Episode 901: A Conversation with Derek Sanders, Chief Revenue Officer at Opptly

Speaker A: Welcome to not only another new episode of the Future of Work Exchange podcast, but the season nine premiere. Thrilled to be joined by not only a dear friend of mine, but also the Chief Revenue officer at Opoli, Mr. Derek Sanders. Derek, thrilled to have you, my man.

Speaker B: Thank you so much, Chris. Really appreciate it. I was looking forward to this. So, uh, thanks for having me on.

Speaker A: I know it's funny, we concluded season eight with an appearance by the very legendary Ms. Lori Hawk. Right. So I think it's my boss, your boss, and I think it's really cool, right, that we can kick off the new season, uh, with you. Right. Sort of like that. The train of innovation isn't stopping. Right. We're continuing with another representative of a very exciting company that's doing really cool things. So Derek, I take for granted that I know you so well and I know that we've got, uh, some, some listeners that know your name pretty well, but I think it'd be great for audience to uh, hear a little bit more about your background.

Speaker B: Definitely. Really. I've been in the contingent workforce space my entire career in different avenues over the past 10 or so years has really been the enterprise space. Before that, I spent about six years with Ronstadt Professional where I manage a large team of recruiters and SalesPeople. But in 2016 I jumped over to TalentWave, which everybody knows, hopefully knows as people, uh, 2.0 now. So I was, uh, one of their newer salespeople when they combined a few older companies into a roll up. And talentwave, uh, was born and then did a short stint at a fintech startup called Quill, where we did supply chain financing, partnered with Beeline, partnered with other VMS technologies to allow supply chain financing. And then in late 2020, jumped back over into People 2.0, where shortly thereafter they acquired TalentWave. So the old crew is back together again. A lot of famous people that everyone knows from the industry. And then about two years ago, actually this week, two years ago, I was able to join the OPWI crew and really excited about the work that we've done here over the last few years. You know, really excited about the opportunity to talk a little bit more about what we're seeing in the marketplace. I know from Lori's conversation, even three months ago, a lot of things have changed and uh, a lot of new legislation has impacted the way that people think about AI. So looking forward to the conversation.

Speaker A: Derek goes, just about to interrupt. I can't believe it's been two years.

Speaker B: Right? I mean, I remember, I know it feels, feels like 10 years and six months all at the same time. At the same time.

Speaker A: Right. And it's funny too. Like I remember having a conversation with, with Laurie saying like, oh, do you know Derek Sanders? I'm like, oh, I'm thinking about that. I don't think we've ever talked. But like, I, I know who he is. Right. So glad to, I mean, it's been great getting to know you so much better over the past couple years, so. And uh, of course appreciate you spending time on the podcast. Podcast here. So something, you know very well being at a company that not only sits at the sort of the cusp of innovation, but you know, you know, you count many MSPs and VMSs as your, your core partners. We're sort of at a workforce inflection point. Right. Um, I use, I use so many different phrases. Ubiquitous, omnipresent. But you know, if you look at sort of where it is in terms of like enterprise technology and software, um, it's really sort of traversing from being a feature, a lot of, lot of platforms and solutions to being sort of like this true enterprise technology platform. That is another layer of intelligence. Right. So obviously this is your life now as AI on stuff, Derek. Right. Like baseball and children. You know, we've had.

Speaker B: Those are very full. But do you have my first job, which is AI first and foremost?

Speaker A: Of course. Yes, of course.

Speaker B: Yeah.

Speaker A: Sorry, children, we can't parent today. We, uh, we have to sell AI.

Speaker B: Yes, exactly.

Speaker A: Imagine that's how it goes. No, seriously though, um, you know, it's a really fun time, I think, to not only be in talent technology, but just enterprise technology in general, given where we are.

