How can AI be used in an outcomes-based recruiting scenario

[00:00:13] Welcome to Episode 81 of the High Volume Hiring Podcast. My name is Steven Rothberg and I am one of your two co-hosts. I'm the founder of College Recruiter and my co-host is Jeanette Leeds. Jeanette, it is great to see you. Happy New Year.

[00:00:27] Happy New Year to you. Great to see everyone. For those who don't know me, Jeanette Leeds, I'm HR tech. What am I? I'm an entrepreneur, an evangelist, an AI, TA innovator. And on this first episode of 2025, we of course need to be talking about AI, but bringing it really to specific. So, so happy to have James here. James Winfrey is the head of business operations at ARIA by LeoForce.

[00:00:55] And I'm so thrilled to have you here for our first 2025 session. Welcome.

[00:00:59] Thank you all very, very much and happy New Year to you both. We've all had a great holiday and restful holiday season, right?

[00:01:07] What's that?

[00:01:07] Excited to hear.

[00:01:09] We should all be getting rest during the holiday season and at some point I'll find somebody who does.

[00:01:15] Exactly, exactly. Well, James, why don't we kick it off with just for our listeners who might not know what ARIA by LeoForce is.

[00:01:23] It's like, it just gives us a quick like background on that.

[00:01:26] Yeah, yeah, yeah. So, so I, I'll date myself a little bit. I've been in the space for about 22 years.

[00:01:32] My teeth at Monster, had a brief cup of coffee at Indeed.

[00:01:37] Before Indeed is what it is today.

[00:01:40] Worked on the social side of things with recruitment, advertising, all with Facebook and partnering with Facebook.

[00:01:46] And most recently here at, at LeoForce where we use AI or ARIA, which is our AI engine to deliver outcome-based recruiting.

[00:01:56] And I really think that if you look at the models today, you want to hire better, you want to hire quicker.

[00:02:02] And so those being the outcomes, we're using AI to drive to those quicker, more efficient hiring outcomes.

[00:02:08] So excited to have this conversation.

[00:02:10] Awesome. I love it. And I love, by the way, that you said 22 years, not 20, 25.

[00:02:16] I think it's been 22 years for the last four years.

[00:02:20] I don't know if it's because I overextended my experience in the space or if I'm like just stuck in time.

[00:02:29] Well, that's sort of like, you know, I'm 24 years old, but I have 25 plus years of experience.

[00:02:35] So, you know, go, go, go figure, go figure.

[00:02:37] Older than 25, less than 70.

[00:02:41] We'll go, we'll go with that.

[00:02:43] For the people who are watching on like LinkedIn, YouTube, listening on Apple, Spotify,

[00:02:49] they'll know that Janette and I are very big believers in data-driven decision-making.

[00:02:54] And one of the reasons that I was excited to have you on as a guest was just when you were like,

[00:02:58] I could talk about outcomes.

[00:03:00] So for the people who don't know much about ARIA, the engine or LeoForce, you know, the corporate name,

[00:03:09] what is the difference when you emphasize the outcomes piece?

[00:03:14] To me, that's saying you're trying to distinguish your product, how it works,

[00:03:19] maybe how it should be evaluated from some of the others on the marketplace.

[00:03:23] So where does the outcomes come from?

[00:03:25] Yeah.

[00:03:26] So if you think about any recruiting efforts that you do as an organization and you look at,

[00:03:31] you know, what's the end outcome here?

[00:03:34] Well, I want a qualified applicant to an endpoint.

[00:03:37] I want a recruiter to just have a qualified applicant sitting within their applicant tracking system for them to engage with.

[00:03:44] Then from that outcome, you look at, well, what is the end outcome?

[00:03:47] Well, I want more qualified applicants at the bottom of the funnel.

[00:03:50] All right.

[00:03:50] Well, then what next?

[00:03:51] Well, I want to schedule an interview with those individuals that are making it through this portion of the apply process.

