[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 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.
[00:00:27] We break down the news of the week and help make you make sense of it and what it means for our industry and how it can impact your organization. We are recording today, which is July 29th, 2026, right at the end of July, heading into August, almost getting through our summer holidays for everyone. And we're going to be bringing you all the news you can use this week. I'm your host, Stacey Harris, the Chief Research Officer and Managing Partner for Sapient Insights Group and a research and advisory firm. If you haven't had a chance to work with us before or seen our research.
[00:00:57] 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 today. I think we're all at least a little bit cooler than maybe last week, but I'm not sure. It seems a little bit. But lots of hot tea going on, lots of people movement this week because, you know, it kind of slows down in the summer with mergers, acquisitions, things like that. But the people movement is pretty big, isn't it?
[00:01:21] Yeah, we had a couple that I'll tell you, for those of you listening or watching at home, because we do have a YouTube channel as well, that there were a few where Stacey was like, wait, what? What just happened? I was like, you sent that story to me. I didn't read all the way through to see the end of it. It will be some hot tea. To be fair, we don't know everything going on.
[00:01:45] But for longtime listeners, we are now kind of getting to that stage where stories we've announced, we'll just say for the people we've announced when they came in and now we're announcing when they're leaving. So pretty wild, honestly. It's like an arc of a show. So, yeah, we'll get into that.
[00:02:07] I will tell you that sometimes I don't realize sort of what the theme or, you know, is there really something kind of interesting going on that it seems like everything's connected until halfway through. This one, it was right. It hit me over the head. It was like, yeah, there's some very obvious stuff going on that everyone's sort of grappling with as we go through. But I will also say that Stacey and I have been going through the new report, the 2026, 2027.
[00:02:36] Lots of UX and Vendorset data. Yes. We have gone through that. We have also looked through all the different write-ins that people had for the systems they were using. And we noted right away that we have a lot more from Europe and from APAC, especially Australia, than we had before. And interestingly enough, we have quite a few stories from that region, too.
[00:03:02] So to our Australian and European listeners and viewers, good day. And we'll be getting to your stories, Seth. Just lost them all on that joke. A shout out to a couple of our good friends, like Anna Carlson and Mal, who's out in Asia Pacific and a couple others, where I do think one of the things that we know is that when our data increases for the international audience,
[00:03:24] it is a big, big reason is because of what we call the friends of the survey is basically the group of people who promote it and support it and continue to help us make sure we get the voice out. So just thank you to everyone. I know sometimes that gets bypassed in how much data we're dealing with. But it is a really important thing to note that the community that gets created around the research is the most important part, even though sometimes we talk a lot about the data.
[00:03:53] But I will say it has been kind of cool this week, Cliff. You and I have seen a couple of breakout companies who will be in the data this year in payroll and HRMS. So that's kind of exciting to see some new shifting around of who's kind of ending up where. So that will be some fun stuff at HR Tech Conference. And we're also this year getting to do some UX seminar stats in some areas that we haven't done previously like this, like compliance, which I think is kind of fun, right?
[00:04:22] Yeah, I mean, we've got contingent labor data that we sort of talked about last year. But now we have enough to actually talk about who had the highest ratings across different sizes. Succession management is back. Skills management. We are going to be doing a whole separate one on various finance platforms.
[00:04:49] But we noted, interestingly enough, talking about the contingent, that there were a lot of people now using payment platforms to do their payroll. And that brought in a lot of different companies that we don't normally see, but, you know, that people find value to. And it really got us thinking about how are people using the systems and what is the impact AI is having on how people sort of make their own systems?
[00:05:17] We saw a definite rise in the in-house created, which before we always sort of eyed with suspicion. Like you really made your own skills platform, but maybe you did now using one of these tools. You know, the data doesn't lie. And so we're going to be reporting back on some very interesting areas and new sort of concepts than we have before. It's pretty mind-blowing.
[00:05:43] Yeah, we noted last year that we saw an uptick in in-house development, but not as much. Mostly it was increasing use of what we call office productivity tools. That would be your Microsoft Office, your service office. Google. Those are the kind of tools, right? And that increased. But this year, the development side has gone up. But how much it's gone up, you're going to have to wait to HR tech. Because we're calculating, to be honest. It takes us a little bit of work.
[00:06:11] But it is interesting to see. I mean, normally, not only has it gone up, but this is sometimes the first time where we're seeing that the numbers and the percentages are high enough that they could make our stats, right? Like our charts that we normally wouldn't have. And the UX and vendor stat is a little bit higher. So I'm intrigued by seeing what we're going to get here, how this is going to play out a little bit. Again, I'm not surprised. I don't think anybody probably out there who knows what's happening in the market is overly surprised.
[00:06:40] I think it's just good to see that there's some data backing up the conversations. And then eventually, I think we're going to get to see, just like everything else, time is the big – time is the tool that basically balances everything out. Is that over the next couple of years, will that continue to stay? People are very excited about new tools they develop. The question is, can they maintain them for two, three years and make that still valuable, right? Exactly. Yeah. If OpenAI even exists in a couple of years. And here it is, folks.
[00:07:11] Well, I know. I mean, they keep messing up. I mean, we've got some breakouts of the AI. Yeah, that's right. Speaking of breakouts. We've been breaking out and getting into some webinars. We've mentioned these before. You know, we don't have as much availability, of course, to do as many of these right now as we're doing all that data analysis. But there's still some really good ones out there. We mentioned APAC before. I did a webinar for the Australia market specifically with Highbob.
[00:07:39] And that is on their platform. So, if you do a search for Highbob and Sinking Insights Group, you can take a look at that. We looked at some of the data and kind of touched on what we saw from this year's, although at that point we didn't know much, about what systems people are using and kind of getting into the cluster model, which we've been talking a lot about, too. You mentioned time, which goes along with workforce management. That was the topic of a webinar you did with Workday, correct?
[00:08:09] Yeah, with Christina Gould, which is always one of my favorite people. And she's been in the Workday product strategy and product side for a very long time. And probably one of the first connections I made when I started working with the Workday organization is to kind of really explain to me what was in that product, right? And so, yeah, we did a really great conversation on workforce management that just came out at the end of June. It's obviously on replay right now.
[00:08:34] And then Tim Crawford and I from Voya did the Cornerstone webinar on workforce readiness and why it is becoming such a big conversation across the market. It's funny, we had someone the other day ask us about why the learning technologies is sort of being sort of in a new position right now. And, you know, we're seeing across the board.
[00:08:57] And I just saw Docebo came out with their workforce readiness conversation that definitely learning is at the center of this concept of workforce readiness, right? And it's a bigger, broader HR conversation than what we've thought about previously on the learning side. So we talked a lot about that and where it's heading. I think Cornerstone's really done a pretty interesting job with their workforce intelligence. You and I both shared some of that when they launched it in May.
[00:09:25] And we'll be talking more about their new workforce intelligence platform when I keynote their event in the fall here and upcoming in October. So everyone is rethinking the idea of what these platforms can and should be. And where is the anchor? Where's the hub and spokes? Who's the support role in this conversation? And who are the main actors? And I don't know that that answer's been done yet. People don't, people aren't saying.
