Ep 126: Aiming AI at Human Bias and True Potential to Succeed with Trent Cotton

[00:00:09] Hey everyone, it's Bob. Welcome back to Elevate Your AIQ, your go-to source for insightful conversations on human-centric AI readiness, talent transformation, and the future of work. In this episode, I'm joined by my friend and human-centric AI Council collaborator, Trent Cotton, for a conversation I've been looking forward to for a while. Trent is currently the Head of Talent Insights and Analyst Relations at iCIMS. And beyond his day job, Trent is a multi-time author, working on his next book,

[00:00:37] host of the Human Capitalist Podcast. He brings more than two decades in talent acquisition, along with a refreshingly data-grounded, no excuses take on why hiring feels so broken right now, and why AI tends to be more of a mirror than a culprit. We get into the real tension between experience and skills, what the latest workforce data is actually telling us, and why the next generation of the workforce may be far more ready for this moment than we give them credit for.

[00:01:03] Give it a listen, and thank you, as always, for listening and being part of the Elevate Your AIQ community. Hey everyone, welcome back to another episode of Elevate Your AIQ. I am your host, Bob Pulver. Today, I'm excited to talk to my friend, Trent Cotton. How you doing today, Trent? Hey, what's going on? Long time no talk. How's life? Life is pretty good. We got a high school graduation this week, so that's going to get me stressful. Oh, big luck change. Yeah, empty nesters soon.

[00:01:32] You know, it's an interesting time, because it's kind of like you have to go back and go, okay, who was I before there were kids in the house? Right. Yeah. Yeah, the time flew by. And so, yeah, but I'm excited to talk to you about everything that you've been working on with iSIMS, and you've got a great background in talent acquisition across industries, and so I want to hear a little bit about that.

[00:01:57] And I know with your, you know, remit at iSIMS, you guys have been doing a lot of great research lately on, you know, early career, but really just looking at the market in general, generational perspectives, and then, you know, very curious to hear your observations and insights as a result of the work your team is doing. So, yeah, so let's just kick things off with giving my listeners a little bit about your background and your role at iSIMS. Awesome.

[00:02:24] Yeah, so I am the least practical HR person. I did not go to school for HR. Actually, I thought I was going to be a banker. I put myself through school working full-time as a banker, majored in marketing, minored in statistics and finance. I love data. I always believed that, like, data told a story. And so, you know, in banking, a lot of what I did was meeting with commercial clients, you know, high net worth and kind of looking at their data,

[00:02:50] which was the financials, to try and understand, okay, you know, to be able to measure risk. And in 2004, my daughter was a year old. I had been doing banking for about six or seven years and was kind of bored. And I thought, you know, if I'm going to do a career pivot, now is the time to do it. And so I looked at what are the areas in the business world that I really wish that I could change. And it kind of came down to two. The first was audit and the second was HR.

[00:03:17] Well, early in my career, I spent some time in audit and got kicked out, got kicked back over into sales because apparently in audit, they don't like practical jokes. So that left. That did not go over well. I spent a lot of time in HR while I was in audit. So I took on a contract recruiting role and working for a bank and just kind of fell in love with it.

[00:03:39] And one of the things that was different was that I never, I never dropped that, that business acumen, that business vernacular and how I looked at things just because I got into HR. And so I kind of stood out like an ugly duck for probably the first six or seven years of my career. But, you know, I've been in the roles for a little over 20 years. So I've done everything, talent acquisition, HRBP, employer relations, strategy, PMO.

[00:04:06] The only thing that I haven't done in HR and I'm okay with is benefits and payroll. Completely okay with that. Last year was another time where I said, okay, I've done this, getting a little bored. Let's do a change. And a unicorn job fell into my lap. So I am the head of talent insights and analyst relations at ISIMS. I was a power user of iSIMS from 2010 to 2016, 2017. So I love the platform.

[00:04:36] It's great being on the vendor side. And I kind of have a couple of different distinct roles. The first and one of the primary is leading our talent insights program. So every month we put out a free workforce report. We go over benchmarks. So in EMEA, in the US, every month we look at what we call the three-line report, which measures openings, hires, and applications. We also do every month, we try to do like a sector deep dive or a topic deep dive.

[00:05:04] Really just kind of getting in and looking at what's going on in the market, but looking at it from a data point of view. And my job is to take the data and write the so what. All right. Hiring is going up while application volume is going down. It's great, Trent. I can read a chart. But what does that mean for me as a practitioner? That's kind of where I come in. So that's one part of the job. The other part is leading our thought leadership. And then I also handle our analyst relations. In between all that, I get to meet with a lot of clients and a lot of prospects.

[00:05:34] You know, sales brings me in a lot. So it's kind of fun. In fact, last week, I think it was, I was on the ground in Denver with one of our clients doing an AI workshop. So just kind of walking through what is their recruiting utopia look like? Where? Well, could, should they automate? Could, should they leverage AI? What's distinctively human? So it's kind of fun. I get to be the consultant, you know, give them all the ideas, really kind of pressure test some things. But I'm no longer responsible for execution. So it's kind of a, like I said, it's a unicorn job.

[00:06:03] That's amazing. And then, of course, podcasting, writing books, you know, all these other extracurriculars. Yeah. Well, actually, you're going to be the first podcast. I think that I've, yeah, the first podcast. But I am rounding out, it's either my third or fourth book, but it's called The Futurists. F-U-T-H-R-I-S-T. And it's just looking at the five mindsets that I think HR pros are going to need in order to remain relevant in the age of AI.

