109: What’s Next for Compensation Technology with Cliff Stevenson

[00:00:00] Join us on a journey where we unravel the latest trends, tackle your burning questions and explore innovative strategies that are shaping the future of compensation, all with a coffee in hand. Hello and welcome to another episode of Comp and Coffee, where we always aim to serve up fresh insights on all things compensation, HR and rewards with a side of caffeine.

[00:00:25] So I'm your host, Ruth Thomas, and today we're going to be brewing up a fascinating conversation, I hope, on the latest HR Tech Trends with Cliff Stevenson from Sapient Insights Group. Welcome Cliff, delighted to have you on the podcast. We're obviously strong followers of Sapient Insights and enjoy partnering with you. Do you want to introduce yourself? Tell us a little bit about Sapient and the work that you do. Yeah, wonderful. Thank you so much and thanks for having us on.

[00:00:54] Yes, Cliff Stevenson, as mentioned, I'm the Director of Research at Sapient Insights Group. If you're not familiar with us, you might be familiar with the report, HR Systems Survey, which we do every year, coming into our 28th year that's a comprehensive look at all HR tech. So I'm the Director of Research at Sapient Insights Group, a research and advisory firm that does this sort of research in HR and HR technology.

[00:01:20] It also does quite a bit of hands on work in change management and leadership development. So we really like to have my background is I worked in HR for a while before becoming an analyst. Same with Stacey Harris, who many people might know. She was the same. And so we really like to take that perspective on sort of data forward.

[00:01:43] And I think that will become a running theme as we talk is that we always try to start with the data and anything that comes out of that always begins with that foundation. Great. Thank you. Now, we always like to start with a little coffee talk, Cliff. So what's in your cup today? Are you more of a bold espresso kind of guy or do you prefer a slow, steeped, cold brew?

[00:02:08] Well, actually, our podcast, not to step on anyone's toes here, but I just thought thematically it was very interesting. Our podcast called Spilling the Tea on HR Tech. And besides that, I am also a tea drinker, which might be confusing to any listeners that the English contingent is coffee focused here on this side of the pond.

[00:02:29] And I, or tea, or at least I am, I decided, but I have been drinking more coffee lately, but I thought I should at least stick with the tea today. The mug, or anyone that doesn't see it in the snippets, it's just auditory, is what was designed by one of my nephews. So we'll design a little artwork. It's very special.

[00:02:52] Yes. If you hadn't mentioned, I was going to comment on the overlap there of our hot beverages in both of our podcasts. So if you haven't listened to the Sapient Insights podcast, Spill the Tea, it's, it's, if you want to stay up to date with what's happening in HR tech, it's a great way of keeping yourselves up to date on that. I'm, I'm a regular listener. So we'll put the link to that in the show notes at the end of this.

[00:03:16] Okay. So let's get going. HR technology, we're going to be talking about Cliff. It's evolving faster than ever. I think we were both at HR tech in Vegas in October, which is when you do the grand unveiling of the HR system survey. Based on the latest research, what are you seeing as the biggest trends that HR and compensation leaders who are listening to our podcast should be thinking about in 2025?

[00:03:44] Yeah. As we get ready to launch the survey, I'm definitely keeping an eye on a number of things. I think, you know, the big topic and everyone's mind is AI and preparation for that, but there has been some already some interesting indicators about where compensation specifically is going in the HR tech landscape. One area that I found was pretty fascinating was in the level of satisfaction.

[00:04:13] We rate every year. Well, I should actually say the customers rate every year. We ask about user experience and the vendor satisfaction with all different vendors across all sorts of different categories and kind of see where people are satisfied, where they're dissatisfied, where they're looking to change the platforms that they're on.

[00:04:35] And actually compensation was near the very top of user experience and vendor satisfaction. So only onboarding did better. So people generally are finding a lot of value, are enjoying what they are seeing from compensation. But that will only be as good as the data that underlines it. I said I would mention data in our plan that.

[00:05:03] But, you know, out of five, an average user experience vendor satisfaction scores, 3.64, 3.71 is pretty fascinating, right? That's well into the above average and sort of, you know, getting towards some of the highest scores you can have. So, you know, we're seeing that just general impressiveness of, I guess, these platforms and compensation.

