Spilling The Tea On HR Tech- A Big Clarity Deal and More AI Chaos

[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. Welcome to Spill and the T on HR Tech, where we focus on the hottest HR tech news everyone needs to know to be in the know.

[00:00:29] 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. Boy, it's already 8 August, right? August 13, 2026. I feel like we have just started summer and all of a sudden we're wrapping summer up. Kids are back to school. We're starting to work on our paper. It's kind of a crazy time, but we're bringing you all the news you can use this week. I'm your host, Stacey Harris, Chief Research Officer and Managing Partner for Sapient Insights Group.

[00:00:58] We are a research advisory firm, if you haven't worked with us previously. And joining me today for the conversation is my co-host, Cliff Stevenson, Director of Research and Principal Analyst for Sapient Insights Group. Cliff, welcome back to the show. It's good to be here. As you said. What tea do you have today for our first film of tea? A jasmine green tea. A little lower in the caffeine, but. You're doing good. I'm still doing my chai tea. My chai tea that gets me through the day that was given to me by our dear friend Pragya from iSolv.

[00:01:28] I use it almost daily. I haven't changed it up. I do have some mint that I get from my own garden once in a while that I will steep and make my own mint tea. But if anybody wants our tea recipes, we can let them know. But there is some interesting tea. A couple of hot teas on the HR technology space this week. So, Cliff, I'm going to let you take it away. Where are we going to start our conversation today? I'll start by saying that, first of all, we are in short sleeves.

[00:01:55] I think both of us, although the summer is winding down, it is still quite warm. Not owning a whole lot of pink short sleeves. I am unofficially endorsing the Chargers for any of the fans out there, but that is not my normal team. But as you said, we're still staying as cool as we can amongst all of this heat. And I'll tell you one thing that was exceptionally cool was the whole last week. Some of the tea we can tell you. Well, only some.

[00:02:25] We got together. Yes, we got together in what I'm calling a research retreat. It probably should have been a reality show because we were all sharing the large house. And some of us were staying there. Some of us were coming into the Raleigh, North Carolina area and going through all of the data from this year. What we're going to release at HR Tech, and we have a full schedule for that, that we can give you. But I can tell you that there's some exciting stuff. There's going to be some surprising things. Yeah.

[00:02:55] And I am really, really looking forward to it. But what's really interesting, and some of the stories are actually somewhat related to that research and what we found. And we can't really say too much, right? We definitely want... We got to validate it. We call that the first look, right, this week, right? And if we don't double check and make sure we understand the context around it. We actually ran some roundtables this week to do a little bit of sort of understanding of that concept.

[00:03:23] But, yeah, we can't just come out and say, here's what we saw, because without context, data is not valuable. It is really... All the data is interesting, but at a certain point, you've got to add the context and the analyst insights. That's what our job is, right? Yeah, I think you've been reading ahead in the story, Stacey, because there's going to be a couple stories just about that. But I did want to mention, because we've mentioned that we were going to release this at the MAGA session at HR Tech. But as that's getting... As we keep noticing, time just keeps getting faster.

[00:03:52] It's going to be here before you know it. So we actually have a more detailed breakdown of everything we're going to be doing at HR Tech. Yes. So that is the 19th to the 24th of October. So that Monday, we will have a dinner, right? Yes. The unplugged dinner. Yeah, it's a special dinner for customers, friends of the survey. So it is the invitation to get that night. Right. And it will be before the HR Tech Awards event. So we are not coinciding with the HR Tech Awards event, right?

[00:04:22] Mm-hmm. And on Tuesday and Thursday, we're going to be leading some workshops, building a business case, which will be intensive workshops on both those days. The Tuesday evening, we're going to be doing a joint session with Harbinger on, I think, title to be determined, but more or less HR Tech buyer versus developer, meaning buying tools or creating your own using AI. Yeah. And kind of how that all works.

[00:04:51] How those two decisions are being made, what are the big decisions we're seeing being made in that space? Yeah, it's this idea that, you know, for a lot of times we do, you know, who is the HR Tech buyer? What are the things? The vendors are always liking that information. But now I do think both vendors and practitioners are trying to understand, like, where are there opportunities to be a builder? And where are there places where we maybe should just be a buyer? And we're going to get into that, right?

[00:05:17] And that's actually something that we dug pretty deep into with the survey questions this year. We had noted that that was coming up. So we tried to get ahead of that by asking people directly in what areas they might be doing that and some comments around that and all the sort of research around that. So we're very excited because as far as I know, this is no one's done this sort of level of intensive research on this very specific question that is kind of at the forefront of everything going on right now in the HR Tech space.

[00:05:47] And then Wednesday night will be or the Wednesday, sorry, afternoon. Yeah. Night if you're in Europe and our friends in India as well. Sorry, it'll be about 2.30 in the morning for you. That will be the keynote mega session. I, Stacey, draws every year complete sellout, standing room only. I'm telling you this not to riz up Stacey, as the kids say, but because to get there early.

[00:06:17] You know, you want to be able to see it. There's going to be some slides with some numbers on it. You want to be up front so you can see them. Yes. You know, everybody sits in the back and then they all do this. And we're like, we're like, get up front, right? And come early because I start as soon as the clock hits there because we cannot afford to wait for everybody to come in from the sessions. Because we have not enough time to get it out there. Never enough time, right? Yeah. And then Thursday morning, I'll be leading an asset expert session. You can come. That one is also first come, first serve. So please try and get there.

[00:06:47] The topic I'm going to be talking about, and I'll have by that point, I'll be able to share all this data with you live. Because we're going to be talking about AI pricing and resourcing. You know, where is the money going? What do you need to have structurally around AI? How are budgets working, et cetera, et cetera. So I know that's quite a lot. But I did want to, you know, again, get this stuff out early because these things really do fill up.

[00:07:12] And we're already at the stage where people are coming in and setting schedules. And I imagine you as a listener probably are too. Yep. Get your schedule set early. We do have a lot of other events around that that we'll talk about after we get done with the session today. We try not to load you guys up with too much calendar information. But I do think we have some upcoming sort of events we just want people to be aware of. There's some virtual appearances that we're going to go on. You were just on or about to be on, sorry, the Meg and Amy show. Yes.

[00:07:42] Yeah. We do want to note, if anybody knows Meg and Amy show, it is actually one of my favorite shows. It is a podcast by the team who used to run a lot of what happened at SAP SuccessFactors. But both of them also worked at Workday for many years and or Amy did. And I know that Meg worked over at Oracle for many years. So they have this wealth of knowledge in the industry that's absolutely amazing. They're doing a lot of investments and other things.

[00:08:10] They've asked that I join them September 30th to talk about the data. So we're going to a little bit of a preview. They asked me which dates. There was an earlier one of this when I said, OK, that's as close as we can get to when we're going to be delivering the data set. And I'll have more insights that I can share there. So that's going to be September 30th. We did just record today, though, that will be coming out around the first week of October, second week of October, Workday Future of Work podcast with Nancy Weidel,

[00:08:35] who is one of my absolute favorite people in the product area for Workday's HR systems and HR technology environment. And we talked in that presentation. It's a really, really good conversation on why AI adoption is failing at the point of work coming out. And in that session, you will also learn why I believe an Alanis Morissette song is the song for HR.

[00:09:02] And Nancy had another opinion about maybe a Dolly Parton song. So you can come and hear which of those you think is the right answer. Of course, mine is with the Gen X conversation. And then, Cliff, you have a couple of podcasts that you're going to be in. One is the HR Morning Show. Can you talk a little bit about that? Yeah. So that one, there was a live show that we did, but it's recorded. You can go find it. The title of that one, it's the August 11th one.

[00:09:28] But you can also search by the title, which is Ditching AI Resumes and Human-Centric AI Council Agenda. I appear on that as part of Trent Cotton. You may know him from iSIMS. I had me on to talk about this something, a phenomenon that we've talked about quite a bit on this show, which is this ongoing more or less arms race between applicants using AI to generate resumes

[00:09:55] and apply for jobs and the tools that are used to sort of gatekeep that. I definitely am. The tea is so hot. I want to smoke because you had a very specific experience just recently that's kind of along the same bit. Can you talk about that? I can. So for many of you who don't know our process on the research, so we do this big survey a year. And then also, and depending on the year and what we're asking, we oftentimes do roundtables that we put together. And the roundtable, we've been doing these for years, right?

[00:10:24] So we basically send out an email and we basically say, hey, anybody who participated in the survey, here's some three things that we want to talk about. Please let us know if you want to be part of a roundtable virtually. There's usually a small incentive with it because we do realize that we're taking your time and you're going to be sharing some data. We did four roundtables just this week. And what we realized is that only the people who have taken the survey get an email to participate in this opportunity, right?

