Spilling the Tea on HR Tech - How gaps in historical data are driving AI errors across women's healthcare, candidate screening, and HR tech decisions.

[00:00:00] One of the things that we know about Dayforce when we look at the data set on a pretty regular basis is that their customers, they have very specific needs, particularly on the time management side. There was a large grocery store chain that I love very much that was there talking about their 70,000 employees and how much the impact of the time clock made to that frontline employee. Just the fact that you could do seconds and not just minutes, because when you're in California, you're talking down to the second, right?

[00:00:24] The impact of being able to have the weather and information on there for someone who's getting ready to work a shift, shift swapping. Those are the kind of things that I know oftentimes get lost in the AI conversation, but it was great to see those stories and how AI is probably going to have an impact on those, but that those are still foundational to those customers and foundational to what Dayforce is doing. Welcome to the HR Huddle Podcast presented by Sapient Insights Group, the ultimate resource for all things HR.

[00:00:55] It's time to get in the huddle. Welcome, everyone, to Spill in 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. And we are recording today on June 3rd, 2026. We are already into June, bringing you all the news you can use this week.

[00:01:25] We have several friends who have kids graduating from high school this week. Our good friend Aaron Spencer's got a son doing that. We've got a couple others who are graduating from college, so it's a big week for all the graduates. We're very proud and congratulations to all of them. And does someone have a birthday coming up? Maybe you? Mine's not until July. We're good with that. I've got a granddaughter. I've got to get people prepared. Yeah, for my birthday? That big? That big? Appreciate it.

[00:01:51] Anyways, I'm your co-host, or I'm your host, Stacey Harris, Chief Research Officer and Managing Partner for Sapient Insights Group, a research and advisory firm, and my co-host, Cliff Stevenson, who did a great job holding down the fort for me over the last couple of weeks and was doing this walkthrough before me last time. But, Cliff, it's a lot going on. It is the middle of the summer at this point now. It's hot. I just came back from Dallas. You are heading out to a couple of different places. So, what's going on this week?

[00:02:21] Where are we at with everything? Yeah. Well, first I want to say I'm in San Francisco. I tried my hardest to frame the Golden Gate, which is back here. It is being blown out. And I almost thought about doing that virtual Golden Gate one that everyone has on Zoom. Oh, yeah? And I was like, but it's actually very close to what I'm actually seeing out here for SAP. I've just arrived, so I haven't gotten all the new info, but I'm excited. But also, I want to say welcome back to you. I don't know which direction we'll be in video, so I'm going to give you a high five upwards. Down. There we go.

[00:02:51] And for regular listeners, I think I said every, for the last two episodes, some ridiculous thing that you were doing that maybe you had run away at the circus or, you know, had joined the Olympic triathlon shooting team. But that was not the case. That was much more fun. Yeah, that was so much more fun. And there was a time when I was younger when running away at the circus seemed very, very cool. When I was in love with the elephants and clowns before I got scared of them, right? You know, before the- But you got to join the American healthcare circus and stuff.

[00:03:21] I did, yes. Can you talk a little about that? Yeah. So for those of you who weren't aware, and we didn't announce it or make a big deal because I think, you know, we didn't know kind of where everything was going to be going with it. But I ended up having to go in May 12th for some emergency surgery. Everything is fine now, but I had some cysts that had to be removed and they were a little bit larger than we expected, but everything is benign and we are all good.

[00:03:46] But it took me out of a couple of events and it was, we were, it was unexpected. But I think the bigger thing is that, you know, one, dealing with the healthcare environment is really difficult, right? And it's always a challenge no matter who you are and where you're at. I don't find anybody who loves it, no matter whether it's fully paid for in other countries or here in the United States where we're realizing we're going to get a really big bill at the end of it, right? Even though we do sometimes have insurance for many of us. But what I, what was really frustrating and I, and it rolls into a lot of, I think what

[00:04:15] we're going to be talking about Cliff today is I had been having some challenges like many women of my age for several months, for multiple, almost half a year. And there was a lot of doctors that I had seen that were kind of like, Hey, you know, that's it because of your age. It's because of, of, of, of sort of your weight. It's because there was a lot of things they sort of pointed to even with the testing and none of them had the data to say what it was. And because none of them had the data to say it was, or put through any tests to actually

[00:04:44] double check that it ended up getting quite dangerous for me, which is why I had to have emergency surgery. And it just, it was one of those moments where I was like, look, I'm a highly educated business leader who is takes anything a doctor gives me very seriously, make sure that I'm taking care of myself, making sure I do all the things I'm supposed to be doing a bit on my own journey right now over the last for many of you know, for weight loss for a lot of the last year and a half. And what was so frustrating was how little data there was or how, how little expectation

[00:05:12] there was of the type of testing or the type of diagnostics they would have needed to find this. And so really when we finally found it was because I went to the emergency room and I was having some pains and we couldn't pinpoint where those were at. Right. And the emergency room caught it. And you know, one of the things I've been posting a lot about on, on LinkedIn has been about sort of women's healthcare, how little data it wasn't until like 1993 when, when it was required that women were included in all pharmaceutical and medical testing for medical equipment.

