EP 158: What should AI’s role be in talent acquisition? With James Mackey (CEO of SecureVision) and Steve Bartel (CEO of Gem).

James and Steve discuss the challenging question of AI’s role in talent acquisition. Listen to their thoughts on how AI can enable recruiters and the significant risks of AI-driven evaluations. 

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Our host James Mackey

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Chapters

• 0:00 The Role of AI in Recruiting
• 11:47 Ethical AI in Recruitment Tool
• 19:31 Navigating Ethical AI in Recruitment
• 31:24 Leveraging AI in Recruitment Platforms

Transcript

James Mackey: 1:12

Hey, welcome to the Breakthrough Hiring Show. I'm your host, james Mackey. We got Steve Bartel on the show today. Founder and CEO of GEMS. Steve, welcome back.

Steve Bartel: 1:20

Thanks, great to be here.

James Mackey: 1:21

Yeah, it's great to have you. It's always a lot of fun recording with you. I guess, just to start us off here, I guess, like the last few times you've come on the show, we've really dialed in to GEMS product, which is a little unusual. We like with a lot of CEOs that bring on it's not necessarily so product centric in terms of conversations, but it's just. There's a lot going on with Jim right now over the all-in-one product suite that you're developing and rolling out and how you're incorporating AI and the applicant tracking system functionality, and there's just a lot of cool stuff that you're doing. So we've slowed down on that, I think, for today. We I'm sure Jim's going to come up, because you're going to be talking about Jim we're going to do an X, y and Z. That's going to happen. But I think, just talking about how AI is currently being leveraged by recruiters and hiring teams and hiring managers as well, we can talk a little bit about current functionality that recruiting technology companies have incorporated into their products and then I think we could talk about the future.

James Mackey: 2:23

There's also a pretty big debate in terms of how AI should be leveraged and I think I actually like when I was texting, there was a couple of poor things here, like we talked about the role of AI in terms of enabling recruitment versus replacing recruiters in certain stages of the interview or hiring process, and then AI's role in terms of evaluation. Should AI just be essentially leveraged to package data and neat summaries and also to help identify gaps or thoroughness in evaluation, or should AI be accountable for, to some extent, stack ranking or grouping top applicants out from the rest and essentially provide some of the top applicants for, whether at like the resume stage or down funnel to essentially shortlist, if you will, applicants or candidates rather for the roles? So yeah, those are just a couple of things that come to mind for me. Are there any other kind of topics or use cases or anything that are a little bit controversial from your perspective beyond those two?

Steve Bartel: 3:25

No, that makes a lot of sense, and I think there's even a third distinction between AI that's just doing like summarization, maybe putting together packets, versus AI that's ranking and maybe bubbling up the most compelling candidates, versus AI that is actually making hiring decisions and maybe automatically doing so, and so we could talk about the three differences there and maybe where talent acquisition leaders should be a little bit more careful.

James Mackey: 3:54

Yeah, let's do it. Can we just start talking about AI's role in potentially replacing recruiters in certain parts of the process? Because from my perspective, I don't know. I think there's some inherent bias if you're talking to folks in talent acquisition, in terms of AI's ability to potentially replace recruiters at certain interview stages, for instance, or certain parts of the evaluation process, and so I'm wondering I don't even know where to start. I could throw out a couple of recent conversations I've had on the topic to start us off, but I don't know if you've had any recent conversations surrounding this.

James Mackey: 4:28

I guess let me just provide some clarity.

James Mackey: 4:30

Right, you have like interview intelligence platforms like RightHire and Pillar, for instance, or just a couple, which are essentially like AI co-pilots that are integrated with your Zoom and essentially think about a Fathom note taker, but it's trained for interviewing and it's essentially matching list of requirements and taking the candidates answers on the video call that they have with the recruiter, packaging that data to show hiring teams like how essentially folks answered, highlights or whatever from the interview, matching the evaluation to see if there's any gaps.

