Steve 0:09 The HR Happy Hour Network is proudly sponsored by Workhuman. Nearly nine in ten HR leaders say they don't have a strong leadership bench. Workhuman's future leaders gives organizations a smarter way to build one. Future Leaders is the first talent intelligence solution to identify high potential VP plus talent already within your workforce up to four years before promotion. Using proprietary AI and real-time recognition data, it uncovers the leadership signals hidden in everyday work, long before traditional succession planning can, it's a smarter, data-driven approach to building tomorrow's leaders. Learn more at workhuman.com. Thanks for joining us. Welcome back to the HR Happy Hour Network. My name is Steve Boese. I'm with Trish Steed. Trish, how are you? You look great, ready to go back from your recent escapades with the medical community, and yeah, ready to go. Trish 1:10 I have a brand new knee. Like this is a whole new world. I can't wait to get out this fall and like run circles around everybody. So no, seriously, it's been great, and I have to tell you, I mean, we're going to be talking about recognition. You're so good at giving me recognition. So on those few times where I was trying to get back into work, and you'd be like, "You're doing great. That was a very. Steve 1:32 We try, but it's important, and we've talked about it a lot on the show recently. Check out our recent show we did about the Dallas Cowboys cheerleaders documentary because we were just our guest is Tom Libretto by the way I'm going to welcome him right now because he's I'm staring right at him and I don't want to have a long conversation and not not welcome Tom. Tom is the president at Workhuman. Workhuman a huge supporter of the HR Happy Hour Network of course but last week or two weeks ago whenever it was we were doing a show about the Dallas Cowboy cheerleaders documentary, and on that show, really just unsolicited, we said these cheerleaders are dying for recognition in the workplace. They were begging for it from their leaders, and we pointed out how it's a perfect application, right, of recognition tools to where people and Tom, I'll I will start right with that. Tom, are we seeing that with younger people? Because I I mentioned in the pre show these were a bunch of Gen Z folks who were just desperate for more recognition from their leaders in the workplace. Tom Libretto 2:37 Yeah, 100% And you know, I think the the ebbs and flows of demographic changes within the workforce bring all sorts of new dynamics to what what the the current expectations are around what I am going to get from you know from an employer and recognition is something that that kind of transcends those those demographic groups, but certainly for more digitally native, you know, folks like we're seeing with Gen Zers today in the workforce, there is a there's a high premium on on on just general feedback and being recognized for the contribution that one is making to to the workforce certainly as as much if not more than we see in other other age bands. Trish 3:29 Yeah, I wonder too, Tom. And I know we're going to really dive in deep on this today. But as I was kind of watching, and I'm thinking, I I wanted those same moments of recognition or just feedback when I was in my 20s, and there really wasn't a way to do that digitally. That, like, so I think you know that's part of it too, right? They people now entering the workforce, really at whatever age, they know it exists, and so it makes you really crave it. It's like we know our bosses could be giving us more feedback and more recognition or peers, right. Tom Libretto 4:01 Yeah, 100%. Trish 4:02 Yeah, well, Steve, why don't we dive into today? Though we've we've talked a little bit about DCC on the last show, so let's let's get into yeah. Steve 4:10 We can't make this the whole DCC network, but. Trish 4:13 Right. Steve 4:15 Obviously, in in mid to mid to the second half of 2026, 87% of the conversations we're having about the world of work have are about AI, or tangentially or directly involved with AI, AI tools, AI technology. And one of the things we wanted to talk to Tom about is, hey, let's think about AI. Lots of companies are not maybe seeing the returns on investment from AI, or struggling to find it. Struggling to find the applications. I think a big part of the problem is the data that we're asking AI to help us understand, to analyze, to to train the AI models on. And Tom, I know Workhuman has done a ton of great work here in developing tools where AI can help interpret really important, rich data in the organization. So first off, I love I love you to share your thoughts around. Hey, what what do we need to feed into these AI tools to really generate that bang and that return that we're looking for. Tom Libretto 5:21 Yeah, it's it's the question of the hour, and and I think you said it early on. It's the in what formats and where can data be synthesized in a way that is not just readable but interpretable by by AI. That's why you're seeing most organizations now running for the yeah or or mandating that workers across the organization document their their processes their workflows in Markdown files, which is becoming fast the standard for AI readability, so that those things can be more readily automated, and you're not fed, you know, false positives when you're trying to do something with AI that you used to do somewhat differently yesterday. And for us, as a you know, as kind of a subject matter expert in the in the