Executive Summary:
In this episode of The Enterprise Edge, host Mark
Vigoroso sits down with Corey Spencer, General
Manager and Global VP of AI at UKG, for a
masterclass in enterprise AI strategy. Spencer pulls
back the curtain on UKG's groundbreaking Workforce
Intelligence Hub - a platform designed to turn decades of siloed HR data into actionable insights - while candidly addressing the industry's thorniest challenges: AI hallucinations, trust deficits, and the monetization puzzle keeping CFOs up at night. From his home base in Utah's "Silicon Slopes," Spencer shares hard-won lessons from 25 years in SaaS, explains why building customer trust is now a team sport requiring "capability coalitions" across vendors, and offers a refreshingly honest take on why most AI demos impress but few AI implementations deliver. Whether you're navigating the hype cycle, wrestling with agentic AI architectures, or simply trying to understand how multi-agent systems will reshape enterprise software, this conversation delivers the strategic clarity and tactical wisdom you can put to work at your company today. Stream it now and be sure to LIKE, SHARE and COMMENT!
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Transcript:​
Mark Vigoroso (00:01.797)
Greetings everyone. Welcome to another episode of the Enterprise Edge podcast. This is Mark Figueroa, founder and CEO of the Enterprise Edge. Happy to be with you one more time. This is the next to the last podcast of calendar year 2025. So we are winding down a very hectic year. Thank you for being with us all these months. We are joined today by a very distinguished guest. I'm very excited to have Corey Spencer, who is general manager and global vice president of AI.
at UKG. UKG is a widely known brand in the workforce automation space and they are making some headway and making some headlines as well in the areas of how AI and agentic AI and all the flavors of AI are impacting workforce management in all the different forms. And so very happy to have Corey here. Before I go much further,
Let's give Corey a proper welcome. Corey, thank you for being here.
Corey Spencer (01:03.65)
Thank you so much for having me. It's great to get the chance to talk to you Mark today.
Mark Vigoroso (01:09.519)
Thank you, Corey. Thank you so much. Appreciate that. Well, here's what we're gonna do. We're gonna follow our playbook. We're gonna get to know Corey a little bit at the beginning here with some icebreaker questions, try to get under the covers of what makes Corey tick a little bit. And we'll get into the meat of the matter and we'll have some discussion about the business at hand and what Corey is leading at UKG and what some of the goals are for the company.
Corey Spencer (01:25.656)
I'm
Mark Vigoroso (01:37.541)
and the team, and then we'll conclude with a bit of fun with a speed round, getting to know Corey yet, yet further before we adjourn for the day. So let's get to it. So Corey is based out of Utah. I believe the city of Provo, Utah, if I'm not mistaken. Your Provo. There you go.
Corey Spencer (02:00.396)
Yep, yeah. Nestled in the intermountain west in the city of Provo, yeah.
Mark Vigoroso (02:07.256)
There you go. But Corey has also had experience working with and for companies based more out of the conventional Silicon Valley area. And I'm curious, any observation, Corey, in terms of building product, innovating, being in tech based out of Utah, as opposed to say the Bay Area, San Jose area, and anything that maybe would be overlooked or any experiences that you've had that
make it a different place to work from a locale perspective?
Corey Spencer (02:44.556)
Well, I am a transplant to Utah. originally from Winnipeg, Canada, which is flat. so number one thing that's cool about living in Utah is I live about 25 minutes from the Sundance Ski Resort. I've got four sons, my wife and I, and we love to go skiing as often as possible. And I do think the ability for us to spend some time in nature and to unplug and to be in awe of the world.
Mark Vigoroso (02:52.132)
And...
Mark Vigoroso (03:05.86)
Thank
Corey Spencer (03:12.066)
does help create perspective, which I think is really important when you work in tech and your stuff could technically be deleted. But Utah has actually become quite a tech hub over the last 15 to 20 years. There's a lot of companies that have people who work in Utah or have offices in Utah. And I think it has this great mix of being able to work kind of on either coast. It's not...
too far east for the west, it's not too far west for the east. There's a lot of really innovative people who are building lots of cool technology in Utah. They've tried to coin it as the Silicon Slopes instead of the Silicon Valley. Yeah, I don't know how successful it's been, coining that term, but there are a lot of tech companies here and lot of innovation and it's fantastic to get to work in such a beautiful place and still be able to visit all of my colleagues around.
Mark Vigoroso (03:52.932)
Mark Vigoroso (03:58.734)
You
Mark Vigoroso (04:10.84)
That's great. That's great. I love silicon slopes. That's a great, it's a great idea. I've not heard it before, but that does not mean that the branding effort is not working. Speaking of locations, you know, we all work across time zones, you know, not necessarily 24 seven, but we're stretching to collaborate with colleagues around the world. And I'm curious, do you have a routine?
whether it's morning, noon, or night that you stick to, that helps you stay energized, that helps you stay focused, that you really, really don't want to sacrifice each and every day, that just kind of has become part of how you stay productive or creative throughout the day.
Corey Spencer (04:58.872)
Yeah, great question. So as I mentioned, my wife and I have four boys. Every morning I'm up at about 530 and that's just to have a half hour with my wife. And we just, we eat breakfast together. We've been married 28 years. That's something that I simply would never sacrifice. That 30 minutes in the day when I'm in town kind of sets the pattern for our prioritization as a family and then help get the boys up and get...
Mark Vigoroso (05:09.144)
Yep.