[00:06:32] So really excited to hopefully get that to market by the fall. That's my goal. Amazing. So, Taren, let's talk a little bit about the data, some of the data points that you're seeing in some of your latest reports. So let's just pick on. So we're in mid-June, 2026 at the time of this recording. So I know you had some early career research come out in May.

[00:06:56] And I think you did some write-ups around on a similar topic around this time last year as well. So I guess just thought we'd unpack a little bit about, you know, current observations and insights maybe and maybe how that has changed since, you know, similar publication last year. Yeah. So if we kind of start with what's going on in the U.S. market, looking at our three-line report, looking at it as a practitioner. And again, I'm a data guy. My favorite quote is by Deming.

[00:07:26] In God, we trust everyone else must bring data. So I like to go in and interrogate the data. What is it? What is it? And there's been this trend that has been going for probably the last three months where openings are continuing to increase at a rate that hiring is not keeping up. In fact, in our last report, you can see that hiring has been down, looking at kind of a comparative value of this time last year. It's been down below the baseline.

[00:07:52] It only, I think, last month crested at 1% and it stayed at 1% in May. But our increase has been like 15% above the baseline. So that's a huge gap. So what it's telling me is that for some reason there's something going on. Businesses are adding a lot of roles, but the hiring is not keeping up. So it leads me to think there is one of two things. One, companies are shifting where they're investing. So maybe it's not in front line.

[00:08:21] Maybe they're going and trying to hire, you know, all of these AI engineers or anything that is supporting their AI infrastructure gains. Or it could mean that the number of jobs has increased so much that recruiting teams just cannot keep up. So it's one of those two or maybe a mix of both. What's also concerning is that application volume dipped to 10% below last year. So this time last year, you know, we heard it in the market. There are so many candidates.

[00:08:49] I have to spend so much time trying to figure out who's qualified and who's not. It seems like the candidates are backing off a little bit. Maybe they're getting fatigued. I know I was on LinkedIn this morning and saw several candidates posting about how frustrating it is and how many hours they've spent, you know, putting resumes out, trying to interview, trying to get a job and getting nowhere. So there's a lot of noise in the market.

[00:09:13] And it's kind of concerning because if this continues, I do not know how much longer the current recruiting teams will be able to sustain that. Do you think, well, before I ask that, I guess one of the questions I would have is, are people, are experienced people or currently employed people not looking to jump ship?

[00:09:37] Is that, I don't know if your data tells you that if you, if people, if application volume is down because people are just like this, I don't know what is going on right now, but not going to move now and therefore I'm not even going to bother. Yeah, we, we, yeah, unfortunately we don't have a capability of being able to track, you know, are people actually moving. I have seen data from other reputable sources kind of supporting this whole notion or this new nomenclature of job hugging.

[00:10:05] So I do think with all the volatility that's going on in the market, the uneasiness, the lack of, what's the word? You just don't feel stable, you know? So I think that a lot of people that have a job are like, you know what, I'm just going to keep it. I might keep an eye open for something that's out there, but they understand that the competition is tough because there are so many people that have been laid off that are actively looking. So the competition on the candidate side has increased.

[00:10:30] I think it would be pretty safe to say, although ISIM's data doesn't support it, looking at the other data and just based on my experience, I think there is some job hugging going on. Yeah, that wouldn't surprise me. I know there was an observation around the experience level that's being requested for the jobs that are out there. And so I thought we could talk about that a little bit.

[00:10:54] I believe, you can correct me if I'm wrong, but I believe you've said in the past that this isn't necessarily like an AI saying, like we expect you to have all of this great experience. We've always posted like job requirements that probably exceed what is realistic to get into that job, right?

[00:11:17] So mid-level experience versus entry-level candidates, you know, how are you supposed to, it's a catch-22 kind of situation. Yeah, yeah. And if I could tell a funny story just to kind of set the stage, I think it was 2023 in my previous firm. You know, we hired internally, but we also hired to staff on projects. And I remember one of the clients that we were working with, 2023, they wanted five years of LLM experience.

[00:11:46] And like no one decided, like everyone looked at the job description and goes, yeah, that makes sense. And, you know, the recruiters working on it, they were having some trouble. And so, you know, I was like, well, send me the JD, let me look at it. And that was one of the first things that kind of popped up. And so I called the client, I was like, help me understand this. What do you mean? I know that machine learning has been around for a long time, you know, but LLM, yeah. Like with ChatGPT and interacting with, you know, kind of the API aspect of it.

[00:12:14] I said, okay, you want six years of experience with the technology that just kind of came to fruition and went public late 2022. Am I understanding that correctly? It was just that reframe where, you know, the guy was like, oh, that doesn't make sense. I'm like, no, it doesn't. You know, but if I'm a candidate and I'm looking at it and I'm going, okay, well, wow, I thought I had a lot of experience. I don't really have a lot of, I don't have as much as what the market's commanding. So this has been a problem way before AI.

[00:12:43] Let's go back 10 or 15 years. You know, you were looking at jobs and what they were asking for was not in the market. And what they were asking for was not at the pay level that you were looking for. It's just now with AI, now we've got a new technology that we can personify and put some blame on it. What's really interesting, you mentioned in May, we looked at kind of the early career. That was the sector that we wanted to look at.