[00:03:57] Well, then what is the end outcome after that?

[00:03:59] Well, I want to hire those individuals.

[00:04:01] And so we fall in sort of that bottom bucket with regards to driving more quality to the bottom of your apply funnel

[00:04:08] and utilizing the AI to do just that.

[00:04:11] And so I think that in the world that we live in today, some organizations have taught people or conditioned people to think that volume is necessary to succeed.

[00:04:24] And regardless of quality.

[00:04:26] And so we utilize AI to just drive more quality at the end of the apply funnel and only pay for that outcome.

[00:04:33] And so that's what I think is really cool is that your end outcome that we're partnering with you all on, whoever it might be, is I want more quality applications at the end of my apply process.

[00:04:46] Fine.

[00:04:46] If we drive you 10 or if we drive you 100, you only play for what we deliver.

[00:04:50] So like a true pay for performance model, which I think you'll see more organizations moving toward the new year.

[00:04:58] Really interesting.

[00:04:59] So maybe you could tell us a little bit more like specifics or some examples of like how the AI is really being used to improve the recruiter efficiency.

[00:05:11] So it's like, OK, bring it down the funnel, but like maybe just pin in a little bit more on that.

[00:05:15] Yeah. So if you think about what a recruiter spends most of their time doing is is searching and finding.

[00:05:22] And so or or identifying kind of who I need to talk to first and utilizing the AI to sort of validate the top of the quality components of these individuals that are already applied is really an amazing way to kind of cut through some of the noise.

[00:05:42] And so what you want to do is you want your recruiter spending time building relationships, moving people through the apply funnel, not identifying who I want to talk to.

[00:05:49] And so if you've got a large volume provider that's sending in a whole bunch of applicants, utilizing the AI to recognize who do I need to talk to?

[00:06:00] Who do I need to send the thank you but no thank you letter to reducing a lot of that noise by in some cases 50 to 60 percent?

[00:06:08] So a recruiter, I was talking to an organization not too long ago where they have 15,000 applies a month.

[00:06:14] This is for CDL over the road type drivers.

[00:06:18] And when you look at the audience, it's like there's three million plus of these people in existence.

[00:06:24] You're going to exhaust that.

[00:06:26] You're doing yourself and your brand a disservice by providing your recruiters with all of this volume.

[00:06:31] So let's say we cut that in half and that the AI will show you the top 50 percent of those individuals that are matched to those roles that they're applying to and then send the thank you but no thank you notes to the other 50 percent.

[00:06:43] And so what happens there is now your recruiter says, well, I have 50 percent more of my time back or I don't have to figure out who to talk to so I can spend more time building relationships with the right people not figuring out who to talk to.

[00:06:57] Have you ever been to a webinar where the topic was great, but there wasn't enough time to ask questions or have a dialogue to learn more?

[00:07:03] Well, welcome to HR and Payroll 2.0, the podcast where those post-webinar questions become episodes.

[00:07:09] We feature HR practitioners, leaders and founders of HR, payroll and workplace innovation and transformation sharing their insights and lessons learned from the trenches.

[00:07:17] We dig in to share the knowledge and tips that can help modern HR and payroll leaders navigate the challenges and opportunities ahead.

[00:07:23] So join us for highly authentic unscripted conversations and let's learn together.

[00:07:29] Interesting.

[00:07:30] So one of the things that I struggle with and maybe you can help is how do we as tech providers or even as a recruiter know what quality is?

[00:07:42] Because it's one thing to meet with a candidate, to interview them and to sort of have this gut feeling like she's got what I want.

[00:07:55] She doesn't have what I don't want.

[00:07:57] Therefore, it's quality and therefore I'm going to say extend an offer.

[00:08:01] But when you're talking about hiring 100 people, you know, getting 1000 applications and you want to do that in 12 days, like automation has to take over there to a large extent.