[00:10:53] And all of a sudden, we started getting this huge influx of people who we didn't send the email to. Now, we sent it through, I won't name the name of the organization, but it's one of the big marketing platforms because it's easier because we have a very large list of people who participate in the survey. We sent it through that. And what we realized is that that platform had been compromised because it was a direct email to these people. So the only way that that had happened was that somehow that platform had been compromised and people were able to,

[00:11:23] someone was able to get into that and able to basically hack it as best as I can understand and see the page where you could sign up for our environments. And we knew it was coming through that because there was no other way. I mean, they were basically emailing directly that they were interested in participating. That was how they did it. It wasn't like a page that was open to the market. So then we caught all of that spam, but we thought we'd caught most of the spam. But what we realized is we must have missed maybe like a handful of them.

[00:11:52] So the first two brown tables went really, really well, which, by the way, I found it some really interesting ideas about why small businesses are not using their current vendors' product help environments. Let me just tell you, there is a certain tool that makes it easier for them. I will let you know in the report. We're going to share some of that data. But we also did companies over 50 employees. So we made it sort of a very small and very big organizations.

[00:12:18] Our last one today ended up getting spammed in a way that I have not seen since the days of COVID, when people were able to log into a Zoom link that you had open in the market that they didn't, you know, that wasn't secured. All of our Zoom links are secured. Like the only person that can come in we have is someone who is in the meeting, who's been given a direct link, who we have on our list, right? What we realized is we had some really good presentations that all of a sudden we started getting people.

[00:12:47] We make people show up for our roundtables. They have to turn on their camera. They have to talk because it's an open environment. We want it to be comfortable. We don't want people to think other people are listening and gathering data on them, those kind of things. And so we want to make sure it's very open. What we found is there was about six or seven in there that were not – they were names that we had gotten, names we had vetted as real people. So we'd actually gone out and done the vetting that we normally do. They weren't on our list for the survey participant, but they were real names of organizations that we had vetted.

[00:13:18] But what we found out is someone had spoofed all those names that we had sent out during the original survey list, right? Had basically come back with an email and told us that, you know, we were able to confirm they were real people. When they came in, we realized, okay, they're not talking. They're not giving any feedback. So we started kicking them out. What we got was about half of them came back in automatically. Now, if anybody understands Zoom, you know that Zoom clears the IP.

[00:13:46] Once you get kicked out, you can never get back into that IP again. We have this issue every once in a while. Someone gets cut out of other things, and it makes it really hard for them to ever come back in again. They were immediately back in with a new IP, and we talked about, like, oh, they're agents. So we were able to clear them all out again and set up the waiting room, and that basically fixed the situation. But it was absolutely, basically, I think we've done all the things before to make sure that these are very closed environments.

[00:14:11] These agents and these bad actors are really coming around all the different security measures that Zoom has put in place, that, to be honest, the company that we sent our list out through has put in place, that Qualtrics has put in place. We know this. We catch a lot of spam this way and data in our authority set. The agentic sort of number of people who sort of are, like, coming in trying to respond to things is really, really scary, Cliff. And it's the exact same thing you guys were just talking about, right? Yeah, it was.

[00:14:41] Because, you know, we were talking about, you know, it gets like this. And what it's really disappointing because that can create an adversarial sort of relationship. And that is not the goal of talent acquisition, right? The sort of, I don't want to call it a secret. You're trusting if it's a real person. We had someone that we knocked out who was a real person, and we didn't realize it because they hadn't said anything, and we were trying to keep it clean. And they came back and emailed us and said, hey, I was about to answer, and you didn't have a chance. And I was like, okay.

[00:15:09] So we said, well, let's do a personal interview with them. But, right, like it was not how we wanted that experience to go for someone, right? Exactly. Well, I was going to mention one thing really fast because that's actually fascinating because it deals directly into our next story, actually. But I will mention as far as appearances, I was also on the Heroes of HR podcast. It's an ISO podcast, but it's led by William Tincup, who's also part of the Work Defined, in fact, runs the Work Defined network that we're on. You probably heard name-checked at the end of this.

[00:15:38] And Amy Miller talking about another story that we'll go into later. But so talking about this idea of AI identity security deal with one of the big acquisitions of this time period of this fortnight was deal acquiring Clarity. That's the name of the company. Sometimes these company names just sounds like, am I parsing this sentence correctly? But the name of the company they acquired was Clarity. And what does Clarity do?

[00:16:06] They do identity verification, deepfake detection, and fraud prevention. So, again, you can see why a company like Deal would find those types of tools extremely useful for their payroll and HRMS environment. So that was $1.5 billion.

[00:16:30] So, or I'm sorry, they don't think they announced it, but they also said that they had pushed past $1.5 billion. Sorry. Yeah, pushed this deal past the $1.5 billion in ARR and acquires Clarity. I didn't realize that they were there yet, right, with their total numbers. And we know they showed up quite heavily in the survey this year. We got some good data on them this year in the survey.

[00:16:52] They are really focusing on both global and local, both payroll and EOR and some other things. But this issue of, like, deep, fake-driven identity fraud is, like, I mean, we had the experience, and it was frustrating for us, but we have some tools that sort of make sure that we catch it and figure it out pretty rapidly. But I can't imagine dealing with thousands and thousands of resumes under this model, right? It's exactly really, really frustrating, I can imagine.

[00:17:20] And frustrating for the people who are actually putting their resumes in because they can't get through that juggernaut. That was part of the problem. People couldn't get through to sign up for our roundtables because all of these fakes had signed up for it. We had to clear them all out before they could do that, right?

[00:17:33] Yeah, it is interesting because it's definitely a sign of kind of where we are in this evolution and growth of AI is that we've gone past the stories that we would talk about maybe two years ago of here's this new resume parsing AI company to here's a company that can help you determine fakes from the AI generating parsing companies, right? And so it's kind of the next stage, right?

[00:18:01] It's where we're now, we've kind of created the road, right? And now we need to, you know, put the safety measures in place, put on the speed limits and so on. In conversations with a lot of recruiters, this is why they're falling back to direct referrals and the known entities and all of this sort of, oh, now AI can help us be unbiased and help us connect more people and do this. It's lost in this conversation right now, right? Yeah, that's exactly right.

[00:18:28] We're going to be talking a little bit about AI ethics. And that's another thing that we ask about in our resume and it's becoming at the forefront. It's also where some of these regulations are, including transparency models coming out of the EU that are having some effects too. Another one, you know, we got to talk about AI, right? Let's get it out of the way. There's going to be, you know, I think I mention on every show that there always seems to be sort of themes that must be happening because of just, you know, the sort of zeitgeist of the time. Right. As much you can get as I guess in two weeks.

[00:18:59] But there's going to be a lot about recruiting, you know, I think as we talk about this. But in a more standard one, this was Oracle who we brought up in just probably the last few episodes. They had just won the OPM contract, a few other things. And they have gone on to announce that they are going to be using the Google Gemini model for their basically their agent studio. Right. To build out that will be their agentic model.

[00:19:30] And that's fine. Right. But I think the bigger conversation and something that you had flagged as well, Stacey, when you brought this to my attention was that do the models matter? Like, you know, it used to be we talked to them. Right. Right. Like, you know, if especially because most of them at this stage, the differentiation isn't necessarily in the capabilities. It's in the guidelines and the transparency.

[00:19:58] You know, I without again, I can't give anything away. But as we were reading through the comments in this year's report, and again, over 10,000 people took it. So there was a lot. So I didn't get to read them all. But something that jumped out a lot when we were looking at what people liked about the models they were using, what it was was not to say, oh, this got me five milliseconds faster.

[00:20:20] It's that I was able to see how the decision was made and backtrack for any errors so that that transparency starts to become more important when models, you know, rather than what's at the most frontier. In fact, we're going to be talking a little later. I've been doing that a lot. This is what we're going to talk about later. But, you know, it's all episode about the most common usages don't actually require frontier models. And they don't require the models that are pushing the envelope. Yeah.

[00:20:46] I think that's just not how we're seeing people use it, as you kind of mentioned in some of the stuff we were seeing before. Yeah. And I think the model conversation here for Oracle, just to kind of add one additional thing on this, is that they're trying to become more open to their customers and the tools that they need. And I think we are seeing that maybe that's where the models matter, which is each customer is kind of using their own environment.

[00:21:12] So how do you make sure that you are not locking them into something that you are doing versus what they're doing? I think other organizations are taking a different route, which is like ours is sort of working well. You can just connect to it. So, you know, that's the SAP sort of conversation that we saw over there. So last week. So I do think it does probably matter. It was kind of beneficial. But it matters in the sense of how does it work for your customers? Not so much does it matter in the way the tools are reacting right now, right? Yeah.