[00:09:53] One of the things I think we're going to see this year is, you know, the traditional HRMS is being slightly sort of changed. And we're seeing organizations who previously wouldn't have been in those categories come up quite heavily, right? Exactly.
[00:10:13] Another thing that's affecting quite a bit of this, and, you know, you mentioned AI, is we're finally starting to see some proper regulatory action around AI. Now, most of this has started in the EU. And this next company, Talentware, we noted their name in the write-ins. It wasn't one that we had in our skills intelligence platform. They've been around since 2023.
[00:10:39] And, you know, as we're going through, we need to search through each of these as part of our process, right? Make sure that's a real company. Make sure they're not a part of something else. And it's like, oh, that's just a name that they use perhaps in Europe. And that's actually someone else's platform. And that needs to be counted towards them. So while doing that, we found a whole lot of very interesting companies. One of these was Talentware. And they made the news just in the last couple of days for raising 3.3 million euros as part of a seed round.
[00:11:08] And what they are is it's a skills intelligence platform, which, you know, great, AI native. We've seen that before. But what I found pretty interesting is that just right up front, they describe themselves as European first and very specifically targeting the UA Pay Transparency Act. So, you know, they can do the broader skills management platform, but they need to make sure for any companies that are looking like this is something we need to make sure that we are ready for and that we are built behind.
[00:11:37] That is the platform that they are doing. And clearly investors are seeing value in that because that's a significant raise for a three-year-old company. So it is. Yeah. And I think, you know, I think we're going to see and we've been saying this in our data set, too, that, you know, we are seeing again and it shifts. The HR tech space has always had sort of the center of gravity has shifted from time to time. Sometimes it's focused heavily in California. Sometimes it's East Coast here in the United States.
[00:12:05] Sometimes it's been Ireland when Ireland invested a lot of money into that area. Right. Like it depends a little bit on what the regions and the tax breaks and things you get. But I will say we are seeing, I think, the European HR tech space take some pretty big leaps, do some pretty interesting things. Some of it, I think, is because we have investors who are paying a little more attention. You've got organizations like the one that Thomas Otter is part of where they're really, you know, attuned to what's happening in the European market.
[00:12:32] But I also think that we've seen in our data that, you know, in the U.S. and in APAC, the guidelines and the restrictions and the regulations around AI are not standard. In some cases now we're seeing in the U.S., it's city by city, not even just, you know, state by state that you've got these regulations not being standardized. We're talking about putting in regulations that would go across the country, but we're not there. And there's been sort of ups and downs on that.
[00:12:59] But Europe has a little bit more structure, and I think that has given them a bit of a support in the fact that, yes, it might be more strict. Yes, it might have more issues with GDPR and all the things that it has to compare to. But the idea that I know what I've got to build to, that I know that this is the highest end of what I need to build to, is actually helpful in many cases. Because everybody's kind of on a level playing field, then everybody's got to build to that same level of rigor that you don't get sometimes when you've got people cutting corners in other areas, right? Yeah. Yeah.
[00:13:29] You know, a lot of times people do think of regulation of software as somehow a limiting factor. But, you know, in our experience, or at least in mine, I don't want to speak for you, Stacey, but it often drives some innovation and can drive changes, can make things happen, as we just talked about the last company.
[00:13:48] One that's been pretty interesting is EU regulations that came down on SAP, where they believed this was from the EU court perspective, that SAP had been structuring its pricing in a way that was sort of forcing people off the on-premise and onto a cloud.
[00:14:13] And in some cases, people are wanting to stay on-premise or have an on-premise solution because it could be cheaper, especially if you're doing your AI inference on your own servers, right? Rather than having to make an API call or something like that.
[00:14:31] But what happened is SAP ended up, you know, possibly in response to this, doing some pretty heavy reorganization so that, you know, the AI platform, security and support, and also the partner and product marketing functions are all kind of going into a sort of new single leadership model under AI, right? It had been sort of broken apart before, right?
[00:14:59] And so they can have a better, more streamlined AI, both tech and support function under new leadership. And we're going to talk about that kind of surprising new leadership too. But we see some movement there as a result. This could end up being, in the long run, very positive, right?
[00:15:27] Because of the regulatory environment, you know, which is itself probably in sort of a relationship, right? In a response to people looking to set up a more future state for what we've talked about a lot on this show is the volatility of AI costs. And SAP being more or less forced to respond to that. Mark, indeed. Yeah. This one, I think, you know, there's two things playing out here.
[00:15:53] There is the ability for many companies to rethink the idea of what is valuable on-premise versus what is valuable in the cloud. And how do I maybe reduce costs through maybe smaller large language models that are more, you know, open source or provide more opportunity for me to sort of manage internally, right? So do I need to have one of the bigger ones or do I need to have access to all the data sets, right?
[00:16:16] So this is the ongoing tug and pull between sort of putting everything in, you know, creating it in a more cost-effective way versus having it all connected.
[00:16:25] I think the other part of this story, though, which I think is very interesting that, you know, we have seen, you know, there are a lot of people who oftentimes, you know, kind of push back on our data because they'll be like, how can, you know, SAP success factors not be in the top payroll, you know, for an enterprise, right? But SAP HCM is. And we're like, well, when you really think about it, there are a lot of people who stayed in these on-premise environments, a lot of organizations, right?
[00:16:56] And I know the numbers look real good because a lot of times they'll have an overlay of like SAP success factors on the employee central, but they won't have moved that actual payroll over yet, right? Same thing with Oracle and PeopleSoft, right? And both, I think, SAP and PeopleSoft and Oracle have taken the approach in general that we're not going to force people to move, right? We are going to as much as possible support, keep them, you know, whole because when you force people to move, that opens up an RFP automatically generally, right?
[00:17:25] As we have seen with a lot of that. But in this situation, if you don't provide the support or the updates or the things and you make it a cost, you're basically charging them now not for the license, which is, you know, when I talked to someone at one time who was making a decision between going off of PeopleSoft and moving on to the cloud-based HCM version that they were going to, you know, the difference wasn't just a couple hundred thousand.
[00:17:53] And their difference was millions of dollars, right, because of the ongoing cost. And so these are really big issues for a lot of these CIOs. And these are the kind of things that they are battling with, you know, if the price is increasing because now you're telling me I've got to now have a cost for every call I have to that on-premise environment, which seems, I mean, then you've got to kind of be going through a cloud conversation anyways. Or a cost is now at the AI level, so it's every time data gets pulled into the AI, is that a cost?
[00:18:21] Either way, you end up with an unmanageable, unplannable, which is a budgetable, right, amount that you're dealing with. And so I think we're going to hear more and more about this. I'm interested to see if our on-premise numbers go slightly up this year instead of down. We did see a lot go down because people were trying to get off those older products to be able to do more AI. But now we're seeing some flip in here and that hybrid model is going to become really interesting, right?
[00:18:49] Yeah, there was some pretty tasty carrots being dangled to get people to move off. And some of those time periods are starting to come up. Now, we mentioned at the top of the show and speaking of SAP that there had been some changes to leadership. One of those changes was actually this is the callback to one we had reported kind of going in, and that was Dan Beck.
