fewer work opportunities for people, most experts anticipate that rewarding work options will be plentiful.
The key finding of a recent BCG study is that workforce experts anticipate that jobs will flourish over the next fifty years, with four boundaries framing the future growth of the economy (see FIG 2). The study, authored by Leila Hoteit Anton Stepanenko Pavel Luksha Sagar Goel and Leonid Gorenburgalso highlights bionic skills (e.g. tech literacy, data-driven decision making, AI-enhanced creativity, and ease with human-machine collaboration) and creativity as the skills that will be in highest demand. But to complement these skills, workers should also cultivate adaptability and the ability to take initiative.
FIG 2: The four boundaries within which the future economy will grow (Source: BCG)
ETHAN MOLLICK - Reinventing the Organization for GenAI and LLMs
Consider this an early eulogy for the traditional organizational structure, which began in 1855 with the first modern organizational chart and thrived, more or less successfully, until the 2020s, when it succumbed to a new technology, the large language model (LLM).
That’s the bold claim by Ethan Mollick in his compulsive article in MIT Sloan Management Review. While he concedes that previous waves of technology have ushered in innovations that have strengthened traditional organisational structures, Mollick makes the case that GenAI and LLMs are different. He then outlines three principles for reorganising work around AI: (1) Identify and enlist your current AI users. (2) Let teams develop their own methods. (3) Build for the not-so-distant future. If you enjoy this article, I recommend subscribing to Mollick’s One Useful Thing blog.
BCG - What GenAI’s Top Performers Do Differently
The top GenAI performers have the biggest lead across five main capabilities: a clear link to business performance, modern technology infrastructure, strong data capabilities, leadership support, and a grounding in responsible AI.
While GenAI is becoming an integral part of business ecosystems, only 10% of companies have mastered scaling GenAI to create value and secure other benefits from this transformative technology (see FIG 3). That’s according to recent research by BCG, which finds that 10% of companies lead in five key areas: (1) a clear link to business performance, (2) modern technology infrastructure, (3) strong data capabilities, (4) leadership support, and (5) a grounding in responsible AI. A helpful read for HR leaders as they think how HR can lead organisational transformation in the age of AI as well as incorporate the technology into HR programs too. (Authors: Amanda Luther Romain de Laubier Nicolas de Bellefonds Tauseef Charanya Suraj Shah Kevin Nnaemeka Ifiora and Patrick Forth)
FIG 3: Three categories of companies in relation to GenAI adoption (Source: BCG)
PEOPLE ANALYTICS
PATRICK COOLEN - The 10 golden rules for establishing a people analytics practice
A successful people analytics practice starts with the right people analytics leader
Patrick Coolen’s first iteration of his ’10 golden rules for people analytics’ (one prescient ‘rule’ was to combine strategic workforce planning and analytics) was published in 2014 when he was in the early stages of building the function at ABN Amro. A decade on, Patrick updates his seminal article, with insights from his own career journey, Ph.D research, and the evolution of the field itself. As ever, Patrick is right on the mark with his ten selections including these three: (1) The people analytics leader can make the difference, (2) Create a clear people analytics operating model, and (3) Upskill HR in data-driven decision making.
PIETRO MAZZOLENI - Mastering data governance for effective people data platforms: lessons from what we did at IBM
Data Governance is the process that ensures the availability, usability, integrity, and security of data in enterprise systems
Pietro Mazzoleni shares the three key elements related to ‘governance-by-design’ that together provided the fundamental principles underlying the design and implementation of Workforce360, IBM’s people data platform. In the article, Pietro presents the three elements – trust, transparency and compliance (see FIG 4) – and provides a detailed description of each.
FIG 4: Key governance questions to consider when designing a people data platform (Source: Pietro Mazzoleni)
JASDEEP KAREER - The Importance of Data and Upskilling in Driving Growth
Jasdeep Kareer, PhD (née Bhambra) shares key learnings from the recent Peer Meeting for North American member companies of the Insight222 People Analytics Program, which was hosted by Colgate-Palmolive in their global headquarters in New York. The Peer Meeting, which was attended by more than 60 people analytics leaders and practitioners from more than 40 companies was framed on the key findings from the Insight222 People Analytics Trends study for 2023. In her article, Jas highlights five themes from the Peer Meeting: (1) The importance of data and upskilling in driving growth (with insights from Sally Massey). (2) How strategic partnerships and data governance pave the way for successful People Analytics initiatives (with insights from Courtney McMahon Pavel Nouel and Nayana Pai). (3) How insights-driven decision-making and storytelling can drive impactful outcomes in People Analytics (with insights from Durrell Blake Robinson and Mona Routray). (4) Factors influencing the adoption of people analytics (with insights from Patrick Coolen and Brydie Lear). (5) Influencing senior stakeholders with people analytics (with insights from Piyush Mathur). If you would like to learn more about our People Analytics Program, contact us today.