[00:13:09] And we commissioned a survey and it looked at about a thousand respondents. And that was across generations. So we just kind of pulled out that Gen Z and 59% of them said that they report that the companies now expect them to enter into the workforce with mid-level experience. Looking at the time and looking at what's going on in the media, the natural kind of correlation people go, well, that's because of AI. No, it's not. We've had this problem for a year.

[00:13:39] I mean, decades, honestly. They would expect someone to come out of school who's, you know, looking to make X amount of dollars, but they are to be doing a job that you wouldn't even expect two or three year level person to produce. And the way that a lot of the managers and a lot of the organizations justify it is, well, you know, four years ago, we paid X for entry-level talent. Now we're having to pay X times 1.5 because the market has shifted up.

[00:14:08] So now we're going to require more experience. Well, that's, you and I both know that's not how it works, you know? So I think that we've just kind of done this gradual slide to where this has become the norm. Where AI is just kind of amplifying it going, and two, the number of candidates that are entering the market, it's gotten a lot more attention. But it's a problem that should have been fixed a long, long time ago.

[00:14:32] Question I would have is, are people even clarifying what they mean by experience? I mean, are we just talking about tenure? Are we just talking about, you know, industry, knowledge? I'm curious what, if you unpack just the word experience, I mean, can you parse it to what specifically we're talking about?

[00:14:55] Because even if we're talking about AI, I totally understand your point and why you pushed back on the AI experience. I mean, unless you were very technical and already working in machine learning and, you know, sort of predictive AI. You didn't, the LLMs didn't enter your vernacular until, as you said, late 2022.

[00:15:19] So, so you're talking about posting for jobs that you know, very few people would possibly be qualified for that. But, but barring that, it just seems like you're right. People are always looking for, people have always been in that sort of catch-22. Well, we want this experience, but how are people supposed to get that experience if no one, you know, has given them the opportunities to get that experience?

[00:15:48] So, I guess I just get concerned when we talk about, you know, what we're really looking for, what our expectations are. And then with current tech, you know, as you know this well, like talent acquisition technology, looking for these, you know, using, you know, matching algorithms and things like that. You're just, you're automatically eliminating, you're algorithmically eliminating great candidates because of the way that you wrote the job description.

[00:16:17] Okay, there's a lot to unpack there. So first, let's kind of take a step back because I do think that you bring up a really good point. A couple of months ago, I was on a panel and, you know, my grandmother says that even though I can keep my mouth shut, my face comes with subtitles. And this was definitely one of those moments because the moderator, we were talking about trends and things like that. And they mentioned skill-based hiring.

[00:16:41] And I guess I gave a really hard eye roll because they immediately came and said, okay, well, Trent, you apparently have opinions on it. And my opinion at the time was, what in the hell have we been recruiting for if it not for skills over the last 20 years? But Bob, I really had to take a step back and really kind of push myself and go, you know what? We haven't been looking at skills.

[00:17:05] We've been looking at experience, assuming that skills are directly proportional to the number of years of experience. Case in point, you know, my previous role, I put in an AI technical interviewer because I wanted to remove the human out of it to remove some of the bias and really look at what people were able to do because that's what we were going to get paid whenever they went on the job. It was fascinating to me. We had a, let's just say, a senior software developer position open. I don't remember the exact role, but it was a senior role.

[00:17:35] And they were looking for seven to eight years of experience. Well, we had it to where, you know, there was that initial DNQ, but I did not do it on number of years of experience. It was, do you have experience with React, Node.js, whatever else? Yes or no. And then that kind of moved them to the next level. And then if they did have that technology, I wanted them tested.

[00:17:56] And the AI was really cool because it would say, okay, Bob should be at a senior level based off of his ability to be able to meet this problem. Or, you know, he's a mid. And so it kind of helped us be able to open up candidate pools and really kind of figure out, all right, based on this skill set, this is where they should be. So therefore, we should get them for these roles. So we had this guy that the managers wanted. They knew they had worked with them before. He had eight or 10 years of experience somewhere in between there.

[00:18:26] He took the test. The pace of change in the world of business is fast, and that drives incredible complexity for the world of human resources. Welcome to the HR Data Labs podcast, a series of conversations with experts inside and outside the world of HR on how to innovate, measure, and evolve our practices. Our goal is to help provide you with practical examples of how HR has to change to meet the complexity of the business environment.

[00:18:53] Every week, we'll talk to new and different voices on all aspects of HR. And at times, we'll get irreverent. Silly even. And sometimes geek out on the data and technology that underlie the processes that drive the world of HR. But the conversations are always insightful and fun. So please, enjoy the HR Data Labs podcast. Came out with like maybe a B. Let's just say a very hot B.

[00:19:20] We had a guy that had never been to college taught himself how to software and code in software by watching YouTube, by taking online classes, by GitHub. Like just he taught himself. He had two or three years of experience. He loved mentoring. He naturally showed this skill of continuous learning, which is something that we were looking for. And his actual coding smoked.

[00:19:48] And I mean smoked the guy that had seven or eight years of experience. And so after I gave that panel hard eye roll, I was kind of recalled that story. And I was like, you know what? I need to retract that a little bit. The skill-based hiring does make some sense. And then, you know, kind of sticking with that entry level.

[00:20:08] One of the things that stood out to me, I think from last year's report or from our frontline, is that candidates coming into the market are looking and going, okay, ChatGPT is leveling the resume. I can't stand out from a resume anymore because everyone is using some kind of AI to make them look good. And it's making everyone look the same. Candidates have now switched from do not give me an assessment to raising their hand and saying, let me show you the skills that I can do. Let me do that.

