Is AI Helping or Hurting the Recruiting Process?

[00:00:01] Hello everyone and welcome to the Hope @Work. With me today is Marcus Mossberger. She is the Chief Product Officer at Employee. And I have to say, I've been following Employee for quite some time and it seems to change all the time in terms of the different brands that are part of the larger kind of umbrella. So maybe we should start there in terms of who is Employee, what is it that you all do, and then what's your role there?

[00:00:29] Sure. So thank you for having me, Marcus. I appreciate it. So Employee is a hiring technology company and we are a family of brands. So the brands you've probably heard of and love are Jobvite, Lever, and Jazz HR. And what we do is we actually try to bring the absolute best hiring experience to all the folks that use those solutions as well as those that will.

[00:00:56] And the way we think about those solutions are in a way that everybody has unique needs. And so we don't have a one-size-fits-all approach. We don't have just one hammer in the toolbox. We actually have multiple hammers. And so depending on your specific business needs, we have something that can solve them. And so that's essentially Employee in a nutshell.

[00:01:18] I am the chief product officer, as you said. So I'm responsible, myself and my team, for determining how to make these solutions even better every single day and how to incorporate all those nuances and all the things that talent acquisition professionals are experiencing and trying to solve in their daily life. And so I've been with Employee about two years now, and I'm super excited for our conversation. Awesome. What did you do prior to that?

[00:01:48] So prior to that, I actually spent a really quick amount of time outside of the HR technology industry. Thought I'd try my hand at something different in public safety software. But prior to all of that, I have been in HR technology for about, I hate to say, 30 years or so at this point. And I've worked at big companies like ADP, Ultimate Software, UKG and Equifax and super small organizations as well. Cool. OK, so you've really kind of been through the gauntlet then. I love that.

[00:02:17] So you and I have a similar background then. Well, why don't we just jump in? And I actually want to start with kind of the elephant in the room, if you will. So AI. So again, you and I have been around this space long enough to remember when it started to become more prevalent. And my observation was it was the talent acquisition technologies that really embraced AI in the larger kind of HR technology ecosystem first.

[00:02:46] But today, I think we have to admit we're seeing problems on both sides of the table, if you will. So you've got employers and some technologies in the news because they've been using AI to filter applicants. And that hasn't gone very well. But, you know, it's not like the applicants are innocent in this. Right. They're using AI to generate resumes. They're using AI in the interview process.

[00:03:11] So let's start there. First and foremost, how serious is this problem from your point of view? And then from where you sit on your side of the table, what are you attempting to do about it? Sure. So I would say, is it a problem? It's a problem and a solution. Right. So I think that the biggest problem that we're finding recruiters experiencing is that they're really concerned about missing out on good candidates,

[00:03:41] overlooking good candidates because they're spending so much time trying to reduce the overall burden. And so when you think about the fact that AI can, AI bots can actually hit up application processes so much easier, they can apply to multiple roles within the same company, even if they're completely unrelated. Candidates themselves can actually create resumes that look exactly like the job description using AI.

[00:04:09] So all of those things are really increasing the volume of the applications that are coming in. And the recruiter is trying to figure out how do I solve for that? Well, at the same time, not overlooking those candidates that, by the way, they may want to use AI in some way in the job that they're hiring for. And so really what we found is moving the core fix further up in the funnel.

[00:04:35] So finding a way to do a better filtering earlier in the funnel. And so there's a variety of different ways that this can be done, Marcus. And some of the things that we're doing is you can use AI to create assessments that are very specific to a particular role. And using that assessment really kind of filters down those folks that have just tailored their resume specifically to a specific job description or job posting as an example.

[00:05:01] Another thing is that they can use something like async video responses or an async video interview to ask questions to get an idea of not only how that candidate would answer, but how they could demonstrate real fit instead of just describing what is on their resume.

[00:05:21] And so AI can be used in those particular ways to actually surface relevant signals to help the recruiter determine whether or not this particular person is a good fit, while also helping filter down that huge barrage of applications that are coming through. But the reality is that the burden itself, the burden problem is truly self-inflicted in a lot of cases.

