Welcome to the Workology Podcast, a podcast for the disruptive workplace leader. Join host Jessica Miller-Merrell, founding father of Workology.com, as she sits down and gets to the underside of trends, tools, and case studies for the business leader, HR, and skilled who’s bored with the establishment. Now, here’s Jessica with this episode of Workology.
Jessica Miller-Merrell: Welcome to the Workology Podcast, sponsored by Ace the HR Exam and UpSkill HR. These are two courses that we provide for certification prep and recertification, all for HR leaders. You’ll be able to learn more about these courses over at Workology.com.
The resume has long been the cornerstone of hiring, but in an era where candidates are using AI to jot down, refine, and mass-submit applications, many talent acquisition leaders are beginning to ask a tough query: Is the resume still a reliable signal of candidate quality?
On this episode of the Workology Podcast, we’re exploring what comes next. And we’re joined by Mike Hudy to unpack how AI is reshaping not only how candidates apply, but how organizations must evaluate talent to maintain up. From the rise of AI-polished applications to the growing sea of sameness in candidate pools, Mike challenges traditional considering and introduces a brand new approach—one which prioritizes validated skills, trustworthy data foundations, and transparent AI.
We’ll also dig into what HR leaders needs to be asking vendors about AI governance and tips on how to measure the actual impact beyond hiring, plus why trust is becoming an important metric in modern talent acquisition. Before we get into it, I do wish to hear from you. Please comment “podcast” over on our pinned post on our Instagram account; it’s @workologyblog. Ask questions, leave comments, and make suggestions for future guests. We would like to listen to from you.
Today’s guest is Mike Hudy, Chief Science Officer at Hirevue, where he leads the event of data-driven hiring solutions that mix advanced analytics with practical, real-world application. With greater than 20 years of experience in talent analytics, predictive modeling, and assessment design, Mike has built a profession focused on helping organizations make smarter, more defensible hiring decisions rooted in science fairly than intuition. His work centers on translating complex data into tools that improve each 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 Science at Shaker International, where he can be a founding member. He began his profession in consulting and workforce analytics, bringing a deep understanding of each organizational needs and candidate behavior. Today, Mike is a number one voice in AI-powered hiring, advocating for transparency, trust, and a stronger foundation for the way organizations evaluate talent in an increasingly automated world. Mike, welcome to the Workology Podcast.
Mike Hudy: Thanks, Jessica. Great to be here, and searching forward to the conversation today.
Why AI May Have Finally “Killed the Resume”
Jessica Miller-Merrell: I’m so excited. And you have got said that AI has finally “killed the resume”—I’m doing air quotes here. I desired to ask, what did you mean by that, and what should replace it as a primary signal of candidate quality?
Mike Hudy: Yeah. So, once we talk in regards to the—and I’m doing air quotes now, too—the “finally” part… by background, I’m an industrial-organizational psychologist. And in our field, we’ve done actually greater than a century of research on what hiring tools are literally most predictive of success on the job; like, actually inform you something in regards to the person you’re hiring and their ultimate success. That research has shown that resumes have next to no prediction.
So, the “finally” part is like, as a researcher, I’ve known—our space has known—that resumes are really, really bad for a really very long time. Nonetheless, they’ve been sort of the cornerstone of the hiring process. And now, take artificial intelligence, where candidates now have access to AI tools like ChatGPT and other large language models to assist them sharpen their resumes. Some transcend “sharpen” to really write them and tune them to the opening itself—the job description.
And so, what hiring teams are seeing is there’s no more signal in there. All of them look the identical. It’s like, “Wow, this candidate looks great; it looks identical to the job description.” So, the “finally” part is that the hiring world has come around to what we, as a field of researchers, have known for a really very long time: there’s little or no useful signal on a resume. So, it’s finally here. I’ve been talking about it for a very long time, but we’re really seeing hiring teams turn away from it. And so, what do they turn to? What we’re seeing is that hiring teams are beginning to turn to more direct ways of measuring skills, of validating skills—things like assessments, simulations, and interviews.
Jessica Miller-Merrell: I feel like we now have been talking in regards to the resume being ineffective for a very very long time. And I’m excited that we now have technology and tools that may help assist with that. With candidates using AI, as you mentioned, to shine and mass-produce applications, how can HR teams perhaps cut through that noise? And I mean, that is the key sauce: How can we discover true skills and the aptitude of those candidates applying for those roles?