[00:05:30] But that is only girded by this sort of data that feeds into it, right? The benchmarking data, the ability to get real-time data, which is something that we're seeing more and more of. And successful platforms have that, you know, the ability to quickly break down very specific regions or types of jobs and link that all together, right?

[00:05:58] The link between compensation skills is also another growing trend where we're seeing more interest in users and more organizations that are finding ways to link those two sort of areas of HR to make better people decisions. Great. So you're saying UX and vendor satisfaction were high in the compensation category.

[00:06:24] I know in your survey, you reviewed the whole end-to-end kind of HR tech. So compensation sits in the category of talent management. And then, you know, what about HRMS, you know, user satisfaction for them? How did that fare like with other types of categories in your survey? That's pretty interesting.

[00:06:46] I don't know if you really have the report memorized or if you just happened to pick the lowest scores were for HRMS. So it's not super drastic, but the sort of average was about the lowest we've seen at 3.5.

[00:07:12] And then that's actually gone down to, so it was 3.5 last year, or I should say 2023, and in 2024, it dropped to 3.39. So HRMS is very... Do you have any insights? Yeah, insights. Yeah. Yeah, there was a number of things. Remember, with vendor satisfaction, that will be conflated with cost oftentimes too, right? Value for money. We do ask them to evaluate that. But there hasn't been as much service on the vendor satisfaction side.

[00:07:41] You know, we like to look through the comments too. We mentioned big data, but I think qualitative data is just as valid as the quantitative, right? And that gets you that why, those insights. And there seems to be a lot of organizations that are just not focusing enough on customer service and helping people use these tools. As more organizations are seeing a need for compensation tools, specialized compensation tools, they need, they may be new at this.

[00:08:10] They may need some help on understanding the systems. And so they get more complex. That naturally pushes against them being easier to use sometimes. Yeah. And another trend that was in the report was around clustering or, so like companies, organizations looking to reduce potentially the number of vendors. And I could sort of, from my understanding is I get it that they're trying to improve overall

[00:08:39] the employee experience. So what observations have you got there? Because, you know, we've seen a lot of M&A happening in the HR tech space. So what do you see happening there? Yeah, that's exactly it. Stacey Harris has been sort of leading the vanguard on this idea of HR anchor systems and then the clusters that go around them. Now, that anchor system may be different for a lot of organizations.

[00:09:05] We tend to think of an HRMS as an anchor, but it really depends on what business problems you're trying to solve with your HR systems and then finding those systems that work well with those. Now, often that's through your vendor marketplace and sort of preferred ones, but oftentimes it'll also be in how they're structured and how quickly and accurately the data flows between those systems. That user experience oftentimes will be people don't want to leave the system.

[00:09:35] They also don't want to have to phrase things a different way, right? You don't, you want to call the thing the same thing. You want to have the same look and feel. And at risk of repeating the same word to sort of define it, it should be easy, right? Ease of use sort of is definitionally about making it very simple and easy to get to the information you need.

[00:10:01] A lot of the promise that AI is putting forth, at least in the sort of marketing world, is in using natural language of just making it simple to do these sort of complex things of saying, I would like to hire someone in this region that has this many years of experience. What do I think would be the right amount to offer them that is competitively even or even maybe a little ahead of my competitors, right? That is how I'd say it.

[00:10:28] But if you try and put that into a system, right, it can be very complex, if not right. And you're going to need some help from your vendor. Yeah. Great. Thank you. Okay. So you talked about user experience and vendor satisfaction being the ways that you measure within the survey. And PayScale, we ranked in the top five compensation solutions, I think, for user experience and vendor satisfaction this year. So we proudly display our Sapient Insight badges.

[00:10:56] But from your perspective, what do HR pros value the most, do you think, when they're choosing compensation technique? Is it advanced features or is it ease of use? Yeah. And where do you think the preference lies? Yeah. The sort of joke answer is yes, it's those things. So again, we go through the comments and ask people. And it's ease of use and it's access to real-time data.

