Get ready to unravel the future of recruitment with Rich Mendis, the ingenious mind behind HireLogic. We'll uncover how HireLogic's innovative product is redefining recruitment process, engineered to automatically extract crucial interview intel and smoothly integrate it into an ATS. The product's natural language user interface allows you to interact, probe, and juxtapose candidates' responses.
Ever wondered how AI regulations could possibly affect candidate ranking? Rich enlightens us about the prospective evolution and ramifications of this technology. As we navigate the uncharted waters of AI in recruitment, he emphasizes the significance of revisiting this subject in times to come. Prepare for an insightful journey through the transformation of the recruitment landscape.
Special mini series recorded with Oleeo at HR Tech 2023 with hosts Ryan Leary, Brian Fink, and Shally Steckerl.
Listen & Subscribe on your favorite platform
Apple | Spotify | Google | Amazon
Visit RecruitingDaily
Twitter @RecruitingDaily
Join the Secret Sourcing Group
Learn more about #HRTX Events
Get ready to unravel the future of recruitment with Rich Mendis, the ingenious mind behind HireLogic. We'll uncover how HireLogic's innovative product is redefining recruitment process, engineered to automatically extract crucial interview intel and smoothly integrate it into an ATS. The product's natural language user interface allows you to interact, probe, and juxtapose candidates' responses.
Ever wondered how AI regulations could possibly affect candidate ranking? Rich enlightens us about the prospective evolution and ramifications of this technology. As we navigate the uncharted waters of AI in recruitment, he emphasizes the significance of revisiting this subject in times to come. Prepare for an insightful journey through the transformation of the recruitment landscape.
Special mini series recorded with Oleeo at HR Tech 2023 with hosts Ryan Leary, Brian Fink, and Shally Steckerl.
Listen & Subscribe on your favorite platform
Apple | Spotify | Google | Amazon
Visit RecruitingDaily
Twitter @RecruitingDaily
Join the Secret Sourcing Group
Learn more about #HRTX Events
Hey everybody, welcome to Recruiting Daily Sources School podcast. We are live at HRTech in Las Vegas. It is me, it is Ryan Leary, it is Shaly Steckel and Ryan's going to introduce our guest from HireLogic. What's going on, Mr Rich Mendez?
Speaker 2:Hey guys, good to be here.
Speaker 1:Rich thanks for coming on we're excited to he didn't match your energy. He said hey, Rich, how you doing? I'm doing great.
Speaker 2:All right.
Speaker 1:Okay, so Rich, you know it is a the afternoon has kind of taken a little bit of a dip. There's still an energy, there's still a charisma that's going on on the floor. Can you tell the people who did not make it out to HRTech, who should have come to HRTech, what the vibe is? What are the feels?
Speaker 2:This is actually my first time and I'm pretty impressed for an HRTech conference. We've got a lot of vendors, different sizes from, all the way from small startups to some of these names which probably everyone in HR recognizes. It's nice to see the mix and good to see all the AI stuff which we're involved in as well.
Speaker 1:First tick on the bingo card, ai stuff. We should have done that.
Speaker 2:We should have had a car.
Speaker 1:There you go.
Speaker 2:We're going to do it tomorrow.
Speaker 1:Let's just draw it here. There's a FedEx in the Luxor. We can make that happen. We could make that happen. All right, the Luxor. Let's just talk about the Luxor, no no, no, no, let's not do that.
Speaker 2:Let's talk about making it happen.
Speaker 1:So real quick. Can you give us a 30,000 foot view about what HireLogic does and why you?
Speaker 2:came out here. Sure, so we actually we were doing AI before ChatGPT was launched, before it was core. So what we do is we'll listen to any live interview, so be it on Zoom Teams, in person, on the phone, and we will extract all the intelligence about that candidate what are their potential strengths, concerns their skills, all those things as well as information about the interview. How much of the job description did the interview cover? Did the interviewer ask any questions? They're not supposed to around age, gender, race, those types of things and push that all into an ATS so that the recruiter or hiring manager doesn't have to go back in and enter any of that information.
Speaker 1:So anyway it says, don't have to enter in any of that information. Is that information accessible via search like a Boolean search string or?
Speaker 2:even better. So the search capability is the ATS. It's available, based on however the ATS works. However, what we've done is we've also put on top of all this interview intelligence a ChatGPT interface, so you can go up and say, hey, of the six sales people we interviewed, which one has the best ability to prospect on their own, which is not something you can really ask in a Boolean search, but this will interpret that you can ask.
Speaker 3:you're just not going to get an answer. You can ask anything you want.
Speaker 1:Who is the best sales person? He's being super humble here.
Speaker 3:He tells you how it works, but you've seen it in action.
Speaker 1:Right, I've seen it in action.
Speaker 3:But like have you seen it? I don't think so. You need to see it.
Speaker 1:I do need to see it. It is sick.
Speaker 3:It's probably the best product software package I've seen this year. That's how praise comes from. We pretty much caught him off in the midstream there, so go ahead and finish your talk. No, no.