Corey Spencer (05:27.054)
going, but I'm usually down here in my home office by 6 37 in the morning because most of our company works East Coast. A lot of them work in India. So usually by seven, I'm going and have back to back meetings. I mean, it's, a, there's a lot going on at UKG. That's really exciting. I find it exhilarating. And then by the time it's around dinner time, I spend a lot of time with the family again, helping with homework, spending time with my wife.
But the fun part about AI is even when you're not working, there's so much to learn. And I have this like endless pit of curiosity. So in the evenings I get back on to see if there's additional things that customers need or additional people I can assist. But then it's usually two to three hours of just learning what's happening, building apps, using new AI tools, playing with the latest, you know, models around video generation and image generation and creative. I have a
deep passion for AI and creativity are starting to co-mingle. So playing with the newest stuff there becomes a little bit of an addiction. So sometimes my wife and I have to talk about maybe, maybe let's go for a walk. I will say the other thing that my wife and I do every night at eight 30, we go for a walk. We go for about a two mile walk just around the city. live very close to a BYU university. So we walk down around BYU and come back.
Mark Vigoroso (06:38.126)
Ha
Mark Vigoroso (06:48.206)
Wow.
Corey Spencer (06:54.51)
And it's kind of those two bookends of starting my day and ending my day with the person who's most important for me just helps create perspective on everything else.
Mark Vigoroso (07:01.081)
Yeah.
Mark Vigoroso (07:04.388)
That's phenomenal. No, that's great. That's great. congrats on 28 years. That is phenomenal. That's great. That's great. Well, you know, it's...
Corey Spencer (07:10.605)
Yeah.
Mark Vigoroso (07:18.008)
you know, it's like where's the line between passion and addiction, right? I guess it's hard, but you know, I'd love to maybe pick up on that theme a little bit and segue into kind of what you're actually up to at a UKG and maybe even draw a thread from some of your previous stops with Adobe and Alterix and New Relic and some others.
Because I think all of our careers, they're kind of like storylines that unfold and sometimes they're in unexpected ways, right? But there's common threads, I think, that at least I can look back and see how various things that I've learned over the years have served and are serving me in various ways in my current role, right? So with that as sort of a backdrop, I know you've in some previous roles, you've obviously done a lot of work around
analytics and platform development, various kinds. And I know that you've been involved with what I think UKG has been calling the Workforce Intelligence Hub. And if I'm not mistaken, was just recently sort of announced or unveiled at your Aspire event. And so I guess the first part of the question, just to establish a baseline, is what is the Workforce Intelligence Hub? And then...
Second part of that question would be, you what are some of those lessons that you have learned in your previous stops at some of those companies I've mentioned that you have applied and are applying in how you're thinking about an architecting capabilities like the intelligence hub specifically, but more generically, agentic AI and AI agents in the context of workforce management. So that's like.
That's like a three page question that I just read to you. yeah.
Corey Spencer (09:12.586)
Yeah, I'm going to try to be brief, but I want to also be very detailed. I'm actually going to take your second question first around what we learned and then why we're applying it here at UKG. So my entire career, I've worked with data. My very first job at a college was working for a startup called Omniture, which went on to be Adobe Analytics. And this was an
Mark Vigoroso (09:20.887)
Okay, okay.
Corey Spencer (09:35.882)
2002, 2003, and it was the beginning of digital analytics. Just finding out how people are using your website, where they're coming from, what keywords, and it was revolutionary new technology. And it was the beginning of people saying, oh, we can start to democratize this data and start to use it to make better decisions more quickly. And I'm relatively new to the world of HR tech, but for 20 some odd years, it was all about data.
And some of the things that we learned, we learned at Adobe, we've learned at Alt Church, we've learned at New Relic, is that a unified data platform becomes so incredibly central to anything that you want to do when you're trying to value at a higher pace and at an expedient value set. That's when you talk to organizations, even today, and you talk to CIOs, they will tell you that 90 % of their frustration costs
and time is just spent getting the data together so that people can make the right decisions. And that in the world of AI is more important than ever. AI, the source code of AI is data. And the lifeblood of AI is context. And it needs those two things. It needs as much data as possible. And that data set can't just be deep. It has to be broad because it needs as much context as possible. So when people hear like large language models,
Mark Vigoroso (10:37.688)
Mm-hmm.
Corey Spencer (11:00.568)
hallucinating, which is nice way of just saying making mistakes that usually happen because the large language model feels pressure to fill in the gaps. And when it doesn't have data, it just starts to put words in order to fill in the gaps of trying to answer the question that the human being asked. And that almost always happens because it doesn't have data. It doesn't have context, but the reasoning continues to try to do what it can.
Mark Vigoroso (11:04.792)
Yeah.
Corey Spencer (11:28.428)
And so we learned that at Adobe before generative AI was, was super hot. learned that at Alteryx because it was the core competency that the team, that that company builds was to help people unify data. so coming here to UKG, one of the things that drew me to it was that because of our history, this kind of federation of acquisitions, we've had to do something that's very unique. We have to have a subject matter expertise in the data across a very broad set.
of workforce management, HR and pay use cases. Because we have enterprise level software for paying and payroll for HR administration and for workforce management and time and scheduling. We're the only company in the world that has to have a perspective on that data model, not just for analytics, but for actual execution. So from the moment that somebody applies for a position,
And then they get that position and then they get their uniform and then they get onboarded and then they get their first shift. And then unfortunately they miss a shift and they get paid and everything that happens, all their promotions and their performance evaluations, their culture surveys, all of that can be captured in UKG. And it didn't start out that way. It started out as silos because of these acquisitions. And over the last couple of years, the company has worked very diligently in creating what we call people fabric right now, which is.
a unified data layer across all of those use cases. So this becomes really valuable for AI because it means that as we train models and we start leveraging or as we are building models and leveraging large language models, the context is complete. So even if someone misses a shift, that doesn't happen in a vacuum. That means that there's additional context. And so whether our customers are using us for just one of our products or the full suite, the AI is more intelligent because
It's being trained on a broad set of context and deep decades worth of data. Does that make sense so far Mark?