[00:05:48] So companies are trying so hard to make applying to a job so frictionless that it's making it easier for bots to get through or easier for people who aren't qualified to get through. And so what you want to do is you want to actually have intentional friction. You want to find a way as you're going through the process to insert a targeted question that can't be answered by a bot as an example,

[00:06:14] so that you can filter down to actual interest without excluding really good candidates. Okay. Interesting, because you started the conversation talking about the fact that you were in the hiring technology space and you used the term experience. Like what is the experience? I assume you want to have a good experience for the recruiter and the hiring manager, but certainly for the applicants as well,

[00:06:41] whether the applicant is an external applicant or maybe even an internal applicant in the organization. So really interesting. It's almost like we over-rotated to this frictionless experience and that got us in a bunch of trouble. So now we're purposefully adding a little bit of friction just to be able to validate. These are human beings that are actually applying for roles and that they're qualified human beings. Is that an accurate statement? Absolutely.

[00:07:10] Yeah, I think you're absolutely right. We've made it so frictionless, but that made it a lot easier for other people to raise their hands. And in an economy like we have right now, there's going to naturally be more people, right? So how can the technology help you determine who are the folks to prioritize in this situation and then spend time with those folks? Because at the end of the day, the most important thing for any recruiter I've ever talked to is the connection with the human.

[00:07:37] Really spending time with them to determine whether or not they're the right person for the organization. But just as importantly, if this is somebody we're really interested in, how do we make them understand that this is the right organization for them as well? That can't be done through technology. That has to be done through the real human connection. Yeah, totally agree. And you mentioned early on that you want to avoid missing good candidates. So I want to talk about that a little bit. So get a little personal here.

[00:08:04] I've got a really good friend of mine that's been out of work for quite some time. He's very talented, intelligent individual, great experience. And he actually sent to me a job description that he or a job posting rather that he applied for. And then he sent me his his CV, right? And I'm telling you, they were almost identical. He didn't use AI to like create the resume to reflect the job posting.

[00:08:31] He was a perfect match for this job. And he didn't even get a look, not a call, not an email, nothing. And I'm like, how does that happen in today's world where wouldn't the AI at least as it's filtering these individuals pick up on? Oh, yes, this matches, this matches. And again, I know that every situation is different. And maybe in this case he applied and they already had somebody perfect for the role.

[00:09:01] And so they didn't even give him a call. But that is one of a thousand stories that I have heard lately, especially from the younger generation that is extremely frustrated. So how can we ensure that we're not using technology to, again, over rotate with too much friction and too many hoops to jump through? And then all of a sudden the organization itself is missing perfect people.

[00:09:29] And some of those perfect people, there may be internal candidates as well. So, I mean, how do we address that? So there's a couple of different ways to address that. I do think it's a big problem, right? Because with the barrage and volume of applications that are coming through right now, it's just harder and harder for recruiters to find those diamonds in the rough as we like to think about that.

[00:09:52] And so one of the things that we've done is we've said, let's just not use a candidate matching type of capability out of the box where it says, here's a resume, here's a job description, find the best one, and then just prioritize. Don't disposition, but prioritize for me which ones to start with. What we've done is we've taken it one step further. We've said, allow the recruiter or the person responsible for that particular job to actually decide based on the skills, what level of skill do you need?

[00:10:22] Like how important is a specific skill versus a must have versus not as important and so on. So almost turning the dials, if you will, a little bit to try and hone in on those folks that have on their resumes or in their application process shown real experiences that are attuned to exactly what you're looking for.

[00:10:44] And by allowing the recruiter to kind of turn those dials, to kind of hone the matching capability in a little bit more, that actually allows them to find those folks that really truly not only maybe match their resume to the job description, but in the case of your friend's situation, have real life experiences on that resume that filter up to the top that are caught by that filter that says this person has exactly the requirements that you're looking for and the amount of the requirements that you're looking for.

[00:11:14] And so at that point, it helps prioritize that person. The other piece is that you could actually take everybody through with an async, an AI async interview process. You could take everybody through that, right? That's right. You don't have to naturally filter out anybody. You say, let's put everybody through that.

[00:11:31] And then based on the answers that that person gives, and if they can give real demonstrated examples of the skills that the job description requires, well, then you're going to actually get down to the brass tacks and the folks that you want to spend time with. And so I think it's not one answer from a technology perspective.

