Podcast 447: Beyond Skills Assessment in AI Hiring With Mike Hudy, HireVue

Narrator: Welcome to the Workology Podcast A, uh, podcast for the disruptive workplace leader. Join host Jessica Miller Merrill, founder of workology.com as she sits down and gets to the bottom of trends, tools and case studies for the business leader, HR and recruiting professional who is tired of the status quo. Now here's Jessica with this episode of Workology.

Jessica Miller Merrill: Welcome to the Workology Podcast sponsored by ace, the HR Exam and Upskill hr. These are two courses that we offer for certification, prep and recertification, all for HR leaders. You can learn more about these courses over@workology.com the resume has long been the cornerstone of hiring. But in an era where candidates are using AI to write, refine and mass submit applications, many talent acquisition leaders are starting to ask a hard question. Is the resume still a reliable signal of candidate quality? In this episode of the Workology Podcast, we're exploring what comes next and we're joined by Mike Hoodie, Chief science officer at HireVue, to unpack how AI is reshaping not just how candidates apply, but how organizations must evaluate talent to keep up. From the rise of AI polished applications to the growing sea of sass in candidate polls, Mike challenges traditional thinking and introduces a new approach, one that prioritizes validated skills, trustworthy data foundations and transparent AI. We'll also dig into what HR leaders should be asking vendors about AI governance and how to measure the real impact beyond hiring. Plus why trust is becoming the most

Jessica Miller Merrill: important metric in modern talent acquisition.

Jessica Miller Merrill: Before we get into it, I do

Jessica Miller Merrill: want to hear from you.

Jessica Miller Merrill: Please comment podcast over on our pin post on our Instagram account, it's Workology blog. Ask questions, leave comments and make suggestions for future guests. We want to hear from you. Today's guest is Mike Hoodie, Chief science officer at HireVue, where he leads the development of data driven hiring solutions that blend advanced analytics with practical real world application. With more than 20 years of experience in talent analytics, predictive modeling and assessment design, Mike has built a career focused on helping organizations make smarter, more defensible hiring decisions. Rooted in science rather than intuition, his work centers on translating complex data into tools that improve both candidate quality and hiring outcomes. Prior to joining HireVue, Mike held executive leadership roles across HR technology spaces, including the Chief Science Officer at Modern Hire and Executive Vice President of UM Science at Shaker International, where he is also a founding member. He began his career in consulting and workforce analytics, bringing a deep understanding of both organizational needs and candidate behavior. Today, Mike is a leading voice in AI powered hiring, advocating for transparency, trust and a stronger foundation for how organizations evaluate talent in an increasingly automated world. Mike, welcome to the Workology podcast.

Mike Hudy: Thanks, Jessica. Great to be here. And, uh, looking forward to the conversation today.

Jessica Miller Merrill: I am so excited. And you have said that AI has finally killed the resume. I'm doing air quotes here.

Jessica Miller Merrill: I wanted to ask what did you

Jessica Miller Merrill: mean by that and what should replace

Jessica Miller Merrill: it as a primary signal of candidate quality?

Mike Hudy: Yeah, so when we talk about the. And I'm doing air quotes now too. Um, the finally part by background. I'm an industrial organizational psychologist, and in our field, we've done actually more than a century of research on what hiring tools are actually most predictive of success on the job. Like actually tell you something about the person you're hiring and their ultimate success. That research has shown that resumes have next to no prediction. So the final part is like, as a researcher, I've known, our space, has known that resumes are really, really bad. Ah, for a very long time. However, they've been kind of the cornerstone of the hiring process. Um, and now take artificial, um, intelligence, where candidates are now have access to AI tools like ChatGPT, other large language models to help them sharpen the resumes. Some go beyond sharpen to actually write them, tune them to the opening, um, itself, the job description. And so what hiring teams are seeing is there's no more signal in there. They all look the same. It's like, wow, this candidate looks great. Looks just like the job description. So the finally part is the hiring world has come around to what, like us, as a field of researchers have known a very long time. Like, there's very little useful signal on a resume. So it's finally here. I've been talking about it for, uh, a long time. Um, but we're really seeing hiring teams turn away from it. And so what do they turn to? Uh, what we're seeing is that hiring teams are starting to turn to more direct ways of measuring skills, of validating skills, things like assessments and simulations and interviews.

