What Can AI Actually Do For The Frontline?

[00:00:00] Most AI conversations have focused on people sitting behind desks. But as we all know, the frontline workforce has largely been treated like an afterthought, even though they account for 80% of the global workforce. It's kind of crazy, right? Meanwhile, the teams responsible for keeping businesses actually running every day are still dealing with slow hiring, constant turnover pressure, and operational systems that create more friction than they do relief.

[00:00:26] Now that AI has moved beyond reporting and into workflows, companies are forced to rethink how they hire, how they schedule, and support frontline workers at scale. My name is Noah Mithrush, and today we're joined by Sean Baer, CEO of Fountain, a platform focused on frontline hiring and workforce operations.

[00:00:45] Sean, before we get into the technology side of the discussion today, when did it first become obvious to you that frontline work was being left behind while the rest of the market accelerated around this whole digital transformation movement?

[00:01:02] Yeah, I mean, I think a couple of seminal moments that really came to mind around this frontline workforce. First, I'm a multi-time entrepreneur. This is my fourth company. The third one relied on frontline labor.

[00:01:18] And so I saw firsthand how important these frontline workers were. We wound up, that company was acquired by a company that had thousands and tens of thousands of frontline workers throughout the U.S. and throughout Canada and throughout Western Europe. And all, regardless of region, regardless of language, two things were universally true inside that organization.

[00:01:43] One, it was really hard to hire these frontline workers. And it was really, and maybe even harder to retain these frontline workers. And that became really, really challenging, especially on things like, it was an automotive company. So automotive technicians, you know, people who could take care of cars or people who needed a specific kind of license.

[00:02:05] Those were even more challenging. And so it became really apparent that somehow the HR software that was built, that we were paying millions and millions of dollars for, didn't serve a really important population of our workforce really well. That's kind of when the light bulbs started to go off to say, wow, there's something here. I mean, obviously, I think it's been reinforced throughout over the last couple of years.

[00:02:31] Right. And, you know, like you said, software that's actually built for a good part of the workforce. We're talking about 80% of the workforce, right? Not a small amount globally. Absolutely. So when we think about AI, you know, fountain, as you say on the front of your website, the number one agent for what is it? Let's see here. I'm going to back up. The number one agent that runs your frontline operations. That's right.

[00:03:00] AI has become one of those terms that instantly triggers skepticism because people have heard so many skepticism. No, a healthy skepticism. Right. So because people have heard so many inflated promises already, right? And they don't know what to expect. So why should HR and operations leaders actually care about this moment in particular? And has the industry maybe earned some of that distrust around AI?

[00:03:29] Yeah. Yeah. It is, you know, you could probably fill an encyclopedia with AI buzzwords and hype. You know, one of the things I think, first of all, I think HR leaders should be right to have some healthy skepticism. I know I said that in jest, but I think they are the right to.

[00:03:52] You know, a lot of software that is built even pre-AI, you know, really failed to deliver on some of these really amazing outcomes that it promised. And AI, you know, for all its hype, right? There is a lot of hype. There's a lot of buzz. The question is, what of that buzz and what of that hype is actually going to drive an outcome for my company? Like an outcome that really matters to my company.

[00:04:21] So one, I think you have to have some healthy skepticism and really drill in. Okay. I understand what your technology can do. Like, what is it actually doing for real customers like me? Because that's a proof point that matters to me. If you can tell me that a company that's as big as mine, that's like mine, that's already seeing AI deliver great outcomes, then I should be listening. We do think, so that healthy skepticism point is number one.

[00:04:51] Number two, we do think we're in an interesting moment in the evolution of AI and what it actually can accomplish. And those moments don't come around very often.

[00:05:05] And I do think we're at a moment where the technology has advanced to a place where specifically for frontline, the world of frontline work, AI has real powerful capabilities that maybe don't exist in the knowledge worker universe. Now, I want to be really clear. I think one thing we are upfront about, we think frontline workers are going to be relatively unimpacted by AI.

[00:05:32] I know there's some people out there that think the robots are going to be making our hamburgers and our smoothies soon. It's going to be a long time before the hamburgers are made by AI. The hamburgers are going to be made by humans for a long time. The thing that is changing, though, is that the operation of working a frontline operation is going to be impacted by AI.

[00:06:02] Meaning AI can have a profound impact on your ability to hire better talent. Assess whether that talent is going to be a really good fit in your organization. Onboard that talent. Check in with that talent. Interview that talent. Attract that talent. Retain that talent. AI can do a really, really incredible job there that, frankly, humans would not be able to do.