[00:11:50] It's a couple of different answers along the way to kind of cut through the noise and try to get to those folks that really have demonstrated in some way the fact they've done the things that their resume seems to indicate that they've done. Yeah, well said. There's not a magic wand, so we should probably not be holding out hope for one. It's a combination of ways that we can address this challenge.

[00:12:12] And you hit on one that I find fascinating that I'm seeing become a lot more prevalent, and that is it's almost like we're using AI to combat AI in the form of these scalable simulations and interviews that can be done by AI. So previously we would say, okay, I got 1,000 applicants to this job. I can't interview 1,000 people, but AI could. AI could literally interview every single one of those applicants.

[00:12:42] And so that's what I'm starting to see is not just using AI to do that, but also using AI simulations, like AI-driven simulations, to put you through your paces to say, okay, you have self-reported on your resume that you are really good at customer service. Let's see that in action, and let's actually put you through a simulation where you have a customer that comes in, and they're unhappy,

[00:13:10] and you've got to do some problem solving, and you've got to demonstrate the ability to think on your feet and to be diplomatic and tactful in the face of an angry customer, whatever it is. And so I'm starting to see that become a lot more prevalent. Are you seeing that as being more widely adopted in the future, or do you see that as a kind of a niche thing that's just being explored in small ways today?

[00:13:38] No, I'm definitely seeing it more widely adopted. I think there's a couple of reasons for that. If you think about it, it's no different than the in-depth assessments that would have been done much further along the process, right? You get through interviews, you'd have a couple of people you think are pretty good, and you put them through some in-depth assessments to kind of test their skills. I think AI allows you to move that further up in the funnel. So I'm not talking avatars that are talking to you and giving you a problem. That's not what I'm talking about, but AI can do those things that you talked about.

[00:14:08] Like, you've said you can do X. Well, let's have the assessment or the bot, if you will, whether it's delivered via chat or text or what have you, let it put you through your paces at a high level much earlier in the process because it's more affordable to do it then.

[00:14:27] Not the deep assessments, but a high level assessment to get really to that, to have the candidate demonstrate their abilities to what they said that they could do on their resume, which again is another filtering opportunity for the recruiter who can then decide who are the people that I'm going to spend time with because those are the folks that I'm willing to invest the time in.

[00:14:49] Those that have proven, have true demonstrated evidence of that fact that they can do the things that they've indicated that they can do. And so I've definitely seen it much more now. And I think AI and advanced technologies allow us to bring that way higher up in the funnel for that filtering capability. One of the things that we do is we actually have assessments that can be done and they're created using AI to determine what is the type of assessment.

[00:15:17] It could be a behavioral assessment. It could be a competency assessment. It could be a cultural assessment. It could be an intellectual, whatever it is. The recruiter decides which combination of any of those type of assessments it gets created and then it actually gets pushed out. Candidate goes through it and now it's just more information for the recruiter to make determinations on. And those determinations really get them to the recruiter screen, but also can be packaged up for the hiring manager to take a look at later as well. Okay.

[00:15:47] Okay. I like that your use of the language kind of at the top of the funnel, right? Where we previously may have waited to do some of these assessments and done some of the filtering. And now we can do a lot more at that top of the funnel. So, okay. I want to go back a couple of years to a Deloitte human capital trends report. I think it was 2024. And in one of the sections of the report, it talked about the end of jobs. And it wasn't talking about jobs going away.

[00:16:17] It was talking about the fact that in the future, maybe we won't hire people for jobs and positions because they change so frequently. Maybe we'll hire them for their skills, especially their transferable skills that allow them to be successful in a wide variety of roles. So, I'm curious to hear your point of view on where we're headed because the half-life of skills has dropped dramatically.

[00:16:45] I read recently that somebody said skills used to last, you know, 20, 30 years and now it's only seven. I read somewhere else that it's like, no, no, no, no, that's not true. It's like less than two years now, depending on what the skill is, obviously. But I think we all recognize that skills become obsolete way faster than they used to.

[00:17:06] So, are we going to see a change in the future in terms of how recruiting gets done where, A, we're not bringing in people for jobs, but we're bringing them in based on the fact that we can use them in a variety of parts of the organization.

[00:17:20] And, B, will it be more, would be more advantageous for us to just validate skills versus looking at resumes or traditional degrees and those kind of, you know, old school ways of evaluating your capability of doing a job? Where do you see these things heading? Yeah. So, you know, I think skills-based hiring is really interesting. And I do see it being, it's here today and it's going to continue to be the wave of the future.