Mark Vigoroso (13:30.616)
Yeah, yeah, yeah, yeah, yeah, I'm with you. I'm with you. Keep going.
Corey Spencer (13:33.198)
So when we talk about that, so that was like number one lesson, our CPO, Suresh Patel and I, we both worked at Adobe. Number one lesson we learned is, unify data, bring it together, do the heavy lifting for the customer so that they can get value from it as quickly as possible. And then when we layer AI on top of that, there's a whole new mode of engaging with that data and automating it and reasoning with it that comes along with that shared unified data model.
And I'll say the other really important part is that we're building right now is that that data model can be extensible so it can add data that is unique to a customer specific business. So maybe weather data is really important to your frontline management team and you want to bring in that weather data and the AI system can become more intelligent because you're bringing in real time signals from third parties. Or maybe that's from a competitor of ours. You're bringing in data from
you're not using us for one of our core capabilities, but you still want to leverage the complete data set. In 2026, we'll enable you to bring in that data from competitors so that the AI can be intelligent, even if you're only using us for part of that entire data collection. Now, you asked about Workforce Intelligence Hub. Our products are built. Workforce Intelligence Hub is the way that we reconcile and
bring all that data together for orchestration and for joining the data and for accessing the data. And then our applications are built on top of that workforce intelligence layer. So we bring all the data into People Fabric, workforce intelligence layer is kind of that compute layer and access layer that brings the data together that AI can use and our applications can use. But one of the things that we found, and it's another lesson learned from other industries, is that the second you start doing that, and we found this very quickly with our customers,
they want access directly to that data outside of the traditional applications. Maybe they want to understand, hey, I'm bringing in additional data and I want unified analytics across all of that, as well as some other data that I'm bringing into your system. So I want to access your workforce intelligence layer to get analytics across a bunch of things. One of the things I love to show when I demo is hours worked by gender. Those are two
Corey Spencer (15:54.114)
disparate different data sources, HR and WFM brought together, but you could also bring in other things, like if you wanted to learn about your total cost of labor, you've got ERP data. And the powerful thing about bringing that into a workforce intelligence hub that's running your workforce is it's not in some third party cloud data warehouse where you've got a bunch of data engineers putting together a dashboard that shows you an insight, and then you got to figure out how to take advantage of it.
Being able to bring that data into your workforce intelligence layer in your HR and workforce management and pay system allows you to take action right away. And it also allows the AI to suggest actions or take actions on your behalf. So it centralizes a lot of that. So workforce intelligence have a, we have three additions of it. The first is analytics. Yeah, you can start to bring in more data and get better insights because you have one central place. The second one is benchmarks.
So the addition around benchmarks is because we have all of this data, we're starting the process of aggregating key performance indicators and anonymizing that data to show you where you might fit with some of your peers or other companies in your industry or region to show you, your attrition is half of what the industry standard of what UKG normally sees. So again, because we have that data coming into the workforce intelligence layer, we can give
customers some unique insights into where they fit. We get that question all the time. The simple question of, is this normal? Right? Is this the market or is this us? And we can start to create some more transparency with that. And the third edition, so we have insights. So getting more insights from your data, we have benchmarks, understanding more where you fit. And then the third is just plain on old access. We have all this data here and I want to be able to use
Mark Vigoroso (17:33.492)
Hmm.
Mark Vigoroso (17:50.148)
Hmm.
Thank
Corey Spencer (17:52.962)
web hooks or API, maybe I'm building an MCP server and I want to start leveraging this data for my own AI. We have an enterprise level offering to be able to get access directly to that people fabric data for your own business use cases. So that's what Workforce Intelligence Hub is. It's the same services for the most part that our applications leverage that we're now publicizing and allowing our
customers and partners to leverage directly at that intelligence layer.
Mark Vigoroso (18:26.372)
Fascinating. I mean, it's like, well, there's so many use cases that you've described that I think are,
taking various forms and shapes now in many different places across the industry. When you talk about analysis, data analysis itself, getting to some sort of insight or intelligence is sort of been turned on its head, right? In terms of drawing those analyses and sort of conclusions, if you want, as well as taking some sort of action in response to those conclusions.
We're basically at the point where that can be very, very much assisted by AI and in some cases, maybe even autonomously acted upon by AI, 18, 24 months ago was not the case. So it's tremendous. I mean, it's just a tremendous pace. And I've asked folks this in previous conversations about, we all know the hype, we all know the
Corey Spencer (19:20.91)
100%.
Mark Vigoroso (19:32.724)
superlatives that are being used to talk about AI. But it's really true. I don't think the disruptive nature of this phase that we're going through has a real precedent. I mean, we've seen disruptive change before, but I don't think we've seen it at the pace or magnitude that we're seeing it. Certainly HR tech is yet another example of that. I don't know if you would agree with that or not.
Corey Spencer (19:58.107)
I mean, yeah, I mean, this is my day in day out is it. This is what I tell my team for about a decade. I thought tech got kind of boring. Like I didn't know another cloud migration. Please just put me out of my misery. Like it was, it was kind of rinse and repeat. And we saw the same things over and over and everybody was kind of chasing similar.
