Heidi Barnett

▶0:00HostWelcome to the Built by People podcast, where we share the stories and insights of the world's top HR leaders. Join us as we dive deep into the minds of HR executives, uncovering their strategies, challenges, and triumphs in shaping today's workforce. Heidi, thank you so much for joining us today. And as a starting question, I always love to ask if you could share a little bit more about your career journey.

▶0:47GuestSure. Thank you so much for having me. I'm super excited to be here with you guys. I— my career journey has started in marketing, actually, and started at ApplicantPro and we were, we're an ATS. So I was working with them and started to build my career with them as they grew as well. I think I was the 7th hire at ApplicantPro, part-time content writer, and then really started to grow up in the organization. Love the work I did there, became eventually our chief revenue officer, running a lot of the teams that touched our clients. And then after that, in the last 3 years, served as CEO of ApplicantPro, and then we made a transition. And integrated into iSolved so that we could better help our customers be able to have, you know, a more holistic and integrated solution set that happens. And we're seeing that in the market, a lot of pull in that way. So, there you go. There's my career journey. Now, I am serving as president of iSolved Talent Acquisition. So, we have a standalone ATS business within iSolved and then an integrated solution there as well. So, most of my career has been spent in the HR space. Before that, I worked an onboarding company. And so most of my career has been spent in the HR space.

▶2:03HostHeidi, can you share a specific time when your team faced an overwhelming number of job applicants, as many fast-growing companies do, and what challenges that created and why it was critical to address that situation?

▶2:15GuestYes, for sure. So one, one clear moment stands out. In 2024, we had a lot of those mass layoffs in tech. I think Meta and Google and Amazon, and a lot of them were downsizing. And so, we posted a developer role around that same time, and normally we see about 20 qualified candidates in over a 5-day time period. But that time, in 2 days, we had over 125 qualified candidates that came in, and it was really overwhelming for us to sit down and say, you know, and the pace wasn't really slowing for us, and it just felt really overwhelming. We wanted to spend some time, of course, looking at our own process, but then also, about our customers. We knew if we were experiencing this, then our customers were experiencing the same thing. So we started, typically we had focused on sourcing, right? We wanted to cast the widest net. We wanted to get as many candidates as we could, and we had to adapt more over to a source or a screening story and to see if we could really work to help screen out and screen in the proper candidates. And so the first thing we really focused on was our job screening questions, knockout questions, weighted questions really helped us to filter on that front end. So really well-written job screening questions can help with that. And the weighting eventually came into some of the AI tools that we have. So we'll get to that in a, in a second. We applied video interviews, and that really helped us to be able to not go back and forth on a phone screen, right? So this is the one-way video interviewing, and it made a massive difference. And then our pre-hire assessments as well. Helped with benchmarking against top performers. So, if we knew we wanted to clone somebody and we love them, we would create a benchmark and then put all applicants against it. So, it was easier to look just in a, in a split second, oh, hey, this person is an 85% fit against our favorite employees. And so, that really helped. And then the birth of our candidate matching AI came into play, and it really helped us to surface applicants that were the best fit according to job description and other indicators that were coming in there. So, All of those really helped with that, you know, our concrete steps and tools to, to really help to curb some of that, some of the challenge that came in with so many candidates in an influx.

▶4:33HostCan you walk us through the concrete steps and tools, especially any AI-driven solutions that you implemented to manage and prioritize candidates, and also describe any obstacles you encountered along the way?

▶4:44GuestYeah, I gave you a few of those, but I want to go just a little bit deeper on the candidate matching here. Really, I think this This also helped with the obstacle question that you, that you brought up. I think our biggest obstacle was the job ad. So a lot of our candidate matching is based around the true job ad that was written in order to be able to match with the resumes that are coming in. And then there's some other pieces that come in and, and really help to educate around that. The more generic it was, the more mismatched the candidates were. And so that's really one of the challenges that, that came out of this is that we had to rewrite and be really clear. Prescriptive about the different things that we were looking for. And in order to be able to do that, you really have to understand what the job does, which I think is a challenge for a lot of our practitioners, right? They're trying to be generic. They're not in the day-to-day. And so there's a couple of questions that I think make a big difference when writing the job ad. I love to include a day in the life section. If you can get a hiring manager to really help lay out for you what a day in the life would look like, you can write that into the ad. You start to get a little bit of a story, you get a more of an emotional connection, and then you get more excitement around it, and people will come to the ad looking for something very, very specific, and they'll come in excited about it. And so the candidate matching, when you have the job ad in conjunction with the candidate matching and your job ads— that, I mean, sorry, I said job ads, what I meant were your job screening questions. When you have those in conjunction with one another, you really start to create this AI-driven solution that's going to show you who is the best fit and then really show you where they're lacking. So, when you look at our candidate matching, you see that there are a certain percentage of fits. So, visually, you can, you can pop it open and see them right away. The other thing is, as you dive a little bit deeper, you can see these maybe aren't— this is where they're weak. So, let's say I'm looking for a marketing person and I have HubSpot as a requirement. And HubSpot expert, maybe they don't come in as an expert. I can look at that and say they're a little bit weaker in this area. How important is it for me, right, to be able to, to interview and, and look at this person?

