Episode Transcript
[00:00:00] Speaker A: Okay, everybody, I've got a really fascinating podcast with Eric Mosley, the CEO and founder of Work Human, which used to be known as Globoforce. This is one of the only billion dollar human capital, HR related technology and services companies I can think of that has survived 26 years. The same executive team is still there. They haven't been acquired, they haven't gone out of business, they haven't gone public for their own reasons, and they' continue to grow. And what you'll hear from Eric is the story of what we used to call employee recognition in the employee recognition market. I won't tell you the whole story in the preface because you're going to hear it from him, but let me just tell you how important this company is. Amongst the many, many other vendors that are working on this, we are suffering with a significant decline in employee engagement. The 2025 Gallup study shows that not only did employee engagement drop by around 10 to 12% this year, but Manager Engage dropped even more, which has to do a lot with AI and young employee. New employee engagement has dropped even more than that. And you're probably seeing all the research on employee well being, frustration, hopelessness, fear, et cetera, and that's the reason nobody's changing jobs. So what Work Human has done, and you're going to hear this from Eric, is they have come up with a really spectacular solution over the years that allows you to build a recognition program in all directions, not just from manager down to employee, but every in every possible direction that not only really increases the engagement and retention and interest and excitement of employees, but gives the company and the employees all sorts of vital intelligence. They call it human intelligence about skills, capabilities, strengths, weaknesses, trends, things that leaders need to know about individuals and about teams and about the company because of the richness of the data in the recognition program. So let me turn it over to Eric and I think you're going to learn a lot from this podcast. Thank you.
[00:02:08] Speaker B: So Eric, thank you for making time to talk to me today. You're really a pioneer and an icon in this world. So it's a wonderful opportunity to talk about Work Human and the history of the company. So let me just start by giving you a couple minutes to just talk about the history of the company of Work Human and its prior name or prior life, however you want to describe it, and the core of what you guys do. And then we can talk about human intelligence and all the different things coming next.
[00:02:36] Speaker C: Well, you know, delighted to be here with such an industry pioneer as yourself. So It's a privilege. Yeah. Work human. We started, I founded the company over 20 years ago in Dublin, Ireland. We started with the aim of creating a recognition platform that would be suitable for the world's largest companies. You know, big global organizations with many, many employees. Because there didn't exist anything like that before.
It was always very country specific. You know, you would have multiple vendors in multiple regions of the world. We came to it with the aim of creating a single platform which would allow companies to, for the first time, speak to their employees with one voice. You know, because they'd run this one program geared to getting all of their employees to recognize one another all over the world.
[00:03:29] Speaker B: So if you go back 20 years, was it really recognition or was it service awards? It was kind of different, wasn't it? What was it?
[00:03:37] Speaker C: Well, back then, recognition, it's a recognition being around for, you know, decades. And it was, it's unrecognizable to what recognition has become. You know, we talk about social recognition now, but back then it was very much a manager to subordinate activity where a manager would give their favorite employees an employee of the month award. And then of course there was service awards, anniversary awards. You know, if you were with the company for 20 years, you get the gold watch. When we came to it, we were very much from the software world in the middle of the Internet kind of revolution. And with the advent of social technology, social media, we also were informed by this new concepts of, of just social aggregation of people and social graph.
[00:04:28] Speaker B: Instead of just coming from your manager, coming from peers.
[00:04:31] Speaker C: And yeah, so the difference became one down to everyone, peer to peer. So from employee to employee, also manager to employee, but also employee, subordinate up to manager. So everyone, you know, whenever anyone in the company sees great work or performance or wants to say thank you, that's what social recognition is. You're basically, you're taking a percentage of payroll, you know, half a percent, 1% of payroll, and you're delegating that to the entire workforce to just be on the lookout for great performance and to appreciate it, to show gratitude for, for that performance. And that's what social recognition is. It's really, it's gone from, you know, hierarchical to being a social movement in a company. We were called global Force back then. Global Workforce. That's where that name came from. And what happened is probably about six years ago now we had launched the Work Human Conference, which we now call Work Human Live. And that had grown and doubled every year. It was, it was a real success. And it started to become Almost bigger than we were. So we decided, well, we better do a reverse takeover of the brand. And we, we, we took the Work Human name as the company name, the conference name, the product suite. Everything is Work Human, you know, and Work Human really is a kind of a call to action and a purpose for our company. It's all about bringing humanity to the, to the workplace. So it's turned out to be a fantastic, you know, call to arms for the, the world's companies.
