Bennett Sung - HR Tech 2024 - AI's Current and Future Role in HR
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The world of business is more complex than ever. The world of human resources and compensation is also getting more complex. Welcome to the HR Data Labs podcast, your direct source for the latest trends from experts inside and outside the world of human resources.

Listen as we explore the impact that compensation strategy, data, and people analytics can have on your organization. This podcast is sponsored by Salary.com, your source for data, technology, and consulting for compensation and beyond. Now, here are your hosts, David Turetsky and Dwight Brown. Hello and welcome to the HR Data Labs podcast. I'm your host, David Turetsky, live at the HR Technology Show in...

Las Vegas, Nevada. And I have with me one of my best friends for a very long time, Bennett Sung. Bennett, how are you? I am doing well. Thank you for having me back on the podcast. It is absolutely a pleasure, and especially when I get to do it in person. Right. And actually see your face, your smiling face, and see your new hair color. Newest hair color, yes. And it is a orangish-reddish-yellow. Yes. Okay.

With gray highlights. It sure is. It's three months old, but it's still showcasing its color. Yes. I don't mind. Gray is great. Yeah. Gray is trendy. I love it. It shows your maturity. Right. I'm sorry. Exactly. I didn't do that. Don't worry. So...

What we do for every one of our guests on the HR Data Labs podcast. Bennett, what's one fun thing that no one knows about you? Ooh, any new fun thing? You need a new fun thing. I've already told probably the most hilarious one. Fainting from formaldehyde in an animal science class. That was just to bring everybody back to you.

Back to that, the old way. Back to three years ago. Yeah, right. Well, this time, you know, I had a bit of an incident getting to HR Tech. In a context like I had, I tripped on these amazing new shoes of mine that are red. Yeah, they're Pumas. They're really nice red Pumas. You know, so, but I tripped over them and I had to like, like brace myself for a face plant. No. Yeah. And now, now I can barely shake hands. Yeah.

So when was this? From Seattle to Las Vegas. No. Yeah. So I got off the train in Seattle and boom. I was on the ground. I said, oh my goodness, what's going on here? This is not how I wanted to start my HR tech travels. But nonetheless, it is what it is. But you look okay. Yeah, I'm fine. Show me your hands. Oh, yeah. Do they look bruised? No, not at all. That's what I thought.

So for those of you who don't know Bennett, Bennett's a brilliant guy. I met him a long time ago when we were working for ADP. His company had just gotten acquired and my company had just gotten acquired by ADP. And we kind of bonded over that newness. But also, we did a lot of conversations around talent management and how working in an environment of payroll and HR and how do you...

get more people thinking about the world of not just at that point applicant tracking or talent management, but also what does that really mean in the context of business? So that's been a while ago. It has been a while and things have changed. I mean, priorities...

priorities definitely have shifted yet at the same time stayed relatively the same. Because I think when I began to kind of look, do a reflection of, you know, the, since back in 2006, when we were together, right. You know, so many things have stayed the same.

Right? Some of the topics have relatively stayed the same. Right. Candidate matching. We're still struggling to figure why that, we can't figure that out. Right. Right? And there's just a lot of small little, small little mini milestones we all have to accomplish in order to get

get that piece of functionality right. And I'm not sure if we ever will be happy with it anyways. Well, it's so difficult, and especially in the world now where, and we want to talk a little bit about artificial intelligence, but within the world of applicant tracking and recruiting, there's still so much complexity with where am I hiring people? Who am I going to hire? And who do I actually get to talk to, given the fact that a lot of the AIs are actually filtering people out? Yeah, for sure. I mean, AI is definitely helping with...

getting through the volumes of candidates, especially in the employer-driven world we're living in today. I'm sure next year will be a candidate-driven world again, and that is going to be a different strategy. But nonetheless, I think AI certainly has been helping folks out in the context of, I'm getting work done for you. I'm getting work done. I'm looking at people. I'm not sure if they're correct, but I'm looking at people, and I'm thinking and predicting that this person should be put in your...

interview bucket and not the disposition bucket, right? So, you know, we're going to continue to see how that continues, how that evolves over time in terms of its accuracy and, you know, its context of not making sure that decisions are not based on previous biases and such. Which has been the case. Which is why, which is,

Why on the recruiting side, it's very much, for me, one of the biggest –

hurdles and challenges going to facing recruiting technologies today. All of them are quoting AI. They're making recommendations. Right. You go talk to EEOC, which I have. They are very critical about decision-making capabilities that are not made, that don't have a human in the loop. Right. Right. So, so, so I think one of the keys there is have the artificial intelligence help create the artifacts, but have a human review them. Yeah.

