AI adoption by employers and candidates is not what you think

[00:00:13] Welcome to episode 123 of the High Volume Hiring Podcast. I am Stephen Rothberg. I am one of your co-hosts and joined today as almost always by my awesome co-host, Jeanette Leeds. Almost always. I've missed a few. It's true. I'm traveling a bit. And our guest today is catching up in terms of the appearances. That's by popular demand. We have Trent Cotton from Ice Booms. How are you two? It's been too long. It's been, what, three or four weeks? Yeah, sounds like that.

[00:00:43] What's going on here? Trent, I'm like a rash. I just don't go away. But get some ointment and you'll be good. Let's just move on. Let's move on. Okay. We are here today to talk about the new iSims report on AI and adoption and who's adopting it, who's not, and what part. And I am so fascinated by some of the stats coming out of this report.

[00:01:10] I think it's really, some is surprising and some is like, okay, we get it. So maybe let's kick it off. First one. I know, I feel like the headline stat when I read through the report is that the number, tell me if I got this right, 69% of companies say they're using AI and talent acquisition. Yes. But only 18 are using it broadly across the whole hiring lifecycle. That was interesting, wasn't it? So just real quick, I do want to give credit also where credit is due.

[00:01:38] Madeline Laureano with Aptitude Research. We partnered with her. Yeah, I know. She's such a jewel. Fascinating. And just quick story, Tim and I, Tim Sackett and I started talking about this in July of last year. And then he brought in Madeline and at HR Tech, literally on the back of a napkin, we're like mapping out what is this research? Because there's been a lot of noise around AI. And so we really wanted to dig in and say, okay, what's really going on in the organization?

[00:02:02] So yes, 69% of the 412 respondents said that, yes, we're using AI and automation in our practice. The 18%, that was interesting because whenever I saw 69%, I was like, hang on a second, that doesn't sound quite right. The 18% sounds right because those are the few that are actually using it from point A to point B. So from sourcing to onboarding. So it's not as pervasive as you think.

[00:02:30] And one of the things that the research found is that because of that 18%, we want to understand what's driving that. What's driving it is that right now, most TA organizations are using AI for use case specific scenarios. All right. So I have a problem with screening. So how can AI help me with screening? They're not looking at how can it help with interviewing or assessments or communications. That's the only thing that they're looking at.

[00:02:56] More generally, I think it's more in the communication, you know, including job description, email campaigns and things like that. So it was really, really interesting. And because of those use cases, you know, three of us have talked about how I kind of chase data. So when we were mapping this out, it was like, well, let's find out this. Well, what if we only find that 18% are doing it broadly and others are doing it use case? Well, let's look at what the use cases are. So we looked at each stage of the funnel.

[00:03:25] And that to me is the meat of the report. It's 31, 32 page long report. So it's not like a little touchy-feely thing. This is like there is meat on the bone here. Like there is real stuff. I have been chomping at the bit to get this out into the market. The 18% that are using sort of broadly versus call it two-thirds that are using at least piece.

[00:03:51] Is it that 69% is on their way to becoming like the 18%? You know, are these organizations looking for their biggest pain points and starting there with the idea that maybe over a year or three years or something, they'll roll it out more broadly? I think we have two competing related scenarios that are driving the use case specific.

[00:04:20] Regulatory, compliance, risk, all of those concerns. So especially in the enterprise, you know, you've got these AI committees. You've got legal that says, okay, we need to review anything that's AI. And so I think that, you know, if I were sitting in a practitioner seat and that's the environment that I'm in, I'm thinking, okay, where is my greatest pain point? And let me see if I can. That's a hill that I'm ready to dial. Now, moving it broadly across, that might open up more risk.

[00:04:47] And it may just get a quick no from legal, but maybe if I can just get them to look at how can I use it here for interview scheduling or, you know, using some type of transcription service to be able to go in and create a candidate profile. Let me see if they're okay with that. A couple of clients that we've had speak. It's really interesting. They've kind of gone in and said that it's easier for them to get automation approved with legal and compliance than it is with AI. That two-letter word just makes everyone kind of go, eesh.

