The HR Leader's Guide to Multiagent HR Architecture, with Josh Bersin

In this episode, I talk with Josh Bersin about the latest report out from the Josh Bersin Company report, The HR Leader's Guide to Multiagent HR Architecture (part of the HR 2030™ initiative), which outlines how HR leaders must govern over 151 specialized AI agents and 8 functional "superagents". The core thesis is that orchestration, not agent count, drives value. Josh Bersin is an analyst, author, educator, and thought leader focusing on the global talent market and the challenges and trends impacting business workforces around the world. He studies the world of work, HR and leadership practices, and the broad talent technology market. He is often cited as one of the leading HR and workplace industry analysts in the world.

• 0:03Speaker 00

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• 0:35Speaker 00

  Josh Bersin, welcome to the conversation today.

• 0:37Speaker 01

  Thank you, Jonathan. It's great to be here.

• 0:39Speaker 00

  It is great to be with you again. You're joining us from San Francisco. I'm south of Salt Lake City in Utah. Today, we are going to be talking about the latest report out from the Josh Bersin Company, The HR Leader's Guide to Multi-Agent HR Architecture, which is part of the HR 2030 Initiative, which outlines how HR leaders must govern specialized AI agents and functional superagents. I'm super excited to have the opportunity to sit down with you and to discuss this. We were scheduled previously, had some travel mishaps and whatnot. We had to reschedule. Anyways, thank you for your patience, and it's a pleasure to sit down. As we get started, I wanted to share Josh's bio with everyone,

• 1:23Speaker 00

  though I think everyone knows who you are, Josh. Josh is a well-known analyst, author, educator, and thought leader focusing on the global talent market and the changes and trends impacting business workforces around the world. He studies the world of work, HR, and leadership practices, and the broad talent technology market. He is often cited as one of the leading HR and workplace industry analysts in the world. And I think this may be, I don't know, the third or fourth time we've had an opportunity to chat. Always a pleasure. Before we dive on in, anything you would like to share with the audience by way of your own background, personal context, or about the company, and then we'll get going with the report?

• 2:04Speaker 01

  I mean, it's, at the moment, it's all about AI.

• 2:08Speaker 00

  Yeah.

• 2:09Speaker 01

  It's all everybody wants to talk about, so we're spending most of our time on that. But of course, we don't have to talk about it today, but there's lots of other questions about pay, and job titles, and roles, and structure of companies and so forth that's being affected too. But let's talk about AI first.

• 2:25Speaker 00

  Yeah, for sure. It's ubiquitous. Like, it's really at the forefront of everyone's attention, which is understandable, sometimes frustrating. It is what it is, right? Well, first, tell us a little bit about the report generally. Maybe even start with HR 2030 generally, the initiative, and why this report?

• 2:51Speaker 01

  Well, we started HR 2030 about a year and a half ago, because there's been this proliferation of ideas of what AI could do to different parts of HR, from recruiting, interviewing, assessing, training, employee experience, analysis, pay, rewards, et cetera. And there's going to be hundreds of use cases of AI, of course. But we wanted to kind of get the big picture. So we sat down and said, let's imagine we could fast-forward ahead four and a half years. What do we want the world to look like, ideally? And so, we ended up with an architecture of 160 agents, which sounds ridiculous, but it makes sense when

• 3:35Speaker 01

  you think about it, interoperating with each other in a certain sort of networking effect, with eight superagents that manage and coordinate the 160 other agents. And we introduced this, and the reason we, the way we came up with that is we had spent the last 10 or 15 years studying HR and the different, the 95 functional domains of HR. So we kind of knew a lot about the complexity behind this. And we knew, and we had already done a whole bunch of research on what we call systemic HR, which is HR operating as an integrated system, as opposed to a whole bunch of standalone services. And, you know, I didn't really know what people were going to think of it. It was almost an intellectual

• 4:18Speaker 01

  exercise, as much as anything else. Then we built a whole model, a visual model of it and everything. And we started showing it to people, and they said, oh, my God, please come help us, because we're getting flooded with vendors, and tools, and technologies, and bots, and stuff. So it's become a body of research and a set of tools to help companies decide where they are, and where they want to go in their roadmaps, because this is a lot of, lot of change happening out there. And then, the multi-agent architecture piece is one chapter. There's a bunch of elements to this, but to deal with the issue of, this is a system of agents, this isn't an agent. And you could have debate. I just had a meeting

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  last night with a computer science guy, you know, is a big agent better, or a small agent? And that's part of what this is about. And if you do have a bunch of small agents, how do you manage them all?