[00:20:38] And then you go and find me the job that is the best fit for me. So I think that this whole narrative of experience versus skill set, I think that AI to the positive is actually going to help us be able to finally flip that. Because you can be working for 10 years at something and still suck at it. Or you could be working at something for two years and be a master at a level where you can actually develop a team and co-train people. So I'm really kind of excited about that.

[00:21:06] I think that's the first level set that needs to happen. So that's kind of step one. Step two, we need to get rid of this job ad. I'm not going to say job description because it's not a description. I don't know when we over-rotated. And if I'm thinking about it, I was probably leading the charge of making it more marketing, more of a commercial. When really we should be focusing in on, you know, if you think of the medical commercial, that little blah, blah, blah, blah, blah, blah, blah, that they say at the end.

[00:21:35] You know, all these little side effects. That's really what you need to know. It's not, you know, oh, this person's on the beach now. Now they can take this medication and go to the beach. That doesn't really help you. You know, what you need to understand is what is it diagnosing? What are the side effects? Are those actually side effects that you'd be able to live with? If we take that approach and look at what the JD should be, I should not be surprised as a candidate. If I read the job description, it's looking for these five skills. Whenever I go into the interview, I should know that they're going to ask me about these five skills.

[00:22:06] I should be ready for it. It should not be a surprise. Whenever I look at some resumes now, it's like we need a dynamic, you know, blah, blah, blah, strategic. Like how do you measure that? How do you evaluate someone's dynamic? You know, it's completely, it's perceptive, right? So if we need to start looking at how do we pull the skills and evaluate someone's job fit, first, we need to focus on skills and not experience.

[00:22:31] Second, we need to redraft and just actually blow up our whole approach to JDs and start treating it like a scorecard. It should be a scorecard that the candidate looks at and evaluates. Yes, this is something that I think that I would do well and it's something that I want to do. But it should also be a scorecard throughout the interview process. Now, that's step two. If we look at, you know, you kind of mentioned algo, I can never say the word. Algo, you know what I'm trying to say.

[00:23:00] I have a stutter and that's one of the words I just cannot say. If we look at how an AI looks at a job description to try and evaluate the skill set of a candidate against, if skills are buried in there, then how effective is that job ranking? But you can't blame the AI because it's just using the information that we gave. So it's really kind of a three-pronged problem.

[00:23:26] First, we need to get rid of this dependence on experience, assuming that it relates directly to skill and ability. Not true. We need to change it. Next, we need to change the job description to actually be a job scorecard, something that the candidate understands going in full transparency. This is what we're going to be evaluating them on, and there will be some consistency in a scoring process throughout the interview process for all people involved.

[00:23:55] Then we'll be able to layer on that AI to actually really get in and rank the person and improve the ability for us to predict whether or not that person will be successful in the role. It sounds easy, but if you think about it, it's not a technology fix. I mean, technology, we can fix that tomorrow is the mindset of a lot of people that are HR professionals, hiring managers. You got to get rid of some of the stuff that we've been doing because it hasn't worked for us.

[00:24:25] We got to change. I feel like it's the, call them educated candidates, right? They're not just blindly applying to things and throwing their hands up. Oh, woe is me. Like, this is terrible. I'm never going to get a job, whatever.

[00:24:39] But doing it with their eyes wide open, understanding some of the things that you just described, and finding places that have woken up to the fact that they could give someone this pre-hire assessment, whether that's technical or behavioral or both.

[00:24:58] You know, we could probably talk for a while about, you know, these AI, you know, fluency, AI readiness kinds of assessments that gets into mindset, not just, you know, skills and what tools have you been using, right?

[00:25:12] It's like when you and I were applying to our first jobs and we had like Microsoft Word and, you know, those kinds of things on our resume, like that stuff, your ability to adapt, your ability to learn things on the fly and have that mindset knowing that you can take those things on should be more important than some of those years of experience.

[00:25:34] I think, I'm pretty sure you've talked about this probably extensively, but like just these, these sort of proxies that really are not strong signals of someone potential to succeed in that job or to be, you know, an active, constructive, you know, positive influence on the organization and the organizational goals.

[00:25:58] Because hopefully, hopefully we start extending, you know, 10 years again and we start increasing, you know, engagement levels and morale and things like that, that have been on seemingly on a steady decline for quite a while. I mean, we should be thinking about how do we fix some of these things in the short term and the long term for the betterment of the organization and these individuals.

[00:26:22] You know, you're alluding to some of the things that I have talked about quite a bit, which is this should be a win-win scenario that everyone's looking for. And if you're not a fit for that organization, why are we trying to, you know, force someone through the process? We should be able to pick up stronger signals earlier in the cycle and only have conversations with those people.

[00:26:47] And that might mean a level of transparency and trust that we just haven't really experienced in most organizations before. Yeah, but Bob, if you, if you look me getting into HR, if you were to look at your, your standard recruiting position and looked at my resume, whenever I was applying, I never should have been hired. I didn't have the experience on paper.

[00:27:15] Now, I was managing four states for a mortgage broker at the time. I didn't have a recruiter. I was the one that was recruiting my own team. I had a vested interest because I made money off of what they produce. So I wanted to make sure that I was bringing on the best, but also needed to bring on people that I knew that I would be able to lead. So I had that level of experience from my previous banking days. I had relationship experience. I knew how to manage a portfolio.

