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

Connect with Richard:

• 

Connect with David:

• • 

• david.turetsky@salary.com

Connect with Dwight:

• • 

• dwight.brown@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• One Model
• Salary.com Consulting practice
• HR Data Labs Season 8 Episode 12
• HR Data Labs Season 8 Episode 10

• “If I can leverage technology to get me to 90/10 consulting vs. data engineering, I can do a lot more of the stuff that I’m paid to do.”

Announcer: 0:01

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. 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.

 

David Turetsky: 0:38

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 One Model. Richard, how are you?

 

Richard Rosenow: 0:51

Good to see you, David! I feel like we were just here last year? It's very similar.

 

David Turetsky: 0:55

We were. It was over there. It was on the other side over there.

 

Richard Rosenow: 0:57

Yeah, that way.

 

David Turetsky: 0:58

Yeah, that way. It's really great to do visual references on a podcast, right? But we're here before everything gets started, so you're gonna hear a lot of beeping and a lot of commotion, hopefully not a lot of yelling. But if there is, it's just part of the broadcast, I guess. So, Richard, how's everything been going?

 

Richard Rosenow: 1:16

It's been a lot of fun. I mean, 2024 has been an exciting year, interesting year for a lot of companies and but coming back. I mean, the energy of this, especially when things are just getting set up, and all the all the vendors are here, getting things ready, preparing everything, getting the floor set. I mean, just, there's an excitement in the air. And just HR Tech's a special time, so I'm excited to be here for it.

 

David Turetsky: 1:34

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 a lot of people we know and love, and they're just, they're brimming to get started.

 

Richard Rosenow: 1:47

Absolutely!

 

David Turetsky: 1:47

Brimming? Is that the right word? We'll go with it. How about that?

 

Richard Rosenow: 1:51

Sure.

 

David Turetsky: 1:52

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?

 

Richard Rosenow: 2:01

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 six foot seven, and I have been scaring people at conferences because my LinkedIn photo does not look six foot seven, apparently.

 

David Turetsky: 2:10

Well, yeah.

 

Richard Rosenow: 2:12

If you see somebody head and shoulders come up and say hi to me, and it's, uh, I promise I won't loom. And, uh, yeah, happy to see ya!

 

David Turetsky: 2:20

Yeah. Well, you know, I'm five, I'm five eight. Now that my hair is all gone, I'm five probably seven and a half. But yeah, when I saw you, I was like, hey, hey, how's the air up there?

 

Richard Rosenow: 2:31

Well, we'll put a picture in the podcast notes maybe

 

David Turetsky: 2:33

That would be a great idea. Yes, let's do that. Let's do that. So today, our topic is going to be the what's going on in the world, and especially the world of HR tech, HR analytics and and you know, of course, we have to cover off on that two letter acronym that everybody loves and knows.

 

Richard Rosenow: 2:49

Of course!

 

David Turetsky: 2:57

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.

 

Richard Rosenow: 3:07

It's been a big year. And I actually remember

 

David Turetsky: 3:07

Well, I'm actually looking at you like one of the questions we talked about last year was around, are that, because the hype cycle is still there, right? we starting to see, like, AI just hype, or AI native? 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. I think we're starting to see that a little bit. Because everybody had the same marketing last year. Everybody had AI last year, right? This year, there's actually some like oomph behind it, especially in a couple of the... you don't think so?

 

Richard Rosenow: 3:37

Absolutely!

 

David Turetsky: 3:38

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 the Frau. The burrowed Frau is that Give us an example for like the person

 

Richard Rosenow: 3:49

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 who's sitting at home going, Yeah, okay, well, I've heard AI little bit more around that, sort of like... Sort of like it was answering questions last year. This year, it seems like it's taking actions. 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 Dreamforce and is going to take my job. So kind of make that agent real for me. Oracle. A number of the big HCMs 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 are what's happening. We're seeing a lot more of these autonomous software that's that's going to take on some of those things What, what agent is it, and what is it going to help with? And that were really had to be human in the past, and human decisions. But the software is going to try to start make some of those probabilistically, and Yeah, I'll give, I'll give, kind of a simple example then 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 what is it going to replace? example is like, hey, I want to order a pizza. I can talk into my phone to my chatgpt enterprise, I can say, hey, please order a pizza for me. You pick the place, and it goes ahead and dials, places the order 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, and then that figures it out from there.

