From data to impact: Leveraging AI in Total Rewards

Speaker A: Foreign.

Speaker B: Hi, everyone. Welcome to today's new Shape of Work conversation. Uh, my name is Gord Frost. For those of you who have listened to the podcast before, you know that I've kind of become one of our periodic hosts for the podcast, sharing those duties with my esteemed colleague Kate Bravery, who is not with us today, but I am thrilled to have my colleague Jess Von bank here with us. So, Jess, thank you, and welcome to the podcast.

Speaker A: Thank you. I love that title, Periodic Host. I feel like I'm going to take on that title in my email signature. Periodic host.

Speaker B: Periodic host. Among other things. Right, so I'll let you introduce yourself more in a minute, but I'm excited to have you here just because you're one of our experts in AI and how AI is impacting the future of work. Right. And I know that's a topic that's top, um, of mind for everyone today, and I'm even more excited because you and I recently presented together at a Mercer conference in Europe, um, which went great. I was really excited by both, you know, the presentation that we did, and more importantly, the response that we got from clients and from organizations and from Total Rewards leaders that we met with there and, and the learnings that we got from that, which will be a lot of our, um, the topic of our discussion today. So before we dive into that, maybe just tell people a little bit more about yourself and then we can dive into the conversation and let them know about. About what we saw when we were. We were in Europe together last week.

Speaker A: Yeah, absolutely. Well, thanks for having me. Uh, I work in our global digital transformation practice, serving, uh, all of hr. Uh, so it's always fun when I get to sort of dive bomb into a discipline like Total Rewards and see what's going on in that space in particular. Uh, and thanks for calling me an expert in AI. I don't know if anybody is an expert in AI. It changes so fast. I do like to say you cannot be a steward of something without being a student of it. So I study AI a lot. Um, there's some interesting research, including some of Mercer's own research that says the more you understand AI, the more it scares you. Yes. And I can comment on some of what I heard and learned just this week at AI World, hosted, uh, by Oracle. But, yes, that's what I. That's what I do alongside being a periodic host. Ah. On some of our thought leadership like this. Yeah.

Speaker B: Well, look, thrilled to have you here today, and it's been really fun, you know, collaborating with you on the last conferences we've done and on other pieces of work at Mercer. Uh, but maybe if you could kick us off, um, as you said it was, you know, you cross all the different domains of HR and you've even worked more broadly, you know, in the, in the studying that you do of AI and the impact on work and stuff like that. And so I was wondering if you could start off and maybe just like, take us up a few levels and, uh, as you think broadly, what are the challenges that organizations are facing? What are the challenges, maybe particularly in rewards, within the HR function, which is a little bit different from the other disciplines? Like what are, what are some of your observations there if you were to compare and contrast and, you know, insights that you might share with our listeners?

Speaker A: Yeah. So working in our transformation practice, uh, we call it digital transformation, but I feel like everything now it's AI transformation. Let's just call it transformation. It's, you know, organizations, uh, trying to move the needle forward and change the way they work, like tangentially, fundamentally change the way they work. Unfortunately, most organizations lean into technology alone to change the way they work. And then we see studies like the MIT study recently that says 95% of AI initiatives are failing. That's kind of the problem. We call too many things transformation. We're not actually changing the way we work. If we just lift and shift to a new piece of technology, we'll make the same mistake with AI. We'll throw AI at everything and say, look, we're doing transformation when we're not pausing long enough or we're not willing to go as deep as we really need to go. Transformation is a culture shift for first. It's a mindset shift, and it has to do with the way we lead. This is a leadership moment. Then you sort of earn your right into technology and AI as part of that conversation. Um, and I see that, you know, I saw that with the Total Rewards fine, uh, people we were spending, uh, time with in Barcelona. I see no matter the discipline, I, I see that. I see a sort of rush headlong in, into tech thinking. It's going to be the silver bullet that's going to magically create efficiency and productivity. It's going to drive innovation across the way we do things. It can help, but we have to be willing to, um, actually reimagine the way work can get done. I think with Total Rewards professionals, they have an added challenge. So much of AI and innovation is getting AI as close to your data as possible. Your enterprise data, not public, like your Enterprise workforce data. The closer you can get AI to that, the more truly intelligent and contextual and impactful it can be and total rewards. People know more than anybody how precious your business and workforce data is. So I think rightly they're a little bit more uh, cautious. Uh, there's a lot of discipline around data, but there's also sort of like our data isn't even ready to get AI close to it. I observed both of those things.

