Andreas Welsch 0:01

Everyone's talking about AI agents, individual ones, multi-agent systems where agents work with each other. But the question is what actually happens when you want to introduce that into the HR landscape. Today, we'll talk about just that, so stick around for more. All right. Welcome back for another episode of "What's the BUZZ?", where leaders share how they have turned AI hype into business outcomes. Today, we'll talk about the top criteria for multi-agent systems in HR and I could be more excited to welcome my guest, Kris Saling to the show. Kris, thank you so much for being with us today.

Kris Saling 0:33

No, thank you so much for having me. I'm really excited about this topic. It's gonna be fun. I.

Andreas Welsch 0:36

Fantastic. Hey we've been following each other on LinkedIn for a long time. And I'm really excited that you are taking time outta your busy schedule to be with us today. But maybe for those of our audience members who are not familiar with you yet, can you share a little bit more about yourself, who you are and what you do?

Kris Saling 0:50

Sure thing. So I'm Kris. I'm Kris Saling. I'm a career Army officer. I work for the Assistant Secretary of the Army for Manpower and Reserve Affairs, who is basically the closest that we have for our Chief Human Resources Officer for the Army. And that means we're responsible for how we manage the talent acquisition, the training, the personal files, everything for about 1.1 million people. So it, it's a significant lift and my job is to bring new technologies and AI and partnerships into the personnel domain and it keeps us pretty busy. 'cause we've got a lot of stuff to manage,

Andreas Welsch 1:27

I'm sure. Wonderful. But I also know you are saying your personal opinions and experiences. Yes. With us today. I know you've been in the data and AI space for a long time, so very thankful again to learn from you and bring your expertise. No,

Kris Saling 1:39

I appreciate you bringing that up. 'cause I am, I'm sharing my own personal opinions. My personal background. This is not any official Army opinions, but I've been working in this space for the Army for about 10 years and that's been enough to accumulate a fair bit of research.

Andreas Welsch 1:54

Fantastic. I'm sure, a lot has happened in the last 10 years looking at emerging technologies.

Kris Saling 1:58

Oh

Andreas Welsch 1:59

yes. Keeps emerging. That's the exciting part about it.

Kris Saling 2:02

Faster and faster. That trajectory is just

Andreas Welsch 2:05

crazy. Yeah. So folks, if you're in the audience put a comment in the chat where you're joining us from. I'm always curious to see how global our audience is, and if you're looking to learn more about how you can empower and encourage your team members to use AI without creating slop or low quality content, consider picking up a copy of my new book, the Human Agentic AI Edge on Amazon, and any any online retailer. Fantastic. Alright Kris. Should we play a little game to kick things off in good fashion?

Kris Saling 2:30

Sure.

Andreas Welsch 2:31

Alright it wouldn't be called, what's the buzz if there wasn't a buzzer? So I'll play a little clip and you'll see the wheels will start spinning and when they stop, I'd love for you to answer with the first thing that comes to mind and why, in your own words, to make it a little more interesting. You only have 60 seconds for your answer, so are you ready? Okay, here we go. If AI were a sports car, what would it be? 60 seconds on the clock. Go.

Kris Saling 2:58

So that's probably your Ferrari, your Formula One racer. It's got a lot of ability to get you from A to B really quickly, but it's one of those things where you need a little bit of instruction and foundations in how these things operate to drive safely. A few years ago, I would've just said plane out a car. But right now it's like this is a speedy high performance car that needs very deliberate instruction and handling to be able to use it safely. And we have a lot of possibilities of, parking it in the storefront if we don't do it properly.

Andreas Welsch 3:27

I love that. Perfect answer. So high speed, also high risk, but if if you're the first across the finish line. Big returns and lots of champagne.

Kris Saling 3:35

Absolutely.

Andreas Welsch 3:36

Yeah. Love it. Wonderful. But obviously we're not here to just talk about cars in that one. But the other exciting part of our conversation about how do we bring AI in agents is especially in, into organizations, right? Whether there, there's in the public sector in the private sector. I think there are some some things that are pretty similar and I know you, you've been working on some of these things. Specifically when it comes to agent systems, multi-agent systems in the HR domain. But I'm curious, what are you seeing? What are some of the foundations about workflows, about how work's done that you need to be aware of to decide where agents even make sense?