[00:05:41] It is just recently that we've identified how little information or how actually the sadder side is how much information came from old outdated thinking that rolled into current day healthcare material for training. And then that is now rolling into AI based training for healthcare guidance and advisory services, because that was one of the things that I did do like everyone else. I went and Googled all my symptoms.

[00:06:09] I went and did all the things that I was supposed to do and said, what could this possibly be? And you know, even though the doctors tell you not to do that, but the doctors use their tools. None of the tools came up with this thing that was actually after I went through it. And I had a lot of really good friends out there who I had conversations with. I tended to find that I have a lot of female friends, you know, Cliff, you and I are in a highly female based industry, right? More than half. I have to say more than half. And you guys know, I don't, I don't do too hard hyperbole with my stats.

[00:06:36] I more than half of the females that I spoke with that were in my age range or slightly younger, either had something like this happened to them or had someone very close to them, close to them, or something like this happened to them, which meant that statistically, that is a pretty high statistic that this would be in a, that large of a data set. And that, and when I talked to a lot of them, it was either diagnosed as something different from what I had, or it was diagnosed under cancer because it was considered cancer.

[00:07:04] Some it was not diagnosed under cancer because it wasn't considered cancer. There's a lot of different things. And so I thought just really for me, I think, and for everybody out there, one, if you are a female at any level, make sure you advocate for getting as much diagnostic work as you can, because it's, it's just dangerous to not know what's happening inside of your own body. But more importantly, we have to really advocate that AI only knows what historically we have been giving it right. And, and I know you and I talked a lot about that.

[00:07:33] I mean, this is very personal to me, but you say, you know, you've got sisters, you've got a mother, they go through this kind of conversation, right? Medications, things they're dealing with. Do you, you know, do you feel like we are taking this seriously enough on the AI side with the amount of information that's, that's out there right now? Yeah. Right. I absolutely, I think people are, it seems to be an all or nothing conversation. A lot of times when we talk about AI, right? It's like, it's scraping all the data, but it's, we keep forgetting that yet there isn't

[00:08:02] the historical data on women's health and women's bodies, along with a few other things. It's like, we're seeing an over-representation of certain kinds of data, right? Yeah. You know, and a complete under-representation of others. And it's one of the reasons why we're seeing a lot of both type A and type B errors from AI, right? Seeing things that aren't there or not seeing things that are there. So, and those can have, you know, we're going to talk a little bit about later about,

[00:08:29] you know, that can have, you know, compliance and risk effects, you know, can affect your business materially, but we're talking about human lives. And I think that it's really important to understand the limitations of these systems. And not necessarily buy into the hype that it's going to solve everything because regardless of how great the model is, right? Even if it was perfection, it's only as good as the data. And we are still hamstrung by our own personal issues.

[00:08:54] I don't even mean that, you know, how our biases work, but our historical biases as a society has caused this issue up to now, which is only going to be perpetuated. You know? Yeah. Yeah. So, yeah, I couldn't agree more. The diagnosis arrow, you know, we get it. You go into the doctor. They're the experts, right? And it feels weird to be like, are you sure? Is there anything else we can do? What about this? What about this? But I do think it's absolutely critical.

[00:09:24] I've seen it happen time and time again. Yeah. Advocation is the big thing, I think, really pushing to get the answers. But I also think to your point, being a, and stating the facts, I have a pretty, most of the doctors I work with are female doctors. I've generally gravitated towards female doctors. So it wasn't even like, I do think they're looking at the data they've got inside their data systems. But we do know that misdiagnosis of females, it's as high as 50% in some categories compared to males. And if you're a female of color, it's even higher, right?

[00:09:54] And so, Jerry Stringer, who does a lot of work in this, I've talked to her about her before and I've reposted some of her work on LinkedIn as well. If you're interested in this conversation, I know, reach out. I'm very interested. We've had a couple of really good healthcare system connected to HR, right? Tool sets that I think could make an impact on this. But I also think it's something that we're all going to have to advocate for, both women and men, because it impacts all of us, like you said.

[00:10:22] But with that being said, I am back and I am ready to go and healthy as can be for the most part. I will say I'm still trying to get my energy back. But outside of that, I've been given a clean bill of health from the doctor, which is why I went earlier this week or yesterday. I made the trip, although I did utilize all the things my doctor, I can't lift luggage and everything right now. And so I had to have someone help me like lift my luggage out. And I had to have, I didn't do the long walks in the airport. So I did a wheelchair. That was a very uncomfortable thing for me.

[00:10:52] Yeah. I was going to say knowing you. Asking for help is not my thing. And so I, there were times where I was like, look, it's a short walk. I can do this. But for the longer ones, I took my doctor's guidance. But I did end up this week in Dallas at the Dayforce Enterprise Leadership Executive Summit. And that was, I did it last year. They had me come back this year to share some of the updated data. This year, I had the opportunity to see David Ossip, who is obviously the founder and has a big play.