[00:06:05] Speaker B: Let me, let me ask you about the rekognition in general then. I definitely want to talk about Work Human and AI. When I got involved in it a long time ago when I, when you and I first met and there was achievers and there were some sort of similar, maybe similar in size to you guys at the time, but you, you've obviously outperformed all of them. There was a question as to why companies would spend money on this and where the money would come from. And I think there we did a big study on the value of recognition and we proved at the time that the companies that were high recognition cultures had a 70% lower voluntary turnover rate. But I felt like we had to go pitch that to companies because they didn't understand it. How has that evolved over the years? Do people understand the value of this 1 or 2 half to 1% of payroll or do you, or is it, is it common? Is it.
[00:06:53] Speaker C: Yeah, so, so I think one of the, one of the kind of breakthroughs that we had, we started our Work Human IQ group, a division of, of Work Human which was basically the organizational psychologists and PhDs in data analytics and data science and now AI people dedicated to analyzing the return on investment for recognition activity and then laterally how you can use all that data for different insights and for AI purposes. And what they found, and what we found was that there's very much a linkage between the activity of recognition, the actual scope and reach of recognition. You know, how many people are giving and receiving on a monthly basis and things like attrition and turnover. You can actually plot a graph and show that if people receive something like five awards in a year, they have, they can have half the turnover of people who don't. And that, that graph literally goes up, you know, if you get to 12, which is really only one thank you moment a month. But if you get to 12, it's something like down to 3% turnover for some companies. So we, we've been able to use the data to map a number of different ROI kind of metrics and once we did that. It kind of overcame the natural kind of pushback on, on recognition as being a software activity, you know, and being too kind of touchy feely and almost emotional for hard nosed business people. When you're able to come to the table with, you know, a multitude of case studies showing productivity safety and you know, in manufacturing plants, increased billable hours for consultants, higher velocity for software developers and ultimately also attrition and you know, turnover statistics, it really cut through and that's one of the foundations by which we were able to kind of blow up and get to the next level. Because then the argument to the CFO and the Chro was if we just re architect our total rewards, you know, we're not, we don't necessarily have to spend more, but if we just take, you know, 1% of everything we spend in total rewards and basically give it to the employees to give to each other, we're going to get all of these different types of ROI and we're going to get all of this data and this data is going to be unlike any other data data we collect because it's basically all of the people in the company transcribing and commenting on all of the work of their colleagues and that's, that's incredibly rich data. So I think the big pivot and kind of catalyst for us was the science breakthroughs around the data attached to rekognition and being able to prove the roi. So much so that we now, in the market today, we now have an ROI guarantee which is kind of unheard of in the software world where if you don't get the guarantee, the ROI that we, we document at the start, we'll literally refund your fees. And people say, well why are you doing that? That's highly, very risky. Well, the reason we can do it is because we know it's never going to happen. And because when you document and we look at, you know, hundreds of case studies, we know that if certain things happen with recognition, if there's a certain throughput, that there'll be an unlock, an unlock of kind of productivity and turnover and engagement and energy in the company.
[00:10:16] Speaker B: You think we've reached a point where most HR executives already know that they should spend 1% of their payroll on recognition.
[00:10:25] Speaker C: Well, the fight's never over. I do think like if I look across the hundreds and hundreds of customers that we have, we have, they're kind of like a bell curve, you know, and the best programs are 1% and more and, but we still have some customers that are below Half a percent and they get exactly the kind of return that we will tell them they'll get. And so it's about a lot of times they're in transition, they maybe have a two year plan or a three year plan to carve off. You know, if they're doing a merit salary increase of two and a half percent, well, they might take 0.2 of that and put it into the recognition budget over a couple of years.