Kris Saling 4:13

I think that's the biggest thing that I caution people on when they're looking at integrating any kind of AI into their workflows. It's a means rather than an end. And I think so many people will sit there and say we need AI to do what? What does it do and what is it for? I separate out those questions as I'm talking to my senior leadership and my decision makers, because even if they understand what it does, the what it's for part, it's like tying everything that we bring in terms of technology, not just AI into our business objectives and outcomes. How is it going to get us from here to that outcome? How is it gonna do it quickly, safely, effectively? And then what needs to change about that underlying workflow for it to happen effectively? I think a lot of what we need to do as we're incorporating agents is not just, by the tool, but we have to look at that entire workflow and see how that needs to change.

Andreas Welsch 5:01

That, that isn't always easy, right? The workflows differ by by department, by team by company. Yeah. Some things obviously are standardized thinking of recruit or retire as a big process. But how do you then decide where it makes sense to, to put an agent in,

Kris Saling 5:18

that, that's one of the things too. The minute you start talking about business process review, everybody rolls their eyes and groans like, oh, it takes so long. It's so hard. But guess what? There's an AI for that. So you can take a lot of these your business SOPs, you can take in our case, army regulations and public publications and policies. And obviously, we wanna make sure it is an appropriately ring-fenced AI tool that, we're using. We don't want something open source to leak all of our information into the out or beyond. But, we do have tools developed that are contained that we can do this with. We can extract those workflows, at least in a draft that people can edit. So they're not going in, filling out time sheets. How much time did you spend on this? What did you do? They have a starting point that makes it a whole lot easier to tweak and edit and say, okay, this step is outta line. I really do this. You can get those things done a lot faster. Then you've got a starting point, which again, I'll take and feed into an AI tool and say, okay, here are, here is the workflow, what are all the different tasks? And give me an estimate on my reduction in time. Increase in efficiency. We've got a number of different metrics that we use in this space to figure out what it is we wanna do. Sometimes it's reduction of time. Sometimes it's cost avoidance. So if I have a human doing it, how many times does a product get sent back for revision? Whereas if a machine does it, it fills it out the right place, it goes forward. And then we start looking at, where's the paperwork pile up the most? And it can be virtual paperwork too. Where are the emails pile up? Where is something sitting in someone's virtual inbox in a CRM? Waiting to be staffed the most, those are usually my favorite targets to integrate an agent into the mix.

Andreas Welsch 6:56

I've been attend a number of conferences. I've talked to with or talked to many peers in the industry who are bringing agent AI in, into their organizations. The thing that I'm always wondering about, especially if I hear vendors talk about all the amazing things that agents can do is how do how do you decide how many agents you need, what they work on, how granular you go? I'm not seeing a lot of conversation there or lot of information being shared, so I'm curious, what are you seeing? Agents, we do a process review. We know we want to put in agents in some places, but how granular do you get, for example,

Kris Saling 7:31

so one of the things I like to do as we're doing this and we're fielding a multi-agent platform, several multi-agent platforms in the Army right now, is just break down the tasks. And not everything needs to be intelligent. So one of the first things I do is I use a framework that we call eliminate, simplify, automate, elevate. So I try to eliminate tasks where we really don't need to do them. And it's does this thing, and again, you can use AI for the research. What is the provenance of this task? Why am I even doing it to begin with? Sometimes it's. Statutory, there's law involved. We have to do it. So okay, leave that alone. Other times it's an artifact of something else just was never really polished off. Or we mark it as something that we look at for elimination. Once we integrate AI into the mix. Simplify the process as quick as possible. This is, this could be something like a RACI chart, who's responsible, accountable, whatever. Who actually needs to see this? So who is my human in the loop that needs to review, the outcomes and critically evaluate them? Then we go to automation. And I deliberately use automation because a lot of times when we have folks, unless they're, working in the space that you and I are working in. They can mean I need a very intelligent system of agents, maybe 16 different agents working together on a very complex task. Or this could just be RPA, it doesn't really need to be intelligent. RPA is my unsung hero in the AI space because I look at a lot of these processes and functions they just need to get done. I don't need to spend the time and the energy and the money on a complex agent, but then it goes from there. It's like how complex is the task? Does it make more sense for the guardrails to have, one agent doing all the work, or do I add one principal agent and then a Supervisorial agent that'll come back through and clean it up afterwards? Because then for me, at least in the workflows that I use to build these. It's easier if it's separate, because then I can troubleshoot the functions in one and troubleshoot the functions in the other. And I'm not troubleshooting an incredibly complex program. And just say for background, I'm a mathematician. I am a brute force coder at best. When it comes down to it, I like to keep my workflows simple so that's one criteria I use. How easy is it for me to go in there and figure out what part of the task went awry?