I mean, the artifacts are still honestly kind of subjective. I mean, you look at resumes. We have no control over the resume or the CV, however you want to talk about it. You look at the content piece created by recruiters and hiring managers, the requisition, that's flawed.

primarily because there's no process of collecting it. Right. Actually, a lot of that's being built by chat GPT because managers and recruiters are, sorry, recruiters, they're a bit lazy about this. Yeah. I mean, for sure. And then you have to think about, is it inclusive? Then you have to look at the languages associated that are being used. But more so, it's the actual process of intake.

Right. You know, is because the whole notion is I as a recruiter and you as a hiring manager, really, this is our S.O.W. to each other. That's right. And if we can't agree upon this, then the first step of the process of going out there and looking at candidates and say, yes, no, yes, no.

I think sometimes humans probably could do a little bit better than AI in terms of getting it right. We'll see. But go back to your original point about the bias. Sometimes we have not gotten it right. And hopefully what we're not training these models on are what had been happening in the past that had gotten us into trouble in the past. Oh, yeah, for sure. Yeah.

By the way, we're recording live at the HR Technology Show and it hasn't opened yet. So you're hearing a lot of the work that's being done to get it to be open. Yes, exactly. In an hour or so.

Let's go to one of the questions that we were going to ask you, which is, to me, one of the fun things about doing these kind of conversations, especially at the beginning of the HR technology show is I think your opinion might be changed by the time the end of the show happens, but maybe not too much.

So one of the questions is, beyond the hype cycle, where does AI land in the HR stack right now? So interesting enough, so I've been consulting with a company called Mimibot. M-I-B-O-T, one of the harder names to say, but still a fun name. But we did a survey of HR folks to get a pulse check on have they made progress since 2016 when we first did the survey on whether or not they're ready for AI. Well, there's a lot of things that

haven't really moved the needle forward. There are obstacles in the way. AI adoption on the HR side, in comparison to the recruiting side, is much slower, much more methodical. And it's because one of the challenges is that now IT has gotten their... They are...

driving in a co-shared relationship with HR. They are driving the initiatives together. So they're, they're both kind of like, you know, obviously, you know, pros and cons and, you know, having their conversations and, you know, debates about, you know, where do we want to take this? What do we want to use? And,

You know, have we set up the processes and the policies at front? Like, do I have an AI steering committee? That's brand new to a lot of things. And, you know, that's just, but it's all for good, right? It's all for ensuring that AI is treated, is done in a responsible capacity or ethically, however you want to use it. Right. And it's like, so I think for the most part, there are a number of things that are,

challenging hr in the context of moving this needle forward faster right first and foremost they're not following the money by not following money i mean they are not partnering as well as they should be with their cfo why is that there just hasn't ever been that that kind of relationship but the reality is the cfo holds the purse absolutely purse strings right it's like

But, you know, so the things like, well, you know, we want to invest in this technology, but it's a new line item. How am I going? What is that? What do you need from us to, you know, get you to sign off on it? What's the business case? What's the business case? What are the outcomes? And by outcomes, we don't mean how much more, what the kind of experiences. Because CFOs will not buy things based on experience. In fact, they're cutting experience.

that are exclusively to experience. Because there's nothing at the tail end that shows me how much revenue I've created or how much cost-cutting I've been able to save. So following the money is important for HR to really master and build that relationship. And then you'll be able to realize when you find the tools that you're looking for, you work with CFOs to then go...

and really build a case that's going to be for the long term, not like this is not one that is going to be cut for the next year. Sure. I would like to think about this, though. There's also another side that the CFO will be interested in, which is the risk side. Yeah. And what are the risks to not only the adoption, but what are the risks to non-adoption? Right. Because so many of their competitors might be adopting AI tools and an AI stack. Yeah. But also...

We've seen a lot of AI in the consumer world. For sure. And GPT-4.0 is available for people to kind of sign up. Yeah. What happens if people do it and they expose data? I'm going to be talking about this a lot. And it's not been done with IT's knowledge or involvement. And so it's being done rogue. Yeah. I think the reality is we already know that it's already being done. So you have to just realize that...