[00:11:24] That is something that came up quite a bit in the comments, right? You don't want to put out a sort of report query and then later get an answer. You want to see it happening in real time. And let's be honest, as the time of recording this, it's a very volatile time in the U.S. job market. And so with large layoffs and people switching jobs, you need to know at that moment what

[00:11:50] is happening and what you need for whatever it is you're trying to do with your compensation platform, right? You want to be able to say, okay, this is what we need for attention. This is what we need for proper talent acquisition, whatever the case is. You need to have that data and watch as it's going along or internally as well, right? And so that ability is something that a lot of Payscale specifically customers really loved seeing, right?

[00:12:16] They love the accuracy of the data and the real-time data that's going on, right? So, you know, customers will do this, they'll, you know, more or less sort of A-B test or at least sort of compare and contrast. And if they see an outlier, they're not going to believe it's true, right? The sort of reliability test. So they have ways of saying, this seems to be accurate and it's getting me the information

[00:12:43] and it's sort of transparent in a sense of understanding where this data is coming from. And it made sense to me for what it is that I'm trying to do, right? It's not conflating this data with other things. So it's the sort of classic reliability and validity expressed usually in the sense of just saying, this is what I really like. It's quick and it's easy to use. There's that term again. Yeah. I would like to say that you were actually number one in most categories.

[00:13:14] So good job on that. Again, that's completely from the customer. I don't get to make those changes. Yes. Yeah. And I think that's interesting because when we do our own research where we do our compensation best practice report, which we go out to HR pros and comp pros annually, we go out and collect that data in November and December. And we're really just asking them questions around best practice.

[00:13:40] But one of the things we do focus on is when you're looking for pay data, you know, what are the key things that are the drivers of that? And accuracy coverage, you know, is another one is like, you know, am I getting, do you have the data that I need? And then increasingly, we're seeing this acceptance of real time data because of the availability that there is now of that. You know, previously people use more static annual surveys.

[00:14:08] And now with the whole world of job postings and pay transparency, there's much more real time data available. And so, you know, people, I guess, are coming to pay scale and our data products in order to get that more current view. So, yes, you will still use your annual surveys, for example, for building your pay structures, for doing your annual merit review, for doing your annual budgeting.

[00:14:35] But having the ability to have access to more relevant real time data is important for getting that ongoing view throughout the year or, you know, views for different things. So, you might not use that real time data to build pay structures, but you might use that real time data or use that cliff to decide on an important hire or look at a potential project where you might be thinking about setting up an office in a different location. So, we are seeing a growing demand for more current data. Yeah.

[00:15:04] And you touched on a trend that we're seeing over the last few years. This may not be something that's specific to 2025, but since the pandemic era, we'll just call it that, more organizations have a large number of people who work remotely or at least in a hybrid fashion, which allows them to expand their geographical reach. And so, they may be looking at, you know, maybe we are going to go into a certain area or maybe

[00:15:32] we are going to look at certain cities because we know there's a higher population of what we're looking for or a new customer or whatever the case may be. So, being able to get into sort of micro regional content is another aspect that we've seen correlated with higher ratings. You know, can I get data on some very, very specific types of jobs, you know, and types of skills

[00:15:58] and, you know, down to the city or possibly even neighborhood level. Yeah. Now, we can't really do a podcast nowadays without talking about AI. We mentioned we were both at HR Tech last year. That was really when I think I really started to feel that there was less talk and more action when it came to AI.

[00:16:25] I felt like we've been talking about it quite in a hype manner and really seeing some of the products and seeing some of the use cases that were starting to be embedded in vendor products at HR Tech and the whole conversation around agentic AI. So, you know, you're a keen commentator. You're a keen observer of this. How are you seeing that come to fruition? And how do HR leaders make sure that they're aware of what's going on and that they know how to use these tools?

[00:16:55] I have to first stop blushing from all these compliments. But I agree completely. There was, as happens with newer technology, there was a period where it was more, you know, the promise of AI, right? I've heard it described as a solution in one type of problem, right? There's just like, oh, there's this cool stuff we can do. And it's like, yeah, but we don't need that, right?