[00:17:50] What I'm starting to see is and hear about is organizations who are decomposing their roles, their jobs, if you will, into all the tasks. And then thinking to themselves, which of these tasks can be automated? Right. Because we haven't done that before versus which are going to require human judgment or are going to require interpersonal relationships or what have you, right? And so I think once you kind of figure out that what can be automated, then you can use, you know, agents or what have you.

[00:18:20] And I am hearing some people are saying we could put AI agents on org charts, which is kind of an interesting thought process. But you can figure out which of those can be kind of offloaded to agents or some sort of technology. But in the meantime, and this goes back to what we talked about before, is that skill validation is really moving towards demonstrated evidence to align with what's truly needed. Right.

[00:18:44] It goes back to the assessments that we talked about before and everything, because resumes can show technical skills and they can show something from even something like 2022. But the reality is, is that skill that was really exciting in 2022 could be table stakes or even obsolete today, as you said. Right.

[00:19:03] And so to me, what I what I'm starting to see is that skills based hiring and the way that they're doing that skills based hiring is really focusing on problem solving skills, focusing on communication when there's ambiguity, ambiguity, learning agility, to your point, I think about the Swiss army knife of employee, right? Somebody who can do a whole lot of different things because that is not typically a whole lot of tasks. It's a lot of different things.

[00:19:33] They happen to have really good interpersonal skills. They happen to be able to real. They have really strong judgment skills. And so we can move them all around the organization. And by focusing on those problem solving skills and the communication skills and learning agility, it allows organizations to treat domain knowledge more as trainable. So whatever we do as an organization is something we can train. But those those skills are things that we're looking for as an organization.

[00:20:02] And, you know, the best example of that is if you think about how long have we been talking about college degrees being like the most important thing that somebody has. Right. And we're already we're already kind of moving past that. We're already realizing that a college degree may not be the skills that you learned. It may just be the ability that you could actually start something and finish it. Be committed to it. And that's commitment and ability to finish is what people are looking for.

[00:20:29] Not necessarily what the college degree was in, because, by the way, my college degree is in psychology and I'm not doing psychology. Right. And you're probably in the same situation, Marcus. So I think that skills based hiring is not a future trend. It's present day and it is really a competitive advantage. And so I think organizations need to jump on this bandwagon if they haven't already. And they need to figure out how to make sure that they're hiring for the skills that they need.

[00:20:58] I don't think that's going to mean the end of jobs, per se. I just think the job is going to evolve. What if I told you that your boss skydives? Tim in marketing is a magician and Sam in accounting does sums in his head while standing on his hands. Seeing sides of the people you work with but never see? Sides that inform and inspire their work and that can inspire yours is what the talent show is all about.

[00:21:22] Email me, Tom Alexander, host of the talent show at talentshow at backboneinc.com and show us what you got. I look forward to seeing you on the talent show. Well, listen, I have a degree in psychology as well. And look at us. We have done quite well for ourselves. So those liberal arts majors have to recognize there's still value in those degrees. My daughters right now, one of them is getting a degree in English. I have no idea what she's going to do with it. I don't think she does either.

[00:21:53] We'll see what happens. So if we operate under the assumption, Dara, that we're going to need people that have these transferable skills, some of them have traditionally been labeled soft skills, which I hate. They're hard skills, okay? They're hard to find and they're hard to teach. So that's why I call them that.

[00:22:13] But when you think about communication, emotional intelligence, empathy, things that, again, people either have or they don't for the most part, are you using behavioral assessments to figure out whether or not people possess those? Or how are you evaluating that particular set of skills? Yeah, so we're providing the tools to allow for that.

[00:22:36] And we believe that whether it's competency assessments, behavioral assessments, part of the interview process, because as you go through the interview process, you should be evaluating those by asking specific questions that leads to those things, right? So I think it's a combination of all of it.

[00:22:54] And for us specifically, you know, our tools are all about providing our customers with what they need and they can decide for any particular job, for any particular type of candidate, how they want to use those tools to assess and get to the particular type of person they're looking for. So I wouldn't necessarily make a recommendation and say, hey, the best way to do it is to use behavioral assessments, because maybe that works for some people, but not others. Maybe competency assessments works for some people, not others, and so on and so forth.