FIG 5: 8 Characteristics of Leading Companies. (Source: Insight222 People Analytics Trends Report 2023)
BURAK BAKKALOGLU – Deploying GenAI in HR | KEITH MCNULTY – How I Created an AI Version of Myself | KATE GUARINO - How to Turn ChatGPT into Your Personal Consultant: A 5-Step Approach | NATALIA GORMANN - Improving Employee Experience with a Solid Data Strategy | PATRICK GALLAGHER - Is It Time to Stop Measuring Employee Engagement?
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) Burak Bakkaloglu dedicates an edition of his If Interested blog to the topic of GenAI including breaking down three layers of GenAI for HR (see FIG 6). (2) Keith McNulty provides a tutorial (including code) on how he built a 'Keith-bot' to answer questions on statistics based on the content of his regression textbook, using a Retrieval Augmented Generation (RAG) architecture. (3) Katie Guarino also provides a practical framework on how to use ChatGPT as your personal consultant and coach on any topic, regardless of your expertise in it. (4) Natalia Gormann discusses challenges for people teams to build partnerships with finance before guidance on how to build an effective data strategy to support employee experience strategies. (5) Patrick Gallagher looks at the case for and against measuring employee engagement, concluding that organisations with mature employee listening and PA functions just don’t need it anymore.
FIG 6: Three layers of GenAI in HR (Source: Burak Bakkaloglu)
THE EVOLUTION OF HR, LEARNING, AND DATA DRIVEN CULTURE
VINCENT BÉRUBÉ, BEN FOGARTY, NEEL GANDHI, RAHUL MATHEW, MARINO MUGYAR-BALDOCCHI, AND CHARLOTTE SEILEROUTLINE - Increasing your return on talent: The moves and metrics that matter
An organization that views its employees as its most important resource can maximize its return on talent by following a holistic strategy—with HR in the driver’s seat.
Drawing on McKinsey research that finds companies that put talent at the centre of their business strategy realise higher total shareholder returns than their competitors, Vincent Bérubé Ben Fogarty Neel Gandhi Rahul Mathew Marino Mugayar-Baldocchi and Charlotte Seiler outline five actions organisations can take to maximise their return on talent. The five actions are: (1) Build a skills-based strategic workforce planning capability. (2) Create a hiring engine that brings in the right talent to fill critical roles. (3) Invest in learning and development. ((4) Establish a stellar performance-oriented culture. (5) Elevate HR’s operating model to become a true talent steward.
FIG 7: Factors that drag down employee and organisational productivity (Source: McKinsey)
PETER CAPPELLI AND RANYA NEHMEH – HR’s New Role
If leaders realized that the true cost of turnover is often a multiple of an employee’s annual salary, they would immediately demand changes.
In their thoughtful article for Harvard Business Review, Peter Cappelli and Ranya Nehmeh set out the case for the HR function to return to its roots as employee advocates. They argue that in a period of low unemployment and labour supply shortages, focusing on cost-cutting and restructuring is counterproductive and the onus should instead be on retention and preventing burnout. To realise this, HR needs to change outdated policies on compensation, training and development, layoffs, vacancies, outsourcing, and restructuring. Cappelli and Nehmeh recommend the first step should be for HR to create dashboards with metrics on the true costs of turnover, absenteeism, reasons for quitting, illness rates, and employee engagement. They contend that: “If leaders realized that the true cost of turnover is often a multiple of an employee’s annual salary, they would immediately demand changes." They also outline guidance on why and how to measure employee stress – particularly with regards to AI and restructuring. The article also provides examples of companies with HR functions that are moving to an employee advocacy approach. These include the likes of Walmart and Neiman Marcus (both on compensation and reward), as well as IBM and Unilever (both internal talent mobility).
DAVE ULRICH - Upgrading HR Professionals: How to Develop HR Professionals so They Rise to Their Opportunity
HR matters. Now more than ever.
In a recent article from his Human Capability Impact LinkedIn newsletter, Dave Ulrich explains why HR functions and professionals are rising in importance, and then lays out a playbook, process and assessment designed to develop HR professionals so they can fulfil expectations and rise to the opportunity (see FIG 8).
FIG 8: Summary and assessment of ways to upgrade HR professionals (Source: Dave Ulrich)
WORKFORCE PLANNING, ORG DESIGN, AND SKILLS-BASED ORGANISATIONS
NICK VAN DER MEULEN, OLGERTA TONA, AND DOROTHY E. LEIDNER – Resolving Workforce Skills Gaps with AI-Powered Insights
As Christina Norris-Watts and Doug Shagam shared with me in an episode of the Digital HR Leaders podcast, Johnson & Johnson has used AI-driven skills inference as part of their skills transformation (see: How Johnson & Johnson are Scaling Their Skills-Based Approach to Talent). In their paper for MIT, Nick van der Meulen Olgerta Tona and Dorothy Leidner provide an in-depth case study on Johnson & Johnson to demonstrate how skills inference can provide detailed insight into workforce skills gaps and thereby guide employees’ career development and leaders’ strategic workforce planning. The paper includes a detailed description of the three steps of the skills inference process (see FIG 9). The sections in the paper on employee trust, privacy and use cases are particularly instructional for companies looking to emulate this work in their organisations.