[00:06:27] And so while frontline labor is probably unimpacted by AI, the actual operation of it is going to be dramatically impacted by AI. So that's the second thing is I think we're at a moment in time where AI can have a big impact on the frontline workforce and the frontline workforce at large. The third thing I'd say for HR leaders, that healthy skepticism is part of their job.

[00:06:51] You know, the person that's in charge of the human resources, the human part of a company should have a very healthy skepticism about bold promises and buzzwords. I mean, they're in charge of the people at the company. And so one of the things, obviously, at Fountain that we're passionate about is human in the loop.

[00:07:14] You know, we believe in AI, but we want to make sure that there's a human in the loop and constitutional AI, which, you know, we've partnered with Anthropic on. But all of our work in AI is backed by humans. And we think that's so critical in the world of human resources. And talking about operational and frontline workforce, something that came to my mind is a story that happened in the recent couple of years.

[00:07:41] So I'm based out of Vancouver, BC, Canada. And it was the talk of the town when a coffee shop opened and they had robots that were serving coffees. The first thing that came to my mind was, well, you know, there's different ways that people are thinking about AI and robots and how it's all coming to life. And people were blowing up social media and the news being like, OK, like the robots are taking our jobs or, you know, they're going after the baristas.

[00:08:12] And it's like, hold on a second. We have coffee makers that you press a button and it does the same thing. Exactly. The way we think about technology and the actual output is, I don't know, I think it's funny. And we're quick to jump to assumptions and reactions around all of the things that are new these days. Yeah, it is. You know, the distinction of where the human starts and where the computer or the technology begins is a really interesting one.

[00:08:41] You know, one thing that's really interesting at Fountain, right? We one of our frontline talent agents is actually an interviewer that does initial interviews with with candidates. And, you know, at first, people were really nervous that the interviewer would be biased. You know, the AI would be biased or that the AI wouldn't feel as human. Right. Or something like that.

[00:09:07] And once customers try it, though, it almost flips the other way. And there's two really interesting kind of anecdotes and stories. One, the AI interviewer doesn't care what sports team you like. They don't care that they haven't eaten lunch and they're really hungry. They don't care that it's been a really long day and they're really tired.

[00:09:32] They give a fair and complete interview to every single candidate, regardless of the time of day that that person calls, regardless of whether they've already interviewed a lot of people. Every single person who calls gets a fair and unbiased interview and a real assessment about whether they'd be a good fit for that job. Now, it is an AI interviewer because you're not interviewing with a human.

[00:09:59] But two things I think are really powerful about it and certainly really, really interesting. You know, how many people apply for a job who never get an interview? The vast majority of people who apply for a job, their resume just never, you know, they never make it to the line where you get interviewed. Right. They get eliminated, actually, even before anyone's even heard their voice.

[00:10:29] Right. With AI, you can actually flip that on its head. If you have a job and let's say you're hiring one person and 400 people apply for that job because it's a really amazing job. You could actually interview all 400 with AI. You could actually ask all 400 a series of 10 or 12 questions.

[00:10:55] And what we might find is that somebody who would have been eliminated in the resume sorting bucket kind of paradigm actually rises to the top. Even maybe they don't have the perfect experience. But the way that they interacted, the way that they answered the questions, the experiences they've had led the AI to say this might be somebody worth double clicking and diving in deeper. And I don't know if there's something more human than that.

[00:11:25] And kind of going to your point around like does the coffee maker, the coffee maker is already a robot. Right. You push a button and it makes the coffee. The only thing that we've done is we've added a robot to push the button, which is totally fine. But it's the same thing on the interviewing side. And kind of where does the tech start? Where does the human start? Becomes really interesting. I think we spend a lot of time worried about how AI is going to negatively impact things.

[00:11:53] And frankly, not enough time on how it might positively impact things. I think that's a natural part of being a human, though, right? We are naturally risk averse, right? We want to try to identify the risks first before diving in. So what's happening now, I think, is completely natural. We're trying to poke holes in it and see what could go wrong.

[00:12:18] But something, a word that keeps coming up in my mind around AI and, you know, just looking at the skills and how people are advancing through different career sets, right? It's you got to be curious. It's good to be curious. The curiosity is what's going to, I think, really help people get ahead. And I guess you can interchange that with skepticism, right? Yeah, exactly.

