Talent Tech Labs Takeover: Meet the New TA Ecosystem 13

Speaker A: Foreign.

Speaker B: Global Solutions presents the Subject to Talent Podcast, a hub for global workforce leaders to unleash the power of the human enterprise. Listen in as we explore the most innovative and transformational topics impacting businesses today. Hello and welcome to the Subject to Talent Podcast. Today we're excited to hand over the mic to our partners at Talent Tech Labs, a quantum work advisory company and part of the comprehensive suite of brands under Allegis Global Solutions. They'll share insights into their new talent acquisition ecosystem. 13 Guest host Brian Delladone, co founder of Talent Tech Labs, has been a leading force in driving innovation throughout his career, working with several engineering and IT services companies. He will be joined by Talent Tech Labs, practice leader of research, David Francis, a, uh, world renowned expert in talent acquisition technology, as well as near and long term technology roadmap strategy and in depth trend analysis. Let's listen in.

Speaker C: Hello, my name is Brian Delladone. I'm the co founder of Talent Tech Labs and your guest host today on this episode of the AGS Subject to Talent Podcast. This is actually a return performance for me. So I'm happy to be here doing this again, but this time I'm being joined by my colleague David Francis, who is the practice leader of our research practice at Talent Tech Labs and he's an expert in talent acquisition and talent management technologies and providing advice to the clients who use these tools trying to perform better in the way they deliver services to their stakeholders. Welcome M to the podcast. David.

Speaker A: Hey, thank you, Brian. Great to be here. And I, uh, guess you've set the bar high since you got invited back. We'll see if I get similar treatment, I'm sure.

Speaker C: Well, uh, let's jump into it and by way of background, a little bit of my history. I've spent the last 25 years in the human capital space in executive roles in IT, staffing, engineering, services, solution delivery. And then 13 years ago, I co founded Talentech Labs. We did that for the reason that we saw so much new technology coming into the talent acquisition space that it was overwhelming. And so there was concern that people were seeing all this innovation, but not necessarily knowing how to make good use of it. And so we started the company really to try and bring clarity to the space, to help people better understand what these tools could do and how they could most successfully apply to their organizations. And so that was the genesis of Talent Tech Labs, um, to bring the story fast forward. Our second investor, when we went out to raise money to grow the company was the Allegis Group. And so we've Had a very long standing relationship with Allegiance and over the course of time they sat on our board for about 10 years and then about uh, almost two years ago made the decision to acquire the rest of Talent Tech Labs. And so we fit into the Allegiance Global solutions business and we're coupled up with the group called Quantum Work Advisors. And Quantum Work is a purpose built advisory firm that helps customers understand where the friction exists in their processes and then helps them execute technology implementations so that they're able to get the most out of the technology that they've invested in. So Talent Tech Labs is sort of the tip of the spear in that Quantum Work relationship where we have our ear to the ground, understand the technology that's coming to market, understand how it works, where it works, and then we're able to bring in our colleagues at Quantum Work to help our clients be really successful at selecting and executing the technologies they've um, decided to um, invest in. So David, why don't you give us a few words about your background because it's really been illustrious and we're so happy to have you on the team I guess for what, the past seven years now.

Speaker A: That's exactly right Brian. Yeah, thanks a lot. Hey. I had kind of a um, circular uh, entrance into the industry. I actually um, started my professional uh, career in this industry, um, doing market research on uh, the contingent workforce industry. And so originally I was an analyst and kind of um, uh cut my teeth doing things like staffing firm kind of performance benchmarking and uh, market share lists and kind of forecasting industrial growth, et cetera. Um, uh, so really enjoy doing that. Um, when I was there, uh, it was kind of the beginning of what was seen as some pretty disruptive trends in uh, the staffing industry. With the rise of things like um, talent platforms and kind of new ways of um, online talent platforms I should say, new ways of kind of intermediating work. And so started doing some research around the technology that was coming to space. What was disruptive, kind of, what was opportunistic, um, uh, know built out a technology practice while I was there and um, you know at a conference I believe it was a, uh, uh, I was doing a presentation specifically on kind of strategic opportunities and risks, you know, kind of related to technology, um, in hiring, uh, met Brian and you know we kind of hit it off, uh, shared a little bit of some of the fun stuff they were doing um, at Talent Tech Lab. So decided uh, to join um, uh, Talent Tech Labs, build out the research practice and it's been of a fun, uh, and exciting kind of roller coaster ever since.

