The Josh Bersin Company

Now that corporate AI tools from Workday, SAP, Oracle, Databricks, ServiceNow and many others have hit the market, it’s time to think about how we apply AI strategically. In this podcast I introduce the idea of Business 2030, a framework built from our HR 2030 architecture.

The simple story is as follows. While it’s easy to think of gen AI as an “automation” technology, its real value is in this ability to integrate vast amounts of data and predict, act, and learn from this stream. Well our companies are set up to operate as functional hierarchies, as we talk about in our research on the post-industrial age. What if you rethink or redesign your company around end-to-end customer experiences, and the functional areas of research, engineering, product design, manufacturing, marketing, and sales were “branches” of this core?

It’s a big idea, but to me it’s the future we all want. Programs like “customer data clouds” were first generation attempts, now it’s time to go further. And for HR leaders, you can help steer your company in this direction because the functional people hierarachies are often getting in the way.

I’ll be talking more about this throughout this Fall and 2027, and it’s all part of the AI reinvention that impacts every employee and leader.

Also, this week we’re launching our new book Superpowered, which describes the new AI-fueled world of “Superworkers,” “Supermanagers,” “Superleaders,” and “Supercompanies.” We’ll be sharing the book at Workday Rising, Unleash, and the US HR Technology Conference this month. Please join the waitlist it will be available on Amazon soon.

I look forward to seeing many of you around the world as we come to the Fall conference season with excitement and much to share. And more information on Workday coming this week.

PS: If you want to dive into any of the research, education, or tools we offer, sign up for Galileo Suite, which includes access to all our research and Galileo Learn, more than 800 courses and tools to help you understand this new world.

Additional Information

Understanding The Post-Industrial Age

HR 2030: The New Growth Operating Model for HR

HR Intelligence Goes Enterprise: The Galileo Jupiter Release

The Leader’s Guide To Multi-Agent AI Architecture

 

 

Chapters

(00:00:00) - Intro: Workday Rising and Business 2030
(00:00:27) - AI as Automation vs Redesign
(00:02:14) - Foundations: Customer Closeness and Delivery
(00:05:05) - The Functional Hierarchy and Industrial Scale
(00:06:52) - Speed, Innovation, and the BYD Problem
(00:08:19) - Mercedes vs Tesla: Subscription Services Misstep
(00:09:48) - Rethinking Company Operations: Business 2030 Defined
(00:10:52) - Cross-Functional Silos in Sales and Product
(00:12:23) - Customer-Centric Super Agents and Amazon Example
(00:14:07) - ERP Limitations and SAP's Industrial Bias
(00:15:04) - Product Management and Customer Data Insights
(00:16:24) - Customer Lifetime Value and Frontline Burden
(00:17:04) - Frontline Workers Absorbing Systemic Failures
(00:18:25) - HR's Role in Business 2030 Transformation
(00:20:12) - HR as Technologists and Cross-Domain ROI
(00:21:41) - Closing Thoughts and Upcoming Events

This week SAP explained its new Joule Work and Autonomous Enterprise strategy at its SAP Connect conference. As you’ll hear, SAP is building a solid set of AI agents to compliment and automate many of its business processes and it’s now all live.

What does this mean for HR? Well SAP customers now have lots to think about – including how Joule may interact with other agents, what agents and assistants to use, and how to interconnect the SAP Knowledge Graph with non-SAP data. It’s a solid strategy for automating existing business processes, and it’s now up to SAP customers and consultants to learn how to use all these agents for business transformation.

Moving to this second topic, I discuss our new research on “Managing a system of agents,” and how you should think through, architect, train, and manage your new family of AI workers. I talk about how specialist agents and Superagents fit together, and I encourage you to read my in-depth article on this important issue. Our new HR 2030 guidebook is available this week.

The future of business and HR is not just about what tools you buy, it’s now up to you (all of us) to decide how we stitch our new self-learning agents together to create scale and growth, not just small productivity gains. SAP is in a good place to help with this transformation, but so is Workday, HiBob, and many others.

Join our HR 2030 program for all the details, license Galileo, or enroll in our new Global HR Excellence Certification to learn all the details.