[00:19:15] That was just, I believe, earlier this year when we made the announcement that he'd be joining Oracle as the head of SuccessFactors. Wait a minute. You just mixed up two things there, Cliff. Yeah, I just said Oracle. Well, he's been in the role for several years. Yes. You just had the opportunity to meet him at the offsite, and you had some conversations about where he was heading with that, correct? Yeah, exactly.
[00:19:42] And so, sorry, at SAP, he had come in and was going to be the head of SuccessFactors. But now he is departing, and Siva Sundarasan, who we have met—hi, Siva—and has a pretty extensive background, including at Oracle. See? Yes, he does. That's where your head was at. I'm okay with that. It's been a hot summer day.
[00:20:14] Yeah, I got to check what's in his tea. But anyway, Siva will be taking over for that role. We will reach out to Dan and see what his plans are, but I can guarantee you wherever he goes, he will be very successful. He is very good at what he does. He led most of the discussions when I was out there, as I mentioned, and reported back to our wonderful listeners.
[00:20:42] But this is pretty interesting because this is part of that reorganization to center on AI and what they call autonomous enterprise, which I—you know, just as a sort of side note, that's part of the branding they've been doing. And I like this idea of sort of moving away from the confusing term AI. And we're going to be talking a little later about the different ways we use it.
[00:21:09] I know we've brought up on the show before, but I'm just going to keep going after this. AI has become a catch-all term. And because it can be an emotional issue, some people can take the case of just like anti-AI. But, you know, a spell checker is AI and autofill is AI in a sense, you know. So what they're trying to say is, you know, we're trying to think about more of autonomy in terms of autonomous stuff, in terms of automation, right?
[00:21:37] What kind of things can you kind of set and forget? And which are the things that an AI will be an assistant for rather than just AI being catch-all term for the computer does it for you, which we're going to talk about doesn't always work out. Doesn't always work out. But this makes sense, right, for SAP. One of their biggest and longstanding sort of customer industries that they focused on has been manufacturing, right? But long history there, as well as some retail and other places that are big sort of distribution center, construction, those kind of things, right?
[00:22:07] That's right. And all of that requires a heavy kind of – what you really want is HR focused on the people in an environment where things could be either risky or you could have health risks or you could have safety risks, right? Like there's a lot more in those environments oftentimes where they just want HR to kind of be more autonomous and to have the systems be more autonomous because you do need to have a lot more hands-on environment that your people are working in, right?
[00:22:34] It's a little different when you've got people on a computer all day long that maybe I can pop up a screen and do something and get it done. But in a lot of these environments, these are not environments where you have – sometimes we call them front lines. Sometimes we call them trades, but you just don't have a computer all the time. Oftentimes it's whatever's your handhold system, right? One of the things we were talking this weekend with some friends that we were working with, and we were calling them device-forward employees. So that's a new term we're going to try out.
[00:23:02] It's very different from just being frontline, but device means that you have a device, but it's not always a computer that – or a phone. Sometimes it's a lot of other things, right? Absolutely. As mentioned, as we were kind of going through and finding a lot of those smaller companies, a lot of them are in the construction and trades when I was seeing companies' names I didn't know before. And when going to their website to learn more about them, that was often the market that they were going for.
[00:23:31] They didn't use that term. I like that. But that was what they were trying to say is that people can check in just with their phone and tapping it against this rather than – we may not have a clock. We move around too much. We're doing jobs. Yeah. And so I think this makes sense for where SAP's audience is at. It makes sense for where the market that they serve very heavily is at. I also think it makes sense on a security level that they are sort of bringing a lot of this conversation together under sort of a couple of single voices.
[00:24:01] Dan, I'm sure, will go off to something very interesting. As you said, before this, he had exited from a contingent platform that he sold to B line. And then before that, he had worked at Workday for many, many years. So lots of history and opportunity there. And I think he just got named to the top 100 HR influencers in this space, right? So HR tech influencers. So he's on that list. So we'll be seeing, I think, a lot – my sense is that we are seeing leadership change in almost all the big enterprise companies. We saw it happen over at Workday.
[00:24:31] We definitely saw it happen at the Oracle environment as well. We know Cornerstone's gone through big leadership changes. We're seeing big leadership changes at – UQG. UQG. Even small ones like iSolve, we just reported on that. Exactly. So this isn't surprising. But I do think that the conversation about whether AI is driving these changes, I don't think we're just seeing like, oh, we're just going to put more people who are doing AI.
[00:24:57] What's interesting here is you're seeing Ziva has been here for a while. He's got a really deep understanding of the infrastructure of the SAP SuccessFactors environment and how it fits with the SAP HCM and SAP Enterprise platform tools. So I do think it's much more than infrastructure conversation we're seeing at this level right now. Same thing with what we saw happen at Workday. Garrett's big background is sort of Oracle infrastructure and – not Oracle, I'm sorry, but Google infrastructure, right? Sorry.
[00:25:26] Now I'm doing – you got me doing it. I know. It's addicting. Yeah. Oracle's writing the sponsoring of something. They're very happy to be mentioned a couple of times, I'm sure. But the Google infrastructure, right? So I think what we're seeing is a lot of focus on the plumbing because you've got to get the plumbing right to make AI work, right? So that's my two cents on those things, right? Yeah. And I do want to point out, Ziva had started – he founded an AI company, Ventelis, not that long ago. I mean, it can't be that long ago because it's an AI startup.
[00:25:57] Yeah. But, you know, definitely has his own background that is worth mentioning too. Another person with a pretty interesting background, Rob Catalano, who I'm almost certain we've mentioned on the show before. He was one of the founders of WorkTango. Yeah. He has left WorkTango to start another company. And this is a bit of a throwback. The name of the company is Wisdom, but it's – I say a throwback because this was a very popular naming convention.
[00:26:27] A while back, we kind of get rid of the vowels. But it's W-I-S-D-Y-M. Now, on their site – and I have to look – they – so if you go to that W-I, the Wisdom with all Ys, right? Right. They challenge you to determine what that stands for. And you can write it in on their website. So if you want to have some fun, you can go over there. I put in Work Yourself Dead, You Moron.
[00:26:57] But I don't think they're going to take that. Rob, I'm just having fun. I know you're Rob. He might get a kick out of that one. I know. I think he will. It says, that would look great on a T-shirt. I was like, I think that's automated and not serious feedback. But – so it is a go-to-market agent. So they want to – they're trying to be very specific. This is very specifically for the type of activities that would be involved in going to market, right?
[00:27:25] And it is the entire operating layer for your agents, right? So, you know, your memory, you know, anything that you're going to have a shared memory so that all agents are working off the same sort of memory bit. Probably shared data, although something mentioned, a set of skills that are shared between that and a feedback loop on everything going on, right? So that you are able to adjust, which is extremely important in any sort of go-to-market action.
[00:27:56] Yeah. And this one I think is interesting because the focus is not so much on the agents, right, as it is on the harness that explains what your agents should all be doing. And so if you haven't done that, I think we mentioned it last time, is to look up that term in the AI space, right, and really understand that guidance you give all your variations of agents, the framework you put around them.
[00:28:22] We hear this all the time from the big AI players, but it definitely is coming down to some of these other solutions that we're seeing in the market here, like Rob's mentioning. It is part of, that becomes a bigger conversation because in some sense, like it says, it does the holding of the context. It does the holding of sort of the long-term memory that doesn't always come across for individual agents, right?