[00:12:47] But you said something that I want to dig into a bit more, right? Where these agents are starting to enter the workforce as teammates, right? So, you know, thinking how software has moved beyond just reporting and analytics and into actual executional roles. So, as you mentioned, screening candidates and then triggering workflows, actually doing the interviews.

[00:13:17] What changes inside a company when systems stop acting like just admin tools and they actually start acting more like operational teammates? Yeah. Yeah. It's going to be a fascinating transformation inside many, many companies. Also, a fascinating opportunity inside of many, many companies.

[00:13:38] You know, I think the ability to say, I want to have this done and I'm going to go do it generally has been a superpower for people inside companies. You know, those people that could just get work done. They just were really, really good at their job and they just, they worked harder. They worked longer. They did more than others. That skill may be less valuable.

[00:14:08] I'd say it's going away. Obviously, that's always maybe a hard thing, a good thing to have. But the ability to deploy systems and technology and to use them effectively to drive outcomes, I think is a pretty big shift. We're only, I think, the beginning of it. Really very, very beginning. I mean, if you think about way back six months ago, people thought maybe it was amazing that, you know, AI could write me a job description.

[00:14:39] You know, hey, can you write me a job description? Oh my gosh, look at this. It wrote the job description for me. Isn't that amazing? You know, or write my resume or whatever the case may be. Um, it, six months ago, it wouldn't have said, hey, write me the job description. Go find me the job boards to put it on and then tell me how I should advertise this job and source these people and find me some good initial candidates.

[00:15:07] Um, it might, might have given you a plan like, hey, Noah, here's, here's what you should go do. If I were you, I would post that job on LinkedIn and Indeed and ZipRecruiter. And then I would message 10 of your coworkers to ask them to see if they have any friends. And then I would do this if I were you. The paradigm shift is now with something like Fountain and FountainQ.

[00:15:34] It will not only give you that amazing plan, it will actually just say, hey, Noah, I'm going to post it on ZipRecruiter, Indeed, and LinkedIn because I think that's the best thing to do. Of course, you're in charge. But unless you tell me otherwise, I'm just going to go start to get you great people for that job. Stand by. You're going to start to see some great applicants coming your way.

[00:15:59] It, that paradigm of going from I'm generating a plan for you to I'm actually delivering an outcome that for you is a big, big leap. And I think it's going to be transformational inside of companies.

[00:16:16] And I think the best operators in the future will be people who know not only how to work great with humans, but know how to work great with agents to deliver outcomes that matter. I keep using this word outcome. We think that's a big transformation as well. I mean, for years, you know, software has been charged on a seat license basis or on a per per X per year basis.

[00:16:45] It's possible in the future that we may get to a world where things are based on outcomes. I mean, how many times have we bought software, paid the annual subscription for it and got none of the value or got very little of the value? Regardless of the fact that we bought it, it just didn't work or didn't deploy it correctly or some version of that.

[00:17:10] In a world where you have outcomes, you can actually say, how about I just fill that great, amazing job you are hiring for? Don't you know, I'll tell you what I'm doing, but I'm just going to go fill the job. If I fill the job, it's going to cost you this. Do you want me to do it? And that's pretty incredible. Or if like a customer, one of our customers opening, I'm opening a new restaurant and my next location is opening up on June 15th.

[00:17:39] I need it to be fully staffed. Go. Not give me a staffing plan, but actually just staffing the restaurant for me is a pretty huge, huge leap. Recognition is one of the most important parts of leadership, but it's also one of the hardest to do consistently. In most cases, the issue is not a lack of intent. It's a lack of clarity around what effective recognition actually looks like.

[00:18:07] That's why Gusto introduced AI recognition scores. This new feature helps managers understand not just how they recognize employees, but how effective that recognition actually is. AI recognition scores evaluate things like frequency, reach across the team, message quality, tone, and alignment to company values. They turn that into actionable feedback that managers can actually use to improve.

[00:18:33] Instead of guesswork, leaders get a clear baseline and guidance on what great recognition looks like. For HR and people teams, it also brings visibility at the organizational level, helping identify patterns, coaching opportunities, and gaps that were previously hard to spot. The result? More consistent leadership behaviors, stronger engagement, and recognition programs that can actually be managed and improved over time.

