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Well, welcome today's New Shape of Work conversation. Today's topic is around the trends that we're seeing in total reward. And I'm absolutely thrilled to welcome back my co-host, Gordon Frost, who's Mercer's global leader in rewards. Gord, it's really nice to have you on the call today and nice to be interviewing you.

Yeah, it's really nice to be back. Thank you for having me. I think there's so much we could talk about, so I'm really excited to have this conversation with you today.

Gord, my worry about this is actually fitting this into 20 minutes. But let's see where we go.

Hoping to do, yeah.

Honestly, I think anybody who works in HR, even if they've got a reward background or a non-reward background, has been swamped with new challenges, whether it's been contending with those raging pay gaps, that we have between new and legacy talent in the post-covid hiring frenzy that we all did.

Then, we had all the fair pay challenges and walkouts, as that cost of living pinch cut a bit deeper than our merit budgets could go. And more recently, we've got the pay transparency laws, sustainability reporting, that I think is just making it even more important to have a very clear sense of what we pay for and making sure that we align to that philosophy.

And I think that is meaning that handling pay conversations well, particularly at this time of year, has just never been more critical. So why don't we cut to the chase. Gord, are we going to have more of the same in 2025, or do you predict that we'll have some new challenges to contend with?

So new challenges makes it. I don't even where to go with that, but I think we'll have a lot more of the same. And I think it will become more challenging, right? Because I think even though organizations have had a little bit of a breather and that rates of attrition have declined a little bit, turnover has not been as big of a challenge, as it was previously because of economic uncertainty and all of the other things that I'm sure your listeners are familiar with, that's not going to last forever.

And that's not going to take the place of the fact that all of the things that you just said are true. We'll see more pay transparency laws. We'll see more information in the marketplace around pay and the relationship between pay and jobs and skills and all of that stuff.

And I think we'll continue to see increasing expectations from both existing employees and new employees to have more information and have more transparency about how they're paid, why they're paid, what they're paid, how that compares to their peers, and how that aligns to the organization's pay philosophy.

So I think all of those things are just going to continue to increase in prominence in the market. And smart employers need to get out ahead of that and have a good story to tell.

Yeah, I agree with you, that comment about increasing expectations. We've been seeing that in global talent trends year over year. Younger and younger generations are getting interested in the total reward proposition. I was just fascinated this year, how many Gen Z's were interested in their retirement payouts.

And I think that's really healthy. And I think, to your point, if you're a company who's been ahead of this curve and is prepared well, it shouldn't really be something to fear. In fact, you'll actually do well out of this increasing climate.

And what I'm thinking about, we are coming to the end of the year, and this is a really busy time for comp professionals. But as we get past this period, how should we be thinking differently? Is there anything that you think executives should really have front of mind, as we close out this year?

Yeah, so that's a great question. And I actually think it's a really, really critical time of year for total rewards professionals and executives because this is the time of year where they're finalizing budgets for the upcoming year. And they're starting to-- well, not even starting, but they're getting deep into the annual pay review and merit cycle.

And often, their annual bonus cycle, if they have incentive plans. But it's the comp season, if you want to call it that, right? And so this is the time of year, where it'll be very visible, the degree to which you're distributing pay adjustments fairly across the organization. Are you taking decisions that are addressing pay gaps or inequities that you have or helping to narrow those?

Are you distributing things like incentive plan dollars or bonus dollars, promotions, performance ratings. All of those kinds of things and the fairness associated with those, I think there's going to become more and more of a microscope on those.

And I fear that this year's merit budgets will be more constrained, right? Because the economic outlook is still a little bit uncertain. Maybe the prior year wasn't as good a year as previous years for some organizations or some industry groups. So I think it'll be the doing more with less.

And given that the budgets are constrained, you really, really, really, really need to be conscious of how you're distributing those dollars. Are you being equitable? Are you aligning to your philosophical goals? And can the decisions that you're making stand up to scrutiny, if you need to be increasingly transparent about this stuff?