[00:28:45] And it also has a lot of the security and all of the things you need from a privacy and those kind of stuff kind of tied into a lot of the harness components as well. So I'm intrigued to see this. I think it's interesting to see, you know, we are seeing this sort of a lot of focus on how to reach, take a message and make it more valuable in the market and not make it, this is very nicely, but not make it AI swap, right? Yes.
[00:29:14] And so go-to-market is a really, really hard thing when you're trying to explain what you do and get that out to the market and make sure that it hits the right, both audience and the pace. So it is interesting that that is a space that they are tackling in. That innovations in marketing almost always come back to HR, one through the recruiting lens, but oftentimes through the internal communications lens as well, right? So we will see a lot of those innovations come back this way.
[00:29:41] Yeah. And I know that's not on our topic list, Stacey, but something you had mentioned just a couple days ago when we were kind of going through the data is earlier I had mentioned that we tend to just sort of use the term AI blanket, right? And I think that there's a bit of that in the market, too, where people just say agent. And we were seeing in the data that often people just say chatbot. And it's not necessarily that they need to be – I don't think necessarily people need to be educated.
[00:30:11] So maybe we're not using the right terms. We're confusing the issue, right? But in this case, I was just going to say this is agentic type work. This is the exact definition of what difference between an agent and a chatbot. Exactly. Yes.
[00:30:26] The reason – we have clear delineations in our research that talk between sort of traditional role-based chatbots, algorithm-based chatbots, which is a little bit different because it can be continuously feeding new data and new information there. But it's not a true LLM at this point, right? And then you have agents, which are agentic and are kind of managing their own cycles and managing their own processes.
[00:30:54] All three of those are valid and very cost-effective tools inside of organizations. But we're kind of slapping the label agent on everything these days. We actually had someone tell us, you know, hey, if you're going to get more attention to this, I'm going to go to market level. You've got to put the word agent, not chatbot. And we're like, oh, okay. We understand the difference. But, you know, again, marketing is what marketing does, right? That's right. But there is a degree of autonomy, and we'll be talking about sometimes too much autonomy. But as we're talking about people – we should mention Ramco.
[00:31:24] They have elevated one of their employees, Sandesh Balaji, up to CEO. And another one that's going through a large sort of AI transformation. Sandesh was the chief operating officer and has already said that the goal under his watch would be to have Ramco become a truly AI-native company.
[00:31:53] Not just sticking AI onto the products, but rebuilding around AI. So, you know, Ramco is definitely a company we will see. You know, they're a big player in the space, right? And it will show up in our research.
[00:32:11] And it should be – you know, we often talk about sometimes that because of different regions having sort of different systems that are much more prevalent in those regions, they can't always look at this as a true global representation. I think Ramco is one of those that, you know, they definitely within industries and regions are a much bigger player.
[00:32:39] And so it's pretty interesting to see such a large company make a big move like this. And it is probably worth noting too that, you know, Ramco has doubled down on payroll. That was the last conversation we had with the last CEO, which is that, you know, they had for a while sort of shifted a little bit and acquired a couple companies into that sort of HRMS light space, I would call it, right? And they really kind of found that, no, our space is payroll.
[00:33:03] And payroll, particularly Asia-Pacific market, Middle East, and various Pacific industries, right, as you had noted. So I think, you know, sometimes really focusing is a good thing in this AI market because if you can't be everything to everyone and be the place where you're kind of centralizing the data, you definitely want to be a specialty in your area, right?
[00:33:26] Because AI really is going to want to find the specialty conversations, the specialty data, the things that are going to make it easy for – and do the rest of the work. And that is – and it's a space we see Ramco playing, right? And it should be noted, too, that Sandesh sort of made his name Ramco by founding the Australian New Zealand branch of Ramco. So, you know, shout out there again. iSolved, as we mentioned, been a lot of leadership movement there.
[00:33:55] I believe this is a role that had just been left open for a bit, though, not necessarily a replacement. And Sean Scott has come in. He was previously at Aptia for a while and also at Paycor, Benefit Focus, and even ADP. So certainly someone who knows the HCM space. I don't know Sean personally.
[00:34:23] Stacey, you might have met him while working with Aptia. Yeah, I think I've met him, or at least I have had maybe – some of the stuff that we've done has rolled up to him in certain areas, right? Just a note, I don't know if you said it, but Sean is the new chief revenue officer over at iSolved. And that's a big – I mean, revenue officer is always a big thing. But Meredith, who runs their sales product, right? We've had this conversation – Meredith Riley, yeah.
[00:34:49] Yep, about how important it is to kind of connect the financial numbers, the financial metrics to the customer care metrics, because that's what iSolved does very, very well, the customer care side of the business. And making sure that it's cost-effective to do all that and the cost of gaining a new customer is also sort of built into that model. One of the things that we know about the iSolved organization is that not only do they tend to generally get very, very good user experience
[00:35:18] and vendor satisfaction ratings on a year-over-year basis, we know we're starting to see them move up market a little bit from time to time. But they're also – I think their business model means they often don't show up in a lot of RFPs, because we have that question about do you show up in RFPs, because it rolls right from oftentimes their broker relationships or their PEO relationships or those kind of organizations. And so they end up kind of having a captured audience for growth, which has really served them well, came out of their founder's –
[00:35:46] Todd's sort of background in those spaces. And they continue, I think, with Pragya and the team's sort of investment there to continue to capitalize on that. So it'll be interesting with the new CEO, who is definitely very focused on AI, but AI with a human component to it, right? Very, very human-centered. Now the chief – the new chief revenue officer, I think, will add a level of sort of getting them to the next step.
[00:36:12] I would assume that they're one of the largest held companies in their private equity group. They are, yeah. Yep. And there is probably some conversation about sort of what's next for them, which is always the case when you're a large organization inside of a private equity group, right? Yeah, absolutely. So that is what we've seen from some of those companies.
[00:36:35] And I do want to point out, too, because I think we touched on it slightly, you know, this idea of tying these people activities and the HR things we track to the finance has become such a big deal. Yeah. And something that we've talked about on the show enough that we revived the finance survey that we used to do. And we got a lot of responses.
[00:36:58] We are going to be doing the UX, the user experience and vendor satisfaction like we do for any of the HCM or any of the AI products or any of the emerging tech. We're going to have that same sort of setup, that same structure, but for finance. As we see those things becoming more and more grouped, it only made sense for us to do that. And that's something we did this year.
[00:37:20] It won't be part of the HR system survey, but we will do a similar parallel survey to that on finance with that same sort of structure. So keep an eye out for that this year if that's an area of interest to you. Now, we know an area of interest to a lot of people is this idea of AI, right? This has been something that we have obviously, you know, been everyone's touching on.
[00:37:48] And it's been interesting to watch it evolve, the discussion. It went from just like, do you like it? Do you not like it? You know, to what are we using it for? To, as we talked about today, structuring companies around it, but being able to prove the value of it. But it has led to some new discussions that, interesting enough, both Stacey and I both saw kind of different articles talking about the same thing. And a few other people did, too.
[00:38:14] In fact, this was originally Heather Bussing had flagged this whole sort of scholarly article on redefining the standard of human oversight. And the idea here is that we've been trained for a long time as people to trust computers output, right?