[00:27:41] I knew how to create, develop, nurture, and execute on a pipeline. Those are all transferable skills. So if I had just, you know, and luckily, thank you, David Mejia and Maggie DeLoreano for kind of looking beyond and going, there are transferable skills here. I think we can teach them the HR and the recruiting aspect of it. Internally grateful. But if it weren't for their foresight and their ability to kind of look beyond and think differently, I would probably still be a banker and miserable.

[00:28:11] You know? So I think that if we take a step back, you know, and let's use that same example. In recruiting, actually in HR, but we'll just kind of really focus in on recruiting. What makes a successful recruiter? Someone who understands the market. Someone who's intellectually curious. They're highly organized, meaning they can keep a pipeline. They understand where things are going. They're process-driven. They're metric-driven. There's a heavy relationship component in it because you're dealing with hiring managers.

[00:28:41] You're dealing with internal partners. And you're dealing with candidates. And the ability to be able to switch between those three personas is a very critical skill. Now, in the new description, if those are the skills that you're looking for, yes. I was overqualified for that because that's all I had done for seven years, right? So I think that's a perfect example of where our hiring process has been broken. Now, you have this broken Frankenstein-up evaluation process, and then you throw AI on top of it.

[00:29:11] You can't blame AI for the problems that were there way before. The only thing that's different with AI is it's going to find them, expose them, and scale them. It's not AI's fault. It's our fault. We've got to fix the structure almost at the ground level.

[00:29:25] Yeah, I mean, I certainly have some of my own examples of where it just seemed like you didn't need to go through the whole exercise of, you know, customizing a resume, you know, matching it to a job description. You know, the scoring is probably a whole other conversation trend, like you alluded to being able to have this, you know, scorecard throughout.

[00:29:53] And I think that would be immensely helpful. But I also know when people start thinking about, you know, responsible AI practices, which you and I have talked about many times through the human-centric AI council that we're both part of.

[00:30:05] And so I think that's, you know, it's an important goal to try to see how we could do that effectively and responsibly because we know AI is being held to a higher standard in terms of its, you know, potential showing that there's, you know, bias underneath, right? Not that the AI is biased, but, you know, there's data that we assumed was sort of trusted and, in fact, maybe it wasn't.

[00:30:35] But AI can scale that bias is the problem, right? AI can scale bias and that's why people are so concerned. It's being held to a lot. It can scale it, but it can also fix it. That's the problem. That's what we're not talking about. And, you know, you and I both have spoken on stage about AI and I'm sure, like me, you probably at the end of it, whenever you open it up for Q&A, in my mind, I'm going five, four, three, I always call them, you know, like compliance Kathy or compliance Ken. I can spot them in the audience.

[00:31:05] I know their hands going up and I know that they're going to bring up bias. So I always like to go, all right, let's just walk through a normal day, okay? A recruiter is recruiting for, let's say, an operation analyst, entry-level position. They post the position today at 3 p.m. By 8 a.m. tomorrow, they have 450 candidates, right? So the non-biased thing, if we're looking, if we're just going to be, you know, following the platform rule,

[00:31:34] that candidate will go through each one of those resumes to determine their top 20. You know and I know that doesn't happen. What do they do? They go back and they look at the last 20 who applied, find the five best, interview them, send them to the manager and call it a good job. No one's talking about recency bias. What if your best candidate was the fourth one to apply? You didn't even give them a chance. But we're not talking about that bias. No, no, no, no. We're not going to talk about that.

[00:32:01] We're also not going to talk about the bias that is inherent in all of us and including our hiring managers. Our guts as professionals tell us that manager is not passing this candidate off because of some kind of bias, but I have no data to prove it. Well, what if I had an AI that was evaluating how hiring managers were looking at candidates and pulling out and going, hey, Bob tends to favor people who graduated from a community college over a four-year degree.

[00:32:31] Is that a huge bias? No, but it's something as a practitioner I would want to know because I want to sit down with Bob and go, hey, just something that I noticed. Can you help me understand why? And if there's not a good reason why, then that allows me to be able to coach Bob to be aware of the bias and to work through it. AI can give us that kind of data. We don't have that now, but yet we just want to blame AI and go bias, bias, bias.

[00:32:58] There's a positive side to this that we're not talking about and that we're not really kind of digging into and going, how can we do this? And then also, too, what if you had an AI that was sitting and listening and transcribing the interview process to help build candidate profiles, interview scorecard, all of that? But what if I was able to look over an entire department and realize that the retention on these three managers within this department, their retention of new hires is through the roof?

[00:33:27] What if I could go and ask the agent, what do they ask? How do they interview? What do they do differently? Take those best practices and give it to the rest of the organization to level up the entire interview process within the organization. There is so much opportunity, but everyone gags on AI bias and the media is not helping. Some of the litigation that's out there is not helping.

[00:33:53] But I just want to encourage everyone, please, please, please look at some of the positive side of what AI can bring because it can analyze data in a way that none of us have been able to thus far. I think those are amazing use cases. And I haven't seen anyone. Well, I shouldn't say that.

[00:34:10] On the providing feedback or analyzing the selection patterns of recruiters and hiring managers, I haven't heard anyone willing to do that. I think that would be awesome and a learning experience for everyone.

[00:34:28] On the interview, what market used to call interview intelligence side of things, yeah, it should not just be looking at summarizing a particular interview and having that fed to the rest of the hiring team. They look at the interview and they look at the interview and they look at the interview and they look at the hiring manager. They look at the call transcript or something like that.

