Understanding Microsoft's Voluntary Exit Program

This has been a big week for the AI industry, NVIDIA, US politics, and our partnership with Workday. In this podcast I give you some bigger historic perspectives on Jensen Huang’s transition, and also explain why this kind of thing happens. I hope this helps you make sense of this exciting, over-hyped industry. Per my many meetings with clients this week, AI is just as transformational as you think – it’s just going to take longer than we hoped to see this technology play out in real-world corporate solutions. In HR, our HR2030® project now has 50+ companies joining in this process, let us know if you’d like to participate. And I could not be more excited about our important partnership with Workday. Read more about it in today’s news release (demo included) and my article with more details. If you want to understand the real business implications of AI on your job, company, and career, read our new book “Superpowered: How AI Unlocks the Superpower of People, Leadership, and Teams,” now available. More Information HR 2030: How AI Superagents Transform HR From Service Delivery to Dynamic Business Enablement The Leader’s Guide To Multi-Agent AI Architecture What is Galileo Jupiter Release And How Does it Integrate with Workday? Chapters (00:00:00) - Introduction and Galileo on Workday announcement(00:00:35) - The Jensen Huang/Nvidia controversy unfolds(00:02:30) - Chip sales to China and political fallout(00:04:00) - Business, profit, and Milton Friedman's capitalism debate(00:05:57) - Lobbying, regulation, and the AI politics parallel(00:07:54) - Growth at any cost: lessons from corporate failures(00:11:24) - Growth as outcome, not strategy(00:13:42) - Companies as citizens and societal accountability(00:14:41) - Hyperbolic AI executive rhetoric examined(00:15:35) - Connecting corporate failures to political upheaval(00:17:29) - Looking ahead: rational AI adoption and HR 2030(00:18:40) - Closing remarks and Workday Rising invite

This week I give you my perspectives on the business lessons from the Blue Wave elections last week. As you’ll hear, the big message is all about what I call “Citizenship.” We are citizens within our companies, cities, states, and country. Citizens have responsibilities and important roles, seperate from “profit seeking.” And what I’ve learned from my book Irresistible, is that individuals always win at the end. Listen up – this is a series of lessons for us as leaders, workers, job seekers, and professionals. The Blue Wave shows us how individuals eventually rise up to do “the right thing,” and this same lesson applies to our companies. To understand the power of individuals in business, please read my book “Irresistible” and our new book explaining the role of AI in empowering people, “Superpowered.” Big announcement coming later this week. Additional Information Why The Blue Wave Will Be Good for Business, AI, and Tech US Workforce In 2035: A Few More Workers, A Lot More Output Corporate Citizenship Redefined: Trust, Inclusion, and Responsibility Chapters (00:00:00) - Introduction and Travel Update(00:00:17) - The Blue Wave: People Always Win(00:01:29) - Two Years of Political and Economic Turmoil(00:02:20) - Lessons for Leaders from the Election(00:04:21) - Why Stability Benefits Business(00:05:44) - Wages, Wealth Gaps, and Consumer Struggles(00:06:10) - Interest Rates, Debt, and Labor Shortages(00:08:18) - The Trust Crisis in Institutions(00:09:10) - Debunking AI Job Loss Fears(00:10:59) - Companies as Institutions and Internal Politics(00:12:05) - Leadership, Listening, and Autonomy(00:13:19) - Citizenship as a Business Concept(00:13:41) - Stakeholders Beyond Shareholders(00:15:04) - Employees as the True Stakeholders(00:16:46) - Citizenship, Bias, and Business Culture(00:18:13) - Competitors, Customers, and Final Thoughts(00:19:18) - Closing Remarks and Teaser

