Is compensation always-on?

[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. Welcome everybody to this episode of Comp and Coffee. Today, my guest and I are going to be debating about whether compensation cycles should move to always-on.

[00:00:24] Compensation has traditionally followed a predictable calendar. We've organized and gathered market data, we've built salary structures, we've planned merit budgets, and we've made pay decisions typically once a year, maybe twice a year, with some ad hoc adjustments in between. But for the most cycle, for a long time, the annual cycle has worked for us. But today's workforce, we're observing challenges that means that that annual cycle is maybe not fit for purpose anymore.

[00:00:51] Jobs are evolving faster, skills are emerging overnight, and AI is changing the capability of what we can do and also changing work. So today, I'm delighted to be joined by Matt Carson from UnitedHealth Group, and we're going to be having this discussion about should we be moving to always-on pay cycles? But before we dive in, Matt, would you like to introduce yourself, tell the organization maybe a bit about UHG and your role? Absolutely. Thank you, Ruth Thomas, and really glad to be here. Thank you for the opportunity.

[00:01:19] I'm Matt Carson, Senior Director on the Compensation Strategy and Analytics team at United Health Group. I've spent about 15 years in compensation across really the full lifecycle of comp projects and processes, things like job architecture, market intelligence, mergers and acquisitions, many, many annual cycles.

[00:01:40] And as of late, most of my focus is on transforming and reimagining the compensation function overall and where and how it needs to evolve. So think about how we redesign the work, how we're using technology differently, and bringing more of an AI-first mindset into the way that the function ultimately operates with keeping the human in the lead, as always. So definitely a timely conversation for me. And again, thank you for the opportunity here.

[00:02:09] Great, great to have you. First time on Comp and Coffee. I'm looking forward to hearing your insights. As it is your first time, we always ask our guests, what is your go-to coffee order? And if you're not a coffee drinker, what would be your favorite morning beverage? Yes, definitely. Definitely a coffee drinker. I'm pretty simple in terms of the order. Either usually a black coffee, I'll dabble with some espresso, depending on the day or the week too.

[00:02:34] The volume, I would say, is definitely higher, higher on the magnitude, probably above the market median, I would say. So simple order, higher volume. Right, get it. I get it. Okay, let's dig in then, Matt. Why do you think we are seeing annual compensation cycles under pressure? And why do you think we're even having this conversation today about why Always On might be the direction to go?

[00:02:59] Yeah, absolutely. And I took some time to reflect on this topic, given I've had a long career here in compensation. And compensation has always felt like an always-on function, in my experience. I think there's really two primary drivers for that. First, pay is obviously one of the most organization's biggest, if not the biggest expense or investment, as I would refer to it as.

[00:03:25] So there's naturally a lot of attention on it and eyes on it from that standpoint. And then secondly, across the 15 years I've been in comp, I've yet to see an organization where every employee is just totally satisfied with their pay and nobody's considering other opportunities. So there's never been a shortage of work for comp teams to do. What I think is changing is our ability to proactively get ahead of it, to see around the corner.

[00:03:54] So the Always On concept, very much shifting from more of a reactionary sense to a proactive mindset. For a long time, I would say the operating model, like again, referenced earlier, largely reactionary, reactive. A challenge would come up, business reaches out, comp pulls the information, analyzes it, responds.

[00:04:18] I think technology now is removing a lot of that friction that has kept us in that reactionary cycle. Think about how much time comp teams have historically spent just assembling the story, pulling data from different systems that information and data may be fragmented, reconciling that on spreadsheets, tracking down the context and all the different cross-functional stakeholders that need to weigh in. So a lot of steps to get to getting complete information.