[00:11:01] Speaker B: You know, the way I interpret it, and this is my experience with you guys, is you're teaching companies how to do this. It isn't just allocating money.
They really have to learn how to do it and create this culture at all levels, not just top down, not just bonuses. Because I could see a relatively naive executive saying, well, wait a minute, I want to use that money for bonuses. Why am I bothering with this recognition money? I'd rather give people a bonus at the end of the year and not spend it on that.
[00:11:30] Speaker C: We do see that argument. And the answer to that argument is the research and the kind of science around the impact of cash bonuses that go through payroll versus the kind of recognition awards that are then spread out through a year.
[00:11:47] Speaker B: I completely get it. But I think a lot of people probably haven't been through the thinking process to understand how powerful this is.
[00:11:54] Speaker C: Yeah. And the breakthrough there is to ask them what do they want to get out of this project? You know, let's document what you actually, actually want to achieve. And then it's about piecing together the ingredients to achieve that. And you need a certain throughput $5,000, for example. Well, that could be $50 awards all throughout the year. And you basically keep somebody energized and engaged and lifted all the way through a whole year. But there's a secondary benefit to that, which is now you've got a hundred awards which have paragraphs of text that describe what that person has achieved throughout that year. And now with the advent of AI, you can then beat all that data into, you know, in AI kind of LLMs and you can create, you can synthesize the knowledge around that person's contribution. So it just becomes, it just becomes much more powerful.
[00:12:46] Speaker B: So you're in a sense encouraging companies to create narratives about performance of every individual from many other individuals to create this giant network of content that can be used to assess skills and capabilities and readiness and engagement and all sorts of things. Is that a good way to think of it?
[00:13:04] Speaker C: Yeah. And I think when that happens over time, we always think about it and talk about it. As if it's like in this one year time span we kind of say, okay, well what's, you know, you put 1% of payroll in a year and this is what you get. You have to remember these recognition programs go on for multiple years. If you think about somebody's career, we have customers that we've had for literally 10 and 20 years. So if you think about what's happened over all that time period, we've, it's almost like you're lifelogging the enterprise and individuals who have started in a company 15 years ago and now they've had many promotions or they've moved departments and they've received hundreds of, of these thank you moments from their colleagues. You're building up quite a picture of, of somebody that doesn't really exist in any other systems. So it's important to view it as like a, you know, a three to five year project as well. And that, that start, people then start to go, oh yes, well you're going to have a lot of data then and that's very valuable.
[00:14:04] Speaker B: So before we get into the data and the human intelligence, I want to ask you sort of more of a business question. So you've built a company that's about a billion two or something like that in revenue. And I have talked to 500 companies that have failed during the last 20 years in HR. I mean, you know, there's just a ton of people who start HR companies and have wonderful ideas just like this and they never come close. Doing what you did as an entrepreneur and an executive, I would like you to tell me what you think has made you so successful and then we'll talk about your product again.
[00:14:37] Speaker C: Yeah, I mean it's a, it's a wonderful question and I think if I could answer it so prescriptively, we'd all go out and do it 10 more times. But I think when I think back on it, a couple of the big differences for us was that we weren't following an industry or an emerging market or a trend. We were almost creating one. And I always advise that when I meet startup founders, if you're not really creating your market and inventing your market, well, somebody else is and then you're always then a follower. The difference for us was we decided we were going to start with global recognition and then we were going to innovate in terms of mobile and social and AI and data.
And so we've created what we think recognition should be and working human should be and we define what that is and that becomes the category and therefore everybody else has to then compete with that. That might sound simple, but there's been many occasions in the past, you know, 20 years where there's intense pressure to follow somebody else's pivot or path. And you just have to, in those moments think that you have a good judgment on where you think this is all going to develop and where it's going. And then you got to stick with that. And for us being able to define what that market was and then go all in on it to help the world's kind of most innovative and, you know, creative and forward thinking companies, we naturally gravitated to them and they naturally gravitated to us. So that gave us the foundation. I think that's the main thing. The second thing then is to have a kind of a mission oriented company. You know, we talk about work human and our purpose, you know, is to make the workplace more human. The company's named after that. It's very few companies out there where literally the name of the company is their mission as well as the name of the company. So being able to be so focused on and where all of our eggs are in the basket, all. Everything you hear about work human is about making work human. And so that kind of singular focus also helps. As I said in the past there's been pressure to do certain things, to follow certain trends, and it's never clear caught whether you should or not. I mean, it's, it's always tough, tough debate. But we have this kind of missionary zeal about following our path and that's helped us.