 

David Turetsky: 5:18

and when? And also, what's the payment that I'm going with? Payment System, what are the payment terms? By the way, this isn't a sales pitch. He's

 

Richard Rosenow: 5:33

I think we're starting to see some pieces of that. So I'll say with with One Model, what we're looking at with the agent side is really on the back end of the product. So 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 Workday and out of Greenhouse and out of all these different places. telling us exactly how it would work. 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 back end to actually write code. Turns out these agents and AI tools are actually really good at writing code. 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. 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, personified. But you're watching this flow happen in seconds, and you see these things kind of across a lot of 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 chat bot is big. There's really, really good use cases for chat bot. 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 get AI to use these tools we built? It's that agent framework is what's coming.

 

David Turetsky: 7:07

So what you've done, though, or 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 that other agent that gave it back something wrong, but, but that's what happens in the world. And so what you're doing is you're creating 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. And we do the same thing when we're dealing with people.

 

Richard Rosenow: 7:59

And it used to be a very human domain, and I think this 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, 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 1000 times. They're doing one thing 1000 times. Let me automate it, right? What's coming up now is you're doing one thing one time. We can automate that. It's we're automating non repeatable tasks with AI that makes a probabilistic decision about what to do. And so that change there allows us to go much further down that slope into these one time things, or one off things that humans used to do entirely.

 

David Turetsky: 8:42

But if we take that one step backwards, then that does replace some of the work that the HRIS 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 do that, and so were we 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?

 

Richard Rosenow: 9:17

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 with the mutual domain. 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 right? Like to be on HR. 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. This is gonna 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. 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. 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!

 

Announcer: 10:11

Like what you hear so far? Make sure you never miss a show by clicking subscribe. This podcast is made possible by Salary.com now back to the show!

 

David Turetsky: 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. 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. 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, because otherwise the clients are going to go, somebody else, somewhere else, to get that data.

 

Richard Rosenow: 11:06

I 100% agree, and this, this is where, like, breaking a job into its tasks is so important. 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. 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.

 

David Turetsky: 11:26

Absolutely!

 

Richard Rosenow: 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. 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 if I can leverage technology to get me to 90/10 consulting versus state engineering, I can do a lot more of the stuff that I'm paid to do!

 

David Turetsky: 11:46

Right. And it's probably much easier to find a

 

Richard Rosenow: 11:48

Yeah! I've seen it with we studied kind of 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 career path. We studied career pathing for people analytics code to be more and please don't, please don't get upset at me by saying this. But those people really don't like talking to other people. They like dealing with data and solving problems. leaders. And one of the funny things is, like data engineering is required to do people analytics well. I have met one people analytics leader that came from a data engineering background. And I find that fascinating, because you see a lot of people analytics leaders from consulting backgrounds and BI backgrounds and analytics or research, but data engineering, for some reason, it's not a career path to the leader, because they they're in HR for a little while, but the data engineers did not go get a master's in data engineering at a high quality school to just do HR. They want to do data engineering. They want to work on big, hairy problems, right? And they bounce between different teams, and they become like managers in that space, and

 

David Turetsky: 12:49

Well, that's how they make much more money, too.

 

Richard Rosenow: 12:53

No, we're in it for the love of the game sometimes on the HR side.

 

David Turetsky: 12:56

Hmm yeah, coming from the Salary.com booth! No, I will tell you that's not true. No, but, but, but seriously, then and so, 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, right?