James Mackey: 5:07

Hey, you said you need to know the salary range that they're targeting, but you didn't cover that in the interview. So you got to ask that in the next one, like stuff like that. It's like packaging data that's staying away from evaluations. But then you have other products that actually are taken as far as stack ranking candidates. We're seeing a little bit more of that top of the funnel right, Like with resume matching. I see application for that down funnel too, so I'm just curious to get your thoughts on that. It's a lot of context, right, but that's the conversation I was hoping to have with you at least to start.

Steve Bartel: 5:42

Yeah, that makes sense.

Steve Bartel: 5:42

So my perspective is that we're very far away from AI replacing recruiters.

Steve Bartel: 5:44

I think AI, though, has the potential to really evolve the job, the role of recruiters, whether they're in-house or agency, and I'm actually pretty excited about what that means for recruiters and for the industry, because I think, in my mind, ai has the opportunity to automate a lot of busy work that goes into the role of a recruiter and allow everybody to show up more strategically.

Steve Bartel: 6:09

Use cases that you talked about around summarizing and transcribing calls, so that recruiters don't have to focus on taking meticulous notes the whole way through and then taking a first pass at putting together a summary of that conversation.

Steve Bartel: 6:29

Now, that still feels very much like a co-pilot use case where the recruiter's in full control, and I think that's great, and I think all AI, in my perspective, should be thought of as a co-pilot experience, and what that allows recruiters to do is be a lot more present on these calls and not have to be taking furious notes the whole time and potentially save up a little bit of time with the write-up. But I still think it's really important for recruiters to look at that write-up, especially the summary, and make sure that it matches what happened, because even if the AI is not actually making a hiring decision, in that case it could still influence hiring decisions if it's helping to contribute to a scorecard, for example and so I think it's super important for recruiters to pay really close attention to that and see it as a time saver, but not necessarily as like the thing that's making the evaluation.

James Mackey: 7:27

Yeah, and I guess like maybe a way to put it is like making the evaluation easier and that's like a sliding scale. So I'm thinking about it in terms of like not AI making a final decision, but I do think that a lot of the enablement tools, I feel like there could be, in some cases, more aggressive steps taken than simply just doing co-pilot work. I think like some of the functionality I'm seeing too for interview intelligence platforms is doing some analytics Like think about like Gong, for instance, right Revenue tool where it's the call record how much time do you speak during the call Just some different analytics around that. So we're seeing some of that which interview effectiveness and providing feedback to the hiring team on doing a better job. Totally, I think it is pretty cool too, but okay, but we get to evaluation right. Like I think the most clear cut or one like we could just start top of funnel maybe work our way down is resume matching right, like for the AI for hiring series.

James Mackey: 8:27

On the show we had the co-founders of a company called Brainerd come on, it's actually on the LinkedIn post I tagged you on today one of the companies and it's they. Essentially it's like resume matching, but it was a lot more than I thought it was going to be. It was pretty pretty in depth, but essentially there's there's certain industries right, like just even take like light industrial, which apparently is like a CEO of a founder and CEO of a company called Qual who came on the show, so it was like 50% of job openings openings or something crazy. Like in the United States, like a lot of companies, a lot of job openings are within that space. Or Brainerd focuses on staffing and recruiting.

James Mackey: 9:04

They're doing a high volume hiring where some of these companies will have like thousands of applications and sometimes they're in there's just not enough time, rather, to get to all of these potential screens, and so they're using a lot more sophisticated resume matching technologies than we used to have to essentially stack rank or evaluate fit, and so in those circumstances a little bit difficult, because it's a recruiter might get to the top the first 50 people, but then they're not even looking at the other profiles, and so you could say, arguably, by having AI evaluate all 1000 plus or whatever, it's actually giving more access and opportunity to more people to evaluate fit. So I don't know. There's just two trains of thoughts here not be doing any kind of stack ranking or it should just be packaging information but not essentially floating candidates to the top right. I don't know if you have any thoughts specifically on this use case like top of funnel resume matching.