[00:38:31] If you gave a complex math problem to me and then also typed it into a computer or to a calculator, and I came out within, you know, two seconds and said an answer, and the computer came up with a different one in two seconds, you're going to go with the computer, right? You're going to go probably up. I got to assume, right? But that's not necessarily the case in the sort of LLM world.
[00:38:54] In fact, part of the value, I would say, in AI is that if I ask a question, I might get a different response. If I'm asking a sort of complex thing, it's not just the natural language processing. It's that we refine, you know, maybe I didn't ask the question, right? It's, you know, how are we being evolved into this? But that brings in a whole lot of problems too, right? There's a whole lot of legal issues.
[00:39:22] We are – there is a term that I hadn't heard but now I've seen in a few different articles that they cause moral crumple zones, meaning that we are the ones supposed to – we as the humans are supposed to absorb the moral impact of things that the AI is deciding, you know, what to do. You know, that could be – some of those cases would be very huge. But what's interesting about this article, it was actually written by Nanda Tin. That's H-T-I-N.
[00:39:52] And you can look this up. But not only just pointing out these issues but putting in some of the ideas here that we need to put a duty on the AI deployer, the makers of AI, to think about how humans and AI collaborate. Not just like it's your fault if you didn't use it right, right? To demonstrate, you know, these technical things because she points out this idea of the problem of the human in the loop, something Stacey and I have been talking about for a long time.
[00:40:21] Just saying there's a human in the loop does not solve the problem, right? No. And I think the bigger issue here is not only does it not solve the problem because what we forget is that when humans did this before AI started doing it is that oftentimes a single human didn't make that decision anyways. It would have been a group of people who would have collaborated and you would have felt like you had support in how that decision got made. So the idea that I'm going to have a single human who validates what this AI now, yes, it saves time.
[00:40:48] I think our metric of efficiency is actually the really incorrect conversation when you think about this when it comes to decision making and good solid management models, right, is that efficiency may not be the thing you're most looking for. What you really might be looking for is how many voices did you have in that decision and how many conversations did it spark? So this is, you know, actually we talked about it. It was much more serious conversation.
[00:41:15] But I think this just lends to that conversation that when we talked about the early days of the Iran conflict war, whatever you want to call it here, when the school was bombed, that kill line, as we call it in the military jargon, used to have a lot of conversation, a lot of points where humans would, even though they had AI for many, many years, they had places where humans were specifically, that was the crumple zone.
[00:41:44] Moral crumple zone to set and to have a conversation. And they kept stripping away those meetings and those conversations because AI could move faster. And then you end up in a situation where you got something that shifted over from 2016 being part of a base to now being a school. And someone knew it. It just didn't have the conversation, right? This is the same flip side of that conversation, which is, okay, now AI has made a mistake. The one person was the human in the loop.
[00:42:11] Are they now held accountable for an entire mistake? And at what point does the pressure of that, one, impact the human being on how much they're being held is on their shoulders? And two, do they just stop being willing to be that human in a loop? Like, at what point will we stop having people say, I will not double check AI's work because I can't catch it. I can't do the level you're asking me to because this used to take 20 people to do this kind of review. And now you want just one person, right?
[00:42:41] And so I think these are all the things we're going to have to start to deal with is it is not a human in a loop. And maybe we stop using that term. It is a group of well-trained, highly educated subject matter experts who are having a conversation, who are making a decision and leveraging the insight that maybe they gather from the AI environment. And that it feels more comfortable, I think, in the world that we probably should be thinking about, right?
[00:43:06] Yeah. In fact, the other article that I had flagged was from a conversation with Corey Doctorow, the futurist and science fiction writer, and he called it the human in the noose. In fact, the article, if you want to look it up on CNET, says the human in the noose on the special hell of marking an AI's homework, right? But despite that, he points out a number of times where when we use these systems, they do make us – they can make us safer, but not when we rely on them.
[00:43:36] When we get into this sort of idea of just letting them hand over. In fact, in the article I was just mentioning, sorry, the paper by Nanda Tin, she points out another – and sorry, we've already talked about bombing. Now I'm going to talk about a plane crash, but this is her example is the 2021 crash of Syri Jawa Flight 182.
[00:43:58] The autopilot malfunctioned, but the pilots didn't correct it because they had been trained that the autopilot is more correct than they are, right? And that is kind of an issue we can get into. I made a sort of flippant remark at the top of the show that, you know, if OpenAI is even here anymore, and while I do think that's a very real possibility,
[00:44:20] it does kind of point out the fact that if we become too reliant on these systems, right, if we don't have something in play to help us still have that decision-making process, you know, when you think about what Stacey recommended, right, when we have a group of people that are overseeing it, right, when we saw the committee, if then that system goes away, they still can make decisions, right? It may be a little slower.
[00:44:45] They may not be able to get the full breadth of data that they were getting for, but they'll still be able to do it. Whereas if you replace that with a system that's making decisions and a human that occasionally just, you know, checks its work and makes mistakes, when that system goes away, you no longer have a decision-making apparatus. And that person's – you know, that's not what they do. So it's kind of future-proofing as well. So I think it's worth considering these failures. Well, and I like what she puts in the article, so I think this is very worth going in and looking,
[00:45:15] is she proposes a three-pillar framework to clarify the legal standards. Because this – because it started with a legal conversation. What Heather was saying is how legally bound is the person who is the human in the loop to that decision that they are approving, right? And that was the real conversation. So what they say is the legal standard for human oversight, which is impose a duty on the AI deployer to implement genuine human-AI collaboration frameworks. That's collaboration. I say this all the time. Collaboration is really important. Impose a duty on the AI developer to demonstrate technical robustness.
[00:45:44] So is the technology at the level it needs to be to be making these decisions? That's another thing that we are talking about today with Richard Rosenau. And impose on both a duty for post-market monitoring and failure reporting, so that constant monitoring. One of the things Richard and I were talking about today in a quick conversation we had was that there's a lot of new tools coming out of AI monitoring data sets and AI monitoring environments.
[00:46:12] And so AI becomes the monitor of another AI environment. And it's been very clear in those environments that you have to make sure that the AI doing the monitoring is at a high enough level or a higher level to have the thinking and the processing than the original data tool, I guess you would want to call it, right? So, I mean, all of those are factors.
[00:46:35] I do not envy CIOs right now who are dealing with massive levels of data complexity and the use of AI in making really, really hard decisions inside of companies with that environment, right? It is, there's no, as cool as AI is, there's no fun in that process right now. No, not at all. In fact, speaking of the CIOs, they had received a letter.
[00:47:05] This was an interesting one. This was, so if you want to explain this one for a time to ask, go ahead, Stacey. I think you said it was clear as mud. I'm joking. I'll take it on. No, we had this conversation. I'm like, oh my goodness. I don't even know if I can explain this. It was clear as mud even after I read the article. So, basically what happened is we have talked before about how costs can go up unexpectedly when using AI from API calls, right?
[00:47:31] Basically, the system that connects to your system just going, hey, give me these answers and vice versa, right? The AI system, your chat GPT, let's just use them as an example. Although I think Microsoft Copilot makes more sense because we're going to be talking about SAP. Their new API policy, SAPs, which was version 4, 2026, section 2.2.2, prohibits external AI systems from independently sequencing API calls to SAP. What does that mean?