[00:11:21] And historically, him and I have known each other for years. It was great to see him up on stage talking about the, not just AI, but the power of the data that they have, right? And the payroll and the HR systems. But what was really powerful was their customer panels, right? I think, you know, one of the things that we know about Dayforce when we look at the data set on a pretty regular basis is that their customers are, they have very specific needs, particularly on the time management side.

[00:11:47] There was a large grocery store chain that I love very much that was there talking about their 70,000 employees and how much the impact of the time clock made to that frontline employee. Just the fact that you could do seconds and not just minutes, because when you're in California, you're talking down to the second, right? The impact of being able to have the weather and information on there for someone, right, who's getting ready to work a shift, shift swapping. Those are the kind of things that I know oftentimes, you know, get lost in the AI conversation,

[00:12:15] but it was great to see, see those stories and how AI is probably going to have an impact on those, but that those are still funded foundational to those customers and foundational to what, what Dayforce is doing. So it was a great session. We did share some updated data about what's happening in the market. And I know you are an SAP. We'll get more next week on that because you're going to be doing kind of like a brainstorming session, I think is what's, what's happening this week, right? In the, in the San Francisco. Yeah, that's right. There's going to be a lot of round tables, a lot of exchange of information.

[00:12:42] I am also, I always find the customer stories so powerful and you've heard it here first folks. We are going to start, you know, already our data is well known. And by the way, the reports out surveys out, go take it. Survey that. You're running out of time, but the time this comes out, you will have less than a week. By the time this comes out, you have less than a week. So go take survey. Yeah. Make your voices heard. Like Stacy said, don't be silent and get that data out there. Help yourselves, help others.

[00:13:11] But within that survey, of course, we're getting very large amounts, but we've really leaned into the last couple of years of really looking at not just that quantitative data, but the qualitative. What exactly are you saying? Let's use your words. We want to start doing that live too. We always report out from these sessions, but we're going to start probably now, probably at this session. But when I'm out there, I want to hear from you. If you, if you see me, grab me. I'd love to hear your thoughts. It doesn't have to necessarily be on the software in question, right?

[00:13:40] At the, whose conference it is. It could be something you're working on. It could be something that you're hearing about. AI mandates. It could be something that you're interested in talking about, about some new functionalities or even just the number of systems you have. I'm going to use this as a segue to talk about the webinar just completed with HiBob for the Australian, New Zealand and Asian audience. So that was really fun just because it got, I got to do it at nine at night. Stacy, you and I were night owls.

[00:14:07] I was like, yes, I'm fully ready for, I'm not groggy. Everyone else would be like, nine at night. We're like, no, not a problem. I've had 11 at night. Yeah. And a lot of that talk was, you know, on the issue of, you know, regulations and using AI and how do we make that connection to the business case? You know, maybe any of those kind of topics that you want to talk about, like, how do we do these things? You know, how do we connect to other people in our departments? Anything like that.

[00:14:35] Come find me where we really want to start getting videos of you out there. You know, of course we have our other podcasts. Stacy mentioned that was at the end where we're talking to, you know, some business leaders and some practitioners and some vendors, but we'd like to start hearing more and more from the customers really highlighting your stories. So just quick promo for that, as well as our survey. And we'll get more from SAP. Voice the customer on the street, right? Is it man on the street? That's right. We're working on that. We're working on the title. Let us know what you think. What's a good name for that? But yeah, absolutely. Go take the survey.

[00:15:05] Check that out. You just did a couple of webinars as well with ADP that I think people will find out. I did. Yeah. So I think we're still promoting because the ADP women at work was such a great session. That was a panel session. If you, if you get a chance to go through that, I still, it's still very valuable. I guess it wasn't just about women and work, you know, women leaders at work. It was about the work that we were doing and how the impact of being a woman in that environment changes some of those things, the dynamics of, of the interactions, the things we have to think about differently. Right.

[00:15:34] Which I think is part of that whole bias conversation we've been having. We also are, did a recording, which is really great of HR and IT and how they're working together with a lot of focus on the practical aspects of, of what is it that, that IT and HR need to collaborate on just on the ground level, like provisioning of technology. Right. Onboarding and offboarding those kinds of things. Right. And so we had a lot of great conversations with Stephen Dental on that. That was wonderful.

[00:16:01] And so, yeah, we've, it's, it's been a nice couple of weeks for webinars and events, even though I've been out of the office. It definitely has, we've kept busy. Let's put it that way. Cliff, right? Yeah. Tammy Smith that we work with. If you listen to this, you did not hear all this work that Stacy's been doing. She's been resting and recuperating. She's certainly not been walking distances of Amy. No, no, no. Yeah. Yeah. I know my friends, friends have all been checking up on me. You know who you are. I do wholeheartedly appreciate it. I got flowers.

[00:16:30] I got cards. I got some inappropriate word puzzles, which I loved. Thank you guys. I got a lot of books from my good friends and a lot of recommendations for videos and movies. The most valuable one was Bridgerton by far. My goodness. You're not supposed to get the heart rate up, Stacy. I know. Great show, by the way. Yeah. But exciting to hear about all those acquisitions that you made. Here's another acquisition that happened. That was the world's worst segment.