area of of HCM and specifically recognition, we feel an obligation to do some of that heavy lifting on behalf of our clients. So we've invested heavily in AI technologies that are interrogating recognition data, so that our clients don't have to, and that we're able to produce post-processed insights out of that recognition data, so that are immediately usable and viable to supplement or, in some cases, completely replace you know some traditional HR workflows. Trish 6:55 I'm glad you started with the HR workflows because I think that's where the natural HR leaders, C-suite, that's where they think to start, Tom. But one of the things that's fascinated me, and I'd love you to get into it a little bit, is when it comes to the recognition data, and it's that zero-party data. And I don't know if everyone's familiar with that term, but if you could maybe explain sort of the relevance of that type of data and why it's it's important early on in your AI journey to understand and how that kind of contributes to your overall success with it. Tom Libretto 7:29 Yeah, of course. So we we define zero party data as as information that is voluntarily provided by by anyone by a worker by an employee without asking them to do so. So essentially, it's the it you know it's it's the knowledge that is resident in the minds of your individual individuals around your workforce that they voluntarily are sharing with the organization, and in our world, that's in the context of of observing values or success behaviors that they identify in their peers in the flow of work, and they're then writing about what they observed in their own words and using really in some cases elaborate language to to describe the contribution contributions that their peers are making. So within the context of that, what would on on face value look look to be a very simple thank you message, there is an unbelievably robust and authentic set of information, you know, contained within that singular moment, and then extrapolated across 10s of 1000s or hundreds of 1000s across an organization, you all of a sudden have a really really powerful data set to work from. Trish 8:50 Thank you. Steve 8:51 Yeah, and if you think about that, Tom, this is one of my favorite. It was a visual diagram. I think probably started with Eric Mosley showing it in one of his keynotes, but diving into kind of the tearing apart like a piece of recognition data and really breaking it down into what it could reveal if it's done, if it's done well, if it's done thoughtfully, and Workhuman also provides tools to help people actually get better at providing those recognitions as well to be a little more thoughtful about them too. So, so we're not just saying thanks, great job, Trish. Right? That's nice to say, but we don't learn too much from that. But if you think about a piece of recognition that might say, Trish, you did a wonderful job supporting our big client. You put in the extra effort, and you worked with the team of developers in India to show dedication, heart, care, and on that back end website development project you did, right? All the things you can put into one of those recommendations, and now all of a sudden we're thinking about teamwork and values and dedication to customer and relationships and inclusivity and more. There's so much there, Tom. I'd love for you maybe to talk a little bit about the power of recognition data. Say compared to some of the other data we throw AI tools at, which is nice, but maybe not as evocative, if you will. Tom Libretto 10:18 Yeah, 100% I mean, the the you know it's almost limitless the types of things we're we're starting to find we're finding really in these in these recognition messages you mentioned a few you can you know writ large you can start to understand collaboration patterns around an organization based on who's writing down you know the facts of who's working with who and who contributed, you know, at what level to a project win or a customer win or, or what have you. We're also picking up really, in some cases subtle, in some cases very strong signaling around skills, hard and soft, that are being demonstrated by employees, and where you know where where our HR strategies are fast moving in the direction of skills based architectures and career pathing and defining you know that what the workforce of the future will need from a from a skills, you know, skills perspective, the authenticity of those recognition moments as a major source of both skills identification and validation, you know, it is a is a huge opportunity for organizations. Trish 11:36 Yeah, I I think especially in these last, you know, I don't put a number exactly, but say five to six years, especially with Workhuman, what I've seen that's been really different from when I was actually in that seat, like VP of HR, CHRO seat, is that there is such a connection that you all are making that you then pass on to the users, and I think that's maybe where I would say organizations aren't fully understanding that value of that connection, right? So I would have thought back in the day, you know, a recognition program was this whole separate thing. It's nice to have it sits kind of over here, does its thing, right? It's it's been a very pleasant surprise, I think, because every time I'm at Workhuman live, for example, I'm seeing real clients talking about, oh yes, compliance is important and all these other things. But you know what? Our recognition data actually is driving our safety results. It's driving our