Speaker B: Right, definitely. And you know, joining the Opalee team. I was actually a partner of Opale before I joined and so, you know, I saw the roadmap from when it was acquired by uh, Tim and Pambo o' Rourke and I saw the evolution of really just being a pure direct sourcing platform and really focusing on what the goodness of the AI was, the outcomes. Right. Ability to replicate successes that we had within direct sourcing. What's the additional use cases? And so jumping over into the OPWI crew, one of the things that we, Laurie Hawk, who we mentioned before, and the other members of the leadership team, Rebecca and Jason, was, you know, what other use cases can we solve for skills? Intelligence is one of our other products and that was born out of our clients demand of we're acquiring a company, we want to rationalize our job titles and rates. Right. We have to be able to integrate those two companies. And our technology allowed us to really facilitate that use case of identifying what they're doing versus the other company and how they merge in together and that provide that new taxonomy of the role and then just other ones that are born out of it. It just seems to be that AI, as you all know, um, is ever changing. And so before I think, you know, I was always joking in 2024 and early into 2025 is like, everyone's like, we have AI, we have AI and that was good enough for everybody. But what does the AI do? And ultimately what does what, what do you specifically need it for? There's a million types of technology out there. There's a large language model, there's close, you know, offline models. There's so many different ways that you can approach how you identify and use the data that you have for ultimately the repeatable outcomes. But that's really where I think we, um, are at a really great point is I think people are educated now. The market's been through the fluff of we have AI. Well, what does AI mean to you? And now people are like, I need to solve this business use case this way. How can you support us? And I think that people are becoming way more educated and they've already invested a lot of money into their tech stacks. Right. If you think about the human capital sector, you know, these are all interconnected, right? If you have a procure to pay system that could ultimately affect what ATS you use, what VMS you use, those are some of the things. And so really what I think that where APWI is in a really great spot is that we are taking the approach of we will work with your native systems, we will integrate into them, and ultimately you can still get the goodness of appli while working with your existing systems that you put so much time, effort and thought around the change management alone. To rip out an ats, the change manager to rip out a procure to pay is so arduous, could take, you know, 9, 12, 18 months. But really if you want to work with your existing tech stack, that's one of the things that we're seeing a lot more companies, they're coming out with new features and those things just to be competitive with the type of companies like we are, where we're more of an add on to an existing platform.

Speaker A: So a lot to cover there, Derek.

Speaker B: Right.

Speaker A: Lots of unpack. One thing that I want to ask right from the get go, um, and I'm not sure like where you stand in this Phrase. But um, bring up the phrase total talent. Right? Everyone talks about it. Um, you know, I'm not going to say that I've been vilified as an analyst, but like, I mean a lot of folks disagree with me that, you know, total talent is more theory or myth and it's not real. Um, I think when you sort of peel back some of like the what does, what does total talent really mean? You see a lot of like real life use cases where businesses are sort of like rethinking the visibility into skills, expertise, their workers across the entire total talent ecosystem. It brings about this idea of like skills become sort of like a universal language across the totality of your talent.

Speaker B: Whether they say it's the connective tissue to evaluate talent. Right? It's uh, you're benchmarking how do I, what does good look like from a job? Is a job description quality and then you're weighing the candidates, right? The candidates. Is this a quality candidate based upon the job? And then last but not least is really my pain appropriately is the uh, rates appropriately. So it's a connective tissue. If you're basing everything upon skills, ultimately, whether you're looking at independent contractors, statement work providers, contingent talent, or I have an FTE need, you're basically benchmarking based upon skills. And I think that's really where the market is going and really it's been going that way for some time and now people are really defining, hey, we are uh, this type of shop and we need to identify these skills for this type of role. I think we're getting closer to that point. Whereas I think even 12 months ago it was, hey, we want to go from role based to skill base. Well, how do we get there? Well first you have to understand the job and what skills makeup of that and then you start moving down the next phase. So I think we're a really good point now where people are ultimately identifying the skills that they're looking to hire for and then we're going to see the evolution of that over the next six to 12 months.

Speaker A: And I think that really brings up an interesting point because um, I remember one of my first, I would say like, you know, sort of like public events with Lori and Opley. I think it was, she and Rebecca were on a, um, an AI focused webcast that the Future of Work exchange hosted. I would say it was summer of 2023. So like that's, you know, it doesn't seem that long ago but in terms of where AI is in the wide

Speaker B: world that's like 30 years ago.

Speaker A: Right, Right. And so, and I think just back then we were starting to talk a little bit about the ide. AI was a great foundational lever for moving to more of a skills based hiring model. Right. But I think, and that's not necessarily a question for you, Derek, but more of like, I'd love to hear more commentary on like where AI fits into this idea of skills based hiring and skills intelligence. And you know, obviously we know that skills intelligence is actually a core offering of the Opalee platform too.

Speaker B: Yeah. And I think that one of the. It's allowing your organization to kind of standardize how you view jobs. Right. So that's first and foremost. I think the other thing too is that what AI allows for you to do is understand the patterns of the workforce. Right. Are you seeing trends of, hey, we have more applications in this area, why is that happening? What are we doing from a marketing perspective? Like those are kind of the key things of the AI will allow you to recognize patterns.