 

Richard Rosenow: 13:22

Yeah.

 

David Turetsky: 13:23

So how does analytics, especially HR analytics, then mature? How does it grow? How does it evolve?

 

Richard Rosenow: 13:31

technologies where you did not have to be more technical to use it. The technology is actually a little bit more human, because it's a natural language interface. We're using plain spoken word, and then it converts that into the queries, the actions, the steps from there. So it turns out HR is really good at speaking. HR is a really good communicator. 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. That's the safe spot for HR leaders, and it's why coding has felt so difficult sometimes. Well, it turns out coding just became natural language. 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, 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 trigonometric it just feels like heavy math,

 

David Turetsky: 14:36

Right.

 

Richard Rosenow: 14:36

But a lot of analytics is knowing what question to ask, right? And knowing to kind of how to parse through the answers you get. 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.

 

David Turetsky: 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 to sniff test, but also can be translated to the business user who doesn't know that it came from a bot.

 

Richard Rosenow: 15:04

Thank you for bringing me back to because, like I am, I could not be a bigger proponent of the profession of people analytics and people analytics leaders. 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 right? And their day job is to do the analytics and the research and drive these human insights, but they have to go fix everybody else's problems!

 

David Turetsky: 15:28

But that's one of the things we were talking about on the way over to the booth was about data governance.

 

Richard Rosenow: 15:34

Yeah.

 

David Turetsky: 15:35

Because, as you mentioned, Teri Zipper did a presentation with Danielle Bushen, who's going to be on the podcast soon.

 

Richard Rosenow: 15:44

Oh, fantastic. She's the best.

 

David Turetsky: 15:46

And and they were talking about data governance and how it's preventing, I think it was pay transparency or it was preventing more transparency.

 

Richard Rosenow: 15:55

Yeah, it was a fascinating session. I think I did not expect. It was early in the day, first day, the room had probably 300 people. It was packed to the gills, people standing in the aisles, people standing in the back of the room, around data governance, which was like, historically, not like a hot topic in the space. Everyone in people analytics wants it to be everyone in an HR space. Like it's kind of new. It's a little bit different. I think what's happening is, because we're starting these Gen AI projects, we're realizing we've never extracted the data properly, and now suddenly this that was overwhelmingly packed with people. It was incredible to see, and that's to a credit to Teri and Danielle are incredible.

 

David Turetsky: 16:30

Yes.

 

Richard Rosenow: 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.

 

David Turetsky: 16:38

Well, if not that, we'll get them on the HR Data Labs podcast and have them do it again. So

 

Richard Rosenow: 16:41

Perfect!

 

David Turetsky: 16:42

But seriously, I'm with you. 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. And having that skill, having the governance and that process and the policy and all those owners that are required to make sure that that data is accurate. Sign me up. I mean, like, seriously, I'm a I'm a fan, I'm an ally.

 

Richard Rosenow: 17:08

Yeah, 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. So every three weeks I get in there, I pull all the jobs down, we get them all updated. And that means we, for the past two years, we've been tracking the jobs in the space. We're starting to see a separation from HR tech and people analytics, 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. And there's a couple fortune 500 that have started to hire people data leaders, chief data officers for HR or HR COOs, that have data responsibilities. But it's a distinct space, 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. So I'm hoping that's my 2025 dream is that data governance continues to emerge, especially in light of Gen AI, that we start to see this discipline start to stand on its own feet.

 

David Turetsky: 18:00

Well, think about the origins of data governance. 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?

 

Richard Rosenow: 18:19

That sounds right. And I think there's like, the HR tech approach to data governance is a little bit like, make the system work,

 

David Turetsky: 18:26

Yes.

 

Richard Rosenow: 18:26

Make sure the system delivers the people in 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 and the appropriate ways to tell a new story. 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 and it's been hard to make the movement happen. 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, 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.