Speaker 2:I appreciate that. Yeah, I mean, look, as a startup C-level exec, I've had to interview a ton of people in my career and it's self-serving for me to say this, but this I never want to do another interview without it, because I'm constantly spending time after an interview trying to give the candidate their due by writing the pros and cons and stuff like that, and this does it for me automatically, which?
Speaker 3:really makes a big difference. Compare this to this side-by-side type of thing.
Speaker 2:I think it's a little bit different than that, because you can ask the same questions and then compare the answers and you can do the ratings and stuff. But imagine you've just over the span of, let's say, three weeks, you interviewed 20 candidates for, say, a customer service rep. Now the research shows you remember the first candidate and the last one, but everyone in between is blended together. So imagine being able to go in and ask questions about the ideal candidate.
Speaker 2:Ask questions about the questions. Questions about the questions or responses, and get that information in a natural language UI Right.
Speaker 1:Okay, the natural language UI, and you use customer success as an example, as a jumping off point. Where do you feel that there's the best use case for this utilization right? Is it high volume hiring? Is it front line? What? Where do we?
Speaker 2:go here. So when we launched this we're not that old. We've been in the market for less than a year. Most of the time we spent training that machine learning model to get really good. We spent about 12 to 18 months training the model, but when we launched it we felt like it would be knowledge workers. That would be the most obvious fit right. You were. Quality of hire is important. You're interviewing, asking them lots of questions. Interestingly, we launched a pilot with a job board and the majority of the positions were all service type workers. So think of like a Popeyes Wendy's Chick-fil-A.
Speaker 1:You're making me hungry right now, like at the end of the day. Go ahead. I'm sorry to interrupt you, my friend.
Speaker 2:But, yeah, interviewing these people. And when we talked to them and asked them, hey, what is it about this, that, why are you using hire logic? They said well, the people doing these interviews are not really trained on how to do it, so we like the fact that it can listen for things they shouldn't ask and give them some training and coaching on. So it turned out that, yes, the output was useful, but the use case was more for improving the quality of the interview.
Speaker 3:Of the hiring manager itself, who are?
Speaker 2:very well trained in knowledge workers, but not very well trained in the.
Speaker 3:Supposedly. Yeah, so you know. Right, you would think. Also, I was going to say I don't know if you found this or not, but it makes sense to me. Logically, If you are looking at candidates that have a very defined skill set, it's a little bit easier to, let's just say, look at homogeneous data and create a pattern. Right, okay, there's 20 candidates and they all know how to write in this particular program. So now you're just looking at the quality of their programming and if you can read the code you can kind of sort of tell. But if you've got 20 candidates and you're interviewing for a very soft skill like customer service, there's not like a keyword that you can look for?
Speaker 2:There really isn't, and so you know it's a lot of behavioral interviewing. Hey, have you been in a difficult customer service situation? Tell me how you solved it. Right Now, the response to that is number one not something that an interviewer typically writes good notes about and then goes back into an ATS and puts it in.
Speaker 1:And number two Exclamation points. Awesome a customer service, and it could be any experience. Any experience.
Speaker 3:And from the experience you can determine if that was a good customer experience or not.
Speaker 2:Yeah.
Speaker 3:But there's not a way to like search that Bingo bingo.
Speaker 2:Yeah, exactly, the other thing we saw in that particular industry is the recruiters. So we also work with staffing and recruiting companies, not just the HR side. So a large staffing company that we're talking to has recruiters that hire for industrial labor. So we generate questions for interviews as well, and one of the questions they said is hey, put in a CNC machine operator. One of the other ones was forklift operator, cause we don't. We've never operated forklifts. What do we ask a forklift operator? Right? So we generate questions and the questions are amazing and they basically covered different types of forklifts and stuff like that. So, in addition to asking the right questions, you then get the analysis which you can mine Interesting.
Speaker 1:What kind of questions? Okay, so we talked about forklift operators and we talked about white collar.
Speaker 3:Tell me, about a time you drove over someone's foot.
Speaker 1:I'm like I'm sitting here and, like I'm, my mind is just racing. I'm like like I know all the questions that I should be asking somebody about Python or about R and how these are implications, but like your use case for a forklift operator, it almost-. What's the first thing you do when you get into the forklift? No, but it empowers the recruiter to ask and to give a more personalized experience. Where we're trying to get to with that, do you offer recruiter training on how to be a better recruiter? I'm just wondering from like an answerly kind of perspective.
Speaker 2:Rich. We don't offer as a service, we don't offer that, but we have a dashboard in our product where, if you're managing a team of recruiters, you can go into higher logic and you'll see up to the last call across your team how many interviews were done, what percentage had compliance issues. You can drill in, see the average interview duration and try and pinpoint. You know if this is your best recruiter. How are your newer recruiters doing relative to that?
Speaker 1:first oh, that benchmarking. That's interesting. Now where?
Speaker 3:you're seeing compliance issues. You're actually driving them back to the point of the compliance issue.
