Spilling the Tea on HR Tech: Workday's $43B Buyout Rumor, Remembering Dolly Parton

[00:00:01] Welcome to the HR Huddle Podcast, presented by Sapient Insights Group, the ultimate resource for all things HR. It's time to get in the huddle.

[00:00:24] Welcome to Spilling the Tea on HR Tech, where we focus on the hottest HR tech news everyone needs to know to be in the know. We break down the news of the week and help you make sense of what it means for our industry and how it can impact your organization. We're recording today on August 27th, 2026. We are at the end of August. Everyone is generally back to school. We've got a few more people who I think are waiting yet.

[00:00:47] And today we're going to be bringing you all the news you can use this week. I'm your host, Daisy Harris. I'm the Chief Research Officer and Managing Partner for Sapient Insights Group. We are a research and advisory firm. And joining me today is my co-host, Cliff Stevenson, Director of Research and Principal Analyst for Sapient Insights Group. Cliff, welcome to the show today. We have a lot of tea. Some sad, some not so good, some good. So lots of tea to spill today. But we are both here in celebration of someone, correct?

[00:01:15] Right. That's right. We can't be mourning. I was like, I'm not going to cry on this. You can't cry on TV, right? We had a lot of passing, but the big one, Dolly Parton. So if those of you that are watching us on YouTube, because we're also in there, you'll see I have my Dolly for president. I have my crown today because Dolly was the queen and she loved her rhinestones and she loved her glitter.

[00:01:38] And I kind of went back and forth between my cowboy hat or my crown. And I thought, you know, for Dolly, I'm going to dress up in my beautiful leopard skin shirt and my crown because that is very much a Dolly expression of yourself. Right? Yeah, that's right. Hopefully, I can't imagine by now that this is how you're finding out.

[00:01:59] I will say that they have asked that in lieu of flowers, any donations, any celebration should be sent to Imagination Library, which was Dolly's charity that gave that use that money to buy books for children. It's imaginationlibrary.com. You'll see a big donate button when you go there. But we'll have a link in the show notes.

[00:02:27] Yeah. Yeah. Dolly was I mean, it was so funny is that we were at our offsite and you had just mentioned her. We were talking about people that we really respected and we were all talking about our favorite artists and what music we loved. And you had brought up Dolly Cliff as someone that you just really thought was amazing. Right? Yeah, she really was. I mean, it's very interesting when she passed because, of course, it's very sad, but it was. Really interesting because it was like people from all walks of life.

[00:02:54] You know, it wasn't just, you know, country music fans. People just knew her from so many different ways through her acting, through her songwriting. But she was also, I guess for the purpose of this show, a huge union advocate. Yeah. I forget the union. I believe it's the International Musicians Union or something of that nature. Someone can correct me in the comments if you like. Yeah.

[00:03:20] But yeah, she was, you know, decades long member, I think, 38, 40 years union member, lifetime union member, and was a big advocate for unions as a way to make sure workers' rights and pay equity for women was respected. She, in fact, there was a story. I was reading all these different stories about her.

[00:03:44] And when she was a producer on a movie or a show, I forgot which, she found out that one of the other producers, oh, it was Buffy the Vampire Slayer, in fact, I do remember now, that she found out that one of the other women producers wasn't being paid the same as a man, so she just cut her a check to equal it out right there on the spot. Yeah, she was, the thing about Dolly is that she was practical, right? Like, her views and her opinions.

[00:04:11] She didn't just comment and make a statement. She would take action. And I think that's something that, yeah, you have to really respect. And she was just always part of my growing up, right? My parents loved her music. I can remember listening to her and Kenny Rogers over and over again in my family house, along with all the other 80s amazing sort of golden hits. I can remember going to the movie theater to watch 9 to 5, right? Yes.

[00:04:41] And Steel Magnolias. And so not only was she just an iconic role, but like you said, she was, she believed in the value of work, but she also believed in the importance of, and the responsibility of the employer to that worker, right? And she had some real reasons for that. I mean, her family was from Tennessee.

[00:05:04] They were from, you know, the working in the hills, employees who didn't have the ability to read, who were working in the mines and the mountains. And, you know, she really did, you know, understand what it was like when both sides of that equation didn't understand each other and took advantage in different cases, right? And so I think, you know, the nice thing about when you think about Dolly's legacy is that she was an amazing businesswoman.

[00:05:32] She was just an amazing philanthropic person. But she was also just, you know, a kind soul who just believed in, you know, if we stopped, you know, looking across each other and started looking at each other, right, and had a conversation, we would actually do so much more, right? And so, yeah, she'll be missed. She'll be missed on a lot of levels, I think, because, you know, as we start to lose our icons of the 80s,

[00:06:01] you know, it is hard to watch the ones who made such a good impact on us, right, leave. We also lost this week Tim Curry, who was another iconic star in the market. And in both those cases, you're looking at people who paved the way in different ways, right? Absolutely. Yeah. Both of them wore corsets and fishnets extremely well. They did as well, yes. And Dolly, I think, would have loved that, right?

[00:06:30] Like, she was all for anybody who was willing to put on a pair of high heels, right? Absolutely. Yeah. So. Yeah. For those of you on the podcast, I'm not crying. Don't put it down. You're so cool. Cliff, really, this one hit him hard. I think Dolly was just, I think Dolly was, for me, was just an idea of how kindness can be utilized in a practical,

[00:06:55] and more importantly, in a way in which you are not making other people feel bad. I think sometimes, you know, we make gestures, and a lot of the times those gestures are, and we don't mean to, but if we're not careful sometimes, it makes, it feels oftentimes like we're going to shame people who are not doing those gestures. And that's, I think she was really good at balancing that idea that, that she wasn't the center of it. She wasn't the limelight, but she wasn't also going to step behind anybody either, which I loved about her.

[00:07:25] She was very much focused on, look, we're all in this together. Let's do it as a, as a team. And if I can help you by being a little bit more aware, or people know me, or people willing to pay more attention to my big blonde hair, then, you know, then it was just as good that they were listening to me because we're going to get this done together. Right. So I think that's, it's a mantra to work for today. Right. That's right. Normally I would do a survey about us all, or a segue about us all coming together. There's no segue there that works out. I wouldn't do that. I wouldn't do it.

[00:07:56] But, yeah. So we've mentioned before, I'm sure regular listeners will know that, you know, we have completed the survey. Thank you all for taking it. We are very close now to, in fact, by the time this publishes, there's a possibility that we'll start making, announcements on who has finished number one and top five in certain categories. I don't think we make announcements until HR Tech, but people will hear from us if you have one. Yes. Sorry, I should mention it as such.

[00:08:26] And, right. I'm glad you bring up HR Tech because that will be the public reveal of this year's data. Now, Stacey and I are in the midst of writing it. I will give a bit of a spoiler if we're going to do a data point in that it will be more than 380 pages this year. You're getting your money's worth. You have to figure out what place or what book it matches then. Yeah, we'll find out at the end. I always do a word count. It was two years ago. It was 1984.

[00:08:54] It was only within 15 words of that. Last year was The Hobbit. It was only within, I think, 45 words. Remarkably close. There's a site online you can do that to figure out. It's going to be, we're getting close to war and peace territory. I know.

[00:09:10] But one thing I did want to bring up, you know, again, sort of spoiler free, but since you've been like me, Stacey, you have some writer's hours late at night and certain things that might have popped out, again, without giving anything away too much.

[00:09:26] I'll say for me, as I'm writing the HRMS section, especially when I was going through the SMB and looking at the companies that are in there, because we're seeing more and more global companies or specialized HRMS than we've ever gotten. You know, that's the beauty of having over 10,000 different people take this survey. It was, you know, we had a period where we were going through and just figuring out what some of these companies are when they do the write-ins.

[00:09:52] And starting to see those that I would have considered, oh, these are like one-offs or, you know, specialized, to actually see lots of people using them. It struck me that there's still a need for specialized, personalized systems. One of the sort of promises of AI was this mass personalization. But I don't know if we're there yet.

[00:10:13] But clearly, people are needing systems that are designed for the trades or designed for, you know, government or education use. And the more you look into it, you realize why that is and what sort of specialized systems need to be there to meet all these different needs.