Jessica Miller Merrill: I feel like we have been talking about the, a resume being ineffective for a really long time. And I'm excited that we have technology and tools that can help assist with that. With candidates using AI, as you mentioned, to polish and mass produce applications, how can HR teams maybe cut through that noise? And I mean, this is the secret sauce.

Jessica Miller Merrill: How can we identify true skills and

Jessica Miller Merrill: capability of those candidates applying for those roles?

Mike Hudy: Yeah, you said it. Um, that is the sacred sauce. Uh, and it's moving away from an industry that historically has relied very heavily on pedigree and experiences and again, um, the research, and I'm going to go back to research again, has shown like pedigree and experiences are far from perfect predictors of future success. Very flawed. Um, so it's moving away from that, it's moving away from what candidates, um, are telling you about themselves to them actually showing you something about themselves. So it's really a move from tell me to show me. And what that means is more direct measurement of skills, um, actually validating skills. So, um, assessments, simulations, interviews, where you're putting candidates into situations and directly assessing do you possess the skill, um, or not. Um, and a great byproduct of this is, um, experiences no longer becomes a barrier. Uh, I'm a father of two 20 plus year old children, one of them who is applying for her first jobs now and she just had an interview and they asked her all about her experiences. She's just coming out of school, she doesn't have experiences. But what she has, because she's just coming out of college and she had a lot of practical experiences is she has great skills, evaluation techniques that actually tap into those skills. Uh, and I really don't care where you acquired that skill. I just care do you possess that skill? And I'm super glad that you have it.

Jessica Miller Merrill: I think that is true for not just college students these days is that people have different skills in different areas. I was uh, reading about the number of uh, millennials that actually have side hustles.

Jessica Miller Merrill: So there might be hidden skills that

Jessica Miller Merrill: aren't articulated or have never been articulated on the resume that could be of value to a current employer or a

Mike Hudy: future one for sure. And the other side of the equation is that, um, you know, we're also coming from a world where jobs largely were pretty static. They didn't change what a job looked like one year, five years later. It required the same type of skill sets. What we've seen more recently is that jobs are evolving faster than ever. AI has been a disruptive in many different areas of our world, but jobs in particular. So job new jobs are being created faster than they, they ever have been before. So this model of I need a resume and I'm looking for somebody who's done this exact thing before and I'm checking all the boxes, it just doesn't exist. But what does exist is, you know what, the job has evolved or I have this new job and it requires these eight skills and I need to look at the candidates to say, do they have these skills? I don't know where they got them. Do I really Care where they got them. I just care do they have those skills?

Jessica Miller Merrill: I love that. You've also been really vocal about the importance of data foundation behind AI.

Jessica Miller Merrill: What are the risks when hiring technology

Jessica Miller Merrill: is built on resume data alone?

Mike Hudy: Yeah, um, it's something that isn't really talked about that much. I mean AI is a disruptor. It's disrupting lots of areas including hiring. Um, but what people don't stop to think about, uh, as often as they should is that AI, um, are modeled on data and they're only going to be as good as the data that they're modeled on. Basically it's the foundation of the house. And you need to know how good is that foundation? Much, um, of what's out there in the hiring space right now that's there for hiring teams, um, has been modeled on resumes. And we've already talked a bit about resumes. You have resumes that are basically spin documents that have real, no standardization. Uh, and now you have AI where candidates are uh, embellishing, they're tuning their resumes to the job descriptions. Um, and that's the foundation of many of the AI models that are out there today. And the reason is that they've always been used in hiring and they're plentiful. So there's lots and lots of data and AI needs lots of data to model on. But the old adage is true garbage in, garbage out. So if you're starting with that foundation of something that really never predicted success on the job and now it does so even less so you have a real problem uh, with your AI tool. At HireVue, we take a different approach. We're not using resumes, we're using skill validation data. We've been doing assessments, uh, and interviews for over 20 years. Uh, and so we have lots of data where we directly measure skills of candidates. And then with that we also have lots of data looking at post higher outcomes. Did it work out? Is that person successful? Did they stay in the job? Um, were they a high performer? So that is the foundation on which HireVue, uh, is building its AI model. So much solid, rigorous, ah, research based, um, foundation compared to using resumes.

Jessica Miller Merrill: I like the post hiring validation because that helps make the hiring better moving forward. And I don't see it happening. It's not happening very often in the HR space.