FIG 9: The three steps of the skills inference process (Source: MIT Center for Information Systems Research)
JORDAN PETTMAN - Workforce Planning: A Beginner's Guide to Strategic Success
Jordan Pettman, one of my many talented colleagues at Insight222, shares some tips and guidance for practitioners looking to start or accelerate their workforce planning efforts. He highlights the Nine Dimensions for Excellence in Strategic Workforce Planning model we use with clients at Insight222 (see FIG 10), explaining that you need to consider each of the decision points that the model presents in terms of getting the foundations right, ensuring your resources are fit for purpose and that you deliver value out of the cycle for the business and employees. Jordan also shares insights from the likes of Jonas Ottiger and Gergo Safar as part of his guidance on two key elements: workforce planning essentials and building skills-based workforce planning.
FIG 10: Nine Dimensions for Excellence in Stategic Workforce Planning (Source: Insight222)
EMPLOYEE LISTENING, EMPLOYEE EXPERIENCE, AND EMPLOYEE WELLBEING
EMILY KILLHAM - From Insight to Action: New Data on the State of Employee Listening (Article) | The State of Employee Listening 2024 (Report)
(Leading firms ensure) listening efforts are aimed at the most important business and talent priorities facing their organizations today.
Emily Killham highlights the key findings from Perceptyx’s third annual State of Employee Listening report, which is informed by survey of more than 750 senior HR leaders from global firms with at least 1,000 employees. These include: (1) 78% of firms surveyed conduct some kind of listening event at least once a quarter, compared to 70% in 2023 and 60% in 2022. (2) Nearly 40% of organisations can share listening data with managers within two weeks. (3) When compared with their peers, the most mature listening organisations are 6x more likely to exceed financial targets, 9x more likely to achieve high levels of customer satisfaction, 4x more likely to retain talent, even during times of high attrition, 7x more likely to adapt well to change, and 7x more likely to innovate effectively.
FIG 11: Employee Listening Maturity (Source: Perceptyx)
NICK LYNN - Trust and Distrust: Why and how you may need to tackle both
Building trust is not always sufficient, you may also need to tackle the causes of distrust. The problems are not always the same. They may sometimes require different solutions.
Nick Lynn constructs a wonderful treatise on ‘trust’ and ‘distrust’ in organisations and offers potential solutions to build the former and tackle the latter. Through analysing four models to build trust, Nick identifies some common ingredients including: communication, consistency, integrity, fairness, empathy, and psychological safety. When it comes to tackling distrust, he assembles four elements of organisational health: work, total rewards, people, and purpose into a framework of employee experience leadership (see FIG 12).
FIG 12: Driving employee experience through connection and contribution (Source: Nick Lynn)
STEPHANIE DENINO, TIMO TISCHER, AND DAVID GREEN - Moving Towards Excellence in EX Management
In the January edition of Data-Driven HR Monthly, I highlighted the fascinating report State of EX 2023-24 study, published by The EXchange, Inc, TI PEOPLE and FOUNT Global, Inc. In this article, for myHRfuture, I interview Stephanie Denino and Timo Tischer, two of the contributors to the study. We dig into what constitutes ‘excellence’ in EX management, the barriers and how to overcome them, and the priorities for EX teams in 2024 (see FIG 13). Stephanie and Timo also provide tips for organisations looking to manage EX more deliberately, which includes: (1) Identifying the moments that matter, (2) Measuring and listening continuously to people’s experiences across these moments, and (3) Clarifying responsibilities (who ‘owns’ which journeys, moments and/or touchpoints) to ensure accountability, and improving high importance / low satisfaction moments.
FIG 13: Top five priorities for EX teams in 2024 (Source: State of EX 2023-24 study)
LEADERSHIP, CULTURE, AND INCLUSION
ANNA BINDER - Build Your Culture Like a Product
Anna Binder, Asana's Head of People, shares her step-by-step guide to intentionally building the company culture, which has helped Asana scale from 100 to over 2,000 employees during the last eight years. The article includes tips on building a people strategy from the ground-up, constructing a culture pyramid to supercharge your organisation (see FIG 14), how to bring conscious leadership to the executive suite, and building trust. A highly insightful and practical guide.
FIG 14: The pyramid of company culture (Source: Anna Binder)
ARNAUD CHEVALLIER, FRÉDÉRIC DALSACE, AND JEAN-LOUIS BARSOUX - The Art of Asking Smarter Questions
Advances in AI have caused a seismic shift from a world in which answers were crucial to one in which questions are. The big differentiator is the ability to craft smart prompts.
The ability to ask great questions is a powerful skill for unlocking value – especially in the age of AI. As such, the cover article of the current edition of the Harvard Business Review by Arnaud Chevallier Frédéric Dalsace and Jean-Louis Barsoux of IMD Business School is well worth digging into. The authors provide a typology of five topics of questions to ask during strategic decision making: (1) investigative, (2) speculative, (3) productive, (4) interpretive, and (5) subjective (see FIG 15). The article also includes a self-assessment that enables readers to evaluate the types of questions that are their strong and weak points, and then provides guidance to help you improve. From completing the assessment myself, it seems I need to work on my subjective questioning technique.
