The Josh Bersin Company

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 Swami, 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