Mike Hudy: That's true. Um, that hr, HR is a little bit unique. It's a really important part of the business but oftentimes doesn't operate in the same way that say, operations would or Finance would, where you're constantly getting feedback. Is it working? You're feeding data in. How are our numbers? Um, HR doesn't go back as often as it should to look at. We're using these methodologies now. We've made this change the process, is it actually quote unquote working? Uh, and that's true of looking at the impact of AI and bringing it to the hiring process as well. Um, the promise of AI is nothing more than promise until you actually document you're getting the outcomes that you're looking for. Now with AI, and not only AI, but hiring in general, the most common and the easiest thing to measure is speed. Like, am I doing it faster? Am I hiring faster? Before I brought AI to my process, it was taking me 12 days to hire somebody. Now I'm down to three. And so we celebrate. We've cut it nine days off. We got a return on investment. Well, problem is that hiring, yeah, you want to go fast. But hiring is ultimately is about getting the right person into the right job. And if you're not measuring, are you getting the right person into the right job? Does speed really matter? Like if we're getting the wrong person in the job and we're doing it really, really fast, speed really doesn't matter. So it's harder to do because you have to look at post hire outcomes. But there's two main sets of data that we look at to look at. Are we getting it right? Uh, one is quality of hire. Whole host of things you can look at from a quality hire perspective. You can look at speed to proficiency, how fast did the person get up to speed. If it's a job that has metrics where you're held accountable, it can be sales conversion rates in sales jobs, it can be net promoter score, customer service scores for service jobs. It can even be supervisor ratings and performance. Or you can ask a hiring manager, would you rehire this person again, some way of looking at quality. And the other thing that we look at is retention. Did the person stay? Um, like you have to keep the person for at least 30, 60, 90, hopefully a year to get a return on that person. So that data is uh, easier to get our hands on and we look at that as well. So when we're looking at skill validation, uh, what we're looking at is it predicting quality of hire and is it helping us improve retention and speed as well. But we have to get it right first for speed to matter.

Jessica Miller Merrill: Going back to the application process, so we're thinking about applying and then how HR Leaders and TA leaders shortlist our uh, candidates in that process because we want to ensure that they're hiring the right candidates and not just people who are polished with, with AI. So how do we rethink that shortlisting process?

Mike Hudy: Yeah, and it really does take a rethinking because what you have with the tools that candidates have and then everything being digital and everything being done online, um, candidates can apply for jobs easier than they ever have. And you have cases where candidates comply to 100 jobs in one day. So what that results in and especially with a lot of the organization, we tend to work with larger organizations that do hiring at scale is there are just a flood of applications and flood of candidates and there's no way that you can use a process where recruiters and hiring managers touch every single one of those candidates. So you really do need to rethink it and you need to rethink it with automation, but automation with science as the foundation. So those things and so what a process might look like today that takes advantage of science based automation is as a candidate I might apply for a job, I apply for that job and I'm going to get a phone call and uh, I am going to have an AI led interview, uh, where that AI interview is going to feel like a very natural conversation. We're going to tell the candidates it's AI, but it's just trying to understand does the candidate even qualify for the job. The basic qualifications. If the candidate does meet those qualifications, they automatically move to the next step of the process seamlessly, which would be say an assessment, a skill validation where or tapping in and measuring directly the skills that a person needs to be successful in that job. From there, if they're successful, we see the system sees that the person has the skills that they need. They might again auto advance right into an on demand recorded interview. In that recorded interview the person, the candidate can take it on their own convenience. And we have a way to actually score that interview using artificial intelligence. So you're moving through automation, you're tapping into skills and you really haven't had to touch a candidate. And you're whittling down the number of candidates to those that are most qualified. And then from there you have a more qualified candidate pool that again through automation they could then self schedule into say a live interview where now I'm going to talk to a recruiter or I'm going to talk to a hiring manager. But what we've done is we've made it easy for the candidate. They've gotten to show themselves by demonstrating their skills. But we've got it down to a manageable number of candidates then that hiring teams can then, um, work with more directly.

Jessica Miller Merrill: Yeah.

Jessica Miller Merrill: And we've uncovered those hidden skills or talents that maybe they didn't articulate very well on, on the resume to make sure that they would work in, in our organization.

Mike Hudy: Yeah, absolutely. Might not have shown up on the resume because they chose not to put it on their resume, but they have that skill. Uh, and then by directly measuring it, you uncover that and you get what it does is also opens up the aperture when you're stuck looking at pedigree and experience. Um, you are looking at a limited set of candidates. That tends to introduce bias into a process as well. You open up the aperture when you're open to just, I just want skills. I don't need to know about what company you work for or where your degree is from or even if you have a degree. I just care. Do you have these 10 critical skills that people need to be successful in the job?

