James A. Seechurn: Hello, welcome to another episode of the as discussed podcast. Today I'm joined by my longtime friend and colleague, Stefan Gaertner Stefan and I work together at Aon and we collaborated on a lot of projects and Stefan is very much the guru when it comes to pay equity and pay transparency has been very much ahead of the curve since before there was even a curve in this space. currently is a senior director at Syndio and has a PhD in human resources and obviously Syndio is a company heavily attached to pay fairness, pay transparency and pay equity. He's originally from Germany and he moved over to the US 30 years ago. In this discussion, we talked a lot about obviously the changing landscape of pay equity legislation and the slight differences between the EU approach and some of the parts of the rest of the world, as well as the US approach and how different states have approached pay equity differently. And then we also talked a bit about the infrastructure around pay equity, namely the job architecture approaches and different ways of measuring the value of work, differences between point architecture methodology, for example, and whole job. classification, which is what most people in the US will be familiar with. then we talked a bit about the ⁓ of that analysis and the tension between the subjectivity and the objectivity of that. We got a little bit sidetracked and talked a bit more about the challenges presented by modern companies terms of equity ownership and how that gets distributed. And finally, of course, the obligatory question around how AI is going to impact all of this. So without further ado, I thought this was a very, very interesting discussion. I highly recommend listening to the whole thing if you are in the area of pay equity. ⁓ And I give you Stefan Gertner. So ⁓ start. ⁓ I just to your thoughts really broadly on the current state of equity and pay fairness. And you've seen ⁓ the world move from a state with no legislation to some legislation to now seemingly endless legislation. ⁓ So what's ⁓ changing in the world pay equity now ⁓ and ⁓ how are companies adapting to Stefan Gaertner: ⁓ Well, it's more a story or a tale of two regions. ⁓ In the United States, I mean, that's kind of the irony of it. The first real wave of pay equity work came about in around 2017. And that was at a time when the federal government was really not pro ⁓ D &I. ⁓ So at the end of the day, what happened was the states took over and the states were driving a lot of the work that happened in pay equity at the time. And similar things are happening right now too. ⁓ I would say that efforts around core DE &I, ⁓ I think they are kind of receding. But it's generally acknowledged that pay equity is probably not part of that. It's generally acknowledged that equal pay for equal work is still a concern in the United States as well. And that equal pay for equal work is sort of baseline fairness. And that doesn't have anything to do with giving somebody preferential treatment or anything. So that's kind of... the situation in United States. So I would basically say as a result, the pay equity business is not gaining major momentum, but it's more like steady as it goes. ⁓ There's not a lot of gain, there's not a lot of loss. ⁓ It's still something that is on people's mind. Obviously in Europe, the situation is very different. James A. Seechurn: Mm-hmm. Stefan Gaertner: There's the European Pay Transparency Directive. ⁓ And this year, 2016, by June, the organizations have to produce ⁓ what's called Right to Information reports. So whenever employees are asking, ⁓ organizations will have to provide them with pay information. Their own, obviously, but also the average pay for men and for women. ⁓ James A. Seechurn: Mm-hmm. Hmm. Stefan Gaertner: working in what is called the same category of workers. ⁓ James A. Seechurn: Okay, so that's not just across the organization, that's for the role or the broad grouping of roles that they're in. So it's actually quite granular, that information. Stefan Gaertner: Yeah, and without getting lost in details, how you define categories, that's an art and a science right there as well. ⁓ But ultimately, that's really the right that comes in effect in June. And the way how countries are transposing these laws right now, it seems that at least some organizations and some countries will have to comply with that by June, others at the latest by January 2027. James A. Seechurn: Mmm. Stefan Gaertner: So, and then of course in 2027 organizations have to produce reports that look at ⁓ pay fairness by men and for women for the entire organization. And these reports will cover the calendar year 2026, which is another reason why there's so much activity in Europe. If you don't have these reports in order by 2027, if you don't get your data right by 2026, It might be that by 2027, we have to pay a lot of back pay. So there's a lot of stuff going on in Europe. ⁓ Asia, similar in the United States, more steady as it goes. So yeah. James A. Seechurn: Yeah, more locally, local laws I imagine as well. Stefan Gaertner: local laws have some interest, especially in the Pacific area, New Zealand and Australia, there's a lot of interest in equal pay for equal work. But other ⁓ regions, ⁓ Singapore, Japan, Korea, there's still activity going on. So yeah, I mean, it's a big question you are asking. ⁓ James A. Seechurn: Mm-hmm. feel like the... Yeah, well, just to come look at some of the trends and I feel like I imagine a lot of places a lot of countries are looking at the US and the EU to see what happens because this is the kind of you know, this is new new ground that we're covering here. So I imagine a lot of countries will follow suit once they've had a chance to see where the legislation lands and how companies interpret that. So there's a lot of interested parties, I think looking at this legislation. But if we look at the EU and the US To your point, slightly different paces and slightly different approaches. To me, it feels like the US has very much led with a pay transparency will solve the issue type mentality. Whereas the EU has led with pay transparency as part of it, but there's a few other things that companies need to do as well with a broader goal of pay equity being sold. Is that a fair characterization or is that just something I've made up based on these headlines? Stefan Gaertner: Yeah. No, it's a fair characterization. I would add though that regulatory is really not the only reason why we are talking about pay transparency today. The other reason is simply technology and a new generation that is just more open to sharing. So if you wanted to learn a little bit more about fair pay, you can go to Salary.com you can go to Glassdoor. ⁓ There's just a lot more information out there and that drives pay transparency. People are just much more aware of how their pay compares with others. And when I talk to clients, I basically realize and I also tell them that if you do this right, it's not just a compliance play. It's actually something that you do that will ultimately benefit your business. You set pay that is fairer. You rely more on data and analytics to get your pay right. And as a result, you're basically getting more bang for your buck when it comes to compensation dollars. And I think from that, if you approach the laws, the regulation in Europe and in the United States from that angle, you'll probably do something that is good for the government, so to speak. but you also do something that's good for you. And maybe that's the best way to think about that. James A. Seechurn: Yeah, I think that it is. And I think when you see the companies that do embrace transparency, I always use the pitchforks analogy. There's always an assumption that all these pitchforks will come out after HR. HR kind of braces itself, all the chaos that will ensue. And in my experience, that doesn't really happen. You tend to get just honest and open conversations. There'll be one or two pitchforks, but they're normally reasonable pitchforks that they probably deserve to be poking people with, right? Because they are the gist of it. Stefan Gaertner: Now. James A. Seechurn: unfair pay scenarios that probably should be addressed, which is kind of the whole point. And in the long run, that's a good thing. Stefan Gaertner: When it comes to pay transparency, it's good to be prepared. But experience in many states in the United States, including California, is teaching us that, yeah, to your point, pitchforks are not coming out. It's not going to be so bad. So curiously, so one of the most concerning regulations around pay transparency is in fact data sharing, where people come and then you have to tell them, James A. Seechurn: Mm-hmm. Alright. Yeah. you Stefan Gaertner: Okay, what's your pay level? What's your range? What do others get? That has always been the biggest concern. To date, there are laws like this out there in some states, including California. And there's a law like that out there, interestingly, in Germany. And guess what? No pitchforks came out. Organizations were prepared, rightfully so, maybe even overprepared, only to learn that A, not that many employees were asking, and B, if they were asking, it became a more or less cordial conversation in most cases. Okay, there are always exceptions. But in most cases, it became a cordial conversation. It became more about, okay, what's