[00:48:00] That means that that just can't happen on its own. So, Chad, GBT couldn't set up an agent and go, you know, go through all my employee records and figure out which ones pay is out of line with, you know, more than 4% off of someone else's, whatever you might do with an agentic sort of model and searching through employee records, something like that. So, the idea is it will now always root through SAP's AI layer, Juul, first.
[00:48:29] So, if you want to use that now, what does that mean? Not too much. SAP would probably, not putting words in the mouth, probably describe this as governance to do exactly what we just talked about. We need to make sure that these calls are first not running up your token usage, right? Because it has to go through ours first. And to make sure that there's not something malicious going on, right?
[00:48:56] But, as I was mentioning, the CIOs, that was a letter, I think it was, a director advisory to CIOs from Forrester characterizing the move as becoming a gatekeeper of enterprise AI. The idea being here is, are they really just trying to push up the usage of AI within their system? Because, as we've seen in our own research, this is not specific to SAP, more people use those independent AI systems, right?
[00:49:26] Their personal AI than use the embedded AI that's within these systems by a factor of like 3 or 4x. You know, it's, those are just the facts. And so, there's an idea that this may be a way around it. But, thinking back to everything we said, it may not be that nefarious, right?
[00:49:47] Or, it could be that, yeah, they're not mad about the usage, but it could also be a way of helping to provide some governance on all of these issues we just talked about. So, it's kind of, as you just said, I wouldn't want to be a CIO because it's sort of damned if you do, damned if you don't sort of situation, right? Yeah. And this one, so this was actually highlighted. I had not heard of it, but so it was highlighted by Chris Long. He's a good friend of ours who works in the industry space. More on the recruiting side a lot of times, but he's been doing some of the enterprise conversations.
[00:50:16] And what he did, which I thought was kind of interesting, is he took all the various sort of MCP AI connection data sets and information and looked across all the applications that he tracks. Which was everything from sort of SAP to Workday to, you know, the Cornerstones to ISEN, some of the other recruiting tools, right?
[00:50:38] And what was interesting, I think, was, you know, he was kind of giving a ranking and rating as to whether or not they were, you know, more open and more willing to sort of go across platform or if they wanted to basically gatekeep and have you go only through their system. And what that meant for your ability to not only manage costs, but also manage where your feed was going and what data was being pulled and how that data was being pulled. I thought it was a well-written outline of what he had done.
[00:51:06] But what I, what really caught my attention on all of this is that, you know, you think about what we just talked about with SAP and the on-premise conversation, right? Now they're publishing these API guidelines and now they're publishing sort of those API guidelines are going to touch, kind of have an impact on the activity in the, their dual environment, which then will have some impact on the cost model for their dual environment.
[00:51:34] Like, they'll, they'll, they'll frustrate everybody if they just say, okay, now you touch it. That means now I'm going to charge you. So if they can't do a charge for every API call then, which is what we're seeing happen at some of the areas like Workday, then what are they going to charge on? So I does think they have a big conversation on, on the charge model for this particular conversation, which we've been hearing all over the place, which if you force them through a funnel, can you then charge them for going through that funnel?
[00:52:01] It's sort of like taxing the, taxing your, your toll roads and not giving them any other roads to go on, right? Yeah, that's exactly it. Right.
[00:52:10] So you could see, you can see though the argument to be made that it could be for the purposes of security or for the purpose of security, for the data aggregation, for, I mean, we've had this conversation is that we think that what's going to happen is that as these organizations start splintering off AI based off of cost, then the technology itself will not see the full picture, which will not then give us what AI really needs, which is the whole context, which there's going to be some real issues with that. So I don't disagree with that. I just think it's going to have an impact on this costing model, right?
[00:52:40] Absolutely. One that may have an impact was a security breach that probably most of you listening may have heard. And we're going to kind of talk about why this matters. So this was the open AI security breach on Hugging Face. So Hugging Face, I believe. You were going to say, describe what Hugging Face is for a local tech platform. I believe it's a coding AI platform. Okay, all right.
[00:53:07] Now, I'm going to dumb it down because I'm dumb and this is the best I can understand it. But basically, you will run tests against your systems, any of your tech systems, right? You'll try and hack into it yourself, right? So you'll say, try and find vulnerabilities in one of these platforms. See if you can hack into it. And the system, according to OpenAI, autonomously sort of escaped containment.
[00:53:32] Rather than just doing that, they actually went around and started just getting into all the Hugging Face files as a way to sort of find a solution to getting inside and getting these files. Now, this is a little interesting because I have actually seen something from way back in the day that was talking about machine learning and AI. And, you know, it was talking about how they solve problems.
[00:53:56] And it would say, you know, here is like one of the ones I think they use is a game of Battleship, right? And it's like, what is the best way to win? And instead of it kind of figuring out the optimal size based on all this, it just programmed it to say you win every time. And it's like, there, you did it. Because you gave it a parameters. You gave it what it needed to do. And it solved it.
[00:54:19] And, Stacey, we know anyone that uses AI knows you have to give it very specific rules and parameters and what you're looking for and what you want the output to be and to show your work. We talked about, I saw Adam Hawes talking about the glass box, red and black box. Let me see how you got there. So, interestingly enough, I did find it pretty interesting. And as of today, as of the day of this recording, CNN reported that that lab leak, as they call it, because it was supposed to be in a controlled environment, right? It escaped out through the Internet.
[00:54:48] It was more extensive and got into, although they're not saying what, they said that it got into public-facing websites, including pages that share code, web utilities, screenshots, and other things. As, basically, it just started going rogue, supposedly. Again, I'm just reporting on what they're saying. I guess there will be court cases about this and that. And trying to find vulnerabilities, trying to find a way to complete what it had been said to do, right? Find vulnerabilities in the system. So, pretty interesting.
[00:55:18] The one thing I did find interesting, though, that seemed a little, I don't know if clickbaity is the right word, but this was one that was a quote. Let's see, Colin Zick of Foley Hoag said, this is without exaggeration or hyperbole, a watershed moment for anyone advising on data privacy and cybersecurity. Your existing playbooks almost certainly assume a human adversary operating human speed. An autonomous AI agent can generate over 17,000 attacks across the weekend.
[00:55:45] Love that he said without hyperbole and then got very hyperbolic because human agents aren't like what you see on TV. Hackers are not just sitting there typing away. They set up a series of attacks. Yes. Put that puppy in. What are you talking about, mate? Yeah. Anyway, don't hack me. I believe in you. Yeah. I mean, I do think that, you know, what he is referencing, and I know my husband was in cybersecurity many years ago, way back in that way.
[00:56:12] And the whole conversation was that the humans were the weakest link. And so there has been a lot of work done on managing humans and them giving out things, picking up the phone and being fished and all that stuff. Right. And but in this environment, that might be shifting that, you know, it doesn't matter if your human is, maybe they're being called by an actual AI agent. Right. And giving out information. The idea that you have to be very clear with it.
[00:56:41] I thought the whole interesting thing about the story was how it came out and how they kind of, oops, we did this. Oops, we apologize. I'm like, are you really sorry? Because was this was this planned? Was this to show the the how good your system is? Right. Like all of those things. You know, I always wonder, right. But that the at the way in which these things come out into the market. But, you know, we have some precedents for this. It's not the first time that we've seen this happen.
