Hiring in the AI Era: How to Embrace and Evaluate the AI-Enabled Candidate

What does AI-powered hiring look like in 2025? From AI-drafted resumes to interview prep, candidates are leveraging tech across every stage of recruiting to stand out, fundamentally shifting how hiring decisions are made.

In this webinar, we dive deep into the evolution of talent acquisition in the AI era. Join our expert panel, featuring Spark Hire, Zapier, and SelectSoftware Reviews, as we unpack the changes in candidate behavior and provide actionable strategies for hiring teams aiming for fair, effective, and scalable processes. What you’ll learn:

• AI’s Influence: Discover how AI is shaping candidate behavior and redefining hiring expectations.
• Authenticity in AI: Explore what “authenticity” means today, as AI has become an integral part of the recruitment process for candidates and employers.
• Evaluation Frameworks: Learn practical frameworks to confidently assess candidates who are using AI within their applications, resumes, and beyond.
• Risk Mitigation: Understand the key risks to watch for and learn how to maintain quality and fairness in your hiring process.

Welcome, everyone. Julie, please feel free to drop into the chat where you’re dialing in from.

I’m a wait for a few hours to drive.

Hey from PA. I’m in New York to get things started.

Chicago. North Suburbs Of Chicago.

Love it. Westchester, New York over here.

And I’m from Los Angeles.

Every coast. Coast to coast.

Oh, lots of Go Rez. Lots of Ohio. Lots of Chicago. Awesome.

Welcome, everyone.

Thanks for joining us today for this session. I’m Jen Sales, CRO of Select Software Reviews, a company focused on helping organizations find and buy the right HR and recruiting tools through our in rep in-depth reviews and buyer guides. We also have a team of HR tech experts dedicated to giving tailored advice and unbiased product recommendations to prospective buyers completely free of charge. We’re gonna share a link, to our calendar so you can book a call with one of our HR software advisors. If you’re looking to buy a new HRIS, ATS, or payroll system, this is a great resource to save you a lot of time in the buying process. Performance management too extensively.

So we’ll share some links there.

In today’s session, we’re gonna explore how candidates are using AI to navigate the hiring process from writing resumes with tools like ChatGPT to preparing for interviews with AI driven coaching. We’ll talk about what authenticity looks like in an AI assisted job search and how hiring teams can fairly evaluate applicants without losing the human element. We’ll also cover some of the risks and challenges this introduces and a lot more. But before we start, we have three amazing panelists with us.

A few words about our guest speakers. First, we have Tracy St. Deak. Tracy is the global head of talent at Zapier on the on the right side of the slide.

Global head of talent at Zapier for the past three years. She has over fifteen years of recruiting experience, including nearly thirteen years at Teach for America. She brings a distinct personal perspective to hiring with a focus on building meaningful relationships through authenticity and employer branding, brand ambassadors, and connections made through the entire recruiting life cycle. Welcome, Tracy.

Next up, we have Josh Tolan, CEO of SparkHire, a hiring software designed for small and midsize employers where people are the key to success. Today, SparkHire supports thousands of organizations helping HR and talent acquisition teams connect with candidates, collaborate with hiring managers, and make faster, more confident decisions. We’re gonna share a link to SparkHire in the chat.

And last, we have Adam Kaczanski, also from SparkHire, senior director of commercial revenue. He has worked for in HR for over nine years collaborating with hundreds of organizations to empower their HR teams and streamline their recruiting processes. At SparkHire, Adam has been a proactive team leader, most recently responsible for establishing and fostering enterprise customer relationships. Welcome. Happy to have you all here. Thanks.

Happy to meet you.

Yeah.

Please note a link to the on demand recording of this webinar will be shared to your registered email. Also, this session has been approved for one HR general credit towards recertification through HRCI and SHRM. Credits are sent out via email a few hours after the event, providing you attended for the whole session. We wanna have an engaging and interactive session, so please use the chat section to leave any comments, questions, or insights, and we’ll try our best to address it. We also have a ten to fifteen minute q and a, toward the end.

To get started, we’re gonna launch a poll, to get to understand how our audience currently sees candidates most likely to use AI during the hiring process. So please enter your input on the poll. Where do you see candidates most likely to use AI tools during the hiring process? Resume writing, interview preparation, live interviews, or something else?

See some resume writing, I I figured.

Yeah.

For sure.

Phil is really good at, like our founder is good at at sifting. He know, he’s like, well, let let’s let’s find someone who didn’t AI to write all of it. You know? Like, a cover cover letters mainly.