fair pay? And maybe some employees got a pay adjustment, but there was not really an avalanche of activity just yet. Yeah. James A. Seechurn: All Hmm. Yeah. I think it is reassuring for anyone that's on the brink of pay transparency that yes, it is a bit of a compliance headache, but it's a good headache to have as the socially and morally correct headache to be enduring, but also it's probably not going to be as bad as you might imagine it's going to be. Stefan Gaertner: Yeah, that's usually the message that I send. And I mean, the flip side of the message is how risk adverse are you going to be? And I don't want to point fingers at organizations or institutions who basically tell their clients to be overprepared because at the end of the day, I could be totally wrong. And maybe... James A. Seechurn: Right, yeah. Alright. Stefan Gaertner: Maybe there will be another launch and all hell breaks loose in June. And ultimately organizations just have to ask themselves, where do they want to position themselves on that risk continuum? Do they want to be over prepared, run a point-based job evaluation system, ⁓ going into extensive communication mode, doing preventive pay adjustments and all that, or do they just want to see go into a wait and see approach? James A. Seechurn: Mm-hmm. Hmm. Stefan Gaertner: to the minimum that is required. And ultimately, that's something that every organization, every compensation leader has to decide on her own or his own. James A. Seechurn: Absolutely. And you made another point as well earlier that there is a there is like part of the reason we do it is for the for the state and for the for the government almost. One of the reasons that the EU adopted pay transparency as our cause and analysis that showed that if you fully incentivized a hitherto under incentivized portion of the workforce, there will be a huge amount of economic benefit essentially because you've got a whole group of people not really participating in the workforce. because their pay is unfair and they don't see the value in doing that. So you've got an economic argument for fair pay. You've got a social and moral argument. You've got a competitive argument for it as well. The only counter argument really is the nuisance that it creates by moving towards and adopting these processes. So I think there's a huge amount of fear of change is really what it comes down to, especially in HR. HR likes things stable and continuous and any change is kind of perceived as almost like a threat to that role. Stefan Gaertner: Yeah, mean, James, mean, it's a really interesting conversation to have, which is all the good stuff that comes from that. ⁓ The openness, the conversations, the fairness of the pay. But I think there are potentially two things that in my mind are probably driving the fear here. ⁓ The first one is where James A. Seechurn: Mm. Stefan Gaertner: Where does this go if you're it to the end? ⁓ And I've talked about that before at conferences. I call that concept the pay equation because ultimately, if everybody knows what their pay is, if everybody knows what everybody else's pay is, if you really have to start managing pay gaps to 0 % And that's where pay gaps by gender, where that gets us is really to a pay equation. It gets us to a point where every employee would have to know precisely how much money they're going to have to make based on their level, their experience, their skill mix, their performance history, et cetera. So I think that's one thing that drives people's fear. Are we ready for the pay equation? James A. Seechurn: Hmm. Stefan Gaertner: And the other one, and we can talk about that too, the other one that I think when I'm sometimes a little bit wondering about where this is all going, is that notion about equal pay for work of equal value. You've seen that in Canada, you've seen that in Europe. And the notion is really quite interesting. It's basically saying that, okay, over the years, ⁓ James A. Seechurn: Mm-hmm. Stefan Gaertner: The biggest chunk of pay discrimination against women and minorities is not about what they're being paid for the job, but really what their job is being paid. So if you are working in HR versus in engineering, for example, we all know that you might be at the same level, but HR employees are making maybe 10 % or so less than engineering employees. And some people who are talking about fairness, James A. Seechurn: Mm-hmm. Yeah. Stefan Gaertner: They're basically saying the only reason why this is existing is because HR is more female dominated as a job and engineering is more male dominated as a job. I am concerned about that concept, not because I don't buy into it. I'm more concerned about it because I don't really know where that ends up. So we all know labor markets. I've certainly as a born in Germany, ⁓ James A. Seechurn: Mmm. Stefan Gaertner: there's a different approach there when it comes to markets still. But in the United States, we believe in labor markets. And I'm basically just saying, if you artificially start to raise the pay for certain jobs relative to others, that would have repercussions on the labor market. So maybe you will have more people trying to crowd into HR jobs and crowd out of engineering jobs. And the labor market is not really balancing this out again because there are regulations that try to interfere with that. So I don't really know where this would go at the end of the day, but that's the second piece that worries me a little bit when I think about these regulations. James A. Seechurn: It's an interesting one, isn't it? Because the regulations exist because companies essentially failed to do what they said they would. So for years, you had all these companies saying, pay equity, we value people, we treat people fairly, we pay. They didn't actually do anything about that. They didn't do their own pay analyses. They weren't transparent. And eventually, legislation came in to solve them. But of course, that legislation always has a kind of backfire effect, like the second order effects they're not sure about. One of which is that, the interpretation of fair pay for fair work. And to your point, potentially artificially interfering with the labor market. But part of me is like, was kind of a problem of companies are making. They had every opportunity to do this themselves by using fair pay without the legislative burden on top of it. And they chose not to do that. So I guess we're just part of the almost at the beginning of a journey, aren't we? With that to kind of figure out what that legislation does to the labor markets and how it gets interpreted. And then we have to iterate on that legislation. Stefan Gaertner: Thank you Yeah, that's right. mean, the Canadians have a similar law in place, comparative worth laws in Quebec. James A. Seechurn: in response to it. Mm. Stefan Gaertner: and in Ontario, especially in Quebec, they have been basically doing pay equity type reports where you have to adjust pay levels for work that is female dominated. If it turns out that these jobs that are female dominated make less than jobs that are male dominated. I don't think I've seen much shifts in the labor markets. On the other hand, I do know that clients are going through this James A. Seechurn: Hmm. Stefan Gaertner: perceive this as a burden, ⁓ as an administrative burden, as a financial burden. And while I'm working on these and I feel that there is something good that comes out of that, there's also a lot where you keep wondering, does this really make a difference for anyone? Is that really helping anyone? And I think the key problem here is, and I don't mean to become too philosophical here, the meaning, the key question is, James A. Seechurn: Yeah. ⁓ please do. Stefan Gaertner: Can you really evaluate the value of work independent of the value that is determined on markets? And how hard is that going to be? Yes, I know there's job evaluations and their point systems. Of course, they are all somewhat still trying to be anchored in labor markets and in real values. Without that, mean, how do you really determine James A. Seechurn: Mm-hmm. Stefan Gaertner: the value of a job. James A. Seechurn: Well, you know, it's an interesting point because the labor markets don't tell you the value of the job. They tell you what everyone else thinks the value of the job is, right? So it's not telling you, it's not really assessing the value to the business of the work that a role is doing. It's telling you what everyone else assumes it to be. And I do think there's, it's obviously important market data because it helps you be competitive, but at the same time, it hasn't helped with the retention turnover and things like the greater in many ways, the more market data there's been in existence. the worst tenure has been in the markets. The tech sector in the US is well saturated with good quality market data, yet has some of the shortest term ever around. So if good quality market data was really keeping people, you might see that impacted, but you don't. So it is a really interesting point, isn't it? It starts to really come back to this fundamental question of how do you value a job and have we maybe over-weighted the market data or aspects of that value in the past? Stefan Gaertner: Yeah, how do you do it? We could probably talk about point-based job variation systems, which in theory at least are the most rigorous ways to determine value. But