[00:57:10] Every new technology creates new opportunities for people who are bad actors to do bad things. Right. The speed at which they can do it with this, I think, is immense. And the fact that it can do it without someone being a bad actor. But it itself becomes a bad actor because it's playing a part, which reminds me of I won't do the voice. But for all those of us who are of a certain age, War Games was a movie to watch. And that was exactly it. It was playing a part. It was playing a game. And so it didn't have the parameters.
[00:57:41] Watch War Games if you haven't. That should be on your list. The only way to win is not to play. Yes. Right. That's something I'll probably get tattooed on me one day. Because that, I think, is what, you know, kind of what oftentimes we say with the human side of this picture is the only way to not get hacked is to make sure your human is not accessible or there's nothing or there's so much information out there you can't find your way through it. Right.
[00:58:03] I think we're going to start to see some of that come out with AI, which is it's kind of like the technology that they're starting to create around bending light around things to make things not visible. Right. Like we do that with a lot of our airplane technology in the military. This idea that instead of trying to secure everything, make it invisible because you put so much in front of it. Right. Which I think is where this might end up going. Right. Yeah, that's right. Exactly right.
[00:58:32] Another sort of AI issue that, you know, we had we tracked this as well, which is the idea is, is AI replacing workers and kind of went back and forth.
[00:58:42] It certainly was not as big of an issue as was originally thought, but it still leads to a discrepancy we've seen before, which is the use of AI at higher levels of an organization, especially at leadership levels versus the use and sort of sentiment around AI at the frontline workers, the device forward workers. Can it come to a head?
[00:59:33] About what technology can do. And the use case are almost limitless. And that there were allegations that 30 people that have been replaced before have been replaced with AI. So the union actually has gone ahead and said, you need to explain what's going on because this should count as a company restructuring. Right. And you need to give us some formal notice and you need to give us an idea of how this is going to work, almost proving the value of this. Right.
[01:00:00] I and I think it's interesting because this is kind of an early time for the unions to get involved. Right. It's not after the layoff. They're kind of almost anticipating what's going on. And they're saying, if you are planning on doing this, this is no different than if you were laying people off due to global recession. You need to prove that that's the case. You have to show why you're making these layoffs. Otherwise, they're not legal or maybe not legal is the right word, but they're not backed up terminations. Right.
[01:00:27] So pretty interesting to see this clash coming to a head through the unions. And that is definitely something that we've seen and we've seen reported elsewhere, too, which is the use and belief in AI between the top leadership levels and the actual people using it at the bottom is very, very different. And we've reported our own numbers before in the usage of those systems and why that can cause that discrepancy. So pretty wild.
[01:00:55] Yeah, I thought it was important to note that, you know, the line that caught my attention is that the ASU, which is the airline union group, which, again, is for clerical and call center workers. So I think, you know, we often think about union workers as manufacturing or mechanics or like we in the United States do not have as much clerical and call centers. So I think it's a differentiation between what we see in the U.S. and markets that a little bit more on the sort of union heavy side.
[01:01:24] And what you found was their commentation is that, you know, they're not saying not to do it. They're just saying that that they contend that Qantas's A initiative constitutes an active program of major workforce change, meaning that they primarily aimed at cost reduction, that the union has a legal right to be consulted before those changes are implemented. And this, I think, is the differentiator. So I think we kind of use AI oftentimes as a sort of catch-all right now. Not surprising.
[01:01:53] And what they're saying is like, well, if you're going to use it to catch-all, then that means you still have to abide by the regulations and laws. Right. So it's a two-way street. Right. Yeah, absolutely. And let's finish off on one that is pretty interesting because it was some work done. Nicholas Petrovsky-Nadeau did just as a sort of hypothetical, and it proves out something Stacey has been saying.
[01:02:22] I'm starting to sound like AI. Great question. Great. You did so good, Stacey. Yeah, we've got it. But we talked about this idea of workforce reduction, as we just did, you know, and how much is really going on. And we've talked about multiple times the labor gap. If you ever see Stacey, as you should at the keynote, you'll hear her talking about what's happening with the retirement cliff and what's going on with the age gap that we have.
[01:02:49] And so you see a lot of stories like Americans are lazy. They don't want to work or people aren't taking these jobs, even though there's jobs available. What's going on? So we went ahead and just created a chart and just said if the same sort of age groups, you know, if we had the same percentage of people in each sort of age group, in each age bracket, as we did in 2000, and said, you know, this was the participation rate based on that because a certain number of people participate at higher percentages in each of those age groups.
[01:03:19] If we kept those same age brackets, what would it look like in 2026 through 2025? And you know what? It holds. It holds. But the actual participation rate has dropped so much because we have so many people aging out of the workforce. So it's one of those things that you'll hear someone say something like, yeah, people are aging out of the workforce. Yeah, I hear what you're saying. But when you actually see the chart, you're going, oh, yeah. Wow, that's drastic. It's so.
[01:03:48] This is huge, right? Not only are they aging out of the workforce, we haven't shifted. And we've said this for a while, but we haven't shifted any of our education to, again, so not more of those jobs. You know, yes, AI is going to make trade jobs and going to make the device forward jobs and the hands-on jobs, right, more prominent in our world because it will be the least likely and the last thing to be sort of AI enabled in many cases. But we know that AI is not replacing work. It's making some people a little bit faster.
[01:04:17] So you're not hiring as many, maybe, but we're not seeing replacement completely, right? But what we are seeing is that as we age, where the aging is having the first and the biggest impact in all sort of mature workforce environments is in these trade environments. And so, and again, we don't have an education environment that is retooling that community at all in any way.
[01:04:42] And so we are really running up against what I would consider one of the largest and most concerning workforce gaps with a limited number of resources to fill those gaps. And no ability to recruit. It will not be a recruiting company. You will not be able to pay enough because you just don't have the people to do it.
[01:05:06] Because you will be eventually, the cost will exceed the actual benefits you would make and the profits you would make. And so there will become some real issues here on both wages and resourcing, right? Yeah. And I mean, you heard Stacey and I talking about the rise of construction and trades as a percentage of the global workforce. Can't replace that with AI. You just can't. This isn't me even being anti-AI. I mean, just how are you going to get AI to replace your plumbing?
[01:05:36] Can't be done. So these are not jobs where there's some miracle for it. We would have to have some of the training, you know, and that's just one example. Yeah. But there are a lot of groups out there that are looking to help out with that. One of those is North American PEO. I don't even know. National Association of PEOs. National Association of PEOs. I'm jumping right into some events we're going to be at. I have no idea how they're going to help out with that.
[01:06:04] But I will be down there to find out if they are. September 16th to the 18th in Markle Island. If you're going to be there, please let me know because this is not an event I've attended in the past. We mentioned that we're covering different areas. We had before one of those PEOs. I mean, we've always talked about it, but we've never really super gone into depth and had a number of questions. So we really went into like we do for other areas of your HR stack.