Speaker B: Yeah, yeah, yeah. And I agree with you. I mean there's on the one hand the confidentiality. Like this is personal employee level data. Uh, so just understanding the risks of ethical use of that data and how do you protect individual confidentiality, that's obviously a top concern that is critically important. But then also you talked about the fact that in is our data robust enough? Like is it, are our jobs matched to the right job codes? Do they need to be re leveled or reevaluated? Do we have them attached to the right salary ranges? Are, ah, they grouped together in levels or in clusters or whatever you're using that allow you to use them for deeper analytics. Like a lot of organizations haven't structured their employee level data to make it easy to use AI for analytics. And then they try and then they realize that they need to do some foundational work first. Right?

Speaker A: Completely. Two of my favorite things I heard at the conference in Barcelona, uh, from practitioners from seasoned professionals. The first one was simplification isn't simple. Yeah, so, so true. And the second was an analogy I'm probably going to use forever. Uh, is that data is like laundry. You can't let it pile up. Sort of you all, you're like, you're always doing it. Like you're, you're always going to have dirty laundry. You're, you always need to be tending to. It's never like just when it's done and you feel like, oh my, look at my data is so perfect. I did, I normalized it. Look at, oh my data is so perfect. That's going to last about two seconds. Um, and so it has to be a habit, it has to be a muscle. It has to be all of those other analogies we love to use. Um, and so I do think that anybody who says they're doing transformation, anybody who says they're driving innovation, especially if you're leaning into AI, I hope you've got change muscle and data fitness at your core.

Speaker B: Yeah, yeah. And I think good using that analogy, like even fitness, like personal physical fitness, it's not like you go to the Gym once and then you're done. I know you need to keep going right in January and then you never see them again. And I've been that person before. And then there's the people that go day in and day out and they make it a habit. And that's the ones that see, you know, real improvement over time.

Speaker A: Yeah, absolutely.

Speaker B: Yeah. One of the other things that I wanted to come back to that I thought was a really great way of thinking about this is in part of the presentation that you delivered, you talked about the different use cases of AI, right. And, and automation when it comes to total rewards and HR in general. And I really like this way of thinking about it because you talked about ways that, um. And tell me if I get any of these wrong, so feel free to correct me. You know, AI can be used to enhance your current processes, to improve upon those and then to transform. Right. And I think a lot of people, as you said, they jump right to the transform piece because that's what everybody gets all excited about. But there are other applications and knowing, and they kind of build like one upon the other. And, and so starting on enhance, you know, that may not transform your business, but if it can still drive improvements in productivity, you know, time savings, things like that, that's still a good starting point that people can use and then they can improve and then they can transform. And if, maybe if you could talk about that a little bit, I think that might be interesting to our listeners.

Speaker A: So the, and the reason I used that is, was it was to give us a little bit of those building blocks. Because our whole keynote and even our recent point of view that we've issued was about helping total rewards people move from sort of a tactical cost center mindset to managing a strategic portfolio and delivering different value to the business. And so the framing, if it's easy for people to remember, this way, it's faster, better, different. Faster is doing the same stuff you're already doing, just faster, more efficiently. A lot of us use chatgpt that way. Draft my email, summarize my meeting notes. I need to create a job description. Now. We all got really excited about doing job descriptions in 5 seconds rather than 30 minutes. Better ask asks a different question. It's sort of. There was somebody else at the conference that was very, very excited about getting 30,000 job profiles down to 900. So better would be using AI or forget AI, just using your brain, uh, more intelligently to say, do I need 30,000 job profiles? Should I be writing all of those faster or do I actually need all of those. So better is a little bit more of an intelligent workflow that asks different questions, that says, do I need to be doing this the same way? It's sort of like uh, the digitalization era where we published job ads in newspapers. Doing digital just meant putting them online. We weren't really changing anything. We were just going from offline to online, which is also fantastic. It's great to go digital, um, to find those sort of incremental improvements, but that's not necessarily transforming the way you do business. And so the third bucket is doing something completely different. If you're transforming, it doesn't look the same when you're done. So transforming would be, do we actually need all of the same jobs we had before? Is that how work is going to continue to get done in our business? Do we still need 500 accountants and 500 recruiters? And you know, and so faster, better, which is just more intelligent, not just automated, but more intelligent and transformative, which is how can work get done and what would we need to change in order to get to that spot?