if you basically argue that the labor market James A. Seechurn: In theory. Stefan Gaertner: is how people perceive the value of jobs. ⁓ I would say the same is probably true for point-based job evaluation systems, because somebody has to make a judgment call. ⁓ Okay, we define criteria, ⁓ but again, what criteria do you define or choose to use? That's a value, that's subjective. You weight those criteria and you can agree on that. James A. Seechurn: Mm-hmm. That's a judgment. ⁓ Stefan Gaertner: but that's also subjective. And then you apply points to each of those criteria. And I think that's also subjective. So. James A. Seechurn: So actually, I want to get into this. I want to get into these methodologies because they're coming much more into light. And I think maybe even people in the US are having to become more familiar with things like point factors than they ever were before, didn't really, particularly in the tech sector and in some of the tech and life science and things like that, point factor hasn't been as prevalent. But maybe could you just talk us through what point factor methodology is and how that differs from other leveling methodologies then? people listening might be more familiar with perhaps from a US perspective. Stefan Gaertner: ⁓ yeah, and it's nice that you're asking because I know you know about as much, you know as much about that as I do as a lifelong compensation consultant. But here's the high level, here's the high level ⁓ summary of what that is. So in organizations, you're basically one of the key tasks that you have because you need to pay people. James A. Seechurn: Yeah. Stefan Gaertner: with very different skill sets and with very different jobs. You want to create some sort of structure to help set pay. And yes, that structure is subjective no matter what you do. ⁓ The easiest approach is basically to develop levels. ⁓ A level would be, for example, something like professional one versus professional two. or an executive or managerial three. So levels of jobs that you put in a hierarchy from top to bottom. And then for each of these levels, you would define basically what these levels represent. Something, do you manage people at this level? ⁓ What kind of responsibilities do you have? What kind of decision authority do you have into each of these? And these levels... These descriptions are typically referred to as level guides. And the most basic of all approaches is to create a level system like that, develop level guides, and then look at the hundreds or thousands of different jobs in your organizations and see with managers and typically compensation consultants how you basically slot these into all of these different levels. Very subjective. Yeah. James A. Seechurn: And that's kind of job matching. Yeah, that's like the job matching process that many tech companies will be familiar with how painful that can be at times. Stefan Gaertner: Yeah. And then you go back and to kind of seeing, okay, what are these levels? And then you typically also work with compensation, consulting firms to see these levels, how do they correspond to levels in the market so that you can price these jobs and saying, okay, this level that we described in that way is typically what you would call a professional number three. What do you know? Radford has a professional number three and they have a market range that comes with it. So you have a structure that would allow you to roughly compare jobs in HR versus finance, versus legal, versus engineering. And then through the market data, you basically can anchor them so that you can price them out and pay them. So that's, I would say still most organizations are doing it that way. Point-based job evaluation systems are not that new. ⁓ They came about maybe tell me 40 years ago when he did this first. So the idea was, hey, maybe we can funnel the judgment a little better. So rather than grouping all jobs into these levels, maybe we can analyze each job at once, one by one, and start evaluating them. on a bunch of dimensions. And I mentioned a few of these dimensions, responsibility, effort, working conditions, ⁓ things like supervisory ⁓ requirements, ⁓ budget, authority, educational attainment, required skills, et cetera. So they basically, most of these systems, many consulting firms have one today. ⁓ You basically identify these dimensions. How many do you want to use? Typically it's seven, six, eight, five, depending on the system you're using. And then somebody goes in, a subject matter expert, somebody who knows the job really well, typically with a consultant to evaluate each job on each of these dimensions on a point scale, maybe from one to seven or one to five, depending on the system. And then on the way how you provide these points, you basically then weight them. So maybe you decide responsibility needed to be weighted much more than working conditions or effort. And then we have a total number of points coming out of that. And then you group these jobs by points. It's really that simple at that point, a job that has somewhere between 50 and 60 points goes in level X and one that has between 45 and 50 points goes into level Y and so forth. The result is pretty. James A. Seechurn: But those jobs, those could be two jobs from two different functions as well, couldn't they? So all the jobs are kind of on a sequential low to high scale. So you will see your HR jobs lower than your engineering equivalence, right? Yeah. Stefan Gaertner: Yeah, that's right. So this was my quick summary into the wonderful world of job evaluations and how job architectures are being made. It's a really big business, as a matter of fact. ⁓ Consulting firms are doing this, organizations have experts in-house. It's usually a very complex project because most large organizations, have thousands of jobs that you have to look into. So yeah, but as I said at the beginning, James A. Seechurn: Yeah. Yeah. Stefan Gaertner: ⁓ It's all subjective still. James A. Seechurn: So either way, just because you're assigning points doesn't make it any more objective. It might feel objective because you can see numbers, but it's still mostly the same criteria from the leveling guide in the first example to the point factor methodology in the second example, right? Stefan Gaertner: Yeah, and I would say that there are some organizations that focus on point-based job evaluation who would basically disagree very much with that statement. And I want to soften it a little bit. I think in a point-based job evaluation system, it's still subjective, but you have something to hang your head on. At least you have a certain level of analytical depth that you can apply to support your judgment. James A. Seechurn: Hmm. Mm-hmm. Mm-hmm. Stefan Gaertner: As a result, I think most people would agree, point-based job evaluation systems are ⁓ more defensible. ⁓ The disadvantage, as most people would see it, is they are less flexible and they are also administratively more demanding because they more costly. Whenever there's a new job, you have to ⁓ go through the whole process and apply point systems. And you know, maybe AI is going to make this a little bit easier down the road, but yeah, that's the positives and negatives. So yeah, but going back to our earlier conversation. ⁓ So in one situation, you basically have the labor market that basically supply and demand that balances each other out. If there's a job that is overpaid over time, you have more people. James A. Seechurn: Yeah. ⁓ Hmm. Stefan Gaertner: taking their job and that balances out the labor markets. And that's subjective because every player certainly applies their own concept of value to it. On the other hand, you have more artificial planning mechanisms, leveling and point-based job evaluation systems. They apply different things. They do different things to achieve similar results with different philosophies. James A. Seechurn: Hmm. Stefan Gaertner: So, but it doesn't, in my mind, doesn't really mean that one is subjective and the other isn't. They probably are both. Yeah. James A. Seechurn: Right. So and I'm interested in your thoughts on this. feel like in the UK, maybe the rest of Europe, when I speak to my my friends in the HR profession, I feel like there's a sense that that job evaluation, that numerical version of that process is more defensible in the era of pay equity and given the legislation, equal pay for equal work type legislation, you've got something that you can point at and say, well, look, we've looked at that. Do you agree with that, or do you think that that point factor methodology is necessary? And if so, might we see more of that? Stefan Gaertner: Well, from a regulatory perspective, there's nothing in the EU pay transparency directive that would demand from anyone to do a point based job evaluation system. ⁓ So that's one thing. And then The other thing is, I mean, think about existing processes for setting pay. Most organization that I am aware of currently don't have a point-based job evaluation system, but they have a leveling system. They have their grades. Many of these, many pay levels, many European countries are regulated through union management contracts. And they likewise very rarely are based on very deep analytical point-based job evaluation systems. So the regulator doesn't really demand point-based job evaluation systems. And I really think if they did, the cost, the