[00:10:35] And you start to really see the whole galaxy of organizations that are out there and how universal, using these space words, that HR and the systems really are. Yeah, I think it's an interesting conversation, right? And we've said it for quite some time, right? This idea of personalization is the ultimate goal of any technology, right?

[00:10:59] Like, you know, the first iterations of it was to kind of standardize everything so that everybody got the same experience. But I think as we've gotten to the next level, it is about creating an environment that fits for each of us individually because it should be able to do that, right? Because it moves faster and it works faster. And I think, you know, you're right that I think the desire, the number one reason the solutions don't meet business needs in our survey, because we ask that every year, is continues to be configuration issues.

[00:11:28] They want to configure it in a way that works for their individual businesses and individual users. And we hear that over and over again. So, yeah, I'm not surprised. But I do think that the question becomes then, and it's hard because you have to get a certain percentage to get in kind of our user experience, Inventor Satin, in our adoption charts, is how, you know, if it becomes too splintered, do we end up again with an environment similar to what we had in the early sort of aughts, late 1990s and before you hit 2000s,

[00:11:58] which was all this software sort of hand built, right? The on-premise environment. I think, so for me, a couple of the big findings I think this year, it sort of leads into kind of where I think the market is heading. And I think it will be interesting. I already got a couple people, like what is the one solution that everyone is using AI to rebuild instead of sticking with whatever they have in there that they can buy off the shelf right now, right? We're going to have that answer when we get back.

[00:12:27] And I will say, as I told someone, it's in the talent areas is what I will say, right? You know, I do think we also, one of the things that kind of surprises me is that there is a lot more, I think, differentiation is a good way to put it, between what's happening at the enterprise level and the SMB level right now. A lot of times those worlds are pretty similar and there's just a little difference in sort of complexity that you're handling.

[00:12:54] But I think with the advent of AI, SMBs are able to, again, to your point, sort of tailor a little bit more their solutions with an AI plus an application conversation, right? Which allows them to buy different technology, to think a little differently about the technology that they might think is important, right?

[00:13:15] And so that I think is really, really interesting in the fact that people are really figuring out new patterns for HR and new patterns for the roles within their organization. And I don't think that's, you know, not layoffs. It's not like we're reworking everything. We still have the same goals in HR, but they're figuring out ways to achieve the outcomes with a different mix of resources, a different mix of skills, and a different mix of technology,

[00:13:45] which I think is, we haven't seen this much, I would say, disruption and differentiation in the market for almost, probably almost 10 years, right? When I first came in and took over the survey 15 years ago, we were in the midst of and sort of the wrapping up of sort of the big talent management surge and all the differentiations in talent management solutions. And the last couple of years, probably the last three years, it has felt like we've been very commoditized in what's happening in the HR tech space. And I would say we probably still are in a few areas like payroll,

[00:14:14] but as we're shifting into this next generation, we are seeing more innovation than we've seen in a long time, right? So I think that's probably my biggest finding is that there is a wide difference in between what enterprises and small businesses are doing. And that actually foretells, I think, a lot of innovation that we're seeing in the market, right? You are the leading foreteller. Sometimes, yes, I can give a hint as to where the market might be. And there's a few places that I think are going right where we thought they were.

[00:14:44] There's a few places that are not. And that kind of blew my mind, which is like, who owns the technology budget? That should see a little bit more than we thought, right? Yeah, show up to HR tech to hear that one. But, you know, I imagine your soothsaying was one of the reasons why you might have been on your appearances on the Workday Future of Work podcast. That was coming out in October.

[00:15:11] So still a ways, but people should start getting hyped now. As well as you were on the Meg and Amy show. Oh, I'm getting ready to go on the Meg and Amy show. It'll be on there September 30th. So we've got another month yet. And I will be – and we specifically went on a little bit later because we wanted to talk a little bit about what they're coming out in the data set. So for those who know Meg and Amy, they are amazing leaders in our HR technology space.

[00:15:34] They have a great podcast and they are sort of – I think as senior leaders in large organizations like Workday and SAP SuccessFactors and Oracle previously, they are now really embracing the idea of what it means to be sort of advisors at, I think, the highest levels, but of multiple organizations and small and big organizations. And that advisor kind of role, I think, is really playing well to where they want to be. It's interesting.

[00:16:02] I've had probably just in the last five or six weeks, to be honest, a lot of women who are – and we're going to talk a little bit about this in one of our last, I think, conversations today, Cliff – of the number of women who we know who are leaving the corporate world in one fashion or another and building out their own environments

[00:16:27] because they really, really want to create a world where they have more control of what they're doing and who they're working with and what's being asked of them. And I would say Amy and Meg have sort of – are a good representation of what we're seeing in a lot of levels in the market, right? Absolutely. And then I was on the HR Morning Show. There's a few episodes you can see.

[00:16:55] Now that's a recorded live, but you can look at a pre-recording sort of television-style morning show. So the specific episode I was on, I was on ditching AI resumes and human-centric AI council agenda. I was there as a guest of the wonderful Trent Cotton out of iSIMS. It was really great to discuss some of the AI in recruiting, another topic we're going to be talking more on.

[00:17:25] I was also on the Heroes of HR podcast with William Tincup, with Amy Miller from iSolved, talking about some of the data, some of what we're seeing, again, without giving too much away, but some of those advancements going on. And that should be out by the time this drops, but you can go into there.

[00:17:50] It's part of the Work to Find Network, which we're a part of too, and you've probably heard Stacey call out before. Yep. So with that said, let's talk about some of the stories that we have. Sometimes during this time of year, as we've mentioned before, we don't see as much M&A activity, but there were a few, and I think regular listeners slash viewers of the show will recognize some of these names.

[00:18:18] But we start off with, I'm only laughing because both these companies are clearly of a time when the naming convention was to cut out vowels or to do misspellings, sort of. So we've got Checker acquires Truve, and it really just sounds like a name generator thing, but Checker, of course, is well-known within the space. They definitely are named for what they do in their space.

[00:18:46] And then Truve, I'll admit I didn't know as well, but I did end up watching a few of their commercials because I want to learn how to pronounce it, first of all. It is within the verification space, but doing a little more on asset and income verification, that some people might have run into when doing credit applications. But it's interesting to see this go into a workplace-type setting.

[00:19:18] Excuse me. Stacey, you've worked quite a bit with Checker in the past. Is that correct? I have, yeah. Yeah, and I think when we think about this kind of an organization, what we're looking at is a mix of sort of compliance, right, along with a mix of sort of the recruiting world, as well as a mix of sort of a candidate experience too, right?

[00:19:46] Because that's all, you know, how things happen, right, through these kind of platforms is part of the employee and the candidate experience, right? And so I do think that we are seeing an interesting sort of mix of how do I connect the dots, right, between something like Truve, which has, you know, those state agencies, the social service programs, you know, those big things, right?

[00:20:17] Who wants to work for those organizations are oftentimes a lot of people who are coming there because they want to serve, be of service to the industry or to the market, right, or to the government, or they want to be of service to people in general, and that's why. So, but that means you also have to make sure that they have a good onboarding experience or they have a good experience sort of going through the process of backgrounding check and those kind of things. And so I think these two worlds fit really well together, and I'm interested to see how this expands

[00:20:46] Checker's just overall customer acquisition and the ability to sort of cross over and cross sell, and this will be really good. So I'm interested to see where this heads, right? I do too. I think it's interesting. I'm sort of top of mind for this space a little bit for me right now because, as we mentioned, we're in the midst of writing the paper, and as I'm getting into the benefits section, the thought of having a system to allow you to see which benefits you're eligible for.

[00:21:11] Some benefits, especially in the United States, are tied to your income, right? There are certain government programs that are tied to your income, so having that built in to your platform that you're using for work could help you with the distribution or eligibility for certain benefits that are provided externally or perhaps in partnership with your organization,

[00:21:36] like you said, especially with working at public companies, and you said government-type companies, that becomes even more of a requirement for both certain jobs and for certain benefits. So pretty interesting. And the more you can speed up any of those processes, the better for the candidate, right? Yeah. Yeah, ease of use, right? When you talk about the comments we see, that's a big thing for benefits, right? It's a complicated issue, so the more you can make it simple.