[00:04:47] And I think that's where AI tech enablement can really change the game for the comp team. And I think really the first unlock for that is just how the comp team uses their time. I know we'll get to more of this probably later in the discussion, but more time to get closer to the business, understand the business context and the challenges that they're facing. More time for strategic problems and just spending less time and energy on all of the tactical so that they can elevate on the,

[00:05:17] what does this mean? How should we consider next steps? What do we need to be thinking about before the next problem or challenge arises? So I think a lot of opportunity ahead. Right. I like that, just the way you describe it of like to stop being reactionary and being proactive because, you know, ad hoc adjustments are essentially reactive half the time, aren't they? Like when you're giving someone an ad hoc, ad hoc, out of cycle increase,

[00:05:44] whereas maybe trying to think more about sensing when someone will need an increase rather than, as you say, reacting to it. So that's what I'm kind of listening to there when I think about always on. But I mean, always on can mean different things to different people. So from your perspective, what does always on compensation actually mean to you? Yeah, great question. For me, always on starts with continuously listening and continuously monitoring.

[00:06:13] So monitoring the market better than a individual person or a team of people can do 20, 24, 7, and always listening to what's happening inside the workforce and really continuously monitoring and listening for all the various signals that have been hard to tell a holistic picture and story of historically. So think about recruiting signals, how jobs themselves are evolving,

[00:06:39] what jobs are now popping up in surveys that, you know, took multiple years to get data for, to show up, et cetera. So the monitoring and the listening are the, the two big things for me. And I just wanted to kind of apply an example to that. So if we take hot jobs, for example, pick your job, forward deployed engineer, et cetera.

[00:07:03] So there are rules today where waiting 12 months to revisit the market just is too long. And it doesn't, it's, it's an old concept. If we apply that to other practical examples of say a real estate agent advising a client on last year's mortgage rates, that wouldn't work, right? Or a financial advisor not considering what has happened most recently in the market.

[00:07:27] So I think comp is catching up to really the, the latest and greatest and needing to be more agile of thinking about really dissecting what can be an annual rhythm and what can be, where are the areas where we need to really respond much quicker. So I think if you surface compression, equity, market concerns, all of that critical information, while the decision is being made,

[00:07:57] you're going to have a much better opportunity to make a good decision the first time, instead of needing to correct it in a big annual cycle, the equity cycle, annual review cycle. So I think again, just a lot of the signs I think are pointing to a more modern and proactive way that we can work through comp issues. Yeah. I mean, I think the first time I'd started seeing in more recent times,

[00:08:21] people adjusting their compensation processes to be more always on was during the great resignation, you know, where we had the whole issue of pay compression happening so quickly. We had people moving jobs and, you know, we did start to see organizations like monitoring a new hire coming in, what impact was that having on pay compression and then like doing internal equity adjustments. So that was, that was something outside of the normal cycle or something that might have worked. So I think that was when I first started to see it in more modern times.

[00:08:50] I mean, there was a point back in about 2015 or 18, when everyone declared that the performance review was a worthless corporate ritual, and we were going to move to continuous performance management. And then obviously comp would follow that never happened. But today, as you say, it's more about skills, hot, you know, skills evolving, AI changing jobs. And that's what we're having to respond to. It's not necessarily the pay compression angle. It's just like work is changing.

[00:09:17] And the skills that we need for people to do that work is becoming hot and then cold and then hot, you know, in a shorter cycle than the annual cycle. And that's what we're trying to have to respond to. Absolutely. Now, it's quite scary thinking about how do we manage always on? Because if I think about my time doing like my many years doing compensation cycles, if we were lucky, like the comp review was the one time of the year we actually

[00:09:43] knew everybody's salary correctly and probably like all their, you know, personal details at that point. And it was such a mammoth task. You know, it took us three months to prepare, like three months to like recover. Whereas it just doesn't seem as hard anymore. And that is due to availability of data. You know, we have so much more data available to us now. And obviously technology that is helping us prepare, analyze data. So is that something you're seeing?

[00:10:11] And particularly maybe we could talk about AI. Like how are you seeing AI influence this conversation? And how have you mentioned AI at the beginning of your introduction? Spot on with all the points you referenced. I mean, this is infused in any and every conversation that I'm in recently. I think AI really, it already has. And it has the potential to substantially change it even more where comp teams are going to be able to spend their time.