confusion, the chaos that would erupt if tens of thousands of organizations in Europe all of a sudden have to go with a point-based job evaluation system. would be very much self-defeating. So I don't think that was really the intent and I don't think that's going to happen either. ⁓ But that doesn't mean that if an organization doesn't see the... So if an organization sees the value of it, ⁓ they should certainly continue to consider it. But I really wouldn't do it just because of the EU-Pay Transparency Directive. Yeah. James A. Seechurn: Yeah. Mm-hmm. which I do feel is something that some organizations and some like commentators have hinted at that they ought to shift to it. So you wouldn't agree with that. I'm on your side of this and neither one is more defensible than the other. I just wonder if it means that if you don't go with point factor, you do need to be maybe better documented in that job matching process because typically that job matching process is more of a conversation almost than the point factor approach. Stefan Gaertner: So, thank you. ⁓ and don't actually break the silence. Over. Yeah, you would basically have to do a better job to at least ⁓ outline what the process was. Who was the subject matter expert, who reviewed it? You would basically have to show your leveling guide. You have to be prepared to show the union, the regulator, or most importantly, the employee, why is the job that you're doing correctly leveled into P3 and P4? ⁓ That's really what you need to do. And again, you don't need a point-based job evaluation system for that. You just need to do your homework. James A. Seechurn: Yeah, that makes sense. So I want to move on. I ⁓ wrote a post the other day on LinkedIn, somewhat deliberately inflammatory, to suggest that in the era of pay equity, we might not need salary bands anymore. So regardless of whether you're using point factor or whole job methodology or job groupings or anything like that, my point was that if you know that everyone's doing or a group of people are doing the same job, The salary band once existed to allow room for negotiation and high and low performance and things. But the data shows us that for the most part, that's plenty of room for inherent bias to operate and ⁓ fundamental lack of understanding about the difference in performance because it is very difficult to establish differences in performance in tightly net collaborative groups anyway. So I'm curious about your thoughts. We talked a bit about this, but what is your initial reaction to that? Is the salary band Stefan Gaertner: Thank Black people. James A. Seechurn: doing anything useful, and some people will actually react to this the opposite, whereas I know you need a salary ban for pay equity because you need to be able to use that to address legitimate differences in pay in a fair way. But what is your reaction to them? Stefan Gaertner: Well, if you are out there on a journey, and you're trying to reach a certain target somewhere. You have to arrive at a certain street, at a certain house, in a certain city. It would probably, just to outline this analogy, it would be helpful to at least, when you start your journey, to understand, what region am I heading for? What city are you heading for? Before you find your way to that exact street and the exact house. Hopefully that was an okay analogy to say that I think the value of bending and leveling is that it's just really, really hard to precisely evaluate the pay that an individual employee should get. ⁓ The more analytical way to say that it's sort of the area of indeterminacy that we develop. So we are reasonably sure. James A. Seechurn: Mm-hmm. Stefan Gaertner: that the fair pay for a certain job falls between 120,000 150,000. But we really have no clue what the fair pay is for that employee within that range. In my mind, that's why we have these ranges. Now we created a certain level of stories around that, where we say, okay, within the range, there's a certain logic that we use. If you're fully qualified, you are in the middle of it. James A. Seechurn: Hmm. Stefan Gaertner: If you're sort of a rockstar, you're on top of that range. If you're kind of still developing, you're at the bottom of it. But I can tell you, and that's my benefit of analyzing this data for 20 years now, that the story and the reality is very much misaligned. So if you look at the hard facts of where people fall within these range, you learn a few things. For example, experience. doesn't really matter much. So one year of experience within that range gives you maybe 0.2 or 0.3 % of pay. Performance is not a huge factor either. So a high performer might get 3 % more than an average performer. That's it. You basically learn that loyalty is not getting you much either. Quite to the contrary. You hires usually get more money than incumbent employees. James A. Seechurn: Hmm. Stefan Gaertner: Which is always interesting. So if you are working for an organization for 10 years and you have a new guy coming in that you train, you probably want to assume that that new hire gets 10 % more money than you do. So that's really the reality of it. So bands are not solving that problem, but what bands do, at least they provide a certain level of guidance. So if... If that level of guidance goes away, I think that wouldn't be bad. But the question is, what would replace it? James A. Seechurn: Hmm. Yeah, and that's kind of, you know, that's what I'm getting to. Like, should anything replace it? Like, are we trying too hard to fine-tune pay to individual scenarios? And is that a false assumption? Because it's a very, you know, it's a very North American mindset, fair days pay for a fair day's work. The problem is you don't know what a fair day's work really is anymore. And to your point, it just creates all this room for the wrong reasons to creep into pay differences. And I feel like... Stefan Gaertner: like. James A. Seechurn: I doubt many companies are going to wake up tomorrow and get rid of pay bands, but I do feel there's a general agreement that we should probably tighten bands because it wasn't really doing anything good having all that, you know, it's 50 % range spreads. So I do wonder if even if we won't see the abolishment of salary bands, the abolition of salary bands, maybe we'll see a slight tightening because pay fairness has taken hold more so than this perceived notion of best and best and worst performance. Stefan Gaertner: Thank you. it wasn't pretty good. Yeah, basically do. All right, we should talk about both. But I mean, realistically, within the next few years, if anything, I would almost I would predict a tightening of pay bands. All right. And the reason for that is that they are really helpful in understanding pay differentials. James A. Seechurn: Mm. Stefan Gaertner: by gender, by ethnicity. They are helpful by understanding pay levels across jobs. ⁓ They're making things more complicated because the tighter the band, the harder the task to really compare a job, say in HR versus engineering, you have to put more effort into it. But I would expect that to happen because it's a really helpful tool, type paid bands, to be compliant with the EU Pay Transparency Directive. with many of the pay equity laws that exist in US states and in other countries in the world. ⁓ But the question is, and James, I know it's you interviewing me, but I do have that question for you. So, I mean, if there are no pay bans at all, the only alternative that I could think of is ⁓ analytical pay setting that functions a lot like a little bit of a passport where every employee ⁓ has a certain passport where you can see what that employee's experiences were, what their skill levels are. These experiences and skill levels have a certain market value. Every job has a certain market value. And maybe you have a fairly complex algorithm that brings it all together. to set the pay right. So you either have that or you just basically let the market do whatever the market does. ⁓ James A. Seechurn: Well, that's the thing. the complex algorithm, you mentioned a few things that I don't think necessarily do need to be incorporated into pay. Yes, you might come into a role and have less experience, but if you're still doing the same role, that lack of experience might be something of benefit. Fresh perspectives, new ideas are often highly valuable. They're just difficult to quantify. More so than the person that's been there for 10 years that can't think outside of the box. So to think about how that might look, first of all, there are some companies that have done this that I've spoken to that have said we didn't want bands. We just have five or six families, broad families, by the way, not the sort of micro granular family designations, but just a broad engineering family, a broad HR family, a broad finance operations family. And you say, yeah, we're never gonna get exactly to the market. And there's gonna be some deviations, but we don't care that much because internally this group of people we value the same and we want career mobility. If people feel like they wanna do a different engineering discipline, they shouldn't feel hampered by the role or think that because they're gonna go and try and do some QA work, they're gonna go down a notch. Stefan Gaertner: Okay. ⁓ Thank you. James A. Seechurn: because that's a lot of value work, for example. So then you start to think, well, where