• 5:11Speaker 00

  Right. Yeah. So 160, you know, specialized agents, the eight functional superagents. You know, we are in the world of agentic AI. Long gone are the days of just, you know, ChatGPT, and large language models, and bots, right? Like, that is so far behind us now. And, and like you said, there were so many tools, so many vendors. I feel like I'm on calls constantly, talking with folks about the new and greatest, coolest thing. And there really are so many cool things, right? There's no doubt about it. I'm sure you're constantly

• 5:51Speaker 00

  on calls, too, about all of these things. And, but, but yeah, getting beyond, getting beyond just the cool little tool, or the new, the new fad, or the new thing that comes out, and every week, there's something new. It's all about the agents, and it's all about how to, how to organize and manage the agents, which, which I just find so fascinating. And anytime I get a chance to talk with somebody who really has figured out how to, how to connect and integrate the agents, that's, that's really what it's all about. Because, because so many people have, you know, have agents, and so many people are talking about

• 6:31Speaker 00

  agentic AI. I've seen very few who actually have it systematized, who really have it integrated, who really have, you know, an approach that seems to be working, yeah, in any systematic way. You know, just so many teams, so many organizations, it's just so haphazard, right?

• 6:50Speaker 01

  Well, in fact, you know, the, the, the misnomer, misunderstanding, is that if I build a little tool, a little AI agent that does something, that's a good thing. Well, it may be, but it may also not be. And I actually think there's a downside to doing that, because if you have your own little personal agent that does something, and it's not correct.

• 7:11Speaker 00

  Right.

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  What other, first of all, what are you going to do to fix it in a big company? And who else is using it, or relying upon it for what? And are you proliferating the mistakes you're making into other people's agents? There's just, you know, these are not simple little tools. They look simple when you play around with ChatGPT, but they learn over time, and they get smarter, and smarter, and smarter. So I'm, as an architectural thinker, really trying to tell people to just think about the big picture before you dive into this cool little thing that you want to work with, in terms of scale, ultimate connectivity, credibility, trust, protection of assets, protection of data, protection

• 7:54Speaker 01

  of security. And I think that's doable. But the funny thing about it, Jonathan, is I've, since we started this, I've been reading up on, you know, all these people that have done lots of work on this, Palantir, and IBM, and others. There's not that much written about how to manage a complex multi-agent system. It's not, because the actual activity and behavior of an agent is not well-known. I mean, the guy I was talking to last night does AI safety research, and he said, you know, there's, there's like five or six different tributaries of research on understanding why an agent does what it does. And so, so I think what we've come up with is enough information, and I talked to SAP in great

• 8:35Speaker 01

  detail about this at their conference. I've talked to Workday about it. You know, I think there's some really important lessons here. So, that, and the other reason that I, you know, I think this, we have to think of this systemically, is if you think of HR as a bunch of standalone services, you're going to get your budget cut. People are going to say, well, why are we spending so much money? Cut the headcount in half, and let's just do less of this, and turn it more over to agents. If you think about it as an enablement function for growth, and that's what I think HR ultimately is, is a dynamic enablement for growth, through hiring, through training, through redeployment, through skilling, whatever it may be, through pay, or job redesign, or scheduling, or, you know, whatever benefits.

• 9:20Speaker 01

  All of a sudden, every decision has an impact on the bottom line and the top line of the company, not just making somebody happy. Then you want these things to work well, so you can learn. Because what we're going to do is, we're going to learn lots and lots of things about our companies that we didn't know before, because AI is so smart.

• 9:37Speaker 00

  Yeah, I love that. The enablement function for growth. I love that framing. And you just mentioned it, you know, a minute ago. I want to double-click on it. You know, the, the failure that comes from unconnected agents.

• 9:50Speaker 01

  Right.