[00:23:23] But I think for us as a vendor in the space, we have to provide all of those options and let the customer decide and we can work with them on best practices. And not for nothing, we have 26,000 customers from SMB all the way to enterprise. And so we have data that can also help customers determine how best to find the best candidates out there, because we have a wealth of knowledge based on our customer base.

[00:23:52] But I think between our tools and our knowledge, we can actually help guide our customers in the best way to get at that true skills-based hiring that they're really looking to achieve so that they can have that competitive advantage. Yeah, that's well said. Again, once again, no magic wands here. And I would argue probably the best way to validate those truly human skills is by another human talking to them and having the conversation. So that's helpful.

[00:24:21] Now, you also made it clear, though, that there's some homework that has to be done that should be being done by these organizations prior to them ever posting a position. And that is the deconstruction of these jobs into tasks and then separating out which of those tasks are automatable and then seeing what's left. And then also acknowledging that some of the remaining tasks, we can augment those with technology so it's not like they're untouched by technology.

[00:24:49] But that gives them a better understanding then of, okay, now we know a little bit more about what we're going to need. One of the narratives in the media right now is that a lot of those tasks that are being automated are the entry-level ones. Those are the ones that, you know, these kids coming out of school are like, ooh, that's what I was going to do and now it's gone. What am I supposed to do?

[00:25:14] And so there's this concern that the shape of organizations might go from the traditional pyramid, right, where you've got mostly entry-level people at the bottom and then mid-level management and then a few select executives at the top to more of a diamond where there's very few entry-level roles. I have a different point of view on that in terms of what the shape of an organization might look like in the future,

[00:25:38] but I'm curious to hear what yours is and if you're seeing some of that trend play out in the world that you live in in recruiting. Yeah, I haven't really thought about what the shape would be of the organization moving forward. I like the concept of the diamond. The thing that scares me about the diamond versus the pyramid is that, yes, entry-level roles appear to be disappearing because of AI. That's real. I get that.

[00:26:07] But if AI handles all of the administrative or volume work that was once done by this entry-level talent, then organizations are essentially killing their bench. Yeah. So eventually they're not going to have people that can move up. And so the longer-term risk, obviously, is to their developmental pipeline, right?

[00:26:34] How do you ultimately have senior leaders if there's no junior folks that are moving through your system? So in your diamond example, you've got a tiny little bunch of people here. You've got a ton of people in the middle, a tiny little bit of people here. Those people in the middle are going to leave because ultimately there's no place for them to go. And there's not going to be anybody filling that middle section because there's nobody to draw from. Yeah. I think, you know, while I don't know the shape that I would give it, I think, in my opinion,

[00:27:04] companies that are really thinking ahead are designing, like, early career experiences around AI collaboration now. Like, they've got to figure out what do my entry-level roles look like so that they can future-proof themselves because they're going to need those people. They may not look like they were before. So, you know, your daughter coming out with her English major, maybe she's not going to do some administrative task, but what can she do?

[00:27:32] What can she do alongside AI, as you talked about, that could provide a pathway for entry-level folks to get into the organization, grow and learn, build those skills, and ultimately provide the bench necessary for these big organizations to sustain themselves? Yeah, I totally agree with you. I think you calling out that they're killing their bench or their pipeline is a really critical element.

[00:28:02] So here's what I'm thinking, okay, in terms of the next shape. I think it's going to be an hourglass. So think about the shape of an hourglass. You know, it's not super wide on top, but there's a few folks. And then it goes, pinches in the middle, and then it goes kind of wide on the bottom again. And here's the reason I think that has the potential to play out. I think that the entry-level jobs are very well suited, not just for AI, but also for these entry-level folks. Because think about it.

[00:28:29] These kids are coming out of school, and they have had technology in their hand from the day they were born. They are very adept at embracing new technologies and adapting quickly to them. And so they're well suited for that interaction with the AI. So I don't think AI is going to completely replace those entry-level jobs. But there won't be as many as there were at the base of that pyramid in an hourglass.

[00:28:55] And then in the middle, I think that's actually where the majority of the administrative, transactional, compliance-related tasks will be automated. The managerial role, the supervisory role that nobody aspires to, right? Nobody says, oh, I just really want to get promoted to manage people. They want to get promoted to make more money and to have a big title. But they don't want to manage people because it's all about compliance and policy enforcement.