Steve Bartel: 10:08

I do, and I think there's also a distinction between top of funnel, resume matching and ranking when it comes to inbound applicants versus AI, narrowing down the total addressable set of everybody out there to folks you might want to reach out to for sourcing. There's a key distinction there. I'm sure you've thought about it, james, but even the EEOC makes a very clear distinction that once somebody has raised their hand for a specific role, either express interest or applied, they become a candidate, and the way that the law treats those folks is actually different from passive talent, whether that's folks that you're sourcing net new externally, or even folks in your CRM or your ATS that maybe applied for a role in the past. If somebody is actively interviewing for an open role or express interest in that open role, companies really need to approach that with a lot more care and thought, because you definitely don't want to discriminate against folks that are actively interviewing. Now I think there's a whole range of ways that you can apply AI and different varying degrees to which you need to be careful. On the one hand, if you have AI that is picking out the top candidates and auto-rejecting the rest, now that starts to get into pretty dangerous territory in my mind, where AI is actually making hiring decisions with no recruiter oversight. If AI is doing its best to rank inbound applicants, I think that starts to trend towards something that's more okay, but it depends on how the AI works.

Steve Bartel: 11:48

My perspective on this is that the AI needs to do a few things. One I think it needs to be built on this new wave of generative AI technology that actually allows for better, more ethical algorithms. It used to be that AI algorithms were a black box where it was trained on tons of human decisions which inherently, are biased and you don't know why it does what it does. So like unknowingly, you could be deploying AI and creating a bunch of bias. Now, with these new generative AI algorithms from OpenAI, from Anthropic and others, you can actually give the AI clear, unbiased criteria for what makes for both an ideal candidate but also the minimum requirements for the role, make sure those criteria themselves are unbiased and then leverage that criteria in the matching and the ranking.

Steve Bartel: 12:45

But you can also get the explainability of AI, explaining exactly why that criteria matched or didn't, and then feed that into making the criteria better.

Steve Bartel: 12:55

Taking that a step further, you could even reduce bias in the process by asking AI to take a pass on the criteria before it even starts ranking people and give recruiters tips on where their criteria might actually be, unknowingly creating bias in the process, and so I think there's like actually a really ethical way to do this. My perspective on this is still that AI shouldn't be auto-rejecting big swaths of candidates and that recruiters should still be reviewing candidates and seeing this as more of an efficiency driver and maybe as a way to respond really quickly to the gems, so to speak, because what we know to be true in recruiting is timing is everything, and if you can bubble the folks up to the top and have a really fast response rate to the folks that are most likely to be a fit, based on the clear, objective criteria again unbiased criteria with the minimum requirements for the role straight from the job description and or from the hiring manager intake process, that starts to feel more okay to me.

James Mackey: 14:01

What's your?

Steve Bartel: 14:02

perspective though.

James Mackey: 14:03

Yeah, I think I don't think AI should be auto-passing on anyone, but I know there's this huge conversation around bias and there absolutely should be. We need to be very careful here. But I think the way that the AI systems can be trained, even now with generative AI and telling it what to do and what not to do and what information to exclude, I almost feel like it'd be easier to train an ai system than a staff of 5 000 people. On some of this, some of these unconscious bias issues and whatnot totally I don't know. I think it's like the reality is that for a lot of these high volume jobs, the vast majority of applications may not even be looked at if the volume is too high. Is that a somewhat accurate assumption in a lot of cases? I think that's pretty accurate.

Steve Bartel: 14:44

I think it is. And for Jam, we're mostly focused on knowledge worker hiring, partnering with a lot of the leading tech companies and large enterprises, and so I have less insight into high volume. But you're totally right. But even for knowledge worker hiring these days something wild I think it's double digit, I think it's north of 20% of our customers have roles with thousands of applicants which you've never seen before. So I think even for knowledge worker hiring it's starting to happen where folks might interview a big batch of candidates but then never even get around to evaluating the rest because they've already filled the role.