does this operate? And there are various companies across Europe, like Mondragon in Spain. is Soma Foods in the UK. They have very flat structures. And these are cooperatives, of course, so it's a slightly different governance structure. But the fact that they're flat means pay is not an issue. And it means that you can have, essentially, a handful of different roles. And their people are paid the same. So it has been used, there are examples, not necessarily in PLC America, but flattening of pay, like closing the gap between top and bottom, starts reducing the importance of pay in these conversations completely. And right now, the reason people go after promotions all the time and the reason people want new roles is often financially driven. It's not an economic event, primarily. not primarily for the new experience or the new challenge, which is a real shame because that's what career progression should be. So I would say if it's a simple enough scenario, which can be achieved by a relatively flat organization, high, low pay, low, pay, then you can just take a group and say, well, if you're in this group, this is your pay. And those are two examples. They also did this at SEMCO in Brazil, which is a famous example in the 80s of a ⁓ collaborative, participative workplace. And they had a very similar scenario. employees figure out how much they wanted to pay themselves and ended up just becoming very, very flat because people didn't like the social discomfort of deciding that that person's worth a hundred times worth that person, et cetera. So to me, it's like, yeah, there are going to be things that you do better than that person, but we just don't care. You know, we're just going to say everyone's pay in a competitive way. And if you're doing that role, then that's going to be your number. Stefan Gaertner: Yeah, you certainly need an organization with a vision and an organization that is willing to take certain chances. But I can imagine, I mean, you mentioned a few cases. I can imagine that it works if you have an organization with a very strong culture that was able to kind of detach itself from the labor markets, always acknowledging that, I mean, that you look at the data and you realize that too, compensation is not the only reason why people join an organization and it's not the only reason why they leave an organization. And there are lots of other reasons why people quit and stay. And in some cases, compensation is not even the biggest factor that drives turnover. So, yeah, I don't think that... James A. Seechurn: room. Stefan Gaertner: We're talking about an end to experimentation. ⁓ So I totally believe that these situations work and that they will continue to exist. The question, however, how can global labor markets function without a market mechanism? And wouldn't the market mechanism always come back to regulate pay levels? James A. Seechurn: brain. Stefan Gaertner: for the vast majority of employees. James A. Seechurn: Yeah, and it will. But the thing is, I don't think the merit cycle is solving that anyway. So when people, if you've got a good person in a good role, an extra 1 or 2 % in the merit cycle to move them up in the band is not going to be the thing that keeps them at the organization. If you've got a really nice bang out performer, you've got to reward them with growth and new challenges and satisfy the principles of self-determination theory. And then the money follows. They get into new, bigger roles. That's what meaningful differentiation looks like. Stefan Gaertner: Thank James A. Seechurn: So I don't think we'd ever be arguing against a sort of, you know, some nature of some degree of free market capitalism in the labor market. But I think this notion that the merit cycle is going to solve a lot of this, the merit cycle is going to keep the high performers through giving a bit more to your better people and a bit less to your worse people. I think that's always been a myth. So I think you could lose that entirely and still probably have the same retention rates, even if you do start deviating a little bit from the market by having slightly broader, broader roles. Stefan Gaertner: Well, I would say this if If you have a situation where you have ⁓ either broad bands or narrow bands, but essentially where employees know more precisely where they belong, where you basically do not focus as much on pay differentiation anymore. If you're taking the pressure out of the merit cycle a little bit, where you kind of either give everybody the same or at least reduce the variability, that James A. Seechurn: Hmm ⁓ Stefan Gaertner: would be good for pay equity for sure. That also would be good for compliance. But I tell you, most of the time when I talk to clients, they are always concerned about where does the pendulum need to swing and what's the optimum here. So if you take money more or less out of the equation to motivate employees. ⁓ James A. Seechurn: ⁓ and then Mm-hmm. Stefan Gaertner: Will intrinsic motivation of employees take over? Will this be enough to push them forward? Or do you still need the money mechanism to keep them going? And I don't know what the answer is. ⁓ James A. Seechurn: Yeah. It definitely depends on the people. And I do think there's regional differences here and cultural expectations. Certainly in North America, I do believe there is a coached mentality of chasing the dollars. That's kind of hardwired into corporate America. But that's a relatively recent thing. And that came about lot in the 90s and Jack Welch and the Vitality Curve and that kind of dog-eat-dog mentality. In the meantime, Stefan Gaertner: Thanks. James A. Seechurn: Real wage growth hasn't increased that much, but executives have taken all the output that those people helped create. So you you've had this divergence, all that effort didn't really lead to meaningful increase in pay. So and then if you look at the research, the research always comes back to intrinsic motivation as the thing that drives quality. So you can get more output and more volume through extrinsic things like money. And that's where sales roles come in. You can dangle carrots and have people go out and sell more. But if we're thinking about Stefan Gaertner: Thank you. And the brief second part. What? James A. Seechurn: The real, to me, when you think about the real risks to business, it's irrelevance I've said this multiple times, like right now, every organization should be worried about their relevance because AI is doing crazy things in the market. We should be scared. All right, so what you need for that is a mobilized, dynamic, engaged group of people that can respond very, very quickly. If you've got a group of people that's been given a set of annual goals, Stefan Gaertner: Yeah, they're turning. Thank ⁓ James A. Seechurn: and they look over their shoulder and see an idea, but they're like, there's no point chasing that because I get paid to do these. That's what crowding out is. That's crowding out in real life. What you want is them to be intrinsically motivated to go and chase the idea that might save your company. And those are the scenarios where you see companies trading these wonderful innovations. Now, pay doesn't really come into any of that apart from as a hygiene factor, making sure they feel comfortable and well compensated. So I do think there's been a historic over-indexing on individualized pay. And we had a debate on this the other day. And I do think there's an expectation that candidates, like good people, want their individualized pay as well. But I also think that's a kind of coached state of mind. They've been told throughout their upbringing that they should want that and they do deserve that, rather than, you know, we should be paid fairly because they're working towards this as a team. Stefan Gaertner: Yeah, I mean, you said it, you called it that we are a little bit over indexed on pay But then again, that also means that there's probably a sweet spot somewhere, or in other words, you could also under index on pay. I do agree. I have talked to people basically saying that pay is everything. So I have done a lot of these employee turnover studies in my lifetime. James A. Seechurn: for him. Stefan Gaertner: And I have been coming across people telling me, why do you even look at turnover and look at the data? I already know why people stay on leave. It's all about the pay. That's to that over index. Sometimes people making it too easy. If you just pay people more, they stay. And I think the counterpoint is if people stay because you pay more, maybe they stay for all the wrong reason at that point. Or worse. James A. Seechurn: Mm-hmm. Well, exactly. Because then you've got someone that's extrinsic. Then you've got someone that's there because of the money and they'll do whatever they can do to get the money. And we've all seen organizations, but operating like that, it becomes incredibly political, incredibly dog eat dog. It's not got nothing to do with your value to the organization, but everything to do with how you advocate for yourself. And that's a very risky kind of company to be in. Stefan Gaertner: Yeah, you can also think about this the other way around and say what's the worst that could happen? Is it worse for you to lose that high performing employee? Or is it worse for you to keep that high employee ⁓ high employee high performing employee for all the wrong reasons? Maybe