[00:17:00] And that is BetterWorks acquired Ripple to this is so Ripple looking at what they've done. You know, the idea here is to collect sort of, you know, signal from the noise from all of your employees to help managers sort of effectively understand all the different data points and summarize that. That seems to make a lot of sense for BetterWorks, one of the leading performance management tools. They definitely show up in our research quite a bit.

[00:17:27] So it makes sense to get, you know, acquire some of the technology when it comes to AI, the sort of purpose built as part of building a complete profile of a employee. Yeah. BetterWorks is an interesting one. They show up quite well in the research when we get enough of them. They don't have a ton that show up in the research, but when they do show up, they show up really, really well. You know, they've been around for a while, probably one of the original solutions that were sort of voice the customer engagement and then, you know, sort of moving into performance.

[00:17:53] But but this gives them sort of a more AI friendly sort of performance management model here. Right. I'm interested to see, you know, I think we will see more acquisitions throughout what we would consider the performance succession management career management space, internal mobility.

[00:18:09] There's a lot of those point solutions right now, I think, in the market who are trying to figure out how they create more of a platform model that plays with the idea of the platform cluster that you and I have talked quite a bit about, which is how do I become more of a destination or at least become a bigger destination to attach to some of the anchor systems? And I do think, you know, we're going to see a lot of these organizations either acquired by a larger solution or grouping together to create more and more holistic environment here.

[00:18:34] So this was not surprising, but it'll be interesting to see how this sort of grows their their ability to be a primary solution that you can attach to some of these bigger platform clusters. Right. Yeah. And speaking of that, because I think, you know, this is a bit of a throwback episode and that it seems to be very AI heavy. And then we're going to talk about some of those stories that strangely all connect. That does seem to happen on the show sometimes.

[00:18:58] But before that, I do want to mention also, as long as we're kind of talking about joining in, especially from these large companies, is that Rod Johnson just this week and just a couple of days ago, I believe, joined UKG as their new chief revenue officer. His tenure is, you know, CV includes Oracle and Infor. Years of experience at, you know, sort of these larger, more complex companies. Very interesting to see just the pedigree, I guess, of what UKG is building over there.

[00:19:28] Yeah. And this one, it was, you know, a lot of what you're seeing with UKG. And we just did a virtual summit for them. So we did. We should probably mention that. I apologize to Jennifer. We should have had it in the earlier comments. But we did a full day virtual summit for them. I was able to attend part of it because I was dealing with some of the other stuff from getting back from surgery. But Cliff, you attended most of it as well. And what we heard really clearly, I think, you know, probably one of my favorite presentations was from Corey, who kind of is their head of AI strategy.

[00:19:56] And him and the head of product really clarified, I think, that, you know, AI is being weaved into sort of the huge amount of data that they have access to because they are one of the largest time management systems in the market. Right. They have a lot of other things in HRMS and payroll and good things. But from a time management perspective, they have some of the largest companies in the world on the original sort of dimensions platform now and that is combined now with the UKG Pro product.

[00:20:26] And it was clear that that is sort of the heart of where they're emphasizing that AI conversation. The data no one even knows that no one even realizes they have was a lot of the conversation. Right. And I think to some extent, you know, the same thing we're seeing with the leadership that Jennifer's building out here. Right. Jen Morgan's building out, I think, you know, leadership that has experience in a lot of different places, bringing together the idea that, you know, some of those hidden capabilities, those hidden relationships will help sort of get them to sort of the next level where they want to be.

[00:20:55] We know UKG is probably one of the next organizations. This is, again, only a speculation because we don't know there's no numbers or anything like that. But we would assume based off of their private equity current ownership that there is goals to get them to an IPO eventually. Right. And so we're assuming that over time you're going to start to see a real big focus on sort of what that that revenue looks like. Right. And what that mix is when you're when you start thinking about a public based organization versus a private equity organization. Right.

[00:21:24] Yeah, it's interesting because over the last couple of years, let's say, as UKG has shifted this focus towards really just saying we are going to be the frontline worker tool. Right. When you think of frontline worker, we want you to think of us. And so there was sort of a it's sort of started at the almost philosophical at the sort of company values. We've talked about that before, how important it is to have that and what does that mean?

[00:21:51] And now we're seeing the execution at the tiniest detail level. I saw a lot of that. There are a lot of talk about very minute, but very important details when it comes to scheduling and, you know, labor optimization and things like that. You know, not exactly the things you want to come out and just like, wow, how cool. But it really does matter, especially when you're dealing with it's not even just about compliance and regulatory things. It's being able to adapt very quickly to shifting things.

[00:22:18] You know, if gas prices go up and this person's going to, you know, maybe can't go this distance and can't make it to this thing. How quickly can you adapt and make sure that you have the right number of people at the right place? And that's what they're working on. We have all this data they're saying and we're going to be able to do it. So I heard a lot of the specifics of, you know, here's exactly how these scenarios would play out with a heavy emphasis on customers, which I said I always love to hear. And they have a lot of those customer stories. Yeah, there's a lot of great examples, I think, that they shared.