James Mackey: 15:27

I think like that, the whole issue of people already aren't happy with the process. Even pre-AI or without leveraging generative AI, they still, oh, I got this, this auto reject email, or I got ghosted by this company, or it's applied to 100 companies today. It didn't go anywhere. People are afraid of bias. There currently is bias.

James Mackey: 15:43

I think a lot of the issues we're talking about with ai are already issues. Uh, I don't see it as like a reason not to use it. I see it as a pause. Okay, we have to focus on this, but I think that if leveraged properly, it's like you're actually able to most qualified candidates, the 900th applicant like this actually gives. They could actually surface that profile. The other interesting one is there was a company called Qual. Their CEO is David Tell and one of the things he mentioned he does like an AI voice agent and the voice agent does like a AI voice agent and the voice agent does like screening calls. Essentially, the AI voice agent does screening calls and what he was talking about is he does a lot of the blue collar, a lot of like blue collar work and light industrial, very high volume, and he said one of the issues in that industry is that people don't have very good resumes.

James Mackey: 16:37

So it's very hard to tell from a resume if it makes sense to set up a conversation with. So by doing this voice AI agent screening, they're actually able to collect a lot more data and they can have every literally anybody can do it, and so any applicant could do it. Have every literally anybody can do it, and so any applicant could do it and so they're able to basically surface the candidates that are truly a good fit, instead of just going off a resume that they have a very resume base where they have a very low confidence and which I I was like oh, that's a really interesting use case for ai and how it could actually, like I don't know, I think better serve people to make sure that their skill set is truly seen and put in front of relevant opportunities.

Steve Bartel: 17:21

It is and that is super interesting and I know for again, we don't focus on this as much for JEM but for high volume hiring. There's oftentimes just basic requirements that folks need.

Steve Bartel: 17:31

that might not always be clear from their background, for example, certain certifications for some industries. And being able to have a set of just very basic questions to see if somebody passes the minimum set of requirements makes a lot of sense, especially if, for whatever reason, your ATS doesn't let you do basic knockout questions. So I totally see a use case for that. I also see a use case for AI chatbots for that same use case. Maybe the video is more interesting, maybe candidates are more likely to try it, but I think AI chatbots could totally ask some of these very basic questions to understand somebody's background better when their resume is sparse sparse.

James Mackey: 18:17

What do you think of screening calls? We can just stick to knowledge, right? Just a lot of our customer base. Both of us, right? Secure Vision and Jim. We work with a ton of tech companies and I'm wondering too. It's a screening calls, of course, at this point, are definitely done by recruiters, right, that's how it's done, but I'm wondering to what extent we could start leveraging AI to actually run screening calls, and I'm thinking from a candidate perspective.

James Mackey: 18:41

I don't know, man, I'm hearing from recruiters okay, people need to be engaged. They want to talk with a person. I'm thinking to myself, yeah, but also they have to take time out of their day. They have to do it in between meetings and before, after dinner with the kids and stuff like that in between meetings and before after dinner with the kids and stuff like that, where, if they had the ability to essentially do a screening process on their own time and be able to collect the information that they need to regarding benefits and professional development and these types of things and interact with the system instead of dealing with all the scheduling and everything like that, I think that there's a lot of people out there that would prefer to do that, and I also, again, I see that as a way like why not let anybody take the?

James Mackey: 19:22

Be very clear with the requirements, but you can let anybody do the screening call or you could send out the screening call to candidates that the recruiter has reviewed. But I just see a lot of the traditional screening call is completely. I honestly I think it could be. I'm open to being swayed in the other direction with some good data points, but I don't see a ton of value in the majority of screening calls. I see that there's that sales element of pulling candidates in, but I also see a lot of inefficiencies and screening calls for both candidates and recruiters which I think ultimately are going to outweigh this human sales-driven approach.

