Aptitude's Kyle Lagunas on AI, Shiny Objects and HR

[00:00:00] Welcome to PeopleTech, the podcast of WorkforceAI.news. I'm Mark Pfeffer.

[00:00:17] AI is everywhere, but what are the changes it's bringing to the fundamentals of how HR works?

[00:00:23] How is it and where is it making itself felt? And how can CHROs sort out the reality of what's

[00:00:30] going on when they talk to vendors who want to hype? My guest today is Kyle Lagunas,

[00:00:36] Head of Strategy and Principal Analyst at Aptitude Research. We'll get into all that and more

[00:00:42] on this edition of PeopleTech. Hey Kyle, welcome. Everybody's talking about AI today, obviously.

[00:00:51] It's been the dominant part of the HR technology conversation for the last two years. But have

[00:00:58] you seen real changes in HR because of AI? Is it really changing the way HR practitioners work?

[00:01:06] I love this question. Let me start by telling you a funny story about how things have changed. So

[00:01:12] I was presenting some research at Unleash America this spring. And I said in front of all these HR

[00:01:23] practitioners, HR's favorite two-letter word. Can anybody guess it? And I was talking about AI,

[00:01:31] AI, but somebody said no. Like N-O, two letters, that's HR's favorite word. I do think that there's

[00:01:40] already material change. Just the new two-letter word is AI for HR. But a lot has changed. You and

[00:01:49] I have been watching like tech trends in the space for a long time. Like even when it comes to AI,

[00:01:55] early AI, early AI that we were looking at, early automation, it was streamlining our work. It was

[00:02:03] helping us to work faster, to move through processes a little bit more consistently, et cetera.

[00:02:11] I do think that we are about to get to a point where the work itself starts transforming.

[00:02:18] You know, like the first several rounds of innovation in this area, we're automating and

[00:02:26] streamlining processes. I think that we are at the cusp of jumping into transformative tech.

[00:02:33] There are things that are core workflows, core processes, core work that HR and talent professionals

[00:02:42] do that these AI agents can do for us. We might get to a point where we are not administrating every

[00:02:54] process. We might be monitoring processes and engineering processes instead of having to manually

[00:03:01] run them. And then that becomes interesting. Maybe HR and talent does shift from, you know, policy

[00:03:10] policing and fielding requests. And we actually start analyzing engagement data, actually, you know,

[00:03:18] not just saying we could like, we start looking for movement in KPIs that are, that we've been waiting

[00:03:26] for because we saving for a rainy day because we're buried under work. I think we're about to get into

[00:03:34] this new, more transformed HR. We're not there yet, but I do really think it's coming very soon.

[00:03:42] What's that going to look like? Do you think? I mean, the AI vendors talk about more efficiency,

[00:03:49] allowing workers to go and do more strategic things. Can you sort of paint a picture of what that would

[00:03:55] look like in real life?

[00:03:56] First of all, the reason why vendors are talking about like efficiency is because that's how they're

[00:04:01] selling this. That's how they're getting business case, like business buy-in for these technologies.

[00:04:08] That's just where we're at right now. I do think that we will be, let's look at the talent acquisition

[00:04:15] function. There is a lot of processing that gets done every single day for recruiting. You know,

[00:04:21] a lot of applications that need to be reviewed, jobs that need to be managed. We're either like opening

[00:04:28] a new rack, managing an existing rack, closing a rack, dispositioning candidates. There are just a

[00:04:33] lot of tasks that we need to do just to run business as usual. Many of these tasks can be automated

[00:04:42] intelligently already. The technology exists. We haven't fully deployed these kinds of capabilities

[00:04:48] yet. But even if you look at like the interview process and aptitude, we've been tracking a rapid

[00:04:55] advancement of what we're calling interview intelligence capabilities, tools that can run

[00:05:01] live transcription of an interview. And it can give you, you know, just like in a meeting like this,

[00:05:07] it can tell us what key takeaways were, but they're also leaning in and giving us insight into new things

[00:05:13] that we've never looked at. Like how much time did Kyle talk versus how much time did Mark talk?

[00:05:19] Did Mark actually end up talking to Kyle about key responsibilities that needed to be covered in the

[00:05:27] interview? Or did they just catch up about where they both went to school and what kind of dogs they

[00:05:31] have? We can then like having an ability to consistently and like effectively track this kind of data.

