Ep 716: Using AI To Transform Quality of Hire

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Speaker B: There's been more of scientific discovery, more of technical advancement and material progress in your lifetime and mine than in all the ages of history Foreign.

Speaker A: Hi there. Welcome to this episode of Recruiting Future with me, Matt Alder. Recruiting Future is a podcast that helps talent acquisition teams drive measurable impact by developing their strategic capability in foresight. Influence, talent and technology. This episode is is about talent and technology. Timely and effective measurement of the quality of hire has long been a significant frustration for talent acquisition, with meaningful data often trapped in subjective performance reviews that arrive too late to be of any help. But what if AI could connect interview assessments, onboarding metrics and performance data in ways that reveal which hires are going to succeed, creating real time feedback loops that continually improve hiring decisions? So how exactly can employers build these types of connected systems? My guest this week is Mark Linville, head of talent at uh, Garner Health. In our conversation, Mark reveals how to identify accurate leading indicators for quality of hire, why authentic interview experiences help predict performance, and how AI helps helps connect the dots we've all been missing. Hi Mark, and welcome to the podcast.

Speaker B: Thanks Matt. Good to be here man.

Speaker A: An absolute pleasure to uh, have you on the show. Please could you introduce yourself and tell us what you do? Yeah.

Speaker B: So I'm Mark Linville. Um, I am the head of talent at Garner Health, which, uh, is series D, uh, Health tech startup based in New York City. Before that I spent, um, about eight years at an edge fund called Bridgewater Associates. Um, they're well known for their culture for trying to understand more about people management as well as being the largest hedge fund in the world. Uh, and while I was there for eight years, kind of made my way through the talent acquisition, um, hierarchy and ended up leading tech recruiting um, for my last kind of four to five years there. The thing that I thought was really cool there, I'll just kind of share one snippet was um, I actually got the opportunity to kind of build a talent function outside of core HR M. So I actually you know, about halfway through worked uh, with the ct, CTO and a couple other leaders to actually take my team. We went embedded within technology and um, to report it up into the cto. Reason uh, for doing that was because we're trying to solve very particular problems for technology hires which we found very different than solving investment, professional hiring um, and needed to kind of build kind of functions and processes and capabilities that were unique to us. And so I would say got my, got my teeth really in you know, first principal designing and having to solve problems for the first time um, at that, at that spot and in particular in that process. And then you know, after that I'd say for eight years it was awesome. I went to ah, another series D, um tech startup here actually based in Nashville where I am, um, where I you know, took the team or you know had to scale again. And so it took the team had to build, grow and fly the plane and continue to, to build it and get all the processes. Um, so that's kind of my path to garner. And then on the personal side I'd be remiss to say I'm not a massive soccer fan. Football, um, probably for most of your audiences. I played in college. Um, I'm a Liverpool fan so never walk alone. And really, really enjoyed uh, the last season getting to watch, getting to watch them with the prem. Um, so that'd be remiss not to throw that in there as well.

Speaker A: Being a Southampton fan literally finished at completely the other end of the test.

Speaker B: The other tents. Well I remember the time in which Southampton basically we always uh, joke Southampton was Liverpool be. We would just keep taking all your players. Uh, and so we were like oh great, fantastic. Um, but yeah, sorry about the relegation.

Speaker A: Oh, it's okay. It happens quite often. So kind of coming back to, coming back to ta, I mean what do you see as the kind of the main market challenges at the moment? And you kind of worked in some innovative structures and seen some different things. Uh, what kind of TA leaders do you think we need to solve those challenges?