Additional Information

Building A Safe Multi-Agent AI Operating System: Lessons from HR and Ferrari

Chapters

(00:00:00) - Introduction and Week Overview
(00:00:27) - SAP Connect: The Autonomous Enterprise Vision
(00:01:18) - Joule Work and Agent Infrastructure
(00:03:00) - SAP's Missing Vision for Future Business Processes
(00:03:54) - Agent Governance and Safety Tools
(00:04:39) - SuccessFactors Agent Stacks and Integration
(00:05:15) - SAP as a Software Company vs Problem Solver
(00:07:07) - SAP's HR History and SuccessFactors Acquisition
(00:08:17) - Acquisitions and Legacy Naming at SAP
(00:09:07) - Overall Impressions of SAP Connect
(00:10:12) - Workday Rising Preview
(00:10:39) - Multi-Agent Architecture and Alignment Risks
(00:11:22) - Pharma Example: Agent Overreach and Legal Risk
(00:13:22) - Protecting Against Agent Misbehavior
(00:14:24) - Human Parallel: Specialization and Ethics
(00:15:37) - Why Specialization Matters More Than Ever
(00:17:02) - Designing Networks of Specialized Agents
(00:17:42) - HR 2030 as an Agent Operating System
(00:19:46) - Multi-Agent Research and Market Landscape
(00:20:54) - Reflections on Tech History and Survivorship
(00:21:51) - The J Curve of New Technology Adoption
(00:23:02) - Closing Remarks and Next Steps

There’s a raging debate going on about something called Superintelligence: the idea of a massive AI network that knows everything and does everything for us each day.

Bill Gates, Dario Amodei, Elon Musk, Sam Altman, Peter Thiel, and Nick Bostrom are all talking about this, and they’re painting a future where we won’t have to work and entire industries and professions will vanish.

In this podcast I try to demystify (and debunk) a lot of these claims, and I postulate that the world may get messy but our imperfect, lovable human selves will still rule the roost, so to speak.

Our new book Superpowered is all about this new world, and I think it’s more positive than you think.

Also listen to my story about my trip to the Ferrari factory in Maranello Italy, and you’ll see how AI may not be as “all-powerful” in our real world than these technologists may think.

Additional Information

Bill Gates on AI Superintelligence

Elon Musk on AI Superintelligence

Dario Amodei on AI Superintelligence

Nick Bostrom on AI Superintelligence

Superpowered: How AI Unlocks the Superpower of People, Leadership, and Teams

We’re All Talking to Each Other Less Than We Did a Decade Ago

Why Technology Failed to Transform Schools

 

Chapters

(00:00:00) - Introduction: Tech leaders' superintelligence claims
(00:01:24) - Effective Altruism and abundance movement critique
(00:02:02) - Lessons from social media's impact on society
(00:03:25) - Why humans get up in the morning: purpose vs pleasure
(00:04:59) - AI's limits in addressing human psychology
(00:07:13) - AI as a tool, not a replacement for human nature
(00:07:39) - Visiting the Ferrari factory: human craftsmanship
(00:11:10) - Ferrari's manufacturing philosophy and human touch
(00:12:57) - Uniqueness, legacy, and refurbishment at Ferrari
(00:15:06) - What Ferrari teaches us about human desires and AI
(00:16:02) - Multi-agent AI and personal application of tools
(00:17:02) - Risks of AI manipulation, lessons from social media
(00:18:31) - Education technology's mixed track record
(00:19:40) - Closing thoughts: philosophy, technology, and business

I’m really excited about the potential for Personal AI Agents. While GrokBot and Muse and Microsoft Autopilot (Scout) are very young (and not at all mature yet), we can now clearly see how personal AI is going to transform and disrupt our business lives.

In this podcast I explain the architecture of HR 2030, our reference blueprint for Agentic HR. You can see how personal agents, action agents, rules agents, monitoring agents, data agents, and Superagents all work together. And it’s important to think this through so you can plan your vendor selection, tech architecture, security architecture, and AI staffing and “management” plan.