[00:05:42] Well, then in TA, we can start to optimize our interview process. We can say, all right,

[00:05:47] of course, 100 interviews that Mark did last year, he never talked about qualifications and responsibilities.

[00:05:55] And actually, it's not just Mark, it's every single hiring manager from this entire business unit. I think we need to do

[00:06:02] interview training and I have the data for it, or, you know, we need to shift the way that we maybe maybe we are

[00:06:09] in having the wrong people interviewing, like, we can start to do more like process engineering,

[00:06:16] process mining, whereas right now we're just doing the processes. And so like, yeah, I hope that kind of

[00:06:22] answers your questions. Like, we're still gonna be recruiting. But the work that human beings do,

[00:06:28] will, I think, shift, not just from clicking to open this rec and scheduling the interview for Mark,

[00:06:35] but I'm going to be spending time analyzing interview trends. And I'm going to be spending time looking at

[00:06:41] quantifying quality candidates. And yeah, it's just going to be, we're gonna be doing different work

[00:06:46] around the same processes. Well, so that leads to the question of whether or not this, these capabilities

[00:06:55] are going to change the way HR looks. I mean, if you get HR from company to company,

[00:07:02] everybody pretty much does the same thing. They may vary how to do it, but they're doing the same

[00:07:07] thing. Do you think AI's capabilities are going to let people do something very different to put more

[00:07:17] power into HR or what have you? I think so. Because look, right now we're just, we're doing something,

[00:07:25] it's a lot of task work, a lot of process work. We don't have time to like step back and take on the

[00:07:34] special projects that we know will make things better. Like we're running processes right now.

[00:07:39] And that's where people, I think oftentimes have misconceptions about what HR does. They look like,

[00:07:45] you know, they're busybodies and they're, you know, they're like just policy police. I'd said earlier,

[00:07:50] like they're just like admin, like glorified admins. Well, people perceive that because that's

[00:07:54] the work that we get bogged down with a lot. You know, but I do think that there are a lot of very

[00:08:01] intelligent and thoughtful and curious people that work in these different functions of HR who would

[00:08:08] rather be able to step back and be continuously optimizing people processes for better,

[00:08:14] stakeholder experiences, for better, more consistent outcomes, for more equitable employment

[00:08:19] opportunities. Like there's just a lot of like strategic level initiatives that we don't have

[00:08:26] the opportunity to take on now that I think that that will become more of the cornerstone of the

[00:08:31] work that we do. So again, like is HR itself going to be completely transformed? I don't,

[00:08:39] I don't think so, but the work that we do will be continued, like will up level quite a bit.

[00:08:46] And what about the financial aspects of this? Do you think these new capabilities are going to

[00:08:53] save HR money and make it a better looking budget line?

[00:08:56] Yeah, I think so. I mean, look, we will never, I don't think we will ever again have as much headcount

[00:09:03] in HR functions as we did during COVID. We staffed up those functions quite a bit, but we did it

[00:09:10] because we had a lot more work to be done in those groups or in those, in those functional areas.

[00:09:19] Now with the, with the layering in, in a responsible way, if we're layering in AI capabilities,

[00:09:27] a lot of that scale of operations that we needed to staff for, I do think that there will be continued

[00:09:34] impact on headcount in HR organizations because we will not need as many people to do as much work.

[00:09:42] At the same time, I, again, because I see so much laundry lists of like rainy day things,

[00:09:51] like think like back burner items on HR agendas. I do think that we will continue to maintain,

[00:10:01] like HR is not going to go away. Basically the HR, the HR people aren't going to go away completely,

[00:10:06] but we do need cost savings. You know, like I, I, I think that there is, it's really expensive to

[00:10:13] throw bodies at some of the problems that we have. And it's not just expensive because salaries are,

[00:10:21] you know, high or the cost of benefits is high. It's not just that it's like, it's makes more,

[00:10:30] we expose ourselves to human error, you know, and we have people that are maybe working inconsistently,

[00:10:38] or we have just a lot of different stuff that we have to account for with human operations that

[00:10:44] I think that we can, I don't know, maybe de-risk and debug some of our HR operations.

[00:10:51] I think we're a ways off though, Mark of like major impact to like, I don't think we're going to over index

[00:10:58] on efficiency in HR with AI. I think people are being very cautious about making sure that what they

[00:11:05] implement is human centric. We want to enhance the, the lives and capabilities of human workers.