[00:18:59] To learn more about AI recognition scores, visit gusto.com. Right. So in the talent acquisition process, traditionally there would be like what differentiates a company might be, how they're writing their job description, and where they're sourcing their candidates from. There's a number of different ways, right? So if they're using these tools, if everyone starts using the same tools,

[00:19:27] and everything is automated and run by agents, where are HR professionals or the operational leaders starting to spend more time and focusing on? And what becomes a differentiator, right? Because employees often now, they want to work for a company for a specific reason, right? The company brand and the culture really becomes more and more important.

[00:19:56] What is the differentiator? Where are people spending more time so that everything's just not cookie cutter? Because it's all running through the same system. Yeah. It's such a pressing question. You know, look, if you have rapid adoption of AI tooling, and everybody else does, what's interesting is you're back in a competitive market again,

[00:20:21] meaning the best people are going to get attracted or recruited to the best jobs, and you're going to be back in a fight for talent on the hiring side. And by the way, the same thing on the retention side. What I think really skilled HR operators are going to start to do is to say, look, all of the blocking and tackling, all of the foundational work is done by agents.

[00:20:49] But the really human things are done by incredible humans and are done really well, right? And so I can envision a world where if all of the basics are roughly even, which one of these companies more closely aligns with my values? Which one of these companies more closely aligns with my life? You know, where I can work, when I can work, how I can work, and make it work within my life.

[00:21:19] Which one of these companies do I feel more recognized, more seen by? Would be a really important differentiator, assuming again, that if all of the foundation, meaning when I get paid, how I get paid, how I get hired, if is it, you know, is it easy? All of that is even because AI tooling is doing and agents are working on it. It's going to come down to those human elements of like, where do I see, where do I feel seen? Right?

[00:21:46] Which one of these companies aligns more closely with who I call myself? Right? And which one of these companies works better with my life? I think that'll be a real important competitive advantage. I think HR professionals probably know that today. You know, they all have an employee value proposition. We all do. It's just that HR is 30% employee value proposition and 70% operations.

[00:22:15] In an agent world that flips, operations becomes a smaller part of it because agents are doing most of that work. You still need some strategy and strategic direction around agents and agent management. But the employee value proposition becomes much more important. And if you can deploy agents more effectively, you actually have more resources to put against, how do I make my employer brand shine in this market? Yeah. Yeah.

[00:22:45] And you said something in there with like the recognition piece. And I, again, I like to distill things down to a single word, right? Where people like to work where they feel appreciated. And I think I look through the generations that are coming through, especially in frontline organizations, where we are starting to see the millennials enter and especially Gen Z. And more and more, they prioritize recognition in what drives value from their company, right? Or just to feel appreciated.

[00:23:14] It's not as much of a, I'm working a nine to five, I'm collecting a paycheck. It's, of course, the paycheck is important, but I need to feel appreciated for what I do. Yeah. Absolutely. Yeah. And there's something that's interesting. So from the HR operations leaders, right, there's a lot that's going on behind the scenes. There's a lot of pressure being put on from the executives to say, we need more AI.

[00:23:42] We need to adopt this faster, which is sometimes creating a gap between leaders and then the actual frontline and what the reality is. So one thing that seems dangerous right now is that executives are experimenting with AI from behind a desk, while frontline workers are actually experiencing the consequences in real time. Yeah. Have you seen disconnects emerge between how leadership imagines automation to work

[00:24:10] and how that is actually impacting frontline teams day to day? Yeah. I mean, I think it's, it's actually predates AI. The best part about frontline is the cycle time is really, really short. Meaning, you know, if you put a new payroll system in or a new performance review system in

[00:24:40] for a salaried professional that works in a headquarters office, it's months and months and months and months before you actually get any real data about whether or not it's working or not working. In the frontline, you know, because it's hourly in nature and shift-based in nature, you actually get almost real-time feedback.

[00:25:04] So you put in a system, you have 10,000 employees around the country or in Canada using it. After the first four hours, you've got thousands and thousands of data points that are telling you whether that system is working or not. Right?

[00:25:28] Like, if you put a new clock in system, you know after 10 minutes whether it's going well or not. And is it adding more friction? Is it hurting? Is it breaking? And so the cycle time is really, really fast on frontline. The best part about that is you don't have to wait. Like, you will know if your employees feel seen and feel recognized very quickly.