[00:17:01] Speaker B: I just wanted to compliment you because I know how hard it is. It's such a competitive market.
Okay, so let's talk about AI and everything. So work human is, you know, such a fantastic concept, but basically everybody's talking about the opposite right now, like, do we even need humans? I mean, and that's the reason we came up with this idea of superworkers, to try to stop companies from talking to their employees about the fact that we don't want so many of you because we're going to automate a bunch of your work, I guess. What is your perspective on that issue? I have my own thoughts on it. On, you know, why are people so focused on AI replacing humans? And then let's talk about the human intelligence stuff you guys are doing, because I want, I think that's just such a fascinating innovation.
[00:17:45] Speaker C: So I think first of all, from an AI perspective and this whole concept of will there be any workers anymore? You'll have to have you know, ubi universal basic income for everyone because nobody will have a job. To me, I think there's no doubt that the world is going to change and change radically in the same way as it did with the advent of the Internet. Let's say that kind of a seismic change. And there's no doubt that there's roles are going to be disrupted, work will be disrupted, and I would also probably say that most companies will be able to exist and fewer employees. However, I think the, the idea that there will be drastically fewer employees is probably wishful thinking for some CFOs in that the, the AI tools, they don't work themselves. I use the, I use the example of software developers. People always think that software development is over, that we'll all be able to, you know, just use ChatGPT to, you know, to generate all the code we want. It's just not going to work like that. To get something that's ready for production takes real skill. Even if every line of that code is written by an AI, to get something production ready, enterprise class out there, you know, being able to evaluate the output, you know, test in the real world, all of those things is going to take skilled computer science people. And my view is it might take a few, a little less of them, but the demand for software is going to go even further through the roof. So the quantity of software that's produced is going to be 5x and the software developers might be 30% reduction, but there'll be 5x more of the number. And when you look at other roles and other companies, I kind of feel like it might be that instead of one company with X employees, that company might have 20, 30% less employees, but there could be five more companies. I believe that productivity is going to explode. The number of companies is going to explode and therefore that will take up the kind of slack that there is. And then this fanciful thinking that what comes out of AI, no matter how developed and professional it looks, is going to cut through. Forgets what how differentiation works. You know, differentiation means you have to be different. That's the name. And if everybody's producing the exact same, you know, output through that's AI generated, you will not have differentiation. And so I believe that that's also, and we're also seeing that now everybody talks the same because they're generating all of their written output through ChatGPT. And it's starting to become so homogenous that nobody is, it means nothing. Amar?
[00:20:28] Speaker B: Yeah, completely with you. Okay, good. We're both on the Same page. So let's talk about human intelligence. So I don't think most people even understand what you're doing yet with, with mining and understanding this feedback corpus. But tell us what you, you know, how does it work and what have you learned as terms of business value of the human intelligence technology that you're building?