Mike Hudy: Yeah, you said it. That’s the secret sauce. And it’s moving away from an industry that historically has relied very heavily on pedigree and experiences. And again, the research—and I’m going to return to research again—has shown like, pedigree and experiences are removed from perfect predictors of future success; they’re very flawed.
So, it’s moving away from that. It’s moving away from what candidates 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 which means is more direct measurement of skills—actually validating skills. So, assessments, simulations, interviews where you’re putting candidates into situations and directly assessing: Do you possess a skill or not?
A fantastic byproduct of that is that have now not becomes a barrier. I’m a father of two 20-plus-year-old children, certainly one of whom is applying for her first jobs now. And he or she just had an interview and so they asked her all about her experiences. She’s just coming out of college; she doesn’t have experiences. But what she has, because she’s just coming out of school and he or she had a variety of practical experiences, is she has great skills. So, evaluation techniques that really tap into those skills… and I actually don’t care where you acquire that skill; I just care: Do you possess that skill? And I’m super glad that you have got it.
Jessica Miller-Merrell: I feel that’s true for not only college students lately; it’s that individuals have different skills in several areas. I used to be reading in regards to the variety of millennials that really have side hustles. So, there may be hidden skills that aren’t articulated, or have never been articulated, on the resume that may very well be of value to a current employer or a future one.
Mike Hudy: Of course. And the opposite side of the equation is that, you understand, we’re also coming from a world where jobs largely were pretty static. They didn’t change. What a job looked like one yr, five years later, it required the identical kind of skill sets. What we’ve seen more recently is that jobs are evolving faster than ever. AI has been a disruptor in many alternative areas of our world, but jobs specifically. So, recent jobs are being created faster than they ever have been before.
So, this model of “I want a resume and I’m on the lookout for someone who’s done this exact thing before and I’m checking all of the boxes,” it just doesn’t exist. But what does exist is, “You recognize what? The job has evolved,” or “I actually have this recent job and it requires these eight skills.” And I want to have a look at the candidates to say: Have they got these skills? I don’t know where they got them. Do I actually care where they got them? I just care: Have they got those skills?
Constructing a Skills-Based Hiring Strategy Beyond Candidate Pedigree
Jessica Miller-Merrell: I like that. You’ve also been really vocal in regards to the importance of the information foundation behind AI. What are the risks when hiring technology is built on resume data alone?
Mike Hudy: Yeah, it’s something that isn’t really talked about that much. I mean, AI is a disruptor. It’s disrupting a number of areas, including hiring. But what people don’t stop to take into consideration as often as they need to is that AI is modeled on data, and it’s only going to be nearly as good as the information that it’s modeled on. Mainly, it’s the inspiration of the home, and you might want to understand how good that foundation is.
Much of what’s on the market within the hiring space straight away that’s there for hiring teams has been modeled on resumes. And we’ve already talked a bit about resumes—you have got resumes which are mainly spin documents which have really no standardization. And now you have got AI where candidates are embellishing, they’re tuning their resumes to the job descriptions. And that’s the inspiration of lots of the AI models which are on the market today. And the rationale is that they’ve at all times been utilized in hiring and so they’re plentiful. So, there’s lots and a number of data, and AI needs a number of data to model on.
However the old adage is true: garbage in, garbage out. So, for those who’re starting with that foundation of something that actually never predicted success on the job, and now it does so even less so, you have got an actual problem together with your AI tool. At Hirevue, we take a unique approach. We’re not using resumes. We’re using skill validation data. We’ve been doing assessments and interviews for over 20 years. And so, we now have a number of data where we directly measure skills of candidates. After which with that, we even have a number of data taking a look at post-hire outcomes. Did it work out? Is that person successful? Did they stay within the job? Were they a high performer? So, that’s the inspiration on which Hirevue is constructing its AI models. So, a far more solid, rigorous, research-based foundation in comparison with using resumes.
Jessica Miller-Merrell: I just like the post-hiring validation because that helps make the hiring higher moving forward. And I don’t see it happening—it’s not happening fairly often within the HR space.
Mike Hudy: That’s true that HR is slightly bit unique. It’s a very vital a part of the business, but oftentimes doesn’t operate in the identical way that, say, operations would or finance would, where you’re always getting feedback. Is it working? You’re feeding data in—how are our numbers? HR doesn’t return as often because it should to have a look at: We’re using these methodologies now, we’ve made this variation to the method, is it actually, quote-unquote, “working”?