Speaker B: Yeah, Yep. And the other thing that I really liked is, um, as you kind of move up that value chain, call it also the way that you need to think, I think about the role of the, of the either rewards leader or the rewards professional or whoever the person is doing that work, it becomes broader. Right. So I think if you're thinking about I'm m just going to do things, you know, better and faster, that's the stuff I do already. Right. So that's within the scope of my job. And you, you gave examples. If I'm writing job descriptions or I'm updating HR policies or I'm putting postings online or whatever the case, but it doesn't really change the nature of the job. You know, if you're thinking about going, going to the next level of enhance, then you're starting to think like, how can I change the impact that I have? Right. How can I deliver something that I haven't delivered before? Right. Or go an extra mile beyond what I did already? Right, yeah. How can I deliver a better experience to employees in the organization by using AI chatbots to deliver not just to answer their questions faster, but to give them a better answer because it's pulling in data from other sets and it knows that if you're asking about topic A, topics B and C are related to that and it brings in that information as well. Right. So I think that those are some examples of better. And then you need to Think about, like, what are the related areas that may be slightly outside of rewards and benefits or another things, but that are related to the same employee need at the end of the day?

Speaker A: Yeah.

Speaker B: And then when I think about transform, that's where you really need to kind of break down the silos in the organization. Because if you're going to do something transformative, it likely means that, for instance, when we come to AI and automation, you need to have access to data sets that for instance, tie together employee data with business data. Right. Because that's one of the other things that we talked about at the conference is this idea of using, um, using rewards to really drive business results and business outcomes. And so in that case, you need to actually work more collaboratively and you have to think of yourself less as a rewards professional or as a rewards leader and part of a business leadership team where you all bring different skill sets together but to solve business problems. And that's where I feel you can really have the transformative impact. Like that's what I loved about the conversation we had at the conferences, giving these examples of, you know, great, if you want to work like there's improvements you can have within your domain and that's great. But if you really want to be transformative, you need to be thinking, you know, across the organization and how you can collaborate with, with colleagues to really deliver a transformative impact on the business.

Speaker A: Yeah, absolutely. I love the way you frame that as a business leader. And when you think about it from a business leadership perspective, what's the impact? Like what is the workforce strategy? What is the. It's amazing to me how often that question is not that well answered. Like, what is your talent strategy and why? U um, and then as a total rewards professional, that's a lever you know how to pull. So you, that's the contribution you can make. So if your organization is becoming skills powered, if you're trying to close skill gaps, what's the lever you know how to pull in order to support that initiative? Do you start paying for skills? What does that look like from a rewards perspective? Or if you have goals around inclusion, you know how to pull the total rewards lever to contribute to that outcome. And so if you can't clearly articulate first of all what those talent outcomes are that you're hoping to support. And if you can't tell the story far enough, it's probably, uh, an analytics problem. If you can't tell the story far enough to say here's the contribution I'm making or our function is making Toward that workforce goal, that business outcome. We've created more robust leadership. Our talent pipeline is more durable and more agile. We're able to pivot talent to work when you know, much faster than before. Uh, we're closing skill gaps. We've created more include, like if you can't connect the dots in that way, um, you're probably not connected enough to the business strategy. Uh, and you probably don't, you're, you're not, the data is not there, you've got a breadcrumb a little bit further than you are already to say, am I helping to support that goal? Uh, and if not, then the strategy is not right or you don't have the insights to be able to tell that story. And that's why we keep talking about total rewards as a cost center. If you can't connect those dots in a compelling outcomes based way, then you're not managing a strategy, you're managing a cost center.