[00:25:55] If they think that the technology is getting in the way of their job, they will tell you really, really fast. The other thing that's really interesting about the AI is then, can you use that AI to actually get even faster on recognizing the challenges? Right? So in an ideal world, if that hypothetical 10,000 person company, if you were the CEO of that company,

[00:26:25] and you had unlimited time and unlimited money, you would try to talk to as many of those 10,000 people as you could. You'd want to hear, you know, how do you like working here? Why did you join us? Are you happy? What do you want to do next? Like, that would be an amazing activity if you could do that. Unfortunately, if you went and spent an hour with 10,000 people,

[00:26:52] it would take you forever to get all that information. With AI, you can actually do that. You can actually check in with an employee every week. Now, again, it's an AI. It's not a human. But you can say, hey, we're going to, if you're interested, if you're open to it, if you want to, you can receive a call from us, from our AI agent. We want you to be as honest as you can be.

[00:27:21] Tell us everything you love and everything you don't love about the tech that we've been rolling out. You will get real honest feedback directly in real time from 10,000 people. And you'll learn a lot in that process. But again, that's another example of using AI to say, here's something I wanted to do as a human. It's just not physically possible. But with AI, it is.

[00:27:49] Yeah, and that is such a gap. I love that, right? When you're implementing a tool or even building a tool, that feedback loop is something that I've seen in my 13 plus years in tech is missed so often. It's especially the end user. How did they onboard onto the tool or onboard into a company? It works in so many different ways. And now there's such an opportunity for fast feedback,

[00:28:14] especially when you're implementing new features or a new tech rollout for the frontline employees. Yeah, that has my mind spinning now. It gets me to another question that I think could be interesting to hear your take on it, because I'm not sure if you've heard what is now dubbed the two-speed economy.

[00:28:38] So it feels like we're entering into what people are saying is this two-speed economy where companies adopting operational AI effectively are accelerating really fast, while others we're seeing are starting to fall behind. So that gap is widening. It's becoming more obvious. What are the warning signs that an organization is already losing ground operationally, even if leadership, you know, they don't want to admit it yet? Yeah. It's such a great question.

[00:29:08] We do see that a lot. And I think you also have this question of which parts of the organization are moving and which parts aren't moving, right? Because you may have departments that are, I mean, just taking new technology and running as fast as they can and grabbing all the AI they can. And then you have other departments that are like, whoa, whoa, whoa, we're going to take a wait and see approach here.

[00:29:35] One, not, again, I'm a technologist at heart, like not having a coherent company strategy for the importance of what an agentic future looks like for your organization probably means you're further behind than you need to be.

[00:29:57] Somebody, you should, I have to assume that competitors are very likely doing the best they can to become more effective using AI in our market. If you start there and you assume that they're doing the best that they can, they will likely win more customers.

[00:30:23] They will likely be able to reduce their pricing and be more competitive in competitive deal cycles. They will likely do a better job of impressing customers who those customers will then brag about them to other people who will then become customers of that other company.

[00:30:43] And so having a coherent strategy about how we as a company are going to adapt in this new AI world, I think is like a table stakes for a leadership team. And that if you don't have it, I think you're probably falling behind in that world. That said, I do think you want to have that healthy skepticism, which we started with. I think you want to really vet who you're working with.

[00:31:12] You know, does this company have all the right certifications? Right. A lot of AI companies will promise you everything. Do they actually have customer proof points? Do they have a large organization who they tell you will get on the phone with you and brag about your AI? If the answer is yes, yes, yes, that might be a partner worth working for.

[00:31:36] The last thing I'd say is looking for partners where you feel like regardless of where that technology slope goes, you've got a long term partner. So what I mean to say there is, as you look for if you're thinking about how to how to approach this and how to stay ahead and how to stay competitive and you're looking for partners to work with. And I hope Fountain would be one of them.

[00:32:07] Finding partners that you can confidently say today, this is a great AI partner today. And no matter what happens in the future, I think this is the right long term partner for us, whether it goes straight up, sort of up, kind of up, not as up, flat. This is the right partner for us. Whatever comes our way, we've got the right set of partners that we're working with, I think is a great strategy.

[00:32:36] Looking for a scalable recognition program that works for your organization's specific needs? Book a demo today and speak with Gusto's recognition experts about our flexible programs. Go to gusto.com to get started today. And I think going into that partnership, too, part of that is, you know, building the trust, which gets me down to an important question.

[00:33:03] I think everyone tries to to answer, especially in frontline workforces where we've talked about execution of tools, things happening in the background. You know, frontline workers are often asked to trust these systems and these processes that they had zero output or that they had zero input on. Yeah. Yeah.