FIG 15: What’s your question mix? (Source: Chevallier et al)
CHRISTIAN HAUDE, IVO BLOHM, AND XAVIER LAGARDÈRE - How Lufthansa Shapes Data-Driven Transformation Leaders
Effective data leaders bridge a crucial gap that still exists in too many organizations. These leaders play a key role in transforming organizations that are leveraging data and AI to increase business value.
An excellent example from Lufthansa on how they created a program to educate leaders on data leadership, and how it provided insights on the roles that people play in data-driven change. In their article, Christian Haude Ivo Blohm and Xavier Lagardere outline the challenge the program was designed to solve, the six different roles for data leaders that were defined (see FIG 16), details of the three training modules: Spark, Inspire and Activate, and four key strategies for success.
FIG 16: Data Leadership: Six key roles (Source: Haude et al)
SHARNA WIBLEN AND DAVID GREEN - Rethinking Talent Decisions and Navigating Subjectivity in HR
Accumulating deliberate, intentional, and informed decisions can unleash exponential returns.
In her book, Rethinking Talent Decisions, Sharna Wiblen highlights an uncomfortable truth: Talent decisions are always subjective. As such, I was delighted to explore this in more depth with Sharna in an article for myHRfuture. In the article, Sharna, an Assistant Professor and Senior Lecturer at Sydney Business School, University of Wollongong, unpacks the nuanced role of subjectivity in talent decisions and the symbiotic relationship between technology and human judgment in the workplace. The uncomfortable truth is that decisions about talent are invariably coloured by personal perceptions, and instead of shying away, Sharna argues that we should lean into this discomfort to emerge with more informed and nuanced strategies.
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 April that I recommend readers delve into:
FRANZ GILBERT, MATTHEW SHANNON, AND ERIN SPENCER - 2024 HR tech predictions: Headless platforms place HR tech in the flow of work – The Deloitte Human Capital Forward team of Franz Gilbert Matthew Shannon and Erin Spencer outline the key HR technology trends they believe will drive innovation in the field in 2024 (see FIG 17).
FIG 17: HR technology trends primed to innovate further in 2024 (Source: Deloitte)
JARED SPATARO, KATHLEEN HOGAN, AND CHRIS FERNANDEZ - Our Year with Copilot: What Microsoft Has Learned About AI at Work - Senior leaders at Microsoft, including Jared Spataro Kathleen Hogan and Christopher J. Fernandezshare insights, learnings and guidance from their experience of using Copilot. For example, Hogan reveals:
Our HR service professionals are able to handle employee inquiries more efficiently. So far we are seeing a 26 percent reduction in initial response time thanks to Copilot.
CATHERINE COPPINGER - Manager Effectiveness: It’s Time for a New Playbook – Catherine Coppinger shares Worklytics research on how companies can understand and improve manager effectiveness. Insights include the impact of isolation on ‘quiet quitting’ and how low manager engagement is a big predictor of isolation (see FIG 18). For more, please listen to Catherine’s discussion with me on the Digital HR Leaders podcast: How to enhance manager effectiveness.
FIG 18: Source - Worklytics
FRANCISCO MARIN - The Role of AI-Powered Passive Organizational Network Analysis (ONA) in Mitigating Burnout, Absenteeism, and Turnover Risk – Francisco Marin of Cognitive Talent Solutions explains how ONA has emerged as a critical tool in identifying and mitigating the risks of burnout, absenteeism, and turnover.
ANDREW PITTS AND CHAD MITCHELL - Mapping and Understanding the Connections Between SIOP 2024 Conference Presenters – Andrew Pitts and Chad Mitchell provide a practical example of ONA by utilising Polinode to understand and map the connections of the presenters at the recent Society for Industrial and Organizational Psychology (SIOP) 2024 conference in Chicago.
PODCASTS OF THE MONTH
In another month of high-quality podcasts, I’ve selected four gems for your aural pleasure: (you can also check out the latest episodes of the Digital HR Leaders Podcast – see ‘From My Desk’ below):
ANDREW STRAUSS AND MATT ALDER - Talent Lessons From Elite Sport – I’ll happily admit to some green-eyed envy towards Matt Alder for the coup of getting former England cricket captain Andrew Strauss onto his Recruiting Future podcast to discuss what elite sports can teach business about leadership.
JOHANNES SUNDLO AND LARS SCHMIDT - Practical Use Cases for Generative AI in Human Resources – Johannes Sundlo joins Lars Schmidt on his Redefining Work podcast to dig into use cases for GenAI in HR including in learning and compensation.
MALISSA CLARK AND CURT NICKISCH - Companies Can Win by Reducing Overwork - Malissa Clark, associate professor and head of the Healthy Work Lab at the University of Georgia, joins Curt Nickisch on HBR IdeaCast to explain how companies unwittingly create a workaholic culture, and what they can do to change this.