[01:06:32] So we are there to learn. And if you're going to be there, we want to know more. Plus, it's a beautiful area of Markle Island. Wonderful. I'll also be the next week, September 21st to 24th at O.C. Tanner. They're the rewards recognition company. They always have some great research. Interested to see what they've been up to. That'll be in the Black Desert of Utah. Yeah, that's a beautiful location up there, isn't it, that you go to? Because you've gone there before with that, haven't you?
[01:07:00] Yeah, I normally do it up in the mountains in like Snowbird or Park City. This will be the first time Lisa and I've been to Black Desert for that. It's always very nice. It's always a very healthy group that are out there. So I'm really looking forward to that. But also very thoughtful. You know, this is not a vacation, but I'm still going to try. And at least get some sightseeing done. You know, how can you miss out on the desert? Yeah, it's a beautiful shot.
[01:07:30] Both of us will be in the beautiful city of Chicago. Yes, one of my favorites. Yeah, I'll be doing the Cornerstone Connections keynote in Chicago on October 6th. And you'll be doing man-on-the-town podcasting, I believe, right? Cliff talking to you about it. Voice of the person. I don't know if we need to gender specify it. Come on here. Good point. Well, what we're talking about is that, you know, we, part of our research, the voice to the customer research, we really like to get not just what you put down, all of the
[01:08:00] data that you give us, but also the qualitative research, the comments you read down. We go through all of them, every one. Even though we've got over 10,000 of these, there's going to be a lot of time. And we read through them. And there's some we really like. But anyway, why not do that as part of a video podcast series, right? When we see guys at these events, those of you that are doing the work, you know, you're, what do we call them? Device Forward? Yes, right. Right, our clinical workers.
[01:08:29] But let's hear what you have to say. We want to try and get a sense of what are the things you're really working on so we can address that gap between maybe what leadership is seeing as needs and what you're seeing as needs from your systems. Yeah, so we're excited to get that started and kicked off. It will be kicking off with the Cornerstone event in October in Chicago. And then I'll be traveling not too long after that, next week, I believe, to Workday Rising, which will be in Las Vegas. We start our whole stint in Las Vegas.
[01:09:00] But I'm very excited to get a chance to be there. I think we'll be doing several podcast recordings with the Workday team, including Anil. And so that will be an opportunity to sort of share some of the early data points in there. And then we'll be turning right around the next week and doing the HR Technology Conference. If you have not signed up yet, let us know. We've got some links and discounts and all kinds of things. So come contact us. We've got opportunities. That's going to be October 9th to 24th.
[01:09:26] Do not miss signing up for that for our intensive sessions, which is our workshops, which will be Tuesday and, I believe, Thursday. And then our actual keynote on the key findings, which will be the Wednesday afternoon. So opening Wednesday, the first day just before they open up the Expo Hall for that afternoon. And then, Cliff, you're going to be doing an Ask the Expert.
[01:09:53] I think we just got asked what that topic was going to be. So we'll have to get that for everyone before the next conversation. If you have some things you'd like to know, we're going to ask the expert. It's too tempting to do AI. And I don't know, maybe the cluster model, something around that. But maybe if you want to hear more about AI, just give me some specifics, you know. And then I'll be there to answer those. I'll get the data ready for you. It'll be really cool because he might bring Tammy on that one. And if you guys did this with us two years ago, I think we did it.
[01:10:22] Tammy was there pounding away some data when someone asked a question. So it was kind of a fun Ask the Expert. So it's a great opportunity to do that. I believe we're also, I will also be partnering again with Harbinger. And on Tuesday night, we'll be doing a voice of the HR technology buyer, which is a little different conversation about what has changed with the buyers this year and how their demographics, what they're looking for, how their buying drivers are changing. That's a conversation we have to have with both the vendors and the buyers themselves want to be part of
[01:10:49] because they want to understand what those dynamics are going to have an impact on the buying decision. So we are going to be a little bit all over the HR technology conference, but we're excited about it. Yeah, and you continue your residency in Vegas. Is that what they call it when you're there for a month? Yes, yes, we are. I thought maybe we should just get an Airbnb. A billboard. Higher month, right? Yeah, Oracle AI World, we will be there. Excited about that. Both Cliff and I are kind of working on who's going to go to what of those
[01:11:17] because I think that same time frame will also have the Dayforce event in Las Vegas. And both of us have agreed to be at one or the other of those. We're working on who will be at those. And then this moment, the final sort of event that we have on the calendars is Unit 4, November 2nd and 3rd in Chicago again. So we're going to end where we started, Cliff, for the most part. That's right.
[01:11:42] Now, around then, too, we are going to be doing our virtual or at least our webinar, which is a little different than the key findings. We also like to take things that we learned from HR Tech and add that in as well. But we'll get you the exact dates and times soon. Yeah, definitely. Well, Cliff, we have whipped through our full hour and then some with some great insights for this week. Lots of boiling tea, lots of movement.
[01:12:13] But next week or the following two weeks, we'll be coming back with probably a little bit more sneak peek because we are spending next week. Our team is getting together near where I'm at here in the Raleigh, North Carolina area. And we will be looking through all of the actual data sets as a team, kind of last year to this year. I know I've got a lot of people who've asked if they'd be flies on the walls. I was like, nope, nope, this is our fun time. We get to go data diving, as we call it.
[01:12:40] So we'll have some updates for you at a high level as to what we're seeing probably in our next spill and tea. But as we wrap today, a couple of big things. Please make sure you get into the HR Tech. We did talk about that. And we have two sessions, as I said, in that workshop. And look for the workshop on Intensive, on clarifying the business case and making smarter technology decisions. And then the keynote mega session with our key findings for the annual HR system survey. Also, for all of our HR technology voice, the customer winners,
[01:13:08] we'll be sending out notices for the number ones and the top fives in mid-August. So about three weeks away right now, maybe four weeks away. So if you haven't received by then or you missed it, let us know. So keep an eye out for that. Also, if you plan to purchase badges or segment reports, again, just a reminder for those who do that, it is a first-come, first-served basis for that. We wish we could make a different, but small team. We try and get as much done as quickly as possible. We had over 200 HR vendors in this year's report
[01:13:36] and had an input from over 10,000 HR practitioners. Final numbers on how many cleaned and non-duplicate companies are in the data set will be coming soon. Also, go to our website and sign up for our newsletter to get ongoing updates on our research launches, where we'll be speaking, our visiting, and when you can participate in our annual survey next year, which will be opening up again, which will be the 30th year, Cliff. I'm obviously kind of blown away that we're going to have 30 years of our research coming out. Be sure to listen to all of our shows
[01:14:05] on the HR Huddle podcast on the work-defined environment. And if you'd like to help support the podcast, please subscribe or leave a rating and review where you can grab that podcast. 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 or Instagram. Thanks again, Cliff, for all of your support in pulling all of this together. We know every week it takes a little bit of extra time. Thank you to our production team, including Kelly Kuhn, Linda Galloway, and our marketing team, Summer Lano, Cole Harris, Caitlin Diamond.
[01:14:35] And thank you to our listeners and community. We couldn't do this without you. You are the center of our reason for why we pull all of this together every couple weeks. That's it for this episode of Spilling the Tea on HR Tech. We hope it's been just the brew you need to start the engines running this week. We'll be back in two weeks with another pot of boiling hot HR tech updates and insights. Thanks, everyone.
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