[00:33:26] How important is employee trust going to become as companies introduce more AI into hiring, scheduling and workforce management? And I'd say one of my top questions with this one is what destroys trust the fastest? What do we have to avoid? Yeah, yeah.

[00:33:46] First of all, the thing that destroys trust is if you make, if you do not meet my expectations, you've eroded that trust.

[00:33:59] I expect, for instance, that you will pay me the right amount of money for the work that I have done and that you will pay me in the method I expect on the date I expect that money.

[00:34:17] That's like, if you mess that up in any way, meaning you pay on the wrong date, the wrong amount, the wrong method, any of those things happen, you've eroded my trust. I expect that when I come to the office or the location, I'm going to be able to start my job, the one I'm supposed to start my job, and I'm going to be able to do my job in the way that I am told I should be doing my job.

[00:34:44] If you get in the way of that, you've eroded that trust. I think what's interesting about the frontline worker is they're also outcome-based. Like, they want an outcome. They want a job they want to do with a shift they want to do it for pay they want to do it for, and they want those outcomes, and they want you as the employer to deliver that outcome.

[00:35:08] I think what's interesting about this population of workers, and maybe most of us, I don't really care that much about the technology. I mean, I'm interested. I'm curious about it, maybe, to your point. But I don't really care what payroll system you have.

[00:35:30] As long as I get paid, again, what I'm supposed to be paid, when I'm supposed to be paid, how I'm supposed to be paid, the actual technology and the code and the actual engineering effort behind it is almost meaningless to me. I don't even need an input on it. You can – I trust you, provided you deliver the outcome that I'm looking for. And that's important to me, which is, in that case, pay. But you could say the same thing on rewards and recognition.

[00:35:58] You could say the same thing on a hiring practice, right? As long as my expectations are met, I don't really care about all that. It's at the end of the day, do I feel seen? Do I feel respected? Do I feel appreciated? Did I get paid? Do I have a schedule that works for my life? If you deliver those outcomes, you and I are in a good place, and I trust you.

[00:36:21] If you stop delivering those outcomes, no amount of technology is going to save you if, for instance, you don't pay me. If you don't pay me, our trust is really broken, and you can tell me all the AI in the world. If my bank account doesn't have money in it, even though I did the work, we probably don't have as much trust. Mm-hmm. And I think all of this transition of strategic resources, right?

[00:36:50] Us being able to focus on more human-centric experiences and away from the task work or the mundane things that we get caught up doing all the time that take us away from the human-centric work and building those experiences is really an exciting time. Awesome. Sean, I could talk to you probably about this for hours. You can talk to me, Laura. Yeah. No, it's been wonderful. I've loved this conversation.

[00:37:19] My last question for you that we always ask our guests to help people continue learning and widen their network of knowledge is, you know, are there any books or other leaders or organizations that have shaped how you think about labor, leadership, technology, and who should people be paying attention to right now? Yeah, such a great question.

[00:37:43] First of all, I wish I had more hours in the day because it feels like there's more articles, podcasts, books, and links than I could possibly read in a lifetime coming out. But a few things I always sort of rely on, I think Andreessen Horowitz puts out some really, really great content about what's happening around this technical shift in the market.

[00:38:13] Obviously, they believe we're in a watershed era. I've got a good friend who writes a blog called The Next Big Thing, really interesting in like where the world is headed. I like Lenny's podcast on product. I'm originally a product guy, so Lenny does a great job of talking a lot about sort of what's happening in the world of product. And I'm also, I love, you know, both how I built this and invest in the best.

[00:38:42] Like those are two podcasts that always get me thinking. And what's really great about them, they're usually not anything that I've ever, you know, they're not in my industry. But it's always great to hear from other entrepreneurs about, you know, where things are headed. So those are a few. And I feel bad because I'm probably leaving out some amazing friends who also write great things. Well, when you think about it, you can drop them in the comments and we can add them in there and we'll create the bigger library. Sean, thank you so much for joining us today.

[00:39:12] It's been a great conversation, like I mentioned. And I hope we get to speak again because the industry is moving so fast and I'm sure we'll have a whole other set of topics to talk about in a few months. So thank you. Absolutely. It's really great to be with you. If you found value in this podcast and want to help others improve their culture, be sure to follow us and leave a rating and review. Better yet, help us get the word out and share this episode with your colleagues and peers.

[00:39:41] Until next time, stay classy, HR.