And that’s true of taking a look at the impact of AI and bringing it to the hiring process as well. The promise of AI is nothing greater than a promise until you truly document you’re getting the outcomes that you just’re on the lookout for. Now, with AI—and never only AI, but hiring basically—probably the most common and the simplest 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 rent someone. Now I’m right down to three. And so, we rejoice: “We’ve cut nine days off! We got a return on investment!”
Well, the issue is that hiring—yeah, you should go fast, but hiring ultimately is about getting the appropriate person into the appropriate job. And for those who’re not measuring whether you’re getting the appropriate person into the appropriate job, does speed really matter? Like, if we’re getting the flawed person within the job and we’re doing it really, really fast, speed really doesn’t matter. So, it’s harder to do because you have got to have a look at post-hire outcomes.
But there are two fundamental sets of information that we take a look at to see if we’re getting it right. One is quality of hire. There’s an entire host of things you may take a look at from a top quality of hire perspective. You’ll be able to take a look at speed to proficiency: How briskly did the person get in control? If it’s a job that has metrics where you’re held accountable, it will probably be sales conversion rates in sales jobs. It will probably be Net Promoter Rating or customer support scores for service jobs. It will probably even be supervisor rankings of performance, or you may ask a hiring manager: “Would you rehire this person again?” A way of taking a look at quality.
And the opposite thing that we take a look at is retention. Did the person stay? Like, you have got to maintain the person for at the least 30, 60, 90—hopefully a yr—to get a return on that person. So, that data is simpler to get our hands on, and we take a look at that as well. So, once we’re taking a look at skill validation, what we’re taking a look at is: Is it predicting quality of hire? And is it helping us improve retention and speed as well? But we now have to get it right first for speed to matter.
How AI Hiring Technology Is Changing Candidate Screening and Shortlisting
Jessica Miller-Merrell: Going back to the appliance process… so, we’re fascinated by applying after which how HR leaders and TA leaders shortlist our candidates in that process. Because we wish to be certain that they’re hiring the appropriate candidates and not only people who find themselves polished with AI. So, how will we rethink that shortlisting process?
Mike Hudy: Yeah, and it really does take a rethinking because what you have got with the tools that candidates have, after which every little thing being digital and every little thing being done online, candidates can apply for jobs easier than they ever have. And you have got cases where candidates can apply to 100 jobs in someday. So, what that ends in—and particularly with a variety of the organizations we are likely to work with, larger organizations that do hiring at scale—is only a flood of applications and a flood of candidates.
And there’s no way that you would be able to use a process where recruiters and hiring managers touch each certainly one of those candidates. So, you actually do must rethink it, and you might want to rethink it with automation, but automation with science as the inspiration. And so, what a process might seem like today that takes advantage of science-based automation is: as a candidate, I’d apply for a job. I apply for that job and I’m going to get a phone call, and I’m going to have an AI-led interview where that AI interview goes to feel like a really natural conversation.
We’re going to inform the candidates it’s AI, however it’s just trying to grasp: does the candidate even qualify for the job, the essential qualifications? If the candidate does meet those qualifications, they robotically move to the subsequent step of the method seamlessly, which could be, say, an assessment—a skill validation where we’re tapping in and measuring directly the talents that an individual needs to achieve success in that job.
From there, in the event that they’re successful—we see the system sees that the person has the talents that they need—they may, again, auto-advance right into an on-demand recorded interview. And that recorded interview, the candidate can take it at their very own convenience. And we now have a method to actually rating that interview using artificial intelligence. So, you’re moving through automation, you’re tapping into skills, and you actually haven’t had to the touch a candidate, and also you’re whittling down the variety of candidates to those which are most qualified.
After which from there, you have got a more qualified candidate pool that, again, through automation, could then self-schedule into, say, a live interview where now I’m going to confer with a recruiter or I’m going to confer with a hiring manager. But what we’ve done is we’ve made it easy for the candidate. They’ve gotten to indicate themselves by demonstrating their skills, but we’ve got it right down to a manageable variety of candidates then that hiring teams can then work with more directly.
Jessica Miller-Merrell: Yeah, and we’ve uncovered those hidden skills or talents that perhaps they didn’t articulate thoroughly on the resume to ensure that they’d work in our organization.