• 9:51Speaker 00

  Which, which is happening all around us. So everyone, you know, we, we feel so special, because we, we create our own little agent. And then, and then, you know, which may work really well for our own little workflow, in our own little world, in our own little silo, but it can cause problems. Like, and

• 10:10Speaker 01

  Well, just yesterday, I don't know if you saw yesterday, Goldman Sachs just shut off Anthropic for all their non-ITF professionals. They don't want people hacking around with it anymore, because it was expensive, and it was actually not working out very well. I think one simple way to think of it is, if you gave everybody a copy of Excel, and said, build a spreadsheet that analyzes whatever it is you're doing, and just share with everybody else in the company. Can you imagine how many errors would be suddenly just proliferated throughout the company? Right? Because everybody's spreadsheet's got glitches in it, and it works for me, but it wouldn't work for you, because you don't know how I did it, or why I did it. You know, it's a little bit like that. And we, you know, so, I'm not saying personal agents are going away. We're all going to have all sorts of cool personal

• 10:52Speaker 01

  agents, but let's just do it in a managed, you know, process. And another analogy that I think's useful, is the more I talk to companies about this, the more I do believe that the agents are like people. So if you think about A new employee in a company. The employee has a job title and a role and a set of goals and responsibilities. And you don't give the employee access to all of the information in the company, all of the secret projects, all the financial information, because they're gonna make a mess.

• 11:26Speaker 00

  Right.

• 11:26Speaker 01

  Because that's not their job. So we think of the agents as individuals with very clearly defined, specific responsibilities, that we want them to be really, really, really good at this. All of a sudden, that point of expertise is really useful to other agents who need it and don't want to replicate it. So I think the division of responsibility, or whatever the right phrase, there is a word for that, I don't think that's exactly the phrase. Clear delineation of who's responsible for what really makes AI safer and more scalable and more powerful. And there's different ways of mixing things up, as the paper discusses, but, you know, if we, if we, and I think a lot of these rogue agents that went nuts and, you know,

• 12:10Speaker 01

  hacked into other websites, were probably experiments where the agent was deliberately given a whole bunch of opportunities to make mistakes, and they were testing how far it would go.

• 12:21Speaker 00

  Yeah.

• 12:21Speaker 01

  And, you know, because these things are probabilistic, they're gonna keep trying to achieve their goal until they find a way to achieve it. And if you give it too much opportunity, it's gonna go down to the tail end of this probabilities curve and do something dumb.

• 12:35Speaker 00

  Right, yeah, yeah, absolutely. One of the things that's talked about in the report is workload reduction. And of course, if we have a bunch of productive agents, this is bound to happen, right? So, proper management of agentic families can reduce HR workloads by up to 40%, is one of the findings in the paper. Talk to us a little bit more about that, what you mean by the, these agentic families. And you talked a minute ago about, you know, reducing HR budgets. If we don't like work systemically together, like different HR functions, we're just gonna find ourselves having our budgets reduced

• 13:19Speaker 00

  versus being the enablement, right, for the business. How do you, how do you look at this finding along with kind of what we've been talking about up to this point?

• 13:32Speaker 01

  Well, the first part of it is how you frame the AI investment. If you go to your boss and say, hey, as a result of spending all this money on AI, I'm gonna cut our costs by 72%, well, you just basically put yourself in a hole. You just did the wrong kind of business case. If you, on the other hand, if you go and say, as a result of this AI project, we're gonna be able to hire people 75% faster, with, you know, two-thirds higher quality of hire, therefore, we can grow our business at a higher rate of speed and do more things than we wanted to do before. That's a different way of framing the problem. So that's the first thing. The second thing is, if you look at the behavior of the HR people in the company today,

• 14:15Speaker 01

  we've got, you know, maybe an instructional performance consultant, an instructional designer, a content developer, an LMS administrator, and training. And then you go to recruiting. We've got a, an HR business partner, a sourcer, a screener, an interview scheduler. We have all these job titles that are kind of narrow and kind of tactical sounding, because what they make up is a step in a process that somebody years ago broke down into a job family. They took a process and they said, let's just take it apart like an industrial process and put it into a job family. Well, you know, if you, if you sort of think about the agents,

• 14:57Speaker 01

  if you had a specialized agent that did each one of those individual steps, and it got really, really, really good at that step, that job's gonna go away. But the person who used to do that job is gonna realize, well, now, all of the intellectual knowledge that I have about that domain, I can train this thing, and I can keep, you know, make it smarter over time. And once I have it to help me, I can do five times as more work or at a higher level than I was before. So all of these individual agents sound like job replacements, but what they end up doing is upgrading or giving people opportunities to work at what are called the top of their license to add more value.