Steve Bartel: 20:01

Yeah, and so the interesting thing with screening calls is now it definitely starts to get into that territory of starting to make hiring decisions, at least if it's fully automated. But let's forget for a second whether it's legal. My perspective on that is if a vast majority of folks wouldn't get screening calls anyways, it feels ethical to give everybody a shot Right, to give everybody a shot right. And today there's just so many applicants that recruiters really have to pick and choose who they do a screening call with. And so for the folks that wouldn't get a shot, wouldn't get that initial recruiter screen, giving them the option to do an AI-based screening call, which would only be upside for them, would actually give them a crack at getting a real interview.

Steve Bartel: 21:02

To me, ethically, that feels like a good thing, even if there's all this sort of legal gray area that companies need to navigate. And, to be clear, we don't have this in Jam. We probably I don't know it might not even be worth us building it because of all of the legal considerations. Actually, did you see this recently that Workday itself is getting sued for providing the algorithm that could be making hiring decisions leveraging AI? This happened just a month or two ago Landmark lawsuit, which is actually going to probably make vendors a lot more careful about any sort of AI that touches hiring. Did you see that, james?

James Mackey: 21:43

I'm looking it up now. That's what I'm doing. So, yeah, a class action lawsuit against workday. Well, this one says 2023. So I don't know if this was like a maybe there's a further iteration or news July.

Steve Bartel: 21:57

It's this. I'm pretty sure it's this year and it's going to court and it's very interesting because previously it was the entire liability was on companies for discrimination or bias in hiring processes. This is the first ever case where it's actually going to court. That Workday could be liable for the algorithms they provide.

Steve Bartel: 22:20

And so I think actually a lot of vendors that build the software are probably going to be like very cautious about anything that touches hiring decisions in the recruitment process, unless maybe they're like a small startup where they don't have anything to lose because of this case that's happening right now, which actually might stifle innovation. And even if we think it's a good thing to give the longer tail of candidates, that wouldn't get a screen, wouldn't give them the opportunity to give them a shot, that could still result in a lawsuit, because how does the candidate know if they were in the long tail or not? And what? How do you actually prove that? And so I think vendors might end up just being a lot more cautious on some of these things. That could be good for candidates but just might be too risky, and so might companies yeah, that's a really a really good point too.

James Mackey: 23:12

Yeah, I was looking at the workday. Yeah, it looks. Yeah, ai powered applicant screening tools discriminate on the bias of race, age and disability disability yeah, that'll be interesting to see what happens. I'm going to look into that.

Steve Bartel: 23:25

Yeah, see, yeah, that is really scary stuff because it's it could be like discrimination at scale first of all, it is scary, if that's truly the case, if their algorithm is discriminating at scale, and that's what these laws are here to protect candidates and applicants from discrimination. Part of me wonders if that was leveraging algorithms that were based on the old way of doing AI.

James Mackey: 23:49

Yeah, yeah, possibly. Yeah, that's interesting. But here's the thing with screening, though. To me it's just take a salesperson, for instance, like what was your quota? What was your quota attainment deal length with transactional consultative six months? What was the ARR? Were you doing new business or upsell than existing accounts? I mean like more that kind of they're not necessarily open-ended questions, they're just very pointed screening questions to just check boxes on yes or no, um, and that's the only information that's being, you know, validated. There's, I guess, follow-up questions right to that's the benefit of generative ai versus the uh, just a static form, and then, of course, packaging that information. But yeah, that's essentially what I'm thinking of, screening, which is, I guess, probably what you're thinking too. But I'm just wondering. That seems pretty clear cut to me.