[00:10:41] And how they get that time back on, we'll call it the more tactical work to elevate to more of the strategic work and to cross-functionally partner more regularly, more intentionally. Because AI is increasingly helping with what I call like the book ends of the process. So the comp practitioners and, you know, many other teams can focus on that important middle that requires the judgment.

[00:11:09] So some of the upfront aspects is all related to the intake, getting that complete information the first time, the back ends of the routing, the implementation of getting approvals, etc. So it's definitely moving very aggressively.

[00:11:27] And I think all in the right direction of getting the right information in front of the right people the first time, as well as being able to make sure that the systems are connected to then report out on the important metrics after the process. And to have all of that data infrastructure to be able to make informed decisions, to have that historical data, to have that data, to do predictive modeling.

[00:11:54] So, again, it just really gives the human more room for the parts that require that judgment, that important middle that they, you know, probably spend a lot of time on those bookends today, certainly. And I think it's increasingly reducing, reducing those bookends to be able to elevate the time spent on the judgment aspect.

[00:12:15] Yeah, because I always think what I remember at the time, you know, when I'd get to the point that I had all the data in an annual review that, you know, it finally all came to me. And I was already at the deadline, you know, I had to just like put all that data together and we had to present it. And I always, you know, my biggest regret was I just wish I'd had more time to look at what was coming back, see what the trends were, you know, where were their problems. We often didn't that time, whereas, you know, that's not an excuse anymore.

[00:12:43] It's easier to bring the data together and then it's easier to, as you say, like see the patterns in the data. And it just makes the role that much more enriching as well, I think. Absolutely. Absolutely. Very different experience for the compensation professional. I think they're living through it now and I think it'll be very different as we continue to work through the year and the years ahead.

[00:13:07] I think especially compensation, I always think it's just been so ripe for like AI disruption or AI enhancement because so many of the tasks that we do are repetitive and cyclical. So if we can have those automated so that we can spend more time analyzing, then that's great for everybody, I think. Absolutely. Absolutely. Okay. So we've talked about like why we think we're moving to always on. We've talked a little bit about how technology is helping that.

[00:13:35] What else can we do to prepare for a more continuous compensation future? Yeah. What do you think is going on here? Like biggest mind shift? Do we need to think differently or what are areas potentially where you should think is a function in investing first? For sure.

[00:13:52] For sure. As I alluded to in my intro, I've spent a significant amount of time this year and my guidance around that question is going to be pretty simple, but I think it's really critical to start with the work itself before starting with the technology or the tool. So there's understandably a lot of excitement around AI agents, automation, whatever the buzzword may be for that week, month, year.

[00:14:51] And then we're going to be saying, yeah. If you ask five people on your team how, you know, what their understanding of a process is, that you're going to have a consistent standard versus getting five different answers. And I know those aren't the flashy parts of AI transformation, but I would call them really like prerequisites for everything else that the AI depends on.

[00:15:15] So, I mean, one tangible way I would advise folks to start working through this or thinking through this if you haven't already is take an activity that your comp team spend maybe the highest or a high percentage of time on today and follow it all the way through. Meaning what areas do the requests typically come in from? How many touches does it go through before it actually gets to the individual doing the work?

[00:15:44] What information does that individual need to do the work? Where is the information coming from? How many systems does it need to be pulled from? Can those systems talk to each other? When you get the data, do you need to, are you doing that manually or is it built in a automated way where you can reproduce that and tweak accordingly? Once you have that recommendation, who does the decision go to?

[00:16:10] Who has decision ownership and then ultimately how it's approved and implemented and is it measured? I think when you actually map that out, the end-to-end solution process, the opportunities become pretty evident as to where the bottlenecks in the process are. And then you can dissect that and isolate, okay, which areas are right for AI?

[00:16:35] Because I think, again, a lot of the AI enablement and opportunities that we're going to be really going after today actually need the time, attention that they would need whether AI was here or not. So it's a really fascinating, I think, inflection point to really go through your core services and how you deliver today.