Speaker B: Yeah, that's a good question. I think. Well I'll steer away at first for the easy one which is AI is going to change everything. And so let me go towards more of like maybe a niche problem. Which I think that as we are continuing to in the TA space be pushed to do more with less with an influx of candidates out in the market. I think really what the main problem is becoming is how are you able to find the right fit for your particular company in today's market? I think um, for so long there was such a focus on just getting bodied in and being able to scale whatever. And there's such more of a focus now I think on getting the right people in and having a super quality of hire. Being able to make one hire, that's an amplifier for two to three other people compared to going to gain two to the other people. And so I think this more with less attitude, um, trickles down directed ta directed to like what we actually have to go and find and hire. And so for me what that means from a, from an actual kind of problem standpoint is do you have to get way more creative and able to get the right people that you want? Are you having to be way more data driven in actually determining like who are the people that succeed, succeed at our company, um, who are people that are quality? And then can we take that back into who, what target companies we were going after or what target people we're looking at. And I think three, you have to do all that while being a leader for your team and not letting them get burnt out. Um, and I think that's another really hard challenge in TA is like there's with the continued push and continued like you know, desire again I guess that to do more with less. You always risk um, ruin on people running, running out of steam and or I think you know the last part of it which will segue into a little bit of how you deal with AI is like how do you also maintain this real human connection and human touch? At the end of the day, no matter what we are trying to do, we're still dealing with humans. Um, and humans um, aren't numbers on a spreadsheet all the time. Um, they have emotions, abilities. And so how are you continuing to have this enterprise scaling uh, hiring practice while ensuring that every single person has a unique human touch and unique human element can experience to their process. I think that's a really big challenge. Um, I think it can become really easy to be super rote to just crank think and go especially when you're being pushed. Um, and I do think then I guess transition into a little AI of like, I think that's also going to become the differentiator between like companies that are using AI well and companies are using AI to just do a lot. Um, I think that companies that use AI well will obviously be able to innovate processes, um, you know, improve processes, look at data different way, solve I would say the low hanging fruit stuff that's like, you know, a pain to, to be hands on keys and recruiting to go and do. But um, what I think it should also do is then like enable recruiters to do the unique element of their job that AI I do not think can do yet, which is talk to a person, assess whether or not they're a good fit, really understand deeply and then sell them on the company. Um, I think those are going to be things that we have to continue as TA leaders to push at the forefront. Um, because I think when we lose that, we lose the combination of what I think this, not just job, but what this, um, what ultimately this sector is, which is a combination of art and science.

Speaker A: What I'm interested in is obviously you sort of talk about data there, you talk about AI's capability to do lots and lots of things. Do you think, or are you seeing, or is it like this in your organization that the boundaries between talent acquisition and talent management and all those kind of aspects of the talent cycle are uh, kind of breaking down and starting to feed into each other more?

Speaker B: Yeah, I think that's naturally what I see happening. It's where I've always tried to push my teams to go. Um, and I think ultimately is where TA is going to end up having to go. I think, you know, obviously the everyone knows what the talent ecosystem or what you know, the employee lifecycle looks like. Right? You have, you know, your business need that gets translated to a person. You have to go attract and hire that person. Then they come in, you have to onboard them, assess them and either retain or exit. And I think the thing that we've struggled with has been how do you take the learnings, host the onboarding or even honestly post the hire and go what's the connective tissue back to recruiting to evolve how TA actually does their job and not just how TA does a job, but how does the manager think about their role, how does the manager think about their org and does that actually affect what you need in the future? And I think we are going to continue and again going back to that theme of with the focus on quality of hire, with the focus on again doing more with less and getting the right people in the door, I think that's going to be even more stress test to Say, okay, for any MIS hire, what did we learn? Oh, for any really good hire, what are we learning and how are we getting more of these people? And it's become, I think as a TA leader, you're going to have to almost become a very seasoned HR BP to start to create those connective tissues conceptually. Hrvp, to create the connective tissue, to actually evolve your own recruiting practice without it being pushed by somebody else. Uh, and I think I can help with that. For what it's worth. I think AI can look at that and go, okay, what interviewers? You know, I think of AI, uh, as like being able to do processes and analyze data in ways that I could never think about. And so it's like cool. Are you able to then like create the prompts to go, all right, like let's see what our core interviewers are. Let's see, here are 10 mis hire and here's the feedback and here's, you know, what we learned. What does it, uh, what do we need to like, how do we analyze that data? How do we actually look at, how do we go, oh man, we've had this interview isn't working or oh man, this, this actual like company or this background is a common theme and pulling those things out so that we don't continue to make those mistakes. Particularly I think for companies that are trying to scale and grow where every single hire is so important, that's going to become critical in how TA does their job going forward.