[00:05:17] I think that's some of the governance is what's driving it. I think the other part of it is that it's still new and people are still a little scared of it. So you've got those two competing things that I think are really driving this. Let's look at it in this particular part of the funnel. Yeah. You mentioned the word automation, right, versus, you know, AI and the risk. So glad that you picked up on that automation and AI.

[00:05:44] So let's just talk about that because there's a stat in the report that says you found 58% of TA leaders can't distinguish clearly between AI and automation, right? Which is, and I've seen this for years, right? It's like it gets confused. It gets mixed up. It gets like blended together. And they're separate and distinct. Right, right. Do you agree? Does that, you know, does that matter? It's kind of funny.

[00:06:13] I forgot what conference it was over the spring. I just asked a general question to practitioners. How many of you have been using automation for longer than five years? And like maybe three people raised their hand. I said, how many of you have been using knockout questions for longer than five years? All of them raised their hand. I said, congratulations. That's automation. You've been using automation. Surprise. Yeah. And then you can have some AI-assisted automation, but automation by itself is really not AI. Right. And there's one question in there when Madeline and I were kind of going through the questions. Yeah.

[00:06:42] It was, where are you using AI in screening? And there was four different ones. And then I put in there automated knockout questions. And she goes, why do we want to do that? She goes, that's not AI. I said, I know that. You know that. We even have a little thing right above the question that says, this is AI. This is not AI. Yep. Let's just see. Guess what the leading one in screening was? I mean, it definitely was that one. Automation. By far. Yeah. Yeah.

[00:07:07] So, you know, that kind of tells me as a vendor that we do need to do a little bit more education in the industry to kind of help make that delineation because it does, you know, it's a little bit lower risk, you know, than having agentic AI, which is completely autonomous. And there's a lot of risk in there and a lot of governance concerns. So I think that part of the aha moment for me was the industry is not quite there yet. So if you hear the talking heads going, everything's going agentic.

[00:07:36] It is. But this report shows you that the industry is not ready for that. Whether you're a payroll pro or an employee wanting to understand your paycheck better, we've got you covered. Tune into It's About Payroll for expert insights on payroll trends and compliance, or check out It's About Your Paycheck, the go-to podcast for employees looking to understand their pay and rights. Two great shows, two great hosts.

[00:08:04] Listen now, brought to you by Work Defined, where payroll meets clarity. You mentioned the candidates a second ago, Trent. Mm-hmm. When I'm reading the report and I'm sort of thinking about it also from the candidate perspective, a lot of the misconceptions, a lot of the fear, a lot of the excitement in some cases is on both sides of that desk. Right? The TA professionals and the candidates, I think in a lot of ways have a lot of the same fears.

[00:08:31] I view it as both a, like a unstoppable force and an immovable object. Right? Employers, and I think the data shows this, the employers are moving ahead very quickly and they're doing it for efficiency reasons. Totally efficiency. Yeah, that was the number one reason. I can't get ahead very quickly too. And I think they also are doing it mostly for efficiency reasons. Write my resume quickly, give me some quick feedback. I don't have to go talk to my sister to help her write my resume, et cetera.

[00:09:02] So I think the report, speak to, if you could, the difference between the desire by TA to use AI for efficiency versus what I would think of as effectiveness, hiring better. So I don't want to give out too much, but we did ask. It's just the three of us. It's just the three of us. Nobody, you know, so this is all private. And as long as this comes out after April the 30th. Download the report. Yeah, download the report.

[00:09:28] So one of the things that we did want to find out is how much time is AI and automation saving recruiters per hour, per week in each stage of the process? And we found that out. So it's kind of in that five to 10 hours per week, per recruiter. And then the next question that we asked after that was, what are they doing with the time? So to your question, they're spending more time with hiring managers. So really trying to understand what is the job?

[00:09:58] What are they looking for? Zeroing in on the right search. And the other part is strategy, like looking at the data and saying, okay, we need to do things better. So part of that is the candidate experience. So yes, they're driving for efficiency, but I was very heartened to see that they're driving it for efficiency to be able to reinvest that time back into the human aspects, which I think that we've missed in the last three or four years. Or 30 to 40. Some of us have not been in there that long. I know, I don't know what you're talking about.