ALAN COLQUITT, COLE NAPPER AND SCOTT HINES - Is Performance Management Fine, Or Rotten To The Core? – An interesting discussion ensues as Alan Colquitt, Ph.D. joins hosts Cole Napper and Scott Hines, PhD to discuss the pros and cons of performance management.
BOOK OF THE MONTH
ANNA TAVIS AND WOODY WOODWARD - The Digital Coaching Revolution: How to Support Employee Development with Coaching Tech
According to Anna A. Tavis, PhD, and Dr. Woody Woodward, PhD, PCC: “Digital coaching is transforming employee experience and the future of work as we know it.” In their book, The Digital Coaching Revolution, they provide guidance on how to scale digital coaching in your organisation – whether the C-suite is already on board or not. The book features case studies from the likes of Visa, CVS, and Hilton, and is a recommended resource for HR, EX, and L&D professionals looking to understand and/or roll digital coaching within their companies.
RESEARCH REPORT OF THE MONTH
ROB BRINER – Evidence-Based HR: A New Paradigm
Evidence-based HR (EBHR) is a process which delivers more informed and hence more accurate answers to two fundamental questions: first, which are the most important problems (or opportunities) facing the organisation which are relevant to HR? Second, which solutions (or interventions) are most likely to help?
These are the opening words to a recently published report from the Corporate Research Forum (CRF), authored by Rob Briner, on Evidence-Based HR (EBHR). The report tackles, the why, what, and how of EBHR, explains why it is not the same as people analytics, provides case studies from Thales, Uber and the Financial Conduct Authority, and provides a practical toolkit for practitioners on the EBHR process (see FIG 19). For more, have a listen to Rob speaking to me in a recent episode of the Digital HR Leaders podcast: What is evidence-based HR and why is it important?
FIG 19: The Evidence-Based HR Process (Source: Rob Briner, Corporate Research Forum)
FROM MY DESK
April saw three episodes from Series 38 of the Digital HR Leaders podcast, sponsored by our friends at Worklytics - thank you to Philip Arkcoll and Laura Morris, as well as a round-up of series 37:
NICKLE LAMOREAUX - How IBM Uses AI to Transform Their HR Strategies – Nickle LaMoreaux, CHRO at IBM, joins me to share how IBM is harnessing AI to transform HR practices, drive business outcomes, and elevate employee experience. One of the examples Nickle shares is IBM’s digital worker, HiRo, which takes on the manual, repetitive tasks of data gathering during our quarterly promotions process and in 2023 saved IBM managers 50,000 hours.
COLE NUSSBAUMER-KNAFLIC - How HR Professionals can Master Storytelling with Data - Cole Nussbaumer Knaflic joins me for a deeply insightful conversation on the transformative power of storytelling in the context of people data and analytics.
CATHERINE COPPINGER – How to Use Passive Data to Enhance Manager Effectiveness - Catherine Coppinger, Head of Customer Insight at Worklytics joins me to discuss her recent research on manager effectiveness, which includes discussion on the impact of network density, team size, and span of control on team and manager effectiveness.
DAVID GREEN - How can HR help create a thriving organisational culture? - A round-up of series 37 of the Digital HR Leaders podcast, with insights from episodes featuring Rebecca Thielen Dorie Clark Didier Elzinga Rob Briner Louise Millar and Olivia Edwards.
LOOKING FOR A NEW ROLE IN PEOPLE ANALYTICS OR HR TECH?
’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 550 roles.
THANK YOU
Reem Janho, JD Michael Griffiths Obed Garcia-Colato Kim Eberbach and the rest of the Deloitte team for inviting me to speak at their Workforce Innovation Forumat the Deloitte University in Texas.
Olimpiusz Papiez for sharing his key learnings on advancing your career in people analytics (with insights from the Digital HR Leaders podcast episode with Serena H. Huang, Ph.D.), on how to quantify the impact of a thriving company culture (with insights from the episode with Didier Elzinga), and on IBM’s HiRo digital assistant (with inisghts from the episode with Nickle LaMoreaux)
Luis Miguel González Soriano for posting about Excellence in People Analytics.
Juliette Matharan for writing about Excellence in People Analytics, and Arnaud COULON for recommending the book to Juliette.
Ancile Digital for including my quote on how HR can harness AI in its post on the best advice for HR professionals.
Mirro.io for featuring me as one of their top HR thought leaders to follow in 2024.
Employ.com for also featuring me as one of their top 16 HR influencers to follow on LinkedIn.
Ganesh Iyer for including the Digital HR Leaders podcast in his list of 25 HR leadership podcasts to subscribe to.
Thomas Otter for endorsing the Digital HR Leaders podcast here.
Kevin Green for recommending series 37 of the Digital HR Leaders podcast.
Thomas Kohler for including the March edition of Data Driven HR in his round-up of recommended HR resources.
Yen Dang for including the Data-Driven HR Monthly in her top 3 newsletters for HR professionals.
Neha Asthana for including me in her group of HR thought leaders and influencers.
Caroline Arora and JooBee Yeow, PhD for recommending me on Mark Shortall’s list of content creators in the people and talent space.
Lars Schmidt for also the Data-Driven HR Monthly (this newsletter!) in his excellent list of HR newsletters to subscribe to.