Oh, interesting. I think that more with less is going to just continue as a mantra for-- it feels like it's going to be in perpetuity. But it is interesting because you've mentioned executive outlook there, and we've just finished our executive outlook study for 2025. And one of the big concerns of executives is actually delivering on the pay expectations of their employees.

So I think this year, with all the elections going on, I think people are a bit sanguine about it. But I think next year, it's really going to bite. And I heard the most challenge around that, coming from people in the EMEA region, saying we continue to forecast growth in China and India, but certainly, India, where everybody is moving parts of their operations there.

That's just going to make it highly, highly competitive. And of course, it might change, given some of the right shoring strategies that might adjust, post the election, but it's going to be an interesting one. The other comment I heard had been forecasting an increase in M&A.

And of course, when you have M&A, you inherit some of the pay decisions of the legacy company that you've acquired. And that can also put pressure on some of those budgets, particularly if that hasn't been as well executed or well managed, as the receiving company.

Absolutely. And even in those situations, as many of your listeners are aware, if they've been through this, you may have two organizations that have had very different pay philosophies in the past, which is normal. So you may be inheriting a set of jobs or a pay structure or pay philosophy that may be very different from the one that you've got.

And so the integration of those becomes even more critical, and I think that a lot of organizations maybe have not been on top of those types of integrations in the past. And so you may be sitting on an organization where you have different employee populations that are paid, slightly different from differently from each other or don't have a consistent pay philosophy.

Or that's what's driving some of the pay inequities or pay gaps in your organization. And previously, when there was less of a drive towards transparency and less regulation around pay gap analysis and pay fairness, could kind of live with those inequities. And that's not going to be the case anymore.

Organizations are going to have to be much more on top of where do we stand, relative to our pay practices? Where do we have inequities internally? Where do we have instances of individuals or groups of individuals that are not paid in a way that's consistent with our stated pay philosophy?

And what are we going to do to-- what are we going to do to address those? Because you do still have time. I mean, I think that's the good news, is that organizations do still have a little bit of time to address these types of issues and make some good progress on achieving better and more equitable outcomes and more consistent practices.

But they don't have a lot, right? And as we said earlier, expectations continue to increase. The legislative landscape is not going to go backwards. It's going to continue to move forwards. And so organizations, I feel like, 2025 really needs to be the year, where people get their house in order, get on top of their issues, really clean up the issues or the pay inefficiencies or the pay gaps they might have.

Because they won't have a lot of time after that to be ready for the EU pay directive, which comes into place at the end of 2026 or 2027 or other pay transparency laws around the world.

And of course, if it's 2026, it's got to be on the preceding year's GAAP. So you're absolutely right. I think the time now is to focus on. That's really good advice for HR. Is there anything else that HR can be thinking about? As I'm listening to you talk there, particularly as you might inherit leaders from different firms, there's a lot that they need to maybe do a better job of moving forward.

Yeah, it's interesting because, on that point-- and I know we'll talk about this, or everybody talks about this-- is the impact of AI. And I think one of the things that we're hearing some feedback on-- and I don't know if you've heard this in your executive survey, I'd be interested to hear the results of that.

People had really, really high hopes for AI to increase productivity, change the way work gets done, really, really have positive impacts on their organizations over the last 12 months. And I don't think they've seen the full value of that. I think it hasn't fully met their expectations in a number of ways.

And I think AI's implications in HR and in total rewards is one of those areas where we've had a lot of hype and a lot of positive anticipation, but not a lot of progress has been made yet. And I think there's a variety of reasons for that.

I think one is the fact that you're using confidential data, so there's data privacy rules around the world, and organizations have gotten bogged down in that, or are trying to balance the need to make sure that they're protecting private data and holding that in a confidential way, but also trying to make some progress with leveraging the benefits of AI and automation.

And I think one of the things that they're finding-- and it really aligns to the prior conversation around transparency and fairness-- is there's problems in their data. And if there's inequities in your data, or your data is not robust, in that, for instance, if your job definitions are not up to date, or if your job leveling is not aligned across the organization, or if your page structures are inconsistent across different employee groups, a lot of very foundational stuff.