Mark Vigoroso (20:01.538)
Yeah.
Mark Vigoroso (20:09.378)
Yeah.
Mark Vigoroso (20:13.176)
Yeah.
Corey Spencer (20:22.958)
value streams and you know, two and a half years ago that all flipped on its head when a totally different way of engaging with software and a totally different way of doing compute and reasoning. And now let me be really clear. I also think all these things have their limits. And so I'm not somebody who thinks that like I get really off putting when people anthropomorphize AI and think of it as their buddy or their coworker. I don't like that. I don't think it's your coworker. think it's a system.
Mark Vigoroso (20:46.925)
Yeah.
Corey Spencer (20:50.382)
And I don't think your co-workers are AI. And I think it's important that we delineate how we use these systems, but they're evolving really quickly. You can even just look at the last few months. If you would have told me that I thought that Gemini 3 was going to completely outseat GPT-5 as the normal consumer-based agent or framework that people would start to use, I'd be like, oh, you're crazy.
But we're seeing that happen in real time. We're seeing people move away from open AI and as they release 5.2, I think they're trying to pull some folks back. But this type of competition is really good for consumers and for partners like us. It also means from a software development standpoint, every couple of weeks you're triple check in. Are we building the right thing using the right technology? So at UKG, we're building lots of abstraction layers so that we can, we're building our own
industry specific or vertical specific models, but we're also building abstraction layers between us and the large language models or any model that we use so that we can do bake-offs regularly and see which ones of these are the best for our specific use case. And I think that's one of the things that really drives what we do is how many lives does this technology affect? Another thing I tell my team all the time is AI is not a feature. The feature is how simple can I make it?
Mark Vigoroso (22:06.724)
Hmm.
Mark Vigoroso (22:11.214)
Boom.
Corey Spencer (22:17.474)
How easy can I make it? How fast can I make it? How dependable can I make it? In the world of UKG, people, not a lot of people get paid to use UKG, but they use UKG to get paid. So how quickly can I get in and out of the system with confidence, with ease, and you get back to your day job of what you're doing that moment. And AI helps us facilitate that in a way that just hasn't existed before, but we're still just scratching the surface.
Mark Vigoroso (22:18.51)
Yeah.
Mark Vigoroso (22:30.862)
Yeah.
Mark Vigoroso (22:43.3)
Hmm.
Mark Vigoroso (22:46.67)
Yeah, I mean, I don't have data on this. You probably do. It talks about how much expense in most corporations is tied up in labor, right? And in some industries, it's like it's an incredibly high percentage of your overall expenses is human capital, right? And maybe it's a less of a percentage when you're talking about more asset-intensive, capital-intensive industries. that's a tremendous...
Opportunity, mean, I think for HR tech in general, whenever you have that much expense, even incremental improvements percentage wise, whether that's in efficiency or productivity or, or what have you, can be material when it comes to like whole dollars, either savings or profitability expansion or whatever else, right?
I don't know if that makes sense. mean, that's just sort of somewhat, I mean, I've never been in HR tech, but that to me is sort of an obvious, when you think about use cases for AI and agentic AI and sort of the low hanging fruit for, you know, where the real material impact is going to be felt. It seems to me that whenever you have such a giant bucket of spend happening, pretty much across all of commerce, it seems to me to be an opportunity, right? For
for efficiency gains and real dollar savings and real compelling ROI.
Corey Spencer (24:17.558)
Yeah, so I agree. Some of this data that's come out recently is showing, so we deal with a lot of frontline companies that have huge frontline contingencies, know, lots of people who work in healthcare, retail, factory manufacturing, logistics. And so people are the number one cost and they're also the number one revenue generator. So when you take a look at some of these companies that, you know,
Managing the right people with the right skills at the right time in the right place is a significant cognitive load to a frontline manager sometimes. So anything we can do to make that frontline manager more effective, we can't replace those human beings, but we can add technology to make those human beings focus on the things that they do best and spend less time worrying about managing software and more time about engaging with patients or.
engaging with customers or focused on the things that they're uniquely skilled to do. And the data shows that those that use AI to help them do that, they have much lower burnout. And I think that goes to show like where, despite all of the, and I think this happens more with knowledge workers, the headlines of AI is coming to replace you. The data that we're seeing is that those companies in our world that are embracing it, the employees are able to focus more on their job. They're able to focus more on what they.
are there to do and in a weird kind of way in the world of human resources, they get to focus more on the human than the resources because they can use AI to do some of those more computationally heavy things so they can focus on what they do best. so I think another thing that was, talked to customers a lot where that plays into is not just efficiency, but creating a better culture and decreasing churn.
Mark Vigoroso (25:54.084)
Yeah.
Corey Spencer (26:15.886)
Some of these companies, just an unbelievable amount of energy on constant hiring and constant onboarding and then offboarding and everything. And if you can decrease that even a little bit by making their lives a little easier, making AI a system, helping them focus on what's most important. It's not just the employee's life who gets better, but the whole company benefits from less churn in the organization.
Mark Vigoroso (26:16.611)
Yeah.
Mark Vigoroso (26:23.896)
Yeah.
Mark Vigoroso (26:41.06)
Yeah, that's a good point as well. Yeah. Yeah, you know, mentioned Corey, you burnout and I'm curious. I'm reminded of a I don't know if you remember this, but I think it might have been a Super Bowl ad at one point where back in the days of e-commerce when that was sort of the
I guess you could say the disruptive trend, right? And there was this e-commerce front end of some retailer and they had put an order form on the screen and they launched the order form and they were watching, the whole team was watching it. And it went from 10 orders to 100 orders to 100,000 orders.
to 10 million orders and they all looked at each other like it was kind of like a be careful what you wish for. And the ad was for something like UPS or some logistics or some fulfillment company that basically said you can't do e-commerce without sort of a fulfillment solution because there's still a physical aspect of getting something from point A to point B. I think that was the story. my, do you remember that? Okay, there you go. So.