[00:11:13] But yeah, I think it, I think it's kind of a toss up to see how, how things end up landing. I'm, I'm, I'm a,

[00:11:20] I'm willing to say that there is probably a lot of impact to non-specialized talent in the HR organization.

[00:11:31] I think that we will need less, what's the word, like people partners. We will need, we, we will need more

[00:11:39] HR business partners and we will need less like HR generalists that are just taking, and I'm not,

[00:11:46] I don't mean to demean the function at all, but that are doing more manual tasks day to day.

[00:11:52] Hey everybody, I'm Lori Rudiman. What are you doing? Working? Nah, you're listening to a podcast about

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[00:12:23] co-host of the Inside Job Boards and Recruitment Marketplaces podcast. And I'm Stephen Rothberg,

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[00:12:45] I wonder if employers and their HR departments are sort of in sync in all this, you know, when

[00:12:54] another major pitch of the vendors is that AI is going to end up saving you money in some way,

[00:13:00] shape, or form. And I keep thinking, yeah, there's going to be a lot of companies out there

[00:13:04] where the leadership is saying, we want to save money. We don't care about all the other stuff.

[00:13:09] Is that a danger that HR is going to be sort of pigeonholed into that kind of functionality?

[00:13:16] I do. I do worry about that. And there's a couple of reasons why I am worried about it.

[00:13:21] One is something that we've tracked in our research. The number one obstacle to adoption of AI

[00:13:29] in HR is not lack of budget. It is not lack of support from the C-suite, from the business.

[00:13:37] It is HR knowledge gaps in the HR organization around how AI works, what it actually is.

[00:13:44] And the reason why I'm starting there to answer your question is those knowledge gaps are important.

[00:13:51] You and I know that we've been giving HR a pass for not being very tech savvy for a long time.

[00:13:58] They're not tech experts. They're people and policy experts. We've said that a long time.

[00:14:02] If we don't understand how these capabilities work and how we can design human-centric solutions

[00:14:12] with AI, then we are going to... By the way, human-centric solutions that will drive meaningful impact for

[00:14:22] the business and will further the HR agenda. If we are not going to take this innovation cycle by

[00:14:31] the horns, we are going to be relegated to the sidelines and the business is going to start making

[00:14:38] decisions about how they want to use AI to automate HR or to make a case to offshore HR operations and

[00:14:52] benefits administration. They are going to make strictly business decisions. And they're going to do

[00:15:00] that because we can't really explain why they shouldn't. Do you know what I mean? And it's just like,

[00:15:07] we're going to say they shouldn't because ethically, we should make sure that we're protecting jobs.

[00:15:15] Whatever it is, we're going to make an HR case. And they don't... We're at a point where... And I hope

[00:15:22] this softens a bit, but there is a hard line that the business doesn't really want to hear from us on.

[00:15:29] They want to make strictly business decisions right now. And they will for the foreseeable future.

[00:15:37] And I think that's why we need to be getting HR really ramped up on just literacy in AI.

[00:15:45] I want us to stop saying like, we aren't sure about AI because we don't want to introduce bias.

[00:15:52] Well, because that's what everybody says. Well, we do need to constantly, whether we're talking about AI

[00:15:57] or not, we need to be constantly diligently mitigating bias decision-making around who gets

[00:16:04] promoted and who gets hired. And like, we need to get rid of bias in general. But we also need to figure

[00:16:10] out how we can use AI to make ourselves more effective contributors and like to be more successful

[00:16:16] HR practitioners. Like we need to figure out how to use this for our own sake. And instead of just kind

[00:16:26] throwing up the boogeyman of bias and then being like, well, we're not ready for AI. Like AI is here,

[00:16:32] whether we are ready for it or not. And I feel like the risk to go back to your original question,

[00:16:37] the risk of the business running with this remains very high until we get our AI literacy up so we can

[00:16:45] lead more of the discussions about how it's being implemented and utilized.

[00:16:50] Now, do you think there's anybody out there, it could be you, who's looking at AI, looking at HR,

[00:16:57] and creating a vision of what future HR should look like using the technology?

[00:17:05] Hmm. I don't know. I think that in some pockets, like basically I'm seeing that we're falling into

[00:17:14] kind of two camps. At the end of 2022, we saw that 75% of HR organizations were investing in AI

[00:17:25] capabilities to some degree, automating processes, using machine learning, blah, blah, blah, blah.