[00:21:41] The tea probably even too hot for this show in terms of it having just happened. But I believe it was today as we're recording that Mark Zuckerberg had written a think piece, probably a strong word, but an essay on the need for more. And a vegetable, isn't that what it was called, I think? Yeah. You have open source AI models, you know, but, you know, with the idea being that we would use his open source.

[00:22:05] So it's just kind of defeating the purpose, but that he's correct, at least in the sense that having an open source model broadly is helpful for the consumer. But more so having that competition, right? Because like you said, what people need is to be able to find things that work for what they're trying to do and not a one size fits all. There's not a model that will win the thing like that used to be the sort of topic, which one will win?

[00:22:35] And it's like, oh, whichever one you need, as long as it's available. Yep. Now, this is pretty interesting, too. Our friends at Darwin Box, I just announced an addition to their platform, which is Cortex, which is they're basically they're completely AI from the platform up, HCM platform.

[00:23:01] And you and I actually got a chance to see a one-on-one demo and rollout of this. You had some very great feedback, I thought, in that. I think I was I really liked the idea of it being everything was in context, right? It wasn't just like we're just pulling in front. They talk about that. They use the term context quite a bit, basically being that, you know, when you ask the question,

[00:23:30] if you don't understand the full framing around it, right, if you're not able to pull data from all the different sources and see how these different things impact each other, then creering or asking AI to do anything is pretty useless, right? It needs to be able to pull this data all the time. This has been the end goal mostly of the cluster model, right? That's the idea of having systems that all have that bidirectional data transfer so you can get that context. So they seem to be building it with that philosophy in mind.

[00:24:01] Anything else that you probably I know your your take on that was much more nuanced. I think when we were going through it, I was just like, wow, shiny. Yeah. I mean, any of the vendors who know who they if you demo me or you give you some marketing content, I'm going to be blunt. It's just who I am. Right. And I even texted afterwards to Opal, who's our good friend who works there with them. I said, I hope they don't they don't think that this means I don't like that. But I'm going to be honest about what I think how it is coming through.

[00:24:30] And I think that's for Darwin Vox. There was a great I think what they're presenting and a concept they're presenting is really, really powerful. What I am telling every vendor right now, and this is not this is not designed to just talk to them, is every vendor saying we are the only ones doing context. We're the only ones doing workforce intelligence at the base level. We're the one doing a people graph. You know, the answer is no.

[00:24:53] Everyone is building this kind of contextualized graph with a people component at the bottom of, you know, sort of or related to their tech thing. But what I do think is unique, there are very unique things about each of them. And I think this is what I basically point out is where do you point out what is unique to you? Because you have to be particularly with the buyers, because the buyers are hearing this from everyone. So if you can't give them a really good conversation on this, they're going to just hear the same noise from everyone else.

[00:25:20] What I did point out for DarwinVox, what I think is really powerful, is that they've been very engineer built up to this point. Stacia Garr wrote a really nice write-up about how their technology with this new rollout, I think, shifted a little bit to taking that very, very good engineering thinking to a place where the end user could start to leverage that power of the engineering.

[00:25:47] Because I always tell people that what engineers do really, really well, if they're good at what they do, is that they can figure out the shortest distance from one point to the other, right? If you give them the leeway to do their job well and you don't tell them you have to follow this process or this path because this is how the industry does it. But I'm telling all the vendors, like, think in a situation where basically if you didn't have job roles in HR and you didn't understand the functions in HR and you didn't understand, like, how do you get from here to here and get this done?

[00:26:15] And DarwinVox has done a really nice job doing that, I think. So, for example, DarwinVox has project management inside their environment. That is a thing that allows them to get to the task level and to the outcome level in a way that others can't get to right now if they don't have project management data inside their system. So I think that the depth of the engineering they've put into that product has allowed them to sort of reshift the thinking.

[00:26:36] And their CEO was very clear that he kind of rebuilt sort of the fundamental architecture of their product through this new context graph. And he feels like this is basically he had to sort of rethink his entire model around building that technology and what AI Native really meant. And I think everybody's trying to figure that out.

[00:27:00] But what I really liked is that they are talking about data and putting in place next steps. They always had a next step, right, a next step on the data. And I was like, look, that next step is the most important, that actionable take action, get the action done. And sometimes buyers miss that even because it happens so fast behind the scenes.

[00:27:25] I was like, you really got to highlight what it's doing so that they understand what it's done for them. And the open box model, making sure they understand every step of the way what is happening on a clarity level. And they had some good tools for making that happen. I think, again, very engineering focused in how they did it. So I thought it was really well. I just think that sometimes, you know, it gets lost in translation when you're kind of you forget that the most important thing to the HR audience is that, one, you have saved them time.

[00:27:53] And, two, you've done it in a very compliant way, right? Those two things you have to have in those conversations. And even if you do it, you have to show it and make sure they see it, right? Yeah, absolutely. And I'm glad you mentioned that whole idea of the next steps because that's really what I took away from another bit of tech that debuted these last couple weeks, which was iSolve's Guardian, which is the AI agent that they put out.

[00:28:21] And it's another one where, you know, they're saying, oh, we're the first to do this. But I think, you know, what I saw from our friend Pragya, who we mentioned before, Pragya Gupta, who works there. Yes, he is phenomenal. Yeah, gave us an individual demo. And what I saw was the idea of going to that next step, right? So it goes through, you put in your payroll, for instance, in the demo I was shown, and it flags a few errors, right?

[00:28:50] And it says, you know, these are errors. These are ones that you could ignore for now or, you know, and here are the ones you have to take action on, right? And one of the ones that was used in the demo environment was, I think, Social Security not matching the records, right? And so, you know, something to be taken care of. Now, normally you get that flag and then you would go in, you'd get their file, put in the information, all that. But it said, do you want this to be fixed?

[00:29:17] Do you want us, you know, do you want me, you know, the agent to do that? Click yes. Then it shows you, you know, basically what it's done. It's powered by Claude. And if you've ever worked in a Claude environment, you are familiar with what it looks like when it does that. It says, here's what I'm doing right now. I'm opening up this file. I'm making this change. Here's what it looked like before. Here's what it looks like now. Here are the people that need to be alerted that this change was made. Do you agree with this? And before it's done, you know, and yes.

[00:29:46] Okay, here's then it's done. And rather than just alerting you, it's actually taking the action to take that next step and actually do something about it to fix that error rather than just alerting you to it. And this goes along, too, with this sort of idea that was a conversation we had with their new CEO, Michael Hask, where he was talking about the glass box, you know, getting it out of this idea of like something's happening. I don't quite know what it's like. Here's everything's happening.

[00:30:16] You know, we talk about that term that I don't think either of us like too much, which is the human in the loop. But that does give it more agency to the human. And this is saying, yes, I will do this work for you, but I'm also not just going to go off and make some mistakes that you don't know anything about. Well, and I think that the big thing about the way he's thinking about it, which I think, again, comes from more of a his background is with other HR applications.

[00:30:42] But he really is saying like, look, HR is fundamentally critical to these conversations. You need to have HR thinking, HR thought process, HR sort of, you know, input into these, not in just a yes, it's done and I've checked the box, but in the decision making process. Right. Like the actual like thinking through why would we do it this way and why would we do it that way? And that I really, really like because I've talked quite a bit about the human in the loop oftentimes is just a checkbox.

[00:31:11] But if you haven't had a chance to think through the conversation, then that checkbox can easily be a, well, the machine did it. Of course, it's right. Instead of a, I'm really thinking about the implications. And I have had a chance to talk through those implications with another human being because that's how we learn and how we think. And that's how I think, right? I talk about Cliff knows this. He gets a lot.

[00:31:43] Yeah, I love the idea of the why, because I don't think AI, I think when we talk about AI failures, just in general, you know, when we talk about the technology of AI. And if you want to do kind of say, you know, pros and cons, I think one of the big cons is that why part is completely lost and it probably can never be fixed in a prediction engine. Right. I'm talking about generative AI. There's lots of different uses.

[00:32:09] But so people we talk, we've seen this phenomenon come up and we brought up on the show that people lose skills by doing this because they don't understand the why. They don't understand why that word choice is being made or why that number is being used or why this was happening. So the more transparency into this allows you to make the sort of pros of it, of doing a whole lot of work or doing a heavy analysis that you wouldn't be able to do. So and helps that transparency can help why this is why this was done this way.

[00:32:37] This is those steps were taken. If you don't have this tool anymore, you at least would now have the ability to recreate those steps. Right. And not completely be lost if it were to one day go away. So I do like that. Yeah, which is a really important note that things might one day go away. But yeah, I think we have to bank on all opportunities. Right.