not just our productivity results, but it's it's driving our attrition results, right? What kinds of of things are you hearing from whether it's current customers or potential customers. Are they making that connection, or are you really having to sort of orchestrate and help them understand how AI and the tools you offer can give them that really connected workforce picture? Tom Libretto 12:56 Oh yeah, I mean it's a it's just fascinating territory, and we love nothing more than when a client will come to us and say, "Hey, we would love to know what type of impact recognition is having on plant productivity, or absentee reduction in absenteeism, or you know the the you know the the old the old chestnut being you know top talent retention, you know, but but it it now you know the the the level of questions are getting way more sophisticated, and and and we're doing we're finding ourselves doing a lot of regression analysis with clients that are providing us some of their own data so that we can correlate it with their recognition program activity and start to show them where recognition activity thresholds are having a correlative effect on on many different types of of business outcomes, not just people outcomes, but real hard financial business outcomes. Trish 13:58 And that's what a CFO is going to challenge any HR leader for right? They're going to go, yeah. You want to, you know, you want to bring in this solution, and I just think, yeah, we we still need to continue as HR professionals to get better at articulating those connections. So I do love the work that you're doing on that because, again, just being an analyst now, hearing that that was like huge. I mean, I'm sitting there thinking, like, oh my goodness, if I would have been able to make those connections back in the day, like that would have been a true game changer. And I'm not exaggerating; couldn't exaggerate enough, probably. Tom Libretto 14:30 Yeah, and I think that you know, to the point to your question, part B of the question about how AI can help expedite a lot of this. I think the, you know, even even today, there's there's a lot of cross you know data cross referencing and sharing that we need to do in order to produce that type of quantifiable ROI evidence you know based analysis. You know I think not so far in the future, the the residents of that recognition data right, alongside an organization's operational data, with AI then on top, you know, becomes a queryable asset base. So I can ask those types of questions and get an immediate answer without, you know, without a research project, you know, that that could take some time to produce the results. Trish 15:21 Right. Steve 15:23 And that's one of the really powerful applications, right? We're seeing with the application of AI against this really rich data set that we've talked about-the richness and the and the value of that data set. Another area, Tom, I know where Work Human spent some time in, has rolled out some really interesting new solutions. Is around helping organizations with identifying potential leaders. Yeah, I'd love for you to talk a little bit about that because I do feel like in the AI age, one of the things we're seeing is companies are struggling here because we might not be bringing in enough new talent because maybe some of that new talent's being disrupted by AI, but maybe we've decided we're going to get a little leaner or a little flatter in the organization, and maybe some of our manager managerial talents left the organization, and now all of a sudden, oh my gosh, where are my next set of leaders coming from? And and I'd I'd love for you to share a little bit about how you guys are approaching that problem. Tom Libretto 16:21 100% It's really exciting. Really exciting stuff. The yeah, again, back to kind of those traditional HR processes. If you think about succession planning as a derivative of of you know kind of the you know the old performance management process that nobody likes doing. No one is particularly good at you know. And by the time you reach the end of the year, remembering the corpus of work and the you know the values and contribution that each employee had is near impossible. Steve 16:52 Yeah. Tom Libretto 16:53 And then if you try to extrapolate out of those types of you know very kind of point in time and fraught processes, who am I high potential talent is that I should put on fast track development programs because they're they're showing the potential to ascend to leadership positions in the future. The whole thing is just you know is just based on a whole string of biases. So what we set out to do was look at what an organization deems as important values, success behaviors, and signals around future talent or around talent development in general. So the first thing we did was built a series of models that understands what's important to an individual company, based on what we see, you know, the recognition activity we see paired with what actually happened from a promotion point of view over time within that organization, who was observed demonstrating these values or skills, and did that lead to promotions? That's a very simplistic way to describe the the initial analysis. We then started to look at can we then predict based on current signal strength around who's being recognized by who, how often, for what, in what domains, what cross domains, etc. Could we accurately predict years before a VP plus advancement would actually happen whether we could we could predict