Jessica Miller Merrill: Let's take a reset. This is Jessica Miller Merrill, and you are listening to the Workology podcast powered by Ace the HR Exam and Upskill hr. Today I'm talking with Mike Hoodie, Chief science officer at HireVue, about AI in hiring.

Jessica Miller Merrill: There is a lot of concern right now around AI bias and legal risk.

Jessica Miller Merrill: What do you think organizations should be

Jessica Miller Merrill: asking vendors to ensure that their AI is defensible and compliant?

Mike Hudy: Yeah, this one's interesting to me because of the bias piece. And before I look forward to and talk about AI, I want to like, we need to be as a profession thinking, um, about the processes that we're replacing. The processes we're replacing are broken and full of bias to begin with. That's why there's so much concern about bias. So when we think about resumes, resumes are biased in a number of different ways. One is because you're looking at pedigree and experiences. There's institutional bias built into resumes. People have had differential opportunities to education into experiences that show up front and center on a resume. Second, they're not standardized. Like, they're not an apples to apples comparison. There's no real. There's a general structure. But what you put on a resume is kind of very subjective and candidates can approach it in different ways. When you think about human, ah, decision makers and interviews, like human beings are subject to unconscious bias. So like, what we're trying to do with tools is hire better people. Yes. But we're also trying to do it More fair to begin with. So we need to first look back to say, you know what, what we were doing in the past wasn't great. We need to be thinking about bias. Um, there's bias in our processes today. How can AI help? Well, AI can actually help if developed right. In that with AI and using skill validation, direct skill validation, you get more consistency. It's standardized. If we have one opening and 10,000 people apply to that opening, all 10,000 are going to be seen, all 10,000 are going to be evaluated the same way. So if done right, and that's the if part, it sets up this more consistent and you drive bias out. And that's what we've seen with a lot of our customers now in terms of developing that if developed right, what does if develop right looks like? And your question about what should hiring teams be asking?

Jessica Miller Merrill: Thank you, uh, for that. And I think this is going to be incredibly helpful for practitioners because AI is in, I don't know, over 90% probably of existing HR technology now. So what's in there? How does it work? Do you have access to the explainability and uh, then the reporting? I think that's going to help uh, us go a long way as practitioners in selection.

Jessica Miller Merrill: Many HR teams are asking to prove

Jessica Miller Merrill: return on investment or ROI on AI investments. How can organizations connect AI driven hiring

Jessica Miller Merrill: decisions to these post hire outcomes that

Jessica Miller Merrill: we talked about, like performance and retention?

Mike Hudy: Yeah, it's really important because uh, AI has a lot of promise, but it is only promise unless you actually document it solving the business problem you're trying to solve. And with hiring, it's really fundamentally about getting the right person into the right job. Um, today what most teams focus on from a terms of evaluating the impact of artificial intelligence into a hiring process is on speed, which is important to do that. And many teams are using artificial intelligence to drive automation as well they should. Uh, so for instance, getting a hiring process down from 12 days to three days, that's great, that's faster. But it doesn't answer the fundamental question is what are we doing to get it right? Um, so speed without quality really doesn't matter. You have to have quality first. Are we getting the right person and then are we doing it fast? So what hiring teams need to be doing and evaluating and looking at the ROI of AI is looking at are we getting the right person, the right person? We look at it in two broad categories. One is quality of hire. Is the person good at the job? Are they effective? Are they a good fit for that job? And there's different ways of measuring that. Um, there are things like speed to proficiency, there are things that metrics, um, you might hold somebody accountable to. In a job, say a sales job, it's your sales numbers, your sales conversion rates. In a customer service job, it might be your net promoter score. Um, you can also just simply ask hiring managers, 90 days in, would you rehire this person were they a good hire? Some sense of quality of hire data to get at. Are we getting it right? And the other bucket of measures that we look at are retention. Is the person staying on the job. If you're hiring a person, they're leaving within 30 days, that's not a great hire and you should be able to do a better job of evaluating that person against the job. Uh, and so we tend to look at metrics like 30, 60, 90 day in very entry level, early career jobs and maybe six months to a year out, uh, retention rates and how we're moving the needle with um, our AI solutions.