Corey Spencer (27:39.104)
I that ad very well, because I worked in New Congress at the time and I was like, I felt that pain.
Mark Vigoroso (27:46.175)
probably dates both of us that we're we're we're we've been around a while but my question the reason for bringing that up is when i think about these early proof cases for ai and you see these outcomes happening sometimes in an isolated maybe not a grand scale but in an isolated way you're seeing people
Corey Spencer (27:49.934)
Yeah.
Mark Vigoroso (28:09.336)
you know, experience less burnout or start to be able to focus on more value added tasks or you're starting to actually see the fruit being born. And I'm curious, is there this sort of moment that you're either seeing or maybe anticipating amongst your customers where people are kind of looking at each other like, be careful what you wish for. Are we going to like lose our jobs? Are we, are we going to be
basically obsolesced out of this equation. And I know there's sort of this sort of generic fear that's been somewhat sensationalized in the media about the rise of the machines and we're all gonna be replaced and.
And I think there's been some very measured responses to those that basically says, no, you're not going to be replaced, right? You you might be replaced by somebody who knows how to use AI better than you do, but you're not going to be replaced by a machine. But I am curious real world in your experience working with customers, are you seeing any sort of rational or irrational anxiety about that seeing success, but then worried about that success?
because it might cost them their job in their minds.
Corey Spencer (29:23.906)
Yeah, it's a really interesting question because I mean, even though a lot of our customers have a tremendous amount of frontline workers, they still have their own payroll teams and their own HR teams that are affected by the technology that we're building as we try to make payroll more autonomous or even forecasting scheduling more intelligent. And I think initially there is intrepidation until
they start using the systems. And on more than one occasion, as we've started to roll out some of these technologies, we've worked with customers who are like, I don't know, anything that we build, the administrators have the ability to keep humans in the loop, which is so important as you build trust in AI systems. So they start saying, hey, we want to be in the loop of anything that AI suggests.
don't let it automate anything until we've approved it. We're like 100%, absolutely, you can turn those flags on. What's interesting is after about two or three months, they go, yeah, we're turning off those flags because A, we're starting to the system and B, I have so many other things that have been on my to-do list or things that are more strategic or more important that I haven't had the chance to do. And by having this automate some of those things, I can go focus on some of this other work. So we haven't really seen the
Mark Vigoroso (30:31.085)
Uh-huh.
Mark Vigoroso (30:46.616)
Yeah.
Corey Spencer (30:48.186)
I think the initial is, let's figure out where this fits in our organization. But we haven't seen the same level of panic of those that start to use it because they go like, it's one less thing. It's kind of, you know, for those of us that remember when computers started to being used or the internet became really big, jobs certainly changed. We don't go to like, you know, not a lot of us go to travel agents anymore, but we do more travel than any time in the world, in the history of the world.
Mark Vigoroso (31:12.259)
Right.
Corey Spencer (31:17.346)
That's because it didn't mean that travel went away. It just meant that those jobs changed and allow people to do more strategic and more interesting things around travel and create better experiences. We're kind of seeing the same thing in the world of HR that as companies embrace AI, it opens up the paradigm for them to say, here's other things we can do now, as opposed to being constrained by any type of old methodology.
Mark Vigoroso (31:28.931)
Yeah.
Mark Vigoroso (31:38.254)
Yeah.
Mark Vigoroso (31:44.517)
Yeah, it's, you know, it's, agree. And I think it's a little bit like comparing a micro versus a macro perspective, right? I mean, they go on the on on balance. It's going to be a net positive, it's going to be, you know, an advancement of unprecedented proportion and speed across almost every industry.
But along the way, it might not be a straight line. It might be a windy road, right? And there might be some couple steps forward and sideways and back. But at the end of the day, if you take a macro perspective, it's like taking a long view of the stock market, right? It's sort of the same idea. But it does bring another question, and probably our last meaty question of the conversation, Corey, and that is talking about scale.
where we're kind of at that point now in this still early days, right? Relatively speaking of the AI era. UKG I think has about or maybe more than about 80,000 customers if I'm not mistaken. Massive base, huge collection of data, workforce data. You could argue.
that could be very useful to train various models on. You've had experience at different scales. You mentioned you started your career at a startup, right? Very, very small scale that grew to a larger scale. And there are different learnings that come based on the scale at which you're operating, right? And I think my question for you is you've got a couple different perspectives on this now.
Most recently with UKG, you have a perspective on massive scale from the point of view of just availability and access to data as it pertains to work and frontline work in particular. And I'm curious, what are some of your thoughts or maybe even early insights on where, I don't wanna say this, where the...
Corey Spencer (33:36.396)
Yeah.
Mark Vigoroso (34:00.249)
where the value really is gonna be created. Maybe yes from your solutions, yes from your platforms, but maybe a little bit more macro just in general from HR tech, where is the value that's really gonna be created at maturity, right? Where you think about maybe fast forward a few years, maybe three, maybe five years, and we are deployed at scale.
maybe 50 to 80 % penetrated across the industries that we're Agentic AI is deployed pretty much everywhere. Are you thinking about where this thing is going to land at least, at least over the next medium term horizon in terms of where is this value really going to accrue in material, financial, operational terms for your typical mid to upper market enterprise?
