Richard Rosenow - HR Tech 2024 - How AI Might Make You Better at Your Job

[00:00:00] The world of business is more complex than ever. The world of human resources and compensation is also getting more complex. Welcome to the HR Data Labs podcast, your direct source for the latest trends from experts inside and outside the world of human resources. Listen as we explore the impact that compensation strategy, data, and people analytics can have on your organization.

[00:00:25] This podcast is sponsored by salary.com, your source for data, technology, and consulting for compensation and beyond. Now, here are your hosts, David Turetsky and Dwight Brown.

[00:00:38] Hello and welcome to the HR Data Labs podcast. I'm your host, David Turetsky, and we are here at the HR Technology Conference 2024. Before everything gets started, I'm here with my good friend Richard Rosenow from OneModel. Richard, how are you?

[00:00:51] Good to see you, David. I feel like we were just here last year. It's very similar.

[00:00:55] We were. It was over there. It was on the other side.

[00:00:57] Oh, on that one. There. Put it that way.

[00:00:58] Yeah, that way.

[00:00:58] Yeah, that way. It's really great to do visual references on a podcast, right?

[00:01:04] But we're here before everything gets started, so you're going to hear a lot of beeping and a lot of commotion. Hopefully not a lot of yelling.

[00:01:10] But if there is, it's just part of the broadcast, I guess. So, Richard, how's everything been going?

[00:01:16] It's been a lot of fun. I mean, 2024 has been an exciting year, interesting year for a lot of companies.

[00:01:21] But coming back here, I mean, the energy of this, especially when things are just getting set up and all the vendors are here getting things ready, preparing everything, getting the floor set.

[00:01:29] I mean, there's an excitement in the air and just HR Tech's a special time. So, I'm excited to be here for it.

[00:01:34] There's a buzz. In fact, there's a lot of people outside who are so anticipating HR Tech. And I saw a lot of analysts already. I've seen a lot of people we know and love. And they're just brimming to get started.

[00:01:47] Absolutely.

[00:01:47] Brimming? Is that the right word? We'll go with it. How about that?

[00:01:51] Sure.

[00:01:52] So, Richard, as you know, on the podcast we do the one fun thing that no one knows about you and we're going to do it today. What's the one fun thing that no one knows about Richard Rosenow?

[00:02:01] I think that at a lot of these conferences, I don't know if I used this one last year or not, but I am 6'7". And I have been scaring people at conferences because my LinkedIn photo does not look 6'7", apparently.

[00:02:11] Well, yeah.

[00:02:11] So, if you see somebody head and shoulders, come up and say hi to me. And it's, I promise, I won't loom. And yeah, happy to see you.

[00:02:20] Yeah. Well, you know, I'm 5'8". Now that my hair is all gone, I'm probably 7'7". But yeah, when I saw you, I was like, hey, how's the air up there?

[00:02:31] Well, we'll put a picture in the podcast notes maybe.

[00:02:34] That would be a great idea. Yes, let's do that. Let's do that.

[00:02:37] So, today, our topic is going to be what's going on in the world and especially the world of HR Tech, HR Analytics. And, you know, of course, we have to cover off on that two-letter acronym that everybody loves and knows.

[00:02:49] Of course.

[00:02:57] But let's first start with HR Tech and let's talk about how has HR Tech evolved and transformed, especially in the last year, because there have been so many things.

[00:03:07] It's been a big year. And I actually remember one of the questions we talked about last year was around, are we starting to see like AI just hype or AI native?

[00:03:14] Right.

[00:03:15] And we were talking about that where last year I was hoping to see this year some of these like AI native startups and companies really kind of embrace what that means.

[00:03:21] I think we're starting to see that a little bit because everybody had the same marketing last year. Everybody had AI last year.

[00:03:26] Right.

[00:03:26] This year there's actually some like oomph behind it, especially at a couple of the, you don't think so?

[00:03:31] Well, I'm actually looking at you like that because the hype cycle is still there, right?

[00:03:37] Absolutely.

[00:03:38] Yeah.

[00:03:38] And where it transforms into actual real work that gets adopted or real savings or whatever that means. That's what I meant. That's sorry. The frow, the burrowed frow is that.

[00:03:49] It was well-deserved. And I think the, one of the shifts I've seen is really this conversation around like AI was chat pretty much alone last year. We're definitely seeing AI into agents and AI into agent swarms this year a little bit more around that sort of like it was answering questions last year. This year, it seems like it's taking actions.