[00:22:05] Another one that we're really sticking to these sort of shorter-named companies. I'd love to see it. Lattice, who we talk about quite a bit. In fact, who came up when we were all meeting in Raleigh because of how much they're sort of expanding their platform, has acquired Pando. Pando, we've mentioned before, they're in the performance management space. They were founded by Barbara Gago, who is pretty visible out there.

[00:22:33] She's been one of the big, I would say, innovation leaders, founders, stars in this space. We'll be talking about a few of the others later on and had worked at Miro before that. So pretty interesting to really join Lattice. And she's going to stay working at Lattice, actually, I believe, as their chief marketing and strategy officer.

[00:23:02] So, you know, Lattice, you know, being able to really expand into a lot of the performance management and using AI to make these assessments, right, for promotion. It's interesting. I know I bring this up every time, but I really don't have a sort of thematic thing in mind. But I do think that we are seeing a little more and more this theme develop that we're

[00:23:29] going to continue to expand on, which is how do you go from having information, having data to taking action on it? In this case, it could be who gets promoted and, you know, what people are coming up. We talked about pay equity. I'm going to give a little spoiler. Yeah. And I really, you know, when you think about a competency rubric, right, and that is tied to specific roles and levels, right?

[00:23:58] Like, and I, it's been ages since I remember early on sort of like reviewing Pando and what it was offering. And I think, you know, these assessments are sometimes can feel very biased isn't the right word, but they can feel very contrived to sort of get a certain answer to some extent, right? Yeah, a little subjective sometimes, too. Yes, right? And so I know when you, when you really look at that and you pull out of it, the behavior,

[00:24:27] which is the big five are based off of a lot of certain behavior modification conversations. And you look at competencies and skills, and you think of that as part of the assessment processes as, as, as at this high level of some of the other stuff, um, you end up getting, I think a little bit more balance in the overall picture. And so I think Pando has done a nice job of that, or at least it did early on when we were talking to them. So I'm excited to see how that lattice incorporates this. Um, I think it highlights too, but we were, we were kind of talking a lot about earlier

[00:24:56] this year, which is that in this space of, of AI, where I can kind of connect the dots, I want as much of this kind of information tied together, right? And, um, so these two acquisitions, one with the, the checker, um, acquisition, as well as this, uh, lattice acquisition, you know, really fits the point that we think point solutions are really going to have to find places where they tuck in, where their data can be utilized more effectively, as you said, to help organizations take actual action, right?

[00:25:26] Absolutely. And, uh, we don't have to wait too long for the, uh, idea of the next sort of, uh, I don't want to call it like serial founder, but innovator, uh, in the space is, uh, Adam Weber, uh, who actually reached out directly to us to talk about something that, uh, actually his new company helm as in like the sort of helmet, right. Uh, we'll be launching September 1st.

[00:25:52] So actually we're, we're announcing this before it actually comes out, but by the time you hear this, it will have, it will have dropped. Uh, but he kind of reached out now. He had been the chief evangelist at 15 five. I believe that's where I met him. Uh, but he has been all over, uh, you know, his LinkedIn, uh, is pretty impressive when you look at, um, where he was at. Uh, he was at CLAAR, uh, as a board member.

[00:26:19] We've talked about them on this show before he does executive coaching on his own. Uh, he was with, uh, Amplify as their, uh, one of the founders and their chief people officer. So, uh, really just, uh, someone who, you know, has a lot of this energy and likes, uh, bringing up these new companies. And this is kind of an interesting one because I'll admit, uh, it isn't what I thought of, but the idea is taking this idea of your executives.

[00:26:47] Uh, and, you know, we've seen sort of team-based assessments and, um, team-based performance management. You could almost think of it as right, which is really his space. And putting that for, instead of sort of individual contributors, doing that for your executive team, rather than looking at individual executives, which we've seen before, right? We've all seen executive development and there are tools for that, but to look at it as your

[00:27:15] executive team and to use those same sort of tools to see, you know, engagement, uh, who is more vocal, who's contributing. Uh, so you kind of get that idea of how your team works together and get those strengths to think of them as an individual team than a collection of individuals. So very, uh, you know, high level of interest. I also reach out to him directly for a comment. Uh, he had given me an entire press release. Adam, I apologize. I won't read the whole thing.

[00:27:44] Uh, I did ask, you know, uh, you know, anyone he wanted to thank. Uh, I didn't get a response in time for recording, but I assumed you'd want to thank, uh, Stacey, uh, for being so brilliant and me, of course, for bringing it up. Sure. Sure. Sure. That's where that went right. With a commentary. Yes. Well, on our brilliancy, I think we're going to have to get into some of the more, um, hotter, uh, funny enough.

[00:28:13] This next topic was one. Yeah. This next one, Stacey. Now, uh, a little behind the scenes. This broke as of recording. Yeah. Yeah. Right. When we were recording two weeks ago, we were like, we, we got off the podcast. We're like, oh, that wasn't there two, two hours ago. Was it? Uh, Stacey, take it away. Cause this is probably the biggest story. Honestly, we have a certain order that we do these stories on, but if we were going to rank them in order of one that broke, you know, containment that anyone could have

[00:28:39] seen, regardless of how minutia, uh, you get at HR tech, this one was bigger. Take it away. You want me to, oh, you want me to. Well, so this, yeah. So, so, so this was the big conversation all over LinkedIn last week. And by the time you guys hear this, it'll, it will now have been about three weeks, four weeks since it's been out, which was, and I got several reporters who called me for commentary and I said, I was going to wait until we got more detail, but silver Lake, uh, is in talks to take workday private, uh, a $43 billion deal, largest potential software buyout in history.

[00:29:07] Um, so that, that was the, that was the, the, um, rumor that was, uh, swirling. And yes, um, I mean, I think the reason I told most of the reporters, well, one is that they were asking late at night and I was in the middle of, of, of actually getting other things done that week. But more importantly, I said, look, I, I, I, our, our process has been to always bring the facts and to always talk about things, not on speculation, but based off of what either customers or the vendors themselves are telling us. And there was neither of that in this conversation, right? Yeah.

[00:29:36] I was actually hesitant. Yeah. Yeah. There was a lot of investors and a lot of influencers with a thought or a commentary on it and what it was going to do for customers. Um, what I, what I will say here is that, you know, there's been speculation for years about whether or not workday was going to be acquired by somebody bigger. Um, right. Most of the time it was another public company. Um, but everyone kind of looks at those numbers and, and there's not a, there's not a real, a real valuable place for tuck in.

[00:30:04] And even if someone had the cash to buy someone as big and as large as workday and one of the larger public companies, right. Um, not in a way that would add value or bottom line to a lot of them. Right. Um, so the other conversation then becomes, you know, private equity, which is, is where the market heads when they either have adjustments to make to their bottom lines, they have to do things outside of the public eye. They have to, maybe they have to restructure some debt. They have to restructure some, some finances, those kinds of things.

[00:30:32] Or to be honest, if there is a need to sort of raise cash to be able to pay for things, those are kind of all the reasons why private equity sometimes is a viable option for things. It's also a scary thing. I think particularly for those of us who have been in the industry, um, and, and buyers who have had their, um, vendors of choice get acquired by private equity. I do not think this is the same story as what we saw with someone like a loss and getting acquired by the private equity firm that now owns in four and those areas. Right.

[00:31:02] Or, um, we've seen, uh, UKG go back and forth with 10 sort of public and, and, and private, um, when they were ultimate versus Kronos. Um, when you're private equity, there's a couple of, of things, especially when you're high tech private equity, right. Um, high tech private equity, unlike a lot of the other environments is generally if, if, if it's done well is bought for growth. So the idea is that we're going to give you a little bit of air coverage, a little bit of cash, uh, to add to your bottom line.

[00:31:29] Um, so you don't have to do all of the work in the, in the, in the, uh, public eye. Um, and what I mean by public eye, I mean, it's basically the stock market because every, as you go ahead, what the most important thing that this did is it raised the stock for work day. And so there was a lot of, a couple of people we know who are good friends who were like, yep, yep. My stock hit the point at which it's going to sell now. Um, we've been waiting cause the workday stock had dropped dramatically with the, and, um, AI apocalypse, apocalypse as everybody's calling it. Right.