this person internally quit already or he starts to hate his job or her job. And maybe it wouldn't be better for that person to find something else than just being around. James A. Seechurn: Varenne, exactly. Yeah. Yeah. Yeah, exactly. Stefan Gaertner: The pay mechanism is absolutely not perfect. ⁓ And another thing ⁓ that came to mind as you said that is, ⁓ so there's research about how cultures form. You call this a self-fulfilling prophecy on ⁓ attractions, election attrition framework. So your culture determines what kind of people are coming to you, what James A. Seechurn: Mm. Stefan Gaertner: kind of people you hire, what kind of people you promote and what kind of people you are leaving. And if you create a culture that is overly concerned with pay, short-term benefits and all that, that's typically the kind of workforce that you would attract. James A. Seechurn: I think the best example of that was Ed Lazear's windshield study and at SafeLite if you're familiar with that one. And ⁓ he got what was I think a 44 % increase in productivity by introducing piece rate pay for you get paid per windshield that you install. But half of that came from increased output from existing workforce. Half of it came from organizational turnover because they attracted people that thought like sellers and wanted to get paid more for the activity. ⁓ Stefan Gaertner: Yeah, talk a little bit about that. James A. Seechurn: Yes, it works because you attracted more people doing the volume that then you got the thing you paid for basically, which is exactly proof of your point, I think. Stefan Gaertner: Yeah, or Enron that tried to liberate the energy market and started to fraudulently ⁓ take money from their clients, including the state of California. And that didn't end up well. So yeah, that's a good examples of organizations that over rotate towards ⁓ monetary incentives. ⁓ So, but again, I think the point is we have to find the right middle ground and you have to stop thinking that James A. Seechurn: No. Yeah, that's right. Stefan Gaertner: Compensation is everything. Yeah. James A. Seechurn: Mm-hmm. Yeah, and there's another one that made me think about as well, which is, what is the phrase? can't, individual attribution error, which is the human tendency to essentially assign responsibility to the individual rather than the circumstances. And it's been shown in the experiments. Even if the experiment's heavily weighted towards failure, if the person fails, an observer will look at that person as a failure. Stefan Gaertner: Look back about. ⁓ James A. Seechurn: So this happens in the workplace all the time. You bring people into a company and you assess them. But they might have brought into a difficult team at a difficult time, difficult circumstances. But immediately kind of get branded a failure or a two ⁓ as performance rating. ⁓ So does a few things. First of all, it wasn't really their fault in the first place. There's so many other factors influencing their success that you didn't take into. But we know the bias enters there. Secondly, the minute you brand someone, a certain thing, they start acting in accordance with them. And this has been shown in research and they did it in schools where you say, okay, this group are high performers, this group are low performers. It was all made up. Yet the high performing group that was completely made up ended up doing better in standardized testing than the group that weren't branded. it's going back to self-fulfilling prophecy, like you mentioned earlier. That's what all of this can be. And my concern is, and this is my warning to many companies is, the more... Stefan Gaertner: ⁓ Yeah. James A. Seechurn: collaborative, complex, abstract, unpredictable work becomes the more these problems really manifest themselves in things like salary band distributions, in things like promotional decisions, and in things like performance-based equity grants. And we're putting more and more faith into that, into a system which is getting, I think, on shakier and shakier ground as work becomes so much more difficult to individually attribute. Stefan Gaertner: Yeah, it's interesting that despite of all this, it just seems to be working more or less, right? James A. Seechurn: Ha! Stefan Gaertner: People go to work, they do perform, they get paid. Organizations for the most part are doing well. I you talked about performance ratings. That's another really interesting aspect of reward systems, as we're talking about subjectivity as our performance ratings. And there's a lot of research out there about performance ratings from different individuals. If you look at one employee, employee being evaluated by their supervisor, by peers, by themselves, these studies basically looked at the correlations between those to kind of assess how reliable are these performance ratings anyways. And you're probably familiar with correlations. The correlation between supervisory ratings and peer ratings and self-ratings, they're typically in the 0.4 to 0.5 range. So there's a certain level of truth behind that, but more randomness than truth behind that. James A. Seechurn: Yeah, like, random, half truth, I guess, is that if it's, yeah. Stefan Gaertner: And that's what we use to basically determine who gets more pay than others. Ultimately, if you get great performance ratings, you also get promoted. So you basically launch to the top of an organization. Yeah, it's interesting to think about all of the imperfections that exist in our systems. And it's interesting to also point out somehow they are still working. James A. Seechurn: Mm-hmm. Yeah, well, at least people are doing things in spite of those systems, I might argue as well, because a lot of corporate America doesn't have much of a choice other than those systems because they're so pervasive. But there are companies that have always thought differently and done things differently. I think that's part of the reason why people like startups so much is that you don't have all this infrastructure getting in the way. You can't just do the work. And there's a sense that you're in it together and you don't necessarily need to have all that. Stefan Gaertner: Yeah. James A. Seechurn: But yeah, as you say, a lot of it's on very, very shaky ground. think the problem is that there's not really a better option as many companies would see it. They don't believe there's a better option. And the idea of just fundamental trust without any numbers is almost unheard of, because then there's almost nothing to point to to show that you're doing things in a fair way. Stefan Gaertner: And it's a great way to do it. Okay. Yeah, I mean, I do work for a ⁓ startup right now and you work on your own behalf. And what's nice about that is, yeah, I mean, you don't have much of that. Everybody is sort of a share owner of this. We all benefit if we are all successful together. It's still a very small group. ⁓ Syndio has a little bit more than a hundred employees ⁓ and we work together towards a common goal. James A. Seechurn: Mm-hmm. Stefan Gaertner: And that's of course the problem. The moment you get huge organizations with 10, 20, 30, 100, 200,000 employees, how do you still maintain a sense of ownership? Or you probably can't. I mean, the remaining sense of ownership is probably divided up among those people who are in the top executive rings. And then there's everybody else and you have to motivate them somehow. Or you have to... James A. Seechurn: Yeah. Stefan Gaertner: to them share in the success of the organization somehow. Maybe that's all inevitable. Maybe just how it goes. Or how economists would call it, diseconomies of scale. That's, yeah. James A. Seechurn: Well, I think that's for. Well, you know that, yeah. Yes, yeah. So this is interesting. A couple of things here. And I do want to get back to a technical question about pay equity as well that I've had, but this is too interesting not to pursue. So first of all, it's the separation of output and capital. So I often mention Luis Kelso, who started in the 60s the ESOP program. And his point was that you can't have a situation where employees are generating all of the capital, then it goes off into somebody else's hand, because then the people with the capital and you make the decisions about the company. and not the people doing the work, and then you've got too much of a disconnect. However, of course, that's only gotten worse in reality. So now you've got a small handful of people controlling most of the worlds, or most of the S &P 500's wealth. So you've got this kind of separation, but employees will, employers rather, will still use the words ownership mentality. So think like an owner, you've got equity in the company, think like an owner. But in reality, even if all the employees got together and said, no, we don't want to do this, they still don't have enough power. to go against the institutional shareholders and the executives. So that's always been an interesting issue for me. Equity is not really ownership anymore. Yeah, you do get a financial share in the success, but you can't pair that with true decision making around what direction your company is going. The decision making is in the hands of somebody else, even if you do have a portion of the equity. Stefan Gaertner: Yeah, it's a really nice European American conversation right now. Getting really deep into the philosophy of capital versus