It's a good, it's actually a really good thing that individuals are using AI and

For purposes of just getting themselves acclimated to what's the day in the life of what I'm doing today, how is that going to change? Until they start experimenting, they will really not really feel the impact. And that's one of the major benefits and kind of missions of AI. At this stage, we're about changing the behaviors of work. So we're encouraging folks to experiment, but we have to experiment with guardrails.

Well, they have to develop these skills because if not, it's going to overtake what they're doing and everybody else is going to be doing it. And they're going to be like, well, why didn't we invest in AI when we should have? We're seeing that. And I think there's also the reality is like you have to understand the problems you're trying to solve.

This is not like, I mean, if you keep on layering technology on technology and they're not really solving any real, real issues, then there's, then again, we're not going to, when it comes to the renewal and they ask for the outcome, that's never going to be very clear. Yeah.

let's look at a consumer technology in the world of AI that everybody adopts. And I'm not talking about Siri or Alexa. I'm talking about like a Grammarly. Yes. Or think about you're just a spell checker or the grammar checker that you use in Word or whatever. For sure. There are so many people that think that

Grammarly is a what lazy person uses it or someone who's uneducated. No, no. A lot of people use it so that they they, you know, what's the right then versus then or or then or what's the right spelling of right or higher or the context. I mean, and getting it right in a business context is so important.

Exactly. But that's a consumer version of an AI tool that people have just kind of built into a lot of tools, whether it's making emails work or... Yeah, I mean, it's all... Anything that is content generation is probably going to have a Grammarly-like functionality built into it without you maybe even knowing. Exactly. And so a lot of times, there's a lot of tools that we're probably using that we don't ever realize. Like, you know, we'll take a step back into the days of Virtual Edge.

That was one of those technologies that got bought by ADP. Yeah. That brought Bennett to ADP, yeah. But what folks didn't know is that it had a built-in candidate matching tool that was using a machine learning, natural logic processing algorithm called Ingenium back in the days. It was like a premise-based machine learning tool. It wasn't even put into the cloud yet until virtual edge got a hold of it.

But the reality is not many folks knew about that in the days. And so, you know, it's been around. Let's just be real. I mean, AI has been embedded in so many different things. I mean, you talk about, you know, Grammarly being lazy. Isn't a calculator the same way? Oh, yeah, absolutely. Excel? Oh, yeah.

Like my mathematical capability of comprehending mathematical equations has definitely slowed down. Oh, sure. Right. But nonetheless, it's the, we know in the back of our heads, it's made us more productive. Yep. Right. We can get to answers faster. Absolutely. Right. But when it comes down to investments in technology and we're asking for lots of new money, like sometimes efficiencies and experiences will not cut it.

Like, we have to, again, not to use the phrase, follow the money. The reality is...

CFOs are looking, how much more revenue are you going to give me or how much money are you going to reduce? Right. Those are the two things that all they care about. So you have to have that business case tight and really focused on those two line items. Absolutely. Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com. Now back to the show.

I want to take us in a little bit different direction because this is a little bit selfish for me. Sure. But you've been in the recruiting space for a long time and now we're seeing pay transparency. I have to take you this direction. Pay transparency is really huge in a lot of different states and it's going to be huge across many organizations who are

using the, let's call it the highest common denominator, whatever the state is that has the most rigorous regulations. Yeah. How does it change the game in recruiting when now they have to disclose the pay range when they're in the midst of the requisition in that candidate cycle? Yeah. I mean, I think in the long term, it's leveling the playing field, right? It's giving, it's going to make candidates more,

Maybe it's going to be a vehicle for screening candidates out or in. Absolutely. Right? Because they have their own personal expectations of kind of money that they want to make. Right. Right? So I do feel from a recruiting perspective, it can be used as an absolute attraction. Absolutely. Yep.