Andreas Welsch 9:45

That makes a lot of sense, and especially the part of en encapsulating or compartmentalizing what are the functions that this agent does and You find at a level that it's not, too expensive or too complex that it doesn't get confused, but you can troubleshoot it too. I think that makes a lot of sense. I've been approaching my late spills in a similar way too, to say, Hey, this is this one function that, that you'll do, or and here are the different tasks and tools that you have. To your point if they're defined roles, it gets easier to troubleshoot or even to, test things in in isolation.

Kris Saling 10:16

I think that's something too that we wanna continue, we wanna think about for our future workforces. 'cause if you're building these things, testing these things and operating these things, you gotta be cognizant of your time. How much time do you wanna spend making sure this works? Or can you add in another helper agent that can help you with that? 'Cause I think, we talked a little bit about the workforce earlier. The large development teams of the past were, are a luxury. We're not gonna be able to have them either because of demand signals out in the workforce or just, scarcity of being able to grow some of these capabilities.

Andreas Welsch 10:49

About eight, nine years ago I was working with fortune 100 company, big company in oil and gas. And I was very surprised when we looked at some of the finance processes, they said, oh, and we had this little custom built tool here that this contractor built for us 15 years ago. We're not really sure what it does. The person has moved on, the documentation got lost somewhere.

Kris Saling 11:08

But

Andreas Welsch 11:08

we cannot touch or replace this because we're too concerned if anything changes, we're not able to pay our bill. It's oh wow, okay. Multi-billion dollar company. With as critical as that. And I'm getting flashbacks of the intern developed apps and things that are floating around in corporations that nobody really knows anymore how they were built or where the source code is, that we might be running into something like this over time with agents as well.

Kris Saling 11:34

We deal with that a lot.

Andreas Welsch 11:36

So I'm curious there how do you see, not just the governance and the rigor of building and documenting agents, but more so when you add more and more agents to that makes to a multi-agent system. How do you think about risk when it's not just. Additive, but it grows exponentially, right? With every agent that you add to the equation.

Kris Saling 11:55

Yeah. I think it's similar to what we did with a lot of our code when generative AI first came out. It's just I thought that was wonderful because I could go into my code and like it was, I wrote it two years ago. I code totally differently now that two years ago. Comment this for me, figure out what each of these modules are so I can swap 'em out. I see us doing a lot of that too, okay. I've got a multi-agent system. Agent, go inventory on my agents, go find everybody, find their core task. And I think that's where, we really gotta get into the individual data, the logs, the metadata, the kind of routine boring information that should be captured in the background and just go, okay. When was this thing built and by whom? When was the last time it was curated? We started doing that a few years ago with different kinda data ca, intelligent data catalog platforms. They could tell you when last time the data was curated. Allow people rating systems. That's one thing I do want for agents as we start fielding them more throughout our organizations. I wanna know what, like the equivalent of the blue meta check mark is for an agent. That has been validated and recently curated, and I want that to fall off if nobody has validated this agent or if it hasn't been curated. I want to know that. Yeah. This thing is, it's been, somebody's taken a look at it. The mechanic has looked under the hood, they've tweaked all the things. It's good to go, or this is used at your own risk.

Andreas Welsch 13:09

I think that's so important. And when we see a revival of citizen development, like yes, build your own agents. I feel that in many ways challenges Exactly. That the governance structure, the knowing what is actually happening in my environment, who spills what agents. What do they have access to? What kind of data do they process or might they inadvertently expose things like that. Yes. And I think especially to get to more of that enterprise grade hardened en environments and governance. I think that's. That's the next level of orchestration of governance solutions. We need to see pretty quickly. To be pace with the deployment of agents.

Kris Saling 13:46

One of the things we wanna do is figure out the balance where we don't stifle innovation. Because now that everybody can develop these tools, you're gonna get some great ideas. And that's really kinda the common currency right now. It's not necessarily the technical acumen because. That, you can develop that. You can have your AI come in and either do some of the work for you or come up with a training plan for you. It's the what It's for piece. And I love seeing it, especially in the army side. I love seeing what soldiers are going and developing at the same time. It's like I want 'em to have a sandbox to do it. And the ability for me to come in and say, this is great. Your answer goes off here. It should be here. Let me help you figure out the right stuff to do to either tweak the model or get the right data to use your model so that you can move over here into the validated side.

Andreas Welsch 14:35

I'm absolutely with you. Governance is, as soon as you start mentioning that term, people go whoa. You're bogging me down. It's red tape, it's cratic,

Kris Saling 14:43

I don't want this. Yeah.