Speaker 2:That's right. So go back into the interview and hey, you know why is this person asking a lot of information about race? So this actually came up where we had an executive recruiting company and one of the things that one of the recruiters would like to do is establish a rapport with the individual. So whenever they had an interesting accent, they would say oh, that's an interesting accent, where are you from? Originally Innocent enough, but not something you should ask. And our software picked that up as a potential and we'll bring them back to that point in the conversation, correct?
Speaker 1:Used for training. Oh, I like to use case for training I love how this could be used.
Speaker 1:Like you know, I think about. I'm selfish. I immediately think about my team and how we can up level my organization, right, you know, that's where I think about that first, but then I think about the broader implication as to how this affects RPOs. Right, because RPOs are sent on an assignment to recruit and spin up very quickly. You need them to be excellent at what they do and you need very little training time. But this offers that element that says this is how they're using the system, this is why they're using the system, these are the questions that they're providing. It gets me really excited about the RPO space, and I don't usually get excited about RPOs. Nobody does no.
Speaker 3:Ryan, just because you had a bad experience, my experience is done.
Speaker 1:My experience is done and dusted, all right.
Speaker 3:It's still seven, two going into the seventh.
Speaker 1:My phone says that it's eight to two going into the seventh. Oh, mine does say eight to two now. Okay, so just real quick if you're tuning in Three weeks later. This is Ryan and Brian watching the Phillies and Brains game as we're having this conversation with Rich.
Speaker 1:So it doesn't look like the Braves are gonna pull through, unless there are. So okay, so moving on. If you come back tonight, you deserve to take the series. I appreciate that. So we've talked a little bit about generative AI. We've talked about trends in training. What trends do you see on the horizon for 2024?
Speaker 2:So, first of all, I think what everyone's gonna have to get ready for is AI regulation that's coming. Oh wow, you're the first guest who said that all day. That's right. I think people are not. They're not paying enough attention to it and it's gonna come. And it's gonna come pretty hard in HR because all of the regulations that are being discussed in AI are around discrimination and privacy and that's right in the heart of HR.
Speaker 3:But yeah, there's already lawsuits Already lawsuits, right people.
Speaker 2:We won't name names, but have already been hit here where you can't use AI for video interviews and stuff like that anymore. But what's interesting is, I think there's an opportunity for HR leaders to actually step up in the organizations and be sort of the people who set the governance and policy for how HR, how AI, is used, not just in HR, but across the company.
Speaker 1:Okay, so across the company. What about these state and city and municipal? Well, state and municipal, and there's the problem, yeah, is that an organization can only do so much? And there's the problem, yeah.
Speaker 2:And HR's been dealing with these things for years, right or not, these are, or ostensibly not, where you're not supposed to ask these questions in this state, or you you know this is the holiday laws in this particular state, right? It's? That's why I guess we have some of our ADP paychecks and other companies to help with those things.
Speaker 3:Excellent, yeah, but basically it's a bias question, right? Yeah, it's a matter of bias and any way that you introduce bias into the process.
Speaker 2:That's right.
Speaker 3:You're going to have some kind of repercussion, and in this case it's uncontrolled bias.
Speaker 2:That's right. Because you don't know what the bias is, and so you know one of the laws are being very clear that you can't have AI tools make the decision on behalf of the human. So even when we present data, we're very careful about how we label it. It's a potential concern, as opposed to giving a score or a ranking or something that determines whether a candidate should proceed to the next level.
Speaker 3:Do you think Because there's the hidden bias?
Speaker 2:Yeah, so now, rich, do you think that the companies that are ranking candidates today is that going to have to go away? They're already in violation of the New York City law 144,. Yeah, yep, so basically LinkedIn, I had the LinkedIn rank.
Speaker 3:Yeah, it's results one through 10 and then 2330. That's a rank. Yeah, if they have some, yeah, just think about it.
Speaker 2:Yeah, that's true. I mean, if they're using AI to do matching and ranking, then yeah, that could be subject to that one. It's interesting.
Speaker 1:I don't know. I kind of think about some of the other tools that I've seen about candidate ranking that ranks a candidate as an A candidate or a B candidate or a C candidate, and then undetermined variable, which throws me for a curve ball because I'm like why don't you just rank them as a D or an F?
Speaker 3:But at what point is this over-regulated?
Speaker 2:So great question Because you have to be able to do so. Here's the problem with this regulation, though. Right Is all the research shows that humans are extremely biased, whether it's scanning resumes, ranking candidates, what have you, it's heavily based on Godin state. So now you've got this AI tool that ostensibly maybe improves it a little bit Learn for humans, but you're putting all this regulation on it, so are you actually killing a solution that might help with the problem before it even has a chance to mature?
Speaker 1:All right, that's a good question. That's a question that I think that Rich means that we should have you back on the sourcing school podcast in the next six months to kind of see where things are headed and where things have developed. I'm Brian Fink, this is Ryan Leary, that is Shelley Stackroll and this is Rich from HireLogic. Thanks for joining us on Olio's presentation of the sourcing school podcast. I'll see you in a minute.