[00:08:15] How do our tech systems know what quality is?

[00:08:19] If the reality is a lot of recruiters can't define what quality is.

[00:08:24] They define it like I know it when I see it.

[00:08:26] It's a little bit like pornography.

[00:08:28] I was like, happy new year!

[00:08:32] 2025!

[00:08:34] Partially recovered lawyer here.

[00:08:35] There was a great Supreme Court case about pornography like 100 years ago.

[00:08:39] Maybe kids referencing, yeah.

[00:08:41] Yeah.

[00:08:41] It is a somewhat G-related show.

[00:08:43] We're going to keep it that way.

[00:08:44] But really, James, like how does Arian know what quality is?

[00:08:49] Is it just looking at the resume and keyword matching and looking at context?

[00:08:54] Because a lot of times what's really great about a candidate isn't on the resume.

[00:08:58] And a lot of times recruiters, hiring managers do a terrible job of writing the job description.

[00:09:03] So how do you do that?

[00:09:04] Yeah, yeah.

[00:09:05] So that's a great question.

[00:09:06] And so from a basic standpoint, it really is the matching of a job.

[00:09:10] An individual and their online profile up against a role and including the company and the company's history and culture behind all of that.

[00:09:20] And so when you think about like high volume stuff, you're like, look, I just need a $20 picker packer, a $20 an hour picker packer.

[00:09:28] Well, that's great.

[00:09:29] But like within the job description, they have to lift so many pounds.

[00:09:32] They have to do all of this from a cultural standpoint.

[00:09:34] I think that that's where the AI just creates these little automations that if I can reduce the noise of all of this inbound by 20%, what do you do as a recruiter with that extra time?

[00:09:49] Well, now I have 20% more in my day to look for those exact ones that I really want to.

[00:09:55] And so it's by no means sitting back and saying like, here are the people that you need to hire.

[00:10:01] It's more along the lines of here are the people that really aren't a match to that role.

[00:10:06] We can put them in bucket B, but you need to focus in over here on bucket A first from an efficiency standpoint.

[00:10:13] And so I think that's where we can really make a lot of impact, especially within that high volume stuff is that you've got these AI engines that now can apply for individuals.

[00:10:24] The AI is monitoring itself now.

[00:10:26] Now it's, you know, I'm excited about what that, what those outcomes can be now.

[00:10:32] And looking at, you use the term automations.

[00:10:35] My mission for 2025 is like automate the automations.

[00:10:39] I'm going to make t-shirts about it.

[00:10:40] Is I really sit back and say, what more can we automate?

[00:10:44] If I get a person that's a good match to your role, they're engaged.

[00:10:48] Why can't we schedule an interview with them right now?

[00:10:51] And if you think about some of these roles, they're on their phone, they're applying at night, not when a recruiter is available.

[00:10:57] Well, now a recruiter has to pick up the phone and try and get a hold of these individuals.

[00:11:01] Well, can the AI just schedule an interview and link up to my calendar?

[00:11:05] And I'm not even there.

[00:11:07] Can they take it a step further?

[00:11:09] I don't know.

[00:11:10] But like really looking at how do we just automate the automations to create those recruiter efficiencies?

[00:11:14] Yeah, no, that's awesome.

[00:11:16] I mean, there's a lot out there that's already happening.

[00:11:18] And so it'll be really interesting to see how far it gets pushed.

[00:11:22] Yeah.

[00:11:23] We'll start seeing a bunch of niche type organizations pop up where it's like, I have some friends that work at one where I'm just focusing in on the application volume for staffing organizations.

[00:11:33] And this is all I do right here.

[00:11:35] And so I'm excited to see what's to come.

[00:11:38] Yeah.

[00:11:38] Any other predictions for 2025?

[00:11:42] Put you on the spot there, but hey.

[00:11:43] I'm in Austin, so maybe a UT national championship.

[00:11:51] I like it.