[00:31:57] Um, and so, and cause work day would, unlike like Oracle, Oracle has, um, the server and the cloud environment sort of is, is keeping theirs slightly afloat a little bit. Right. You've got someone like ADP who has a lot of services, which is right. If you were an all SAS business, you were being hit hard. Um, Salesforce is another one and others who being hard by the AI, um, entry into the market. Um, but the bigger thing then is that not only does this news sort of give a bump up

[00:32:26] to the, to the possibility of the better stock price, but more importantly, what we're really looking at here is that there is some very, very real, um, it's hard to make changes and big changes, layoffs, restructuring, um, uh, investments that seem odd to the public. Um, and that investors don't like because it's risky. Right.

[00:32:52] Um, it without sort of being able to do that, like you would when you were a small business growing rapidly. Right. And so I think that's the, the sort of, uh, the balance people are saying whether silver like, uh, and this is, this has been going on for about two weeks now at this point. So usually these kinds of announcements that are going to come out, usually they don't come out so early that, that then there isn't an announcement a couple of days later. So the fact that we haven't heard anything from anybody, including a Neil Brewster, uh,

[00:33:19] even though the, um, uh, the new, um, the quarter announcements came out, I think it was tonight. Um, and, and although, uh, work days, um, uh, um, actuals were up slightly, um, they were not up as high as they had predicted so that this impacted the stock market at as well this, uh, today. Um, but, um, all that being said, I think the most important thing, Cliff, and you

[00:33:44] and I've talked a lot about this is that, um, as a buyer, um, you do have to pay attention to these things, but I wouldn't, I wouldn't be too worried until there is some data and actual facts. And then when there is your next step, isn't to, to kind of throw your hands up and say, oh my gosh, now my company is being acquired or my company that I bought into is doing something. It's to solidify what, what your path will be with those organizations.

[00:34:12] So I always tell people and say, the next thing after you hear about big news is to then start to connect to who is your customer service representative to your contact who sold you the solution. And just make sure you get real data and real information and not hearsay off of LinkedIn because any decision you make off of hearsay on LinkedIn, um, is probably not going to be a wise decision to make. So, right. Well, let's stay with Workday. Uh, one of the big stories, again, uh, one of the larger stories that's sort of outside

[00:34:40] of the realm of just, uh, HR tech is the, uh, big Mobley versus Workday, which of course is one we've been following here. Uh, but really looking at kind of in a broad sense, setting some precedents for AI usage and who's to blame for, uh, quote unquote, uh, you know, who's responsible would probably be a more legal way, uh, for the use of certain tools, uh, when they're automated specifically within hiring.

[00:35:08] Uh, the store, the story here is just that, uh, the court denied, uh, throwing out the sort of larger scope of this. Uh, I believe Workday's argument again, as always, neither of us are lawyers. So, uh, all of our commentary has passed through a few filters. Uh, but from my understanding is that, uh, Workday wanted to limit the scope and say, you know, this is just a California thing because California anti-discrimination suit and the

[00:35:38] judge said, no, no, no, it's broader than that. So, uh, yes. The other side of this that wasn't just where it was located is that it is now starting to move into actual interviews of clients and customers, which I think that's the bigger part of this conversation, right? Um, nobody wants their data pulled. Nobody's wanted to be a customer who has to go in and, and, and basically, um, share what's

[00:36:05] happened in their organization as a, um, a, um, an organization who's using this technology, but that is what started to happen, right? Yeah, absolutely. Well, some good Workday news. Uh, we've got to prop them up. We should have done this in the middle, right? The compliment sandwich. We did the sandwich, yeah. But I, I do want to make one note before we leave out of this story is that the Mobley story isn't just a Workday story. I think Workday was targeted because they were one of the biggest companies, but you know,

[00:36:34] it's been clearly said, and I think I will say it too, without it, without any, um, without, without, um, any concern about the factualness of it is that Workday was one of the most stringent companies I knew at that time with governance around AI. Even before we had large language models, they had governing boards, they had governing models. They had very strict approaches to the point where oftentimes the, the senior leaders would get into fights about how slow they were moving on things because they were just being

[00:37:04] very conservative about it. So I do think as buyers, um, I think this is a, a, just like the meta law case that we're going to talk a little bit about that just came down this week, which is, you know, basically they, um, they made the decision to settle, uh, on the meta law case around, uh, have doing harm to children that we talked about last time. Right. Um, Workday's not settling as far as we know, at least at this moment on this, we'll see

[00:37:33] once it goes to trial, cause that's when it really starts to get scary because that would be emitting, not just for them, but for every single software solution out there that they are responsible for the algorithmic output. Right. Um, now I do think doing their decision to actually, uh, accept, you know, a, a, um, uh, to,

[00:37:59] to not go through to trial puts more weight on what Workday is doing here. All of these things are slightly interconnected, Cliff. And I think that you do have to, as, so as a buyer here, I think the big conversation for you is watching this and saying, okay, this does have an impact, but it probably has an impact on me in the sense of which of the AI modules do I just go ahead and accept? Which do I actually make some, my, make sure my legal counsel has some say in, right? Yeah. Across any application you're using. Yeah. Wow. Look at that.

[00:38:29] See, I said, we're going to talk about practical stuff today. There it goes in honor, in honor of Dolly, we're thinking about what actions. It's all about practicality, right? That's right. Exactly. Uh, some practical uses that, uh, we found here or that Workday found, uh, they have really deepened their partnership with Compa, uh, company, Stacey, I know you've known for a very long time. Uh, we, I've known them, you know, through, uh, the work that we do.

[00:38:55] Uh, what was pretty interesting looking into this is that Workday, which already, uh, tends to pop up in areas of our report and our survey data in areas you might not think in terms of people really rating them high on user experience and vendor satisfaction. Even in compensation management where there are more specialized tools, this will help them get even more to that. Uh, you know, by deepening this partnership, they can really start getting into not just

[00:39:22] pulling data from annual compensation data, but immediately getting real time information, both incoming and outcoming compensation data to help people make decisions quicker. That's something that, uh, is really becomes necessary. You know, I would say that that was a differentiator for certain compensation, maybe as six years ago, seven years ago, but now it's just like necessary, right?

[00:39:49] You've got to be able to say this is what it is right now, because especially in a more volatile economy, things change much quicker, especially as we have more and more people work, uh, in, uh, hybrid or, uh, fully remote models. Yeah. And Charlie Franklin, who I've known for, for years, I remember him briefing me when he first came out with this idea and everybody thought he was right. Cause they did. Cause, cause, cause basically what Charlie had done and what the comp was done is they,

[00:40:17] they came up with the idea of getting, um, compensation data through the recruiting process. So most of the time the surveys are sort of separate annual, you write down it through your compensation person who does the survey. Right. Right. Early on. And so he said, look, there's, and our recruiters need to understand the ranges of compensation at that recruiting point.

[00:40:43] And so he wanted to bring the compensation data instead of waiting until you got to the final offer on compensation and saying, well, it's just in these three bands to bring it forward to show, look, um, you know, yes, our bands are these three, but right now in this market, in this last month, this compensation range has skyrocketed. And I need to be able to know that as a recruiter to advise the manager to then have the manager decide whether or not they want to go up that high or not. Right. Um, so that was the general idea of comp. And now they've, they now since then are pulling data from a lot of other sources, but

[00:41:12] the idea is that they are pulling the more internal immediate data set. Right. Um, we see this, a couple other organizations are doing this and are growing out what I would say are interesting products about, um, that will allow you to maybe have compensation data that's, um, accurate as of within a month or two. Right. Um, that, um, is that real time conversation about compensation data, um, is, is, um, a great opportunity. And as Christina Golt put her quote in here, right.

[00:41:40] She was, she was very clear on this that, um, you know, what real time data provides for organizations, right. Can be a game changer. And I, and I do agree with her on this because I think, you know, to your point, Cliff, there are so many fast changing things in this market that you cannot wait for a year long, um, survey, uh, to be able to get that insight into people's hands. It's, it's, uh, you know, we get that a lot of times in our conversations. Right.