Speaker A: Right.

Speaker B: And then inferring skills. Right. People are usually terrible at writing jobs or uh, resumes. Right. Resume only tells you so much and. Right. And so how can you get the goodness out of the resume? And that is by some inference of understanding how job skills are connected together. Right. If you say I need a business analyst, we have one JD for a business analyst. And it goes across the organization. Well, for a hiring manager, hey, I'm working in marketing or I'm working in IT or I'm working in engineering. Those are very different skills ultimately that they're hiring for. And so you can allow, the AI will allow you to put the human loop. They still have the handler still while they're still driving forward. But it's allowing to recognize the patterns and infer skills out of that to get to the outcome. Ultimately you're looking to do the, the highlighting the fact that human judgment is still there. Right. I think the, the big question is AI got revenue place everybody in our industry and those things. And so I think that one of the things that I think about which is, well, how at a point in time, if someone's uh, looking at taxonomy or evaluating talent, they're doing at that moment, moment in time. Right. And there could be the way I interpret things could be different the way that you, Chris, interpret things. But what's allowing you to do is from a level playing field, evaluating talent based upon skills. And then you or I, because we're working on two different jobs, we have the ability to input and affect ultimately the way that the job is being understood or the candidates being understood and then I'm affecting how the job or person is being interpreted and then I get the outcome that I want that could be different from the outcome that you want.

Speaker A: And I honestly, Derek, my next question was why do job titles fail in the modern day future of work movement? And that's pretty much it, right?

Speaker B: Subjective. It's subjective, right. Why do it's. If you think about the way that job titles or even job descriptions are had, it's really to put them into buckets, right. It's allowing them for HR reporting or C suite reporting to say, how much are we spending on this talent in engineering? I want to know what I'm spending there. Right. And it's not really understand the capacity of what they could be as well. And so I think that somewhat with the same job title, the example I used a few minutes ago, which is we're hiring for a business analyst, you're hiring for technical and uh, I'm hiring for marketing. But it's the same job description. Well, we need two different outcomes. Right. And so I think that the titles fail or have the ability to fail. Right. Because it's super competitive for talent. Right. We're seeing all the trends in the marketplace of like we're looking for top talent in engineering and technical roles, but it does go all the way down into light industrial roles, manufacturing roles, because the com. It's still competitive for top talent. And so really you have to differentiate yourself in the marketplace to identify the talent. Because if I had talked to a lot of clients, they're like, we get over a thousand resumes for each job that we have. I'm like, who is physically going through a thousand resumes to do a six month manufacturing company contractor job, Right?

Speaker A: Yeah.

Speaker B: You have the ability to implement some AI, to evaluate a baseline and then so you're having consistency in hiring that is not because of the job title, but ultimately the outcomes that the AI is referring and that human loop that is making that decision to move them forward in the process.

Speaker A: So it's, it's sort of this idea that contextual AI enables deeper skills intelligence than just skimming that surface of like keywords and what's included in the typical job description.

Speaker B: Correct? Yep.

Speaker A: Awesome. And so where do you see sort of. And again it's. I always say this, like I, I forget sometimes that there are so many hiring managers and recruiters, HR leaders, contingent workforce program heads that are so I guess they've got the weight of the world on Their shoulders. Right. It's a wonky job market. You have a war happening on the other side of the world. There's for procurement and supply chain folks, it's tariff chaos. There's just so much happening and they've got to focus on their business. Right. And so you know, how do you begin to have that conversation with these types of leaders of getting beyond hype of AI excitement around AI to where you can actually put it in practice? Because I do think that as much as we all talk about artificial intelligence as being this amazing purveyor of the next gen of enterprise tech, there's still that like not gap in education but there's a very minuscule amount of organizations that are really leveraging it beyond like pilot or first year capacity.