[00:32:58] Exactly. Exactly. Someone who took advantage of their opportunities is Anna Goldberg, who's now the VP of AI product management at Cornerstone. Very cool. Promotions from within. Love to see it. Right. Yeah. And I know I've known Anna for a very long time. Some of her earliest days at Cornerstone. She's one of my favorites. Like we get a chance to sit down at dinner and we could just talk all day long about what we're seeing.

[00:33:21] And she started out in sort of talent management and talking about how the performance management was changing and what we really need to think about it. You know, she's a fundamental force behind many of the really big changes that have happened in the Cornerstone platform. And we're going to see some of that come to fruition this year with some of the data that we're seeing. And what I really like here is that she worked for Karthik, who used to be the product manager and who left. And when Karthik left, they didn't just kind of wipe out everybody around him. Right.

[00:33:51] Like Anna was one of those people that really did. Because that happens with all product teams. Right. Like you kind of have the person and then whoever worked with them. Anna was able to. So they kept sort of her skill set, her capabilities, her background, and they moved it through the organization. And it's I just love seeing, again, it shouldn't be this. It should not be a conversation we have to have. But it's nice to see sort of a female name on in front of that AI product management, the strategy role and what we're doing with AI.

[00:34:18] Because I think we do know in the data set, at the very least, that there is fewer women in this role, in this industry. And we just need to keep making sure that that's happening, that women are stepping into those roles just as much as every man is stepping into those roles. Because AI is learning from the people who are designing and building it. And you have to have all voices. Right. So it's a really powerful one. I'm very, very excited for her. And congratulations to Anna. She's well-deserved. Right. Absolutely. I also like the diversity in background.

[00:34:45] One thing that she points out a lot is that she came from adult education. Right. Yes. Rather than sort of wrote, learn some tech, go to work at a company, move up. And said it was a path that's kind of at the fundamental human side of what we are trying to do with this tech at the end of the day is growing and increasing the skills of the humans around us. Absolutely necessary. Right. Yeah. Yeah.

[00:35:10] Then we've got some other news in terms of, I guess you were talking about sort of departures in the last one. This is sort of a sunsetting, maybe not sunsetting, but kind of a big moment. You can't go to the next steps. Right. Yeah. Yeah. Pivoting is the word I think we've been using. Pivoting. Yeah. Yeah. And that said HR.com has gone up for sale. I think on August 3rd, this post was made by Debbie McGrath.

[00:35:36] Having been the owner of the HR.com site and company and just the entire business, along with I think she points out the mypeople.ai that she'd started as well. And that's been quite a long time. I mean, it'll be it'll be one of those sad facts. I mean, assuming that it goes to a new name, it doesn't just go away. But it's a name that is one of the longest lasting in our business. Right. It really has been.

[00:36:06] I've known Deb McGrath since I was a very young analyst. She was one of the first people I got a chance to meet in the market. I think she used to be involved in the I4CP group way back in the day for a while. Right. I know she's Deb's out of Canada. I know the people who work in her team. They're they're amazing. You know, it's great to sort of see them at events and stuff. But Deb, Deb gave me some. I remember riding in a in a in a car with her. We had gotten the same car. We were heading to an event way back in the beginning.

[00:36:36] And I remember asking her what her favorite part of this job and this role was. And and she's and she'll probably not remember this because this goes way back. But I think her answer was somewhere in the range of, you know, it's always changing. It's always you're always reinventing yourself. You're always reinventing the conversation. I thought, oh, well, that that's interesting because that feels overwhelming and very tiring. Right.

[00:36:58] And I think, you know, you and I have talked quite a bit about what makes an analyst an analyst and what makes a research company a research company. Right. And, you know, it isn't just a name or a brand or a survey as much as that's important or data set. Right. It is the inquisitiveness. And I think the thinking of of the interconnected people that are part of that organization, even someone as big as Gartner.

[00:37:26] I know individual analysts there. And that's what makes that thinking interesting for me. Right. I think that's why buyers have conversations is that they know individual analysts who they talk to in there. I know even for some of the smaller firms that we work with on a regular basis, Stacia Gar's organization. Right. Like she's building something where the practitioners will have so much value out of that conversation among them. Right. So she wants to create a new type of entity where they can they can create more conversation.

[00:37:54] But, you know, we've really focused on on making sure that not only are we getting good data, but we're always thinking about how that data is being utilized in the market. Right. Everybody kind of has a little different take on how they do analyst work and how they do research. But, you know, Deb was one of the originals. You know, she was she was one of the her. And back in the day, I remember I was working for Josh back then. I was I met her. I got to meet Lexi Martin, who we took over the survey from back in the day. We got to meet Lisa Rowan then. Right.

[00:38:25] A lot of females in the market. I knew Kathy, Catherine Jones. I try to think of some of the the guys because those are all females. Like John Sumster was that point in time was was one of the ones who supported the work I did. Brian Sumster. There was a lot of, I think, people who understood the ERP space were thinking about how they could change the market. We're trying to make a difference. Deb was a big part of that. So she was.

[00:38:52] I'll be excited to see where where this goes. And I hope it hands at Lansing good hands. Right. Yeah, absolutely. And also well-deserved retirement. Yeah. I don't know if she's retired. She didn't quite say I'm assuming that. I think she says, well, she more or less alludes to it. Right. She said, come to me to plan my retirement. So I assume. Maybe that's it. Yeah. So if you're if you're looking to buy. Help her.

[00:39:21] Help her retirement out. Technology and the HR practices research space. That is a good opportunity for you. Right. Yeah. So another cool opportunity. Vincent Beliveau is named as chief commercial officer at Cornerstone. Someone else I believe you know pretty well. Yeah. I knew. Yeah. I had the opportunity to work with. I've worked with Vincent for years and years. Back when he was. I was a young analyst again. It goes way back. And I was working with Josh and Cornerstone.

[00:39:50] And Vincent was head of the. I think at that point it was just Amia. But it might have been more than that. Sales for the Cornerstone. We connected and collaborated on quite a few things back in the day. So I've known him for at least 15 years if not longer. He's been working with Cornerstone for over 19 years. And then I had the opportunity just this last January, February of this year to go and present

[00:40:14] to his sales team in Bali around sort of key findings of the research data and how that fit with the direction that he was taking the sales team. And it was a really important conversation because Cornerstone is shifting. I think their focus and shifting is a strong word. But but they are they're trying to to build out a bigger, bigger world for themselves. Right. They are always going to be, I think, a big part of the learning and development organizations.

[00:40:43] And that will be their their buyer who they know has sort of built their station. But they were they were always part of the talent management conversation. They were the first talent management systems in the market. And so they always talk to that business partner. But back in the day when I remember working with him, they were also very focused on the CHRO as well. And I know they're starting to rebuild that connection with the CHROs. And Vincent has done that quite effectively in the market that he's been in, where a lot of times Cornerstone is used as a is it what we call kind of a light HRIS.

[00:41:14] Right. They are actually in one of our top five HRIS is in our data set at the enterprise level sometimes. Right. Because it does the job. It has it has effective dating, which is an important thing for an HRIS. It has all the file tracking and for an organization that has maybe multiple payrolls, who is a multinational organization or who needs to create a flexible environment. And now with their new workforce intelligence layer. So I think Vincent has done a nice job of kind of tying all that together.

[00:41:42] And he's taking on this new role now to cover the entire market. We have several good friends who work over at the place. We also know recently that Kate Graham took on some of the new role in the analyst relations leadership. We know some of our good friends who were analysts in other areas have gone over there and taking roles in the analyst relations space. And I do think we're going to continue to see changes in the Cornerstone environment.

[00:42:06] I think they are really trying to think more global, more CHRO focused, more how do they help an organization achieve big outcomes for themselves. Right. So so congratulations to Vincent. I think this is a this is a good move. I will make Michael, Michael, who ran the Americas. I think he's moving on to a new opportunity and has some great opportunities for him. So but I will miss him. He was always a fun guy to work with. I think he knew the U.S. market very, very well. His customers loved him.

[00:42:32] He had brought a lot of great people into the Cornerstone organization who I think will will help them move into the next step of their journey. So good wishes to Michael as well, who will who is moving into another organization as they make these transitions. Right. And again, I love to see this the promotions from within. Yes. Yeah. So very, very cool to see that. So all positive. This next one. I don't know if we'd rank it as positive or negative, but it's a very interesting.