Mike Hudy: Yeah, absolutely. It may not have shown up on the resume because they selected not to place it on their resume, but they’ve that skill. After which by directly measuring it, you uncover that. And what it does can be opens up the aperture. Whenever you’re stuck taking a look at pedigree and experience, you’re looking at a limited set of candidates that tends to introduce bias right into a process as well. You open up the aperture once you’re open to only… I just want skills. I don’t must find out about what company you’re employed for or where your degree is from, or even when you have got a level. I just care: Do you have got these 10 critical skills that individuals should be successful within the job?
Jessica Miller-Merrell: Let’s take a reset. That is Jessica Miller-Merrell and you’re listening to the Workology Podcast, powered by Ace the HR Exam and Upskill HR. Today I’m talking with Mike Hudy, Chief Science Officer at Hirevue, about AI in hiring.
AI Bias, Compliance, and Governance in Modern Recruiting
There’s a variety of concern straight away around AI bias and legal risk. What do you think that organizations needs to be asking vendors to be certain that their AI is defensible and compliant?
Mike Hudy: Yeah, this one’s interesting to me due to the bias piece. And before I sit up for and speak about AI, I would like to… like, we should be, as a career, fascinated by the processes that we’re replacing. The processes we’re replacing are broken and filled with bias to start with. That’s why there’s a lot concern about bias.
So, once we take into consideration resumes, resumes are biased in various alternative ways. One is since you’re taking a look at pedigree and experiences, there’s institutional bias built into resumes. People have had differential opportunities to education and to 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 place on a resume is sort of very subjective and candidates can approach it in alternative ways.
When you concentrate on human decision-makers and interviews… human beings are subject to unconscious bias. So, what we’re attempting to do with tools is hire higher people, yes, but we’re also attempting to do it more fairly to start with. So, we’d like to first look back to say, “You recognize what? What we were doing up to now wasn’t great.” We should be fascinated by bias; 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 now 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 identical way. So, if done right—and that’s the “if” part—it sets up this more consistent process and also you drive bias out. And that’s what we’ve seen with a variety of our customers. Now, when it comes to developing that, if developed right, what does “developed right” seem like and your query about what should hiring teams be asking…
Jessica Miller-Merrell: Thanks for that. And I feel that 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 got access to the explainability? After which the reporting—I feel that’s going to assist us go a great distance as practitioners in selection.
Measuring ROI and the Way forward for AI-Powered Talent Acquisition
Many HR teams are asking to prove Return on Investment, or ROI, on AI investments. How can organizations connect AI-driven hiring decisions to those post-hire outcomes that we talked about, like performance and retention?
Mike Hudy: Yeah, it’s really vital because AI has a variety of promise, however it is just a promise unless you truly document it solving the business problem you’re trying to resolve. And with hiring, it’s really fundamentally about getting the appropriate person into the appropriate job. Today, what most teams concentrate on when it comes to evaluating the impact of artificial intelligence right into a hiring process is on speed, which is vital to do. And plenty of teams are using artificial intelligence to drive automation as well—as they need to.
So, as an illustration, getting a hiring process down from 12 days to a few days… that’s great. That’s faster, however it doesn’t answer the basic query: what are we doing to get it right? So, speed without quality really doesn’t matter. You’ve got to have quality first: Are we getting the appropriate person? After which, are we doing it fast?
So, what hiring teams should be doing in evaluating and searching on the ROI of AI is taking a look at: are we getting the appropriate person? The “right person” we take a look at in two broad categories. One is quality of hire. Is the person good on the job? Are they effective? Are they a superb fit for that job? And there’s alternative ways of measuring that. There are things like speed to proficiency. There are metrics you would possibly hold someone accountable to in a job—say, a sales job, it’s your sales numbers, your sales conversion rates. In a customer support job, it may be your Net Promoter Rating. You may also just simply ask hiring managers 90 days in: “Would you rehire this person? Were they a superb hire?” Some sense of quality of hire data to get at: are we getting it right?
And the opposite bucket of measures that we take a look at are retention. Is the person staying on the job? For those who’re hiring an individual and so they’re leaving inside 30 days, that’s not an excellent hire. And it is best to have the option to do a greater job of evaluating that person against the job. And so, we tend to have a look at metrics like 30, 60, 90-day retention in very entry-level early profession jobs, and perhaps six months to a yr out retention rates and the way we’re moving the needle with our AI solutions.