Speaker B: Yeah. Uh, and that was one of the great parts of the discussion as well, is moving from cost center to really strategic driver of the business or strategic enabler of the business. And how do you make that transition and being able to connect the dots, being able to understand, you know, what levers you need to pull and how that contributes to the broader business strategy. Obviously those are the kinds of levers you need to be able to pull or the contributions you need to be able to make to actually make that transition to strategic driver and enabler in the organization. Um, it really came out so clearly I found that really interesting. One of the other things that we talked about, you, you touched on the idea of workforce planning and I'd love to just, you know, share some of the examples with our listeners of what the vision could be because I felt it was so compelling. So maybe like I'll walk through it a little bit and then would love to get your commentary if there's anything that I missed or that you'd add to it. But I really thought about this, um, this world where, you know, in today's world where we've got issues of trade, right. And tariffs, and so organizations are rethinking their supply chains and they're rethinking where they do manufacturing and where they locate resources and where they're going to hire resources, while at the same time thinking about what are the skills that we'll need in the future and where are we going to find those skills and who will our competitors for skills be? Like, those are complex problems. Right. That you do need to bring together different levels of expertise from across your organization to really solve in an integrated way. Um, and as a rewards professional I think there's a really important role to play around understanding what is the market for talent in different locations around the world. What is the cost of talent in those locations? How would your total rewards program design maybe need to differ in order for you to attract and retain the talent you need with the skills that you're going to need in different locations under different scenarios? And so this idea then of scenario planning or scenario modeling also becomes important. And again that's where once you've got good data and you understand your data, you can use AI and automation to leverage some of that kind of scenario planning in real time. Right. So I think that uh, then you can really demonstrate impact to the business. So it goes beyond just I'm doing my job, you know, more efficiently than I did it before. It's really turning into, I'm able to take the, the expertise that I have within my role and contribute it to solve business problems in real time. Right. Like that's exciting.

Speaker A: Yeah, totally. And what you described has always been challenging, especially for global companies that have compliance and regulation. They have to understand by geo, uh, you know, by geography. Like that kind of workforce planning has always been challenging and exciting. But now consider the great talent remix that's happening not just for all of the reasons you just described, but add the impact of AI and automation. So if you ask the average business what their workforce needs to look like and two or three years or five years, it's not just the sort of the talent conundrum you just painted a picture of. It's what are all of the ways work can get done and what is AI and automation like? What's actually to go away? Like take a call center or health care or any industry hospitality, uh, retail. Are we still going to have, are we throwing bots at all of our customer service stuff or is that still going to be a pool of humans doing that work? And so the impact of all of this on workforce planning and workforce strategy and then looking at the skill gaps that creates and are we going to, you know, sort of repo and upskill those people or is that a uh, future riff? I think we're jumping too fast to riff. Like so you have to add in like layers of complexity to this conversation. When you model the future workforce, it's a human machine teaming conversation. It's impact of AI and automation and, and then it's um, where are we going to find appropriately skilled labor at the Best, you know, at the most effective cost possible for us. Uh, and how are we going to start building that pipeline ahead of time knowing this is happening to every other organization and we're going to be competing for that talent as well.

Speaker B: Yeah. Yeah. So it's funny, as you were saying all of that, I started to feel a little bit overwhelmed that I was.

Speaker A: I know this happens to me a lot. Yeah, I, I hosted our AI forum people.

Speaker B: Yeah.

Speaker A: Yeah. I mean I literally, I hosted one, um, um, one of our most recent AI forums and somebody in the chat, it's very, you know, um, that's very chatty. A lot of like live dialogue and stuff. And it's also Chatham House rules, like we don't record safe space. Like we can talk about what we're nervous about. And somebody said the more you talk, the more overwhelmed. I feel like. I get it. This is a lot of change happening,

Speaker B: really M. So I think probably a lot of people are feeling that way. So if that's how our listeners are feeling, don't worry. You're not alone. But maybe I'd love some observations from you on, you know, from the feedback that we got and the conversations that we had, um, at the conference in Barcelona, what, what stood out to you or what were some of the aha, uh, moments that you had, you know, in, in the conversations with clients that we had afterwards.