I think it also reflects culture. Yep. Transparency is one of those, one of those cultural elements that employees value, you know? And so I think it's, you know, it's also then aligning, like if it is something you really put forth and prioritize and it's going to be reflected in your company's entire, entire way of communicating. So, yeah. So that, those are the things that when I look at transparency,

pay transparency or anything that's transparent. AI, you know, AI is a, it's all about transparency. It's not about hiding anything, right? It's only going to get more granular and more visible and more accessible. Absolutely. When I think about

transparency in the world of recruiting, I also think about what are the programs and how do I educate the candidate and the employee as to what is it the value proposition is that they're working here for? What are the rewards they have the opportunity to get? And also making a more mature relationship

between the employer and the employee because now you're trusting your managers, all the stakeholders in this, you're trusting them to make the best business decisions for the company and the person. Yeah. You know, I think one of the things that candidates and companies don't

prioritize or it's very rare to see they focus exclusively on salary and never really bring in all of the other influencers of compensation, how much they put towards your benefit programs, a contribution to your 401k, all of these things that don't, what they call total compensation statements. Those need to be, those are

amazing recruiting tools if you, you know, when you put it into, when you put it into play. I just don't see a lot of folks. They say, here's your offer and salary and here's how many days you get off. Right? I just don't feel like I have a full understanding of like what value I'm getting from the company. I know what I could bring to them, but I really don't feel like I have a

good sense of the end-to-end understanding of how much they're investing in me. And I think once we get beyond the regulations in pay transparency, what you're going to start to see is that more companies are going to be much more open about their other benefits and other pay elements and things like that because the regulations that exist only talk about base pay. Why? Because it's so complex in the world of everything else. You know, what is an incentive? What's a sales incentive versus a commission versus a

just a shorter long-term incentive. Because those things are so variable by company, by culture, as you mentioned before, that a lot of the regulators, and one of the regulators I was speaking to last week said, look, we thought about doing beyond base pay, but we really couldn't get what a good definition of those other things are. So,

To me, what's going to happen is once we get beyond the regulations, companies are going to use their culture. Yeah. And they're going to say, well, our culture is a total compensation culture. And then people are going to learn more from the beginning. You know, I also just feel like it's an education for candidates and employees. It's like, let me tell you what, let me help you understand the total investment. And that's just, once they hear that, I think they're going to be very excited.

they're going to embrace a lot of the things that they're talking about and they're going to like really revalue the organization that they joined and it's going to give them more motivation and, and, you know, and they're going to feel more, you know, belonged in the organization and valued and such. And that, and that will be a better tool for retention and,

trying to change their pay or giving more of something. Right. I mean, you know, I think there's some folks that no matter what is always going to be the base pay and the salary and that's all they can look at. Right. I mean, I think there's a good part of the bills. They have to pay the bills. Right. I got to keep the lights on. Right. But, you know, I think over time, hopefully as more folks do like start to kind of think about their business

larger picture in life, then they're going to realize the importance of all of those additional pay elements. Absolutely. Yeah.

Hey, are you listening to this and thinking to yourself, man, I wish I could talk to David about this? Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to salary.com forward slash HRDL consulting to schedule your free 30 minute call today.

So let's talk beyond transparency now. Let's go back to our list of questions and get one last one, which I think a lot of people are thinking about. Is 2025 the year that we see AI break into HR in a major way? I know you have an opinion on this. Um...

We're going to wait another year. It's going to be 2026, I believe. So you think 2026 will be the year that HR really adopts AI in a major way? In a scalable way. Okay. We're still... There's...

way too many organizations who haven't even done basic things around using AI. Like, again, I'll kind of reflect back on MimiBot, which does AI employee support. There are 24 digital HR generalists. Who doesn't, like...

One organization, HR, people ops teams want to answer repetitive questions every day. None of them. They hate it. They hate it. Yeah. Slows everything else down. But yeah, no investment. So there's kind of this like double-edged sword of like, do more with less. Yeah.

Yet they never get the investment to actually augment the team to help do more with less in terms of the overall headcount. So for me, I feel like there's still a big stride away. There's a lot of cool things happening here at HR Tech. Oh, yeah. Right? I mean, there's a lot of innovation, but so much like a lot of things, it's all about timing. Exactly.

It comes down to time. Last year, we saw a lot of hype cycle here at HR Tech around AI. Yeah. This year is no exception. Yeah. We're still in the delusional stage. Oh my gosh, yes. We're on delusional. We just don't, because what we don't see is there's not enough, like there's not enough

ability to play with the technology. So the AI, the difference between AI and applicant tracking system is in the case of AI, you have to vet, like, is it doing the job as it's designed? So which means you have to get access to that AI algorithm and you have to play with it. That's right. Else you'll never know if that piece of functionality is

actually what it's being marketed for. You have to turn the dials and make sure that you... You have to turn the dials to the extreme. QA it till the cows come home. Exactly. And that's a major difference. That's between traditional software and what we're seeing today in this AI software. It is definitely kind of flipping or changing the dynamic of how the solution providers are actually selling. But you have to make sure that decisions it's making would be the same decisions you would have wanted it to make. No, exactly. No, and that's...