[00:04:07] And what that opens up for a couple of these companies and part of it too, is I'm coming off a workday rising last, last week and looking at like dream force and Oracle, a number of the big HGMs all just had their big announcements for the year, what they're working on. And across the board, we kept hearing kind of like agents or what's happening. Uh, we're, we're seeing a lot more of these autonomous software. That's, that's going to take on some of those things that were really had to be human in the past and human decisions, but the software is going to try to start to make some of those probabilistically.

[00:04:35] And give us an example for like a person who's sitting at home going, okay, well, I've heard AI is going to take my job. So kind of make that agent real for me. What, what agent is it and what is it going to help with and what is it going to replace?

[00:04:48] Yeah, I'll give, I'll give kind of a simple example that I'll give an example of what we're working on over at one model too, a little bit. So I think about the simple example is like, Hey, I want to order a pizza. I can talk into my phone to my chat GPT enterprise. I can say, Hey, please order a pizza for me. You pick the place.

[00:05:04] And it goes ahead and dials places the order of the pizza, then shows up your house an hour later. You don't have to have that conversation in between. And it might not be that AI talking to a person. It might be an AI talking to Domino's pizza.

[00:05:15] Yeah. And then that figures it out from there.

[00:05:17] But, but does it do all the things that are necessary? Like what I like on my pizza? No, no. I'm being very serious here. What I like on my pizza? Where does it get delivered to and when? And also what's the payment that I'm going with payment system?

[00:05:32] Yeah.

[00:05:33] In terms?

[00:05:33] I think we're starting to see some pieces of that. So I'll say with, um, with one model, what we're looking at with the agent side is really on the backend of the product. So for, for years, we've done data engineering on behalf of our clients. It's a big selling point for one model. If you're trying to figure out how to get your data out of work day and out of greenhouse and out of all these different places.

[00:05:49] By the way, this isn't a sales pitch. He's just telling us exactly how it would work.

[00:05:53] You bring it all back together into the, into the central system. We're starting to see use cases where that, what a data engineer used to do on the backend actually write code. Turns out these agents and AI tools are actually really good at writing code. It's, it's a language that they can deploy and that they can roll out. And so we're seeing that we can actually start to deploy them in groups.

[00:06:11] So having a project manager agent, a data engineer agent, a code checker agent, and an analyst agent that work in tandem that work as a small unit of, of conversations. And like, I remember seeing one of these, like these are early like command line demo kind of things, but it was like the project manager sent a request to the data engineer agent. The data engineer agent brought back something wrong. And the project manager said, that's wrong. Do it again.

[00:06:34] And it's like very, it's almost like a little personified, but you're watching this flow happen in seconds. And you see these things kind of across a lot of the, the conversation now, as we're starting to see how do we actually deploy these things at scale across all the HR tech vendors here. And I think chatbot is big. There's really, really good use cases for chatbot. I think it's table stakes. I think everybody needs some kind of conversational agent now today. That's where people want to interact from here though. That next phase of how do we actually start to use these tools we built and how do we

[00:07:04] AI to use these tools we built? It's that agent framework is what's coming. So what you've done though, or what, what's happening then is we're replicating people in a way in which it's real and they're being trained on the things that a project manager, moving back to the project manager and they, and the, uh, that other agent that they gave it back something wrong. Um, but, but that's what happens in the world. And so what you're doing is you're creating

[00:07:33] these AIs that are replicating the interchange and they're making the interchange work because the logic built into them says, here are the parameters. Here's the parameters by which I work. Here's how I work. And if someone who's interacting with me doesn't give me what I need, I'll wait or I'll tell them how to make it right. So that no, but, but, but, and we do the same thing when we're dealing with people.

[00:07:59] And it used to be a very human domain. And I, I think the space of, you know, what's funny is looking around the floor, RPA used to be a thing. I don't think I see one vendor here that's doing RPA robotic process automation. That was a, that was a hype word maybe two or three years ago. And I think the difference between RPA and what's going on with AI now is RPA was saying your HR administrators are doing this a thousand times. They're doing one thing a thousand times. Let me automate it. Right.

[00:08:22] What's coming up now is you're doing one thing one time. We can automate that. It's we're, we're automating non-repeatable tasks with AI that makes a probabilistic decision about what to do. And so that, that change there allows us to go much further down that, that slope into these one-time things or one-off things that humans used to do entirely.