it now accurately? So we ran some analysis on you know with the help of a couple of our clients, or you know, with their permission, and and and we're hitting at a plus 80% accuracy rate three to five years prior to when people actually got promoted. So we rewound the clock, ran the models, you know, made the predictions and then played it forward to see what actually happened. So we're now able to look at a robust recognition program in a global organization and quite confidently predict the list of people that will factually be promoted within three to five year times to senior positions in that organization. The cool thing is, we can also show them the people that we predicted years ago that would have been promoted but left the organization. Trish 19:29 Wow! Tom Libretto 19:30 And that's the eye opener on the oh my gosh! If only we had known, we wouldn't have number one, we wouldn't have seen that institutional knowledge walk out the door. But we also wouldn't have to bear the cost of, you know, of bringing in a new senior leader at a, you know, in many cases a large expense. Trish 19:51 See, and this is the kind of stuff I think HR professionals get into the business to do, right? It's making those connections, and you mentioned bias. It's. It's such a biased process the way it currently runs. So being able to tie that actual recognition data to it, I think, gives you that subjectivity you want. I can think of every job I've ever worked when we were sitting through you know annual reviews and whatnot, and people just have their favorites. It doesn't mean they're the most qualified. You know, people have people that are super highly qualified they just don't like, or you know, or maybe having a tough year for whatever reason. I guess my question is, can you also play it out the other way? Say you have leaders who have been in their positions for a very long time, and maybe they're not the most successful people who need to be in those roles any longer. Can you tie, or is there starting to be a tie between the recognition data maybe they are not receiving, and seeing that maybe someone else is more qualified? Does is there that correlation yet, or is that still a little ways away? Tom Libretto 20:57 Yeah, there's certainly well intuitively the the absence of recognition is a signal in and of itself, for sure. The other the other element of what we're able to expose with these models, in addition to both showing you know well in advance who your future leaders will be in the future, we also look and expose the what we call who are the talent magnets? Who are those managers in an organization that have a high propensity for developing a disproportionate number of leaders in your organ? Eventual leaders in your organization. They themselves may never ascend to senior leadership level, but you know, good grief, they are they are the the gold dust in your organization that you you want to wrap your arms around and understand what they're doing to cultivate talent at you know disproportionately better than their peers. Trish 21:56 Yeah, I think that would have been super helpful because again, I think you know, think back to even many organizations now, you're sitting there trying to do it just based on what you know and what you see, without anything to back it up. And so, again, back to being in that HR seat, I can't imagine going to my CEO or CFO or whoever and saying, "Yeah, I think this is our talent magnet, and it very well might be, but I have no no data to back it up, so having that, I think, is it's monumentally different than what we've had in the past. Tom Libretto 22:24 Yeah. Steve 22:25 Yeah, and I think that sort of hits up against I think one of the other real strengths of recognition data, and also one of the real challenges of sort of many of the applications of AI right now, which is there is still data, a lot of a lot of data, important data that traditional HCM transaction systems just don't capture. That you know they're not built to capture. They're they don't have a mechanism to capture it. So things like influence, things like this person's a really good teammate. This person really you know puts the customers first all the time, right? Or things that are very, very important to the organization that the transactional system can't really capture. Now, the transactional system captures a lot of interesting things, right? Including who got promoted, right? We'll we'll probably look there to figure that out. But it's missing a lot of really, really important data about how the work is actually getting done, right? And I know Tom at Workhuman and you guys think about that really, really intently, right? And try to surface that to customers. I think that's one of the problems with AI in general in in talent management is it's it can only it can't find those things that are just not in those transactions as as they're typically stored. Tom Libretto 23:44 Yeah, 100% I think the you know most of most of what transpires in in HCM based processes is the is the collection of declarative data, and whether that is you know a a process that involves, you know, doing a write-up on an employee, reading their, you know, their performance and their potential, all the way through issuing a survey to employees to come back into the HCM and declare, self-declare what skills they have or what you know what L and D courses they've completed, and then we imply that they have those skills, but it's all kind of, as you mentioned, transactional and