Skills are changing faster than traditional learning models can keep up — especially as AI continuously reshapes jobs and work. For Cisco, a global technology company with more than 80,000 employees, that reality demanded a more agile, strategy-aligned, and unified approach to development. Enter Edge Up: the brand behind Cisco’s bold learning transformation. In this episode, we speak with Marci Paino, Chief Learning Officer at Cisco — a 2026 HR Pacesetter Award winner — about how her team moved from fragmented legacy learning to a unified, AI-powered skilling ecosystem. Marci walks through four key pillars of Cisco’s learning transformation: AI-powered lifelong learning mindset, Cisco’s strategic capabilities, always-on modern learning, and a unified skilling environment. She also shares the results Cisco has seen: from replacing a 20-year-old tech stack three times faster than the industry average to a culture where employees embrace continuous learning. Marci Paino is Cisco’s Chief Learning Officer, driving AI-era workforce readiness for 80,000+ employees and leading the cross-industry AI Workforce Consortium, backed by 20+ years shaping talent development and digital transformation at companies like American Express, Accenture, and Western Digital. We awarded Marci a JBC Pacesetter award in 2026 for her leadership and strong AI-centric focus. If you have an innovative solution you’d like to share, join our Pacesetters Program. Additional Information Download the Pacesetter Research Irresistible 2027: The Global HR Conference for Leaders and Teams (Sign up now: May 17-19, 2027) Research: From Training to Dynamic Enablement Your Learning: The Global HR Excellence Certification Galileo goes Enterprise: The Jupiter Release       Chapters (00:00:05) - Introduction and Guest Welcome(00:01:54) - Marcy's Role and Cisco Learning Priorities(00:03:40) - State of Learning Before Transformation(00:05:35) - Overview of the Four Strategic Pillars(00:08:06) - Defining Guiding Principles and Culture(00:11:01) - Building the Skills Intelligence Engine(00:14:38) - Modernizing the Technology Stack(00:17:24) - Enabling Modern, AI-Driven Learning Experiences(00:19:30) - EdgeUp Labs and Time to Grow(00:22:43) - Measuring Success and Results(00:27:12) - What's Next: Agentic AI and the Skills Flywheel(00:29:36) - Key Takeaways and Closing Thoughts

More than 70% of US workers are frontline workers and most of our workforce is worried about AI. How do we as a country and we as employers help our people reskill, upskill, and educate themselves as adults? Well Guild, which started its life as a tuition reimbursement management company, has pioneered a new model. By working with employers and credentialed education institutions, Guild gives employers a set of solutions to train, educate, certify, and license people for new, higher paying jobs. For companies in manufacturing, healthcare, airlines, and other regulated sectors Guild also helps “up-license” the technicians and specialists we need. The solution to this problem is complex, and Guild took on this entire problem. The company works with many types of training and learning providers and also provides coaching, career pathways, and their own learning programs to help. For any company looking to fill critical talent gaps with new skills or new licensed workers, Guild is an amazing solution. Guild also focuses on giving employees a new sense of engagement and opportunity. Clients include Amazon, Pepsico, Lowes, and many other large organizations. Bijal explains the market and solution in a comprehensive way, this is a good conversation to help you understand the entire scale of this challenge. She also unlocks some of the secrets of broad reskilling and behavior change programs, including AI transformation. Additional Information Guild Jumps Headfirst Into The Corporate Learning Market The Capability Academy: Where Corporate Training Is Going What Is Task or Work Intelligence And Can We Decompose Every Job For AI? The Global HR Excellence Certification: Learn to be an AI HR Expert Chapters (00:00:00) - Introduction to Bijal Shah and Guild(00:00:51) - Bijal's Background and Path to Guild(00:02:43) - Guild's Original Model and Evolution(00:03:21) - Problems Employers Are Solving Today(00:05:49) - The Role of Education and Degrees Today(00:07:59) - From Good Deed to ROI-Driven Solutions(00:10:00) - Coaching, Human Connection, and Fragile Workforce(00:11:42) - Guild's Core Solution Framework(00:13:56) - Guild Academy and Behavior Change(00:16:52) - Navigator and Local Pipeline Solutions(00:18:23) - Amazon Case Study: Advanced Maintenance Technicians(00:20:28) - Disrupting the L&D Industry Model(00:22:30) - Managing Complexity as a Marketplace(00:23:48) - Cross-Employer and Cross-Geography Aggregation(00:25:54) - State and Government Partnerships(00:27:29) - Motivation, Fear, and Career Reinvention(00:29:59) - Durable Jobs and Economic Rebalancing(00:32:46) - Stitching Together Benefits, L&D, and Talent(00:33:48) - The Future of L&D and Capabilities(00:35:16) - Closing Thoughts on AI and Behavior Change