[00:20:51] Speaker C: We're using AI in many, many ways, right? All across the platform in different ways. But I'd start by just talking maybe a little bit about the data that's generated from Rekognition. And I would also start by saying not all Rekognition programs or activity generates the same quality of data for AI. When you want to have AI systems that are intelligent and knowledgeable, you're training them on data. Not all data is equal. And even within recognition data, not all recognition data is equal. What you really want is a recognition program that has a good throughput, it needs to have volume. And also you want people to articulate themselves in those messages. You don't want somebody just saying thank you, Bob, and that's it. There's not a lot of information density in thank you, Bob. Whereas if somebody writes a paragraph or two paragraphs explaining what Bob did, you know, and explaining the, with some specificity in terms of what exactly was done, how it impacted the person who's saying thank you, how it might have provided value for the company. Now you, now you're getting the information density in the language. And once you have that in aggregate over thousands of them, AI and natural language processing and all the other tools have something to, to work on, to kind of synthesize and store knowledge that's distributed across potentially tens of thousands of these awards. There's a billion awards in our system over over 20 years. So one of the things we saw, and this was probably very early in our AI journey, was that when we put this infrastructure together and we trained it on recognition moments, in even our own company's recognition program, we found that it was very knowledgeable about who was the best performers in different departments, who had certain attributes and skills, what people, who was influential, who had informal power in a department. And when you were to ask it simple questions like in the finance department, who's the best public speaker? Which is not generally what happens in the finance department. It would come back with names. And when you read those names, you were like, oh my God, that's who I would have said. So it knows, it knows what I know. And then sometimes it comes back with a name that you didn't know. And when it did That I would call the manager and say, you know, is this person good at making presentations and public speaking? And they would say, well, I never thought about it, but absolutely yes, always has a great presentation, always a good story arc, you know, all, all that kind of thing. And then what we found was that the AI, it knew what I knew, but it also knew stuff that none of us knew because it was piecing together comments from the colleagues over potentially two years or three years and, and, and then it was providing that back to the user. So that's really powerful. You know, that's. You're basically taking the sum total of knowledge of the individuals in the company, the employee base, and you're creating a one source of repository of that knowledge and then you're providing it back to everyone. So you're democratizing it to everyone back so they can ask any question about talent or performance and get a pretty informed, almost super user, super knowledgeable entity about any questions to do how work gets done in the company. But you don't get any of that if the recognition program doesn't have enough throughput and it doesn't have enough volume of data and people don't articulate themselves in those messages and you have to set up your recognition program to achieve that kind of program or you're hobbling your ability for your AI to be intelligent. You're basically reducing the IQ of the AI down the road. If you're not generating that kind of raw material of the data.
[00:24:43] Speaker B: Now you have a huge ROI of developing the program. Well, let's suppose a company completely understands what you guys are doing. They're bought in, but they're getting started.
Do you help them? Is this a. How do you create recognition activity? Assuming there is some form of recognition culture already, do you, is it teaching? Does the system teach you how to do it? Because I would imagine this is a different way of working than people are used to when they haven't had one of these tools.
[00:25:12] Speaker C: So we see a lot of different companies obviously, and different approaches and well, you know, we have a whole consulting organization and they can advise a company on how to launch a program so that you reach certain thresholds of activity. And this is a very key metric at the start of launching a new recognition program. Because when you hit a certain critical mass, then the program markets itself because you're seeing people around you receive recognition moments and you're potentially participating and congratulating and making comments and there's enough activity that it's marketing itself. But if you don't hit that critical mass, it will, it nearly will wither and kind of just fail. So a big part of the consulting at the start is mapping out a strategy to get to that critical mass. And once you get there, then you can dial back to kind of intense focus and monitoring and really then move into a different phase, which is about the ROI measurement. But it's mainly about marketing the program. It's about making sure that managers know that the certain thresholds have to be hit.
That if, if nobody in a department is, is using the platform to do that, they have to step in and, and kind of grease the wheels of it at the start and reach those thresholds. And then once they do, it becomes organic. And then there's kind of social proof. A lot of times employees will feel inhibited from doing it because they feel insecure about what they're going to write and they feel like do they have permission to, because they're basically given value in points to another employee. So once they see social proof of that happening, then, then it just takes on a life of its own.
[00:26:52] Speaker B: You know, a couple other questions go through my mind. So in a parallel universe to you, there has been this massive focus on skills. Skills based organizations, skills inference, talent intelligence, skills based hiring, on and on and on. Do you find that the companies that have done a good job of recognition programs 2, 3 years in can use it for skills assessment, for skills identification?
[00:27:18] Speaker C: Yeah. So our AI now will create a whole skills graph of every individual in the company. And basically, you know, we've given a number of talks on it. Our head of AI and data science has presented white papers on it. It's, but basically the big difference between skills that comes out of this type of data and everything else is that most of the other skills sources are self proclaimed and the self proclaimed skills are useful, useful to have, but it's a very different situation when you have nothing to do with it. It's your colleagues and the people who've worked with you over the years comment on your capabilities.