[00:17:32] At the end of 2023, which by the way, was like three months after ChatGPT was launched to the public,

[00:17:39] the pendulum swung way back. And we're basically at 50-50 right now. 50% of companies are not investing

[00:17:46] in new AI capabilities and have even slowed down some of the pilots that they had running.

[00:17:51] And 50% of companies remain committed to advancing their AI agendas. The companies that have remained

[00:17:59] on the path, that have continued to invest and utilize and experiment with these capabilities,

[00:18:06] they are, I think they are thoughtfully, intelligently, ethically utilizing AI as a

[00:18:15] cornerstone of their innovation agendas. Like it is a core part of how they want to drive HR forward

[00:18:22] across a number of different capabilities. AI is not the only one, but it is embedded in their

[00:18:27] innovation strategy. The other 50% don't know how, they don't know how this works. And they,

[00:18:35] so they don't know how to incorporate this into their, their innovation agendas. They, they're,

[00:18:41] I think kind of stuck in a rut for lack of a better word. It's, I'm not sure that they know how,

[00:18:48] what the future holds for the function. They probably still have a laundry list of stuff they want to get

[00:18:52] done, but I, I'm worried that they're going to fall further and further behind the curve of like

[00:18:58] just the maturation and evolution of our HR functions.

[00:19:03] Because we're in this transition time and going back to picking on the vendors, but we're,

[00:19:09] we're in this transition period and every vendor out there is saying everything they do is a AI.

[00:19:17] You know, in some spaces is practically to the point where our paper clips are AI based. I mean,

[00:19:23] it's just.

[00:19:23] I know, I know.

[00:19:26] So, you know.

[00:19:27] I say it's AI everywhere all at once. And cause it really does feel like it just like exploded.

[00:19:35] Yeah. Well that's no, that's actually exactly it. It's like there was a switch and all of a sudden

[00:19:41] everything's AI. But if you look at the vendors today and the platforms that are out there today,

[00:19:47] is AI really integral to them or have they just started to slap AI on the package?

[00:19:55] You know, it's not either or there are vendors that are in party a and they're, and it's also not a

[00:20:01] hard line. There's a blur. I mean, any software, any tech vendor, even services vendors,

[00:20:10] every solution provider in our space has been utilizing AI at, to develop new capabilities,

[00:20:16] to really like to refine new feature sets. Like to, it has been a part of their innovation agendas to

[00:20:23] some degree or another. Now, whether they have been productizing AI features is another thing,

[00:20:31] right? Like one thing I've observed as a, for an example, like in the, with all the major HCMs,

[00:20:38] the Oracle, SAP, Workday, UKG, like they have all for this last year and a half, these last two

[00:20:45] cycles have been talking about how AI is a cornerstone integral to their platform, you know,

[00:20:53] and it's been, and they are saying like, we've been, we were the first to, every was the first,

[00:20:58] by the way, to start experimenting with RPA and machine learning and natural language processing

[00:21:04] and dah, dah, dah, dah, dah, dah. Like everybody was first to market with AI. So nobody was, but,

[00:21:08] so there are many who are saying like, acknowledging that we have in our software development been

[00:21:13] utilizing these capabilities. You also do see, and not just in the startup space, also with some of

[00:21:20] these like old, I don't want to say old legacy providers. Some have really been just rubber

[00:21:28] stamping AI features. Like this is AI, this is AI, this is AI. I, Mark, I don't think it matters

[00:21:34] what we call any of this stuff. Like I want to talk about what it does and how it does it.

[00:21:41] And so I don't want them to use AI as, remember when everything was employee engagement, everything

[00:21:46] was about employee engagement and employee experience, everything was, and candidate experience.

[00:21:51] Those are categorical capabilities. Like I want to talk about what does this, what does this feature

[00:21:56] actually do? So I don't want it to say like, we have recruiting gen AI. Well, what do you, how are you

[00:22:03] utilizing gen AI in recruiting? So they will say, we can use gen AI to create a new job description

[00:22:12] that is compliant and that maps to the job family. And I'm like, that is specific to my company. Great.