Vijay Swaminathan, co-founder and CEO of Draup, believes the future of workforce planning is not about studying people; it’s about studying work itself. After nearly a decade pioneering talent intelligence, Draup has pivoted toward the most urgent question every enterprise faces: how do you redesign work for the age of AI? In this episode, we sit down with Vijay to find out exactly how this works and why it’s more important today than ever. The centerpiece of this evolution is a new product called Etter — named from Norwegian mythology, meaning “always in pursuit of” — because AI transformation is never finished. Insights from Etter help companies model their workforce not around job titles and skills taxonomies, but around processes, workflows, and tasks, then simulate what AI automation would actually mean for each one — quantifying the opportunity in dollar terms for the CFO. For one hospital client, Draup sized the AI opportunity at $2.6–2.8 billion. The interesting part of this story is that Draup reveals a data problem lurking inside most enterprises: HCM systems are disconnected from where work happens — in ERP, Salesforce, Jira, and other operational systems. Draup closes that gap by integrating across HCMs, operational systems, and an evidence layer built on Microsoft WorkIQ, which captures how people actually spend their time. But all of this is not just a technology exercise: Vijay and his team take a consultative approach to solving customer problems, supported by technology. Vijay argues you cannot redesign work if you only know the skills people have, you have to start with understanding the work itself and then redesigning workflows and processes with the outcomes in mind. We call this dynamic work design. Work intelligence platforms like Etter are becoming a critical part of the HR 2030 architecture, and this new market is growing quickly. It’s important to learn about solutions like Draup as you build your AI transformation strategy. Additional Information What Is Task or Work Intelligence And Can We Decompose Every Job For AI? Introducing HR 2030: A Vision For Agentic Human Resources How To Take A Pragmatic (and winning) Approach To Skills AI Transformation From the Frontlines (pod) Chapters (00:00:00) - Introduction and Welcome(00:00:49) - Meet Vijay Swamy and Drop's Origins(00:02:56) - The Meaning Behind Drop and Etter(00:04:25) - Five Key Use Cases for Work Redesign(00:06:03) - AI Sandbox and Simulating Automation Costs(00:07:03) - Skills Architecture and Business Model Shifts(00:07:44) - Industry Convergence and Disruption Examples(00:09:03) - Building the Etrontology and Data Integration(00:11:19) - Integrating with Enterprise Systems(00:13:31) - Data Health and Hidden Enterprise Gaps(00:15:05) - HCM vs ERP Disconnect and Workforce Twin(00:16:35) - HR's New Muscle: Owning Work, Not Just Jobs(00:17:57) - Reframing the Story: Processes Over Tasks(00:20:14) - Layered Skills Framework: Root, Core, Tech, Soft(00:22:07) - The Future: Visual Factory Modeling(00:23:06) - Final Advice: Start with the Analyst, Not the VP(00:25:15) - Closing Thoughts and Wrap-Up

As I discuss in this week’s article, Microsoft introduced a “voluntary exit offer” that teaches us some lessons in modern management. It’s not a layoff by any means, despite the flurry of headlines. Listen to me explain why these kinds of programs are part of what I consider “the new model of management.” The old model was “hire to grow.” The new model is more of “grow to hire,” and it focuses on talent density, not the supply chain model of talent management. Additional Information Understanding Talent Density Galileo® goes Enterprise: The Jupiter Release HR 2030®: The New Architecture for Human and Agents Our New Book: Superpowered: How AI Unlocks the Superpower of People, Leadership, and Teams   Chapters (00:00:00) - Debunking the Microsoft AI Layoff Myth(00:00:45) - How the Voluntary Departure Program Works(00:02:08) - Talent Density as a Management Strategy(00:02:28) - Personal Story: Stuck at Sybase(00:04:29) - Making It Easy for Employees to Leave(00:06:02) - Why Most Layoffs Aren't About AI(00:06:51) - Outdated Hiring Models and Growth Myths(00:08:47) - Grow to Hire, Not Hire to Grow(00:09:45) - Microsoft's Legacy and Misreading Headlines(00:10:20) - The Human Side of Layoffs(00:11:23) - Microsoft's Strong Performance and Industry Interest(00:11:58) - Netflix, Amazon, and the Origins of Talent Density

The HCM (human capital management) market is being reinvented by AI. In this podcast I explain why and how HCM may become the center of the corporate AI revolution. Why? There are many reasons, but perhaps the biggest is that AI agents are like people, and the HCM platform will help us manage, optimize, train, align, and leverage these new digital workers. This impacts the revenue and strategies of Workday, SAP, Oracle, UKG, HiBob, Dayforce, Gloat, Darwinbox, ADP, and other vendors who make up more than $70 billion in IT spending. Listen more for details, or read the article published today. Additional Information AI Makes The HCM Market Hot Again: Workday, SAP, Oracle, and More. Galileo® goes Enterprise: The Jupiter Release HR 2030®: The New Architecture for Human and Agents Our New Book: Superpowered: How AI Unlocks the Superpower of People, Leadership, and Teams   Chapters (00:00:00) - HCM market size and context(00:01:29) - Why AI agents can't just bypass HCM systems(00:02:21) - Frontline workforce and the expanding role of AI agents(00:04:10) - HCM becoming a system of productivity and growth(00:04:47) - Six areas of transformation in HCM(00:06:21) - Token-based consumption pricing model(00:07:14) - Labor costs and financial performance impact(00:08:37) - Learning and development transformation(00:09:14) - Easier user interfaces and development tools(00:11:04) - Vendor strategies and market cap outlook(00:12:27) - ServiceNow's role in the ecosystem(00:13:21) - Challenges for smaller specialized vendors(00:14:29) - Microsoft Copilot as a competing platform(00:15:46) - Agent interoperability and network effects(00:16:50) - Sana, Microsoft Graph, and integration choices(00:17:38) - Agent architecture differences across vendors(00:18:57) - Learning from interconnected AI agents(00:19:51) - HR 2030 approach and buyer flexibility(00:21:31) - Closing thoughts and invitation

Today I summarize my keynote from Unleash Paris this week, where I draw a line between generative AI’s birth in November of 2022 and the Railroad Era of 1873. Not only are the parallels uncannily similar, it points out that AI’s takeover of our economy has impacted employment, politics, and our teetering stock and bond market. Here is the article detailing the story. I recommend you read two books after you listen to this. The first is 1873: The First Great Recession, which describes the global financial disaster brought on by the railroad boom. The second is our new book Superpowered: How AI Unlocks The Power of People, Leadership, and Teams. These two books will teach you what is likely to happen in our confusing new AI era, and how you should proceed. I also describe why the economics of AI are about to change, how NVIDIA has become a monopolist, and why there is no such thing as an “AI transformation,” rather a “Business transformation.” And I explain HR 2030 and how to think about your AI architecture before you rush out and buy dozens of new agents. Finally I also explain the four big use-cases for AI in HR, so you can stay focused in your investments. PS: Galileo is now available integrated with Workday, HiBob, Ema, Microsoft Copilot, Gloat, and ServiceNow. New executive compensation benchmarking data from Equilar is now added! Additional Reading Material Time to re-frame HR as an integrated OS? Josh Bersin makes his case Workday and Josh Bersin Company Partner on HR 2030 Education and Research Despite Massive AI Investments, HR Jobs Are Booming. Why? Introducing The Leader’s Guide To Multi-Agent AI Architecture Galileo Jupiter Release goes Enterprise Chapters (00:00:00) - Back from Europe: Unleashed Conference Recap(00:00:49) - The AI Buildout Boom and the Railroad Parallel(00:03:19) - AI Costs, Nvidia's Dominance, and Market Growth(00:04:44) - Inflation, Energy Crisis, and Worker Unrest(00:05:57) - Wage Stagnation, Labor Share, and Employee Engagement(00:07:55) - HR's Confusion and Vendor Landscape (Workday, SAP, Oracle, etc.)