[00:42:07] And it seems kind of hard when you think about it used to be that, you know, two, three years would be the, the average and it would be great. You would do a survey. Our survey now being done yearly sometimes feels like it's too slow. We now have to go out and get interviews and do, do pod, um, do round tables to get some additional data on things that are moving faster. Right. Um, I do really like the longitudinalness. So you can see that things go up or down of yearly surveys, but there's also a place for this immediate, uh, feedback, uh, component as well. I think in all places. Right. Yeah.

[00:42:36] In fact, we're going to even start a new series voice of the customer live, taking our voice to the customer data and start bringing it in at live events. Uh, which is why we give you that list of events, which we're going to be coming up to. But first let's talk a little bit about some other people in the news. Uh, Mike Schumard, uh, joined ISALT, uh, as chief sales officer. Uh, this of course is the one name I didn't look up and maybe it's pronounced, you know, Schumar or something like that. Mike, let me know if we're pronouncing it. It's like, I'm always so careful.

[00:43:05] And I'm like, I think I've got that one. Uh, but yeah, so pretty interesting. He has, uh, an interesting career. I thought it was, uh, fascinating. He'd been, uh, about seven and a half years at Paycor. So, uh, definitely knowledgeable in the space. Yeah. It's, it's interesting to see the, how much ISALT has a, as a new CEO. That's not quite six months, if not even in the role, right? Uh, new Mike Schumard.

[00:43:32] We've seen a couple of other new, we have a, we had a new contact and they're going to, I think have a new marketing role in the organization, right? Uh, on the research and, um, advisory side. So it is, they're, they're definitely sort of re-shifting, um, that leadership role. Some of that, again, it's, it's worth noting that, um, uh, ISALT is one of the larger organizations that is held by, uh, AKKR, right, Cliff? Yes. Um, and one of the things you see with private equity firms is that they will change leadership

[00:43:59] every, almost five to six years. Most of the times kind of, you can kind of almost do it by clock. Um, and it's not just the, the private equity firm who likes to do this. Sometimes this is a, this is a mutual agreement because a lot of, um, the people who, who, with the idea that we're going to exit the private equity firm and go into an IPO, there's a lot of bonuses and things tacked onto that, right? Around those decisions about what is the five-year look for a private equity firm owned company. Um, someone like ISALT, uh, that private equity firm is definitely probably wanting to hold

[00:44:29] onto them because they're doing very well on a cash basis, right? Because of their model that they've got in place. Um, and so I think for someone who's really looking to grow with, maybe with something that's going to rapidly go into an IPO may not have been the, the, the place to be, but there's so much growth opportunity there, um, because there's so much cash available. And because they, they have, I think, um, uh, a opportunity to really work with a lot of the organizations in the market who are ready to, to do new AI, because that this is a point

[00:44:58] at which they're, they're open to that conversation. Right. Yeah. I, without getting myself into too much trouble, but we are spilling the tea. I will say that, uh, sources tell me that, uh, AKR does have some models where they do think instead of companies is, you know, as you said, that three to five year time period, there's value to be had in that, in various ways of either going public or selling, but they also have a seven year plus sort of mindset with certain organizations, uh, of a long-term,

[00:45:28] uh, value. And clearly I solved is one of them, right? So instead he wins the race there. Well, and it, and I think it's important for all HR to understand these business models, right? To understand when they buy into a solution, what is the ownership model? What does that ownership model look like? Like just so they understand what that looks like for their own sort of stability on a financial front. There is, you know, it is rare that you're going to have something bought and sort of torn apart, right? Like that happens once in a blue moon in the market, but you do get, once you get different leadership, you get different cultures.

[00:45:57] And sometimes you have to adjust your thinking because of that. Right. Absolutely. Hi, here's a company that has sort of changed their culture a little bit. Google. It's interesting to think that they were, you know, I, I wasn't, unfortunately I wasn't that young, but I remember them coming up and, uh, they were sort of, you thought of them as a young scrappy startup. And now they're, you know, a megalith that's out there, right? A monolith. And so, uh, this was pretty interesting.

[00:46:26] So this is from, uh, Google's, uh, deep mind, one of the big AIs. Uh, they had a, I don't know if you want to call this a kerfuffle or a bit of interesting news that a little, again, sort of behind the scenes that when Stacy sent me this story, it said something like, this is what I get for reading stories late at night or something of that nature. As I mentioned, we've been working late. Uh, but it was an amusing story, but I think also has some practical lessons, uh, for all of us. Right.

[00:46:56] And the story was that, uh, the deep mind team from Google had said, uh, listen, don't use our regular portal for application. Don't go to like, because we're finding that the AI, uh, is screening people out for some reason that we're not really understanding. So instead they gave a private sort of link, uh, to people and said, please don't group share this either because then we'll run into the same problem. So it's funny.

[00:47:25] They're using AI to solve a problem that doesn't exist and having to go to a non AI to solve the AI's problem. I don't know. It's, it's the most, um, engineering based approach to that. There's like a deep mind AI team says don't apply using your application. There's a non trivial probability that your TV will be screened out incorrectly. Use this form instead.

[00:47:52] Like that was the, was the copy paste that someone had put into it. Now I I'll be, I'll be very upfront on this one. I don't know how true this is. This is what I, a, a researched article. This was, but it was, it was all the buzz for a little while. Right. Um, and, but it was, it's so much of what we're, we're seeing in the market right now. I mean, we, we said last week because of the fraud that we are seeing people just stop using the recruiting process altogether. They're like, let's just go through the referral model. Right. And this is kind of the same thing.

[00:48:21] Here's a little link to a calendar link. Um, and then we'll get you into the process. Right. Like that's what they were saying. Right. Yeah. I mean, it was picked up by Bloomberg. Uh, it is paywall, uh, but we'll love to hear your thoughts on that, but it does, it gets into what you were talking about, Stacey, without belaboring the point of understanding not just what tools can do, but the repercussions of those tools and the long-term impact of those tools. Yeah. I, yeah, go ahead.

[00:48:47] Well, and, and, and the, and long-term impact and the, um, uh, I think again, the document had on it, please don't share this doc widely. Right. Like it just the, the not understanding the behavior of human beings as well. Right. As you said, it was a very engineering solution. No problem. Love to all my engineers out there. My family as well. Uh, keeping it with Google.

[00:49:12] Uh, one of the stories was that they paid 10 million for spirit airlines data, uh, specifically their employee records. Uh, so this is fascinating, right? They want to be, and they want to use this for AI training, right? This wasn't a case of, oh, we're going to sell to these people. This is very specifically, uh, getting back to a central tenant of generative AI. I'm trying to be more careful about saying generative AI when we're talking about these things or LLMs.

[00:49:41] But the, the central tenant is that these systems, these AI systems are only as good as the data on the point in which they are trained. Right? So getting new data, uh, is valuable. You may have seen the story that, uh, Amazon was taking old and rare books, uh, scanning them and destroying them because they were new data, new information for their ability to create, uh, you know, or to, uh, manufacture things through their LLM.

[00:50:09] But this then gets, this is very obviously an HR issue, right? These are employee records. You know, it is not something that we could have regulations for who could have predicted. Maybe you could have Stacey, but most people would not be able to predict that this would have been an issue 10 years ago. We need to put laws around your data, not just when you're working in an organization, but what happens after you leave that organization? Is it allowed to be used for this purpose?

[00:50:35] It's not technically violating HIPAA or whatever your local regulations might be as it stands now. But, uh, it's worth thinking again, future wise of will there be regulations on the past? Am I doing this in the right spirit? Pun not intended, right? Well, another reason I sent this to Cliff is he was a regular customer of the spirit flights. Um, was very sad when that went away.

[00:51:03] Um, but I mean, here's the thing that I think there's a couple of things. One, I can remember, I cannot remember. I think there were big data, so it might have been before you and I started doing the show, Cliff, I can remember early on when I was doing another podcast with someone else. And there was this conversation about how valuable is the employee data? Because that was big data conversations, right? Um, and at what point do you start paying the employee for their data that you can then

[00:51:28] use it to, and we started to see this early on with this idea of, um, if I have ads, can I show them through your company portal technology? Like, like there's all of that stuff that had happened back in those days, right? And I think we'll already start to come up again in, in AI as we start to think about it. But what I, there's, there's a couple of issues here, right? Which is, it says it doesn't fall afoul of the privacy architecture because it, but it, but it kind of could and might.