• 15:40Speaker 01

  So, like most recruiters, when you talk to a senior recruiter, they don't like doing sourcing and writing Boolean searches all day to find people. They wanna talk to people and really make sure they're the right fit and give them a sense of power that they can improve their careers by coming to work for your company. That they're sort of, you know, coaching, salesy kind of people. That's what they wanna do. So if they can offload some of that mundane work to an agent, they can spend more time doing the high-quality work that they really are good at. Same thing's true with training, same thing's true with business partners, employee experience people, I mean, all of these jobs. So, I think there will be jobs that are no longer needed, and some call centers will be smaller. But I'll give you an example of where this didn't work out.

• 16:23Speaker 01

  So a large bank that's been working on a big global onboarding agent system for a while. Originally, when they built the, started the project, the goal was to eliminate this big call center that did global onboarding, because they do onboarding in 130 countries. So they have a whole bunch of, you know, rather country-specific regulatory things to do. So they started building the agent, they took all the documentation they had, they started rolling it out. What they found is that no matter how hard they tried, most of the work these people were doing was not written down.

• 16:56Speaker 00

  Right.

• 16:56Speaker 01

  So there was no way to train the agent to know what these people knew unless we interviewed all these people and asked them hundreds of questions about, well, what do you do in this case? What do you do in this case? What do you do in this case? What do you do in this case? And eventually, they're, I think, getting to the point where they're starting to see maybe headcount reduction. But by training this thing into a super agent, they're gonna have scale and quality data about their workforce that they didn't have before as a result. So, you know, maybe the original goal of cutting the size of that call center is somewhat realized, but the better goal is faster hiring, more accurate hiring, more legally compliant hiring and so forth. So, most of these projects seem to go in that direction.

• 17:39Speaker 00

  Yeah, well, and I recall, I don't remember how many months ago it was, but it was relatively recently, you know, the story out of IKEA with, you know, their, their updates with their call center. You know, they automated with AI. And they had the choice that so many organizations have. Do they lay off all of those people? Now, now they have, you know, you know, so many of the customer service calls being handled by AI. Well, they did, you know, something totally different. They, they upskilled their, their people. They essentially made them into kind of like mini interior designers.

• 18:20Speaker 01

  That's what I would have guessed.

• 18:22Speaker 00

  So now, now when people-

• 18:24Speaker 01

  So they can sell more stuff and do better stuff.

• 18:26Speaker 00

  Yeah, so now when people call in, they go, they go through the initial screening with the AI, they get passed along to this kind of mini interior designer who can provide better customer service, who actually can sell more stuff. They actually saw an increase in sales, quite a dramatic, dramatic increase in sales. They increased their business, they didn't lay people off. Like, it enabled them to grow, right? And it wasn't, it wasn't a reduction. And, and that's exactly what you were talking about earlier. So it's a different mindset, right? And I think it's different than when you have like an oracle that's just like, we, we're gonna lay off 40%, we're gonna send out these emails at 6:00 AM and just let go of people.

• 19:09Speaker 00

  Like, that's a totally different way of thinking about your workforce than, you know, like how-

• 19:15Speaker 01

  Well, there are companies, I mean, I mean, honestly, I'm sure there are companies that could afford to lose 40% of their people, and maybe that's just, you know, they just hired too many people and they're not managing well. But, but, you know, my, you know, belief is that in most cases, if you look at a project that looked like a cost-reduction project, but reframe it as a customer intimacy growth project,

• 19:37Speaker 00

  Yeah.

• 19:38Speaker 01

  you will find that the second opportunity is sitting in front of you, but you weren't thinking about it that way.

• 19:43Speaker 00

  Yeah.

• 19:44Speaker 01

  And I know in the case of HR people, people don't go into HR to do administrative work. They don't like doing it, nobody likes doing it. They go into HR to help people and to help the company grow. So this tends to be the heart and minds of what HR people have been trying to do their whole careers. And we're trying to make it easier for them to do that.