declarative based. When when all of a sudden you supplement that with what really is a written narrative about each individual that is way more robust and authentic because it's coming from your peers without asking them to do so, and when when that happens, they tend to write verbose you know dialogs and narratives about the employees. You get this incredibly rich tapestry that you can you know you can either use to supplement what's. Collected via other means, or in some cases, refute it and replace it. Trish 25:07 Yeah, I think this is just all a good example of you know it's more than just an award, right? It's more than just some icon that you get through an email or maybe points for something. That's all important. Those things are important to the individuals. I'm not wanting to downplay that, but it's so much more valuable when you're looking holistically at how recognition truly impacts the health of your organization. So, I guess my question around it would be for organizations maybe that have not thought about recognition or AI, for example, in in terms of workforce decisions, what would you offer as advice, maybe to them, in just starting to consider some of these things? How should they maybe go about the first, you know, year or two of of looking into this? Tom Libretto 25:54 Yeah, I think in in in the past, the you know the value proposition around a an employee recognition program investment was largely around strengthening and elevating culture as a you know something you take off of the wall and start to experience in practice, and with that comes higher levels of employee engagement and therefore productivity. We're we're we're almost overly excited of flipping that you know around and saying well the the real win and value proposition is that rich narrative that you're collecting around the employee base that describes how work is happening around the organization that is in you know that is you know arguably as powerful as the the motivational lift you get with recognition itself. So the you know the the those are two sides of the same coin because they don't compete with one another. They're completely complementary. So I you know if I'm if I'm a CHRO today, I would be really excited by the proposition of I get to elevate the culture, drive employee engagement, heighten levels of productivity across the organization, reduce you know reduce unnecessary attrition and therefore cost, and all of a sudden I have this rich you know, tapestry of data that can be mined with AI to tell me things that I would never know about my organization otherwise. It's super exciting. Trish 27:31 It is exciting, and this is something. This isn't new. People might think like, "Oh, this is new because of AI. You all have had this stance for literally decades, right? Tom Libretto 27:42 Yeah. Trish 27:42 When it was still just being drawn on a on a whiteboard or something, right? And so I just want people to understand this isn't new thought for you all. You all have been operationalizing this each step of the way, and it's it's very important, I think, to me as someone who used to buy a lot of technology. I know that when you need to spend your money, there's there are many considerations, right? I think part of it for me as an HR leader was trusting that the organization has been truly thinking about these things all along, right? And then figuring out how technology can make that happen. So I just want to commend you personally for the work you all are doing is doing just that, right? It's it's the problems that we've all had since the beginning of the workforce, right? And so we knew what the problems were. We just really could not address it. It's exciting to think if this is where we are now. I can't even imagine where you all are going to be in a year. You know? Yeah. Tom Libretto 28:39 No. No. I mean, every day is bringing a new, you know, aha moment, which is which is an incredibly exciting place to be at the moment. Steve 28:48 Yeah, yeah. This is, I mean, the value proposition here, the ROI, the the justification for doing a really robust, well thought of, well funded recognition program was always there, even if when it was just engagement, productivity, that was a very compelling argument. And and now, when you say there are all these additional workforce insights that can be layered on top of that, and all this rich information and all these answers you could get about the your organization and the people in it, on top of all that, to me, it's it's such a compelling story, and one that hopefully more organizations are are going to get ready to write for themselves. Tom Libretto 29:27 Yeah, and look, the the we you know the there's also the well, how in the world am I going to find new investment to you know to put into something like this if I don't already do it? And the reality is the the those investments already exist around an organization, and we often we often talk about the yeah the the the bonus pools that are created within large organizations are are massive amounts of money that have a shelf life of emotional impact and engagement impact that is measured in day. You know, maybe sometimes a week or two after it lands, and those are big monetary awards at those point in times. You know, that flow directly into someone's you know bank account, and and it's forgotten. Where at half of the cost of that, if they're you know if the you know those awards are metered out on a way more frequent basis across the length of a year, you're you never