Corey Spencer (34:43.65)
Yeah.
Corey Spencer (35:00.014)
It's really, really good question. So here's a couple of things that I'll say on this topic. I think we're gonna see a rise of multi-agent systems that go across companies and that these systems, and frankly, we're one of a few companies that are very close to Google. They get the chance to meet with Google every single week.
working on A2A, which is their agent to agent protocol and working with other companies on like, Hey, how do these things properly share tasks with the rights and responsibilities of that data intact, maintaining the sensitivity and privacy of that data, but also being able to have the context to be able to be efficient and to work together to automate across things. And to me, that's the next horizon.
Mark Vigoroso (35:29.88)
Yep. Yep.
Corey Spencer (35:52.372)
is when these agents can become self-discovering agents with other agents from other companies, maintain the guardrails and the protocols and start to bridge the gap between those. Without feeling like you're at a digital DMV where you're like, is this the right agent? No, is this the right agent? No, I get another agent. like, without the burden being on the user. So we're working towards a world where you can just say, hey Siri, tell UKG I need to take next Thursday off. I shouldn't have said.
Mark Vigoroso (36:09.292)
Yeah.
Corey Spencer (36:21.784)
the name and yeah, no, shut up. But I think the, and that's just a, a metaphor or an example of where I see these things starting to go is that the lines between these systems become not blurry, but intelligent. And where are you, the technology for HR should fit into the life of the users of the technology and not the other way around where the users of the technology are having to learn the software.
the software should learn the user and should feel embedded into their lives. Now that might be on this phone. might, I don't know, like, you know, Sam Altman and Johnny Ives might create a pendant that we all stick in our forehead three years from now that talks to us. I don't know what that looks like, but I think that first and last mile interaction with the user is going to be dominated by the technology that becomes most adopted by the consumers.
Mark Vigoroso (37:05.422)
Ha ha ha.
Corey Spencer (37:17.134)
And it's our job as SaaS vendors to make sure that we embed ourselves in the technologies that our customers are using and the technologies that their end users are using so that it feels seamless as you're doing these things. That if you're onboarding, feel seamless across five or six different companies, even if you're onboarding using UKG, but you have four or five different companies, it should be intelligent and easy and feel like one solid conversation. And where that shows up to the end user,
Mark Vigoroso (37:27.278)
Yeah.
Corey Spencer (37:44.622)
should be dependent on what makes most sense to the end user. And I think that's always been the dream of us that have worked in SaaS for 25, 30 years is like, boy, like we have all these artificial barriers and I got to know your APIs and you got to know mine. And there's all of these things and there's a lot to figure out around how we compensate each other and how we monetize some of these things. And I think that's probably where we're the least mature as we have this conversation.
But this idea of saying that we can help you run your workforce and maybe for 40 or 50 % of that time, you're not even logging into UKG because we're integrating with your AI systems and with your other vendors. And we're making sure that the personalized and bespoke experience for your users feels personal, understands their needs, understands their goals, and is helping your employees be as successful as possible.
That's our goal. And it means that we're going to have to work with lots of other AI systems. I do see some competitors in our space saying that they want to be like the source of record. I think source of record is more of an IT perspective. But I think the workforce platform for how these things work together is definitely right in our center lane and our peer purview. And because of the scale that we have, that's the big difference is we have the data.
Like when I think about the difference between my startups and right now I'll just be like a little off the cuff. Most AI startups, what they're doing is they're using an incumbents data and they're using a large language models compute and they're building a little app that does one specific thing and they're selling it to the customer and God bless them. get it. I think that makes sense for where you are. But as we have the data and we're moving further with AI and large language models are becoming easier to integrate with.
Vibe coding tools are becoming easier to create bespoke experiences for the customer. I see those three coming together and a lot of the AI startups, if you don't have the data, I don't know how you'll survive the next five years. I think the difference again, that's why I started with the data when I first started talking, I'll end with the data because we have it, because it's unified, because we understand it, it just creates a whole different perspective of scale. Does that make sense?
Mark Vigoroso (40:07.556)
Yeah, yeah, no, that's great, Corey. I Well, you know what, I'm gonna have to park. It's probably enough for another two podcasts to be honest with you. I think this is fascinating. I will pick up one of those threads as we segue into the speed round here. And that is sort of your startup experience. I have a similar background in the sense that I have worked for.
startups, you know, I've worked for pre revenue pre funding startups, and I've also worked for multiple 10s of billions of dollars, Fortune 500s and everything in between and it is a unique perspective that not everybody has. And so with that as a backdrop, I'm going to give you a hypothetical. Which is if you were to start a brand new company today, in the HR tech space,
Corey Spencer (40:50.456)
Okay.
Mark Vigoroso (41:00.29)
which I know you're not a lifelong HR tech guy, but if you were, I don't wanna say how would you compete against UKG, how would you, is there anything that you would make sure, either from a product perspective, a data perspective, a culture perspective, that was true from the very beginning, right? Having the hindsight that, you guys have hindsight now that's,
better than 2020. So what would you make sure is true for an HR tech startup if they were to start on December 12th, 2025?
Corey Spencer (41:39.084)
So I'm gonna, I'm gonna cop out of this a little bit, cause it's gonna sound generic, but it is, it is like tattooed on my soul. It's the thing I talk to my teams about the most and it's so important in the world of AI. Find a problem that you fall in.
Mark Vigoroso (41:42.092)
Okay.
Yep.