Speaker B: Yeah, I think the, the intelligence of the AI is really where it shines. Right. And so most companies, I ah, would say platforms, whether it be us or one of our competitors or larger in the human capital HR tech space, the eight folds of the world, right. It's the context becomes so valuable. It's the context of the job. Matching is one thing, right? We have a match and score algorithm that you know, we think is best in breed because we do these different things. But ultimately you can get a score from anywhere. Right. I've seen other technology demos where they're like this person's a 38% match. And it's like why am I spending my time evaluating the 30% match? So the context is super important to say what does good look like from our organization perspective? How are we evaluating the jobs? Because again, the human loop is still of the utmost uh, importance. But how are we understanding the skills that we're hiring for, how they're going to use them and how they're going to evolve? Right. We're really on the front end of recruitment which is how do I identify talent? What does good job description look like? But as you go forward, the intelligence of the AI is of the utmost uh, importance to drive context. Right. So as you're impacting looking at a job, well you can say great, we call this a business analyst, but they worked at another organization that would call them an infrastructure analyst. Just call it that. Well that person, if you're using traditional recruiting sources and those things, that person may not surface up right off the bat because you're not giving enough contextual evidence to show why they're matched from a skill perspective, not from a title perspective. And so the way that you can impact reading of the intelligence of the context of the role and the person will really help drive that thing forward and get through all the muck of, hey, we have AI and I don't know what it does, but we have AI. We're checking a box. We're seeing more and more end clients and partners that, uh, you mentioned the MSP and the bms partners that are kind of like, what's next? What's driving things forward? How can we show our clients value? Not that we're blocking, tackling that we have this capability, but we're actually having business outcomes that will impact your business, make your employment brand better, make, make your recruiting cycle time shorter. Right. Those are the outcomes that we're looking for.

Speaker A: So talking about outcomes, you talked about cycle times and I, uh, was just spending, um, the morning looking through some of our direct sourcing benchmarks. Right. Direct sourcing. I think, uh, it's, I think it's a fun topic. Right. I think it, it's a very interesting, it's an interesting arena and I like where we are today with direct sourcing because like, we're past, like, I feel like as direct sourcing really started to get hot, we hit pandemic mode. Right. And then.

Speaker B: That's very true.

Speaker A: A lot of folks saw direct sourcing as like, well, this is really cool. We can scale up, scale down based on save money. Right? Save money, of course.

Speaker B: Yep.

Speaker A: And then obviously reduce some of those, you know, like foundational hardcore recruitment and hiring metrics. But anyway, this is a long winded way of me saying that, you know, you know, just a couple weeks ago, Ardent Partners released our annual direct sourcing and digital Staffing Technology Advisor report, which is, uh, a vendor landscape platform evaluation report and was pleased to honor OPLI as one of our market leaders.

Speaker B: We were very pleased as well, by the way.

Speaker A: Awesome. Well, it's good to hear.

Speaker B: Yes.

Speaker A: You know, highest ranking, and solution strength, which is sort of the, you know, the baseline, uh, technology and automation innovation. But beyond the report and uh, where we are in direct sourcing. Right. Want to sort of pick your brain a little bit about how OPLI fits into that direct sourcing space. Where you personally see it, where the, where the solution sees it. You know, think of this as sort of like our direct sourcing lightning round. Right. We talk about all things related to that arena.

Speaker B: Yeah. And when I got here. Right. That's what we led with. Direct sourcing is a foundation that, uh, our organization was built upon because we did see the market trending that way. And I still think, to your point, it's such a valuable Use case when done. Right? Right. What I think happened during the pandemic and during kind of I was at, uh, another organization, I kind of saw what happened when things went well, which is we allow the curation and technology provider to leverage the brand. And really the purpose of direct sourcing, which is to act on behalf of the clients, it's the ability to leverage their logos and brands and their brand reputation and be able to use that Persona to reach out to candidates, to get them more involved in the community, to keep, create talent pools to message them about upcoming roles. Right. That's really the intent of direct sourcing. And what it morphed into, in my humble opinion is hey, how can we get the cheapest for the most value? Right. And that's still a valuable use case. But unless you're taking the necessary steps to set up appropriately, we have really good examples of things done well. We have examples of things that haven't gone well. Um, and where I see direct sourcing ultimately going is being very targeted and outbound on the talent pools that you're leveraging. Right. Segmenting that talent to know I have these repeatable positions that ultimately come up all the time. I always need to recruit for that. But then there's a special, especially the purple squirrels, right. That's not the intent of direct sourcing, but you can use it as a recurring channel to ultimately fulfill those less repeatable jobs. But the intent is the repeatable jobs, the baseline to build out your talent community, to reach out to your candidates, to inform them of what's happening and advise them when a role is available because they've taken the time to register with your talent community, to go through the process to be made available. And really that symbiotic relationship between curation which the people that are outreach, whether that's internal ta, where there's a third party curator or your MSP leveraging the technology in the way that was intended to be built out, which is using direct sourcing as a mechanism to market to candidates, to inform them and to engage with them. That's really where direct sourcing shines. And there's a really good examples across the industry of that happening not just with us, but other technology providers, other curation providers. But I think that as we get further along, as it becomes more specialized, I will, I think we're going to see more and more direct sourcing specific to sectors as well as roles. Right. Shrinking that talent pool and really use it the way it's intended and ultimately you have the higher outcomes, which is More fills cost savings and allowing your curation partner to leverage your brand, which I think is a paramount because that's the intent of direct sourcing. It's to leverage a brand on uh, behalf of a client, whether it's internally or externally, to reach out to people that you otherwise probably would not have access to. And build a community.