• 20:02Speaker 00

  Yeah, yeah, absolutely. So we've been talking about it all along, but the super agent architecture, so again, we're taking individual AI agents, they're being grouped into these eight functional super agent families. The whole idea here is that you're gonna have these HR domains where they're grouped together functionally, they're working together. So you're not having these chaotic, you know, individual agents off doing their, their own unmanaged kind of free-for-all, their own thing.

• 20:32Speaker 01

  Well, if you, yeah, the example that I think is, like, if you compared SAP strategy versus Workday's, SAP's building hundreds of agents, Workday built 17. Workday basically built 17 super agents. SAP built 80 or 90 individual agents. I'm sure they're both gonna end up in the same place, but, so you go to an agent and you say, I need to grow my team to do more work, to sell more stuff, write more code, whatever.

• 20:58Speaker 00

  Yeah.

• 20:59Speaker 01

  The agent doesn't know what the word "grow" means. Does it mean we're gonna make them taller?

• 21:06Speaker 01

  Does it mean we're going to have more of them? Does it mean we're going to improve the skills of them? We need to teach the agent to think broader. So the idea of the super agent is the super agent is the place where you add, what Workday calls it, the decision intelligence agent. You give it bigger questions and let it go to the specialists to give you scenarios for answers. Because most of the HR problems that I run into have many, many possible solutions. I mean, every HR issue has dozens of potential solutions. We can fire the person, we can give them a coaching plan, we can train them, we can move them to a new job, we can give them a new manager, we can pay them more money, we can pay them less money. I mean, you know, come up with 50 ideas on how to make this stuff work better.

• 21:52Speaker 01

  The agent should be able to go and get the information from other agents on what the scenarios look like, and then give you, you know, an intelligent set of recommendations on what the right solution would be. And the more these things work through a company's evolution over time, the smarter they are that, you know, in this case, redeployment is usually a better solution than hiring from the outside. Because the last 10 times we hired from the outside, eight of the people didn't work out and we had to hire somebody else. So whereas, you know, that might be in the head of a HR business partner, or it might not. So a human may not have that insight. So there's a lot of, I mean, I think a lot of HR is sort of art and science mixed together, and it's probably too much art because we don't have enough science.

• 22:39Speaker 00

  It's the alchemy of HR, right?

• 22:41Speaker 01

  So I, I mean, and I've seen this in recruiting and recruiting, because that's where the most mature AI has been implemented. And recruiting, these recruiting systems are very smart about who's going to be successful. Like, like Delta Airlines has been running a bunch of AI in their recruiting. And they told me the other day that the most successful flight attendants are not coming from customer service or hospitality jobs. They were school teachers. Because I don't know exactly why, but you could guess, school teachers are kind of good at, you know, taking care of people that don't behave and being nice to people and listening and teaching and being patient. You know, that kind of personality would be great to have somebody on a plane with you.

• 23:22Speaker 01

  So, but that may not have been obvious from the skills model that some I O psychologist did.

• 23:30Speaker 00

  Yeah, super interesting. Well, Josh, this has been a great conversation. I know we've only scratched the surface. There's so much more in the report and in the initiative, all the way around. I encourage people to check that out as we wrap things up for today. I just wanted to give you a chance to share with the audience how they can connect with you, find out more about your team, more about the report, and give us the final word for today.

• 23:52Speaker 01

  Well, we're, almost everything we do is on joshbersin.com, J O S H B E R S I N dot com. Or our Galileo agent, our Galileo agent has everything we've ever done in it. It's a, it's a link from that website. But for people that really want to dig into this more, we, Jonathan, we launched a, an HR certification program called G H R E, Global HR Excellence Certification. And so take a look at that on our website. It's a 12-week in-depth sort of master's degree where you get your hands on using this, building agents, implementing systemic HR and real case studies. And that's another way for people to learn about this. So those are some of the things that, that, that we can offer.

• 24:34Speaker 00

  Wonderful. Again, Josh, thank you so much. It's been a pleasure. I encourage the audience to reach out, get connected, check out the website, check out the academy, check out Galileo, all the things. And as always, I hope everyone can stay healthy and safe, that you can find meaning and purpose at work each and every day. And I hope you all have a great week.

• 24:56Speaker 00

  Thanks for joining us for this episode of the podcast. We hope you stay healthy and safe, and please join us again soon.