[00:08:41] Mm-hmm. But, but if we take that one step backwards, then that does replace some of the work that the HRS analysts used to do or the administrators used to do, which was, I need to make sure that the data is right. Let's just say we're training our bots to do that, or RPA was training to do that.

[00:09:02] And so where you are in that case, are we really replacing people or are we just replacing those tasks that are just so terrible that now that person can make sure that the results are right and that they're doing some value added shit instead of.

[00:09:17] Yeah. I think it's augmentation. I think you definitely still need the human in the loop, especially because we're making very human decisions within the HR domain.

[00:09:24] But this area to be able to kind of take some of these things off the plate and allow you to go beyond and push HR into those more creative spaces, that's where HR should be.

[00:09:32] Like to be in HR, if, if I was passionate coming out of high school or college, I was like, I want to be in HR someday. I want to change the world through the lens of HR.

[00:09:41] This is going to sound bad. I love people analytics and HR tech. That's probably not the dream of changing HR. HR really at the core is like, I want to influence the workforce. I want to change people. I want to work with people.

[00:09:52] And those kind of ideals of what it means to be human at work and to support humans at work are really not always in the weeds of mechanically changing these technologies.

[00:10:03] So if AI can take on more and more of those pieces, if we can raise the floor, hopefully we can get the HR people back into doing HR things with humans.

[00:10:11] Like what you hear so far? Make sure you never miss a show by clicking subscribe.

[00:10:16] This podcast is made possible by salary.com. Now back to the show.

[00:10:22] And I think that's a really great segue to say, and how does that help us now focus on those value added tasks like analytics?

[00:10:30] And I will disagree with you when I talk to people or in the past, when I've talked to people, especially about HR analytics, it was about them providing a more value added service to their clients because they weren't giving them the information or insight to be able to run their business better.

[00:10:48] And so I was hoping those administrators would then be able to see the insights, see the patterns in the data, and then become more value added is a horrible way of putting it, but more value added to their clients.

[00:11:03] Because otherwise, their clients are going to go somewhere else to get that data.

[00:11:06] I 100% agree. And this is where like breaking a job into its tasks is so important.

[00:11:11] Because when I was a people analytics leader, when I was building my team, one of the reasons we went with technologies was to de-skill the team.

[00:11:18] I said, I want to remove my team's skills and I want to have a team that's less skilled in SQL and more skilled in consulting.

[00:11:26] Absolutely.

[00:11:26] Both of those things are needed to do people analytics. You have to be in the data. You have to understand the data.

[00:11:30] But if I have to hire someone that's 90% data engineer, 10% consultant, I'm going to be able to deliver very different insights and outcomes to my HR partners than if they're 90-10.

[00:11:39] Right.

[00:11:39] And if I can leverage technology to get me to 90-10 consulting versus data engineering, I can do a lot more of the stuff that I'm paid to do.

[00:11:47] Right. And it's probably much easier to find a consultant who you can train to do SQL than it would be to find a SQL person, a SQL engineer, or someone who knows how to write code to be more.

[00:11:58] And please don't get upset at me by saying this, but those people really don't like talking to other people.

[00:12:06] They like dealing with data and solving problems.

[00:12:09] I've seen that with, we've studied kind of career path.

[00:12:14] We've studied career path for people analytics leaders. And one of the funny things is like data engineering is required to do people analytics well.

[00:12:20] I have met one people analytics leader that came from a data engineering background.

[00:12:24] And I find that fascinating because you see a lot of people analytics leaders from consulting backgrounds and BI backgrounds and analytics or research.

[00:12:31] But data engineering, for some reason, it's not a career path to the leader because they're in HR for a little while.

[00:12:36] But the data engineers did not go get a master's in data engineering at a high-quality school.

[00:12:40] To just do HR.

[00:12:41] To do HR.

[00:12:42] They want to do data engineering.

[00:12:43] They want to work on big, hairy problems.

[00:12:44] Right.

[00:12:45] And they bounce between different teams and they become managers in that space.

[00:12:48] And they move up in their own role.

[00:12:49] Well, that's how they make much more money, too.

[00:12:51] That's true, too.

[00:12:52] That's true, too.

[00:12:53] No, we're in it for the love of the game sometime on the HR side.

[00:12:56] Hmm.