To the following people who sharing the March edition of Data Driven HR Monthly. It's much appreciated: David Simmonds FCIPD Hafiz Adam Hanafi Reshma Mawji Hakki Ozdenoren Jo Iwasaki Aravind Warrier Katrina A. Stevens, CHRE Muhammad Firdaus Chrechen Jeja Kouros Behzad Arin Buawatthana Abid Hamid Robert Rogowski Terri Horton, EdD, MBA, MA, SHRM-CP, PHR Anvita Patnaik Paola Valerin Francisca Solano Beneitez Beverly Tarulli, Ph.D. Nicola Vogel Alexander S. Locher Kingsley Taylor Jacqui Brassey, PhD, MA, MAfN (née Schouten) Ralf Buechsenschuss Aysegul Tigli Philipe Ferreira Jane Datta Malgorzata (GOSIA) LANGLOIS Karen Edelman Indre Radzeviciute Hallie Bregman, PhD Adam McKinnon, PhD. Amanda Painter Adam Tombor (Wojciechowski) Chris Lovato Nabil Dewsi Tatu Westling Kristina Schoemmel Janeen Rabinowitz Susan Knolla Dan George Catriona Lindsay Patricia Carmona Ulrich E. Basler Caitie Jacobson Warren Howlett Jackson C. Trent Melissa Hopper Fritz Ankit Saxena, MBA Martha Curioni Anna Nord ?? Amardeep Singh, MBA Irada Sadykhova Christina Bui Higor Gomes Tanya Pastor Danielle Bushen Nicole Lettich Ken Clar Kerrian Soong Laurent Reich Stephen Hickey Olivier Bougarel Jana Glogowski Marcela Mury Tina Peeters, PhD Aimee Wilkinson Ludek Stehlik, Ph.D. Phil Inskip Adam Gibson Daniel Bosman Todd Tauber Violeta Lennon Soojeong Bae Aurélie Crégut.
UNLOCK THE POTENTIAL OF YOUR PEOPLE ANALYTICS FUNCTION THROUGH THE INSIGHT222 PEOPLE ANALYTICS PROGRAM
At Insight222, our mission is to make organisations better by putting people analytics at the centre of business and upskilling the HR profession The Insight222 People Analytics Program® is your gateway to a world of knowledge, networking, and growth. Developed exclusively for people analytics leaders and their teams, the program equips you with the frameworks, guidance, learnings, and connections you need to create greater impact.
As the landscape of people analytics becomes increasingly complex, with data, technology, and ethical considerations at the forefront, our program brings together over one hundred organisations to collectively address these shared challenges.
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.
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:
June 4-5 - Insight222 European Peer Meeting (hosted by Nestlé in Vevey, Switzerland) - exclusively for member organisations of the Insight222 People Analytics Program
June 25-26 - Insight222 North American Peer Meeting (Minneapolis, US) - exclusively for member organisations of the Insight222 People Analytics Program
September 16-19 - Workday Rising (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)
October 22-23 - Insight222 North American Peer Meeting (hosted by Workday in Pleasanton, CA) - exclusively for member organisations of the Insight222 People Analytics Program
November 12-14 - Workday Rising EMEA (London)
November 19-20 - Insight222 European Peer Meeting (hosted by Merck in Darmstadt, Germany) - exclusively for member organisations of the Insight222 People Analytics Program
More events will be added as they are confirmed.
GenAI
2024年05月02日
GenAI
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.
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
GenAI
2024年02月18日
GenAI
2024年的HRTech：GenAI、分析和技能技术In 2024, the field of Human Resources is experiencing a transformative shift with the integration of cutting-edge technologies such as Generative AI (GenAI), advanced analytics, and skills technology. This article by Dave Zielinski, featured on SHRM Online, delves into the evolving landscape of HR, highlighting the significant impact of these technologies on enhancing the employee experience, improving regulatory compliance, and revolutionizing talent management. Industry analysts and thought leaders share insights on the growing importance of GenAI in HR processes, the challenges of maintaining employee experience in cost-cutting scenarios, and the potential of predictive analytics in optimizing workforce planning.