Speaker C: And it's been great to have David on the team. You know, when we were a startup, we were just really rubbing two sticks together to try and understand what was going on. But when he came to the company, we really were able to double down on our research chops and, uh, built a really robust research and advisory business on the back of the expertise that he and his team have grown, uh, up to provide the company. So let's go back to the beginning. Our mission was to try and bring clarity to the space. And one of the first things we did was come up with an infographic that plotted out the technologies across the talent acquisition continuum. In your mind's eye, just think about a little place mat that identifies where all these technologies come to life. The first version of this was actually some sticky tabs on our office wall in New York. And fast forward, today we're up to ecosystem version number 13. So almost one a year has been our release frequency. So David, help the audience understand visually, if you can, what that ecosystem looks like and what makes it important to understand how the space lays out.

Speaker A: Yeah, sure. I'll maybe walk through the, uh, structure of the ecosystem kind of conceptually, and then I'll give, um, you a little bit of thought, like, kind of why we designed it the way that we did and made some of the decisions around it, um, that we did. So way we organize it, um, we have actually a couple of different ecosystems. Today. We're focusing on the, uh, talent acquisition ecosystem and, uh, the design of that particular ecosystem. It follows the journey of a candidate kind of through the hiring life cycle. So we have what we call stages, which is source, select, engage and Hire. Inside of those stages we have kind of super categories, or we call them verticals, um, which are sets of kind of related tools and capabilities. And so as an example of that, in the source stage you have, uh, the job advertising vertical. Under one of those verticals you have kind of all the, uh, point solutions or kind of individual categories. We call these sub verticals. And these tend to be companies that would show up together if a company was going out to do an RFP or if they were looking at a particular solution space, it should be companies that have, uh, similar features and functionality and would commonly show up together kind of in, um, RFPs. So as example, under again, job advertising, uh, you have things like job boards, um, uh, uh, uh, programmatic advertising, job distribution, uh, et cetera. Um, and then inside of each of those sub verticals, you have the collection of vendors, um, associated with um, those particular, um, sub verticals. Now one of the design decisions we made is um, and if you look through, we've got quite a few vendors, I think more than 500 in our talent acquisition, ah, technology ecosystem. You'll see that there's uh, one vendor kind of in one, uh, sub vertical. And it was kind of an intentional design decision to do this because part of our, Brian before, part of our kind of mission or founding ethos was we wanted to, you know, help practitioners and organizations kind of cut through the noise and get a sense of like what a company's kind of actually do. And in most cases, if you, you know, ask a, you know, a company what they do, the answer is yes. And so, um, not particularly helpful from trying to make a decision point. And so while there are many cases where a particular vendor might have, and we're seeing this more and more actually as it's become kind of easier to develop. Uh, but, um, a vendor may have offerings in kind of multiple different sub verticals, uh, or capabilities in multiple different sub verticals. Um, we identify them in the category from which we think they derive kind of the majority of their business, or it's the main reason or solution that customers, um, are actually buying. Um, and from a practitioner's perspective, I think this is really useful in a couple of things. First off, it just gives a really clear, succinct, um, way to understand the kind of layout of the land, who does what, um, and also we have some, we put a great deal of thought into kind of which, uh, particular vendors actually do or don't make it onto the ecosystem. And so it can be useful to gauge a sense of, um, who's impactful or innovative in the space as well.

Speaker C: Thanks for that. A little hard to visualize. So if you want to get a quick snapshot, just go to talenttechlabs.com and you'll see a dropdown that allows you to look at the ecosystem. There's an interactive version of it there. So it had been rocking along over the years and it's really withstood the test of time because the way we've characterized those pieces of functionality across the ecosystem have become sort of industry, um, nomenclature as to how you describe functionality. So it's pretty ruggedized, it's pretty, um, tested by the market and it's, like I said, withstood the test of time. We see it showing up in people's offices all across the country and so, uh, it's gotten out there. Virally and so we're really pleased about that. But there's a big change that um, happened, a couple of big changes that happened from ecosystem 12 to ecosystem 13 that I'd like to drill in today with David. So why don't you talk structurally David about what's happened with the growth of the extended workforce and what that caused us to do and the way we characterize the space.