[00:43:02] We're getting out of people. We're going into the ethics topics. Right. Yeah. Yeah. Yeah. Yeah. And some data analysis. And this actually kind of gets to what you were just saying is that understanding different ways of looking at the data and thinking about why. Right. A little little training on this. And who better to train us than a lecturer at Berkeley? Dr. Mary Kate Stimler, who's just been knocking out of the park lately with some. I sent you two articles in one month from her, which I didn't realize. I was like, he's like, oh, you must literally like what you said.

[00:43:31] I'm like, oh, I didn't realize it said two articles that we were talking about. Yeah. Yeah. So, Dr. Stimler, thank you so much for all the work you've been doing. This is really fascinating stuff. And I'd also like to take you off with a little bit of comedy, a little humor brought in, too. But this is what she started to look at some of Anthropics' own data that got released just this month.

[00:43:53] And she's following up on a New York Times post from Oxford economist Carl Frey, who had talked about the chore economy, meaning this idea that AI is doing what other tech has done, which is pushing the sort of tasks we used to give to professionals onto ourselves. Right. We're taking that on. The example that Frey had used, or is it Frey? I don't remember. Talks about like when washing machines became available.

[00:44:23] We used to go to a laundromat or a laundered person. Sorry. Laundress. Yeah. Yeah. I'm trying not to use laundress because it's short. Yeah, but we know that at that day and age, it was 90% female. Yes. Yeah, of course. But the idea being that now we do our own chores, right? You know, these machines come in. And so what Dr. Stimler did is look at what they could see from how Claude is being used, right? Yeah.

[00:44:47] And found that in specialized domains, there was a massive shift towards doing your own work, right? In fact, 97% of cardiologist work was being performed is a couple examples. And, you know, she started to think about the why that is, right? Because she was saying, you know, it seems like healthcare, it seems one of the ones that should be least affected, right? You know, that's such a specialized skill.

[00:45:12] But actually what's happening is the discrepancy between the amount of people who have that skill and the people who need that skill is so broad, right? It's not going to be at the middle of the night you're having a heart pain. You're like, I need this sort of specialized information. You're not going to be able to reach a cardiologist. So people are turning to Claude for these type of things. And so this idea being that we are taking on self-servicing in a wide variety of places than we had before.

[00:45:40] And that is where we're seeing a whole lot more professions being impacted than maybe we would have not thought of, right? And I really liked the credit counselor, right? Like she noted that the credit counselor used to be a specific role. And now they're saying one of the top things that you're doing with AI is actually getting counseling on your finances, right? 80% of it is personal. Millions of people are quietly becoming their own financial counselors. I will say I had a situation kind of like this.

[00:46:09] I was just thinking as you were reading this clip, I was like, boy, my dad ended up over the weekend having something happen to his eye. He had some floaters. And he wasn't telling anybody. So he finally told my mom about it. And I was freaking out because I'm thinking stroke and all these things, right? So, of course, I put it all into chat GPT. I was like, what is this, right? What is this quick and perfectly? And it gave me some options. It did say that we did take him to the emergency room. I don't think he would have gone to the emergency room without that. And I don't know that I would have found that in a book.

[00:46:37] I might have been able to find some of it online. But it walked me through the questions to ask him that I don't think I would have found on a website, right? And I wouldn't have known to put in. And then it said, your dad should probably go to the ER. It was an issue with the retina moving in his eye. He now has a doctor appointment. Everything's sort of being taken care of. But it was a scary moment for me. But I will say it would have been scarier if I hadn't had some – you don't want to get medical advice from chat GP. We get that.

[00:47:07] That was not what I was doing. It was walking me through the questions to ask so I could convince my dad that this was a time to go to the hospital. This is not going to get better, right? But I think that's kind of what it's saying, right? Yeah, and exactly. And if you think about that, right? So I have a good friend that's an ophthalmologist. My sister also works in an ophthalmology office. And those kind of things normally would be like you would have had to have called. It would have had to have been 11 at night and someone – that's not going to happen. So it's not like we're taking anything away from them.

[00:47:37] They would have walked you through those questions, right? Exactly. But if that's not available, again, they don't lose any business from that. If anything, you know, it may have helped. If anything, it brought someone to him maybe earlier than they would have come because my dad probably would have waited another week or two knowing my dad because he's stoic and that's what he does. And it might have been at that point too late for them to do anything, right, based off of what we understood what might be happening. And if there was nothing happening, great, right? Like that was a good opportunity. So I agree with this.

[00:48:04] That's why I think why I – even before this experience this weekend, I thought Dr. Kate Stimler's sort of approach – Mary Kate Stimler's approach to thinking through this built off of Carl Frey's sort of commentary is sometimes I think we think a lot of it is all bad. And it could be detrimental, right? But I think that there is some benefits and we have to figure out how to balance the benefits with the risks with the opportunity, right?

[00:48:34] Well said. Another one on job replacement in AI or at least maybe not job replacement but how AI is being used. It was a pretty interesting story that came in. Also, again, looking at some of the data that was done and this was that only one in 20 HR jobs requires AI skill based on an analysis of all these different posts.

[00:49:01] But what was pretty fascinating in the graph that I've got and I'll – as always, we'll have links to these stories – is that the different roles within HR, there's a lot of difference in what's impacted. And the number one was learning and development. It's not a huge difference but it is the top. Often we think about recruiting, right? Because we – of all the things we're talking about, right? It seems like one of those things, yeah, AI is going to be a big part of that.

[00:49:28] But we forget that how much learning and development is being impacted by AI. I mean, you can tell from the number of stories we've been talking about. Right. Yeah. And this story I flagged comes from our good friend Johanna Sunlow who many of you know has kind of created a whole brand on understanding AI. You know, he was working in a corporation, started using AI.

[00:49:55] He's now created his own sort of, I think, consulting and advisory and sort of keynoting. So if anybody's looking for a speaker on AI, I always recommend Johanna's. He – we had a chance to catch up at HR Tech Europe event. He sat in my session. It's one of those things where, oh, when Johanna is sitting in your session, like, I got it right. I got it. He's interested in seeing what I did. But what I really liked is he started that analysis with his own region.

[00:50:20] He was looking specifically at sort of last couple years at what was happening in HR ads in the Sweden area to see how much AI was being utilized. And he was using the job descriptions to that. And then he expanded it and then he looked across all of it. Right. And I think, you know, to your point, yes, we can tell you in our data learning and development has arisen. We can already tell you that that's going to happen. We don't buy my watch. We'll know by how much sooner.

[00:50:47] But we also know just creation of documentation has become a big conversation. And that, I think, is part of learning for a lot of people and part of process and part of onboarding. So how you think about learning changes that conversation as well. Right. Yeah, absolutely. There's a couple other interesting facts. It's something that we've seen in our data as well, which is that the number of skills. Right. The skills are being looked for.

[00:51:12] It was very low for responsible and lawful AI. Right. Like ethical use. Something that we have. Yeah, that we have. He said, I thought it would be higher for HR leadership because maybe that was more of a hope. I was like, yeah, I feel the same way. You know, it's something that we have pointed out for our data as well. When we ask people, you know, what are the guidelines you have around the ethical use of AI? That seems to have been for a long time.

[00:51:39] I don't want to call it an afterthought, but that may probably be the best way to describe it. I think it's been an afterthought. I mean, I don't think people are doing it on purpose, but I do think that they're assuming someone else is doing that. I think that's what's happening in a lot of cases. Right. So, yeah. Yeah, absolutely. Absolutely. So, continuing our Dr. Stimler, Dr. Mary Kate Stimler love here.

[00:52:01] Another one that was really cool, which was to look at job losses and the impact AI had on job losses. And what she was specifically looking at was this idea that sort of a cursory glance at the data made it seem as if the AI job losses were supposedly focused on entry-level jobs. Right. I think it's one of those things that kind of – Which is a thing that we've all been hearing. It's the expectation of what AI is going to do, right? Yeah.

[00:52:31] And I think it kind of is somewhat commonsensical, or at least it seemed to make sense on a surface level, right? That, yeah, these seem like jobs that would be that. But she found, too, was some pretty interesting thing that actually there was a whole lot of – she looked at age groups that have been impacted and found that actually there was plenty that's being impacted kind of across these age groups, right?

[00:52:58] You know, people that were well into their 30s and even 40s have been impacted quite a bit, much more so. She also points out the fact that, you know, that's because we've had more time, you know, in that. But so her, I think, key line from this is that experience buys times but might not buy immunity. Yeah.

[00:53:21] And she also says, like, Amy, you can always tell someone who really understands data because she goes, these are very broad brushstrokes. Google's economics team is cautioned against interpreting this data without attention to interest rate effects, et cetera, et cetera, right? And she's basing this, at least her analysis, is based off of, again, the analysis being done by Stanford's famous Canaries study. And I was laughing because you don't like my canary in a coal mine than when I say that for our data sometimes.