labor. James A. Seechurn: Right? Yeah, we have to be careful here. We'll be here forever. But I did it. Stefan Gaertner: You All right. mean, okay. But that is the problem. I mean, if you don't instill any sense of ownership in a large chunk of your population anymore, because the hole is getting too big. ⁓ And honestly, look, I mean, if you look at management texts, it's typically... In blood organization, the philosophy is more like the business first, the individual second, or the worker second. If you start off like that, you shouldn't be surprised if employees no longer feel any ownership because you don't, as a manager, no longer feel responsibility for the employees either. So yeah, and I think at that point, you're living in the system that we're living in where you basically just have a very different contract between yourself and your employer. You have a certain job to do, you get a certain amount of money for it. If somebody like your supervisor tells you that you did a great job, you might get a little bit more money. And then you do this for three or four or five years and you move on. And maybe that's just the kind of situation we are in because we are living in the world where you have really large corporation where again, it's not easy to ⁓ to feel ownership. Yeah. James A. Seechurn: There are alternative models, like, again, you know, it's not going to happen anytime soon, but like Patagonia is an interesting example of an employee-owned company that's been successful for a long time. And in Europe, there are more examples of employee-owned companies out there, but certainly the PLC model results in exactly the situation you described. It's almost inevitable that this kind of relationship between employee and employer is going to happen because all the words in the world around ownership mentality and we're in it together and all that kind of stuff. Well, when when the going gets tough and that company gets rid of 20 % of population or spends all its money on a share buyback rather than reinvesting it back into the R &D function, the actions speak louder than the words ultimately. So that's the that is the inevitable reality. I think the disingenuous thing by companies is to pretend that's not the case. And personally, to act like you're some kind of Stefan Gaertner: Thank you. James A. Seechurn: wonderful cooperative, participative, employee-owned company, whereas in reality, you're a PLC with institutional shareholders that call the shots. That to me is the disingenuous part, and I'd rather those companies are a bit more honest about the reality. Stefan Gaertner: Yeah, So yeah, now we got very deep into the philosophy of incentive systems. James A. Seechurn: Yeah, we have to turn back. Let's row back from this hole we've dug ourselves into. So I did want to go back to a question about pay equity. I'm actually curious about your thoughts on this. But it's my understanding that in California, they've added equity into the mix now. When you're assessing whether pay is fair or not, you now have to include equity. So my first question is, is that the case? Did I read that correctly? And then if so, yeah. Stefan Gaertner: Yes, equity and other compensation elements as well. So theoretically, you would also have to include in your consideration benefits. So it's more like a total rewards philosophy. James A. Seechurn: Hmm. So benefits probably isn't as difficult, it's a bit irritating to add into a pay equity analysis, but equity in particular, particularly for a VC backed private company when everyone's got different awards with different strike prices and different investing schedules, that's quite tricky. Have you done that analysis yet? Because there are private companies that fall into this category, right? Stefan Gaertner: Okay. Well, I must admit I don't work a lot with really small companies, pre-IPO companies rarely. ⁓ But I would imagine, I mean, there were a handful and the logic is basically that you run these pay equity analysis, evaluating equity at the value it had at the moment when it was granted. And that's probably the easiest way to think about it because James A. Seechurn: Mmm. Okay. Stefan Gaertner: That's the one thing that organizations actually control. The actual value that employees get from it depends a little bit on how long they're to stay, how the value develops over time, when they decide to exercise the grants. None of that can really be controlled by the employer. The only thing that the employer really can control is the value of the stock or the equity when it was granted on the day it was granted. James A. Seechurn: Mm-hmm. So with an ISO, a stock option, the value is zero when it's granted. you just, are you obliged to basically calculate an assumed value then? Stefan Gaertner: Yeah, you do a black schools computation or something around trying to figure out, what is the theoretical value of that stock option? What would be an independent person pay for this thing that we're giving the employee if we were to offer it to that person right now? Yeah. James A. Seechurn: Hmm. Yeah. And could he use the preferred less the 409A and that, know, a typical 409A and they preferred spread. Could he use that? is that in the led on a national how much you know about the details in the legislation? Does it specify how you value the stock? Okay. Stefan Gaertner: No, no. And there are details that none of these regulations I would imagine would ever specify, ⁓ especially when it comes to technical computations. What typically I would recommend to clients is ⁓ do what you think is right, do it in good faith and do it consistently. So if you decide to evaluate these stock options in a certain way, James A. Seechurn: Mm. Stefan Gaertner: Just keep doing it the same way ⁓ as you continue to do this analysis and be prepared to answer questions. Regulators are probably not going to press you on that. And by the way, the EO pay transparency directive over there, ⁓ they include equity as well. They include bonus, they include all sorts of benefits. We typically walk through clients through all of their rewards to basically then decide James A. Seechurn: Yeah, okay. Stefan Gaertner: which of these elements does it make sense to actually include them given your own reward system and your reward philosophy. James A. Seechurn: Okay, so it does add a bit of complexity to that pay equity analysis because a lot of companies do this fairly regularly now the pay equity analysis, because it makes sense to do it for various reasons, but it does add a layer of complexity to them. Stefan Gaertner: Yeah. And even without that law in California, can tell you that almost half of the pay equity projects I'm working on would include equity and bonus for that matter. Yeah. James A. Seechurn: Hmm. Interesting. So, one last question then before we wrap up. No conversation at all anymore is complete unless we mention AI. So ⁓ does AI impact the world of pay equity and pay fairness? Stefan Gaertner: ⁓ Well, like everything with AI, ⁓ it's really hard for us to understand in detail what's going to happen. But there's one thing that I think is not going to happen. ⁓ Let me start with that. ⁓ I personally don't think that AI is going to replace compensation decision-making. So we at Syndio are at this. in detail and you may have seen some of our postings lately. And we do believe that AI has a role to play when it comes to helping along with the process. ⁓ to your point earlier, there is a band within which recruiters and managers typically set pay. And yes, ⁓ if you have good policies and if you have good analytics, you can probably limit the discretion of recruiters for fairer pay compensation. But I don't think you want to go to a place where you replace recruiter discretion with AI discretion. Because the way it goes today, ⁓ AI is almost like a black box. There's a lot of information that goes in there. Unless you train the AI to execute faithfully mathematical equations, James A. Seechurn: Mm. Stefan Gaertner: ⁓ statistics, the AI will really ultimately start to make stuff up, much like a recruiter or a manager would. ⁓ in my mind, it's just moot to discuss whether the AI decision is more precise. If it's still made up, if you can't really explain where that comes from, it's not going to improve your decision. Besides, you don't even know what kind of biases crept in. Yeah. James A. Seechurn: Hmm. ⁓ Right. Stefan Gaertner: So AI certainly has a role to play, but it's not going to ⁓ replace good analytics, math statistics and human decision making. But it's certainly going to help in some aspects when it comes to compensation decision making. James A. Seechurn: Yeah. And I think you mentioned human decision making as well. The fact is we're all people that need to work with each other and people need to understand people. And I think it often is in the era of pay equity and pay fairness for good reasons, but also a question like some challenging reasons. We're almost trying to take the human component out as much as possible, but also in many ways, the human is the best person to make the judgment as to whether to hire a person and what they deserve in that process. maybe an interview that makes you realize that they're brilliant. They're just not brilliant for this role. It means you can take that person and deploy them somewhere else. But I think there's almost like a slightly perverse hope