And that's the hope. That is why also the solution providers are going to be pressured to provide transparency on how the algorithm works. Right.

Which I think a lot of folks feel like, oh, that's like secret sauce. I can't give that away. Well, you're not going to have much choice because you're going to have legislation telling you. Legislation is going to tell you, you've got to expose this all. Absolutely. This cannot be hidden. Well, if you don't have it through legislation, you're going to have it through lawsuits. So...

Right? Do you want reputation hurt? Exactly. Like we already see in the consumer side. Yeah. Right? I mean, the consumer side just saw Air Canada go through a lawsuit because their chatbot was delusional about bereavement travel policies. Yes, I heard that. And then you hear the same thing happening in, I think, New York cities. They're giving wrong information about everything because their chatbot is ingesting wrong information.

outdated information so yeah the reality is we ought to hold everybody accountable you got but you also you know realize that you have to understand again what are the problems you're solving how would you go about solving that and it's no more different from the days of assessments right yeah of course assessments were one of those tool early ai tools that were done by pay on paper yeah and but nobody they had to provide the evidence and the receipts to to defend what's

what potentially could be a lawsuit or a reputation issue. Well, we also saw work day. Oh, good. Get. We're still, we're, we're waiting for that judgment to happen. Right. We kind of, it's been exposed. It's nothing secretive, but now it's going to be, you know, who's accountable. Right. For the, the algorithm itself. I think this again, this is history repeating itself. It comes full circles. Like we're, we're here again, right.

20 years later, 10 years later, still talking about the same things, maybe in a slightly different context, but the ramifications and the thought process is all the same. But I think you mentioned before, transparency helps provide that layer of trust, whether it's talking about a chatbot, whether it's talking about an AI assessment of a candidate, and why did you choose this one over that one? And why did you choose to let this one go from the process? Or pay transparency or whatever. Yeah. Transparency.

treating people with respect and providing them the insight to understand why a decision was made or how it was made. Everybody wants an answer. Has to. They have to. Or it will get legislated or it will get a lawsuit. You know, and we're just going to be where we are today with frustrated employees and candidates where, you know, it's like, why didn't you choose me? Well, you can't really say. There is these communication, like,

kind of restrictions about what you can say, what you cannot say. It's like, you know, let's open, we have to open this up. Like,

Like, there is no reason why you couldn't tell somebody they were not chosen for this particular reason. The problem is that they actually and probably the AI doesn't actually know the reason. Right. They just made it. They probably just made it up. Well, and for those of us who have gone through the process of trying to apply for a job. Oh, gosh. And getting an email back five seconds after you hit submit that said, thank you very much. You've got really great experience, but we've gone on with other candidates who are better suited than you.

Bullshit. Right? Come on. What they probably forgot to do is close the requisition. That is going to be my guess, that the requisition for it was forgot to be closed. Oh, that's hysterical. And so what they're saying is, ooh, we've got a dismissal from these people all the same way, and this is how we're going to do it. We're going to give them the most generic email that you can possibly exist. But doesn't that, I mean, talk about reputational risk there, though, Bennett. I mean, isn't that, like, really embarrassing? It's embarrassing, but you know what?

Who's talking about it? You'll get a few of these naysayers on TikTok. We've heard them all. Some of them are very, very self-promotional and will put themselves out there saying, oh my goodness, can you believe what just happened to me? But most people just...

They're accustomed to it. They're just like, okay. Yeah. Right? I mean, there's nothing else I can do about it, right? Right. I mean, I can call them. Nobody answers the phone. Nobody even responds to the emails. Right. Oh, there's no phone number. You can't call a recruiter and say, why? I'm going to figure out a way to reach the recruiter. Why did you choose me? I'm the best candidate. Exactly. I mean, it's a vicious cycle of things. It is. There's legacy practices that are still in play that just have to be kind of like,

well, let's, let's find a way to tell people why they never got the job, why they were, why, what, why their compensation is where it's at. Right. Right. Or why the answers to these questions are the way they're at. Right. So is this, it's all comes down to, again, changing the, changing the, changing the behavior to change the culture, to reflect and change and get different outcomes. Absolutely. So, and, and let's just say this, cause I, I,

Oh my gosh.