[00:51:56] And so one, I think I do wonder if, if we're going to see some GDPR or some, um, because the idea of GDPR is I can pull out my data at any one point in time. Well, if you've sold it to another third party, um, where does that leave a GDPR requirement? Right. Um, and the second one becomes, you are not allowed to have, uh, a share private information like HIP information, like employee assigned, aligned with my private details. So if my email, so what they said, and we took all the emails to train it, right?

[00:52:25] Well, if my email was talking about something privately with HR, so did they really scour to figure out which emails they could and could not use? Right. There's that conversation too, right? Um, is, is, did they handle this appropriately? And we don't know until someone, um, uh, basically goes to them and basically says, Hey, I want to understand this, right? Um, you know, 80,000 accounts, 500 million Microsoft team chats. I just think about all of the team chats we have, right?

[00:52:54] Um, this is, this is a very real thing. Uh, and I think employees will start to ask for some of protections in this area. I know we, when we first started doing the survey, um, when I first started working with, with Lexi, when the very first things we had in almost all of our surveys was your data will never be shared outside of our organization, um, in, in a, uh, in any fashion other than an anonymous format, right? And that's still the case. We'll never leave connected to your name, any data that can identify you.

[00:53:23] Well, that me includes, um, and we, so we have sort of some strict policies with our data process, right? Where, where the data is held, who has access to it. We have some strict policies about what's connected to it. And even to some extent, how you can connect what systems someone has to that. Um, we've had conversations in the organizations, if there's ever any, um, uh, we could never sort of transfer that data anywhere because of that one statement we put on our survey. And we did that for that exact purpose. So it could never be, someone couldn't buy it and use it in a way that would be inappropriate

[00:53:53] is that that data is always secure because of that statement we put on it. Um, but I do think that that is a concern for employees because we don't sign anything that says our employee data will always be private or will not be used in this fashion. GDPR only kind of says you could pull it out and ask it to not be used that way. Um, there are a lot of legalities here that I think we're going to get into in the future and we need to get into in the future, right? I do. I do agree with that. It's interesting because, uh, actually I amended our language just slightly this year because

[00:54:22] of these systems abilities to put sort of two and two together, except, you know, it's 2 billion and 2 billion together. Uh, I said that not only would the, any data be used to be anonymous, but it can only be, it can't really even be dissected because there, if you said you worked at a company that had 278,000 employees, it's like, okay, there's only one. 278,000. Yes. So it's like, there's nothing to be put in language.

[00:54:49] Like we wouldn't share any data that, uh, even unknowingly could be traced back. Right. There's, we can't just sort of pick and choose what we think, uh, is identifiable data. Uh, it has to kind of come in a way that is so broad that it's unidentifiable, but still useful. Uh, little side note, just as a joke here, I, I put in a note for Stacy, but, uh, Stacy had mentioned, she said this to me cause she knew my love for flying spirit.

[00:55:16] Uh, and they, I, while trying to understand why Stacy said this, cause they always writes all something like, you know, what it thinks or to give some background. It's like Stacy wrote that this one hits home for you almost certainly referencing Cliff's aviation background. And it's like, uh, citation needed, uh, is that why she said that? Thanks. All those rare books are not helping. No, they're not helping at all. Right. Yeah. Yeah. So, uh, amazing.

[00:55:46] Uh, well, speaking of AI, this is a sort of natural segue into a academic paper. And I'm bringing this up because this is, this, I might actually use one of your favorite terms of phrase Stacy, the canary in the coal mine, because I do think in this case, we are, I, we are trying to look at data and look at some of the information coming out that may be a harbinger of something negative. Some of the negative impacts of AI that we need to think about.

[00:56:14] And this one, uh, is from, uh, I believe a Chinese, uh, university. Uh, it's called the generative AI learning penalty evidence for Chinese secondary education, uh, for Americans. That's what we call high school. Uh, but the idea being that they looked at three factors. They looked at, uh, score. They took two groups, right? I don't think you get away with this in America, maybe, but, uh, they took it two groups. One used generative AI LLMs and the other one didn't.

[00:56:44] And they looked at, uh, three factors, which is, uh, amount of time it takes to complete their homework assignments, the scores they got on those homework assignments and the scores that they got on exam grades. And those that used generative AI, uh, to complete their homework assignments, got them done. Uh, on average, it was 62 minutes went all the way down to 43 minutes.

[00:57:09] And, and I think even lower, uh, it was their homework scores were going up. Uh, you know, averaging, uh, so, you know, I, you know, roughly let's say 20 points higher, but their exam scores plummeted, absolutely plummeted, uh, to the point of what would be a failing grade in the United States. So what does this mean? If you think about what we're doing with work, you know, you can use AI and you'll see boosters

[00:57:38] of AI and people talking about the positive, I'm getting my work done faster and it seems to be better. But what happens if you don't have it? And also what happens if you don't have access to it, even if AI sort of sticks around in its current form? You know, we've been talking about token usage. Yeah. How much have you really been upskilled? How much are you learning? Do you know how to code now? Do you, do you, or do you know how to use a system to put something out that looks right to you? How much are we really learning?

[00:58:06] Maybe that doesn't matter, but it's something to think about as we're using these tools. When we talk about those long-term repercussions, it's interesting to see these studies coming out. Yeah. And, and, you know, and this is a wonderful example of, I think, you know, the difference between, um, is it hurting learning versus is it helping us get the outcome we want? Right. Two different conversations. Right. Um, and I would say, yeah, it's no different. Um, it decodes back.

[00:58:35] I think you can always go back and find some examples of this. Um, I used to generally be able to read a map just generally, not great at it, but I, but I could kind of figure out North and South and which direction and where the, today, if you put a map in front of me, I'd be like, oh my God, how, where is all this? I'm probably better at it than, than a millennial and, and better than a Gen Z, but I'm, but I'm going to be struggling with it. Right. Um, GPS without a doubt created an environment where we could get anywhere at any time. And it is so useful when I'm traveling.

[00:59:04] Like when I was traveling, if I didn't understand the language, I wouldn't have been able to get here. I would always have had to have an interpreter with me and someone with me. But now I can, with the GPS, make some judgments about where I'm going and whether or not it's safe. Right. Um, I could not have done that in the world of maps. Right. And so I think, you know, this similarly very much. Yes. Can it help us do things faster and better? We talk about this all the time at work. Um, you know, one of the things I did a couple of weeks ago is we're, we're, we're training,

[00:59:31] um, one of our new analysts, Alison, uh, Williams, who is amazing by the way, and she will be at HR tech and she will be doing briefings. So people, please, please, uh, get a chance to meet with her. Um, but I was teaching her how to pull data and analyze data. And one of the things that we do in that process is we pull it out. We put it into a spreadsheet side by side by side with all the different cohorts and variations. And there's no real good way to do that. There's a lot of tools that could pull that out automatically. And Claude could definitely do that for us.

[00:59:59] But part of what we teach a lot of our new analysts is it isn't, it's the process of, of looking and pulling each time makes you kind of forces you to see the differences as you're doing that process. Is it the, the fastest, the easiest? No, it's not by any means. Right. Uh, do I do it that way still? I, I probably take a couple of shortcuts here and there when I'm trying to work on stuff. Right. Um, but it, but it was important to me and, and important enough that I felt that Alison should still pull it that way because it promoted the learning process.

[01:00:29] My outcome was her learning, not just getting the work done. Right. Um, and so I do think, yes, of course it's going to make it so that you don't learn because you, you're, you're getting that data in a different way. You're not forcing yourself to learn it as you're going through the process. Right. So I do think we are going to have to think about, it's not like you want to restrict the use of it. It's to teach people when it's best to use and when it's not good to use. Right. Absolutely. Well, well said.