[00:17:01] Make sure you have a really good understanding of that as you try to automate and get more on the forefront of the AI. So I like the way you describe kind of like getting some of the fundamental basics right, you know, to set you up for this like always on approach. So you say like data governance, who needs to approve what job architecture, as you say, is so critical. I think two blockers you talked about as well.

[00:17:29] I mean, when I think about trying to move to an always on cycle, there are two things that have always been a problem. They have always been argued that this is the reason we can't do it. One of them is how do you maintain peer relativity? Because if you're making decisions individually through the year rather than in a focal review, as it has been called, how do you make sure that you're maintaining equity? Well, we have enough data. We have enough ways of kind of looking at that to be able to see that proactively now.

[00:17:57] And then I guess one of the biggest ones has always been budget. Like how do you manage budget outside of the annual cycle? So it's easy. We have our annual merit budget. What do you do for the rest of the year? Like, do you just let everyone go free wheel and they can just spend what they want and then you deal with it by the time? Or are you allocating budgets? That's always been a challenge. I don't think we've wholly got the answer, but I think it is solvable. I don't know. Is that something you thought about at all? Totally, totally. Totally.

[00:18:25] And that's what I would say has been a lot of the focus in some of my work lately of probably be repeating some of the points I said in the kind of last answer. But I think it goes back to decision rights, understanding your total reward strategy too. And flexibility within a framework is a concept that we talk about too. So I think you also need to have some core anchor points you can lean on, but then also define.

[00:18:55] And this is the critical part within all of the process mapping and AI enablement is the, again, the governance, decision rights. What do we want? How much manager autonomy do we want to give? And defining all of that, because if you don't have that defined upfront, you're a broken process and a ambiguous process. Probably a better term is only going to compound the problems as you try to try to automate that.

[00:19:24] So it's critical. I think there's a key, I think finance has always been a critical partner for comp. And I think that's going to grow even more, more and more as technology infuses into the day-to-day work, because we want to work smarter, faster without sacrificing the quality or the budget discipline.

[00:19:50] So, and that's going to require a very close, synced up, hand-in-hand partnership between comp and finance. I also think we're starting to see that switch from, you know, we all got a budget. We thought of it as a cost and we were maintaining the cost. Whereas now it's about how can that spend accelerate the business? Like if we invest in that person, we know that they can go and do this.

[00:20:12] And I think that mindset is also like making it easier to have that thought of like actually adjusting pay outside of the cycle. I think, yeah, the whole like discretion versus like how much discretion are we going to give managers? If it's, if we have solutions and we have data and solutions that can like tell a manager what they should be paying based on all the data that's available, does manager discretion actually need to exist? I think that's a whole other debate. We could probably have a whole other podcast on that.

[00:20:43] Yeah, I think again, it's been a very time. This is a timely conversation for a lot of the discussions I've been in is I think that the guardrails and there's two ways to look at it. If you do kind of go the more giving, giving more, more leeway to do that, making sure that you have all the guardrails and the protocols to measure that and keep a very, very close pulse on that. So it really, really important point.

[00:21:11] And I think for everyone to be considering to like, it sounds great to be able to, you know, give, give managers more autonomy. I think that's probably a lot of teams and organizations and goal, but you can't, you can't lose the budget discipline, the pay equity, pay equity proximity and the pay for performance aspect.

[00:21:34] So the guardrails would really, really need to incorporate all of your, your key total rewards guiding principles and throughout, throughout any agentic solutions. Guardrails are so important when it comes to explainability as well, you know, because it's, it's in the age now we live of pay transparency, which really changes the whole way.

[00:21:55] We think about how we deliver and talk about compensation to our employees, you know, that those, those guardrails and explainability are also really important. Okay. Let's future cast a bit then, Matt, if we were to look ahead and imagine a world where compensation is more continuous, recognizing that this is probably one of the biggest shifts that, you know, the profession has experienced for a while. Where do you think we'll be in three to five years with this?