Trying to find the best candidates in a very, very big sea. And these tools are trying to help the recruiter get the best person because it's about their reputation. It's about the recruiter's reputation. Equally as that. Yeah, for sure. And they're looking for these technologies to be able to make their lives a little bit more livable. Yeah. To be able to do that. Yeah.

I think some of the technology, you know, at the end of the day, you have to look at the people processing and people processing technology tools, right? So the people is not the issue, usually. The process, it could be improved a little bit. The technologies are the ones that, you know, have...

Much further to go because expectations are just greater. Oh, yeah. Right. It's like, I just don't want you to tell me the exact match. Well, now also expand that exact match. Yeah. Who could do who potentially could do this? Like there should be tiers of candidates in your pool. Until you talk to them and talk to them, you'll never be able to get a full sense of whether or not they'll be a good fit for

fit into the organization, the role of the team, right? So at the end of the day, some of the technologies have to understand, like, how do I build a pool of candidates that are based on potential? Right.

Well, and God knows we also need succession plans, right? Oh, yeah. And so somebody moves on. Is that term still used? Well, yeah. People leave. Oh, I know. People go. Yes. And actually, that's a really important thing. People are going to be going a lot more now because the bubble of baby boomers, the people in my generation, you know, Gen X,

we're going to be retiring. I mean, I'm 57, so I got at least 20 more years. We'll be doing 20 more years of podcasting. Yeah, I'll be talking to Bennett in 2037. Is it still going to be in the same facility? HR Tech Show will still be here in Vegas. It will. It will never go away. Never. But I mean, seriously though, there's going to be this demographic bubble that is leaving, and there are going to be a lot of

holes left in organizations. I mean, look at retirement statistics are going to go through the roof soon. And the recruiter's job is going to be not just about filling today, but also filling tomorrow. Yeah. I mean, and I think this is why organizations need to really get a handle on retention, right? Because the reality is I am... It's like...

retention that you can control. Right. And so, because the, the, it's just recruiting and retention are the same coin, but on the opposite side. Absolutely. So we need to continue, you know, when, when retention is high, recruiters are just strapped to refill seats. Absolutely. Versus refilling for the future. Right. So, so we got to help recruiters.

We all have to help each other by really addressing being rigorous on retention so that recruiters can actually recruit for the future, recruit to fill, find the folks to be able to come in to support or replace the...

The retirements that are going to happen in droves. Absolutely. And they need skills. They need experiences. And so organizations have to figure out, well, how do I get these individuals the experiences that they need? Yep. Right. And that's not so much saying they need technical skills. Sometimes it's just, it's just, they just need the experiences of being in leadership, the experiences and doing specific types of things.

projects. Or mentorship, too. Being able to do it the right way, not just being able to do it. Exactly. So we're going to be talking about this for another 20 years. For the next 20 years, we're going to be talking about how AI took over the world. Took over the world. But you know, what's also interesting is this new genre of chatbot called AI agent. We've heard about it from Workday, Salesforce, and such. There's all these agents coming about.

What's really exciting about that is that it's really going to change. It's really going to... This version of AI that's currently being pushed out is going to be able to be proactive in seeing the areas of gaps in organizational systems to then figure out how to get them back in order, right? So, which is...

One of the hardest things, which is why a lot of companies have struggled with their HR tech stacks, is that they're so kind of non-integrated. Data is all kind of all over the place. And so it's very hard for them to realize, oh, my goodness, I have all these gaps in compliance because people moved or we went from remote work to you better get back into the office, folks, which means it's going to have a trickle-down effect on taxes. Right.

So, and all sorts of other things, but none of that. So what you're talking about agents are, these are little AI bots that, that serve a specific,

specific purpose they do a specific job and they're trained on one thing yeah and they fill that gap or it may be it may be what someone used to do or it may be something completely new it's probably somewhat and somewhere in between it's because i mean anybody if we had the time and the time enough more time and you know to be able to go into our systems and

Figure out the data and then look at it and then realize, oh, here are the 20 compliance gaps that we have right now. And here's a list of things that we need to do to fix them. Mm-hmm.

all of that's very routine. Those are all routine things that can be nicely performed by an AI agent. Right? And so, so, so a lot of times the noncompliance is because our systems are just out of order. Right? Because they're missing something. They're missing things. They're missing, they're not talking to each other, they're missing data points, all sorts of things. And so, so we have, I think it's,

the AI agent is a great way to maybe scale the fixing of a lot of systems because the AI agent can't be really, you know, do it's like true automation until they get the data straight. Yeah.