Speaker A: Yeah, sure. So for some historical context we you know even since our early days we always had kind of uh, a uh, lens on towards what was happening in the external uh workforce uh in a lot of ways. You know that, that includes you know basically the hiring of non um, full time employees essentially of, of all different kind of category types. So freelancers, independent contractors, you know, temporary staff log, et cetera. Um and the you know, kind of the, the, the source to hire workflow is actually relatively similar um, in both of those you know, environments. And you know both of those kind of uh, constituents are big um technology. And so we've always had a lens on uh, the extended workforce and the technology related to support it. In ecosystem, I believe it was ecosystem 11, it was a couple of years ago we added um, we kind of broke out the technology that was specifically focused on the extended workforce and we created kind of this wave at the bottom and you know, gave a little bit better sense like okay, what's, what are tools that are designed for you know, talent acquisition teams and full time hiring or what are the tools that are designed for um, uh the extended workforce teams um and hiring and managing that particular category of labor. One of the things we kind of learned and found is that uh, as we've worked more with the teams responsible for managing that particular category of labor is that the use cases and the workflows, it's so nuanced and it's so different than um, you know, talent acquisition. Even though there's you know a talent acquisition component to um, really is kind of a different beast. And even in you know, areas where there's overlaps kind of in, in categories, um, so you know, uh, people use assessments or you know teams use assessments to you know, qualify candidates in their you know, full time hiring. And that's a category of tool that's also used, you know, in contingent labor. Uh, but in even the cases where both are using assessments like the specific vendor mix actually tends to be different. Um because the features and functionality that you build to support those different audiences is going to be different because the workflows um, of those two audiences are Different. So anyways, Net Net, um, we thought that the extended workforce ecosystem as a category is important enough that it really needs its own standalone ecosystem. It shouldn't kind of be a, you know, a footnote in the, in the Talent Acquisition ecosystem. And so what we decided to do is we removed all of the um, uh, extended workforce related subverticals, uh, from Talent Acquisition Ecosystem 13 and we are going to be launching a new standalone contingent workforce, um ecosystem, um, which we think is going to be a lot more valuable to uh, the practitioners that are responsible for managing that particular space.

Speaker C: And that's coming out pretty soon. From what I understand and typical uh, in our product releases like this is we put out an explainer report which helps people understand what makes up the space and what's changed. And so David, I know you're working on that now but um, you know, I'm sure that this, there'll be a webinar to announce it and hopefully uh, we can get you all invited to uh, be able to listen in on that. But besides that structural change, there's a lot that's gone on on this ecosystem that's changed as a result of the emergence of AI. Um on ecosystem 12 I think we had AI addressed basically in two bubbles, maybe three. I think one was called generative AI, one was AI audit tools and I think the other one might be RPA robotic process automation. But in ecosystem 13, what happened, David? We went up to five or seven bubbles, um, describing.

Speaker A: Yeah, so in, yeah ah, last year um, uh we had a, we had a couple of bubbles and specifically so first is just a kind of a clarifying point Brian. AI has been in the industry since the industry was kind of founded. Right? Um, and so there have been AI solutions or companies that are providing AI solutions in our ecosystem since we started creating um, the ecosystem. What kind of changed was the rise of uh, generative AI and large language models which provided kind of a fundamentally different um, approach to how you can embed AI in your systems or deploy those solutions to end clients. And there's been kind of a renaissance in terms of capabilities uh, and new solutions, um m around that. So uh, kind of with that context, last year we had generative AI, um as a standalone category. We had AI algorithm and audit as another one. This year we launched an AI solutions uh vertical and so basically an entire kind of solution space where the bulk of the um, the features and functionality related to um, AI capabilities, um, foundational models or generative AI models are still in that vertical. We changed the name to foundational models to better reflect the foundational role that they play in a lot of upstream or I should say downstream uh solutions. Um and we launched several new subverticals. Uh so AI recruiters. Um, these are tools that organizations use to automate using agentic AI capabilities. Some parts or possibly all of the um, you know the recruiting process or what normally a human recruiter, sourcer or you know a uh coordinator would do. Uh we launched AI workers which uh is a solution category designed to basically be deployed inside of companies where you know an AI agent can take on parts of work that was formerly done by humans but it doesn't necessarily need to be in the talent acquisition department or HR department. So it could be things like you know an AI sales agent, um you know AI software developer, AI medical scriber, um you know AI research analyst things uh, of that nature. We launched a new category which has really only been around you know for you know probably the past 18 months or so, um for candidate AI tools. And this is kind of an emerging category of tools that you know may be the bane of existence for many uh large organizations struggling with um, you know an influx of uh, uh applications that are AI assisted or candidate fraud in the application process. I think these are really tools designed to help candidates apply and mask for jobs kind of custom or hyper personalize their application. Um and then in the actual um interview process or selection process they're providing in some cases kind of real um time feedback for things like interview kind of assistance etc. Um we kept AI and algorithm audit. That's a category that's that's um, continued uh to grow. And the last category we added was um, AI infrastructure. Um and these platforms are kind of a different flavor of um the solution. Basically they're um, generalist AI agent builders. Uh typically you know or kind of most notably um offered by the largest technology companies like you know the Microsoft and Servicenows and Salesforces uh of the world. Uh but basically what they let organizations do or practitioners inside of organizations is build their own kind of custom agents and kind of a no code environment define the rules and guardrails around how those agents are deployed or what they're able to do or what systems they can interact with. Uh, but it basically lets them build kind of their own custom uh AI workers on demand and then get charged kind of piece bill for it based on the utilization or work that those um agents actually do. So yeah, a lot of change uh and a lot of uh, kind of exciting innovation happening particularly uh around AI solutions. Brian.