You know, you're a strategic thinker, you're, you know, you're good at public speaking or presenting or you're, you know, you're an AI guru or whatever it happens to be. If multiple people are making the same comment over time you start to hit a probability that the person really does have that skill. If one person says you're a world expert at public speaking, well, you know, that's one piece of data. But if over a two or two year period multiple people have said that and they haven't said it about other people, then the AI can then start to say, okay, the threshold of probability that this is real has now been passed. And we can say with a certain.
[00:28:42] Speaker B: Amount of time because you're more valuable than all these other skills tools, it seems to me. Because if you're really seeing behavioral activity in real world work, that's.
[00:28:50] Speaker C: Yeah.
[00:28:51] Speaker B: What about another sort of use case or family of use cases is if you're doing this over a long period of time and somebody's watching the data and you suddenly see trending down, can you spot dysfunction before chaos and disaster?
[00:29:09] Speaker C: You can in a number of different kind of vectors or situations like. So, for example, you can start to spot disengaged employees. You know, giving recognition is almost as powerful as receiving recognition. And, and, and, and giving you recognition has all sorts of kind of personal benefits in terms of, you know, it comes, you think about the old gratitude journal where they say, you know, every, every morning, fill out what you're grateful for. And, and, and that kind of works. And the reason why it works is it puts you in a positive mindset where you're looking at things positively and the glass is half full and you're feeling good about, and that you carry that through today in recognition. When you give thanks, you have to articulate why you're giving thanks. And so you start to look at the people around you in a more positive light. And it kind of sets you up to be a little bit more sympathetic, a little bit more grateful for your position. And when you're like that, you're more creative. And, you know, what about the argument.
[00:30:09] Speaker B: That it's always positive? Yeah, well, do people debate that with you?
[00:30:13] Speaker C: Yeah, yeah. Over the years, many times a customer or prospect will ask for, well, where do we put the negative stuff? And the answer to that is, well, not on our platform. So when I say somebody can become disengaged, what you can find over time is you can see that the tone of how they speak sometimes subtly changes. And that can be that they're, you know, burnt out. It can be that they're becoming disengaged, so their activity can become visible in that way. You can also start to see departments where maybe there's a leadership change and the whole kind of morale or tone in a, in a department changes. You can start to see that as well. And then the metrics of productivity and engagement within that department in comparison to other departments starts to, starts to change. So you don't necessarily need the negative to start to see negative trends. Can Be, you know, that can be very valuable as well.
[00:31:04] Speaker B: Boy, I, you know, I. Eric, I mean, I've talked to you multiple times. I can see why you've been so successful because you've just done such an amazing job of understanding all the implications of this and the power. You know, one question that sort of comes to my mind if I think we're going to go through a period over the next couple years where everybody at work is going to have an agent they're communicating with constantly. It'll either be on their phone or they'll be wearing it, or on their watch or in their glasses or on their desktop. And this agent will be helping them with their job, but periodically asking them questions. How's it going? You know, what do you think of this? What do you think of that? I mean, I have, I see survey vendors, we're talking to some startups now that are basically going to disrupt the whole idea of surveys because, you know, why would you have a survey when you could have a conversation with an agent? Do you see that as a trend that affects you? How do you see the daily life? Because filling out a form, right? I mean, I haven't used work crewman for a long time. I think you fill out a form, right, and you kind of submit it. Where do you think that's going to go?
[00:32:04] Speaker C: Yeah, I mean, I think, well, we, so we integrate with a lot of platforms, you know, from like social media and communications tools like Teams and Slack and other things like Outlook and email.