[00:10:29] I am sticking with my 20. I love it. 20. It's a good number. It's a good number. Okay. I want to circle back to the whole point around risk in AI. The report said only 15%, I was fascinated by this stat, only 15% of employers say they're confident that AI reduces bias. I was blown away by that because I would have thought it would have been way, way higher.

[00:10:51] And then there was a related stat that was 45% have no governance framework at all. So you're like, okay, is it reducing bias? Some don't believe that. I'm a big proponent of it does reduce bias, right? Maybe not. It doesn't get rid of it all, but it reduces. But then there's no framework. It's trackable. Right. I mean, that's the thing. How about that? Oh, yeah. You're going to... I know. We could do a whole separate. You're going to unscrew a can of the... I know. I had to. I had to.

[00:11:22] But the thing that, you know, as a former practitioner, whenever I saw that 45% of the organizations, and a lot of these were like upper mid cap and enterprise. So we're talking about large organizations have no governance framework about AI. So getting back to the first question of, yes, it's 69%, but why are only 18 using it across the board? That's another reason. That to me is that three-legged stool. Because if you have no governance framework, then yes, you are going to have to limit where are you testing AI before you go all out.

[00:11:52] So and just whenever I read that, I know Madeline and Tim and I were talking about it and all of us had acid reflux of going, oh my God, why would you not have a framework in place before you put in a new technology? But 45% of organizations, they're not doing it. It's a little concerning. I think we've got time for one more quick question. Go for it. And I'm going to grab it this time. Do it. This time it's my turn. Okay. I'm in TA. I'm starting to use AI. I'm not one of those that has the governance policy. I'm not one of the 18% using it end to end.

[00:12:20] I am like almost all right. I'm feeling my way. I don't really know what I don't know. I read the report. What's my one aha moment that I can take back to my manager and say, we should be doing this or we shouldn't be doing this. It's the number of hours saved and what you can do with those hours. To me, that is the gold. And if you're thinking about, all right, I think it was five to 10. So let's say seven hours per week is what's being saved by a recruiter.

[00:12:48] Your average base salary is $70,000. So that comes up to roughly $35, $34.80 an hour. You guys can do math in your head. I'm like, sure, if you say so. Sure. Watch somebody in the comments go 382. Even just checked your math. I could see it. Someone checked my math with AI. But there's a hard cost there, you know? So you can go to the business and say, yes, this new tool costs $40,000 a year, but we're

[00:13:16] saving $85,000 just on these cost of inefficiencies and the hidden cost of not being able to have the time to go and source the talent ahead of the opening and the future of the organization. So I think there's some meat there that I think that I would start with. But please, for the love of God, AI does not solve all problems and it is not the Midas touch. You can't just throw it in there and expect for all of it to work.

[00:13:45] If your data is crap, your AI is going to be crap and your output is going to be crap. So start the right way. But at least you'll be able to scale all of those problems. Yes. Yes. It's like, you know, throwing oil in the air and turning the fan on and going, oh, wow, it got all over the place. Yeah. Quickly. Well, I've never heard that analogy before, but I love it. I've heard of throwing something else at a fan. Yeah. Not oil. I was like, oil, that's a new one. Okay. Stephen, you like how quickly I was able to make that.

[00:14:15] Yes. And I'm like, what? Keep it PG, folks. Keep it PG. Yes. But the report does come out on April the 30th. You can go to isims.com backslash insights and you'll see it there. Thank you both for always being willing to bring us on and talk about the data. I love hanging out with the both of you. Awesome. It's great having you. And again, to all of our listeners, definitely download this between Trend and Madeline and Tim. I mean, putting this whole report together, it is gold. It is so timely.

[00:14:44] It is just, it's packed full of so much great info that will, you know, make you do your jobs better. So thank you for putting together all of this. Yeah. Thank you, Trent. Until next time. I'm out. In the next report. Awesome. Thanks, everyone. Cheers.