Speaker C: So how many companies do you think we've added to this ecosystem over the last one?

Speaker A: Uh, it's a good question. We ended up net, I think even with the removal of all of the contingent workforce related um, subverticals, um, I think we still ended up net right near or above um, where we were at last year vendor wise. So we've added I think more than 100 um, new solutions. There's also several companies that get removed each year, um, for a variety of reasons. Um, some get acquired, some go out of business, some uh, don't meet the qualification, uh, et cetera for inclusion. Um, so yeah, quite a lot of vendors added this particular year um, which also kind of necessitated some of the changes we made structurally to make uh, ah, the extended workforce its own ecosystem. Because the infographic itself was getting pretty busy quite frankly.

Speaker C: So I want to come back to the area of those candidate tools because I can't be in a conversation with a staffing executive, um, an msp, an RPO, or a talent acquisition team who isn't really paranoid about what these tools are doing to either sort of really create the Hollywood resume in flight capabilities or ah, what that is um, done to facilitate um, the emergence of fraud in the way people are showing up in atss and on job boards. So any, any advice there to, to the buyers on how to combat these tools in the candidate's hands?

Speaker A: Yeah, yeah, I've got a couple of thoughts maybe before I ask the question directly on kind of, you know, strategies for mitigating, maybe a quick philosophical discussion, I think there's a, uh, you know, a question about my, my own, you know, like are these tools good or bad or right or wrong? Um, you know, my sense is I actually kind of like the fact that now, you know, one of the, you know, I would say silver linings of AI coming to market is it's now giving a new tool set, you know, candidates. And so um, I don't think it's necessarily a bad thing that candidates have this new tool that lets them, you know, be able to apply to more jobs or present themselves in a, in a better fashion. And even from you know, an employer's perspective, whether that's a, you know, staffing company or direct employer, um, you know, having more people apply to your jobs isn't necessarily a bad thing. Um, now the downside is, is if people are using it in ways that are unethical or that kind of clearly run afoul of what it is that you're trying to hire for. So people are misrepresenting themselves or saying that they have skills that they don't, um, or cheating on ah assessments or the interviews then that could be a problem. Um, and so there's a couple approaches I think I would take. So first of all, um, in many interviewing platforms now, in many digital interviewing platforms and assessment platforms, um there are ways you can kind of AI proctor um an interview or an assessment. You can't do this with 100% certainty. But there are ways to basically flag uh those interviews or those assessments that look like they are using um, A.I. uh, uh, to basically cheat the system or cheat in the process. And so I think that's going to probably become ah a de facto standard at some point where you have to incorporate some type of AI proctoring in order to um, at least get uh, an understanding of. And there's some pretty advanced techniques that you can do. But at a minimum what you want to be able to do is understand, be able to flag um, who are the candidates that you think are at risk or cheated on a particular um assessment or interview and then you can create whatever follow uh up you want to do to make um, a decision uh, one way or the other on that particular um, uh candidate. The other thing that can kind of be baked in natively is large language models are, they're basically just prediction engines. And so they're probabilistic prediction engines. But you know one of the effects of that is that they're you know the way that they write is also predictable. And so there are some tools that are out there with some confidence that you can basically you know, use these to get a pretty good sense again not 100% accurate, um, but you get a pretty clear directional picture of like is the content that has been submitted by this particular candidate like AI generated or not. And so you might have that as a, as a filter or a flag kind of right at the get go. I think the last thing I would say, you know again maybe this is all end philosophical is that you know, um, I think it might be amiss if the you know, kind of the basically the decision, one of the decisions to be made is to what extent are we going to allow candidates to, or not to or not to use um AI And I don't know that anybody or many, many organizations I think at large haven't yet kind of formally decided what the policy is here. But I think there does need to be a policy. And so in some cases um, you may have like a zero AI policy for how you complete a problem. In other cases, there are organizations that have kind of leaned in and the expectation is like, well, do we even want people that like, don't use AI applying for our jobs? You know, maybe not, because that's kind of the future. And so let's create a structure in which they can't, can use AI, but do it in a way that kind of also complies with like, our internal policies. So, um, little bit of philosophy, hopefully a little bit of tactics, uh, hopefully helpful.