[00:22:19] That's actually sounds like a really great feature. I can click a button and have it generate

[00:22:23] a job description for me. Like I want us to be more descriptive with the stuff because AI is not

[00:22:30] a magic thing. Like it's, it's not as simple as is this real AI or not? It's we're still need to be

[00:22:38] evaluating what, what does this deliver for me? What does this like do for me? Um, I, Madeline is our,

[00:22:46] our founder, my business partner, I, you know, Madeline Lerano, she was having a call with

[00:22:52] unfortunately like a fortune 250 company that like the, the head of TA operations, uh, she was talking

[00:23:01] to them about their agenda for the next couple of years. And they say, are, they were excited to tell

[00:23:06] her we are going all in on AI. And she's like, awesome. What kinds of things are you thinking

[00:23:11] about? And they're like, AI, we're going to do AI next year. And it's like, so, cause your question

[00:23:18] is like, are vendors doing some of this like stuff and giving us like just really generic points of

[00:23:22] view and just like slapping AI on all their products, you know, practitioners are also teams

[00:23:28] are also treating this kind of generically and not being extremely intentional. So I think that for

[00:23:35] anybody that's trying to discern the vendors, how they're thinking about AI and what's air quote real

[00:23:41] versus not is lean in, you know, listen to how, what, how they're describing capabilities. I I've talked

[00:23:48] to vendors over the last six months that, that even acknowledge we're not going to really talk about AI

[00:23:53] a bunch, but you're going to see in our software, like that it's working behind the scenes to help make

[00:23:58] this possible. And I like that. I'm like, all right, well open up the hood. Let me see how it made that

[00:24:03] possible. Cause this looks super easy. Like lean in, explore what you don't know. Don't act like

[00:24:10] answers are obvious and see how vendors respond to that. Are they, is their story going to unravel

[00:24:17] quickly? And they're like, Oh, actually we, we literally just have a plugin with chat GPT and

[00:24:22] you query here. It's actually going over there. You know, like you can peek under the hood and start to

[00:24:28] figure out how this stuff really works. And we should be like, we should all be approaching every,

[00:24:34] every claim from every vendor with a grain of salt, just like we always should have. Um,

[00:24:41] I think that the answers become pretty clear pretty quickly. And by the way, you learned something while

[00:24:46] you are probing with them, which we need to be doing too. Well, are there dangers here? I mean,

[00:24:51] you know, it seems like AI capabilities are coming in waves, you know, December was video month.

[00:24:59] You know, everybody was, was rolling out their video generator. And does that pose particular

[00:25:06] dangers for HR if they're starting to use tools that aren't really quite stress tested? I mean,

[00:25:14] we're talking about dealing with people here. Yeah.

[00:25:18] Are there landmines at HRS? Yeah. I, I think that, look, the biggest landmine is not evaluating

[00:25:29] closely enough, like not scrutinizing claims, not examining, not evaluating how vendors like what

[00:25:38] kind of audits they have paid to have done of their algorithms, you know, of, um, how have they

[00:25:44] validated through third part engaging third parties, like safety and reliability of their offerings.

[00:25:51] I was talking to the head of hourly recruiting for a major hotel and hospitality chain just yesterday.

[00:25:59] And I was asking her about how she would recommend evaluating new use cases of AI for talent acquisition.

[00:26:08] And she said, start with impact. She's like risk. You need to be aware of risk, but start with impact.

[00:26:16] And I said, what do you mean? She's like, we were looking and we thought that if we implemented this

[00:26:22] tool, she was talking about an interview scheduling tool. Um, and we deployed across all of hourly

[00:26:27] hiring, just the time savings alone on a, on a conservative estimate was going to save us millions

[00:26:35] and millions of dollars. She's like the, what that did once we had that number in front of us of the

[00:26:41] impact opportunity was then it justified us spending three and a half months with our compliance team,

[00:26:49] our legal team, our risk team, putting our prospective vendors for this pilot through the ringer

[00:26:56] to make sure that this was going to work as described, that it was going to be compliant within

[00:27:02] the territories that we wanted to deploy it in, that we knew what risk factors existed. And we asked,

[00:27:08] we worked with them to find out if we get audited, how do you support us? If we have claims of

[00:27:12] discrimination, how do you support us? Like literally because they knew how big of the opportunity

[00:27:18] was for AI to give them some money back. Then they were able to rally all the troops in their or

[00:27:23] across the organization to help them evaluate these use cases closely. And guess what? They rolled it

[00:27:32] out. It went off without a hitch. And then they, and then they have launched it into new territories

[00:27:38] and then into new, because the business case was just too compelling not to. They front loaded their,

[00:27:44] their AI journey with deep, deep evaluations of risk factors and compliance, like, et cetera.

[00:27:52] They didn't do what TA often does, which is see the shiny object and say, Oh, I want that.