Some important concepts:

HR “agents” do more than HR – they enable dynamic growth, productivity, and cost savings – so take a problem-oriented view

Clearly defined agents won’t misbehave, as you’ll see in our architecture

Companies will implement Superagents based on business priority, not just for ideation

SaaS incumbents are likely to deliver many of these agents, so SaaS Apocalypse may be over

Agent training, context, and maintenance is critical – and this gives you scale and growth – so new jobs are needed

Frontier labs may or may not survive this enterprise model

Personal AI agents will be massive and likely disrupt many HR tech vendors.

Lots to learn about here, read more on HR 2030 and call us or get Galileo to learn more.

Chapters

(00:00:00) - Introducing HR 2030 Architecture
(00:01:02) - Three Tiers: Super Agents, Agents, Personal Agents
(00:02:20) - Building the Agent Database and Use Cases
(00:03:23) - Strategic Planning vs Buying Point Tools
(00:04:13) - The SaaS Apocalypse Myth
(00:04:47) - Rise of Personal Agents (Muse, Grokbot, Scout)
(00:05:43) - What a Personal Assistant Could Do For You
(00:06:24) - Why Personal Agents Beat Vendor Platforms
(00:07:28) - How Personal Agents Know Everything About You
(00:08:06) - Positive Uses and Privacy Considerations
(00:09:34) - The Digital Twin in Practice Today
(00:10:14) - Personal Agents Helping Employees Grow
(00:11:01) - This Is Coming - Being Realistic
(00:11:25) - How Agents Will Interact With Employees
(00:12:09) - Impact on HR Software Vendors and Coaching Tools
(00:13:42) - How to Access and Engage With HR 2030
(00:14:17) - Galileo's Role and Ongoing Development
(00:15:48) - Falling AI Costs and Open Source Models
(00:16:21) - Copilot and Running Open Source Models Cheaply
(00:16:50) - Business 2030: Dynamic Enablement for Growth
(00:17:12) - Not About Headcount Reduction
(00:17:36) - Upcoming Conferences and Closing

Andreas DeNeve, founder of TechWolf, believes AI can “infer” your skills with great precision. After years studying this problem TechWolf has built a variety of AI language models to observe and identify your skills and tasks, which then form the basis of many decisions in business.

TechWolf monitors and indexes all your work activity and then builds a “skills context graph” to show you a skills map of your company. This is a big idea, one which companies like Eightfold, Seekout, Findem, Sana and many others are pursuing.

In TechWolf’s platform employees retain full control, validating inferred skills in the flow of work, with a 90%+ acceptance rate across white- and blue-collar workers.

The interesting part of this story is that this approach competes with the massive industry of testing and industrial psychology, used for decades to assess fit for a job. Andreas describes the company’s successes and how they’re building an entire “work intelligence” platform for AI transformation.

Skills Inference, which is a new AI application, competes with testing and assessment, which is used for screening and recruiting. Andreas discusses how inference is used for talent management, workforce planning, and AI enablement and transformation. Now TechWolf positions its platform as a system to help discover what tasks and skills can be automated by AI. (Now called “task mining.”)

This new market is growing quickly, so it’s important to learn about products like TechWolf in your new HR 2030 architecture.

Additional Information

Introducing HR 2030: A Vision For Agentic Human Resources

The Four “New SoftSkills” We Need To Thrive In The Age of AI (pod)

How To Take A Pragmatic (and winning) Approach To Skills

AI Transformation From the Frontlines (pod)

Chapters

(00:00:00) - Introduction to TechWolf and Andreas Deneve
(00:02:35) - Andreas's Background and Founding TechWolf
(00:03:59) - The Origin of Skills Inference from Work
(00:05:14) - How Skills and Task Inference Technology Works
(00:07:50) - Handling Non-Digital and Frontline Work
(00:10:38) - Data Readiness and Honesty with Clients
(00:12:08) - Employee Data Ownership and Transparency
(00:13:58) - Evolution of Use Cases: Talent to Workforce Planning
(00:16:01) - Rise of Work Intelligence and Task Proficiency
(00:17:09) - AI Transformation and Role Redesign Use Cases
(00:18:09) - Measuring AI Usage and Cost Efficiency
(00:19:36) - How Insights Are Delivered: Agents and Platforms
(00:22:31) - Biggest Challenges in Building TechWolf
(00:24:19) - Expansion to the US and Future Plans
(00:25:31) - Closing Thoughts and Final Wisdom