[00:12:57] Yeah.

[00:12:57] Right.

[00:12:58] Coming from salary.com booth.

[00:13:01] No, I will tell you that's not true.

[00:13:02] No, but seriously then.

[00:13:04] And so that enables when you get the right balance and now data analytics can now focus on being able to deliver on the promise of being able to find insights in the data now that we're not scrubbing the data, now that we're not focused on the SQL side.

[00:13:22] Right?

[00:13:23] Yeah.

[00:13:23] So how does analytics, especially HR analytics, then mature?

[00:13:28] How does it grow?

[00:13:29] How does it evolve?

[00:13:30] Yeah.

[00:13:30] And I think one of the funny things kind of coming out of this past year of seeing the kind of Gen AI conversations evolve, it's one of the only technologies where you did not have to be more technical to use it.

[00:13:40] The technology is actually a little bit more human because it's a natural language interface.

[00:13:44] We're using plain spoken word and then it converts that into the queries, the actions, the steps from there.

[00:13:50] So it turns out HR is really good at speaking.

[00:13:54] HR is a really good communicator.

[00:13:55] That's a big part of kind of who we are within HR is we're here because we need to be able to write job descriptions, communicate boundaries, write policies, communicate and coach and mentor.

[00:14:05] That's the safe spot for HR leaders.

[00:14:07] And it's why coding has felt so difficult sometimes.

[00:14:10] Well, it turns out coding just became natural language.

[00:14:13] So ideally what's going to happen here is we're going to be able to see these technologies start to, through the natural language interface, deliver some of these insights in the way that these HR people are really good at asking for things.

[00:14:25] And analytics might look a little bit different then where a lot of times we think about analytics and it's, you get these like dreams of calculus and trigonometry.

[00:14:34] It just feels like heavy math.

[00:14:36] But a lot of analytics is knowing what question to ask and knowing to kind of how to parse through the answers you get.

[00:14:42] And if we can do that through natural language instead of code, it's really going to open up analytics to a lot more people across HR.

[00:14:48] But it's still going to need that person to make sure, as we were talking before, to make sure that what's coming out of the algorithm not only makes sense, pass the sniff test, but also can be translated to the business user who doesn't know that it came from a bot.

[00:15:04] Yeah. And thank you for bringing me back to you because like I am, I could not be a bigger proponent of the profession of people analytics and people analytics leaders.

[00:15:10] I think what frustrates me is when I see them get hired to do people analytics and then day two, they have to be a data engineer and then day three, they have to be a technologist and they have to go fix all these other things before they get to do their day job.

[00:15:22] Right.

[00:15:22] And their day job is to do the analytics and the research and drive these, these human insights.

[00:15:26] Right.

[00:15:27] But they have to go fix everybody else's problems.

[00:15:28] But that's one of the things we were talking about on the way over to the booth was about data governance.

[00:15:34] Yeah.

[00:15:34] Yeah.

[00:15:35] Because as you mentioned, Terry Zipper did a presentation with...

[00:15:42] Danielle Bouchin.

[00:15:42] Who's going to be on the podcast soon as well.

[00:15:45] She's the best.

[00:15:46] And they were talking about data governance and how it's preventing, I think it was pay transparency or it was preventing more transparency.

[00:15:55] Yeah, it was a fascinating session.

[00:15:56] I think I did not expect...

[00:15:59] It was early in the day, first day.

[00:16:01] The room had probably 300 people.

[00:16:02] It was packed to the gills, people standing in the aisles, people standing in the back of the room around data governance, which is like historically not like a hot topic in the space.

[00:16:10] Everyone in People Analytics wants it to be.

[00:16:12] Everyone in the HR space, like it's kind of new.

[00:16:15] It's a little bit different.

[00:16:15] I think what's happening is because we're starting these Gen AI projects, we're realizing we've never extracted the data properly.

[00:16:21] And now suddenly this, that was overwhelmingly packed with people.

[00:16:25] It was incredible to see.

[00:16:26] And that's to a credit too.

[00:16:28] Terry and Danielle are incredible.

[00:16:30] And so the business acumen, the way they were able to tell the stories, the candid nature they had, I hope that was recorded because it was an incredible session.

[00:16:38] Well, if not, then we'll get them on the HR Data Labs podcast and we'll have them do it again.

[00:16:41] Perfect.

[00:16:41] But seriously, I'm with you.