[00:22:47] And I thought that was probably, I mean, I really like it when the companies show the examples that have real life situations built into them. And I think we definitely saw that from the UKG Summit. We'll have a little bit more once you and I both get a chance to look at the recording sessions and make sure we kind of sync up on what we heard. But we'll probably post something out on LinkedIn as well. But it was every company over the last couple of weeks has now sort of given us their view of what's happening in AI.

[00:23:14] And I think everyone is taking, you know, some are doing agent of record. Some are doing system of work. Some are doing a focus on sort of content at the center and contextualization, like we saw with Cornerstone. And some are really honing in on those industries that they serve best and the amount of data that they have access to. And so I think, you know, we're seeing all flavors of this right now in the HR space.

[00:23:35] The one thing I will say is that no matter which event we went to, price was the number one question for every analyst and every practitioner. And it was the biggest conversation. So if you think you're having challenges with it, every one of the vendors are having challenges. No one has a great answer. I just saw a good post put on someone had done one on Workday's cost and Oracle's costs. And I know we noted that, you know, that is the number one thing holding back big enterprise organizations from acquiring AI where they need it. Right?

[00:24:05] Yeah, it's unpredictability. You know, I was talking, I mentioned this in the last podcast, but talking to Manchu from Cornerstone and Mini Paris as well. These are all, you know, public conversations. I put this in the post. You can check that out. We're also going to have a longer blog post you can check out about this. They heard that loud and clear. And they said, you know, it's not even about the cost itself. It's not about the rising costs. It's unpredictability of costs that they found that their customers can't deal with. If they knew it was going to be X amount, even if it's higher, sure, they can budget that in.

[00:24:35] But it's not the way corporate budgets work. You can't just go, oh, it's this much. And one story that we're not going, well, we are going to talk about it because I'm talking about it now, that at first I didn't know if it was relevant. Plus, I figured everyone's seen it, is that we're now starting to see usage-based pricing for the most common consumer AI tools. We're seeing it on GitHub Copilot, which is the most used coding AI platform. We're seeing it across many of Claude's products and Claude Code, in fact, specifically, on and on.

[00:25:04] And immediately we saw a drop in the number of people using it. It caused a huge uproar. And, again, people kept saying, I don't care if it's $100. Just let me know. I can't have it be, you know, it's this much and I'm out. I can't do my work anymore. Right? So it's that unpredictability that is interesting. And when we talk about the pricing, it was so interesting. The Cornerstone heard that and said, we are not going to that model. We are not going to a usage model specifically because we hear the concern about that.

[00:25:32] Another concern I do want to point out, though, and this one was kind of interesting. This is a lawsuit. So anything from here on out, if I forget to say allege, anything I'm saying here, this is just part of the lawsuit. There are no facts here. This is all a story that is in development. And so take that with anything from here on out. We're saying alleged either directly or it is implied.

[00:25:56] This is from lawsuit Kistler versus Eightfold Eight AI, which is a class action brought by. It is. Yeah. As you said, I don't have it here in the notes. No, it was from the EEOC originally. Optimize your business with a workforce AI edge powered by Vizier. Make smarter decisions and achieve better outcomes with real time AI insights delivered to every people leader. Explore more at visier.com.

[00:26:23] And it's towards justice is the nonprofit that originally brought it. And according to the lawsuit itself, it wasn't just that Eightfold AI system was biased. What was going on, according allegedly was going on, was that the Eightfold AI system, one that we also track and have kept an eye on, according to this lawsuit, it was going through. It was ranking candidates. And it was cutting some out of the process without anyone seeing them.

[00:26:51] Now, the lawsuit isn't about the bias or whether it was even correct in doing that. The problem was that it was doing it in secret, meaning there wasn't human oversight on it, not whether or not people were getting cut unfairly, whatever that might mean. The lawsuit's concern and the broader concern why we wanted to talk about it today is because it brings up another point of AI, which is the sort of autonomy. And we always talk about agentic AI and their value in being able to make its own decisions.

[00:27:16] But then, you know, you can imagine why a lawsuit might get brought up because it was just happening supposedly, allegedly, without being told to do this. And this taps into some concerns that I saw. And we should know it's being it was doing it as part of the design of the system, right? Like it wasn't like the and the configuration and setup of the conversation. Right.

[00:27:39] But I think that the bigger side of this, if I understand it, is that is that also to that the the person doing the sort of putting forward their their their resume didn't know it. Right. Like so it was it wasn't also just it wasn't about sort of that that middle. It's it's it's that the does the candidate does the person who's being impacted by I have a right to know that AI is at the front of that. Right. Right. And that's supposed to be part of the Fair Credit Reporting Act. That's where this fall fell under, I think, more or less because we didn't know where else to put it.