[00:29:24] And it's not fun. And so I think that's actually one of the best elements of AI is giving that to the technology. So we'll have very few people doing that because the technology can do the performance evaluation for you. And then you put your stamp on it and add some elements that can't be captured by the digital kind of paper trail that's left behind.

[00:29:48] And then you've got, I think, the potential then for a few more folks in the executive ranks because we're going to need more people evaluating everything coming out of AI, all of the data and all the reports. Because we need people to make the decisions, okay? We are not ceding the decisions to the technology. So I actually think we could see a few more at the top. So again, wider than the pyramid, but still not necessarily that many at the top too. So we'll see.

[00:30:17] We'll have to have you back on the podcast in like five years to see if the shape of organizations has resembled an hourglass yet or not. I love it. And by the way, I think it'll make our kids and their kids graduating college and understanding that there's really a place for them in these organizations. It'll make them very happy because right now they're feeling the pinch. Yeah.

[00:30:41] Now, the only thing I don't have figured out then is, okay, if you look at them, so they're going to, these kids will have a place to land in the organization. But if you're taking the middle rung out of the ladder, how do they get from down here to up there? So there's going to be a challenge no matter what. It's interesting. I mean, look, it's a really fun time to be in this space because, you know, I think back to, you know, been around long enough.

[00:31:08] So of you that we've seen client server go to, you know, I mean, mainframe going to client server from client server, go to cloud. Now we're seeing SaaS, you know, getting eaten by AI. And so we've seen all these iterations and some things have stayed the same and some things haven't stayed the same. But this is the fastest I've ever seen something go from, you know, typical what we've typically known to be, you know, cloud-based SaaS solutions to agents and agents sitting on top of everything.

[00:31:38] And so, you know, I think we may not have to wait five years, Marcus, for me to come back on the podcast to see whether your hourglass has come to fruition. Yeah, yeah, I think you're right. That's a good point. And you're so right. It is such an interesting time to be alive. I tell my wife all the time, I'll never be able to totally retire because just sheer morbid curiosity, I want to see what's coming next. So, okay.

[00:32:04] So we talked about kind of the media portraying these entry-level jobs as vulnerable. The other thing we're seeing a lot in the media is layoffs. And a lot of the layoffs are a result of these larger institutions making huge investments in AI infrastructure. And they're like, I need money to pay for this stuff. I'm going to take it from payroll. Now, that's not true for all of them. But, you know, it's funny.

[00:32:32] These executives don't seem to realize there's more than a couple buttons on their desk to, like, make decisions. It's like they have a layoff button, a hiring freeze button. They have a reorg button. Now they have an AI button. And it's like you guys realize there's other, like, ways of going about this. Like, we're big on strategic workforce planning. That's what we do at Litics. And yet most organizations haven't chosen that path.

[00:32:59] Are you starting to see organizations recognize that there's a wide variety of ways to address the workforce-related needs, to plan for the future? Again, you talked about them needing to understand the breakdown of a job into tasks. Or are you still seeing a lot of organizations just trying to do things the traditional way?

[00:33:21] Yeah, I think I'm still seeing organizations or CEOs treating the workforce like a balance sheet rather than, you know, a capability portfolio. So, yeah, I think for sure I'm still seeing that. Now there are some glimmers of hope. There are some organizations that are thinking about it differently. I think, you know, one of the challenges – and I do think the decomposition of the job is really important.

[00:33:44] Because I do think when you split that in half and say what can be automated versus what truly requires a capability of some sort, which requires a human in the loop, if you will. I think the more we see that, the more that I think that the CEOs and C-levels will really, truly view their organization as a capability portfolio. The problem is – and this is a little bit outside of, like, my purview in talent acquisition. But it's really about workforce data, right?

[00:34:13] And it's siloed all across the organization. HR has some and finance has some and operations has some and all that kind of stuff. So truly understanding what you have from a capability perspective is really hard to pull together. And so in order to do effective workforce planning, as your organization does, you need to merge all of this data and not just focus on annual headcount plans. That's right. Big organizations, not a problem. They have mature people analytics functions where they can kind of pull all of this together.