[00:11:53] We are recording this show in advance.

[00:11:55] And so this prediction might not age well.

[00:11:58] No, very true.

[00:12:01] Jeanette, I think we have time for one more question.

[00:12:04] You want to grab it?

[00:12:05] I do.

[00:12:06] Okay.

[00:12:06] So we're talking about the outcomes at the beginning, like outcomes based.

[00:12:11] Can you maybe give like an example where you've seen like really like a case study that you've seen where it's just been so, so awesome.

[00:12:20] And maybe a defined outcome because you were saying like, okay, there could be so many different types of outcomes.

[00:12:25] Maybe we could give our listeners like a fun case story.

[00:12:29] Yeah, so we work with a lot of healthcare organizations as do I think a lot of people right now.

[00:12:34] And we just recognize that there's a lot of need in that space.

[00:12:38] We also recognize that our industry, me coming from a large job board was partially responsible for it, have created a lot of noise around these recruiters.

[00:12:48] And so we work with a large, well-known healthcare facility where they're looking for RNs across all of the markets.

[00:12:55] We started off very, very small in one market in this outcome based scenario.

[00:13:00] And that meaning is I'm looking for an OBGYN RN in Detroit, Michigan.

[00:13:05] Okay.

[00:13:06] You're only going to pay for OBGYN RNs that apply to that job and end up in your applicant tracking system.

[00:13:13] You're not going to pay for the one from Dallas that accidentally applied.

[00:13:16] You're not going to pay for the one in Austin, Texas that is a pediatric RN.

[00:13:21] And so looking at that outcome base is really what I think a lot of organizations wanted to be was a true pay for performance model.

[00:13:30] You could give me a million dollars a month in budget, but if I'm just going to drive you four applies, you're only going to pay for those four blocks.

[00:13:37] And then next month, I'll try harder to get to that million dollar budget.

[00:13:40] And so this large healthcare facility started out in one market.

[00:13:46] They were probably our first actual client that adopted this new product line two years ago.

[00:13:52] We're in 25 markets with them right now, and they're still going strong into the new year.

[00:13:58] So really, really exciting stuff.

[00:14:00] I think you'll see a lot of us yelling and screaming and telling our story from the rooftops because it is something that we know is working.

[00:14:09] And we're really excited about more of this outcome-based recruitment solutions, utilizing AI.

[00:14:16] That's awesome.

[00:14:17] I love it.

[00:14:17] I mean, from that efficiency and using your marketing dollars, like really, really well.

[00:14:23] It's awesome.

[00:14:23] And my dog, if you just heard of this, it's going to be a thing for next year.

[00:14:28] Awesome.

[00:14:29] My dog is literally like, yeah, good job.

[00:14:33] Literally, I don't know what the microphone picked it up.

[00:14:35] Yeah, your dog is wearing like the UT Austin, like the orange and the longhorns and whatever.

[00:14:43] Well, James, this is awesome.

[00:14:45] I'm a huge fan of looking at outcomes.

[00:14:49] Accountability is great, right?

[00:14:50] Because if you're doing a great job, everybody should know it.

[00:14:53] And you should just know like, hey, I'm doing a good job for my boss, my customer, my vendor, whatever it is.

[00:14:59] And it should just be really, really clear and less on focus, less like did she show up to work and work eight hours and more about what did she get accomplished that day?

[00:15:09] Exactly.

[00:15:10] I think that's really great for everybody.

[00:15:11] So thank you very much, Jeanette.

[00:15:14] Awesome to see you.

[00:15:14] See you again in a couple weeks.

[00:15:16] And James, congratulations with Ari.

[00:15:20] Exciting for it.

[00:15:20] Thank you all for having me.

[00:15:22] Cheers.

[00:15:22] Happy New Year, everyone.

[00:15:23] Yeah.

[00:15:24] Happy New Year.

[00:15:24] Take care.

[00:15:25] Thank you.