[00:53:48] But she literally says, you know, sticking with the canary metaphor, we can see the gas rising through the mine, right? And as you've got a couple – few feet on you, which means you've got a little bit more, you know, length and longevity, but it's eventually going to get to everybody, right? Like, I think that's what she was saying. And she basically did that by looking at the employment index and looking at the fact that it doesn't really change. It's not really saying that these workers at this age – it's basically saying that this would have been a fall off.

[00:54:18] And so basically we're just seeing that, you know, it's rising up and it will hit everyone kind of at the same level it would have hit if we wouldn't have had AN. It would have been any other technology, right? And we should mention, too, that the data said she's using is ADPs because BLS data is – as we mentioned on the show, we were saying we'll have to keep an eye. And sure enough, it's become very – I wouldn't say error-filled. We just don't get it a lot of times.

[00:54:44] It's just not getting it as often and as likely, which makes some of the data from these vendors much more valuable right now. We're having this conversation – data around compensation, data around hiring, data like that become – and we said that, that business data is going to get better. It's going to be critical, right? Yeah. Yeah. You know, I take it back. I thought that the theme was going to be recruiting. The theme is definitely – it's kind of analyst, data analyst 101. Who's the analyst and why are they an analyst, right? Yeah, because this was pretty interesting.

[00:55:12] This was a post that Tal Neuron CEO put out. And he had said that he had had a conversation with a client that said, I don't know why anyone would pay for your data when you get the same answers from Chad GPT. So he said, you know, obviously, he's like, I don't agree, but let's see what happens. So they tried it.

[00:55:33] And I said that putting in all of the, you know, same stuff using the models, it said the estimates diverged from 46% to 189%. And, you know, basically looking at some of their compensation data from that. So – and he noticed that just kept happening, right?

[00:55:57] So, you know, he kind of pointed out something that's known that LLMs – and I really – I keep – I'd rather say generative AI, but LLMs too. But they are often confidently wrong, right? But this goes into a few different things, right? It's being able to – you need to have the context and experience to understand data that you're seeing and kind of know. You know, you need to be able to validate it. All those transparency steps are no good if you don't understand the why.

[00:56:26] Why is it this happening? We have seen this – we see this sometimes in our own data where we've seen something like, this can't be right because that's not what this company does. You were mentioning Cornerstone showing up. We had to go back and check on that. We had to make sure they actually were selling something that could be used as an HRS. We're like, how is this on a learning tool? And we were like, oh, oh, yeah, they do have the capabilities. It's just – it's being used differently, right? Right.

[00:56:54] So if you don't have that, and we go to your example before, having the right questions to ask is much more useful output, right? Than saying, okay, get a small knife and go towards your father's eye. Right. Yeah. My dad might have said no to that one. Yeah.

[00:57:14] But I do think this is – I love – I mean, his exact words, I thought, and I think we could say it, is this – said it was a bit soul-crushing to hear the feedback because it's so egregiously wrong, right? And we failed to get ahead of this risk to better educate the clients more directly and proactively. Like that – I think everybody is feeling this way, this angst of, you know, it would be like, you know, oh, I can do, you know, surgery on my dad. No, all of us asked was the right question so I knew to get to the professional, right?

[00:57:45] Yes, it might be able to give us an indicator that there's a problem, but it is not going to be able to give you the data you need to make the decision based off of or to take the action that you need to take. And that's really where I think, you know, he was talking about was that conversation, right? Yeah. And this is – you know, I promised there would be things that we're going to talk about later on. So this is my – this is your payoff for that earlier call forward.

[00:58:11] And this is exactly more or less what you were getting to on your Tuesday evening joint session during HR Tech with Harbinger, right? Which is buying versus developing. What are the things you need to look out for? What are the pros and cons? What are the positive outcomes? What are you looking to do? And how can you maybe find a middle ground for it, right? Exactly so.

[00:58:34] So this is a real example of this phenomenon that we've seen in effect for a while now, which is these companies adapting to the fact that AI tools are – could be – I remember hearing about shadow ERPs and things like that. You know, and is this a risk? And what do we do? And I think his point that, you know, we need to educate our customers. It's not just like, no, you're wrong. It's not the right thing here.

[00:59:02] You know, it's understanding how best to use these tools so that they don't make these mistakes. I'll give a little highlight for one of my roundtables. It was very interesting. And what we came up to is a very large group of sort of people on the roundtable. And it was very clear that they were frustrated that the vendors weren't doing more education. They were like, stop marketing to us and start helping us understand more. Then that was it. That was almost like a point blank commentary from them.

[00:59:30] So I will share that, that if the vendors don't – you know, we really need to think about how do we educate. And it's a tough thing because you have to almost say, I'm educating you, yes, and yes, the point is that I'm going to sell you more. So you have to have a trusted education model, right? But it is – the buyers are really looking for it, right? Yeah. Exactly. All right. So we're going to continue to see – You want me to do the title on this one, Cliff? Yeah, I'm not reading this. This is a family show. Okay. Read it. Okay. Yeah. So this is Jess Von Banks' article.

[00:59:59] I was very – I was like – I said this to Cliff. I said, Cliff, you're going to like this. And I like Jess. For those who know Jess, she says it as it is. She's very forthright. She's trying to sort of build a vision of what AI is going to be like in our market for some – her and Jason Aberbrook are doing some new things called – I think it's a new name called – it's no longer the now of work. It's something else. But she does some wonderful write-ups on things, right?

[01:00:21] And so her article here is, it's pretty ballsy of open AI to claim AI replaces workers when the data says otherwise, right? So you can now explain what the article is about. Yeah. So this is, again – this really is – I really didn't plan this, but this really is – we're doing analyst training here. But what she was looking at was an open AI article and the headline talking about AI has changed the way people work, right?

[01:00:50] And so there's one way of looking at that data saying that all of these – which occupations – like all these different occupations were using AI. And, you know, so it's like all these sort of things. You know, it's almost like Dr. Stimler's look at that. But digging a little deeper, you know, even by their own data that was published, it's that – what was it?

[01:01:16] Yeah, 43.5 of, you know, mass cross-occupational boundaries at a high at 6.9. But 62% of work use was generic writing, summarized scheduling, and similar activities. This goes to another one. Data validated. Backs hang off that we're using frontier models for non-frontier problems. So basically, what does that mean? That means that looking at the professions that are using AI the most actually is not a great indicator of which are being the most impacted because they're using it for just generic tasks that are across all professions.

[01:01:46] Everyone needs, like you said, Stacey, documentation and, you know, that sort of – Undergaffling and – Yeah. The nature of what's in that documentation changes across profession. But the generic idea of I need all of this stuff to be written down, I need a process document created out of this, or I need some help with these sort of more generic tasks of scheduling and things of that nature. That's the same across.

[01:02:07] So really, it's understanding that we're using AI, but we're kind of, in a way, using it the same, right? The nature of what we're doing it. But a lot of us are doing kind of – I think almost anyone here could, you know, any of our listeners, you know, feel free to write in and tell us if you're using something you think that, you know, only your profession does. But I'm going to hazard a guess that the way you're using it is probably the same as someone in a totally different profession, right?

[01:02:34] You were asking it, can you go through my emails and flag the ones from this person? Or, you know, can you summarize everything right here into a paragraph for me? You know, even if it is like a hardcore healthcare techs as opposed to, you know, design of, you know, mechanical device.

[01:02:54] Yeah. And her whole comment about, you know, OpenAI making this claim that it's displacing, you know, workers or that it's even taking part of their job was, again, I think she's really pointing out that, you know, what we should be really looking for are the interesting, significant opportunities that where it's making a difference in people's lives or making a difference in the actual fundamental work. Right. And to some extent, you can't get there if you if all you're doing is basically improving on a path that we've been doing for a long time.

[01:03:22] I think she calls it, you know, you know, basically she calls it cow paths. You know, it didn't create the cow path. And it's the same thing like we put a road there, but that was the road that was created long before because of where the it wasn't an engineer who designed that road. It was it was the way the river went that designed that road. Right. Well, what I think. Yeah. Yeah. The shift in this conversation is we need to stop if we are going to do something interesting. Right. Maybe stop looking at the roles we have.

[01:03:50] Maybe start looking at something completely different. Because I think that's how some of the most interesting things have happened in our market. Right. As much as people might really, really, really hate what Jeff Bezos did with Amazon, he wasn't looking at how he could sell more books. He was actually looking at how he could create an environment that made the book management easier. Because because the biggest issue in book selling wasn't selling the book. People wanted the books.