Steve Bartel: 25:05

So my read is if AI is helping to understand whether somebody meets the minimum requirements for a role that are outlined in the job description and are very clear objective criteria Like, yeah, what was your quota attainment, and that there's clear minimum requirements for the role, I think that's okay, especially if it could help plug in gaps from screening questions that maybe somebody didn't fill out in their application or something like that. Yeah, whether they have a certain certification or like compliance thing, that's important for the role as a minimum requirement. Like that kind of stuff feels okay to me. I think it's when AI starts to make an assessment on the less objective criteria that that we just need to be a little bit more careful, in the same way that recruiters need to be more careful, yeah.

James Mackey: 25:51

There's those nuance that I don't know why this, even stuff like tenure, potentially, or I don't know. That's not necessarily like a bias thing, but there's even like nuanced things like that where a lot of people lost their job during COVID, during due to like layoffs, and so if you see somebody in the tech industry who's had three one year stints and there's, you could train an algorithm to say you want an average tenure of X, y, z, then folks who like literally had zero control over being laid off could be essentially, yeah, discriminated against to some extent. I don't know if technically that would be considered discrimination, but there's just, there's a lot of. That's just an example of something where you don't really maybe think of, of how somebody could be essentially pushed out of an opportunity.

Steve Bartel: 26:35

Yeah, and taking that a step further, there actually is a real of how somebody could be essentially pushed out of an opportunity. Yeah, and taking that a step further, there actually is a real chance of discrimination there If, for example, like women who have children might have briefer stints because they had a kid and, for a lot of reasons, maybe they wouldn't go back after that, and so there actually could be gender discrimination, depending on how AI is thinking about that.

James Mackey: 26:55

No, you're absolutely right.

Steve Bartel: 26:57

I think this stuff is really complicated and it's evolving really quickly. But my high level thinking around this stuff. For that reason, I think companies and vendors should be really careful when it comes to any sort of AI in hiring decisions and even this example of how, like looking at whether somebody had short stints could inadvertently discriminate against women more than men. You wouldn't even expect that. At first glance You'd think that's not discriminatory. It's like a great reason to tread carefully that's a really good point.

James Mackey: 27:38

Yeah, I guess that's like why you see some of the more established players iron pillar in the interview intelligence space.

James Mackey: 27:45

It's really it's coming down to enablement and they're going more of the co-pilot route. And then it's like they're doing the data packaging, essentially summaries, identifying gaps in evaluation, which I thought was pretty cool and they're, of course, doing easier stuff. They're pulling together role requirements, customized interview questions, populating that Hiring teams can add their own and putting that as part of the evaluation criteria, and then again tracking to see what's been covered and what hasn't, to ensure consistency and that different folks are being asked the same questions, and all that as well. And then they're integrating with the applicant tracking systems to essentially put together scorecards, which maybe you start to get a little bit into evaluation, but it's reviewed by people, but it's just essentially doing the AI note taking and then putting the summary into a scorecard format. So that's more of the path, but all of the interviews are still being done by recruiters and it's just sitting in, like how we have Fathom and Firefly sitting in on this conversation.

Steve Bartel: 28:45

Yep, exactly, and I think that's a good approach, a good thoughtful approach for now, and it's the same approach that we're taking to our applications of AI when it comes to ranking and matching your inbound applicants. Ranking and matching and applying that to resurfacing people from your CRM or your ATS for a role. Or, yeah, ranking and matching at the top of the funnel for external AI sourcing. Yeah, for sure, sure really thinking about it as a co-pilot approach yeah, yeah, that's.

James Mackey: 29:16

It's all really interesting stuff on the ai front. Have you, are there any additional use cases that you've been thinking a lot about recently?

Steve Bartel: 29:26

so when I think about ai use cases, it comes down to really what's been enabled by the new generative AI technology shift in terms of like, where we think and focus, and for me it's anything tech space, which is why, like the resume matching and ranking piece is pretty compelling, because, after all, resumes are techs, so are qualifications for a role, and so I think AI can do a really good job of ranking and matching there.

Steve Bartel: 29:56

And then I also think transcription's gotten a lot better to your point around the interview intelligence and call recording software similar to Gong in the world of sales, and so I think that space is really interesting too.