You know?

You see the words being pulled.

I’m not great at at but, you know, either way, people are gonna use AI. So awesome. So we’re gonna launch into our discussion on market dynamics.

A shift in the HR hiring landscape, the need for faster automated processes to keep up with limited resources and personnel, enter AI and hiring on both sides of the process.

So panelists, how has the workforce and consequently hiring practices shifted in recent years?

Yeah. I mean, I’d love to hear Tracy’s perspective on this.

You know, from what I’ve seen over the last few years, especially now with the rise of AI, there’s been this convergence, which has created a lot of challenges for employers. You know, there was the rise of easy apply over the last five or so years where, you know, job boards, LinkedIn, Indeed, etcetera, made it so easy to apply for jobs in a couple of clicks. And now with AI, you still have easy apply where it’s still easy to apply for a bunch of roles, but you also have AI enabled candidates where now it’s also easier for them to follow a best practice, which is what we’ve always said of personalizing their resume and their application for roles.

So now it’s like an infinitely scalable process for candidates to apply for roles. And you know, from what I’ve seen and from what our customers are seeing, that’s leading to, a, a lot more applicants entering the top of the funnel, and then, b, a challenge in differentiating those applicants because as they come in, they kind of all start to regress to the mean when everybody starts to use AI in the process to personalize things. You start to see a lot of similarities in application question answers, start to see a lot of patterns across resumes.

Maybe that’s what Jen was just referring to as well while the poll was going on.

And so the convergence of these things, I think, has just created a lot of challenges. That’s at least from my perspective. But, Tracy, you and your team live this every day, so we’d love to hear what you think.

Yeah. I mean, I think you said it for sure. We are seeing a much higher volume of applicants who appear completely perfect based on our job description. And so as a result, our recruiters are spending so much more time on application review.

They’re spending so much more time on recruiter screens because it’s hard to differentiate between the real top candidates and the ones that just look really impressive on paper. And of course, with the rise of use of AI, we’re also dealing with a bit more fraud as well. And so that is also taking a lot of our time and attention away from really working with the serious candidates, the top candidates that are most qualified for the role. But we just have to sift through all of the noise and try to figure out ways to cut through it.

So, yeah, couldn’t agree more with what you said.

Tracy, really catch what happened?

How do you, like, how do you fish it out when they get past there and you’re now doing that phone screen or you have that first one to one with that candidate?

How are you able to surface, alright. This was an AI resume. This is how they got in the door.

Yeah. Well, one thing that’s interesting at Zapier is that we’re we’re resume optional anyway. So we actually we we look at your resume. We’ll look at your LinkedIn if you decide to submit it.

Honestly, about 90% of our applicants do, but we are resume optional for a reason because we wanna look at how you answer those application questions, which are a bit more detailed and specific to the job itself. Now, of course, people can use AI to answer the application question. So, you know, we’re not we’re not completely solving the problem there. But on our one on one screens, we’re able to dig a bit deeper, really understand results.

And I think at that point, we can usually tell when folks are legitimately sharing about their experiences versus it’s a little fluffy. But that’s why we have the hiring process that we do because then we have a technical assessment. I mean, have other aspects throughout. But I think to Josh’s point is what we’re dealing with is there’s so much noise at the top of the funnel.

We’re spending so much time there even just getting to the point where we’re working with actual serious top candidates.

That was gonna be my next question is with all this noise at the top of the funnel, what kind of challenges does that create for your team downstream when they’re spending so much time early in the process?

I mean, it’s it they it’s capacity. They’re not able to spend the time that they wanna spend with the top candidates, whether it’s answering questions or prepping them or anything like that. Like, we’re just not able to spend as much human time with the candidates that we want to, with the hiring managers, etcetera, because we’re dealing with this volume. But there’s obviously ways that we’re using technology and AI, which, of course, is the premise of this conversation to speed that up. But that’s a big focus of ours, is how are we ensuring that our recruiters are spending time on the things that really matter that lead to rigorous hiring decisions and better decisions and not on just trying to figure out who’s cheating or not cheating by using AI on their resume. Like, that’s not a good use of of anyone’s time.

Totally. Yeah. You gotta focus on what matters. And, you know, the other thing I’m interested in on your take is Zapier has an amazing employer brand.