 

David Turetsky: 19:03

Yeah. And I think what that's going to also drive is maybe the focus on privacy and focus on stability inside firewalled.

 

Richard Rosenow: 19:10

Yes, yes,

 

David Turetsky: 19:12

Databases or structures, so that we're not exposing all of this data in ways in which we didn't realize.

 

Richard Rosenow: 19:18

Oh, absolutely. And let me, let me be really clear, please do not hook chatgpt directly to your Amazon warehouse like that. Yeah, and that is a tough thing. We are seeing on the floor. There are some vendors that have popped up that are finding fast ways to get to people analytics with Gen AI and it's like, oh, there's not a fast way here. There is a hard way to do it and the correct way to do it. But just hooking the LLM directly to the data, it's not there.

 

David Turetsky: 19:39

Oh, and I am so scared about not just the more enterprise versions of this. I'm more scared about the more pardon the expression, cowboy.

 

Richard Rosenow: 19:49

Yeah, there's a lot of that happening.

 

David Turetsky: 19:50

Versions where, you know, commercial models of chatgpt are everywhere, and people are asking chatgpt questions. And when people ask chatgpt questions. Question here in the wild, your IP address, maybe your company name, is also in there too, and so that stuff's out there.

 

Richard Rosenow: 20:07

Oh, survey. 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.

 

David Turetsky: 20:15

Lovely!

 

Richard Rosenow: 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, and it's producing results which are actionable, helpful, potentially, 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, we're going to have to have zero data retention. We're going to have to have data deletion policies. You're going to have to have a really strong handshake between the systems and chatgpts of the world,

 

David Turetsky: 20:41

But, but private cloud based, right? Or private cloud based, or they, or they are they actually going to the public cloud for that?

 

Richard Rosenow: 20:49

So here's an interesting one. This is something where we're starting to see is that Open AI is starting to sound a lot like AWS. I think five or 10 years ago, if you said, Hey, we put all our data in AWS, people might have been a little nervous, like, hey, that's, that's public cloud that's out there, but it's like, it's become part of the infrastructure now. We're seeing signs that enterprise AI, tools like chat GPT, as well as the others, anthropic, you name it, are starting to become that, sort of like, hey, this might just be the infrastructure layer. And then if that happens, and if they can prove themselves, and if they can kind of stand up like AWS has, we're going to see cloud based adoption.

 

David Turetsky: 21:22

But there still needs to be the segmentation of my data against your data neither the the twain shall meet.

 

Richard Rosenow: 21:29

Yeah, oh, yeah. Definitely get an enterprise license. Yeah. Don't be doing this on your personal license.

 

David Turetsky: 21:33

But that's what I'm saying. I think the consumer facing version of chatgpt, 4.0, everybody's just going after it, putting lots of shit in it. And, pardon my French, stuff in it, and it's really not bounded by anybody.

 

Richard Rosenow: 21:47

No, it's, it's, um, it's an exciting cowboy world right now.

 

David Turetsky: 21:52

And if you're an IT leader, hopefully you're clamping down on the usage of chatgpt in its unlicensed version, and providing your people with the knowledge and understanding about what happens if your data goes into the cloud.

 

Richard Rosenow: 22:07

That's definitely another big trend we're seeing is there's this there's this shift towards these enterprise AI layers. So a lot of SaaS tech companies, we've

 

David Turetsky: 22:14

So wait until next year? gotten really used to having a front door that the customer

 

Richard Rosenow: 22:16

Wait until next year always. Next year to be comes in our front door, uses our tool, leaves and goes goes about the day. Maybe everyone's trying to kind of capture the better!

 

David Turetsky: 22:21

Wow, I don't know about that. Next year they customers and bring them all into their product as often as they can. We're realizing that these enterprise AI layers are may be taking over, so we may be answering to the AI overlords, creating a very different experience that the customers and clients are frankly preferring where they would much rather work with a enterprise chat GPT or like a glean or these other kind of layers that have come out that then works with an AI or an agent that talks to other systems. 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? And so those enterprise layers, I think we're seeing a glimmer of that this year. 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, are going to realize that these enterprise platforms are really good at recreating that on the fly. but

 

Richard Rosenow: 23:18

We'll send our agents to this podcast.