that we can take the human out almost entirely out of the process because it feels more defensible. But as you say, an AI itself has its own biases in it and its own flaws. Stefan Gaertner: Yeah, if I may and James, you can cut some of this out if it goes to detail. But I have my own journey towards AI. ⁓ As you probably know, I have been in People Analytics for more than 20 years. ⁓ I have a PhD in human resources. And as such, I have been very much at the forefront of innovativeness in HR over many years. And I can tell you, ⁓ have done a lot of things with data in this space. And we have used a lot of methodologies to analyze data. One of the technologies that I've been using quite extensively is called neural networks. And I can tell you that neural networks have been applied to data problems for a long time. And it did not kick off a revolution in people analytics. ⁓ So now, why am I saying that? I'm saying that because when it comes down to it, what is AI really? AI is the application of neural networks to human language. And we are able to apply neural networks to human language because of these large language models. Large language models is nothing other than people who are being able to convert words into numbers. They take the context into account and then they train those over years and years and finally realize that something really good came out of it. So when I look at this, I'm basically saying AI doesn't really add anything new to the world of people analytics because we have been applying these methodologies to numbers for two decades now. What AI is really doing, it's applying ⁓ something entirely new to the spoken word, to the spoken language. ⁓ And that to my, in my mind, ⁓ creates a bunch of use cases, which are not really in the people analytics space. So, and you have used AI. I mean, it's amazing. It's amazing what comes out of that. I mean, they write texts for you, they write job descriptions for you. They build prototypes for you. James A. Seechurn: Yeah. Stefan Gaertner: they can almost anticipate what you're going to say. But in my mind, they're not really doing anything new and different. So why am I saying that? So in the context of HR, the use cases that I'm seeing for AI is not that they're going to come up with a better way to combine data, numerical data, to create a better job offer. In my mind, they're not going to do a better job for us to add five plus five or making financial statements more accurate. They're doing all of these things probably much faster. And I also don't think that AI is going to help us think new thoughts, entirely new thoughts that have never been out there before. So with all that said, ⁓ James A. Seechurn: Hmm. Stefan Gaertner: When it comes to and compensation decision-making, I think we will still have to rely on humans to make the decisions because AI is basically drawing their insights from the very same data that humans do. Maybe more data, but ultimately, what data can you actually even access to determine if Joe or Maria is paid fairly? Data that we didn't access in the past before. James A. Seechurn: Hmm. Stefan Gaertner: But what's worse with AI than with humans is this. If a human makes a mistake, we forgive him or we can blame him for that. Or we accept it, we're used to that. If AI makes a mistake, who are we going to blame? So that's why I think human decision makers and organizations will continue to set pay or to at least play a role in pay setting more so than AI. Human decision makers will still ultimately have to make the decision as to who gets hired or who gets promoted, AI can help, but really only in a supporting role. And that's probably the future that we are moving towards. I hope this was not too much detail, but this is kind of the journey where I came from. I am basically listening to people these days, believing that AI can do things that really cannot. Probably because they didn't have that experience. James A. Seechurn: Hmm. Hmm. Stefan Gaertner: working with a methodology that's underneath AI for so long James A. Seechurn: No, that's a really interesting, there's not too long at all. It's a very interesting perspective and it's quite a reassuring perspective as well because I agree with that. It's definitely speeding things up. It's speeding, but it's speeding things up that I would have done myself or other practitioners would do for themselves. So there's definitely a threat to the volume of HR jobs, I think, given how much more efficient one can be. When you think about something like job matching, for example, reading through a job description and comparing it to a catalog of jobs. Stefan Gaertner: Yeah. James A. Seechurn: you can get an LLM to do that pretty quickly and to the same degree of accuracy. But as you say, it's just replicating something that humans have done before because it's human knowledge that's trained the LLM in the first place, but it does make it much, quicker. So I think the optimist in me wants to believe that it's going to free people up to do much more interesting work. The pessimist in me thinks that the companies that can will squeeze out much more from the resources that they have because they know how much more the LLMs can do on their behalf. I guess we'll have to wait and see for then. Stefan Gaertner: Yeah, I like to compare this what's in store for us to the Industrial Revolution. The Industrial Revolution allowed us to replace muscle power with machine power. ⁓ Didn't replace people, but made them a lot more productive. And the same happens now with AI, where we can basically ⁓ multiply our brain power tremendously. ⁓ James A. Seechurn: Mm-hmm. Right. Stefan Gaertner: And that will disrupt everything to your point. It now comes down to the question, what's going to happen faster? Are we going to develop new things and new ideas that add value faster? Or are we just going to focus on replacing or doing the things that we have been doing all along and just making them cheaper? If the former happens, ⁓ boy, what a wonderful ⁓ experience. we will have over the next few years. If the letter happens, there's going to be a lot of hardship. James A. Seechurn: Yeah, and we talked a bit about politics, I feel like there needs to be some degree of political change to help the former happen rather than the latter. And that when I look at particularly things like hiring statistics around younger people in the workplace, and when I speak to people in engineering organizations that would rather use Claude than hire a first year graduate, then it does leave me a little bit concerned, I have to say, because there is an economic argument for just, you know, not delivering that value back to the people. And I do think things like reducing the number of work, typical working weeks to spread the work around the same population when there's less work to be done and things like universal basic income even further down the road. Like some of these things need to be explored, I think, to make sure that, you know, that efficiency gets distributed to the right people. Stefan Gaertner: Yes, yes, yeah. I mean, I have experienced Claude developing a technology prototype basically in two hours. And it's tremendous. Just imagine the programming hours, the coding hours that would have to get into this just a year or two ago to accomplish the same thing. ⁓ Yeah, I mean, for people like you and I, who have the experience and the context knowledge, AI is probably going to be a good thing. I mean, we can do things much faster and more effectively than before. We cut out the middle person. We don't need a coder or an analyst to accomplish these things. But most... James A. Seechurn: Yeah. Yeah, it's pretty crazy. Right. Mm-hmm. Stefan Gaertner: Critical question right now is what are we going to do with the graduates who basically go in and don't have the experience? What's going to happen there? So yeah, I don't think anybody knows the answer right now. James A. Seechurn: Right. Now I'm keeping an eye on that one. I'm very curious because you can't have a situation where no one's coming out of college with a job to go to. I that's completely unsustainable and it's terrible. It means terrible things for the future of the workforce as well, because they're just not getting that first year of experience that's so critical to their working world. But anyway, we'll end on a positive note, which is then for the time being, AI is a good thing. And I agree, for the time being. tremendous fun as well, I have to say. Stefan Gaertner: Yeah. For the time being, it's amazing, isn't it? Yeah. Yeah. James A. Seechurn: ⁓ But Stefan, think that's plenty there. I want to say thanks so much, because it's always great to chat and we've been working together for years now and I love hearing your perspectives on not just pay equity, which is your area of focus, but more broadly on the world of work and how you see things changing given the number of companies you work with and how thoughtful you are about the shape of work as well. So I just want to end by saying thanks so much for your time. Stefan Gaertner: Yeah, and likewise, I enjoy reading your LinkedIn postings and your books too so far. Let's see how many more are going to come. So congratulations on a great career there. Take care. James A. Seechurn: One more on the way, but that's currently... Thank you. All right, Stefan, thank you very much. Take care. Okay, bye.