Steve Bartel: 30:09

For us at Gem, we've got an ATS and a CRM, and so we're gonna be focusing, instead of building that, on strategic partnerships with some of these new startups that are doing that, like BrightHire and others. But I think that space is super interesting and I do believe it can drive real value for recruiting teams and save a ton of time when it comes to the note-taking. But I also love what you were calling out in terms of enablement and coaching recruiters, making sure there's consistency across interviewers, helping companies identify which are their best interviewers, which are the ones that need more enablement. I also think that potentially I don't know if these platforms do this, but if I was them, I would be building in little feedback loops to ask and run feedback that recruiters and hiring managers and hiring teams are writing through a set of prompts that checks for bias and maybe gives them some coaching in real time in the moment as they're putting together the feedback about where they could be potentially biased or introducing bias into the hiring process.

James Mackey: 31:14

Yeah for sure. Yeah, it's a really interesting space and these products are evolving a lot faster over the past year, which is really cool, and the other definitely, steve. An episode to check out is with Nikos, the CEO of Workable, and they've done essentially a massive overhaul on their product suite and they're aggressively implementing AI in each aspect of their business, which was honestly for a larger, more established player. I wasn't, for whatever reason, going into the call. I wasn't expecting to see that aggressive of a shift from workable, but yeah, they're like we're doing this, we're doing this, we're doing this. Different stages of the funnel, everything from recruiting to employee management, essentially more of the HR side of the house. So that was really interesting. It's a pretty cool episode. He drops a lot of cool insights there too.

Steve Bartel: 32:10

Oh, that's awesome. I'll definitely check it out. Actually, here's another killer use case of AI that we've been thinking about that we're going to be building into our platform. So we've talked about the ranking and the matching piece. I think it's going to be amazing for the industry if AI can be writing even more hyper personalized messages and helping recruiters with that draft creation. And here's the killer use case that I think we're really excited about at Jam. So for us, like, ai is part of a broader end-to-end platform and we think there's a lot of advantages to that.

Steve Bartel: 32:42

But imagine if somebody's already engaged with your company.

Steve Bartel: 32:46

Maybe they've attended a recruiting event nine months ago, or they were talking to James Mackey, like one of the agencies that we partner with, six months ago about a different role, or that they applied two years ago.

Steve Bartel: 33:03

Maybe they were silver medalists and that from the rejection reason and the interview notes all really valuable touchpoint context in terms of who this person is in your relationship with them really valuable touchpoint context in terms of who this person is and your relationship with them. Now imagine if they're a good fit for a new role that you just opened up. Ai helps you surface that person, but it also helps draft a highly personalized first draft to that person, referencing the relationship that you have with them. The fact that I know James was chatting with you about this other role that's really similar to this new role we just opened up that we really enjoyed getting to know you as part of the hiring process 18 months ago. We think you'd be a really incredible fit for this new role, for personalized reason, a, b and C, based on your background, maybe even based on the scorecards from back then. And, by the way, we've got another event similar to the one that you attended nine months ago.

James Mackey: 33:59

Wouldn't that feel amazing to a candidate and wouldn't that just be a really great thing for candidates and companies alike. Yeah, yeah, that's awesome. So is that something you're doing now, or when are you going to? When is Jim going to be able to do that?

Steve Bartel: 34:08

We're going to be able to do that in the next three to six months, and so we're just super excited about what the future holds.

Steve Bartel: 34:15

Before we do that, we're heads down on applying the same, like AI ranking and matching, that we've built for AI sourcing, which is, in general, availability to AI ranking for your inbound and also for candidate rediscovery. And that's when I think, like this stuff is all going to come together in a really compelling way for our customers and for the market, because then you're going to have the AI ranking and matching technology threaded across all the important channels, whether that's rediscovery, inbound, external sourcing. Plus, you're going to be able to feed in all those touch points and relationship context data, which you can really only do if AI is part of this end-to-end platform that has that complete source of truth for every relationship and touch point. Feed that into the messaging for each of these channels to make it hyper-personalized. You're only going to have to set up like the criteria, the matching criteria, once, and then AI is just going to go to work for you across all three places instead of having to configure this in different point solutions. Yeah, we're really excited about how it's all coming together.