If that hasn't been addressed, then it's very difficult to use that employee data in any AI model and get robust outcomes. And so I think people are realizing that they actually need to go back and do some of the more mundane but foundational work around their data integrity and the robustness of their data before they can even think about, what kind of AI models or AI tools or automation might we be able to benefit from.

So I think that's the other thing that people will need to spend more time on. And it's like when you buy the new house, and you want the beautiful kitchen. And you want the luxurious bathroom, and you want the fancy garden, but what you really need to do is fix the foundation.

People are going to need to-- and it's not beautiful, and it's not glamorous, and it's not exciting, but you got to do it, or else, you're not going to get to have the benefits of all of the other exciting stuff that people want.

Oh, Gord. You're such a killjoy. I just want to jump to all the sexy things that I can do with AI. You are right, most people have not seen the return on it, so all of-- we did a comparison between at the beginning of the year, what projects people wanted and what percentage of productivity improvement they would deliver.

And then at the end of the year, we looked at how many of them to come to fruition. And you're right, many of them have been paused, and paused for good reason, because we couldn't reliably or defensively scale them, or they've been paused because the true cost of doing them has made them prohibitive, or because they found a different way of solving the challenge.

So I think there has been that. But on the point about AI, I always got more excited about AI's opportunity in total reward to improve prediction, what strategies would retain which workers or personalization, a total reward package that's just focused on me. But I haven't seen as many innovations on that, that I would like to-- I wonder if you've seen some innovations in that area that got you excited.

Yeah. So I agree with you. I think those are really exciting, right? And I think that the potential for AI to deliver on true personalization, much better prediction and predictive analytics, like that's valuable. And I do think that that's possible as well. I haven't seen a lot of organizations that have truly achieved the benefit of that yet.

I see a lot that are working on it. And so that's where I think we'll start to see more organizations that start to get that right over the coming years. And I think that will also show us that they'll start to reap the benefits of that, and then they'll start to push further ahead.

And you'll see more differentiation between the winners and the losers, or those who are really reaping the benefits of AI and automation and better predictive analytics and more personalized total reward solutions, and more engaged employees at the end of the day, and then others who are lagging behind.

And so I do think people need to get on top of that. I don't think it's fully there yet. And I think going back to the reasons why is, they're realizing that their underlying data is not good enough. So you want to do the predictive analytics, but then, you realize that all of our jobs aren't coded to the right job codes.

And so we really don't who's in the same job versus who's in a different job, or what level their job is at. And so until you've addressed again, a number of those underlying issues, it's hard to get the benefits. But I think once you do, it will really start to become transformative.

Yeah. So I think what I'm hearing from you is you've got to do the hard work before you can do the fun work. But I do know that AI has been fantastic in helping people with flagging to managers those pay differentials, the pay AI. Just want to have you, though, one trend that we did see this year was more people wanting to bring skills into the comp agenda.

I think AI has a place to play in that space. Are you seeing many people get a handle on skills and whether the skills are going up and down over three months and trying to make some more informed decisions around pay, that does link them to their, maybe, future work skills strategy?

Yeah, so that's a great area. And I am starting to see some really good traction in that area. And I think that's an area where we'll continue to see innovation. And so, for instance, the idea of how do we start to get a better handle of the skills that we need in our organization and how those align to the jobs that we might have in our job architecture or our job library.

That was a huge undertaking for a lot of organizations, especially if they have thousands or maybe tens of thousands of jobs across their organization. AI can really help with that. So there's AI models now that you can use, and we're starting to experiment with those.

As you know, at Mercer, that can help organizations say, well, if these are the jobs that I've got, what are the jobs-- what are the skills that are most likely to align to these jobs from a market perspective? And then which ones are the ones that I actually need most in my organization?

That was a very large task, previously, for a lot of organizations, and so they may not have embarked on it. But now, with AI, they can do that a lot more quickly. They can use data from external market data to help drive those decisions.

They can use Mercer skill library or other resources that we or others could provide to help them do that a lot more quickly. So it reduces the investment that's required, and then it makes the ROI much more attractive. So we are starting to see some innovation there.