Andreas Welsch 14:45

Yes. But at the same time, to your point you need to strike the balance between knowing what is happening and giving people guidance, but at the same time, embracing and encouraging that level of in innovation. Because at the end of the day, people that are the closest to the work. Also see where the issues are and they very likely come up with solutions for it if they know enough about the technology and what it can do.

Kris Saling 15:04

And if they know about the domain side, this is one of the arguments we have right now too. Because it's. I come back to the analogy that AI is a car a lot because you need folks who understand how it works from a mechanical standpoint, not maybe from an engineering one, from scratch standpoint, but you know how much domain knowledge is sufficient for you to be able to build a good and useful tool. Somebody who has just driven a car a few times probably isn't necessarily gonna be the person you want building another one, although they are a good test market to tell you what features it needs to have. And I bring that up just because we've had some people who have built decent tools, but are, they're foundationally erroneous just because they don't have, they've been a user of the domain system that they're trying to improve. They don't understand all of the foundational underpinnings, and that's a challenge, like how do we get them that, right? I love their initiative, but how do we get 'em the right level of understanding of the system?

Andreas Welsch 15:54

I think that's so key. How do you do that? Do you have some formalized training programs? Do you have office hours? Do you have town halls? How do you source information and at the same time are only an arm's length away if they need help.

Kris Saling 16:08

Yeah. No I wish I had a good answer for that. We've talked about this a little bit too. We focus so much on providing the AI education and the tool education. That we've really forgotten that we need to provide the domain education too, especially where these AI tools are taking over from where people are getting that domain education to begin with, and they get it unfortunately by doing the toil, by doing the rote boring tasks that build up your understanding of the overall system. And then you go, okay, this is why we do it. And once we automate it away, you understand like what went into that automation. But if that automation is there instantaneously or it's already there when you get there, how do you understand those foundations?

Andreas Welsch 16:52

So in a way, like we still teach the foundations of electricity. Electrical engineer, even though we just plug in a device into the socket. We still need to understand better physics or chemistry to be able to work at higher levels of it.

Kris Saling 17:04

Yeah. There's there's a little more on the input and output and all these things that we need to understand than it just goes input output into a black box. There's logic that went into the building of the box. How do we see that and understand it in a way that provides that kind of domain expertise that I think we're really gonna be thirsty for in the coming years.

Andreas Welsch 17:23

Now, obviously a lot of that innovation when it comes to agents, when it comes to multi-agent systems. It is similar to how we think about humans, how we think about human teams. In my book, I make the case that need to have a job description for your agent. It'll need to abide by some kind of code of conduct, whether it's values or or professional standards. Think of IFRS or similar ones. You need to tell them what tools are available, what data is available, what other agents are available to collaborate with. And how to find them. That's how I usually think about them as digital employees, just in that sense of governance, guardrails, guidelines similar to humans at the same time. They're just a piece of software. So you're still responsible for the decisions and actions that it takes. Yes.

Kris Saling 18:06

Yes.

Andreas Welsch 18:06

But from a, from an HR point of view how do you see that? It's evolving rapidly where we're talking about hybrid workforces of Humans and systems or humans of, and AI. A lot of times I see HR isn't even at the table. What are your observations in

Kris Saling 18:21

We, we are constantly fighting to get, be at the table on the HR side. 'cause you're just like that's the human side. You're just doing the transactional human work. We need to be doing more than that. We need to be doing full spectrum talent management. Which I separate out from HR. 'cause HR has, your transactional functions, you're making sure people get managed, they're paid, their documents are correct. We wanna make sure that we are developing our workforce, that we are categorizing and reevaluating. That becomes a continuous evaluation of the skills inventory and the requirements of the job. And that way we can make more effective pivots, more effective matches, and we can create tools. I call this our Waze model for talent management. It's again lot of analogies of cars,

Andreas Welsch 19:00

right?

Kris Saling 19:00

'Cause Waze, you give it your data and it tells you how to get from point A to point B using the rest of the data repository and what it gets from the Department of Transportation more effectively. I wanna create tools like that for my employees so that they're not just, filling out another, taking a skills assessment, filling out another preferences survey. I want them to be able to see, here are the things I can do. Here are the jobs out there that I'm qualified for as I grow my career through this organization. Or in the cases, I say I see some of my tasks getting automated. I need to figure out what my next progression is. Let's do this. And that also gives you a way to use the tools and benefits you offer as a company and say, now I'm not just giving you CPE credits. I'm not giving you certification or certification funding. I'm not providing all these other things. This is how you use it to get to that next goal.