So what you're saying is that we need another layer to help fix the data, and then things will be okay. I think it's a good, it's a good, you know, it's not a patch, but it's a good thing that it's a good starting point because I don't think people know where to do, instead of like starting from ground zero and implementing new systems and importing all this data, it's like, okay, I think we can fix existing data

Through these AI agents. And you really get the actual integrity of the data in the way that we need it, TB. So that we can now just move on from that and then look at other AI tools to be able to layer on top of that. Well, and those agents don't stop. They continue doing their job. Refining, refining, refining. Always refining. Yeah. And that is a job in itself. Right. That's worth refining.

It's priceless in my books. Well, and since it's an AI, it's probably relatively inexpensive compared to people that you would have been paying for doing that. Yeah, exactly. And they'd be bored out of their freaking brains. Yeah. Talk about things, probably some jobs that will cause them to want to leave. Yes. Right? Unless they really, I don't even think the most obsessed data person would want to stay for that kind of job.

Nope. Nope. I think you're right. But that's kind of an exciting part of where AI is today compared to last year. I mean, all of this AI agent conversation, and we're seeing it in real life, is really kind of another layer of vision. It's a refinement. It's a refinement. And I think it's also going to help refine the employee engagement. So when you think about, remember the days of employee self-service portals? Oh, sure. ESS. Yeah.

Well, they're still here, but... Yeah, they're going to get replaced. They're going to be replaced by these AI agents for employees. So the AI agent is going to contact the employees and say, hey, have you updated your W-4 in a long time? It's going to enable them to go in there and say, tell me, I need to fill out my expense report. Well, this is how you do it. And I'm going to lead you through. Don't worry, you don't have to log into Concur. You don't have to log into Expensify. Here's an expense that we found on your Amex. Yep.

Did you take a picture of that receipt? Yep. If you did, give it to me. Great. Send it to me. Yeah. Send it to me at this address. Yep. Yeah. So it's really going to, you know, because when you pull back the IT applications that is being supported in organizations, it's astounding. Yeah. There are some companies that have 600 employees and have 600 applications. Hmm.

It's like application. It's suffocation of applications. And like, these are applications, like maybe once a year I use it. Yeah. Right. It's like, who's the expert in this? Yeah.

AI agent. Hey, help me figure out how to use this one tool that I do once a year called benefits enrollment. Yeah, the benefits enrollment thing. So, you know, I mean, there's some exciting things coming down when you look at like trying to stream, simplify the employee engagement in these technologies. And then also, most importantly, really helping employees

and operations teams really get the things that they need to get done so that they can focus on things that maybe move their career forward or help bring in new solutions to address other problems. Well, scalability. Scalability. It provides them with scalability that they wouldn't have had because they have to call every employee to make sure that they're doing their benefit enrollment. Right. Well, instead,

train the bot to do it and then let me tell you bot keep reminding these folks go into the system oh who hasn't done it get send them another reminder oh you need help we are let me help you recommend I can recommend you something have your dependence changed no next step yeah so exciting things I mean that's what they're calling at least in the healthcare world they've called this digital front door mm-hmm

The digital front door to employee engagement, which just enables organizations to still have all these disparate systems in the back office, but really could provide a seamless one user interface to the employee, which is really kind of what employees crave because it's a consumer approach. That's right. That's right. ♪♪♪

Well, Bennett, I think we could talk about this forever. Oh, yeah. Give me a couple more hours. But I know the show is going to start soon. Is it really? Yes. How long have we been here now? Well, we've been doing this now for 38 minutes. There we go. We're actually 39 now. Right. Bennett, thank you so much for your insights. It's such a pleasure to talk to you. Always. Thank you and take care and stay safe.

That was the HR Data Labs podcast. If you liked the episode, please subscribe. And if you know anyone that might like to hear it, please send it their way. Thank you for joining us this week and stay tuned for our next episode. Stay safe.