[01:04:19] It was the distribution process. It was how you got the book to people. It was it was how I reached people who didn't have a bookstore near them. And and that was the process that he really went after. It was a very different dial. He stopped looking at the sales piece and he started looking at the distribution conversation and that changed the whole model. Right. So I do think we have to really think a little bit about, you know, are we just are we just paving over the cow paths? Right. Exactly.

[01:04:43] And also, I should mention that, you know, these sort of headlines, you got to think about sort of again, the why, you know, why open AI has extremely strong vested interest in creating the sense of fear and inevitability about AI's transformative power. Right. They need it to seem scary. So that it seems like if you're not doing it, then you're going to get left behind because they need to somehow. Make some money for some things they've got.

[01:05:12] Stuff likes to point out. Yeah. Yeah. But yeah. So then then there's kind of this idea of some specific use cases. Right. This is Dr. Andrea Derler on looking at some performance and engagement data. Yeah. And she's our good friend here in the Raleigh area of me. I keep trying to get lunch with her. We haven't had a chance to catch up recently, but she works for Vizier and she is basically their chief researcher economist.

[01:05:43] She's an amazing sort of thought leader in this space and has done some really cool looking at the data that Vizier brings in. Right. Yeah. She's looking at the same thing. She's looking at age group breakdowns of who's considered high performers. Right.

[01:06:07] And found that aging experience seems to have, you know, sort of won the day, I guess. Yeah. The age group's, you know, kind of, I think around 40 to 45 years. I'm sure she meant to include us as well, but generally get the highest ratings. Right. So, but then there's actually a bit of a drop off. Right. So there seems to be a bit of a sweet spot.

[01:06:38] So, but understanding that is also important too. Right. Is understanding the context around that, you know, much as I was pointed out in the earlier one, that there's also sort of external effects that could be getting into that. Right. Of, you know, how we view people. You mentioned too about these tools used to remove bias. Right. And how managers see people and all that.

[01:07:00] But, you know, having that ability to kind of look at, you know, what are the groups that tend to get rated more can help you understand what may be happening beyond what's just, you know, we think we'd love to have this idea. And you have to also look at like sort of those people have more in their career. Others, you know, over time does too much information or is it age bias? Right.

[01:07:29] Like there's a lot of things that could play into this. And I love that, you know, Andrew really looks at this and she says, you know, I think her first comment is performance ratings are one of the most distrusted tools in HR. Right. Like we don't trust it. I would say that's probably quite true. I think you're going to find there are some things that are happening with the AI world with companies in that that is that whether or not it's going to be used effectively, but they are shifting their thinking on what tools they want to do this kind of conversation.

[01:07:58] Yeah, exactly. Anyway, I one that I was watching over this last couple of weeks, which was tech HR India, not to be confused with HR tech and a few of the things going on around that. There was a World Talent Council roundtable is actually brought for the World Talent Council Roundtable. And I was looking at some of that and watching that as it came out of India.

[01:08:25] You know, what I just flagged this one because I found it very interesting that a lot of what was highlighted for how AI is impacting the workforce in India, which has traditionally long been, I would say, you know, different. You know, the challenges they're facing, you know, are usually different from the US. I was pretty fascinated by the fact that how much this is. You could just say this is the same thing that we're seeing in the US, right? A lot of talent demand.

[01:08:55] I talking about finance, wealth management, infrastructure, electronics and manufacturing move towards more blue collar and frontline workers. Same as we've been seeing as well. One I did find pretty interesting, this idea of the forward deployed recruiter. So the idea that especially in my days of recruiting, it was very delineated where you had one person doing sourcing, someone else that did the recruiting in terms of, you know, making the calls, right?

[01:09:22] Someone else, you know, with the screening sort of section and, you know, looking about all that. But with these tools, you can do some of the sourcing yourself and, you know, one person can do that, but they're using the tools in front of them. That's the idea of using, you know, I think you were, you've talked a few times about forward deployed engineering. This is sort of taking that concept of forward deployed recruiting of taking these skills and putting it into the spot. I really like that concept.

[01:09:48] Yeah, and when you're doing that large data, when you're doing that large of groups of recruiting, which is what happens, particularly in the India market, we know in certain areas, it is kind of a necessary thing to sort of have that sort of rethinking of that, right? And I think so. I'm not surprised that's coming out, right? Absolutely. Now, speaking of, you know, you were mentioning all the women getting hired and women in the workplace. This was pretty depressing, but it was, again, another one where there's some different. Yeah, you read the data. Yeah.

[01:10:16] Yeah, it's another interesting one where we're going to look at the data a few different ways, right? Because there is some stories here. We did get the jobs report, and this was one way of looking at this came from the National Women's Law Center that flagged as women accounted for 100% of the labor force decline, right?

[01:10:38] So, basically looking at the number of women that were gone and then taking that against, you know, the total number. The 23,000 jobs that they said they lost, right? Yeah. Yeah. And so, women accounted for all these losses, losing 32,000 while men gained 9,000, right? So, you could say the women just dropped out. So, pretty interesting. But, you know, we did look at it, and there were some other people that did some work around this as well.

[01:11:06] And it's not quite as dire, but it's still pretty dire. But what was really interesting was that women did gain 11,000 private sector jobs, but the net loss was driven almost entirely by government jobs. Lost 43,000 government jobs. It's almost certainly probably due to government cuts, right?

[01:11:29] And that they seem to be disproportionately losing their jobs, meaning it's not a statistical, normal statistical variation. You couldn't say that this is just sometimes there's some fluctuations. It does seem to be a disproportionate amount of losses coming out of the government and sometimes even part of stated policy of what's going on in the public sector, here in the United States at least. Yeah, this one is hard to sort of – it's like, first it was like, yeah, they weren't reading the data right.

[01:11:59] Let's actually go back and look at it. You and I both had that same reaction. We're like, 100%, that seems odd, but we understood where they were coming with the data. But then when you realize that most of this is happening inside the government, I know both of us have family and friends who are in different government entities. I have a lot of family who's in the military. I know I've talked to them a lot about the fact that in the military you're seeing women not only sort of losing opportunities and losing jobs but sort of being completely cut out of options for certain things that would not have even been thought of just two years ago, right?

[01:12:29] And so when you think about a career that you put into the military, most people sort of bank on if I've been there already 16 or 15 years, I'm going to retire from the military. There's some value to that. We all know that. That's part of why people put that much into a service that actually you don't often get the kind of pay you need to, and many of them are losing that opportunity to do that. And that's actually really sad and really scary, I think, and same thing with the other government jobs, right? Yeah, absolutely. We're seeing it across the board. And, you know, it's – yeah.

[01:12:59] And honestly, it's – It can have an impact on our economy one way or the other, right? Yeah, exactly. That's what I was going to say. It was ostensibly done in the name of efficiency, but it ends up being – it just ends up being a net loss no matter how you look at it. So for all the reasons you just laid out. So this one is kind of interesting. And this is – this was brand new. This was only the last couple days.

[01:13:24] But as part of EU's Artificial Intelligence Act, which we talked about on the show, going through it, we're starting to see things that are happening now. I think in the last show you mentioned the sort of locus of control moving away from the U.S., and this is certainly a sign of that.

[01:13:44] Because Anthropic, the makers of Claude, has added basically what they're calling invisible watermarks, basically signifiers within the text to show that it was created – the text was created by Claude, right? So the idea being here that there are a bunch of tools that are out there that are supposed to help you identify whether something was written by Claude. It's meant to be presented as original work.

[01:14:12] We need to know whether it is or isn't, right? It's that verification that we started right off the show about. This is one of the tools. The book that someone had – they had tried to pull back from because they thought it was written by Claude and they didn't know for sure, right? Yeah. Right, exactly. So, you know, there's all sorts of areas where, you know, some aspects of HR, you know, people will probably be like, yeah, that's fine. We'll admit it was written by AI. In some cases, they wouldn't want that to happen, right?

[01:14:38] And I think it's been, what, two days since this was released, and already we're seeing people find workarounds to try and overcome this. It's your whole – everyone's trying to get into your workshop thing again. Right. Right? I mean, so, yeah, that maybe we'll end where we started, right, which is, okay, they're going to find workarounds. We figured – we thought we'd figure this out during COVID, and now we got another round of this, right?

[01:15:03] What I really – it was interesting because I've been actually saying for a while, there's got to be a way that we can sort of mark things up that would help, even videos like deepfake videos and stuff. And, you know, it was so instantaneous. Less than 24 hours, and they came out with ways to get around this. Now, Claude can say they have done what they've been asked to do. That's an important component of this.

[01:15:24] And I think, again, for the less savvy, the less technical, the kids and the students and most of the general population, this will be fairly easy to sort of – it will help people kind of see, yes, this has been created by AI. This is not. So I don't think it's not valuable on some level, right?

