The Josh Bersin Company

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(00:06:51) - Outdated Hiring Models and Growth Myths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