So in some ways, your team has always probably been exposed to a larger than average volume of applicants just because a lot of people wanna work at Zapier. Why wouldn’t they? It’s a great company to work for. But there’s certainly a lot of employers that just maybe don’t have, you know, as well known of an employer brand.

And so now these types of companies where historically maybe they’d struggled to get applicants are now inundated with applicants, and they don’t necessarily have the infrastructure, whether it be the team or the process to deal with that.

I guess from your perspective, like if you were sitting in somebody’s shoes like that, how would you approach that challenge?

Yeah. It’s such a great question. I mean, well, we we go a little bit back, Josh, so you know my my views on employer demand and how it’s such a critical piece of any process. It has to be woven into everything that you do.

But I would say that if you’re getting hit with an influx of candidates and you’re not sure about the quality of those candidates, it’s really thinking through what are some of the things that you can do that sort of knock out excuse, like, knockout questions, for example. How can you cut through the noise earlier on so that, again, you’re getting the human in the loop where the human needs to be in the loop. And so that’s where I would say, you know, in this age of AI making things easier, what are the right tools for AI for your process and your philosophy on candidate experience that would make sense in your context to be able to cut through some of that noise and put the human where the human needs to be.

Taking, like, just a little bit of a step back to get just slightly meta for a second, before my team started using any AI tools really in our process, we had a a real alignment conversation with my leadership team to say, what is our philosophy on AI? What is our philosophy on where humans should be in the loop, where they have unique value, where technology can actually help speed that up so that we can balance hiring quality, we can balance speed, we can balance efficiency, but really still stay true to our candidate commitments to be human centered and to be candidate centered as much as possible.

And so because we started with that philosophy, knowing that the technology that we probably need is not even either created yet or it’s not quite where we need it to be yet, it made it easier for us to say, yep. That’s where the human needs to stay in the loop, and that’s actually a solution we need to solve for by finding technology. Once the technology gets there, let’s implement because that’s not the place where we’re adding the most value. Rather than kind of chasing the next shiny AI tool that comes up and figuring out how we could use it, we started from what do we believe about the candidate experience, and then where can we actually figure out where tools are necessary.

So for a company that is maybe sifting through a ton of that high volume, I still say go back to that philosophy. And then for everything that you don’t need a human in the loop, see if you can find a better technology solution for that.

How Go ahead, Jen.

And how can technology help organizations balance hiring quality and speed, and how can it hurt?

I mean well, so Zapier is an AI orchestration platform, so I’ll say that up front because our mission is to make automation work for everyone. So we are very AI forward.

One of our values is build the robot. Like, don’t do things as a human that technology can do for you. That’s just one of our values. So I’ll say that we might be a little bit unique in that regard, but I think technology is improving so much that you can make really intentional decisions about where a human should be involved.

We don’t have to assume anymore that we have to do everything manually. And so there’s tools that can speed up so many parts of the process, right? So for me, I would say when done incorrectly, though, it can hurt. Like, I am not at a place where I would ever allow AI to make any talent decisions for me.

Hiring, promotions, performance reviews, absolutely none of that. But I am at a place where I rely on AI to surface insights, to surface data, research about the talent market, like things that they can like, AI can summarize for me. That concrete evidence or data that I can then use to make better decisions is how I really see the impact of of AI on our work and how we balance that rigor and quality, but also speed. Our CTO says, and I actually love this.

I say it all the time. You can delegate the work, but you can’t delegate the accountability. And as humans, we’re still accountable for the decisions that we make, and technology cannot do that for us. So it can help and hurt.

Yeah. That’s that’s a great perspective, and it kinda takes me back to one of our conversations, Tracy. As you mentioned, we go way back where I think your quote was something about every opportunity to cultivate a candidate matters. And so I like your perspective of we’re going to use AI in the hiring process, but we’re gonna be thoughtful about where we use it.

I think a lot of companies, out there are making the mistake of looking at every problem, in the hiring process or everything to be solved is is this nail with AI as the hammer. And so it’s like implementing AI for AI’s sake when really what you need to be doing is thinking through how can we use AI to highlight what actually matters and bring those insights to the forefront. So as people, we can make better decisions with that information. And so we can spend our time as recruiters in cultivating those relationships with candidates and playing our part of, you know, what really matters in the hiring process and building those relationships, showing that authenticity.

And that’s what’s going to, you know, attract somebody to work at the company. So I think it’s, like, the balance of finding the best use of people time and finding the best use of AI resources, but always through the lens of, like, what really matters here versus just implementing something because you can.