[00:34:50] But what are the patterns that you're seeing in terms of who's coming through and what is, as you look at them, if people have onboarded, looking at the patterns of success and things like that. I think the traceability of all of that data throughout the life cycle is incredibly valuable. And you're right. Those are perfect examples that should get people to think perhaps a little bit differently and more optimistically about where AI can come in and improve this process.

[00:35:19] I mean, whatever we've been doing is not working. No. And, you know, there's a lot of hype around the litigation around AI being used in hiring. I get it. It's new. You know, there are some concerns. And I'm not discounting the concerns whatsoever. So please hear me out there. Don't come for me on LinkedIn because I am supportive and it's a conversation that needs to have.

[00:35:45] But we don't talk as often about how many EEOC claims have been filed for one particular organization because of their hiring practices. That doesn't make the media as much as it did several years ago, right? It's still there. It's still proliferant. But now because we have AI, now all the focus is on it. I'm just asking for a little bit more of a balanced narrative in the market.

[00:36:14] No, I agree. And I also think that AI can do a better job. I feel like it can do a more proactive job even as you're thinking about whatever the job description, job ad, however you want to frame that. Even before that gets posted, I mean, we've had AI-powered sourcing technologies and ways to go out outward and do this well before ChatGPT.

[00:36:43] Because I was evaluating those solutions as one of my first jobs. A useful. First RPO firm. Yeah. So these capabilities do exist. You don't have to wait for someone to explicitly, you know, apply to the job to start thinking about who are the candidates. Not to mention, you have a candidate relationship management system. You have, you know, silver medalists.

[00:37:07] And you have all these other people who are probably reasonably qualified that could fill these new openings that you have. And I mean, that's a whole other topic with candidate, you know, rediscovery. But the point is, you've got all kinds of mechanisms where AI can help you. And you can continually sort of train the AI on what good looks like. And so you could start to do some of the things that you said earlier, Trent, which is...

[00:37:36] Life is too short not to work on solving cool problems with interesting people. And recruiting allows us to do both. I'm Brando, host of the Rebel TA podcast. A show where we share stories with people and talent leaders who are on a mission to empower the candidate employer relationship in the future of work. You know, don't discount the experience.

[00:38:01] The experience is great, but let's not make it a disqualifier if you don't have eight years of experience. This person is willing to take this, use this, interact with this AI interviewer in a pre-hire, you know, screening. You can give them different assessments. The people that are really interested in the job would be willing to spend the time knowing and hoping that this is a sort of win-win scenario.

[00:38:26] Otherwise, it's just this dysfunctional, you know, mess that we've sort of been experiencing. And you're going to continue to get this revolving door of talent. And I do want to, I want to kind of go back to the sourcing aspect of it because I'm actually, in my practitioner days, I leveraged an AI sourcing tool.

[00:38:50] So the thing that I loved about it, so you remember the example I gave a little bit earlier of the, you know, I need five years of LLM experience and we were sitting in 2023. I didn't necessarily, you know, kind of come and just go, hey, Bob, you're an idiot. What are you talking about? Because that doesn't really win you any favors with the hiring manager. I had my sourcing tool. And so I went in and uploaded the job description and it would do a count of the number of candidates.

[00:39:17] And so whenever I was on the phone with the manager, I said, the JD, as written, looking at just the skills, this is how many candidates you have. And it was zero. And he says, that's not possible. I said, well, let me go and remove this number of years of experience with LLM. And then immediately, you know, the floodgates open. He goes, oh, well, what if we add this? We'll take this away.

[00:39:40] And so we were kind of going through and looking at the market and looking at the talent that was out there and crafting what was that scorecard actually going to look like. So using data and leveraging, you know, the power of AI, being able to go and pull in this market data, I was able to advise rather than saying, I can't go, I can't find anyone. Right. So I think that's going to kind of change and elevate the recruiter experience and what they're able to do. So that's on the front end.

[00:40:10] Now I've got an agreed upon scorecard. I can go in and start evaluating the candidates. I can build in the AI interviewer to evaluate their skill sets based off of that because I've used AI to help paint a picture, to come up with a scorecard. Why not have it evaluate? I can get it to help me create a scorecard that interviewers use. And then at the end of it, I have a pretty good indication of success, which means retention.

[00:40:38] Because what we're missing is we're thinking that these two are separate. They're not. I can have eight years of experience. I've got 20 something years of experience working as a practitioner. Would I be qualified for a CHRO role? Absolutely. Yeah. I've kind of done that type of role just on a smaller skill. So, yes, I'm qualified and I have the skill set, but is it something that I really want to do? Is it something that I'm really passionate about?

[00:41:05] Using and leveraging AI to augment the recruiting process allows the recruiter to sit down and say, candidate, you're qualified. Do you really want to do this? Are you really going to be passionate about this? You're fixing the retention problem in the recruiting process, not after the person starts. So, again, there are so many benefits that if we take the time and get rid of some of the scaffolding, I call it the Frankenstein that we have developed of let's throw on this, you know, tactic here,

[00:41:34] let's throw on this tactic here. Let's get rid of Frankenstein and let's redesign the process. Yes, it's going to make better candidate experience, a better hiring manager experience. And ultimately, if we're really looking at how recruiting drives enterprise value in an organization, it's going to line it up better with retaining the top talent in the market because they're doing something that they're qualified to do, that they enjoy doing, and that they can continue to grow in doing. And I think those are the three things that we've been missing.