[00:16:43] I've wanted data governance to take on a new role within the world of HR for a long time because, you know, as everybody knows, I think HR data is crap.

[00:16:52] And having that skill, having the governance and that process and the policy and all of those owners that are required to make sure that that data is accurate, sign me up.

[00:17:05] I mean, like seriously, I'm a fan.

[00:17:08] I'm an ally.

[00:17:08] Yeah.

[00:17:09] I'm walking away a little bit inspired from that session because I think one of the things we've seen, we run a job board for people analytics.

[00:17:15] So every three weeks I get in there, I pull all the jobs down, we get them all updated.

[00:17:18] And that means for the past two years we've been tracking the jobs in the space.

[00:17:21] We're starting to see a separation from HR tech and people analytics.

[00:17:25] And we're starting to see them kind of shift just a little bit apart and create space in the middle for this data domain.

[00:17:31] And there's a couple of Fortune 500s that have started to hire people data leaders, chief data officers for HR or HR COOs that have data responsibilities.

[00:17:40] But it's a distinct space.

[00:17:43] And if you look at like the education it takes to be an analytics leader or a tech leader, it's not exactly the background of data architecture or data model or data governance.

[00:17:51] So I'm hoping that that's my 2025 dream is that data governance continues to emerge, especially in light of JetAI, that we start to see this discipline start to stand on its own feet.

[00:18:01] Well, think about the origins of data governance.

[00:18:04] Didn't it really come out of the need for databases to or for data structures to kind of be able to talk together and for there to be a system of record for certain things and know what the system of record is?

[00:18:19] That sounds right.

[00:18:19] And I think there's like the HR tech approach to data governance is a little bit like make the system work.

[00:18:26] Yes.

[00:18:26] Make sure the system delivers.

[00:18:27] The people analytics approach to data governance is a little bit different because it's I need the data to be able to perform something downstream and to be able to listen to the data in the appropriate ways to tell a new story.

[00:18:38] And I think for any people analytics leaders listening, like we've all been pounding the table for 10 years, like data governance is required.

[00:18:44] And it's been hard to make the movement happen.

[00:18:46] I think with this push for Gen AI, it's a perfect thing, like lasso your hopes there, push them in front of the executive board and say, hey, if you want to do Gen AI projects,

[00:18:55] if you want to hook ChatGPT up to something, you must do data governance and then get that done so we can do our good people analytics downstream.

[00:19:03] Yeah.

[00:19:03] And I think what that's going to also drive is maybe the focus on privacy and focus on stability inside firewalled.

[00:19:10] Yes.

[00:19:11] Yes.

[00:19:12] Databases or structures so that we're not exposing all of this data in ways in which we didn't realize.

[00:19:18] Oh, absolutely.

[00:19:19] And let me be really clear.

[00:19:20] Please do not hook ChatGPT directly to your Amazon warehouse.

[00:19:24] Yeah.

[00:19:24] And that is a tough thing we are seeing on the floor here.

[00:19:26] There are some vendors that have popped up that are finding fast ways to get to people analytics with Gen AI.

[00:19:31] And it's like, oh, there's not a fast way here.

[00:19:33] There is a hard way to do it and the correct way to do it.

[00:19:36] But just hooking the LLM directly to the data, it's not there.

[00:19:39] Oh, and I am so scared about not just the more enterprise versions of this.

[00:19:45] I'm more scared about the more, pardon the expression, cowboy.

[00:19:49] Yeah.

[00:19:49] There's a lot of that happening right now.

[00:19:51] Versions of this where, you know, commercial models of ChatGPT are everywhere and people are asking ChatGPT questions.

[00:19:57] And when people ask ChatGPT questions, those questions are in the wild.

[00:20:01] Your IP address, maybe your company name is also in there too.

[00:20:05] And so that stuff's out there.

[00:20:07] Oh, survey data is a scary one because that's one of the number one use cases we've heard for like why ChatGPT is really helpful with HR teams is parsing through survey data.

[00:20:15] Lovely.

[00:20:15] But what's happening is, again, the cowboys are throwing it into ChatGPT on their demo license they have for themselves, their personal license.

[00:20:21] And it's producing results which are actionable, helpful potentially.

[00:20:25] But that lack of governance at the core to get this right, to bring these LLMs to bear, which I think is going to happen.

[00:20:32] We're going to have to have zero data retention.