(00:09:24) - Wall Street Pressure and Buying AI the Right Way(00:10:05) - AI as Growth Driver, Not Cost Cutter(00:10:24) - Productivity Needs and GDP Growth Math(00:11:49) - Building AI as an Enterprise Architecture(00:12:42) - Build vs Buy and Managing AI Agents(00:14:08) - Big Vendors' Advantage and Data Ownership(00:15:09) - Framing AI as Business Projects, Not Tech Projects(00:15:51) - The J Curve and Vendor Market Shakeout(00:17:00) - HR 2030 Resources and European Riots Context(00:17:54) - Frontier Vendor Hype vs Market Reality(00:19:02) - Four High-ROI HR AI Project Areas(00:20:06) - Learning, L&D, and Galileo Learn(00:21:06) - Redesigning HR Business Partner Roles(00:22:12) - Other Opportunities: Onboarding, Payroll, Compensation(00:23:22) - Skepticism Toward AI Hype Figures(00:24:31) - Data Center Overbuild and Economic Outlook(00:24:58) - Superpowered Vision: Wages, Growth, and HR 2030(00:25:51) - Europe's Pragmatic AI Approach and Closing Thoughts

This week we launch our new book “Superpowered, how AI Unlocks The Power of People, Leadership and Teams. Based on more than 100 case studies and years of research on organizations and careers, this book sets the stage for your future as a Superworker, Supermanager, or Supercompany. Whether you’re a human resources professional, a people manager, a business leader, or a professional preparing for the future of work, Superpowered offers a way to think more clearly about AI and the opportunities it creates for human contribution. No prior AI expertise is needed to benefit from Superpowered – the book introduces its own frameworks, examples, and practical guidance for adapting to AI. For readers of Josh Bersin’s Irresistible, Superpowered acknowledges the reality of work today, and prioritizes preserving the employee experience and the human contribution to our organizations. For those starting with Superpowered, Irresistible offers a complementary blueprint for leading teams through change and disruption to create enduring companies that thrive with improved customer satisfaction, employee retention, and business agility. What Business Leaders Say “Superpowered is the clearest roadmap I’ve seen for turning artificial intelligence into human advantage: more capable workers, denser talent, and companies that grow by developing people rather than churning through them.”—Laszlo Bock, CEO coach and NYT bestselling author “AI is moving from tool to teammate, and that shift is going to change how every company operates. The companies getting this right are redesigning their operating models around what people do best. Superpowered lays out how to make that shift, and it’s a book that every leader needs to read.”—Aneel Bhusri, Co-Founder, CEO, and Chair, Workday “For decades, Josh Bersin has helped leaders anticipate how work is changing. In this important new book, he goes beyond describing AI to explain how organizations can redesign work so that technology amplifies, rather than diminishes, human potential. This is an optimistic, practical and timely guide for anyone leading organizations through the AI era.”—Lynda Gratton, Professor, London Business School, and author of Living the 100-Year Life: How to build a career that lasts and a life you love. “We know AI is transforming our jobs and lives, yet many of us have been left wondering exactly how this change will unfold. Superpowered offers both an inspiring vision of the future and practical, real-world advice on how organizations can create a more innovative, productive, and humane workplace.” —Arianna Huffington, Founder & CEO, Thrive Global Read details, endorsements, and testimonials. Order on Amazon today.   Chapters (00:00:00) - Introducing Superpowered: AI and Work(00:00:38) - Lessons from Irresistible: Empowering People in Organizations(00:01:47) - Economic Inequality and the Role of Capital vs Labor(00:02:24) - AI as an Empowering Force, Not a Job Killer(00:04:38) - Growth vs Cost-Cutting: How AI Adds Value(00:05:00) - The Driverless Car Analogy: Redesigning Work(00:06:33) - Agents as Digital Workers and Scaling Human Capability(00:08:37) - Will AI Replace Leaders? The Human Spark of Creativity(00:10:10) - Careers, Longevity, and Continuous Learning in the AI Era(00:11:46) - Book Background and Closing Thoughts