[00:42:03] Yeah, and I think the hiring organizations are earning, you know, credibility. They're earning trust and respect because you've gone and taken a more sort of novel approach, you know, based on the guidance that you just expressed, Trent. And they've really said, you know what? We're not going to follow these traditional paths and we're not going to...

[00:42:30] We've come up with a way to figure out that the best way to hire great people who are passionate about what they do is to make sure all these things are in alignment and, you know, experience, you know, be damned. This person has what it takes. They've got the right attitude. They've got the right mindset. They're, you know, AI ready.

[00:42:55] That would be another aspect of this because, you know, three years ago, recruiters... I mean, I know just from my nieces who've been applying to jobs, like, three years ago, if you brought up how AI may impact that career path that you were interviewing for, the recruiter may have been like, I don't even know what you're talking about. I'll have to trust that you have done your homework and you came here, you know, prepared. But it's true. I mean, that's completely changed. Three years ago, no one was giving...

[00:43:25] No one was even asking AI-related questions about how AI is going to impact the future of work and the future of this work. Our work. Let alone... Yeah. Let alone actually evaluating, you know, their fluency or readiness or however you want to sort of evaluate that. So that's a whole other sort of, you know, skill set that's going to have significant, you know, value. I think most, at least knowledge work, most of those job descriptions now encompass, you know, some level of AI knowledge,

[00:43:55] knowing that you're going to use AI when you get here in some capacity, right? And so the better and more equipped you are, the more familiar you are with what that actually means, right? I think this ties a little bit to some of the, you know, human capitalist, you know, work that you've been espousing for a while, right? So I think there's a lot that's changed.

[00:44:18] And I think we're underestimating just how ready some of these younger folks are coming out of school. I mean... We're not just coming out of school because one of the data points that really jumped out at me is that a third of them, and this is, again, in our free workforce report in May, a third of them said that they are actually teaching themselves new AI skills.

[00:44:44] So they are probably one of the more perceptive generations that we've had. You know, we've had others that kind of look and the woe is me, you know, the market's changing, not Gen Z. Gen Z is looking at the market and saying, okay, school didn't prepare me, I'm going to prepare myself. And this has changed over the last year because whenever we looked at it last year, you know, where do you expect training to be? A lot of emphasis was on the job, you know, the employer to do the training, school to do the training.

[00:45:14] This flipped. I mean, just in the span of a year, they're looking and saying, okay, I can't really depend on those two institutions. I own my career path. I need to go and learn these new skills in order to remain relevant and in the workforce. And so I think that that's something that needs to be taken advantage of. You know, you've got, for the most part, a generation that is committed to continuous learning. And adaptability is built on continuous learning. They're mutually exclusive.

[00:45:43] So, you know, the whole aspect of someone being highly adaptable, the thing that's driving them is that they like to learn new things. You know, they like to remain relevant. Well, look at Gen Z. They are highly adaptable and they're showing very positive trends towards continuous learning. Why are we not just scooping them up and putting them into roles? Because that's going to be the workforce of the future. They're already showing and exhibiting those soft skills that you desire for that in order to meet that future work.

[00:46:12] They already have it. We're just not giving them a shot. Yeah. Yeah. I mean, that's, that describes some of my motivations for being involved with my school districts on the AI task force. Right? These kids are, they can do a lot more than you think just because they're not doing great. Grades aren't great. I mean, you can have a whole separate conversation about what grades really mean. But, you know, I wasn't a great student.

[00:46:39] There's lots of people that aren't great students and they still have very successful careers. It's just they haven't been challenged or they haven't been given the right opportunities. There's all kinds of reasons why that could occur. But I want people to realize, you know, students, parents, educators, that these kids are capable of a lot. We've just got to let them sort of explore their own, you know, creativity and curiosity and got to give them the mechanisms to do that.

[00:47:07] I mean, my daughter's about to start college in the fall and I was very encouraged already by, you know, I did my own research about whether they were adopting AI across the university. And literally like two weeks ago, they announced that there's now a new major, two new majors, a Bachelor of Arts and a Bachelor of Science, both tied to AI. So the Bachelor of Arts is tied to human-centric AI. So I'm not pushing her to change her intended major yet.

[00:47:37] But to me, it's very encouraging that the school is embracing this and they recognize that to prepare these kids properly for to be, you know, the future workforce and to evolve with the future of work, maybe build the future of work themselves. This is the kind of, you know, program that is going to enable them to do that. So I'm pretty psyched actually to see that. Yeah.

[00:48:06] You don't want to get me started on how education institutions do not prepare kids for the real world. Just don't even open up that can of worms with me. But I'm glad to see it. And locally here in Atlanta, I've seen a couple of different colleges do the same. Also, there's one that actually created this program.

[00:48:26] And the way that I've read through the program is it's basically someone that majors in business, but they have a little bit of tech because they understand that one of the key drivers is adoption. So you can go and buy all this wonderful AI infrastructure, but if no one's using it, you know, there's a change management aspect of it. There's an adoption aspect of it.

[00:48:47] And this degree actually kind of sits in the middle between tech, kind of like a technical project manager, but more of an emphasis on the human aspect of it and change management and AI. And I think I've been trying to get my nephew. I was like, you need to do that. I said, you'll be able to print money in the future by the time you get out of school, because I think that's going to be the next clip that we hit. Yeah. No, I think there's going to be a lot of amazing opportunities for these kids. But you're right. I mean, education system has not traditionally kept up.