Speaker C: No, I'm sure that's really helpful. So, David, I'm in conversations with many leaders, um, and they are just really almost in a state of panic with fear, um, of being left out and maybe not moving quick enough. On the other hand, they're seeing the pace of change happening so quickly, they're wondering, when should I lean in? So before we answer the question of how we might guide those bits of, um, challenge, maybe we could talk about the impact that some of the best tools in the space are having on the processes they're trying to impact. So maybe if you could just summarize some real high level. Which of these tools in these verticals that we talked or subverticles we talked about is having the greatest impact at

Speaker A: this early stage, uh, for AI solutions specifically or just kind of across the board?

Speaker C: Well, I think we can talk about predictive as well as generative, and then maybe a little bit of the agents.

Speaker A: Look, I think, uh, I'll maybe start kind of the solution set at large and then we'll talk about AI specifically. So, um, uh, there's impact first. Again, I'll maybe unfortunately start a bit philosophical, but impact can be measured. It can be measured a few different ways. And so, uh, typically when we go into an organization, like if we're doing like advisory work, you know, part of our mandate is we have to, you know, find uh, out what's broken, find, uh, out what the challenges are and then, you know, help advise, uh, on solutions that can, that can fix those challenge. And typically there's like, you know, uh, it's a sophisticated organization. You want to have kind of a business case or some ROI associated with, you know, whatever new investment that you're going to be, um, making in the talent space. Uh, it can be a little tricky because there are some things that are very easy to measure ROI on. Um, and there are some very impactful things that are important, incredibly important, maybe some might argue the most important, but are a lot harder to like, assign. Um, first they're hard to measure, and then Maybe hired to kind of assign a dollar value. Um, so it's like a couple specific examples. If you're doing, you know, if you're, if you're trying to source and you know, you want to, you know, a direct measurement would be like, okay, can we increase like the volume of quality applicants that we have coming through our pipeline? Okay, that's a very kind of easy to measure, easy to track, typically easy to associate with, you know, what solution was driving that particular result. Um, if you're looking towards, you know, which employee that we hired is actually staying on longer, like which employee is actually like driving business results for organization, um, and you know, how did we make the determination for that, that particular candidate being like a high quality candidate or a high potential candidate. That's a lot harder to measure and you know, kind of a lot harder to um, uh, ah, track. But ultimately that's probably like the most important thing that you want to be doing is hiring organizations is like hiring people that are going to you know, um, you know, improve your bottom line, uh, uh, uh, or drive business results in some way. So anyways, um, just a way to say when you're thinking about kind of how you put business cases together or look at the performance of any of these tools, um, it's important not just to think about the things that are like easily measurable, um, but also about stuff that's a little bit harder, harder to measure and making sure that those kind of, you know, maybe non um factored costs are getting taken into account, um, on which areas are kind of most um, impactful. Well, there's a lot of uh, uh, investment happening right now around um, AI kind of broadly speaking. One of the interesting things that we found, we did a survey where um, uh, we asked uh, talent leaders to answer a series of questions related to their use and kind of sentiments towards um, AI and uh, AI solutions in the talent space. One of the interesting things that we found is that uh, about three quarters of companies um, had some kind of a pilot or um, were planning some kind of a pilot, um, in the next 12 months. Um, and when you asked them like um, what's their strategy, uh, you know, the vast majority, about three quarters as well, um, had said that they don't have their AI strategy kind of figured out. And so there's a little bit of the carpet for the horse, um, in terms of where uh, you know, it's been kind of most impactful. It depends on um, uh, for AI solutions specifically, um, it's tended to do really well in Kind of higher volume, earlier career contexts, uh, to date. And so um, uh, but that's, that's also kind of partially driven by the fact that this has been um, that's where the pilots have happened. Um, typically in kind of a defined population, usually with early career or high volume, kind of more repeatable, um, uh, candidate processes. And so how um, extensible that'll be to other parts of the organization that are doing, you know, maybe more complex hiring, more senior level hiring. I think the verdict is still a little bit out, but um, we've seen some kind of early promising results there too.