Jessica Miller-Merrell: So, what I hear you saying is it’s really not a short-term ROI. It seems like a yr, 18 months, 24 months for us to type of understand trends. Is that this doing—is it helping? Is there a Return on Investment in relation to artificial intelligence? It’s not fast.
Mike Hudy: Oftentimes, it’s not fast. The closer to “fast” could be for those who implemented like a 30-day hiring manager survey that asks “Would you rehire?” But again, that’s only at 30 days. You recognize, what you said about hiring an individual at 30 days might change at six months. But for jobs which have high turnover rates where candidates… if you have got a high turnover rate inside 30 days, you may know slightly bit quicker then. But yeah, by and huge, for those who give it some thought, you hire someone, they should be trained, they should undergo the educational curve after which be proficient. Numerous jobs, it’s six months to 12 months until they’re proficient. So, you might want to wait that long to say, “Hey, did it work or not? Did we get it right or not?”
Jessica Miller-Merrell: I feel that’s vital to call out because a variety of times people want fast—I mean, all of us want fast results, right? And to grasp the larger picture, was this a superb investment? But not only do we’d like to grasp how AI is getting used after which our processes should be in line, but then we now have to have those checks and balances after the actual fact to learn, because we may not get it right the primary, or the second, or the third time. So, it’s going to take time to actually understand the method and who’s one of the best fit for the role.
Mike Hudy: Yeah, that’s right. Checks and balances and fascinated by it as like an ongoing feedback loop. You learn from the information; you inform the models based on that. If it’s not getting it right at the extent you would like, you update your models after which it’s a continuous loop. It should never be “set it and forget it.” It should at all times be getting that post-hire data feeding back into the closed loop.
Jessica Miller-Merrell: You’ve mentioned that many corporations are at different stages of AI adoption, and we’re seeing that within the news. What does a sensible maturity curve perhaps seem like for HR teams who are only getting began in AI in hiring?
Mike Hudy: Yeah, when I feel in regards to the AI maturity curve—simply to not overcomplicate it—I feel of it, and we see it with our customers, in three fundamental phases.
The primary phase—sort of the entry-level adoption—goes to be bringing AI for automation. So, we take a look at our hiring process and there are repeatable administrative tasks that we do this we are able to take AI, apply it to that, and take resources out. We don’t need as many individuals to do those administrative tasks. After which it speeds it up—the speed of hire. We hire people quicker. So, as an illustration, not too way back, scheduling interviews was a really manual process comparing calendars. Now there’s a number of great tools, including what Hirevue offers, to automate that strategy of scheduling interviews and taking hours and person-months out of the hiring process. So, that’s phase one. I feel most organizations when it comes to applying AI are using that. You quoted 90%, and we’ve seen very similar stats around 90% of hiring teams are using AI of some sort of their hiring process.
But phase one is automation. Phase two—now I’m going beyond automation and what I call it’s signal extraction. Signal extraction goes to be a method to pull information from candidates that human decision-makers are still going to make use of. So, as an illustration, I’m doing an on-demand interview or perhaps a live interview and AI might be used to present a transcription of that interview. So, I don’t should return and necessarily watch it; I actually have a transcription that I can undergo faster. We, at Hirevue, also provide AI summaries—not an evaluation, but a summary. So, if I actually have 100 candidates to guage, 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. It’s helping, it’s augmenting the human decision-maker. And so, that’s sort of the subsequent step within the journey.
After which sort of the final word is once you’re using artificial intelligence to show you how to with skill validation. Now, I’m starting to guage; I’m directly measuring those skills and AI is supplying you with an evaluation of that candidate against those skills. The human decision-maker continues to be making the choice, but candidates are sort of prioritized into tiers—best fit to least fit—that 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’ve got… they’re still using automation from the first step, they’re using signal extraction, and so they’re using skill validation. But that’s typically the journey that we’ve seen going through the maturity curve.
Jessica Miller-Merrell: Thanks for breaking it down into easy steps, since it doesn’t feel that way once you’re starting your journey.
Mike Hudy: Yeah, once you’re in it, yeah, you’re within the midst of it of course.
Jessica Miller-Merrell: Let’s perhaps look ahead and speak about what hiring looks like in a world where it’s skills-focused, not resumes, and the talents are the first currency. How should HR leaders start preparing for this now?