[00:27:58] And then like, show me, show me how it does it in this language and show me how it does it in that. Like,

[00:28:03] they didn't want just the dog and pony show of the features and functionality. They knew that the

[00:28:08] deeper evaluation needed time and it needed to not just be TA taking the salesperson's word for it.

[00:28:15] Do you know what I mean? And so like, there are landmines potentially anywhere. The biggest one is that

[00:28:23] we are looking at the shiny object in front of us and buying these tech, like we used to buy any tech

[00:28:31] of just like, what does it do? That sounds cool. How much does it cost? We need that extra layer of

[00:28:37] scrutiny into every use case because I don't, I don't know compliance law in labor law in Malaysia and, or

[00:28:46] Minneapolis. Right. Like, so it just, it's a long winded way to answer. Like I, I'm not going to

[00:28:52] say there's like one thing that we need to all be looking for out for in terms of landmines. It is

[00:28:58] more of like, be prepared to do some real, like scrutinized evaluation, heavy scrutiny evaluations. And we

[00:29:05] should be, the vendors should be ready for that too. Do you think most HR organizations are taking that

[00:29:14] approach or are they kind of sticking with what they know in terms of?

[00:29:21] Yeah. It's, it's a, I always hate when I'm like, it's a mix, but it is because there are like,

[00:29:27] cause risk profiles for every company are, is different, but I will say that we're noticing

[00:29:34] that a lot more companies big and small are creating internal AI councils, which are coming up with

[00:29:41] you know, AI governance and principles that can be used. Cause like HR is interested in AI sales is

[00:29:47] interested, excuse me, ops is interested, IT, you know what I mean? Like everybody is interested in, in

[00:29:52] AI across all of the company right now, all organizations right now. But so these internal councils are

[00:29:58] creating governance and rules and they are, I think helping to, to set expectations with different

[00:30:07] stakeholders, whether it's HR or sales, you know, others I think are running the same RFP process as

[00:30:16] they used to. And functional leaders like HR are asking their HR oriented questions around, can it do

[00:30:24] this? Can it do that? Yes. No. And then they're leaving it to their colleagues in IT and compliance and

[00:30:31] risk to ask their questions. Like, how do you deal with this? Are you compliant with that? And I don't

[00:30:37] know that we are all really on the same page. With those, I, I think it, we run into risks.

[00:30:47] We run into the risk in those situations where there's not a unified, a shared goal for implementing,

[00:30:55] evaluating AI where we're not actually on the same page about how this is going to work and what we

[00:31:00] need to make sure we're looking at. HR starts getting a lot of questions about a company's,

[00:31:06] a potential vendor or potential use cases risk. We are the first ones to get asked questions about

[00:31:11] this or that, or this or that. And we don't know the answers. And what happens is it feels like we are

[00:31:17] getting buried under red tape. And so I do see often time, like too many times this year,

[00:31:24] those HR and TA leaders that are trying to drive innovation, they're overwhelmed with the obstacles

[00:31:31] and the, like the, uh, and so then they just move on to something else, like a path of least resistance.

[00:31:38] And then they abandon that project because it's just, I don't have the time to figure this out

[00:31:42] right now, you know? And that is where I'm thinking we, there's too many companies that are in that ladder

[00:31:51] camp where HR and TA again, lacking AI literacy are unable to address just even early evaluation

[00:32:00] questions from their colleagues in it and compliance and legal. And so these projects are stalling or

[00:32:06] being abandoned. Kyle, thanks very much. It's great to see you. I appreciate your time and I hope we'll

[00:32:15] talk again. Oh, I love it. Mark, we're just at the beginning. Can you believe that? Like this is

[00:32:22] literally the beginning of AI and HR, real AI and HR. So let's catch up again in the new year because

[00:32:27] I think we're going to have even more to talk about in just several months. Absolutely. Thanks.

[00:32:44] My guest today has been Kyle Lagunas, head of strategy and principal analyst at Aptitude Research.

[00:32:49] And this has been People Tech, the podcast of WorkforceAI.news. Where we're part of the

[00:32:56] Work Defined Podcast Network, find them at www.wrkdefined.com. And to keep up with AI technology and HR,

[00:33:07] subscribe to WorkforceAI today. We're the most trusted source of news in the HR tech industry.

[00:33:14] Find us at www.workforceai.news.

[00:33:19] I'm Mark Zaffer.