Yeah. Yeah. And and when I I know when I talk to customers, it’s about setting the proper expectations, right, of of what you want AI to accomplish for you. And it’s, you know, Tracy, going back to what you said earlier, it should empower but not replace.

How are you to make better, quicker decisions but not replace your ability to make better or quicker decisions? Because also as a recruiter, AI will never fully understand your company culture. Right. AI will never understand what your company’s initiatives are over the next six months or twelve months, only you will. So you can use AI until you’re blue in the face, but it’s always gonna stop short of making the critical decisions that you absolutely need human intervention to make.

Yeah. 100 agree.

Tracy, you’ve written recently about Zapier leaning into AI for an AI an AI first approach to hiring. Can you walk us through what sparked that shift and what it actually looks like in practice? That’s a great article. I think we’ll share it after.

Yeah. Yeah. For sure. So okay. I’ll I’ll step back a little bit and talk about market and Zapier context, which I think will be relevant, and then I can go as in-depth as you want around how we actually implemented it.

So, I mean, in the market, right, the bar just keeps rising. Like, companies that don’t embed AI, that don’t embed AI fluency into their hiring process eventually will fall behind as AI capabilities accelerate. And so we think about not only future proofing our talent, but future proofing our company because we’re not just hiring for today’s jobs, but things are changing so rapidly. We have to also think about hiring for roles that will evolve dramatically in the next two to three years and hiring for people who are onboard and ready to embrace that curve.

Another thing that my CTO at Zafir says, I get a lot of I get a lot of good quotes from him. He says, you know, we’re not about the snapshot. We’re about the curve where people are. So however technology is today, where people skills are today, that’s one tiny snapshot in time, and it’s probably the worst it will ever be.

And tomorrow, that’s the worst it will ever be. Right? And so we need to think about where are people on the curve and how we’re going to be able to hire for the future. So that’s what I think about the market, and I think I think companies are are really starting to understand that and get on board.

But internally at Zapier, we have more AI agents than employees right now. So we have far, far more than over 800 agents supporting under 800 people. And I think this represents the future of work, like AI human collaboration where people orchestrate AI agents or workflows rather than just doing the tasks themselves. And so at Zapier, 89% of our team already uses AI daily.

Like, we we sort of track that and report on that and see what sort of productivity gains that we’re getting across every function because of that. So all of that context is important because as an AI product, we need to be our best own case study. So it’s so important that our workforce is on the cutting edge of that. So that’s sort of the context.

And then just to get a little bit more nitty gritty into implementation, we’re sort of on this journey from what we call AI adoption, which is like using AI just to do your existing work faster or better, saving a few hours or a lot of hours here and there, to what we call AI transformation, which is really rethinking what work gets done, how, and by whom. So that’s why AI fluency is now required for a 100% of our new hires. We measure it in three ways, or we look at three sort of themes, mindset, strategic acumen, and builder skills. And that’s a little bit different.

Builder skills in particular is a little bit different per role. But then we measure it on kind of like a four point four levels. Unacceptable usage, which is people who are, for whatever reason, still resistant to AI tools, or they use them recreationally. Like, I took a picture of my fridge and asked ChatGPT to create a recipe, you know, things like that.

We would actually now, at that point, consider that unacceptable usage for what we need at Zapier. Then we go up to capable, which is you’ve repeatedly used AI with purpose. Like, you’re showing forward momentum in in learning, you’re you’re building emerging depth in how to use it, how to tweak things, how to prompt better, that’s capable. That would kinda be our minimum expectation for anybody at Zapier in any role.

Adaptive is where I would say we’re sort of aiming for most people to fall in the future at least to start, which is embedding AI in personal workflows. You’re optimizing processes. You can say, I did this, and I got this ROI. I saved ninety hours a month because of that.

And you can actually really say those things and talk about that with real depth. And then transformative folks would be people who have already embraced and understand that AI can fundamentally change how they work. And they scale that impact not just for themselves and their function, but to others as well. That’s sort of like the golden goose.

Like, we don’t we don’t see that very often yet, but I think we’re getting more there. So that’s how we measure AI fluency, and we measure it in multiple parts of our hiring process. We also, by the way, very much encourage and embrace the use of AI in our hiring process, which I’m happy to talk about later. But we do measure that in multiple places, and we also pair that higher expectation with higher support.

So that’s how we really believe you kind of lift all boats rather than create barriers. We do wanna make our bar accessible to people even if they’re less familiar with AI when they first apply for our process. I’ll stop there. I’m happy.