[00:17:21] And I, having just actually come from my previous appointment before this, was listening to quite a bit of HR-focused AI applications talking to venture capitalists, right? And I noticed immediately that they were talking about present-day solutions rather than future solutions, right? So yes, thinking about how you are going about your business as an HR professional, if you

[00:17:51] are looking at some of these models and you're looking, what is it you are trying to do? Because if you just see all the things that AI could do, it'll seem, yeah, I can do that. But then you'll find that you're not using these increasingly expensive platforms. And, you know, we all know they're very resource intensive. But if you find a way that it provides value in terms of your resources, right? You are saving hours by doing this, or you're able to do something in a way.

[00:18:21] And often we're using it as a point to turn, right? But you need to be able to think, okay, well, this sort of natural language processing might be of great value. Maybe it can also help take large amounts of data that we're getting and take some insights from it or find anomalies in here saying, can you look at all of these different regions? Let's use the example we just were at. We're thinking about hiring in a few different cities.

[00:18:49] Which of these are we seeing the less variation in pay grades? You know, that would take a personal long time. But it may be, if worded correctly, if the system's robust enough, it may be able to get you that answer immediately. And show the data and show the sources to understand what it was looking at. But it all comes down to the data. And I think that is where you need to sort of start thinking about future-proofing any

[00:19:18] application of AI that you may be doing. Think about your data sources. What data are you able to provide? Because that's the only way to get any sort of predictive natures or to get any real true insights is the data that you come in. You may have more sources of data than you realize. And there may be more ways you can partner with your existing platforms to find the data that you need.

[00:19:42] We found in our own research that besides, I think, two vendors, most people were unaware of the AI features that already existed within their HRMS. So, you know, it's interesting. As you said, there was sort of this hype. And now there's a pullback where they're saying, OK, and some people are a little, you know, they're not the vibes are off with AI. They're not liking what they're seeing. So they're saying, OK, we're not going to mention it is there or it's working in the

[00:20:10] background or the agentic model, anything to sort of shift the conversation. But again, if I be do some research, find out what you have available and think about in terms of solving specific problems and coming in that way. And you'll probably find some good use cases and you'll be less likely to be blinded by a sort of future vision of how your business could be run completely by AI and do all these great things. And there's certainly nothing wrong with that.

[00:20:38] But the present reality is always very important while not sacrificing any sort of future dream. And I think the way you do that is by focusing on what you're trying to do now and prepping for the future by having clean, reliable data that can be used for any AI models. And I think I was listening to one of your podcasts. You and Stacey were talking about this. And one thing that really resonated with me where, you know, HR folks are maybe thinking

[00:21:07] about how an AI can make the work that they do better. But I think you both mentioned about thinking about how it can enable the business as well and not just the HR teams. And I think that's where we're really going to see the unlock in terms of the power of HR systems in terms of answering potentially those difficult business questions that will actually, you know, deliver better business outcomes as well. Yeah, that's exactly it.

[00:21:37] And if used correctly, I think that is that would be more of my dream. Then I think people should be going towards making a more human centric workplace, actually, by the use of these machines. We've all seen value in our personal use of taking mundane tasks, repetitive tasks, tasks that are prone to error because of fatigue and doing that so that we can then think about the right questions to ask.

[00:22:07] Humans are still always going to be the most successful at asking questions. AI may get better and better at answering them, but we'll never know which questions to ask. So the more we can do that and the more we can think about supporting the business, which ultimately supports the people, ideally, then, you know, that I think is what we should be working towards in both our sort of framing of AI, but our usage of AI going forward.

[00:22:37] Because just like with any new technology, there's going to be sort of, you know, a cycle, right? A sort of undulating ups and downs as we find value in it. And then maybe there's some areas that aren't as valuable. But there's certainly some promise there if used sort of responsibly and correctly. Yeah.

[00:23:01] And I think really understanding, I mean, AI is often using quite a blanket term. And particularly, you know, in the marketing that we're seeing, you know, hitting the buyers in terms of like everybody's got AI and everyone's got something that's got an AI name.