And then the other thing that I would predict is that, that can help to then justify why you may have differences in your pay practices, between individuals that are in similar roles. But you may have a reason for paying one differently than the other, and it may be due to their level of skill that they have. So I think that will be an area that we'll see more innovation in as well.

Yeah. Well, I look forward to that. Because I think you're right, it is the missing piece of the equation, but it always felt like a pretty large, daunting task. And I love the combination now of combining the valid data that's on fact with the scrape data, so you can make more informed decisions.

We also, on this podcast, don't just have HR technical folk, like you and I, who probably have enjoyed this last bit, where we've nerded out, but we also have just either young professionals who listen to this or anyone who's in a managerial leadership role. And as we do, consider that we're heading to the festive season.

Everybody steps back reflects on, am I in the right job? Am I being paid the right wages for this job? Could next year look different? A lot of them will be thinking about, how do I negotiate the best deal for myself in the new year? So I'm curious, as a global expert, what advice would you give? I'm personally curious as well. So, yeah, how can we all be thinking differently about entering those conversations in a healthy way?

Yeah. So I think the answer is data. And so back to the geeking out again, I think this is the answer, both for employees and managers and employers and total rewards is, everybody needs to realize that the availability of data is only going to increase. So data about jobs, about the rates of pay for those jobs, around an organization's pay philosophy, around the competitive marketplace.

All of that data is going to become more and more available. How you use it becomes really important. And so as we all know, we all have access to data through all kinds of means every day now, that we never had before. You need to be savvy about how you use it.

So you don't just believe the first data set you see, or the first data point that someone sees, or what somebody might post on social media or something like that. But using those different sources, using a multitude of different sources to try and get a better understanding of what is the real worth of my job in the market, what would it what would it be worth, if I were to be on the market.

You may find information about, if my organization is trying to fill similar roles in the market today, are they publishing pay ranges for my job externally? Are competitors publishing pay ranges for my job externally? That kind of data will become more and more available to individuals, which will help them negotiate or help them have more knowledgeable discussions with their manager.

Organizations will start to publish more and more data on their own websites or intranets or postings around their pay philosophies, the pay positioning that they may offer for certain jobs, more information around how pay gets delivered within their organization. So I think there'll be more information from employers as well, that employees can use and leverage. And I think that puts a greater onus on everybody to really upskill themselves.

With that data, I think it puts a greater onus-- going back to the employer perspective again-- on really ensuring that you're arming your managers, with the right information to have good conversations, when it comes to year end or the annual pay review cycle and to really think about, how you're communicating with your employee population, what data they might have available to them externally, what data you might want to provide to them.

So that you can really start to own the narrative in a much more proactive way than you may have in the past as an employer. So I think that that's another area we'll see a lot of movement on over the coming years. As more data becomes available, employees will become more savvy. Employers will have to become more comprehensive in how they enable people.

It will require managers to get better at how they have these conversations. Everyone will need to raise their game around the discussions about pay, the discussions around promotion, the discussions around pay philosophy, all of that kind of stuff.

It's so interesting, you talk there about multiple data sources, which, of course, in the world of assessment that I'm more familiar with, you need that to make good, valid decisions. And it's just the same here. Make sure you've gone out there and looked at lots of different sources in order to make your case.

And I think skills data as well is going to rise in importance. So being equipped with that in your back pocket, I think, for both parties is really good. We are coming to the end of our time, so maybe one more question. Gord, you've just spent the last few months in conference season, at lots of different reward conferences around the world.

I don't about you, but I found this year, particularly with the AI discussion, really, really vibrant. But it also has thrown up just some really interesting questions. I think I shared this with you before. I was at the Mercer HR Leaders Conference in Singapore. It was back in October.

As everyone uses those sliders to warm the audience up before they get started, there was some talking about pay transparency. And they said, have a look at the people around your table and put your hand up if you're interested in hearing what that person's paid. Of course, everyone's hand goes up.

And then they said, put your hand down if you're not comfortable sharing it. And they use it just as an opener to talk about what do we mean by pay transparency and what are the pros and cons of that. But it really did linger, and a lot of people talked about it to say, well, obviously, what we mean by transparency is very different things.