Speaker A: I was going to say. Then the next thing I wanted to address too, right. I mean, here's where I think Opoli is really special. Obviously I'm sure you agree, but having a platform in the direct sourcing space that is a true real deal AI technology led enterprise solution really allows you to sort of leverage your functionality across ats, around vms, msp, human capital, ecosystems. Right. So it sort of positions you a lot differently for direct sourcing because you have so many different sources of data, so many different sources of skills, intelligence and data. It's really, you know, we throw the on innovative a lot, but it really is an innovative model within direct sourcing because of what you are as a technology.

Speaker B: Yeah, I think that, you know, with our proprietary AI. So let me highlight that we started building our LLM in 2015, right. So before AI was AI, we started building our taxonomy, which is how do we get candidates in, evaluate the resumes, build out a skills model ultimately to show whether it's title, uh, skills or similarity scores, to pull that in together. That's how we ultimately get to the score. Right. The connective tissue between what we call them versus what their skills are and come together. And then really when, you know, when Lori Hawk took over, right. Leaning into the AI first, we saw where the market was going. We beefed up our data science team. We worked with a few outside providers really to hone in on this is how we're going to market, which is we're going to evaluate talent based upon skills, match and score. That was the initial use case. As we've come out of that, we know that our AI can also do on I'm, um, evaluating talent, but I can also evaluate jobs. Is it job quality? We just did a project for one of our clients where they haven't touched their job, their job descriptions and understand the skills since 2020. Right. Six years ago. The world's changed, we've changed. I, uh, have less hair, you know, more gray in the beard. Those things are happening. But think about the jobs that were being posted in 2020 pre pandemic to where it is now.

Speaker A: Derek, we weren't in our 40s back six years ago.

Speaker B: That very, very true. I Was uh, bright eye and bushy tailed and the world of AI changes fast and so do we, so that's right.

Speaker A: You know, so what I want to chat about next is because again, I think if you're an AI platform, you need to be able to talk about the greater picture around AI. Right? Sort of like not just because I feel like AI within our discussion today has been both macro and micro, but more in the micro because we're talking about talent tech in that ecosystem at a macro level, right. There's sort of this, um, you know, big discussion around, you know, AI in human judgment. And you call, you know, some. I mean, I'm not sure how you specifically refer to it, but you know, a lot of folks in this space, human in the loop, but um, what is that biggest area of AI that businesses really need to overcome? Maybe, you know, is it, is it governance? Is it judgment? Like it's, there's, there's so many different questions here, right? That is, boil down into one area of our discussion. But you know, there is that sort of AI versus human versus AI and human together discussion that's happening across not just our industry, but all industries. It doesn't, not just HCM and talent tech, but you know, ERP and fine tech and procure to pay. It's happening all across the board.

Speaker B: Right, agreed. I think that kind of a two, two part answer. One is I think the pace of AI is outpacing the way that organizations can handle it, meaning new features, new products, new advancements. Is this conflicting with our infosec policy? Is it conflicting with any of our data posturing? Those are some of the things that we're seeing more and more large language models ultimately. Is that right for our organization? We need time to evaluate it. We need the right people in the room, we need the right AI counsel. So it continues to go faster and faster and faster and tech is outpacing the way that companies evaluate and engage talent. The second part is really around governance. To your point, right? What is the government doing? Right. New AI laws are coming out in different parts of the world all the time. One of the things, Chris, that we talked about, which is we just launched our data center last year in Ireland at Compass EU. We're EU certified, we're SOC2 type 2 point, ISO 27001. But everybody has different requirements, right? And we've done the tried and true test of being overly cautious to accommodate for those things. But one lawmaker, one, you know, California or Washington or New York state changes things the way that you evaluate talent could be impacted. And so by choosing the right provider and choosing the right tech stack, are you, do you have your ERPs, do you have your VMSs? Do you have your ATSs? Are these things connected together? Do they uphold your data posturing and your compliance strategy? Are they doing these things? And so having the right partner and having the right certifications, everyone loves their badges, is really, really important to make sure that you're not going to get in trouble six months from now, from here, because you did something that wasn't appropriate to the law.