[00:28:09] But the idea being that if decisions are being made about you by these systems, you have the right to know it. And so you could imagine why that would kind of be like your credit score. Right. Bingo. So there's supposed to be specific procedures. You're supposed to know what's going on. And they're saying that that was not reported as part of this. Yeah. And it's a big deal. Yeah. It was interesting. So I was looking on the lawsuits, the attorneys, one of the attorneys at least involved.

[00:28:32] And it was kind of interesting because there's sort of they literally call it framing the issue, you know, saying that, you know, some stats that 52 percent of workers is from Pew Research. I say they feel worried about how AI may be used in the workforce. And 45 percent. This was from Stanford study. 45 percent said that they don't believe in the reliability or accuracy of AI. So, you know, this is sort of tapping into this sort of public concern. Right.

[00:28:58] So we've got that going on while at the same time there are concerns about the people they're using, even the people that are feeling AI are worried about the sort of uncertainty of how is it working in terms of usage and consumption of inference and what's that going to cost me? So it is kind of interesting. We, you know, not to sort of blow our own horn, but heck, why not? You know, no one can stop me. We started predicting this, I would say, almost a year ago, Stacey.

[00:29:24] We said, you know, right now we're hearing about the sort of possibilities, capabilities, right? We're wide eyed. We're looking towards the future. But we know with all tech, there is a bit of a roller coaster of sentiment. Right. We're always super excited when cloud, the cloud, we can do everything cloud. And one goes, whoa, hang on. I don't know about this. You know, there's going to be security and privacy issues and everything else. And then, you know, we sort of even it out. But, you know, we tend just as humans to get like reverse polarity. I think somewhat.

[00:29:54] And I think we are on that downswing now, right? We are. Yeah. There's always a swing, right? And I know sentiment is definitely on the outside. We're hearing more and more about it. But I think, you know, there was a time. I know sometimes I sound like, yeah, when I walked uphill both ways in the snow, right? But there was a time when I remember people saying, oh, the Internet was going to kill the library and nobody was going to read anymore. And, you know, we fear the things we don't know. And there are a lot of risks.

[00:30:24] I'm not saying there's not a lot of risks with it. But I think, you know, we and it hasn't helped any that there are a lot of people trying to make money off of the fear mongering. We had that conversation online this week with and, you know, sometimes you just need to follow the money and not listen to the hyperbole. But I do think that, you know, we have to acknowledge that sentiment. And that's what I think is right now. There's a lot of poo-pooing. It's not important, but it is important, right?

[00:30:50] And I also want to take this time to I'm just shouting out Marissa Cabas, the wonderful journalist. She has joined LinkedIn specifically as an AI hater. Love her. Love her. Just shouting her out just as free exposure. But go check that out. It's really fun. But, I mean, she's also been doing some great work about detentions and other things.

[00:31:07] But so interesting enough, just, you know, for listeners, viewers, both you and I, Stacey, both sort of found some interesting stories about this idea of the intersection between what is going on with this sort of general job market and AI, right? Because they're such big stories, we kind of tend to assume they're related. But you and I both came to two completely separate studies, both more or less saying they're not related, but showing different data. Fascinating stuff, especially if you're kind of a data nerd.

[00:31:36] Because I saw what Stacey sent me. I said, oh, yeah, that makes total sense. And then I had one that I had seen that also made sense. So we'll start with the one that you sent because it was a little more, this is from Benedict Evans. We'll have a whole link because there is a very large blog post. It was a good story, right? If I recall this one, it's been a while. But, you know, Benedict had, I think, posted this article and he had posted through this research.

[00:32:05] And it really was sort of saying, analyzing which jobs companies and industries are most exposed to the AI. And that everybody's trying to analyze this, like every consulting firm, every online assessment, every HRMS right now is doing a, which is going to be impacted by AI. And I've been very much like, I guess, it's a, one thing I think we're finding in the market is that, you know, it's a very task level analysis right now. It is not every job and the whole job.

[00:32:33] It is, it is task, even engineers, like there was a big post this week by, by one of the NVIDIA sort of investors in a couple of them where they were basically saying, yeah, maybe it's not going to be an apocalypse of AI replacing everyone. Because we're actually hiring more engineers than, than, than we, than we were previously. And we're like, well, yeah, because you're in, you're creating a demand for the space that you are improving on and efficiencies, right?

[00:32:56] And what I thought was sort of interesting in this one is that the survey data, you can kind of see that the jobs that are sort of having an impact is, you know, they, they kind of showed 50 years of financial automation that, that, that, that there was incremental changes in every business model. Right. But the thing that really changed with sort of the internet and other things, right, sometimes wasn't the thing you thought was going to be incremental changes.

[00:33:22] The big thing was that actually that we, no one thought the taxi business was going to be the business that was in, that was most impacted by our phones coming in place, right? Or by machines coming in place.

[00:33:36] And I think that was what the story was kind of getting at was that while we're looking at the incremental changes and incremental jobs that are mostly connected to the spaces where we're all looking at something off to the side is where the real change is happening, because there's a need or a, a, a thing that could not get solved any other way until this new technology was in place. And that thing is going to disrupt a whole other market. We're not even watching. Right. Long distance. We would just have this conversation phone lines, right?