Mike Hudy: Yeah, I feel you said it in that step one—and what we’ve seen nearly every organization, speaking of journeys and curves, are somewhere of their skills-based hiring journey. And that goes back to something I discussed earlier about how you may’t take a look at the pedigree and experience anymore; jobs are changing fast. And so, as you said, skills really are the brand new currency. So, sort of getting that skills-based hiring process… these jobs defined when it comes to skills versus what traditionally shows up in job descriptions.
Then, where we’re beginning to see things going and can proceed is, now that we now have skills as our currency, it opens up a world where we’re now not considering nearly external candidates. Where skills is the currency, we needs to be taking a look at external and our internal talent and fascinated by mobility. And we begin to take into consideration not “talent acquisition” and “talent management,” but only a “talent function” where we’re moving talent across the organization and we’re considering each internal and external candidates in the method. These look very similar and so they’re based on science, but they’re based and rooted in skills from there.
And now, that is yet to occur, but again, I feel it’s where things should go and eventually will go. The world of hiring… the inspiration you had was resumes, after which one other foundational element of hiring was the job requisition. And so, that’s one other thing that I wish would just die: the job requisition. Since it just sets up this grossly inefficient process where I actually have a gap, I put my opening on the market, we now have all these candidates come and apply, and I actually have this funnel where 100 candidates start and I’m hiring one or two of them. And 98 great candidates who’ve all these different skills get the “thanks, but no thanks, we found better-qualified candidates.” It’s because we now have this requisition-based model.
Again, with skills as a currency, what we should always and hopefully can be moving towards is knowing the person’s skill profile. Every candidate involves a company, they get seen, their skills get evaluated. After which as an alternative of being on a rec, it’s pointing them to different opportunities based in your skills profile. “Listed here are 15 different jobs that our company has that you just might find an excellent fit.” Internal talent for mobility—same thing. We don’t just open up a rec.
So, that’s, again, where I feel the long run goes. It’ll—it’s challenging. It wasn’t easy to let go of the resume, and it’s not going to be easy to let go of requisitions because we now have a number of processes built around requisitions. And we now have our Applicant Tracking System that’s built to serve up and manage requisitions. But when it comes to where the talent space must go, that’s where it must go.
Jessica Miller-Merrell: Well, thanks, Mike, for taking the time. I feel like for a variety of us, our roles are rooted in these processes and in these sort of old ways. So, it seems like we now have a variety of unlearning to be doing and relearning of our own within the years to return. So, really appreciate your time to talk with us.
Mike Hudy: Yeah, absolutely enjoyed it.
Jessica Miller-Merrell: Yeah, where can they go to learn more about you and the work that you just and Hirevue are doing?
Mike Hudy: Best place to go is to, after all, our website. A lot of great information on Hirevue’s website: Hirevue.com. After which me personally, you may at all times hit my LinkedIn profile. There’s not too many Mike Hudys on the market, so for those who seek for me on LinkedIn, you’re probably going to search out only one Mike Hudy out on LinkedIn.
Jessica Miller-Merrell: We’ll include a link to Mike’s LinkedIn profile in addition to the brand new Global AI and Hiring report available by Hirevue. So, ensure to have a look at the transcript of this podcast interview. Thanks again, Mike.
Mike Hudy: Thanks.
As AI continues to evolve, the organizations that win won’t just be those who adopt it fastest, however the ones who use it probably the most thoughtfully. Mike reminds us that hiring higher starts with higher data, clear intent, and a commitment to balancing technology with human judgment.
For those who’re fascinated by how your organization evaluates talent in a world beyond the resume, this conversation is one you might want to be an element of. Let’s shape it together. You should definitely take a look at the show notes for the links to Hirevue’s latest AI and Hiring Report and extra resources to support your personal AI journey. Thanks for listening, and we’ll see you next time.
For those who enjoyed this episode, make sure to subscribe, leave a review, and share it with one other HR leader. That is the Workology Podcast. Thanks for joining us. It’s sponsored by Ace the HR Exam and UpSkill HR. Workology has a learning platform for HR certification and recertification in addition to Manager Training. You may also take a look at our recent tech marketplace at www.marketplace.workology.com.
This podcast is for the disruptive workplace leader who’s bored with the establishment. My name is Jessica Miller-Merrell. Until next time, hearken to Workology’s podcast on all of 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.
Resources
Mike Hudy – Beachwood, Ohio, United States | Skilled Profile | LinkedIn
2026 Global AI in Hiring Report | Hirevue