Speaker A: Yeah. You know, I want to acknowledge that we are still all on a journey and we're all somewhere on that journey. We don't have to be in the same place. If you work in a highly regulated industry, you are probably choosing to lag behind a little bit. If you are, uh, I don't know, fortunate or unfortunate enough to be in a bleeding edge in industry where, uh, where you have to be first, uh, where you're, you're pushing the envelope out of necessity. There's competitive pressure, there's stakeholder pressure. Um, you have a lot to, you know, trailblazing is not easy. There's a lot of risk to be assumed, uh, if, if you're first. It was telling to me that in the same room, like when I did a session, uh, last week in the very same room, we had one person, one global total rewards leader, massive organization, say that he is planning to stand up an agent to manage open enrollment for all of their US constituents within 12 months. That's his goal. That's the mandate. They're driving hard toward it. And in the very same room, another total rewards, professional, global organization, complexity. All of the same things. No better no, worse said, what is an agent? How is that going to work? So that's all of us. We're all somewhere on this journey. It's not a level of education comprehension that. That's not the difference. The difference is our appetite and our organizational ability to move that fast, to take the risk, to put the governance in place, to get your data ready for AI to be close and cozy to it. Um, like, I think we're overcoming the education, like, sort of the understanding. And that part of it, it's sort of the more, you know, like, the more you understand, the more you have to understand the risk, the responsibility, the guardrails, how you're actually going to govern and manage the change that goes along with doing some of this stuff. Um, so I want to acknowledge, like, it's okay to be somewhere on the journey out of necessity. You might be watching and learning. Uh, and that's okay, too.

Speaker B: Yeah, no, that makes total sense. And I agree with you. Like, everybody's on this journey. I like positioning that way. We're all at different points, both based on our own skills and capabilities and the nature of the organizations that we're a part of, which does dictate this to a large extent. Um, but at the same time, um, I was at another conference last month in Asia where one of the speakers said, it's a little bit like when you're driving through fog, right? When you're driving through fog, um, you. The worst thing to do is to stop, right? Because if you stop, you know you're going to get hit, right? So you need to be cautious, you need to be thoughtful, you know, you need to be, you know, careful. Um, but you need to keep moving, right? You do need to keep moving forwards. And I thought that was such a great analogy. We've had a couple of them today. But that analogy really stuck with me, Right? Like, yes, the future is unclear, right. And we may not know exactly what lies ahead of us, but we do need to keep moving regardless of where we are today. Right? And I thought that was an interesting piece of advice, um, and one that I think really applies in this case as well. And so maybe a couple of thoughts that you can leave people with before we wrap up, and then I'll jump in with a few too, on, you know, regardless of where you are today on your journey, what are some recommendations you would give around what specific actions or tactics HR and rewards professionals could be thinking about engaging in themselves to continue moving forwards on their journey?

Speaker A: I would say own not just your journey, but your Strategy, like, understand what you're trying to do. One I loved our, our, um, panelist speaker from British Airways, personalization. Hyper personalization is one of the things that she talked about. A lot of ears perked up in the room. That is a, that is a valid and incredible goal to have. How do we make the experience of rewards and recognition, employee experience, feel like we know you, like you're, you're one of ours and we know you, we understand you. Everything we give to you is hyper personalized. It's catered to you, it's individual, it's high. Jess, we understand you need this and did this and, uh, what a great goal. So it's not, you know, sort of like the rush to transform at all costs or, or just for efficiency and productivity, to save a few cents on the unit of productivity. Um, it might be to create a more personal human. Yes. Tech can feel more human. Like if you're able to cater and individualize experience and create more engagement and loyalty in your workforce. Like, so own your strategy. Like, understand what you're trying to do and why so that you have even the right measures of success. Um, understood, you know, what's working. And so, um, learn from everybody else, but make it your own. What are you trying to do? Why? What does success look like? And how are you going to keep refining and sort of continually improving toward that goal? Um, it's not a, you know, set it and forget it sort of thing. There we did the thing. Uh, we can move on to the next thing. Um, it's probably, it's probably something you're going to start with, with a group that's ready, that's asking for this change, test it, refine it with them, and then roll it out more broadly.

Speaker B: Yeah, no, I think that's great advice. And the point that you were making, I think is so important. We touched on a little bit before, but I think it's important to reinforce it's about not necessarily like AI or automation for the sake of AI.