Speaker A: Yeah, I mean a hundred percent. I think it uh, it kind of solves so many problems about data being in different systems or not existing. So it's creating more data for us for things like interviews and then, you know, the ability join it all up and actually create some kind of actionable things that make things better. It's just, you know, it's kind of a massive opportunity come back to AI shortly. But just to kind of dig into the human side of things a little bit in terms of candidate experience. What's your kind of take on the candidate experience? What does that look like in your organization?

Speaker B: So I think at Garner we have a really unique and I think like, I would call like a very true to self kind of experience where you know, I think there's been a lot of um, push for obviously standardized interviews and really trying to make sure that we assess people correctly throughout uh, the process and are able to go like apples to apples versus you know, very subjective. I think um, Garner does that. I think the added element of what we do that's really cool from a can experience standpoint is like, we try to make it authentic to what we think life will be like at Gartner. Um, and so we try to schedule our. We try to like, structure our interviews around real life Garner problems and then structure it around how we think you will interact in real life with us. And so obviously we have our kind of interview that is focused about our culture and we dive deep and we have a very upfront culture and we want people to authentically experience what that looks like, um, in the day to day. And some ways we actually expect some people to opt out of that. And we're okay with that because we'd rather have them opt out now knowing, you know, open kimono, what everything is compared to get in and also know, like, wait, I was lied to my interview processes, not what's going on. Like, no, no, no, no, no. We want you to know. Um, and so I think that's really, really big from a culture perspective. But then I think the other thing that's super cool that we do is we really try to structure our, uh, like meat and potatoes interviews around, like I said, real life Garner problems and solving them with, like, real life Garner people and solving the way that we think Garner would try to solve them. And so, you know, in my space, right, instead of, you know, asking about a time a search has, you know, been off the rails or whatever, and how do you deal with hi managers. Like, I create literal. I have literal three prompts that are problems that we deal with on a day to day basis at Garner. And we talk about that. And then it's literally just like a session of, here we are solving this, or hey, I haven't even solved this problem yet. I would love to get your idea. So there's no right answer for like, what the thing is. But really what we're trying to get at is how do you think about, like the problem that we're trying to solve? How do you design and diagnose whatever the issue is? How do you get more information? And then how do we do it in a way that's actually, um, like I said, authentic to like, how I think we will work at Garner together and structure interviews around that for our working sessions and our case studies. That I think is just like a slightly different take on, um, you know, candidate experience, which is just like, hey, we want you to really, like, the goal is we want you to know what it would be like to work at Garner and full stop. And then we think we can get a good assessment through that as well. But ultimately you can also assess us and determine if that's a company you want to join and this is a place you want to be and these are people that you want to actually be side by side with as well.

Speaker A: Support for, uh, this podcast comes from Pebble. I know that many of you listening are hiring internationally and you'll be all too familiar with the wall of paperwork, rules and approvals. And you know just how much this red tape can slow things down. That's what pebble is designed to fix. Pebble makes global hiring simple through embedded compliance and AI driven workflows. The pebble platform takes the delays and guesswork out of going global. So you can move fast without adding risk. The bottom line is that anywhere is possible with Pebble. With global hiring simplified, founders and HR leaders can spend their time on focusing on what and where comes next for their business. Pebble has a special offer for recruiting future listeners. Pebble is normally $399 a month per employee, although a no brainer for what you get. But right now there is a limited time offer on their site that makes it even easier to get started. Go to high Pebble AI before it's gone. That's HIPEBL AI. Terms and conditions apply. You mentioned quality of hire earlier. We were talking about joined up data and all those kind of things. From a TA perspective, how do you judge quality of hire? Because obviously that's something that could take quite some time. It could be quite subjective. How do you kind of judge the quality of people that you've hired?