[00:20:34] We're going to have to have data deletion policies.

[00:20:36] You're going to have to have a really strong handshake between the systems and ChatGPTs of the world.

[00:20:41] But private cloud-based, right?

[00:20:43] Or private cloud-based?

[00:20:45] Or are they actually going to the public cloud for that?

[00:20:49] So here's an interesting one.

[00:20:50] This is something we're starting to see is that open AI is starting to sound a lot like AWS.

[00:20:55] I think five or ten years ago, if you said, hey, we put all our data in AWS, people might have been a little nervous.

[00:20:59] Like, hey, that's public cloud.

[00:21:01] That's out there.

[00:21:02] But it's like it's become part of the infrastructure now.

[00:21:05] We're seeing signs that enterprise AI tools like ChatGPT as well as the others, Anthropic, you name it,

[00:21:10] are starting to become that sort of like, hey, this might just be the infrastructure layer.

[00:21:14] And then if that happens and if they can prove themselves and if they can kind of stand up like AWS has,

[00:21:20] we're going to see cloud-based adoption.

[00:21:22] But there still needs to be the segmentation of my data against your data.

[00:21:27] Oh, absolutely.

[00:21:28] And neither the twain shall meet.

[00:21:29] Yeah.

[00:21:30] Oh, yeah.

[00:21:30] Definitely get an enterprise license.

[00:21:31] Yeah.

[00:21:31] Don't be doing this on your personal license.

[00:21:32] But that's what I'm saying.

[00:21:34] I think the consumer-facing version of ChatGPT 4.0,

[00:21:38] everybody's just going after it and putting lots of shit in it and, pardon my French, stuff in it.

[00:21:43] And it's really not bounded by anybody.

[00:21:46] No, it's an exciting cowboy world right now.

[00:21:51] And if you're an IT leader, hopefully you're clamping down on the usage of ChatGPT in its unlicensed version

[00:22:00] and providing your people with the knowledge and understanding about what happens if your data goes into the cloud.

[00:22:08] That's definitely another big trend we're seeing is there's this shift towards these enterprise AI layers.

[00:22:14] So a lot of SaaS tech companies, we've gotten really used to having a front door that the customer comes in our front door,

[00:22:19] uses our tool, leaves and goes about their day.

[00:22:22] Everyone's trying to kind of capture the customers and bring them all into their product as often as they can.

[00:22:25] And we're realizing that these enterprise AI layers are creating a very different experience that the customers and clients are frankly preferring,

[00:22:33] where they would much rather work with a enterprise ChatGPT or like a Glean or these other kind of layers that have come out

[00:22:40] that then works with an AI or an agent that talks to other systems.

[00:22:44] So why would I want to go in and make all those transaction moves within another system if I could have an AI system do it for me instead?

[00:22:50] And so those enterprise layers, I think we're seeing a glimmer of that this year.

[00:22:54] I think next year, a lot of these SaaS tech companies that have relied on their UI, their interface, their front door for too long

[00:23:00] are going to realize that these enterprise platforms are really good at recreating that on the fly.

[00:23:06] So wait until next year.

[00:23:07] Wait until next year, always.

[00:23:09] Next year's going to be better.

[00:23:10] Wow, I don't know about that.

[00:23:12] Next year, they may be taking over.

[00:23:14] So we may be answering to the AI overlords, but...

[00:23:18] We'll send our agents to this podcast.

[00:23:22] HR Data Labs has been taken over by robots.

[00:23:25] Yeah, that could happen.

[00:23:27] Yeah, you could probably do a much better job of hosting than I can.

[00:23:29] Certainly, they'd be much more energetic than I am.

[00:23:31] Oh my goodness.

[00:23:33] Hey, are you listening to this and thinking to yourself,

[00:23:36] man, I wish I could talk to David about this?

[00:23:38] Well, you're in luck.

[00:23:39] We have a special offer for listeners of the HR Data Labs podcast.

[00:23:43] A free half-hour call with me about any of the topics we cover on the podcast or whatever is on your mind.

[00:23:50] Go to salary.com forward slash HRDLConsulting to schedule your free 30-minute call today.

[00:23:59] So we've obviously been speaking about AI the entire time, Richard.

[00:24:04] Is there anything else that's kind of, for you, that's kind of pushing HR tech, HR analytics?

[00:24:11] Or is it really just being able to wrap our hands and heads around how AI is actually affecting the world of HR tech and HR analytics?