接受SHRM Online采访的人力资源行业分析师、从业者和思想领袖表示，今年，人力资源职能部门将采用生成式人工智能 (GenAI)，投资于提升员工体验的技术，并采用强大的预测分析和技能技术。
人力资源领导者将转向技术，这些技术不仅可以提高法规遵从性，还可以帮助其组织做出更好、更快的人才决策并重新定义工作方式。
有远见的公司将继续投资 EX
一些分析师预测，随着高管将注意力转向降低成本和提高效率，远离包容性、公平和多样性等问题，员工体验 (EX) 将在 2024 年出现“衰退”；灵活的工作安排；和员工心理健康。员工的工作选择将减少，雇主将收回一些影响力。
不过，尽管许多组织可能会在 2024 年减少或冻结 EX 支出，但专家对此类举措的后果提出警告。
JP Gownder 是 Forrester 的副总裁兼首席分析师。他在博文中写道，根据 Forrester 研究，66% 的技术决策者表示，他们将在 2024 年增加对 EX 或人力资源技术的投资，其中许多投资将旨在提高效率，而不是 EX 结果。
但逆流而上的领导者将在 2024 年获得实实在在的好处。
“通过开发成熟的 EX 计划，您的组织可以提高生产力、降低人员流失率并提高创造力，”Gownder 写道。
其他专家认为，足智多谋的人力资源领导者会在预算紧张的情况下找到投资 EX 的方法。
管理咨询公司光辉国际 (Korn Ferry) 首席人力资源官 (CHRO) 业务的高级客户合伙人丹·卡普兰 (Dan Kaplan) 表示：“人力资源部门将被迫在低迷的市场中保持参与度，甚至在成本削减和削减的整个过程中也不例外。” “这将是一场艰难的舞蹈，但最好的人力资源领导者会找到办法做到这一点。”
光辉国际 (Korn Ferry) 专门负责人力资源问题的高级客户合伙人胡安·巴勃罗·冈萨雷斯 (Juan Pablo Gonzalez) 表示，组织对 EX 的承诺在 2024 年不会减弱，但 EX 看起来会非常不同。
“EX 的本质可能会变得更加个性化，同时也会变得不那么个性化，”冈萨雷斯说。“例如，通过使用 Microsoft Office Copilot、Workday 和 Salesforce 等大型软件平台中已有的人工智能功能，雇主和员工已经改变了他们的 EX。正在发生的情况是，员工与技术的互动越来越多地取代了与人的互动，但与技术的互动已经变得更加适合员工的特定需求和情况。”
亚特兰大人力资源咨询公司 IA 的创始人兼管理负责人 Mark Stelzner 表示，虽然由于组织面临控制盈利的挑战，预算将在 2024 年重新分配，但良好的 EX 相关技术投资将继续为公司带来红利。
“我认为投资 EX 实际上会提高效率并降低成本，”Stelzner 说。“到 2024 年，我们可能会看到组织不断转向‘流程主导、技术支持’的理念。端到端流程的优化通常会导致诸如消除现有技术债务以及统一工具和技术等决策，以减少员工的困惑并优化个性化，从而减少集成良好的接触点。”
Gartner 专门研究人力资源技术的副总裁分析师 John Kostoulas 表示，做出更具战略性的采购决策和改善现有技术生态系统的治理是改善 EX 的两个关键。Gartner 最近的研究发现，60% 的人力资源领导者认为他们当前的技术阻碍而不是改善了员工体验。
Nucleus Research 专门负责员工体验的研究经理 Evelyn McMullen 表示，仅仅为了提高效率而不是 EX 结果而设计的技术投资可能被证明是短视的。她指出，改进的 EX 通常会带来更好的绩效并降低与营业额相关的成本。
麦克马伦说：“考虑到劳动力市场和求职者优势的不断波动，减少 EX 预算的风险尤其大。” “当控制权不可避免地回到求职者手中时，保留 EX 投资的组织将能够更好地捕获和留住最优秀的人才。”
GenAI 从实验转向加速采用
到 2024 年，通过更多地采用该技术，人力资源职能将从涉足 GenAI 转向更深的领域。
随着领导者制定更严格的 GenAI 治理计划以及使用该技术的风险开始降低，人力资源和招聘部门将越来越多地使用其 HRIS 平台中已有的 GenAI 工具来编写职位描述和面试指南、创建敬业度调查、开发培训课程、分析数据，并制定政策。
世界大型企业联合会 2023 年底对首席人力资源官的调查发现，61% 的首席人力资源官计划在 2024 年投资人工智能以简化人力资源流程。
分析师 Eser Rizaoglu 表示：“许多人力资源领导者的 GenAI 之旅仍处于起步阶段，但要么通过现有的人力资源技术提供商获得 GenAI 功能，要么到 2024 年中期购买新的 GenAI 工具。” Gartner 的人力资源研究和咨询实践。
Rizaoglu 表示，许多人力资源技术供应商仍在努力弄清楚如何充分利用 GenAI 的功能，同时平衡保护数据、确保有效治理和考虑道德因素的需求。他表示：“在实现这种精细的平衡之前，GenAI 能力在人力资源领域的大规模扩散将面临挑战。”
Stelzner 表示，虽然去年 GenAI 带来了兴奋并刺激了人力资源领域的实验，但“冷酷的现实”是许多组织仍然没有准备好全力投入。
“到 2024 年，GenAI 采用率的任何增长都可能是渐进式的，包括更好地利用聊天机器人、增强员工沟通的个性化、更加关注人才招聘领域的可能性以及系统升级和实施测试的自动化。”他说。