Speaker B: Yeah, if I fully ever solve this, I'll just start my own company and let you know. Um, so I can't say I'm the right answer. Um, but I can tell you what at least I've done and what I know our problems are about it today. And so I think the thing you stated up front is like, yeah, I would say typically I've seen quality hire go in two different directions. One, which is people just look at the scorecards and feedback from the interview and try to assess like, hey, is this a really strong hire, an okay hire? Or whatever. Um, the other side of it, which is I think the unfortunate side of quality hire, at least for how it's been structured today, is this, um, lagging indicator of performance reviews. Right? So like you hire someone and then really you can't tell if they're a quality hire for six or 12 months. Like that feels way too long, particularly for how fast and the velocity. We need to like adjust and Evolve things. And so what I am continuing to do and what I've continued to push to do and you know we're working on this, you know, actively at ah Garner is trying to figure out how fast of an indication can I get through structured check ins, right? We talk about 30, 60, 90 onboarding which is like why is there not an assessment piece of that? Why can't we actually have true signal early on to understand if things are going on or off the rails? I um, think that actually AI can actually help in onboarding so you can actually get to the ramping period to be down so you can actually get to true assessment faster. Um, I think though that the data that you have to start to structure is you have to have your core competencies really lined out for the role and have a clear understanding of what success looks like and have like clear criteria. I think that is the first and foremost thing that a lot of people don't do super well which is say hey, what are our core competencies, how do those apply? And then like literally then level down. What does it mean for this role? And can I actually measure expectation against that in a disciplined way? And I think it's really almost in that way, like an act of upfront thinking, structure and then discipline that you can get to a much higher fidelity, quality and higher metric early on. And then I think the layers, that's your early indicator one. And I think the layers then that you take from that have to be attrition, promotion and performance management, um, all wrapped together and basically say hey, here's my quality of hire based upon what I see. And then you can throw in a few other structures like nine box and stuff like that. But I would typically say like less is less is more in this space. So like do the uh, upfront work, make sure you like have managers actually be able to give you signal within I think 90 days. I think you can get signal within 90 days at least directionally. And then you have to layer in the, that like that performance management data. Um, which I think if you have the infrastructure set up right from the performance management standpoint you can actually get to be much less subjective and way more objective.

Speaker A: No, absolutely, that makes perfect sense. Circling back to AI, we obviously talked about AI kind of in every sort of facet of this conversation I suppose in terms of sort of a summary or to give advice to people who are listening. When it comes to thinking about what AI can do right now, how it can change the game, what would you say? What would your advice be? To people about how they should be thinking about AI right now.

Speaker B: Well, I think my first advice would be don't be scared of it. I think that, uh, ultimately I've seen, or at least I've read a decent amount of tension of, like, will AI end up replacing a particularly recruiting or TA or HR jobs? And I think that instills a weird, um, faux dynamic with AI that I just don't think needs to be there. And so I think my advice would simply be like, one, don't buy into that narrative. Um, because I still think no matter what AI will be able to do for us, I think until it gets to a point where it's just not going to do hallucinations, you're not going to be able to always set in front of a candidate because even if it does a 1 out of 100, if you lose that one person, you're going to be in trouble. Um, and that could be the person that's a hire. And again, it gets back to the human element. But I think that's just the thematic thing of don't be afraid, experiment, try things out. Um, then I think practically where I've been focusing a lot of, like, how can we think about AI is can we actually think about AI first and foremost on cleaning up our processes. And so anything that you do today that was like, you're literally sitting down to write hands on keys on something, like, I would say, immediately try to do that with AI. Just like literally go, okay, wait, what was I about to go do? Was I about to try to write an assessment? Was I about to try to write a job description, whatever? Like, nope, don't go, go to AI. Uh, think about that. Um, and then the second piece, which is like, again, I think about processy and analysis and say, okay, you know, I don't think that TA folk, myself included, are always the most quantitative, um, junkies out there. And so as much as we might try to want to live in Excel, I don't know if we all can. Um, and so it's like, how can you, how can you amplify, uh, AI to help you do that analysis piece? And then how can you really, I think, think about building a system which AI is an assistant compared to, um, replacing you? Um, and then I think there's interesting places that I've begun experimenting that I think people are going to be experimenting going forward, which is really interesting of what does AI do with interviewing in general? Um, and how do you think about that, not just from the assessment side, but also like, I said that can experience side. And so my, my guess is you have to have some sort of combination of AI can do a certain amount of things, but then you would still need a human element touch. And so how do you think about creatively trying to meld those two things of um, where's the AI assist and then what does a human have to do? Um, I think interviewing is again the really interesting one, um, to try to get at because as, as best you can. Even AI has its own subjectiveness at times. But as best you can, I think you can create enough assessments that are objective that it can run. I think if you think about high volume recruiting or if you think about very high level of applicant pool that you're trying to sort through, I think there are clever ways that AI will help you out there. Um, obviously there's plenty of tools out there to staff crank resumes and stuff like that. But even when it gets to more qualitative stuff, I think you can start to experiment and kind of lean on it um, more to then create more efficiency for your recruiters to actually focus on the right people.