Andreas Welsch 19:52

I think that's so important right now because I see in the private sector, a lot of talk about, Hey we cut off the bottom. We give that to AI. We don't need entry level people or roles. AI will do all of that. And then some organizations are already waking up to the fact that oh boy we need to build a talent bench. Who's going to succeed? Our senior leaders and experts that are in these roles now, once they move up. Out. But at the same time, there's not a lot of clarity on what does it take to become more senior and an expert now that AI supposedly can, can cover the bottom part of it. That's where I think it's it's so important what you're sharing. Yes, part of your work will eventually be delegated to AI and to agents, but what is that other part that we want to help you qualify for and learn about and become more proficient in? Again, you. You have a hundred percent role again, not just a 60% or 50% role.

Kris Saling 20:43

No, exactly. I think we have to be more flexible in the way we think about careers and everything's gonna be evolutionary. It's we're going to the job that you take. It is not gonna be the same job a couple years down the line. It's gonna be very different. Your task load is going to evolve. You're gonna be partnering with automation more than you thought you would. You might have a totally different role that you've moved your way into, but I think providing the, doing the skills inventory, providing these different pathways that people have in order to be able to grow makes it a decision on their part rather than a decision that's made for them as these tasks are getting automated away.

Andreas Welsch 21:20

Sounds like in a lot of ways when you have ownership of the decision and of your own destiny It, it feels a lot better because you feel a lot more in control rather than something happening to you it's happening with you or because of you or 'cause of your own initiative.

Kris Saling 21:33

In all of the talent management efforts I've been a part of on the army side, we've really prioritized autonomy and agency. It might sound counterintuitive with the military, but we are an all volunteer force. Everybody has. Raise their hands and signed up to do this. And retaining our talented workforce means they're continuing to volunteer, so we want them to continue to volunteer for things that are good fit. For opportunities they see beneficial and for things that will make them good citizens whenever they finally depart service. 'Cause I've said often both in official and unofficial capacity, the army is a net exporter of talent. Now after a certain amount of time, whether it's their initial term of service or a career where they retire from service, we are giving people back to the American community and our goal. Is not to just keep people forever. It's for them to go out and be massively successful contributors to the American workforce. The more we can give them to be able to do that, the better off we all are.

Andreas Welsch 22:27

That makes a lot of sense and I think especially in this time when. There is change when there are opportunities. To evolve and, make the next career step as you as you progress. Now, Kris I can't believe that we're almost at the end of the show. Feels like time has Oh my goodness. So quickly. But I was wondering before we wrap up, if you can summarize the key three takeaways for our audience today.

Kris Saling 22:48

I, I think the key takeaways are that the foundations still matter. You brought it up with how we teach physics and electricity. We have to think about the foundations from all the different other aspects of our business and the aspects of the technology, and how are we going to incorporate a baseline understanding of those. The other, this is a fast car. It's moving quickly. If we understand how to drive it appropriately, it's gonna be great. And we have to realize that there are a lot of other cars on the road and we have to be able to play well with them. I think that comes in from the governance aspect. 'cause everybody kinda goes, ah, this is bureaucratic. It's the, it's traffic laws. It keeps us from crashing. And then I think the last piece is that the human element, everybody worries that we are gonna have these fleets of agents that are gonna take everything. We also thought once upon a time that everybody's jobs are gonna go away. 'cause we implemented, machinery and manufacturing and some of your older audience will remember. All the effectiveness that email was gonna give us, because, you weren't traveling from desk to da, it just made more work. So our work is gonna evolve. AI is gonna take our tasks. I don't think it's gonna take, our jobs as long as we're able to keep using that that eliminate, simplify, automate, elevate framework and we keep elevating our human workforce into the work humans can do. And then getting rid of the other things that keep us from doing that work. Humans can do.

Andreas Welsch 24:09

I love that. So encouraging and powerful to, come back to our human capabilities for sure in, determine what is it that makes us really good and makes us really human at what we do as well.

Kris Saling 24:22

Lovely.

Andreas Welsch 24:23

Kris, thank you so much for sharing your expertise with us. It was a pleasure having you on. I learned a lot about how to think about multi-agent systems, how it it evolves, how it involves HR as, as well. And I hope you in the audience are taking away a lot of good learnings as well.

Kris Saling 24:38

Awesome. Thanks so much for having me. This was a really fun conversation.

Andreas Welsch 24:41

Awesome. Thank you so much. And folks, see you next time for another episode of "What's The BUZZ?". Bye-bye.