[00:24:19] That's a really good question.

[00:24:20] I'm trying to...

[00:24:21] And it's hard because literally everybody here, I'm looking at a bunch of different signs,

[00:24:24] and it's literally just AI on every single sign, which is unfortunate.

[00:24:27] That's definitely a bit of the hype cycle.

[00:24:29] I think there is a bit of a change in the air around partnership.

[00:24:32] We've seen a couple of the big HCMs kind of open up and start to bring in more partners that were maybe a little bit closed before.

[00:24:38] I think that has a...

[00:24:40] You mean in the marketplace or the API movement?

[00:24:43] Yeah.

[00:24:43] And being able to kind of like understand that there's not a one-stop shop.

[00:24:47] Right.

[00:24:47] I think we've heard that for years.

[00:24:48] Like, come to the one-stop shop.

[00:24:49] Just come here.

[00:24:50] If you're within our walled garden, you'll be okay.

[00:24:53] And we're seeing a lot more of these kind of recognition that this is an ecosystem of tools.

[00:24:58] And maybe it is in the face of this existential changes that are coming that it's like, hey, we got to go together.

[00:25:04] So I don't know.

[00:25:05] I'm feeling a little bit more camaraderie this year.

[00:25:07] Maybe that's just me.

[00:25:08] Well, I think it's been very cyclical.

[00:25:10] It was in the 80s or 90s.

[00:25:13] Maybe it was the 90s and early 2000s that we started hearing about more openness and more...

[00:25:20] Well, we had a best-of-breed timeframe with probably the 90s.

[00:25:25] And then probably in the 2000s, we had a...

[00:25:28] Well, the HCMs are kind of building everything themselves and we're going to have it on one platform.

[00:25:32] And that went for a long time.

[00:25:33] And then it kind of fought between best-of-breed and the platforms.

[00:25:37] But now, I mean, with APIs and with marketplaces, yeah.

[00:25:42] I mean, well, they're also getting a piece of the action too.

[00:25:44] Because if you're in their walled garden and if you're in their marketplace, they're going to get a piece of your action.

[00:25:49] Yeah.

[00:25:49] It's a very different look at kind of the financial nature of what it means to purchase and purchase within and with.

[00:25:55] Yeah.

[00:25:55] Yeah.

[00:25:56] It's a bit more nuanced.

[00:25:57] But I think it's ultimately to the benefit of the customer when you can have a little bit more openness and connection so you're not feeling locked in.

[00:26:05] Co-opetition.

[00:26:06] Co-opetition.

[00:26:07] There we go.

[00:26:09] Let's bring it back around.

[00:26:10] Yeah.

[00:26:10] Why not?

[00:26:19] So, I think what I'd love to do is at some point maybe pull you aside at the end of the HR Tech Show and say, okay, so we talked at the beginning.

[00:26:30] Is there anything to change your mind at the end of the conference?

[00:26:33] That'd be a lot of fun.

[00:26:34] Yeah.

[00:26:34] It'll be me just like fully beaten down, tired, just bedraggled.

[00:26:40] And you're going to change your name to Richard A.I. Rosenau.

[00:26:43] Yeah.

[00:26:44] Just fully adopted.

[00:26:45] Sure.

[00:26:46] Why not?

[00:26:47] Or it'll just be A.I. Rosenau.

[00:26:48] A.I. Rosenau.

[00:26:49] Yeah.

[00:26:50] Poor Al Adamson.

[00:26:51] Poor Al Adamson.

[00:26:52] Every time I see his name, I'm like, A.I.A.I.

[00:26:54] Exactly.

[00:26:55] Love Al Adamson.

[00:26:56] I think he's going to be here, too.

[00:26:57] So maybe we'll try to pull him over at some point.

[00:26:59] We will.

[00:26:59] Yeah.

[00:27:00] All right.

[00:27:00] Richard, thank you very much.

[00:27:02] Absolutely.

[00:27:02] David, thanks for having me on again.

[00:27:03] My pleasure.

[00:27:04] Stay safe.

[00:27:05] That was the HR Data Labs podcast.

[00:27:09] If you liked the episode, please subscribe.

[00:27:11] And if you know anyone that might like to hear it, please send it their way.

[00:27:15] Thank you for joining us this week and stay tuned for our next episode.

[00:27:19] Stay safe.