[01:00:55] I think that, uh, yeah, I think that we'll have to, uh, sort of play those out as the case, but I do think it's worth thinking about what you said at the very beginning. Right. Uh, which is, you know, you, we may be learning to do these tasks, right. But are we, we really sort of solving the problems, right? Are we getting the outcome we want? Yeah. We're getting the outcome. And, and, and it's not bad to have an assistant to help you do it. Sure. If that, the goal is to get it done faster and, and you don't care about the learning because

[01:01:25] that may be the only in the last time you do it. It's the only time I'll be in India and that one street will be, so do I really need to know how to read that map about India at that one time? Right. Yeah. And you want to probably know because I'm not going back. Right. Yeah, exactly. And I think, you know, you can also point to the positive aspects, right. Of doing the, the homework faster and getting a better score. Uh, you know, sometimes we, like you said, you don't necessarily need to learn to do it on a test. You don't need to intrinsically take it in, but it is worth noting how these systems work

[01:01:55] and not just taking it as a blanket. Oh, it's getting things done because like you said, we should focus on those outcomes. And, and maybe they're learning something you weren't testing on, which was how best to utilize that AI and, and use the right prompts and get the right information. Right. Like that, they might've been learning that and you're not testing on that. Right. Interesting. Just saying, you got to think a little bit deeper on some of it. Right. I know we have to make it out to the end. If you're able to. There's a lot of villainy in it, but I do think there are things we got to, got to keep in mind. Right.

[01:02:25] Yeah. Got to separate the weed from the chaff there. So we're going to get a few international stories here, even though I do, you know, as, as the world, you know, sort of compresses, we, these all matter, especially because as we talked about, the EU is leading the way when it comes to regulation of AI. This one actually came from the Netherlands as the Dutch Data Protection Authority, their sort of oversight of all those things we were just talking about, Stacey.

[01:02:53] They actually do have a governing body looking at that sort of stuff. has flagged AI hiring tools of saying that they will have to, you know, pass review and that they, you can't just automatically use these sort of systems that they have to pass sort of a regulatory environment because they are seeing that some of these AI hiring tools,

[01:03:18] as we discussed earlier, are sometimes not meeting the requirements put out under the EU AI Act. And so we start, this is sort of the early steps of something we've been talking about for a few years now. You know, we get a sort of broad-based law. Yeah. Right. And then we start seeing what does that mean, though? What is the practical application of that?

[01:03:43] And we're starting to see what that will really mean when you're out there trying to buy or trying to use certain systems for your organization. And yes, the Netherlands is only one part of the EU. This is not a blanket EU thing. But each of them will interpret it differently. But we will start to see impact to your own organization because of these regulations. What I really, I think people really have to kind of note here, it isn't just I know how the decision was made.

[01:04:13] But I'm also providing to the candidate the information about how that decision was made. Yes. So they can challenge it. And I think that's the big thing a lot of people will get lost in in this conversation is it's not just about whether AI is making a decision, whether the human is making a decision, but there's also a component of, for example, you know, right now someone might get screened out because they have a financial role and their financial score for their creditworthiness

[01:04:42] is seen as poor, right? You can basically connect those two dots and say, and say, look, the reason you were you were no longer on option for this job is your your credit score was really poor. We have to consider that when we're doing a high level financial role that that could create a situation where you are in an environment that would have a risk of possible financial issues, right? And I think that, you know, those are directly connected.

[01:05:09] But if the AI is making that decision and it and it has that plus two or three other things and it doesn't want to give that back to the candidate, that lack of challenging, that reporting back to them is part of the problem. We did a demo this week earlier with Payscale on some of their newer products around their salary solution, pay factors, and some of the newer tools they have in that environment.

[01:05:35] And what I really liked about one of the things they were showing us was an executive dashboard and more importantly, the reporting capability of that kind of walks through step by step how salary bans and how salary decisions were made and that ability to print that out and give it to somebody, right? That simple little thing of adding that not just I can see the history in the chat window,

[01:06:00] but I have a button to push and a printout in a formal environment that I can give to the candidate, that I can give to the executives, that I can give to any governing body. Our software has to consider that as they're thinking about all these little chat windows off to the side, right? 100%. I think you've been reading ahead, Stacey, because that is a direct tie to our next story. This was a story that came out of HCA Mag.

[01:06:28] So it's about Australian women. And it's basically taking the point that we're not seeing the movement up in the sort of succession within Australia that one would hope to see. It's not equal. And this is even after a pay equity law has been put into place because that is just one step, right?

[01:06:54] So, you know, there is the idea of like, okay, we have now shown that I, that, what was it? I'm trying to find the exact percentage or at least we got the dollar amounts. Female CEOs earn 83,000 less on average than their male peers, right? So we now know that because of that transparency. What's that next step? That printout, here you go and you go, what do we do about it, right?

[01:07:21] There's a great line in this that says, visibility without intent is theater rather than transparency. And Australian women have stopped buying tickets. Love that because it speaks to something that we have been talking about this whole episode. And we've talked about pay equity before. Specifically, females not being paid as much as males, which is, that's great. That's that first step of the law of seeing that transparency. But that is only as good if you do something about it. Yeah.

[01:07:51] And it highlights that, you know, in just one year, the expectations for career progression have almost halved in a single year. They're crashing from 30 to 17% of women feeling that they have opportunities. Where 81% of men see a clear path for the women, right? So there's this disparity between women's view of where they can go and what men feel they have the progression to go.

[01:08:19] And I think what the pay equity has done is it's shown a light on what's really happening. It's like, you know, sure, we're a clean restaurant. You put a light in the corner and you see all the little rats running around, right? It's like, yes. But now do I have an exterminator, someone to come in and clean that up, someone to help move it forward in some way? And if there is nobody working towards that, moving it forward, and you aren't forcing people to move it forward in any way, just the awareness is important.

[01:08:47] But it can be actually very depressing for women who know that, like, hey, my colleague is making that much more. And nobody has said anything. And no one made the effort to change that, right? Absolutely. And I think it's a great article. Hopefully I can get a chance to connect it too. Because it does also give some practical advice. What should we do about it? And so, you know, things like, you know, publicize your commitment to doing it.

[01:09:15] Formalize things like partnerships, sponsorships, and mentorships. Not just allow it to kind of keep happening. And give that reasoning. So defend, you know, they call it defending the infrastructure. But idea being like, why is this happening? Explain the reasoning going on, you know, and say these are the steps we're doing to address that. Yeah. It's funny. There's been a lot of backlash on the meta conversation about them basically doing the settling.

[01:09:42] And people are afraid that, yes, that admits that they did wrong. But does that mean that all that admits they did wrong and they're going to just keep doing it? Of course. Because you've kind of given them the okay to do it. It's kind of the idea. I've shown you. Now I can just keep doing what I'm doing is the thing. Because one of the things that they noted was that, you know, women, the benefits fell from 28 to 17%, right? Mm-hmm. In this cost-cutting environment. And there was definitions and structure. Sorry.

[01:10:11] But that insight and awareness does not make for action, right? Yes. Exactly. And a lot of sort of early laws and regulations really are just about that first step of just saying, you have to say what's going on. But it's up to you to do something with that. You can't just sit around and wait for that to happen. And it's interesting what you said about the AI thing, right?

[01:10:40] There's a saying that, you know, basically when a fine doesn't stop you from doing business, then it just becomes part of the cost of doing business. Yeah, part of the cost of doing business. Yes, exactly. So it's, you know, you don't want to get into that sort of state there. And then sort of wrapping up, I wanted to bring this up because this was actually a story. I don't think she knows that we're going to put on the one.

[01:11:07] But this comes from one of the people that if you listen to the show, you have actually in a way interacted with. And that's Linda Galloway. You've heard her name in the thank yous of this. But she goes through, she takes all this, and she writes anything that's sort of public-facing about the show, the descriptions of it, anything that's going on our social media, making sure people are linked, that the stories make sense.

[01:11:33] Think of her almost as maybe, if not an editor, but definitely a big part of how we package this and communicate it out. She's a wonderful writer and a great person. And she knew that we talked a lot about AI, and she was curious if we had seen the story. And this was from about a 6,000-word essay that Bill Gates, founder of Microsoft, I'd love to have to introduce who he is. If you don't know him, oh, Willie Gates, you know, he's over there writing us letters.

[01:12:02] But although I personally disagree with some of his conclusions, I do like the way he framed what makes AI as a little different. He said, you know, it could be the greatest equalizer ever invented or the worst source of injustice is one of his phrases there. And he said that often, and we've been just as guilty, or I've certainly been as guilty of this, often people use sort of false analogies, right? We think about it all the time.