 

David Turetsky: 23:22

HR Data Labs has been taken over by robots! Yeah, that could happen. Yeah, it could probably do a much better job of hosting than I can. Certainly they'd be much more energetic than I am.

 

Richard Rosenow: 23:31

Oh, my goodness.

 

David Turetsky: 23:33

Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this. Well, you're in luck. We have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast or whatever is on your mind. Go to salary.com/hrdlconsulting to schedule your free 30 minute call today. So we've obviously been speaking about AI the entire time. Richard, is there, is there anything else that's kind of for you that's kind of pushing HR tech, HR analytics, 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?

 

Richard Rosenow: 24:19

That's a really good question. I'm trying to and it's hard because it literally everybody here. I'm looking at a bunch of different signs, and it's literally just AI on every single sign, which is unfortunate, that's definitely been the hype cycle. I think there is a bit of a change in the air around partnership. 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 close before. I think that has a

 

David Turetsky: 24:40

You mean, in the marketplace or the API movement?

 

Richard Rosenow: 24:43

Yeah. And being able to, kind of, like, understand that there's, there's not a one stop shop,

 

David Turetsky: 24:47

Right.

 

Richard Rosenow: 24:47

I think we've heard that for years, like, come to the One Stop Shop. Just come here. If you, if you're within our walled garden, you'll be okay. And we're seeing a lot more of these kind of recognition that this is an ecosystem of tools, and maybe it is in the face of this existential changes that are coming that say, Hey, we got to go together. So I don't know. I'm feeling a little bit more camaraderie this year. Maybe that's just me.

 

David Turetsky: 25:08

Well, I think it's been very cyclical. It was in the 80s or 90s. Maybe it was the 90s and early 2000s that we started hearing about open, more openness and more well, we had a best of breed timeframe was probably the 90s, and then probably in the 2000s we had a, well, the HCMs are kind of building everything themselves, and we're gonna have it all on a platform. And that went for a long time, and then that. But then it kind of fought between best of breed and the platforms. But now, I mean, with, with APIs and with, with marketplaces, yeah. I mean, well, they're also getting a piece of the action too, 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.

 

Richard Rosenow: 25:52

more nuanced. But I think it's, 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 you're not feeling locked in.

 

David Turetsky: 26:05

Co-opetition.

 

Richard Rosenow: 26:06

Co opetition. There we go! Let's bring it back around.

 

David Turetsky: 26:10

Yeah. Why not? So I think what I'd love to do is, at some point, maybe pull you aside at the end of the HR Tech showing and say, okay, so we talked at beginning. Is there anything to change your mind at the conference?

 

Richard Rosenow: 26:33

That'd be a lot of fun! Yeah, it'll be me just like, fully beaten down, tired, just bedraggled.

 

David Turetsky: 26:40

and you're gonna change your name to Richard AI Rosenow,

 

Richard Rosenow: 26:43

Yeah, just fully adopted.

 

David Turetsky: 26:45

Sure. Why not? Or it'll just be AI Rosenow.

 

Richard Rosenow: 26:49

Yeah, poor Al Adamson. Poor Al Adamsen. Every time I see his name, I'm like, AI AI!

 

David Turetsky: 26:54

Exactly!

 

Richard Rosenow: 26:55

Love Al Adamsen, and I think he's gonna be here too, so maybe we can chat with him at some point.

 

David Turetsky: 26:58

Yeah we will. Yeah, all right, Richard, thank you very much!

 

Richard Rosenow: 27:01

Absolutely! David, thanks for having me on again.

 

David Turetsky: 27:03

My pleasure. Stay safe.

 

Announcer: 27:05

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