[00:23:19] And I think really just understanding what does that mean and what was, you know, because we've had at Payscale, you know, we've always had machine learning, for example, in our data sets. So, you know, we manage massive data sets. And ever since those data sets began, the way that we maintain those data sets, the way that we make sure that the data that's put in there correlates is through machine learning.

[00:23:45] And that is a form of AI. And so sometimes when people go, oh, well, we don't want any AI. And we're like, well, you've been using machine learning since day one in terms of if you've been using our data sets or if you've been using any larger data sets. And then, you know, really we're looking, you know, we're introducing AI in terms of enabling workflows. So, for example, in the job pricing workflow, helping you to make better choices.

[00:24:14] So giving you like, OK, you're searching to price this job. People who've priced this job have used these matches before. So we're trying to like reduce the time so that you're not choosing from a massive list. And then we're looking at AI in our job description management tools. You know, that's been one of the biggest use cases so far alongside talent acquisition, I think. So I think really taking the time to work with your vendor and understand what do you mean by AI?

[00:24:42] What are the use cases? And that can help you get through that difficult sort of legal processes at the beginning where potentially your legal team are saying you can't use anything with AI or you need to switch the AI features off. Yeah. Yeah. One of the sort of standout data points that we came in is actually asking people directly what how are they approaching this idea of ethical use of AI? Right. What what is what are the guidelines that they have?

[00:25:10] And 64 percent of respondents said either they didn't have any formal ethical guidelines or they didn't know of any in the organization. Fundamentally, I combined those two answers because fundamentally they mean the same thing. You're not doing them. You know, you're not following any guidelines. I. So, you know, yeah, it's there's a little just. You know, just kind of a gap in knowledge of AI because.

[00:25:39] There's a bit of a combative nature between the sort of marketing push towards this and, you know, sort of evangelical fervent desire to use AI for everything that you hear a lot in broader media. And obviously from those that have these systems and they're putting them out there and then sometimes. But the opposite is true, too. Right. There's plenty of media that sort of scares you off of AI.

[00:26:07] And so and making you think that, you know, that there is no ethical use or something of that nature. Right. So it's it is, as you said, sort of finding the right language and understanding what it is you're trying to do, because it's probably more of a combination of machine learning, large language models, natural language processing. And, you know, what it really comes down to, you said these things have been around for a while.

[00:26:31] If you're playing 20 questions, you know, and it's if that if this then that and you take in new information, you know, that's what the system is doing just writ large. Right. So, OK, we now know that it has four legs and, you know, antlers. It's a moose. Oh, no, it could also be a deer because I've now learned that that's what machine learning is.

[00:26:52] So, you know, understanding those sort of limitations and and also possibilities, I think, is key to avoiding both the fear and the overhypeness. Sometimes we get around, you know, until the next whatever the next new technology is. I know. I quite often liken it to, you know, I've been around long enough in HR tech for when compensation went on to SAS technology.

[00:27:19] And at the time, you know, I mean, I started Curo that was acquired by Payscale just before the SAS explosion, you know, in 2008, 2009. And when we started Curo, which was one of the first kind of SAS based compensation companies, everyone was like, well, why would we give you our data to put into the cloud? You know, and there is all this reticence at that time. And then, you know, three or four years later, it was accepted that all HR technology was going to the cloud.

[00:27:46] And everyone was like, oh, that's fine. So sometimes there's just that fear of, you know, is it going to be safe? What's going to happen? And so I liken it back to that time when I think about how people are sometimes reticent about AI adoption. Yeah, exactly. So I always try and say, yeah, those are the sort of two ends of the spectrum, I think, within the world of HR technology. You have the cloud based thing that really did sort of revolutionize and change how we went about it.

[00:28:15] Then you have something, let's say virtual reality, where, you know, there was a lot of talk about it. And then we realized there's only a few use cases and there's sort of new ones that come out from time to time and you can find value in there. But it isn't on itself solving any problems. No technology solves the problems by itself. It's always a mixture of people, processes and technology.

[00:28:39] So as we look ahead, what do you think HR and comp professionals should be doing now to prepare for the next wave of HR tech innovations? What things should they be looking out for and how do they stay ahead of the curve? So I alluded to this earlier or directly stated it actually, and that they need to be very mindful of their data.