Jessica Miller Merrill: So what I hear you saying is it's really not a short term ROI. It sounds like a year, 18 months, 24 months for us to sort of understand trends. Is this doing, is it helping? Is it, is there a return on investment? Um, when it comes to artificial intelligence, it's not instant.

Mike Hudy: Oftentimes it's not instant. Um, the, the more closer to instant would be if you implemented like a 30 day um, hiring manager survey, that would you rehire. But again that's only at 30 days. We know what you said about hiring a person at 30 days might change at six months. Um, but for jobs that have high turnover rates, where candidates, you have a high turnover rate within 30 days, you can know a little bit quicker then. But yeah, by and large if you think about it, you hire somebody, they have to be trained, they have to go through the learning curve and then be proficient. A lot of jobs, it is six months to 12 months until they're proficient. So you need to wait that long to say, hey, did it work or not? Did we get it right or not?

Jessica Miller Merrill: I think that's important to call out, um, because a lot of times people want instant. I mean we all want instant results, right? And to understand the bigger picture, was this a good investment? But, uh, not only do we need to understand how AI is being used and then our processes need to be in line, but then we have to have those checks and balances after the

Jessica Miller Merrill: fact to learn because we might not

Jessica Miller Merrill: get it right the first or the second or the third time. So it's going to take Time to really understand the process and who's the

Jessica Miller Merrill: best fit for the role.

Mike Hudy: Yeah, that's right. Checks and balances. And thinking about it as like an ongoing feedback loop. You learn from the data, um, you inform the models based on that. If it's not getting it right at the level you want, you update your models and then it's a continuous loop. Um, it should never be set it, forget it, set it and forget it. It should always be that post, um, hire data feeding back into the, into the closed loop.

Jessica Miller Merrill: You've mentioned uh, that many companies are at different stages of AI adoption and we're seeing that in the news. What does a realistic maturity curve maybe look like for HR teams who are just getting started in AI in hiring?

Mike Hudy: Yeah. When I think about the um, AI maturity curve, just to not overcomplicate it, I think of it in and we see it with our customers in three main phases. The first phase, kind of the entry level adoption is going to be bringing AI for automation. So we look at our hiring process and there are repeatable administrative tasks that we do that we can take AI, apply it to that and take um, resources out. We don't need as many people to do those administrative tasks and then it speeds it up at the speed of hire. We hire people quicker. So for instance, um, not too long ago scheduling interviews was a very manual process comparing calendars. Now there's lots of great tools including what HireVue offers to automate that process of scheduling interviews and taking out hours and person months out of uh, the hiring process. So that's phase one. I think most organizations in terms of applying um, AI are using that. You quoted 90% and we've seen very similar stats. Around 90% of hiring teams are using uh, AI of some sort in their um, hiring process. But phase one is automation. Phase two, now I'm going beyond automation and what I call it is signal extraction. Signal extraction is going to be a way to pull information from candidates that human decision makers are still going to use. So for instance I'm doing ah, ah, an on demand interview or even a live interview. And AI can be used to um, give a transcription of that interview so I don't have to go back and necessarily watch it. I have a transcription that I can go through faster. We can also in HireVue also provides AI summaries, not an evaluation but a summary. So If I have 100 candidates to evaluate, I'm getting job relevant information in front of the human evaluator in a more efficient manner. So that's like skill extraction. I'm not evaluating the candidate. I'm just supplementing the human. I'm helping. It's augmenting the human decision maker. Um, and so that's kind of the, the next step in the journey. And then kind of the, the ultimate is when you're using artificial intelligence to help you with skill validation. Now I'm starting to evaluate, I'm directly measuring those skills and AI is giving you evaluation of that candidate against those skills. The human decision maker is still making the decision but candidates are kind of prioritized into tiers and kind of best fit to least fit that. Ah, hiring teams can then go to a shorter list. So that's the journey. But then like our most advanced on the maturity curve, customers are using all three of those. You're still using automation. They're Susie, in step one they're using signal extraction and they're using skill validation. But that's typically um, the journey that we've seen uh, going through the kind of the maturity curve.

Jessica Miller Merrill: Thank you for breaking it down into

Jessica Miller Merrill: simple steps because it's, it doesn't feel that way when you're beginning your journey.

Mike Hudy: Yeah. When you're in it. Yeah. And you're in the midst of it for sure.

Jessica Miller Merrill: Yeah.

Jessica Miller Merrill: So let's maybe look ahead and talk about what hiring looks like in a world where it's skills focused, not resumes. And, and those skills are the primary currency. How should HR leaders start preparing for this now?