[00:22:20] Yeah, I would say, I think the best comp functions will be operating with dramatically more leverage. They'll be able to see around, around the corner again, all back to that concept of less reacting, more partnering cross-functionally with proactively with talent acquisition, with finance to, to bring more holistic end-to-end solutions, stories.

[00:22:47] So all the right decisions can be made at, at the right time in the, in the process as how work and information flows. I think a lot of the routine analysis and administration will be, be automated or self-service. A lot of those first level questions. I think there'll be more tiered components of with, as AI starts to get infused more in organizations.

[00:23:14] I see it playing out with more kind of tiered, like, and risk levels where can answer, can questions be, be answered right away through, you know, an agentic front door. If, if they have higher risk levels, like, it'll go through a process to elevate where comp isn't that frontline getting many of the questions that they would historically have, have gotten.

[00:23:40] So, but I think for, for that to happen, comp is going in partnership with the larger HR organization or people team is going to really need to buckle down and focus on creating enterprise, an enterprise governed data foundation with really, really clear decision frameworks that you can, can really articulate what, what that means.

[00:24:04] And how it fits into, into, again, saying it a lot, but like the, the agentic solutions and how everything can ultimately work towards more, more automation and getting the simple questions answered before they may take up an individual's time. If there's already a clear answer for it. Yeah. I think I'm aligned there.

[00:24:27] I think this whole, I like to think of a world where it's, you know, it's sensing, you know, that, as you say, I think there's a piece of what has to happen first, which is the data structure, the governance that you've talked about, but then it should be able to run as a system. And we should be much better at sensing, like there'll be signals when, and when someone's pay needs to be adjusted.

[00:24:48] I think one of the challenges we often have in making pay decisions today is we don't always have the data points that allow us to make the complete pay decision, which is why that human in the loop is still needed in a lot of pay decisions because ultimately pay is about people. And we don't have every data point on a person, like maybe they were paid something because they came from a different function or they came through an MNA.

[00:25:13] And I think increasingly, if we can gather more data that will allow us to make that, to be even like some of the more challenging pay decisions that involve those people data points become more automated as well. And yeah, see if we can get there, I think.

[00:25:28] Totally. The last piece that I wanted to mention on that topic is, I think after the decision is made to being able to much more accurately measure, did it actually work or what was the ROI on that implementation?

[00:25:45] I think compensation will ultimately get much better at connecting decisions to outcomes instead of really simply a lot of, you know, the transactional work and moving on to the next fire, fire drill. So I think being able to look back, reflect, prove that, hey, this investment actually took us from here to here. And this is why we should be able to think about making that next investment.

[00:26:13] So I think it'll give the team, as I started, a lot more leverage to really show up as really quality, valid, trusted business partners. That's the exciting bit as well. I mean, we've talked about always, you know, we're going to manage comp strategically, but we have always found it very hard to prove the impact. So we made decisions, you know, maybe sometimes we made big programmatic comp decisions in order to like deliver a business outcome. But it was often very hard to join the dots.

[00:26:43] Whereas that's the exciting bit now is the potential to be able to say, as you say, to like track the impact and join the dots more and really start to treat compensation as strategically that it should be. Yeah. It's an exciting time to be in comp, I'll say. Yeah. I mean, so scary and exciting. Some people still scared of AI. Some people are like embracing it. Some people it's now part of their everyday life.

[00:27:08] But I think, you know, yeah, a lot of change to come, but exciting change and allow us to really operate, as we've said, as strategic partners. Well said. So I think compensation isn't becoming more complicated because organizations want it to. It's becoming more dynamic because work is changing so quickly and we have the availability of data to make better, smarter decisions. So excited to have you here today, Matt, to talk about this topic of Always On.

[00:27:37] Interested to hear what you think, the audience? Are you moving towards Always Compensation? What are the challenges you're facing? Let us know. You can get in touch with us at coffee at payscale.com. And we'd love to hear your thoughts on the topic that we've discussed today or any other ideas for future episodes. But thank you again, Matt, for joining me. Thank you for giving your time. And it's been great to have you as a guest on Comp and Coffee. My pleasure. Thank you for having me. Thank you.