And I would see that the input mechanism of saying thank you. So fundamentally, recognition and appreciation is a core human need. So regardless of how it happens, it's needed. So that will always be true, you know, because, you know, Maslow's hierarchy shows we need to feel that when we do work, somebody notices that we're seeing, right? So, so the activity has to exist. The happens can be a whole gamut of different things, but in the situation where you're, you mentioned where like there's an agent and you could just tell the agent, hey, I'm feeling, I want to thank, you know, Jill because she did an unbelievable job. That could be one of our agents, it could be somebody else's agent. But the key thing is that that data gets into a central reconnaissance repository for this and then is fed back out to the world or to the company, to the employees. You then get into, well, how valuable is that data? And how do we, how do we encourage that data to be rich and have information density? So we would, we would advise that there's some sort of kind of threshold of quality of, of that recognition moment.
[00:33:23] Speaker B: So you would be in the back. One of the other things that's happened over the many years that we've been doing this and you guys have been doing it is these feedback based performance management tools which to some degree have come and gone. I mean I don't think any of them have been super successful. What's your feeling about performance management these days relative to rekognition?
[00:33:41] Speaker C: So as you know, we have a tool called Conversations which is basically a kind of a continuous performance management tool where you have check ins, you have feedback, you can request feedback. And then there's also a thing called Reflections in it which is where you can have a quarterly or semi annual check in based on your, your performance. Like, like an annual performance review that happens twice a year, four times a year or whatever. That's a place and that is a place where some potentially constructive criticism can live in it in a kind of a tool like that. That's also great data for bringing into these central AI kind of repositories and entities. And so we do bring in data.
[00:34:22] Speaker B: From your human intelligence engine could mine that data as well as the recognition data. Or does it?
[00:34:29] Speaker C: It does, yeah, it does.
[00:34:30] Speaker B: Okay, so you're getting a pretty big perspective on each individual.
[00:34:34] Speaker C: Yeah, yeah. And, and, and it's, it definitely enhances the picture. Now I would say that the recognition data is actually much more powerful because it's kind of ad hoc and it's.
[00:34:45] Speaker B: You know, it's, it's more, it's voluntary, not forged and. Yeah, yeah, yeah. I mean, I gotta hand it to you Eric. I mean you have built just an amazing thing here. All of the aspects of work and the psychological issues of work and management and breaking down the layers between managers and individuals. I mean if I think back about when I first found out about this space in the early days, it was really just a way to sort of distribute money to people and badges.
I come so far the good old days. I mean, one more thing and then I'll let you go. What do you think the big challenge is next for you guys as a company?
[00:35:21] Speaker C: I think for us like we're really excited at the moment. This AI stuff is, it's an amazing thing. When you look at a kind of a skunk works proof of concept kind of thing internally and the hairs on the back of your neck stand up because you're, you're kind of blown away. That doesn't happen very often. Maybe every four or five years. And, and that's the situation we're in now with this, with this AI stuff. We're in a unique position in that we have access to this recognition kind of corpus of data. And so we were uniquely able to kind of innovate on it. But the next phase, I think, and the stuff we're working on at the moment is kind of predicting the future career paths and the future of, of people and employees in organizations. And we have had some incredible breakthroughs there. And people don't kind of understand it. It's hard for them to see. How can you predict who's going to be a great leader in a company if they're only in middle management or, you know, and it's an amazing thing if you look at how people talk about them, when they thank them over time, you can see they use language about certain individuals that they don't use about other individuals. A good example is we had an employee who in many, many mess many recognition messages, people commented that they showed up to work like they were a vp, and people presumed they were a vp, but they weren't a vp. And then when they did become a vp, they all kind of said, I always thought you were a vp. That's a classic example of where in the data, if you can take it from many, many different places and synthesize it together, you can predict who are the people who will become the next leaders. So we're working hard at productizing that and making sure that that's available.
Because if you're Microsoft 10 years ago, to be able to predict the next Satya Nadella from the ranks, that's the holy grail for HR for me. And we're getting there.
[00:37:14] Speaker B: Okay, Eric, thank you so much. I mean, I again, I just want to congratulate you on all you've achieved over these last couple of decades. And thank you for sharing all this with us. Lots and lots for people to learn here.
[00:37:25] Speaker C: And thank you for all of your support and all of your advice. The oracle of HR is the way I think of you. So it's always great to talk.
[00:37:33] Speaker B: Great. Thanks, Eric.