see an engagement decline. It is always up, and therefore productivity motivation and the data collection aspect are always are always on. You know, so those budgets exist. It's simply a matter of redirecting them into something that has proven to be, a you know, a much higher return on you know on an HR investment. Trish 30:54 Yeah, I think too those those types of bonuses you're describing or amounts of money those often become expected, and that's also why they don't have that much impact, right? If you know that I'm going to get a 10,000 bonus every year, yeah, it's nice, but whatever. I know I expect it. I think the type of recognition you all are doing is what's unexpected, especially because it's coming with that description created, whether it's by a peer or a leader or whomever is giving you that, it's it's heartfelt. You all are actually helping people, not just write some, you know, hey, great job, right? Tom Libretto 31:28 Yeah. Trish 31:28 So I think that's part of it too. It's it's unexpected, and to me, like I said, I think if someone were to to just send me that paragraph they wrote, gosh, that's going to stick with me for maybe forever, that might actually be the first time someone has said something that nice to me about my work, ever. And so the money comes and goes, right? Yeah, it's those moments that that's what keeps you on a team. That's what keeps you with a leader. That's what keeps you with a company. Is those feeling like I matter. What I'm doing here matters, and it's not always money. Tom Libretto 32:03 No, no, no. You know we're we're blown over every day with the stories that come in from our clients and the verbatims that we read on LinkedIn, where people are sharing. Yeah, you know, even you know, even to the money point, what they were able to buy with their points, and they're giving public thanks to their company for you know for for them being able to do that. Whether it's take their you know kids on a trip to Disney World, or we we saw one come through from our client LinkedIn about you know someone who who downloaded a best buy, not a Best Buy Home Depot gift card, to buy the lumber to build a handicap ramp for his for his elder grandmother, and he shared that publicly, and it was just-I mean, it's just heartwarming. Trish 32:57 All because someone thought to say thank you to him. Yeah, yeah, yeah. Steve 33:01 It's simple, right? Yeah, I think we've we've we've really, you know, scratched the surface only. But there's so much there about just all the amazing things that are happening with recognition, with AI, the combination of the two, unlocking so much capability and insight, and again, still doing all those wonderful things that it's done for years and years and years, right? That none of that's gone away. So I think it's a compelling story. It's it's one you should get connected to. You go to workhuman. com, of course, to learn more. Tom, anywhere, anything else you want to just share with with the listeners before we let you go and you get back back to your busy life and and back to the farm. Tom Libretto 33:43 Yeah, yeah, right. The I think the the the other just one last innovation, so to speak, that that we really hit on here starts to move the value of this recognition data well beyond HR, because again, resident in all of those messages are descriptions of the types of projects people are working on, and when you kind of zoom out and look at that writ large across an organization, you can start to you can start to understand how aligned a workforce is or isn't to the key strategic initiatives of that company that the CEO announced to the board and to the street as the top priorities in the organization. So we, you know, we built a capability called Topics that is that you know that part of the product that is now interrogating those recognition moments to see the trended alignment with what the what the company itself has declared to be the company's priorities, and whether or not people are actually working on those things, which is which is a again fascinating territory for AI based you know capabilities and analysis, but using that rich data source and applying it to a you know a very very different set of questions within a within an organization. Steve 35:07 Yeah. Trish 35:07 Yeah, Tom. Every time we speak with you, I feel like we get those new little those new little nuggets, right? So now we'll have the next time we talk, we'll have to see like how how organizations feeling about that. If if there's misalignment, maybe right. We'll have to have a future conversation. Yeah, yeah, yeah. What they're actually doing with them, but. Steve 35:24 Yeah. Trish 35:24 Thank you so much for your time. This has been so so valuable. I mean, I feel like I learn every single time. Steve, I know you do too. Steve 35:31 Yeah, it's great to see Tom again and to talk more with our friends at Workhuman about what's going on and just yeah, really encourage folks to check check out what they're doing there. If you haven't in a while, you'll be really pleasantly surprised too, right? If you come back to Workhuman, if it's been some time. But Tom Libretto, president of Workhuman, thank you so much. Trish, thank you. Welcome back. Sort of. We've been back, but welcome back officially. And that's it. Go check out all the show archives at hrhappyhour.net or on our YouTube or Spotify or everywhere else. My name is Steve Boese. Thank you so much for listening, and we'll see you next time. Transcribed by https://otter.ai