[01:15:40] But I do think that, you know, the nefarious players, the people trying to sort of beat the system, the seven bots who got into our roundtable who thought they were going to get a small gift card for doing that, which it was not, right? They're – you know, we're always going to have some level of that. They're always going to find a way around it, right? But it is interesting to see that they – how quickly Claude came to the table with what they thought was the right answer for the European market, right?

[01:16:09] Yeah, absolutely. And let's close out with, I think, a pretty interesting article from – we mentioned a few different friends. So a really good friend, Pete Tiliakos, he's been on some of our sister podcasts and probably seen him around. He does really good work. Also, I should mention one of those people that transitioned from military to the private sector. Yeah. He's often talked about, you know, that sort of idea of like the GS work versus, you know, whether that was even worth it anymore.

[01:16:40] So he was really calling out that, you know, as time has kind of – as we're at the stage we're at, right? And we've talked before about HR systems, the technology becoming more entwined with financial technology, fintech. His office is short, too. And he talks about how earned wage access is kind of the next level. We've seen it often thought of as just like, yeah, it's an option. You could stick with it.

[01:17:06] But he's really saying – and he's a great writer and a couple of good turns of phrases, which is something about – oh, yeah. Employees do not experience pay as a back office process, right? They experience pay as life and moments, right? And that idea of being able to, you know, easily digitally transfer money, it seems like a natural step to go into earned wage access almost as a sort of natural inclusion.

[01:17:33] And that it needs to be looked at as a more easy-to-access process, something we'd always track in our survey as well. And, in fact, it was only a few years ago that we saw the shift go from earned wage access only – or not only, but primarily being offered to salaried workers instead of hourly workers.

[01:17:52] And because, yes, it was easier to calculate, but now that there are a number of different options, we've finally seen that go to where it probably sort of more naturally should be, which is more easy access to hourly workers. Yeah. And Pete mentions here that he – I think he says only a third, it might be. I'm not sure the exact number in here that he puts down. Yeah, one-third of the U.S. workers have access to the financial wellness solutions, right?

[01:18:16] Less than – our data shows on the earned wage access side that only about 14% to 15% of organizations are even offering that. And most of the time it's because leaders don't feel it's valuable, right? And we'll see what this year's data says, but I don't know if it's going to go up much. And Pete really points out something you and I actually talked about quite – about a year and a half ago when we had talked to some of ADP's customers who were using their earned wage access solution.

[01:18:43] And they were talking about how it helped the payroll function actually be able to address real issues where they could run real-time pay for people who maybe had a missed account, some process, some sort of underpaid, something like that, right? And it became – it reduced a lot of the issues for payroll and a lot of the frustration with payroll for people. Pete mentions all of that.

[01:19:06] I just thought, you know, we have to keep reminding people that your lived experience is not the same as someone else's lived experience. And you may want to rethink, you know, what's important to not just your employees. And I realize that right now businesses are in the driver's seat, but that's not going to be for long based off of all the data we're seeing, right?

[01:19:26] Something as simple as providing some flexibility in payroll options along with maybe some financial wellness and financial education could be really, really powerful for workforces. And I think it's one of the underutilized, underthought-of conversations. And Pete does a nice job walking through that, right? Absolutely. Check him out. Well, if people want to live their experiences with us, where can they find us in these upcoming months? We're going to be in a lot of places, Cliff.

[01:19:55] I think you're going to start September 16th and 18th back on the road again, and you'll be going to Marco Island, Florida. And you're going to be at the NAPEO event. Do you remember what the acronym is? North American PEO, Professional Employer Organization. Something that we have also expanded our coverage of in our survey and research this year. We are really looking forward to seeing some companies that we've known for a long time that will be down there.

[01:20:25] I saw Prism HR will be there. But also some of these newer companies as we're expanding everything that we know and the sort of questions that we're seeing and the data we're getting in. Really looking forward to diving deep into that space. So that'll be really great. Plus, it's just a really, really nice area. Really beautiful. I was talking to Pragya Gupta from iSolve, and she had just been there the week before, just on vacation. There you go.

[01:20:54] It'll be a nice opportunity for everybody to sort of get together again. Then all the way across. So in September, you're then heading off to Utah for O.C. Tanner's event, right? Yeah. Another area, you know, we don't talk as much about the rewards and recognition space. But, you know, O.C. Tanner, one of the big leaders in that space, has been doing this conference for a long time. Like WorkHuman, it's kind of a broader view, right? It's really getting to the human side.

[01:21:24] WorkHuman does that with their conferences. O.C. Tanner's Influence Greatness does the same as well. And always come away learning quite a bit from that. So really looking forward to that. And then we're going to start our longstanding residents in the Las Vegas area, or at least getting ready to start it. October 6th, we're going to be at the Cornerstone event, their Connect event. And I'll be doing a keynote session. You'll be doing our person-on-the-ground podcasting opportunities.

[01:21:51] So if anybody wants to become a little bit famous to get on a podcast, this is a great way to do it. So look up Cliff there. And that's going to be in Chicago. And then we'll be jumping right from there to Las Vegas to our Workday Rising event. I'll be doing there. I'll be doing a podcast there as well. We'll be doing an event there. And then the very next week, we'll be doing our HR Tech Conference October 19th through 24th. So Cliff told you all the things that we're going to be doing at that event, but definitely make sure you sign up for that.

[01:22:21] And then, Cliff, I think you and I are still deciding what we're going to do October 25th to 28th, because we have two back-to-back events, Oracle AI World and Dayforce, right? Yeah. And, well, not too far apart. But if you know Vegas as well, it doesn't matter. The hotels can be next to each other, and it's almost a mile apart. But, yeah, Dayforce Discover and Oracle AI World will be going on. We'll have to start looking at apartment prices down there because I think that'll now be our third week in a row, your third week in a row in Vegas.

[01:22:52] But, yeah. But you can definitely catch us. Just let us know, too, if you're going to be at any of those and where you'd like to meet. We can always figure that out. We want to make sure that we're providing you the information you need. So if you have one or the other, let us know. And then, finally, at least for now that we know of, we'll be at Unit 4's event back to Chicago. It's interesting.

[01:23:19] I was thinking about this about five or six years ago, maybe a little longer than that, maybe closer to 10. Chicago and Vegas sort of split, right? HR Tech would even be in Chicago sometimes. Those were like the two big areas. That goes way back, for those who remember it. And then that locus of control that we talk about, it moved completely to Vegas around the pandemic years. And it seems to be, they're wresting it away. Certainly won't hurt my feelings. Shout out to Las Vegas. But I spend way too much time here. Yeah.

[01:23:49] I know someone told me there might be an event in New York and that same week. And then another one in San Francisco. And I was like, either of those finds, great. Let's go, right? So we'll see. There might be a few others that we add to the end of the schedule. But Cliff, we are way over our hour. We didn't even do our speed round. We were just rushing through everything. We do want to appreciate those who take time to walk through and listen for all the conversations and updates that we have as we wrap.

[01:24:14] Just again, as we said, make sure you sign up for the HR Tech Conference to enjoy and get the earliest findings on the data this year. Also, if you are an HR Technology Voice the Customer winner, we'll be sending out notices for number ones and top fives. That was part of what we were doing last week. I was looking through all that in the upcoming weeks. And also, if you plan to purchase badges or segment report, a reminder that is first-come, first-served basis for those who are still trying to make decisions about where they're at. There's only so many of us. We have to put all that together. It's an important process that we go through because we need to make sure it's right.

[01:24:45] Also, just as we wrap up today, I just want to remind everybody, if you do want to get more details on where we're at all the time and you're not going to listen to the end of the podcast, sign up on our website for our newsletter to get ongoing updates and research launches where we'll be speaking or visiting where we're at. Our team does a wonderful job tracking all that. Be sure to listen to our shows on the HR Huddle podcast on the Work Defined Network. And if you'd like to help support the podcast, please subscribe, leave a rating, and review where you grab your podcast. We do know we go a little bit long. We have seen comments on that. So we are trying to work through it. It's hard to get through all the data and all the information.

[01:25:16] 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. And finally, thanks, Cliff, for all your help in putting all this together. We couldn't do this without you. Thanks to our production team, including Kelly Kuhn, Linda Galloway, and our marketing team, Summer Alano, Cole Harris, Caitlin Diamond, and everyone else who spends a little bit of time helping us get this up and running. Thanks to our listeners and community. We couldn't do this without you. You are the most important part in this conversation. And that's it for this episode of Spilling the Tea on HR Tech Cliff.

[01:25:46] We have exhausted the week upcoming, but 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. So please join us.