[00:34:04] We basically said with mobile phones, you would then get rid of all of your home phone lines. Well, that took 10 years to get rid of home phone. And those same businesses that were in that went into the mobile phone business. Right. And so that was kind of, I think where the story was heading. And it was what I found most interesting. I don't know, Cliff, if, if that fit with the other one, you were going to be talking a little bit about. Well, no, but actually another one from that same one from Ben Evans work was that, you know, they talked about even with just computers, right?

[00:34:31] I mean, not even going to like the specifics, but, you know, once there kind of been the advent of personal computers and we have all these systems, there was a prediction even back then that we weren't going to need accountants because basically, you know, who needs math nerds? We've got computers now. And instead, accountants kept going up. I know it's a bit of a platitude that I do think is overused that is basically, you know, AI isn't going to replace your job.

[00:34:57] You know, it's just the people know how to use AI is going to replace job or something like that, right? The job itself is going to change. And I don't know if that's necessarily the case, but I think they are trying to make the point with data and statistics. Again, we'll put this in there that maybe that's a little oversimplified, but that is what's happening, right? Is that the job, you know, accounting still needs to exist. It still has specialized knowledge and there is still work to do. It's just the tools do adapt and you adapt with them.

[00:35:24] It's not that accountants were out of jobs or that, you know, taxi workers are out of jobs, but the way, the structure around it, the underlying fundamental aspects of work sometimes change. Yeah. And I think the biggest thing here is they showed that for every new technology that increased people doing that work oftentimes versus reduced it, right? And especially in the areas where we thought it was going to basically replace those roles.

[00:35:48] And not to say that not everything, you know, we don't have as many people building, you know, buggies these days, but there are still very good buggy makers, right? In the market. And the Hermes story about Hermes bags and using your talents in that way is still a very valid story about sort of rethinking the model about where skills fit, right? So, sorry, off on a tangent there. No, that's brilliant.

[00:36:10] And so this other paper, this was the paper itself is called the remote work junior hiring research paper from Lambert and Schindler, but it was flagged in a Financial Times article. And they, this is an academic paper, but they are arguing the case that the real fundamental shift that's changing where why we're not seeing so much junior hiring is not because AI is doing those jobs. It's because we've moved to so much remote work.

[00:36:34] And so the idea being here that, you know, I'm going to just kind of more or less paraphrase and sometimes quote here is that in knowledge work environments, right? What we sometimes just call white collar work, right? You learn through proximity. You basically, they call it informal apprenticeships. You watch other people work. You learn all of these skills. You go up, right? They're basically, you start here and you work your way up, sort of the classic model.

[00:37:00] So that sort of fundamental way we do work, as we were just talking about, that line has been severed with the increase in remote work. It has also brought other benefits, but it has changed the way we do work. And so we don't have a sort of automatic pipeline of people within. Now, I would argue that there has been so many other benefits to that, right? Because it allows people from different geographies or people wouldn't normally be in there to work, but it will require us to shift how we're doing.

[00:37:30] And I do think it's especially susceptible to the pressures of just general economic anxiety, right? It's a lot easier to sort of not hire and cut people without that sort of proximity. So it's interesting because both are sort of saying AI is not having the direct effect on jobs you might think it is. And both of them make compelling data-backed arguments. Very data-backed arguments. And I think that idea that AI is impacting just junior workers or disproportionately impacting workers.

[00:37:58] And we had Jason Everett just put a post out about this, about how are you training your entry-level people? I really like this when you look at the data because I do think that working from home has a much bigger impact in a lot of ways than we gave it credit for. And a lot of the coming back to the office, it didn't fix that situation because we are still working the same way. We're just doing it in another location. We are still not collaborating. We're not taking the junior person with us to go do a thing, right?

[00:38:26] We're still doing everything very virtually, even in that environment. And so I think we've had to rethink how we train analysts. We've had to rethink how we train people coming into operations. And I think that is something people, if you just blame it on AI, you're really good. You're going to lose that story there, right? Yeah. I was thinking about, I'm trying to still find a great metaphor for this, but I heard something earlier about when mines started to go away from just sledgehammers and pickaxes and mines.

[00:38:53] The automated system didn't make it so that, okay, great. Now we've got the carts and we've got these machines can do it. If I went down into the mines, I could be like, well, we put all the workers back in the mines. The way the work is done has shifted. There are still mine workers, but just going there and being next to the machine, that is not really what's happening here. You don't need to just do that. You need to think about how that shifted. Because there are still miners to this day. There are still coal miners to this day.

[00:39:22] So miners in different parts of the world, all over the globe. And it is a very hard job and, in many cases, a very health or lifetime reducing job, right, too, from a health perspective. So, you know, but this is the value, I think, of these conversations is that assumptions get made because of whatever's gone on the news or because of whatever hyperbole is being, you know, or whatever your neighbor is saying or whatever. You know, we have this issue across the board, around the globe.