James Mackey: 35:24

Yeah, that's really awesome. I know we're talking about once a quarter here. We should definitely revisit on what you're doing right now and over the next three to six months as that continues to develop and more lessons learned. And then when you start to, it'll be really cool too when you start to roll it out to customers as well, just to see their feedback and different iterations of the product and how you're of those features and how you're making it better. It's going to be really cool. But yeah, I agree, that's just a totally different level of personalization and that's also just really cool for jim because I know, like with the product suite play, that your team's gone in that direction. It's like all of the systems, all the products essentially working together to pull even more personalization, probably even smoother than I. Would it still work if they have a different ats but use jim for sourcing? Are you still pulling in that data for personalization or is it essentially if they're just using your CRM and ATS?

Steve Bartel: 36:16

Yeah, that's right. I think one of the unique things about GEM is for that specific piece, we're going to make this work whether you use GEM, ATS or another ATS. Now, things will work a little bit better together if you use Apple products for everything. The handoffs and your AirPods just know how to sync a little bit better to your iPhone than maybe an Android and your Apple Mac. But, yeah, no, I think we are very committed to remaining ATS agnostic and knowing that there's a lot of different ATSs out there, and we want to be able to support customers of all sizes and shapes regardless of what ATS they use.

James Mackey: 36:55

So on the ATS side, I know we got to jump in a minute, I have a hard stop coming up here in a few minutes, but are you now so? I knew you were rolling out the ATS in tiers, right Starting with SMB, then pushing up market. I think initially I don't know if that was something from wrong, but how's that going? Are you like, has the ATS like fully rolled out? Are you aggressively selling that product now? Like, where are you in that product life cycle and iterations thereof?

Steve Bartel: 37:20

Yeah, so it's going incredibly quickly. We only started building the ATS a year and a month ago. It's wild and there's so much to build for an ATS, but we already have so much.

Steve Bartel: 37:33

Yeah, it's do it well, but we already have a hundred plus customers using it and it's now in general availability for companies of up to 500 employees.

Steve Bartel: 37:44

So we're moving up market incredibly quickly and we're starting to sign on customers that are in that thousand thousand plus range. Actually, the last three months, I personally went and talked to a lot of our biggest best customers that leverage GEM about becoming design partners, and so we've got this amazing enterprise design partner program where we have commitments from five to 10 upper mid-market smaller enterprise customers, many of them several thousand employees or more, lots of them public companies, where we've, of course, gave them a really compelling offer on the ATS piece, but they've all pre-purchased the ATS as part of a three-year contract and are working hand in hand with us on bringing it up market, which we're super excited about because and I can't talk about these companies just yet, but they're some of the most incredible logos in tech top AI companies, amazing either pre IPO or post IPO tech companies that that anyone would have heard of some of the best brands in tech.

James Mackey: 38:49

Oh, that's amazing. Congrats on that. Uh, yeah, I'm excited to have more of that conversation too, next time we record. Yep, that would be great. I'm excited to have more of that conversation too, next time we record.

Steve Bartel: 38:56

Yeah, that would be great. I'm really excited for it as well.

James Mackey: 38:59

Yeah, for sure. Hey look everyone. Thank you so much for tuning in. We have a lot more episodes coming up for AI, for Hiring series. It's going to be great. We've had some incredible guests thus far and we're going to have even more. And, steve, thanks so much for joining us today. As always, it's a lot of fun and you dropped a lot of great insight for our audience, so I'm really appreciative of that.

Steve Bartel: 39:21

Likewise Great to be here. Thanks, James.

James Mackey: 39:23

All right, thanks, bye.