Speaker A: Right, right.

Speaker B: It's in order to support a business objective. Right. So to understand, like what is the business problem that we're trying to solve and how do we use technology and AI through rewards to solve that problem. So to your point, if it's more, we want to drive better personalization so that we can drive a better experience for our employees, then that's the objective. And how do we use the different tools in our disposal to do that? Or if we don't have those yet, like, what foundational work would we need? To do, to be able to deliver that outcome. Right. If the business problem is the workforce planning stuff and being more agile in workforce planning, as we talked about a few minutes ago, that's a great business objective. And then, okay, what are the capabilities that we would need to have? What would we need to change about our data or our processes to be able to do that and deliver on that outcome? Right. We had another organization that said our objective is we want to deliver fairer pay to our employees through the year end salary review cycle. And we'd love to use AI and automation to ensure that we're doing that while also doing it faster and better. Right. To deliver a better experience to people. And so if we wanted to do that using automation again, what are the things we need to do with our data? What are the processes we need in place to put in place? What capabilities would we need to have on our team to deliver those outcomes? So I think your point of like having an outcome like a business outcome in mind or maybe two or three of them that you want to drive towards and then say, what are the actions we would need to take? Is it the robustness of our data and in what way? And does it need to connect to other systems or does it need to. Do we need to make sure we've cleaned these aspects of it? Or is it a capabilities that we don't have? Or is it around data security and we would need to ensure we can enable data privacy around these sets of data. I think by having the business objectives in mind, that allows you to then get granular about the actions that you need to take.

Speaker A: Totally. And it's okay. Like if efficiency and productivity is the thing that creates capacity to do the other stuff, like that's okay, but I see too much like, oh, 10% efficiency gains or you know, we're able to run this cycle or this, you know, thing a little bit faster. Uh, so maybe we don't need that headcount. Like that's such, that's such short sighted thinking. Like yes, do the things better and faster to create capacity to get to those loftier goals you probably have.

Speaker B: Yeah, that is such such great um, input and uh, and advice for people. So maybe I'll just take a minute. Um, if, if, if we. Sorry, if listeners, not. I was going to say readers. I don't know if anybody reads the transcripts of our podcast. Maybe they do. Maybe, um, maybe they do. Um, but if listeners would like to find out more, we've also drafted a white paper which um, we'll Include in the link to the podcast, um, it's on mercer.com it's called rewards Revolution. It's been developed, um, based on interviews with senior rewards and HR practitioners around the world. And we've synthesized a number of strategic imperatives that organizations can put in place not just to leverage AI and automation in the rewards practices, but really to position the rewards function as a strategic function and strategic advisor to the organization going forward. So by all means, there's more case studies in there, there's more examples, um, there's links to other tools and assets. So we'll include that, um, in the information for listeners and you know, with that. Jess, I'd really love to thank you for joining us today. Um, I think this has been a great conversation. I know that the clients that we've met have been super interested in this and again, it's a priority for all of them. I don't know if you've got any kind of final thoughts or ideas that you'd want to share with people before we wrap up.

Speaker A: Uh, no, it's been m, my pleasure partnering with you on some of this. And I would say for all of the professionals, uh, who are thinking about innovation and transformation and sort of taking the next step, continue to learn from your peers. That's why these events, sort of these mini conferences or whatever, where you really can get with your domain specific peers because there's so many headlines, there's so much hype out there. If you can really learn from, uh, from your peers and from colleagues, uh, to see what challenges they have, what they're, how they're overcoming them, um, I think that's sort of like the safest, sort of community driven way to understand, you know, how, how to move forward in your own organization.

Speaker B: Perfect. All right, well look, thank you again. Uh, thanks to our listeners who've taken time to listen in today. Um, if you're interested in any of the topics that we talked about, whether it's total rewards or AI or the impact on HR and broader transformation, all of those are available on Mercer.com um, you can find more there on the, on the thinking page on mercer.com Jess and I are both also active on LinkedIn, so you can find out more on our profiles there. And uh, we look forward to sharing, uh, more exciting information with you as we look to our, our following podcasts over the next couple of months. So thanks everyone and have a great day.

Speaker A: Thanks everyone.