Corey Spencer (41:55.886)
Don't fall in love with the technology. Don't fall in love with the solution. Don't fall in love with anything but the problem. Get really close to the customers. And if I starting right now, I would do countless interviews. And I know this sounds generic. It literally wouldn't mean which industry I was in. And I would fall in love with the problem. I think the biggest problem I'm seeing right now across AI, and that's why you see these headlines like 95 % of AI projects fail from MIT. It's because people are falling in love with the solution.
Mark Vigoroso (42:21.452)
Yeah. Yeah.
Corey Spencer (42:24.972)
And there's this idea of like, Hey, I need you to go build an AI thing or I need you to focus and cram AI into the wherever you can fit it. And I think that gives you a great demo, but it doesn't add any real value unless you've deeply fallen in love with the problem and you understand how to solve that problem or you have some theories on how to solve it. And then you work your way into, okay, what are the tools? And I think AI is a whole new word. I love, I hate the term vibe coding. think it's really cringy.
But I love to do it. It's something I'm trying to get all my kids to do it. But almost every weekend, I try to build another app. I build apps to help me write stories. I build apps to help me create synthetic data sets for testing. I build apps to do all sorts of different things and the ability to quickly iterate and play with that stuff. And we've actually we've gotten vibe coding tools for all of our product managers here. And when somebody says, hey, I've got an idea for a feature, I go, cool, don't send me a PowerPoint. Send me a prototype.
get in one of these vibe coding tools, build it, send it to me and show me that you've fallen in love with the problem by coming together with a solution. And I think that's probably the, while we have an advantage of data and startups don't have, startups have the ability to be nimble and to throw stuff at the wall and see what works a lot quicker. When we release something, you're right. The biggest companies in the world are going to take it for a test drive the next week. And so...
We've got to measure our innovation and our risk constantly. If you're a startup, know, fall in love with the problem and just start iterating daily, hourly, and talking to people until you think you've found the right mix. And then I'd say, you know, just be careful about your dependency on which data you get to. I think data is going to become really expensive for startups in the future. I think incumbents are going to protect it a little bit more. And so they'll have to be really careful about that.
Mark Vigoroso (44:06.094)
Yeah.
Mark Vigoroso (44:13.635)
Yeah.
Mark Vigoroso (44:20.708)
No, that's very wise. I love that. I love that. All right. Couple more then we'll adjourn. So there's this other trend macro you could call it that I think is fascinating and it's sort of an inevitable, I think, outcropping from agentic AI. And that is this sort of commercial impact on enterprise software consumption. Basically the idea that, you know, for years and years, enterprise software has been sold.
Corey Spencer (44:26.232)
Sure.
Mark Vigoroso (44:46.668)
and consumed on a user base or a seat based approach, right? You how many humans are using this piece of software, XYZ? And what we're seeing now is more and more outcome or consumption based models where pricing is predicated on impacts realized or other types of units of measure around consumption.
And part of that is because value is being created through agentic driven use cases without any human or very minimal human initiation or intervention. And so we are seeing that we're seeing some hybrid models there where there's sort of a combination of seat-based user-based consumption based out outcome based. And to the lay person looks like it's getting kind of complex, right? In terms of how enterprise software is being.
valued and priced. And I'm curious where UKG is on that journey. Is that something you all are looking at, concerned with?
Corey Spencer (45:51.118)
It is. Yeah, Mark, that's a great question. And it's really interesting to see because you're right for like, you know, the vast majority of my career, it's you've got to how many seats are you buying? And that math becomes really simple for a salesperson to be like, well, how many users and how many months or how many years and, know, multiply this by that and I've got your cost. And in the world of AI, that doesn't quite work because the value of the systems can be disproportionate to the number of people that are using it.
Mark Vigoroso (46:07.714)
Yep. Yep.
Corey Spencer (46:20.974)
And I think the other thing is that AI can generate a lot of activity without necessarily generating a lot of value. So if you're just saying consumption by compute, well, that kind of puts all the eggs in the value proposition of, know, I just think incentives are important. And if my incentive as a software provider is just to drive more compute and AI is a way for me to jack that up without necessarily giving you more value, I think your incentives are off on both sides. So a lot of our software is still
Mark Vigoroso (46:26.798)
Yep.
Mark Vigoroso (46:46.702)
Yep. Yep.
Corey Spencer (46:50.498)
in our space, Pepham per employee per month. But some of our more agentic stuff will be unveiling different type of pricing where we think that the correct monetization opportunity is really around the value to the user. Is AI generating valuable actions that you take that change your business? And that's not just us. We are seeing that across the board. You're seeing like, there's other large enterprises that are just like,
will charge you however way you want. Like we don't know what it's going to be, right? And, and I think, that's part of the discovery process is figuring out where the correct value proposition is for the user and, but keeping those things aligned. And I really do believe that from an alignment standpoint, as a product developer, the more value I drive to you, the better. And the more value I drive to you, the more willing you are to pay me money. And that
Mark Vigoroso (47:21.229)
You
Corey Spencer (47:45.986)
that's where we'll see more and more of that pricing start to mature, not just in HR, but kind of throughout the entire industry.
Mark Vigoroso (47:54.608)
Yeah, it's really interesting just from a, you know, it's almost like a, there's several like business school case studies there in terms of just how value is created, tracked and monetized in a fair and equitable ways. It's fascinating. It's a really interesting time in that area, but it also is there's a lot of confusion. I know.