Speaker A: So, Derek, I do want to move on because, I mean, we can talk about this for hours, right? You know, I think the larger discussion around the, uh, macro elements of AI is a, uh, discussion unto itself. But I want to wrap up by talking more about the future of oplee. Right. I mean, I think that what's. And honestly, I'm a little jealous too. Right? I mean, I think if I, I was not an industry analyst and a thought leader, I would be on your side, obviously. And I think what's really exciting makes me a little bit jealous of what you guys do at opli is that you are in this constant state of evolution and innovation.

Speaker B: Right.

Speaker A: So whatever peaks you can give us into the future of opli, we'd love to hear.

Speaker B: Yeah, I think it's the way that the, the labor market is going to, is becoming more global, more dynamic and more focused on skills. And I think we play in a really nice match here based upon the way that we were built, based upon our governance and basically the opportunity to work with our clients that are identifying new use cases for us to engage with. Right. We're not changing our model, but as clients come up and as we own our own tech and we have the ability to affect our LLM, we have the very nice ability to pivot and to accommodate and put it on the roadmap ultimately to accommodate where our clients needs are going to be. Our, uh, leadership team, Lori being our leader. Other members of our team have the ability to take in feedback from our clients. One of our products is called Wise, which is our market analytics platform that gives basically monthly updates on how you engage talent in the world, what the talent availability is, how many places have been. I, I don't know anything about the Poland labor market. I need to hire 15 engineers. What do I do? Where do I look? Who am I competing with for talent? But what if you're not looking for specific talent in a job title, but more industry wide? I want to be educated on Those things, those are new filters that our clients are demanding, right? That, hey, I don't care about. I'm competing with Coke and I want to compete against Pepsi. I'm more competing for talent across the entire consumer goods sector.

Speaker A: Who are the.

Speaker B: What does good look like? How many places are going? So I think one of the things that we continue to see is that talent can be made anywhere. Talent can be engaged multiple different ways. I have been a WH worker my entire career, but I think I'm an anomaly based upon where the market trends are going. We're seeing a 50, 50 mix of full time hiring versus non full time hiring and non full time hiring. Could be I'm an independent contractor or I own my SOW business, or I'm a gig worker and I want to only work 10 hours a week. The flexibility and kind of tying back into the point of where we're seeing the market going, you have to allow for different types of talent to be engaged and to be engaged with, to be show that you guys are being forward thinking about the way that you engage talent. Right. Doesn't mean that I have to be in the office 40 hours a week. It means that I need to get my work done. And how I do that is the way that I choose. And so by allowing different talent pools and allowing different market segments, you can find the right talent for your organization, whether it be FTE or otherwise.

Speaker A: So we're going to wrap up here. And this is also a very important question, Derek, your top three Seinfeld episodes.

Speaker B: Ooh. I mean, there's so many to choose from. Uh, it's tough. Marine biologist is definitely number one. Of course. Yeah, it's gotta be number one. I want to say that one, but it's probably not appropriate for this webinar. So I'll. I'll save the other two. But as a whole, I think we talk. We are common love for, uh, Seinfeld. I think anybody in their late millennial

Speaker A: people, that geriatric m. Whatever you want to call us.

Speaker B: Right, yeah, Geriatric, millennial. Anybody that was in College in the 2000s, I think can understand that that was that and PTI were on TV all the time while we're going to college. Unfortunately, my wife still. I still watch Seinfeld. My wife doesn't enjoy it. So it's, uh.

Speaker A: I'm in the same boat, man. Same boat. You know, Sarah always asks. She's like, you've seen these hundreds of

Speaker B: times, hundreds of times. And it's background noise at this point. You know, I have three young boys. They're running around and they're, you know, the office and Seinfeld are pretty consistent. Are. And they'll even sit down with me and watch for a little bit. So it's pretty fun.

Speaker A: That's, you know, showing the next generation the greatness of 90s, where we've been

Speaker B: before and where we're going in the future, you know.

Speaker A: Great way to wrap it up. Well, Derek Sanders, chief Revenue Officer at opli, we appreciate you taking the time to join us here on the seasoned premiere of the Future of Work Exchange podcast. Thanks again.

Speaker B: Thank you so much, Chris. Thank you for having me. And, uh, it was a great time. Thank you.