▶6:56HostLooking back, what were the most important lessons learned from that experience and how did it change your approach to recruiting moving forward?

▶7:04GuestI love this question. I think the biggest lesson for me is that volume doesn't necessarily equal value. So a lot of times you'll hear HR professionals say, I got 150 candidates for this job, right? It makes us feel good. It's like a little warm blanket that we put over ourselves. However, unless there are— the candidates are qualified, it's not great. And it's also, you know, just like one warm blanket is great, 75 warm blankets is not necessarily good when you're trying to sleep, right? It's overwhelming. And so that's the thing that was the biggest lesson. Unless your inputs, your job ad, your screening questions, your assessments are all aligned, The AI won't really help you, right? It's, it's maybe going to give you a little bit, but it's, it's just going to get you faster to the noise overall. And so really starting to treat your hiring as a system and not necessarily just a linear pipeline, right? This goes back to my marketing experience of leads. It doesn't matter how many awesome lead forms you get in unless they're qualified, it's not going to convert and get you to where you're looking for. And so we really wanted to optimize each step so that the recruiters could focus on connecting with the candidate, not just sifting through and sorting through them. A number that I read is that, um, SHRM, um, says that it takes about 23 to 25 hours per candidate to find a hire. Like, those are crazy, right? You look at that as like, that's a whole day of your life trying to get to that candidate. So the faster you can get to them, but not only fast to sift, but fast to convert, right? That's really where we want to focus people.

▶8:41HostIf you were to tackle a similar challenge again, what would you do differently? And are there any common pitfalls that you'd advise others to avoid?

▶8:49GuestYeah, if I could do it again, I would fix the job ad first, right? So let's, let's work on the filter on the front end and make sure that there is that deep connection. Common pitfalls to avoid, you know, thinking just shifting your focus. More candidates isn't necessarily the best option, or, you know, more candidates isn't always better. So looking for the quality scores and then use your AI as a tool, right? You're— a lot of people will try out AI in various forms and functions and say, it just doesn't work for me, or I don't like it, it's not returning it. But we have to remember, we have to train it over time, right? So if we don't like the inputs, that we need to adjust and then give that feedback wherever possible. And I think the other thing is that there, there is a heavy sense of overwhelm, right? And when we're feeling overwhelmed, then we're going to shortcut things. And so we over-automate. Sometimes we under-automate, sometimes we over-automate. If we over-automate without thinking, then it can also, you know, mess us up on the backside. So I think that those are, those are some of the pitfalls. I think that we've got to shift. Some of it is mindset and some of it is actually that tactical Let's go look at our job screening questions. Let's go look at our job ads. The AI only is as good as the inputs that come in.

▶10:08HostWhat parting advice would you like to provide to our community?

▶10:10GuestParting advice, I would say utilize the AI. Utilize AI. Let's make sure that it is helping you though overall. Utilize those inputs, work on your filters, educate back the AI functionality so that it gets better and smarter over time. And I think the other thing is that when I talk about AI is it's that it's not the wand, but the wizard where the magic comes, right? So we are the wizards in this scenario and AI is our wand. So it's only as good as the inputs that we give it. You know, when you're using ChatGPT, it's your prompts. When you're using the AI functionality within systems, it really is only as good as we are at giving it the information and inputs. So that's my advice is if it's not work, like first of all, try it out. If it's not working for you, work on the input, see how you can modify the inputs to be able to help you get the results that you're looking for.

▶11:06HostHeidi, thanks so much for joining us today on the Built by People podcast. It was a pleasure to have you involved.

▶11:11GuestThank you so much.