Mike Hudy: Yeah, I think you said it in that the first step and what we've seen, just about every organization speaking uh, of journeys and curves are somewhere in their skills based hiring journey. Um, and that goes back to something I mentioned earlier about um, can't look at pedigree and experience anymore. Jobs are changing fast. And so as you said skills really are the new currency. So kind of getting that skills based hiring um, processes jobs defined in terms of skills as opposed to what uh, are traditionally show up in job descriptions. Then where we are starting to see things going and will continue is now that we have skills as our currency it opens up a world where we're no longer thinking just about external candidates where skills is the currency. We should be looking at external and our internal talent and thinking about mobility. Um, and we start to think about not um, talent acquisition and talent management but just a talent function where we're moving talent around the organization and we're considering both internal and external candidates and the process. These look very similar and they're based on science but they're based and Rooted in uh, skills from there. Um, and now this is yet to happen. But again I think it's where things should go and eventually will go. The world of hiring the foundation, you had resumes and then another foundational element of hiring was the job requisition. And so that's another thing that I wish would just die is the job requisition because it just sets up this grossly inefficient process where I have an opening, I put my opening out there, we have all these candidates come and apply and I have this funnel where 100 candidates start and I'm hiring one or two of them and 98 great candidates who have all these different skills get the thanks but no thanks. We found better qualified candidates. It's because we have this requisition based model again with skills as a currency. What we should and hopefully will be moving towards is understanding the individual's skill profile. Every candidate comes to an organization, they get seen, their skills get evaluated and then instead of uh, being on a wreck, it's pointing them to different opportunities based on your Skills profile. Here's 15 different jobs that our company has that you might find a great fit. Internal talent for mobility, same thing. Uh, we don't just open up a rec. So that's, that's again where I think the future is going. Um, it'll, it's, it's not easy, it wasn't easy to let go of the resume and um, it's not going to be easy to let go of requisitions because we have lots of processes built around requisitions and we have our applicant track and tracking system that is built to serve up and manage requisitions. But in terms of where the talent space needs to go, that's where it needs to go.

Jessica Miller Merrill: Well, thank you Mike for, for taking the time.

Jessica Miller Merrill: I feel like for a lot of us, uh, our roles are rooted in these processes and in these kind of old ways. So it sounds like we have a lot of unlearning to be, to be doing and relearning of our own, uh, in the years to come. So really appreciate your time to chat with us.

Mike Hudy: Yeah, absolutely. Enjoyed it. Yeah.

Jessica Miller Merrill: Where can they go to learn more about you and the work that you and Hirevue are doing?

Mike Hudy: Best, uh, place to go is to of course our website, lots, uh, of great information on HireVue's website, HireVue.com and then me personally. You, um, can always hit my LinkedIn profile. Uh, there's not too many Mike Hooties out there, so if you search on me on LinkedIn, you're probably gonna find just one my cootie. Uh, out on LinkedIn.

Jessica Miller Merrill: We'll include a link to Mike's LinkedIn profile as well as the new global AI and hiring report available by HireVue. So make sure to look at the transcript of, um, this podcast interview. Thanks again, Mike.

Mike Hudy: Thank you.

Jessica Miller Merrill: As AI continues to evolve, the organizations that win won't just be the ones who adopt it fastest, but the ones who use it the most.

Jessica Miller Merrill: Thoughtfully.

Jessica Miller Merrill: Mike reminds us the hiring better starts with better data, clearer intent, and a commitment to balancing technology with human judgment. If you're thinking about how your organization evaluates talent in a world beyond the resume, this conversation is one you need to be a part of. Let's shape it together. Be sure to check out the show notes for the links to HireVue's latest AI in Hiring report and additional resources to support your own AI journey. Thank you for listening and we'll see you next time. If you enjoyed this episode, be sure to subscribe, leave a review, and share it with another HR leader. This is the Workology Podcast. Thank you for joining us. It is sponsored by ace, the HR Exam and Upskill. Uh, HR Workology has a learning platform for HR certification and recertification as well as manager training. You can also check out our new tech marketplace@www.marketplace.workology.com. this podcast is for the disruptive workplace leader who's tired of the status quo. My name is Jessica Miller Merrill. Until next time, listen to Workology's podcast on all your regular podcast outlets and head on over to workology.com for more great information, resources, articles and research. We'll see you next time.