Speaker C: That's great. That's really helpful. So in your view, do you think this accelerated pace of innovation coming to market is here to stay? Is this pace going to be what we have to look forward to?

Speaker A: Uh, I think yes. And just for a little bit of context, we kind of saw more new vendors come to market in this last um, iteration of updating the ecosystem, um, than the entire time that I've been here, um, at Talent Tech Labs. And there's a couple of reasons for that. Um, the biggest reason is one of again the silver linings or side effects of this new generation of AI that's now available, um, is it's largely been democratized. And what that means is basically anybody has access to it. And so it's made that significantly easier. Like there used to be like if you wanted to build a technology company, you used to know how to code and um, uh, uh, do all these different technical things. You have to raise money. It was pretty challenging. Now it's significantly easier to kind of stand up a solution, um, uh, even among kind of non technical folks. And so it's um, kind of democratized access to literally the best AI that the world um, has ever known. And so on the back of that there's been this massive explosion in new solutions and not just in the talent space but literally across the board. Um, and so the question that we get asked is, well there's a couple questions we get asked. The first one is what you just did, is this enduring? The other one is, okay, if there are so many different kind of new solutions coming to market, who specifically um, is going to win? So on your question, Brian, I, um, do think that this kind of trend is enduring. So if I had to like kind of try to articulate the state of the market right now, I would say it's a very, you know, we were definitely like probably an 11 out of 10 in the hype cycle. But at the same time it is still like a very. I think this is going to be an enduring, like this, this technology is transformational and I think it's going to be enduring. Um, and the pace of innovation is probably only going to accelerate, not decelerate. And so I'd maybe liken it to a little bit like 1999 where we had pet.coms uh, in all of their ilk, but we also had generationally defining companies that are now the largest companies in the world that came out of that period too. And um, I think that's kind of where um, we're at today, where it's very early stages and what's probably going to be, uh, many years long kind of transformational process. And I think another thing that's going to happen is a lot of the big companies that are kind of dominant market leaders today, like, aren't necessarily in a, in, in a safe position. And so like, who wins in the future? I don't know which specific vendor is going to win, but if you look at like the big categories like ats, or CRM, um, you know, or matching or AT job advertising and sourcing, like who wins in each of those categories, like there's, you know, that's, that remains to be seen. And just because you're like the largest company in the space today, like that doesn't necessarily like, you know, ensure your success or uh, continue dominance, um, you know, five years, five years from now. And so kind of the, the, the, the pace of disruption is accelerated, uh, significantly.

Speaker C: So sounds like we're in a good business.

Speaker A: We're uh, like we're in a good business. Yeah.

Speaker B: So.

Speaker C: And you know, really honest speaking, speaking candidly though, you know, the, the, the, the, the, the genesis of Talent Tech Labs is trying to bring clarity. Our job has gotten harder with this, um, pace of change. But you know, we really are putting all of our efforts into trying to keep a level head around what these mean for the market and what winners and losers might look like so that companies can make the most informed decisions they can on how to structure their technical, um, solutions to deliver the services they need to. David. Um, this is great. Maybe tell the folks how they might listen in on um, some upcoming webinars that we're offering to um, unpack some of this stuff. I think you got a couple coming up pretty soon.

Speaker A: Yeah, we have one. We'll be launching the, um, we're doing a webinar on the, the extended workforce, um, in October, I think it's October 23rd if I'm not mistaken. So encourage, um, you to, to register for that and listen to it, I believe. In November, we're launching our, uh, third edition of the Talent Management Ecosystem, so we'll have an accompanying webinar. So welcome to join us, uh, for that as well.

Speaker C: And certainly you can go to talenttechlabs.com to see our event schedule and, um, the research that we give away for free to help people understand the space. And hopefully someday we might have the opportunity to support your organization. David, it's been great having you on this, uh, on this call and appreciate the insights you've shared. Um, hope we have a chance to do it again soon. But, uh, thanks for all your contributions today and the insights you share with our audience.

Speaker A: Thanks so much, Brian. It's been fun. Take care, everyone. Bye.

Speaker B: If you enjoyed this episode, please subscribe, rate and review us on Apple Podcasts, Spotify, or wherever you get your podcasts. And if you have any questions, send them to subjecttotalentligiusglobalsolutions.com follow us on LinkedIn with the hashtag SubjectTotalent and learn more about AGS@allegiusglobalsolutions.com where you can find additional workforce insights and past episodes. Until next time, cheers.