[00:29:00] There is a growing sort of battle between IT and HR for some of these technologies as the line continuously flurs between what we even call HR tech and what we call just work tech or productivity tech. We even see that from different companies like Microsoft and Google previously, or I guess still currently dipping their toes in some, you know, traditionally HR tech world.

[00:29:26] And then we see that from the top of the world is that who owns the data kind of controls the future. So making sure that you have the data, you understand how these systems work so that when they come out, you know, all the sort of data governance.

[00:29:41] I speak and tech and processes that you're going to need to know who has access to data security and privacy, you know, data terminology, data definitions, how everything sort of flows. That is extremely important to know.

[00:29:59] And I also, we didn't really touch on it, but as we see and probably are going into an era of a little more economic volatility, what usually happens during those times is we start seeing more hiring in the contingent labor market, right? Gig workers, contract workers, temporary workers. How are you planning to use compensation and management tools to support those workers? Are they going to be on the same system?

[00:30:28] Do you treat them different? Are all regulatory sort of check boxes being checked? Do you have a system that can support that if your organization needs to increase its percentage of contingent workers, move towards that? Are you prepped for that? Are you going to be scrambling, be ready for using compensation management tools in new ways that you might not have thought of both in terms of these cool new technologies, but also supporting different kinds of workers that are going to be entering the work.

[00:30:59] Yeah, I think definitely I see that as the next area of where HR tech is going to have to evolve. And we're already seeing it in obviously some of the HRMS vendors are getting there first because they try and capture who's in the workforce and not down to the complexity of what are you paying them. So we saw some of that come out in the back half of last year with organizations being able to manage different types of workers.

[00:31:27] I definitely see all HR tech solutions being able to support those different types of workers as being a key area of development. Absolutely. We're seeing it's still slow growth, but we do still see people interested in on demand pay and expanding that to different types of workers, sometimes called earned wage access listeners. But the idea being that you're paid rather than, you know, semi monthly, you could get your pay after two or three days as requested.

[00:31:56] And how does that feed into your broader compensation plans and anything like that is another area just as these new ideas come up? Do you have the systems in place to support these? Yeah. So we've been referencing the annual HR system survey through our chat today. So you mentioned that you're going to start to go out for fielding.

[00:32:24] What's your normal fielding period for that? Roughly, it's going to be between May and towards the end of, you know, about April, May. It kind of depends on we have partners that we work with to get that out to current customers and prospects, whoever the case might be, our distributor network. And they will get it in April. And so that through towards the end of summer for those in the northern hemisphere.

[00:32:50] So, you know, June and July, definitely by the end of July, we have to have it wrapped up so that we can be ready for an increasingly early HR tech in Vegas, the U.S. version in September. This year. So that is the period they should be seeing it. If you have any interest, of course, we'll have links. We'd love to hear from you. We've had more numbers every year.

[00:33:16] And having that data allows us to do wonderful things to sort of put a bow on that whole concept. But we only have that information because of all of you that take it. So we'd love to hear from you. If you're listening, we want to hear your voice. And if you're a Payscale customer, we help you to collect responses. So we partner with you for that.

[00:33:38] So obviously, if you're a Payscale customer, you may hear from us and you'll get the opportunity to participate in that study for this year as well. So, well, thank you, Cliff, for joining me today and sharing all your insights. Do go and look at the Sapient Insights website and do listen into their podcast. As I said, great way to stay up to date with what's happening in HR tech and various trends that are coming your way.

[00:34:07] But thank you again for being with us today. It was my absolute pleasure. Thank you again. Love to see coffee and tea coming together. Coffee and tea. Maybe we should mix them. What do you think that would be like? I haven't tried that. Maybe we do that. Okay. Well, thank you all for listening in. And as always, if you have any topics that you would like us to discuss or anyone you'd like me to talk with, then reach out to us at coffee at Payscale.com. Thank you very much for tuning in.

[00:34:36] And we'll see you next time for another hot take on all things compensation. Thank you. Thank you.