[00:39:50] But if you go back to the data, now the data isn't always, I think sometimes we read data incorrectly, right? Like, just because one thing goes up doesn't mean another thing goes up. And we always have to keep that in mind, right? But I do think that data gives us a lot more room to think about the unanticipated outcomes, the things we don't theorize. Because oftentimes research is done on a theory and we think it's happening. Sometimes it's great to see what maybe we didn't expect to happen, right? So, yeah. I know we're running out of time and we've got to wrap up today.

[00:40:20] So I will let you move into your speed round in our events, I think. Oh, yeah. Well, let's go in. So, as I mentioned, I am right now at SAP's event. We're going, I'm going to come back pretty fired up. You'll probably see some content. Check out LinkedIn, our YouTube channel. As we start putting in some more of those sort of short videos, we want to hear from you. I would ask, as we're going to these events, I'm going to name a couple more really quickly. Let's see.

[00:40:45] By the time this comes out, you still will have time to meet me in Denver for Prism HR Live, part of the Venture Family of Solutions. That one will be really nice. It'll be at the Gaylord there in Aurora next to the airport. Good to go to nice locations. I love Denver. I never get to go to Denver. I'm good friends in Denver. And it's nice and it's awesome. But you'll be in beautiful Southern Florida. I'll be in beautiful Sunrise, Florida. UKG, we're doing a payroll customer conversation.

[00:41:15] And that's kind of fun. We're going to be really brainstorming how we rethink the idea of payroll. So, yeah. And then you're going to be, and then I think the next, we have a couple other events that we may be at throughout the summer we're still working on. But I think the next documented thing is the OC Tanner in September, right? Which is in Utah. Yeah, absolutely. An amazing event. Always a good venue. I would say along with WorkHuman, probably the most sort of non-traditional outside of, you know, anything you could consider sort of corporate. It feels good. It's very humanistic. Very interesting.

[00:41:44] But we'd love to see you at any time. As Stacey said too, at the beginning, please share your stories. We're trying to, you know, please, I don't think there's any value in remaining silent. Let's all share our data of any kind, but especially anything around our health, because that is most important. And also, I do think, Stacey, that I love that you even shouted out the minors. I don't know how many of our listeners are coal miners, but any of you, we respect your work. I don't know how much of that's our demographic. I have some family members. Even if you can't hear us.

[00:42:13] Yeah, I have some family members. A friend I just met, actually, at the event I was just at. We were just commiserating about the fact that our families are both from sort of Appalachian-based towns, right? Like mine was in Pennsylvania and Ohio. Hers were in West Virginia. So we've definitely got family members in those areas. I do want to mention one other thing. I will be keynoting the Chicago event for Cornerstone. And I think that's in September as well. So everyone should be aware of that. So if you are looking to sort of go to the Cornerstone event in Chicago, that is.

[00:42:42] And then I will be at the Workday Rising event. And you may be as well. We're still working through that, which will be in October. I think early October. And then we'll be doing HR Tech Conference. So we will have some big announcements about the HR Tech Conference coming up in the next couple of weeks. So watch for that as well. But with all that being said, I know I've got to jump for a call, Cliff. So we're going to have to wrap things up today. So my apologies, everyone. We're going to give you a short show for once, which may be kind of nice. You can get through your driving or whatever you're usually doing.

[00:43:11] As we wrap today, a couple of things again. Just a reminder, the survey is open. Go take the link now. Tammy will not keep it open longer because we have specific things where we're trying to get our data done this year. So you really got to go and take the survey. She may take some bribes if you have really good chocolate. But that is about the only thing that will help people keep it open. Also, if you want to hear more about where we'll be at, what we're doing, be sure to go to our website, sign up for our newsletter and the ongoing updates where we'll be speaking, visiting, and how you can participate going forward.

[00:43:40] Be sure to listen to our sister HR Huddle podcast. At HR, we have a problem run by our Cliff. Our wonderful friend, Cliff Stevenson. Cliff Stevenson, because we haven't updated the data here. I've got to write the new name in here because I still have Terry's name in here. You also have the pivot effect, though, if you do want to hear Terry. We also have the pivot effect with Terry and Susan. And now we will be doing the man on the street, the voice of the customer podcast. So we've got lots going on in the podcast run. If you'd like to help support the podcast, please subscribe and leave a rating.

[00:44:08] That is the best way for us to get your feedback. Do you like these shorter ones? Does that help? To stay up to date with immediate breaking HR tech news and get all the behind the scenes content, you can follow us at Safian Insights on LinkedIn and Instagram. And a thank you to our production team, Brand Method Media Group, who helps us produce our podcast run by amazing founder, Kelly Kelly, and our marketing team, Summer Alano, Cole Harris, Caitlin Diamond, Linda Galloway, and all the other people who get this up and on their own. Now we've got, I think, Kelly. Yeah, right. We've got a team that's just growing. Thanks to our listeners and community.

[00:44:37] We couldn't do this without you. And that's it for this episode of Spill in the Tea on HR Tech. We hope it's been just the brew you needed to start the engines running this week. And we will be back in two weeks with another pot of boiling hot HR tech updates and insights. Thanks, everyone. Bye.