埃森哲进行的研究发现，GenAI 有潜力改变组织 40% 的工作时间。“这并不意味着 40% 的工作岗位将会消失，而是反映了工作方式的转变，”负责该公司人力资源转型和交付实践的埃森哲董事总经理迈克尔·本亚明 (Michael Benyamin) 表示。“技术将取代一些任务，让员工在工作中变得更有生产力、更具创造力和效率。人工智能是人类能力的倍增器。”
随着 GenAI 开始增强或转变更多的工作角色，人力资源和学习领导者将需要创建敏捷的学习计划，以重新培训员工使用快速发展的 GenAI 工具的技能。许多工人几乎没有接受过如何使用该技术的培训。
Salesforce 于 2023 年进行的一项调查发现，62% 的员工表示他们缺乏有效、安全使用 GenAI 的技能。波士顿咨询集团的另一项研究发现，尽管该技术有望从根本上重塑他们的工作方式，但只有 14% 的一线员工接受过与人工智能相关的技能提升。
Benyamin 表示，随着 GenAI 在工作场所变得越来越普遍，人力资源部门必须帮助制定负责任和道德的人工智能使用政策，并制定培训计划来解决偏见、歧视、数据保护和适当数据使用等问题。
更加关注变革管理，提高新人力资源软件的采用率
专家认为，许多人力资源领导者将寻求通过采用变革管理策略来提高 2024 年技术投资的回报，例如确保员工使用新采用的技术解决方案。
人力资源面临的一项持续挑战是管理云技术供应商源源不断的更新和新功能，导致许多人力资源软件即服务 (SaaS) 许可证闲置。位于加利福尼亚州帕洛阿尔托的 SaaS 智能平台 Productiv 于 2023 年进行的一项研究发现，组织中 53% 的 SaaS 许可证总体未使用。
位于阿拉巴马州亨茨维尔的人力资源咨询和研究公司 Lighthouse Research 的首席研究官本·尤班克斯 (Ben Eubanks) 表示，许多组织低估了如何确保员工在新的人力资源平台和应用程序推出后定期使用它们。
“人力资源和人才技术不是‘按下开关就可以开始’类型的解决方案，”尤班克斯说。“但许多雇主仍然这么认为，并低估了采用该技术所需的行为改变。”
重新思考员工敬业度调查
更多的人力资源和执行团队将重新考虑如何创建敬业度调查以及分发调查的频率，以减少“调查疲劳”。
ServiceNow 高级副总裁兼员工工作流程产品总经理 Gretchen Alarcon 表示，随着组织继续努力寻找“秘方”，让员工在 2024 年更频繁地重返办公室，人力资源领导者将需要使用更有意义的方法测量工具。
她说：“组织将利用员工的声音调查和反馈来分析在办公室花费的时间与员工情绪和生产力的关系。” “这将使领导者能够根据数据而不是假设做出决策，这样他们就可以根据员工的需求、行为和提高生产力的因素来调整重返办公室 [RTO] 策略。”
从改进的技能技术中获益
转向基于技能的招聘和晋升策略的人力资源和招聘领导者将受益于技术的发展，例如使用人工智能和机器学习自动创建、组织和更新员工技能数据库的技能本体，从而显着减少体力工作量人力资源部要求。
下一代本体论和其他新兴技能技术可以使人力资源领导者更轻松地识别组织中的技能差距，然后相应地调整招聘或学习和发展计划。虽然市场上没有真正的端到端技能技术解决方案，但许多人力资源领导者正在将人工智能驱动的点解决方案结合在一起，以创建有效的技能数据库和评估工具。
“到 2024 年，随着组织采用技能智能技术，他们将开始认识到，这不是拥有最大的技能数据库，而是一个不断更新的丰富且互联的技能数据库，”Alarcon 说。她补充说，此类数据库使公司能够了解人才缺口是否是由于缺乏合适的人才或缺乏技能造成的，以及他们是否需要为未来培养、购买或借用人才。
预测分析工具变得更加强大
人力资源从业者和分析师认为，人力资源部门将受益于日益强大的预测分析工具，这些工具将改善劳动力规划和数据驱动的决策。
光辉国际 (Korn Ferry) 的冈萨雷斯 (Gonzalez) 表示：“凭借更大的数据集和改进的算法，人力资源部门应该能够采取一些措施，例如缓和过去几年的招聘盛衰周期。” 例如，冈萨雷斯表示，雇主不会雇佣数千名员工，然后在六个月后解雇其中一半，而是能够更好地预测在合理的时间内他们需要的员工数量和类型。他说：“然后他们可以雇用和培养一支更稳定的员工队伍，以造福所有组织利益相关者。”
Stelzner 认为，许多人力资源部门由于没有充分发挥数据分析的潜力而错失了机会。他说，如果未能投资分析人力资源数据所需的工具和技能，可能会导致洞察力缺失，并阻碍人力资源战略与更广泛的业务目标保持一致的能力。
“从历史上看，人力资源部门也一直在努力解决数据的准确性问题，”斯特尔兹纳说。“这会影响该职能部门依靠报告和数据分析来通知和支持其决策的能力。更糟糕的是，企业的其他部门已经接受过培训，预计人力资源系统会提供有问题的数据，因此在数据清理、报告和分析方面还有很多工作要做，以重新获得整个企业的可信度。”
Dave Zielinski 是 Skiwood Communications 的负责人，这是一家位于明尼阿波利斯的商业写作和编辑公司。
作者：Dave Zielinski
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