[00:34:42] But the mid-market companies don't really have that luxury. So, you know, to me, my hope is that there are organizations out there today that are democratizing people analytics in some way that every organization, regardless of size, can not only access them but afford them. And in so doing, that will start to even the playing field for all. So that's my hope. But I think ultimately I'm not seeing it move as fast as I would like it to move.

[00:35:12] But I'm always essentially and ultimately optimistic. Good. So when I see glimmers of hope, I assume that, you know, the light is the light is not that far at the end of the tunnel. Well, I'm glad you're seeing glimmers of hope. This is, after all, the Hope at Work podcast. And so that's actually probably a good place to land.

[00:35:31] But I will tell you also that one of the things that we're doing at Lytx is to, especially for those mid-market organizations that don't have a large team of data scientists and analysts or, you know, people analytics folks, is we're making it as easy as possible. By using AI to say, okay, let us aggregate the data for you. And instead of it taking months for you to collect all of this data and understand what it tells you to do, it literally takes minutes.

[00:36:00] And that is a big change. Again, that's the beauty of using this new AI technology. So anyway, let's land on the question of hope because you said you're seeing some glimmers of hope. So what makes you hopeful and optimistic about the future of work from your vantage point today? So I would say there's a lot of different things that give me hope.

[00:36:25] I would say, and if I'm summarizing everything we've already talked about, AI is definitely forcing a genuine rethink of how things, how work gets done. Not just how fast it gets done. Because certainly there's a component here, a productivity uplift. And that's where the CEOs are using a blunt force to kind of cut the workforce as opposed to really thinking through it. Because there's a productivity uplift there.

[00:36:54] But I think that in combination with skills-based hiring that we talked about before, when it's really done and done right, makes things more equitable. So I think it opens doors for people without elite credentials. So if you didn't go to Harvard or Stanford or Yale or wherever, but you can really demonstrate your ability to do the work. You've got the skills. Then I think that actually really gives me hope. I think it changes the way people think about hiring.

[00:37:24] And look, the disruption, absolutely real. No doubt about it. But so is the opportunity for us to build organizations that are more intentional. They're more adaptive. And really, in my mind, and this is where the glimmer of hope comes from or the reason for optimism comes from, is ultimately makes those organizations better for the people that are in them. It does. Well said again.

[00:37:49] I think what you've captured there is it democratizes the landscape to give people an opportunity to be successful. And they don't have to come from an affluent background and go to Stanford or Harvard or any of those Ivy League institutions to be able to be successful. You also said something else, though, before we sign off that I want to highlight, because you mentioned it's not just how fast this stuff happens.

[00:38:14] And I think that's where the world of recruiting has lived for so long, is the two metrics that all these organizations evaluate when it comes to talent acquisition is time to fill and cost per hire. And they don't look at, well, did we hire the right person? Is the quality there? Are we hiring people that are wildly productive and successful?

[00:38:40] And so I like the fact that we're getting away from some of those traditional metrics in embracing and rethinking and reimagining these traditional ways of working and exchanging them for contemporary ones. Yeah, no, I completely agree with you. I think that, you know, people think about like speed to hire and being picky, if you will, as opposites. And it's not really true.

[00:39:10] I think you can increase the speed to hire. So we talked about speed with AI, right? So you can increase some of that by cutting through the noise. But by cutting through the noise, it gives recruiters the ability to take their time and really figure out who is the right person and making sure that they're driving up their quality of higher metrics, right? Because really, if we're being honest, what's more important, time or quality? In all cases, it's going to be quality. Because you can put somebody in place really quickly.

[00:39:40] But if they don't stick, they don't stay, then it doesn't help you. But if you take a little bit more time, you go through the process, you let AI do what it was meant to do, which is deal with the more manual administrative pieces of hiring so that it can help you hone in on the connection with the person. Spending the time with the right people and making sure they're the right people for your organization. Your quality of hire goes up. And in that case, so does your retention. And so does ultimately your bottom line.

[00:40:08] And that is the stated promise of AI is giving us more time to spend on those things that only humans should be spending that time on. So hopefully we'll see that come to fruition. Dara, thank you so much for taking the time to join the podcast. We'll have to decide what the timeline is to have you back on so we can compare and contrast the new shape of organizations in the future. But thank you again. Really enjoyed it. Thank you, Marcus. This was great. Thank you.