Speaker A: No, that makes perfect sense. And I suppose to round things off, as a final question, what do you think the future might look like? So how does this all pan out? So if we had this conversation again in three or four years time, what would we be talking about?

Speaker B: Yeah, well, I think it might be in like two months time, uh, because AI continues to evolve rapidly. And so I think I say, I say all these things I today and like in two weeks that could have a completely different version out that uh, changes the game. And so I do think this is the tip of the iceberg when it comes to this conversation. I think though, if I had to try to throw on my Nostradamus and figure out what the future would look like, I think that ultimately you're going to have clean recruiting teams and you're continue to be having to do this less and more, less with more. But I think what you're going to end up having is, is way more of a question on how has actual work changed and what type of roles you actually need to hire now in three to five years. Like I think that's the thing of uh, where we're going of like I think anything that we're recruiting for today is going to be vastly different in three to five years. And I think that's the uh, you could argue that's like the biggest change that's happened. And you know, since the, you know, I don't know, 2000s and like you know, coding really was coming at like. I just think that ultimately like these roles that we're hiring for are going to change and everything's going to have I think within three years an element of either AI or a different technology piece to it and everyone's going to be a technologist. And so to be more than I think a question of what separates people out. Right. It's not just their ability as a tool but it's like going to be I think their ability to think. And I think we're going to actually see a bit of a reversion back to like abilities, capabilities, uh, first principle thinking as a means to find the best problem solvers that will then cobble together and use all the tools that are available for everyone at the, at that point versus I think today where we're still looking for a very skill heavy um, environment at times. Like I just can't imagine in three years that that's the same story. I think that like the, I think the way the AI works and the way I, the way it's going to affect all these jobs is going to be drastic. Um, I think, you know, I think obvious tech side that's true. I'd be interested to see where that goes on the go to market side because I actually don't know. I um, think that there's still an element if I think about sales or I think about marketing, there's elements in which I think AI helps you but you're still in that person to person human connection piece. But I don't know if companies change how they accept sales calls or how they accept pitches. Does that then evolve how you have to actually try to get them? Um, and so that's, I think that's going to be the biggest thing which is like we're going to have to shift and evolve and become even more fluent with AI and with, honestly my guess would be with what our businesses are doing to like in a deeper way to actually solve the talent problem because it's going to become I think a very like hydraulic, like a very um, a lot of people are going to have the same skills. It's just going to be very hard to separate out the chess.

Speaker A: Absolutely fascinating times. Mark. Thank you very much for talking to me.

Speaker B: Yeah, it was great. Thank you so much. Really enjoyed it and hope you have a great rest of the day Matt.

Speaker A: My thanks to Mark. You can follow this podcast on Apple Podcasts on Spotify or wherever you listen to your podcasts. You can search all the past episodes@, uh, recruitingfuture.com on that site. You can also subscribe to our weekly newsletter, Recruiting Future Feast, and get the inside track on everything that's coming up on the show. Thanks very much for listening. I'll be back next time, and I hope you'll join me.

Speaker B: Um, this is my show, Sam.