[00:49:17] AI is definitely no exception. If anything, it's sort of the poster child of not being able to keep up. But I'm just, as with all things AI, I try to stay cautiously optimistic. And yeah, and I mean, just going back to, you know, how we're evaluating folks and their potential fit and potential to succeed in roles. I mean, I just remember, you know, I didn't spend my career in HR. I pivoted into this space during the pandemic.

[00:49:45] And I just remember one job that I was really interested in working for a vendor. They would basically, they were using, they were drinking their own champagne, right? So they would just take their pre-hire assessment and say, I don't even need your resume. Take a look at this job if it's interested, if you're interested, you know, fill out this thing. Three rich text questions. This is before ChatGPT, so I was not even tempted. I had no, had to do it myself.

[00:50:13] And from that, I was friendly with the founder. So I was able to see what the dashboard looked like behind the scenes, not just the constructive criticism and the readout back to me as a candidate. But both of which I very much appreciated. First of all, immediately replying to the candidate, which people are still not doing, with this is how your answers were interpreted. And here's some constructive, you know, feedback in the future.

[00:50:40] And then to be able to see the dashboard and the scoring and the breakdown and all of that. I was like, what do you even need the resume for? I mean, yeah, if you want to see some of the experiences just to sort of layer it on top. But it's certainly just one set of signals. But the stronger signal and the more accurate signal was just let's learn about Bob and let's learn about how he thinks.

[00:51:07] And then he can adapt to the environment. He'll gain the industry knowledge. And today, you know, you could just use some of these AI assistants and gain that knowledge. So it's just things have changed, you know, pretty, pretty rapidly in the last couple of years. But if you think about, you know, the resume, I think, needs to be retired. It's well past its retirement age.

[00:51:32] If you think through the last couple of people that you have interviewed to hire on your team, can you remember where they worked before? No, it's not relevant, right? It's not relevant. But you remember what you hired them for. They have this skill set. They have that skill set. And I think that is probably where we need to go in the future. The first person that I interviewed at this recruitment process outsourcing firm, they gave me this person's resume.

[00:52:02] And I was like, you got to be kidding me. Like, this guy has never done this job before. There's grammatical errors all over this resume. And this resume, I don't even know what to do with this person. They're like, just trust me. You got to talk to him. And I talked to him. And he was just like, this guy's unbelievable. And he turned out to be an amazing technical recruiter, having no recruiting experience of any kind whatsoever. He was a problem solver. He was curious.

[00:52:33] He asked good questions and was just immersed himself in the job. And I haven't talked to him in a long time, but my understanding is he's done very well for himself. He's working for some early stage startup doing their early technical hiring. So you just don't know until you give people a chance. And I know that could expand the already big talent pool out there.

[00:52:58] But you've got to think about the types of filters, the types of screening that you're doing. Are you actually looking at the right things? Forget about how people have hired in the past. Just think more deeply about, and then to your point, using AI and all these interesting and novel ways where you're actually mitigating a lot of the human bias that has plagued hiring teams and the market for so long. So I think all of that is really important to think about.

[00:53:28] And we finally have, I mean, you can go back and fix tech if you know that there's a problem because it's being audited. Can't really do that with humans. So Trent, I want to be respectful of your time. Any sort of closing thoughts about what early tenure candidates should be thinking about, what CHROs should be thinking about, and anything coming up with ISIMs and what's on your plate? Yeah, absolutely. So let's start with just general HR.

[00:53:58] I want to do a call to throw away your processes. Stop being married to them. They're not working. It's a Frankenstein. Go back and use this time to completely retool what you're doing to make sure that it is fair. It is equitable. And expect that of yourself before you expect it out of AI. The next is make sure that you have a governance framework in place. We did a definitive guide for TA. Let me try that one more time.

[00:54:27] A definitive guide for AI adoption in TA. We released it in April. And one of the things that stood out to me is that 45% of organizations that were surveyed have no AI governance framework. That's concerning. So if you want some information, some guidance on how to leverage AI respectfully and responsibly, you can go to the ISIMs Trust Center.

[00:54:53] We've got a ton, I mean a ton of data out there for you. Go and build that governance framework today. Make that a priority. My advice to early career is to keep going. I know it's tough. I know that this market is very unforgiving. But continue to reinvent yourself and reinvest yourself and continue to try and acquire some of those new skills. The market will catch up with what you need.

[00:55:19] And as far as ISIMs goes, as a practitioner, I was constantly looking for benchmarks in the market. I like performance data, but comparing your performance data to your own performance data, you're kind of looking at data in a vacuum and it never goes well. We look at every month the three different parts of our line, which is the openings, the hires, and the applications. We also look at applicants per opening and we look at time to fill.

[00:55:48] It's a free report. So go to isims.com backslash insights. You can find the monthly reports. You can also find our anchor reports. So there's a CHRO report from last year, frontline hiring report from last year, and our AI adoption report that we released in April. So if you can't find the data, send me a message on LinkedIn. And if I don't have it, I will find a way to get it to you. Trent, thank you so much for spending some quality time with me. Great insights and takeaways. Always enjoy it. Yeah.

[00:56:19] And we will see you soon. And I'll put those links in the show notes for everyone as well. So thanks everyone for listening. We will see you next time. Thanks again, Trent.