There’s a big flurry of interest in Job-Task Analysis (an old discipline) to break down jobs into tasks, categorize and inventory these tasks, and then “Voila,” you can build AI agents to automate these tasks. It sounds like a great idea. So data providers like Lightcast, Draup, SAP (TechWolf), Reejig, and Workday (using Stanford data) are busy building these huge “Job-Task” data sets so you can now look at your company or department as a “basket of tasks” and apply AI to build just the right agents to streamline work. (And also make decisions like centralizing some tasks or building better learning to cross-train people for such tasks.) As you’ll hear me describe, this kind of work is extremely valuable for regulated, structured, safety-related, and operational jobs. If we know that every airline pilot has to master the task of “lowering landing gear at the right speed and time” it makes perfect sense for the aircraft to do this (or tell the pilot) automatically. Similarly if every sales person has to update Salesforce in a 2 minute “task” we should build an agent to do it accurately. So there are task automation opportunities in every business area, every function, and every job. The challenge is that these “tasks” don’t always stand alone: they’re related to broader job roles and business processes. So taking an inventory of tasks is great (we know all the tasks an HR Business Partner does, for example), but we have to think strategically about which of these are suited for AI, which are suited for regular IT automation, which could be outsourced, and which are very “context sensitive” and may need to be treated differently. In our interviews with clients we find a hierarchy to this work. First, what is the business goal for this job or work? Second, what are the broader product, service, or customer outcomes we’re trying to achieve? Third, what are the steps or tasks and are they “rigid or mandatory” or are they “judgemental and optional?” Fourth, how do these individual tasks relate or chain together? And Fifth, what human or technical or judgement skills are needed? In other words, just listing the tasks and knowing the “size” of each is not enough. We really do need to re-engineer these things in the context of outcomes, customers, important workflows, and the subtlties of “doing the task well.” And when this is done well we can use it for recruiting, training, performance management, and AI agent development. What we want to prevent is a project to do task analysis for its own sake, because this kind of work has been around for decades and it’s often used primarily for safety, compliance, operations, and highly structured work. I think that AI, with all its learning capabilities, may make this domain far bigger than ever before. So this domain is a very fast-growing part of HR 2030 and our ongoing work to “agentify” our companies. We are doing case studies in this area now – please contact us if you’d like help or share your work intelligence project. More detailed article here. Additional Information How To Do Job-Task Analysis Workday Introduces Work Intelligence to Help Customers Design Work for AI Lightcast Work Intelligence Draup Task and Work Intelligence Guideboooks TechWolf (SAP) Work Intelligence Reejig Operating System for Work Chapters (00:00:00) - Introduction to Task Intelligence(00:01:33) - How Task Data Is Built from Jobs(00:04:32) - Where Task Analysis Works Well(00:06:32) - The Risk: Institutionalizing Old Ways of Working(00:10:07) - Lessons from the Skills Taxonomy Era(00:12:17) - Finding Value: Cross-Functional Job Consolidation(00:14:41) - Process Mining as a Related Approach(00:15:20) - Using AI and Digital Twin for Process Mining via Email(00:18:05) - Data Retention and Practical Challenges(00:19:16) - Final Thoughts: AI Must Serve Business Goals