[01:12:32] Oh, it's like with this technology or this technology. But he mentions PC because what he knew. He mentioned, yeah, that took over like 20 years because first software had to be written. People had to understand the connection between hardware and software. There had to be a point when it was available. Even when I was young, I was one of the few people on my block that had a computer, if not the only one, because it was something my dad used for work. And I knew a lot of people had never seen one, which is hard to imagine that, right?

[01:13:01] But that's the short amount of time, what, 20 years since? 19 and 3. And then people had to learn to use it. And what's interesting about this new technology is none of those barriers are in place, right? People understand how to use it. It's readily available, all of these things. So it's sort of accelerated its use in. Stacey, I think you and I talk often about the fact that for most of the technologies, like the internet, yeah, it didn't, it wasn't, everyone was using it right away.

[01:13:31] People, when they found a use case for it, they went into it. Right now, it seems like it is a bit of a force feeding to many people. And I think that may also account for some of the sentiment swing that we're seeing. But also, again, it's something to be wary of because we're using this in the workplace. Not everyone's going to have the same feeling or even usefulness for it that everyone else will have. Yeah. And I don't disagree with him.

[01:13:58] I mean, I'm one of the people who often does equate it to other products or other historical events, right? I think that's how I learn best is by connecting dots on things, right? But I agree with what he's saying is about the speed. And we have noted many times that it is the speed of the AI that's more dangerous than the actual AI itself, right? Like AI has its quirks. It's going to have ups and downs. It's going to have positives. You know, every model is its own little entity to some extent, right?

[01:14:25] It might be that they kind of all do the same thing, but they really are their own behavior-based tools, right? And you and I have talked about the fact that part of the reason why I am a big, very big conversation I have with the market, I think sometimes get pushback is I do think you need to treat it more like a human than you do a tool. My belief on that is because humans, right, we grow rapidly. We change rapidly. Behaviors change.

[01:14:54] And you have to modify and manage those behaviors. These technologies change on a regular basis and they are being changed and being developed by a human who influenced them and a human who made some decisions. And then the tools make the decisions themselves based off of that influence. And so I do, I agree with Bill in the sense that this is faster than I think anybody understands it's happening.

[01:15:18] And I do think it will, it will create a lot of pain both in the job market, I think for our children and for our economies. Mm-hmm. Mm-hmm. But I also think that having been through the era that Bill was part of, I can remember, you know, I was the first kid on the block with the Apple IIe, like I said, right? Oh, I love that one. Good job. Yeah.

[01:15:44] I was the first kid that went to programming camp, right? I hated technology because my dad was into it. He was, he worked at all, he was always like, you got to go to technology. It's going to, it's going to be where the money is, where the job. And I can remember I went as far away from it as I could when I went into writing and creative and, and, and, you know, my background was radio and TV. And I was like, yes, we're not going to do the internet because I'm going to be this generation of this other thing. And my world has come back to it, right? Yeah.

[01:16:14] Interesting. You can't run from these things. I think you have to find a way to work with them. I guess that's my only feeling is that he's right. It is, it is dangerous. It doesn't mean we turn it off. And what he is advocating for is more government oversight, more policies, more oversight from businesses themselves. And we did that during the internet. We put a lot of policies in place eventually, not as many as we probably needed, but we did, you know,

[01:16:39] just something as simple as that used to, you know, people won't remember this, but there was a point in time in the internet where we were making a decision. If people could own pieces of the internet. So you would not have a worldwide web that would be accessible by everybody. And we kind of said, nope, everybody's going to have access to it. There is a piece in time when we used to say, you know, who owns the radio waves? They do still have that, right. You know, in place, right.

[01:17:01] It is those kind of decisions, I think, that will make a difference as to whether or not this becomes a positive or a negative impact on our world, right? Yeah, like you can't run. Flight is not an option, but you can still fight. Fight over flight. There we go. One thing that will not be a source.

[01:17:29] Our backs might have some pain on that because it's never the best beds we go. So Cliff is expanding AI conversations and HR conversations into the non-HR realm.

[01:17:58] It's one of our favorite things to do.

[01:18:41] They're only a couple weeks away, yeah. In the Windy City. The keynote? Yeah. And as you're getting ready to register for that, please come see us. We will be sharing a little bit of early data, but we'll be talking a lot about the relationship between the CHRO, the CIO, and the CFO.

[01:19:10] And we'll also be really discussing the kind of the budgetary impacts of what's happening with AI and how to address that and handle that, both from a learning and a talent side and from a workforce intelligence perspective. Again, onto that practical conversation, we know that where the money falls is where the market goes, and you've got to understand that in your business, right?

[01:19:48] You've got the crown and everything to go with it. Yeah, I'll get an opportunity there to speak with Anil, the CEO, and a whole leadership team. We'll be getting some updates from them. I think I'll be doing a podcast while I'm there with the team as well.

[01:20:13] And then we'll be heading home for a few days to come back on October 19th through the 24th, right? We're still discussing whether that makes sense. I think it depends on how late in the day I have to go home and whether I can get out that day or the next day for the trip. Do I want to be home for two days or is three days enough? Those are kind of conversations, right?

[01:21:47] Homegrown? Yeah. Homegrown, owned, developed HR technology, right?

[01:21:52] We did the crown last year.

[01:22:24] I think it was the year before we did the fortune teller. This year, we're, so last year was data queen. The year before that was fortune teller. I don't know what we're going to do this year. We will come up with something. There will definitely be pink, that much I can tell you. But outside of pink, you know, whether, maybe we'll be the data diva this year. We'll see. Maybe I'll come with a bow or something like that.

[01:23:35] Well, I actually just confirmed that today. I will not be at day force. We had that. I did ask them. They were very sorry to hear that I was not going to be there. But Cliff, they were very excited that you were going to be there. So you will be doing the extended stay for the day force event. I will be heading home after the Oracle event. So I'm excited. Now, it's not the day force event. It's the actual staying in Las Vegas. I got the week before. Remember, we're training. This was how we were going to do this. I got the week before. You did the week after.

[01:24:47] There are a few others on the list. I know UKG is doing something in New York that week, that same week. We'll probably do that. We just got an invitation to a couple of other events. Yes, exactly. All right. Well, Cliff, it's a little bit shorter. I don't think we're full. Maybe a full hour at this point in. So how did we even get there, Cliff? It's not feasible.

[01:25:17] All right, everybody. I honestly do not feel like we talked for an hour and 30 minutes. Because I think we went shorter last time. How did we do this? All right. We were having a lot of conversations about DAL at the beginning. That was it. So we'll go with that. But we are wrapping up today. Is there any last minute reminders, Cliff, before we say our closing today?

[01:26:17] Yeah. Yeah. And just a reminder that if you do want to find out more about where we're going to be at and what we're going to be doing, please make sure you sign up for our newsletter to get ongoing updates on our research launches, where we'll be speaking, as I said. And when the surveys will be out, the data will be available, and when it will be launched. Be sure to listen to all our shows on the HR Hidal podcast on the work-defined environment. And if you'd like to help and support the podcast, please subscribe and leave a rating and review where you grab your podcast.

[01:26:45] To stay up to date with immediate breaking HR tech news and get all the behind-the-scenes content, you can follow us at Sapient Insights on LinkedIn and Instagram. Actually, really good places. I'll give you one clue that came out of the data set. One of the top social platforms people use to get their information is on LinkedIn. So definitely join us on LinkedIn. Cliff, as always, thank you so much for all that you do to put this all together. And thank you to Kelly Kuhn, Linda Galloway, and our marketing team at Summer Orlando, Cole Harris, and Caitlin Diamond for all their help in getting this up and running and out the door.

[01:27:15] Thanks to our listeners and community. We couldn't do this without you. And Cliff, the time has flown by. And we will say that we have finished our 9 to 5 today in honor of Dolly. And we will miss her greatly. But that's it for this episode of Spill the Tea on HR Tech. We hope it's been just the brew you needed to start engines running this week. And we will be back in two weeks with another pot of boiling hot HR Tech updates and insights for all of you. Thanks, everyone.