And how do we communicate what is appropriate and what you shouldn't be sharing. It just, I think, opened our minds to just some of the nutty challenges that we've got to address, but also, if we get it wrong, some of the unintended consequences that can happen. And are we prepared for that?

Do we have a budget to address some of that, if it does highlight some gaps. I'm curious from your travels, was there anything that stuck in your mind? Was there any client presentation that you thought, that's a little bit different way of thinking about it. Or was there any blind spots that you walked away from?

Well, you know what, I think that instance that you just described of the putting up your hand of what if you'd like to see what your neighbor makes, but then not wanting to do it yourself, I think that's a great example. And it's just one of the indications of the challenges that we'll face from a pay transparency perspective and a fairness perspective.

But if I go in a completely different direction, going back to the discussion of AI we had a few minutes ago, one of the things that I thought was great at some of the conferences that I went to in my travels through the fall, was for the organizations that are really starting to make good progress on AI.

It's not about buying technology or implementing technology. It's really about upskilling your team and building competence in your team around the different potential use cases and having more, I would call, digital fluency. And so the companies that I thought were doing that really well were identifying very narrow use cases where they could do a test and learn.

And so they were taking an approach of saying, we're not going to deploy this massive AI tool across our whole organization. We're going to pick just one area, where we think it could really help, or where we've got a problem that we think maybe AI could solve.

And then we're going to put a small team together, and we're going to see if we can solve it or solve it, or develop a different approach, or whatever the case may be. And we're going to use that to learn and build skill and build confidence. And then, we'll take on maybe another one or another two to grow from there.

And I thought-- I mean, that might seem relatively basic, but I think that it's that change in mindset of AI, I think, will change the world over the longer term. But we don't need to look for these Big Bang applications that are going to change everything overnight.

If we even look for small applications that could improve an internal process, or help us tackle a project that would have been too difficult to tackle otherwise, or address another challenge that we may have in our organization, then we can build small wins that help to position us for the bigger wins in the future. And that just seemed like a really, really smart approach to me.

I agree with you, and I do think, as the full costs of becoming more transparent, you need to have the business case. And I think that's a trend we've seen more broadly. We've moved away from the incremental productivity gains to real problem statements that we're trying to solve.

In fact, I think many companies are now governing on the problem statements, and I think that's really pushing us forward. I also just love the fact that HR is learning together. I mean, generative AI, in particular, is new for all of us. And so it's very lively. You've got the junior person, the most senior person, learning together.

You've been part of some of those art of the possible workshops we've run with whole teams. And I love that because, I don't know, for me, it's a once in a lifetime opportunity for us all to be innovating with an absolutely, the same level playing field, because no one's got experience from a previous company of how it's been scaled or done well, which is quite exciting. Gord, I know we could talk for another 30 minutes, but I'm going to have to close this there.

We'll talk again. Yeah.

We will definitely talk again. In fact-- be careful what you ask for-- we will also be talking again at the beginning of January, because what I thought we would do, Gord, then, is we'll switch chairs. We've got global talent trends out. We've got the executive outlook out.

I'll be deep in some of those trends, and we'll have a conversation about what are the trends that really took hold, what are the ones that have just been really challenging to make progress on, and how are people thinking about rewards in 2025. So we'll come back to this topic then.

Yeah.

Thank you, everybody. Thank you, everybody, who's taking time out to join us today and listening to our conversation. I hope you found it interesting. If there's any of the topics that Gord's touched on today, AI, total rewards, do check out our Thinking Page on mercer.com.

Gord, I know you've contributed to a lot of content on there, that people can get a read of, or any of the other topics that we have on our New Shape of Work Podcast series, where we often deep dive on client innovations. Looking forward to kicking off with you in 2025. Gord, thank you so much for sharing your thoughts and wisdom today. I've certainly learned a lot, and it's been great.

I've really enjoyed it too. Have a great day.

Thank you. Thanks, everyone, for joining, and have a good festive season. We'll look forward to kicking it all off again in January. Thanks, everyone.

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