And a lot of learning curves, especially when you're talking about salespeople trying to communicate clearly and effectively to a potential buyer how it all works, right? That's that's that can be an obstacle in the immediate term. anyway, all right last question, Cory. This is This is one around partnerships. I am I'm doing quite a lot of work myself in just helping to Broker and facilitate partnerships across a lot of different areas and I'm curious
I'm gonna share a statement with you. And I wonder the degree to which you would agree or disagree with this statement. And the statement goes like this. There will be room for what I would say many winners in the agentic AI era. There's not gonna be like one man standing at the top of a mountain, right? There's gonna be many different winners, many different.
swim lanes that they're that they're occupying. And so a lot of it will have to do with building, establishing and growing trust with the end user community, the buyer community, the corporations, and that building that trust is a team sport. And that what I mean by that is that that trust will be sort of strategically built.
purposefully built by partnerships across the ISVs, the SIs, the hyperscalers, the data platforms, the management consultants. There's probably six or eight different major categories of software and services providers that have been and will continue to form these strategic relationships. whether they're doing it deliberately or not, I think what they're doing is trying to earn trust.
Mark Vigoroso (50:12.835)
that as a group, as a collective, as a coalition, they are trustworthy and they are worthy of bets being placed upon them, not as individual corporations, but as complimentary groups of, I call them capability coalitions. They all bring something to the table. They all stand to benefit monetarily for sure. But.
Corey Spencer (50:26.531)
Yeah.
Mark Vigoroso (50:39.097)
But to me that, and it's a very long statement I'm saying because it is a very multi-layered phenomenon that's happening. I'm curious if you agree that building trust is a team sport and that we will continue to see these capability coalitions and is UKG on that bus? Do you guys agree with that in terms of building trust with key partners?
Corey Spencer (51:04.062)
Yes, so first, I agree with the statement. And I think more than ever, my 20-some odd years, or 25 years of working in SaaS, customers are expecting us to get our stuff together.
as groups that work together. And because things are moving so fast and AI is moving so fast, they're expecting us to work together. And that's why I mentioned this kind of multi-agent system of systems working together to solve problems is gonna be so critical in the future. And it's behooving on us that work in the industry to make this as transparent, auditable, easy and trustworthy as possible. Because this...
the opacity has to be obvious to the CIOs, the CTOs, the CIROs, and the CEO. Anybody in the organization has to be able to understand why was this decision made? Why did the...
multiple systems working together say this is the right thing to do. And if we don't have our lines of communication clearly defined and clearly understood, then we're going to end up pointing to each other and then the whole thing falls apart. So we are on that board or on that bus. We are working with partners regularly. Earlier this year, we announced a partnership with ServiceNow.
Mark Vigoroso (52:06.5)
you
Corey Spencer (52:23.01)
one of the biggest companies in the space because we think there's a tremendous amount of crossover between us. We're engineering some new and inventive opportunities for go-to-market as well as agent-to-agent collaboration. A lot of the companies that we talk to, and I'll even say, I'll go one more, and the companies that I talk to, not only are they looking for the vendors, but most of them are building their own AI technology as well. And so they're becoming...
a development partner and a customer at the same time. And so this trust is internal to them and external to the partners that they're leveraging, which adds one more cast of characters because a lot of them want to have a central place that all their employees go that has chat and his voice and is branded and understands their business, but is leveraging all these other systems. So I think there's a lot of work still that is being done on this technological work and
Mark Vigoroso (52:53.955)
Yeah, yeah.
Corey Spencer (53:20.032)
organizational and cultural work. But I think it's like, like you said, I think in most cases we all win or lose together. And especially in the world of AI where so many promises have been made and we risk fatigue. It's more important than ever for all of us to work as unified teams that are trying to solve bigger problems than just our quarterly earnings. Does that make sense?
Mark Vigoroso (53:27.46)
Yeah.
Mark Vigoroso (53:42.928)
Yeah, that's great. No, it does, Corey. I appreciate that. think it's, it's, well, it's it's fascinating. I do think that, you know, there are companies that I think have got this and sort of understood everything you just said, and they're acting upon it and
And you can see it by the actions they're taking in the market, given your ServiceNow example is perfect, right? Where the companies that are in fact forward leaning, forward thinking, being led by strong leaders are seeing things sooner than others, right? They're seeing things as often as the case with great leaders is they're anticipating things four steps ahead of everybody else. And they're laying those groundworks, they're making those chess moves.
a lot more astutely than others. And it's interesting to see it all play out, because even with, I think even with...
not everybody being world class leaders, there are still gonna be winners in the ISV space, in the SI space, just because of how huge the ground swell is gonna be, right? And how many people are actually going to benefit and profit, quite honestly, from all of the innovation that's happening now, all of the productivity that's gonna be created, and all of the economic value that's gonna be created. So.
Corey Spencer (54:49.517)
Yeah.
Corey Spencer (55:03.662)
Absolutely.
Mark Vigoroso (55:04.645)
It's really it really is a rising tide. It's gonna lift a lot of boats I think regardless of how you know prescient you might be as a leader But anyway, I know we've gone a little bit over this Cory. This has been phenomenal I really thank you for your for your time and your generosity of insight and coming to us straight from the silicon slopes
Corey Spencer (55:14.187)
I agree.
Corey Spencer (55:19.395)
Yeah, no worries.
Corey Spencer (55:26.606)
You can see them behind me. These are the silicon slopes.
Mark Vigoroso (55:30.214)
No, I love that. I'm going to use that. Appreciate everyone paying attention and coming along with us. Wishing everybody a happy holiday season. We are going to adjourn for now and we will catch you on the next episode of the Enterprise Edge. Have a great weekend everybody. Take care.
Corey Spencer (55:41.23)
Absolutely.
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