I could talk about this all day. Yeah.

That was that was awesome. I think, you know, I mean, I took a lot away from what you just said, but the things that jumped out to me as you were explaining that spectrum is, two things related to just buy in, for AI in organization. One, if as a company, you want to be an AI enabled organization or an AI fluent organization, it has to and it matters to you. You have to make it matter.

So you have to make it a thing, and that means you have to measure it. You have to monitor it. You have to have programs around it if you want organization wide adoption of it. So that’s from the company side of it.

From the practitioner side of it, what I really heard in the way that you’re evaluating candidates use of AI and where they’re at on that spectrum is you have to take yourself from looking at AI as just like a pure efficiency tool of I can save two hours here or save two hours there to making it more tied to outcomes and actual business repeatable results. And so if you’re a practitioner and you’re trying to make a pitch internally to your stakeholders to get adoption of AI or to allow you to purchase this AI oriented tool, that would be my advice is you have to anchor on, like, the results rather than the efficiency because your stakeholder doesn’t I mean, they care about your efficiency, but they don’t really care about your efficiency.

If you save two hours, they’re gonna you, what are you doing in those other two hours? And so that’s what really jumped about to me in the way that you guys are thinking about AI is you’ve made it matter as a company, and it’s, like, outcome oriented. And I think when those things come together is where you can really accomplish great things. So I don’t have a question on that, but Yeah.

I just wanted to highlight my takeaways and and hope that everybody, drew that same insight.

I’ll just say very briefly on that is I like the framework of I think when AI was first started to be seen in more tools, people were like, oh, it’ll save me so much time. I’ll be able to do so much less work. And I think it’s really important to reframe that. AI will not allow you to do less work.

AI will allow you to do more work. And and what I mean by that is more impact. It will maybe feel like the same amount of hours or whatever, but you will have more impact, better outcomes with using these tools. And I think that’s just a little bit of a mindset shift that people have had to make.

Awesome.

This is fascinating. I I wish we could talk longer on it, but I just wanna stay on, on schedule. So, let’s transition transition to about SparkHire AI. Adam, if you wanna, share your screen.

And Absolutely.

This is usually where I mess up. I even said that before here, sure I’m sharing the right screen. So just let me see what I can do here and make sure I’m doing the right thing. Okay.

You got it.

Oh, I should just end the webinar right here. I mean, I I got something right. I’m I’m out of here. Okay.

Alright. Thank you. And I I am really excited to talk about SparkHire, an organization that I’ve I’ve been lucky to be a part of for a number of years.

But I think it’s as we talk about SparkHire and and how we view AI and and AI’s role in the world of recruitment, I think just a little bit of background about SparkHire. So SparkHire is you’ll see here is two solutions. We have Meet, which is focused on video interviews, behavioral assessments, and reference checks, and then Recruit, which is our ATS. Now this graphic is it’s cool.

I’ll admit that. But it’s also there for a reason. It’s to show you that what makes us unique is that they can be used separately or together. So if your only focus is video interviews and reference checks, you can use that without having to bring on recruit as your ATS.

If you want recruit as your ATS, we include Meet because we wanna give you a holistic solution that provides you with a multitude of options to find the best candidates in the most efficient and successful way possible.

So that’s a bit of background about us. I wanted to do that quickly because there is a lot more I wanna cover in the next, you know, seven to eight minutes.

So what you’re going to see here, there’s a lot going on on this slide, and this was actually filled in by attendees at the recent SHRM conference. So we asked them what’s your biggest headache, and we put four items on the board, and you could see where the clusters are. So you’ll see here tons of app unqualified applicants getting in the way to hiring top talent, which is also something we talked a little bit about earlier today, and hiring manager collaboration slowing down the process. What’s unique about this is this was already in alignment with what some of our AI initiatives were, and I’m gonna highlight that in a moment.

Obviously, also have competing priorities, budget or resource constraints, which is pretty much all the time. You know, HR and talent acquisition teams are usually the teams that have to fight the hardest for budget. We always know that and respect that. But that top of funnel that Tracy was talking about earlier, as well as collaboration with hiring managers or just collaboration across the team are two of the biggest challenges that our talent acquisition teams are facing right now.

So our narrative and this is going to echo a lot of what we already discussed, which is which is great, is first and foremost with Spark Hire, our view is that you’re not hiring with AI, you’re using AI to hire. And what that means is what we were saying, you know, you’re using AI to empower you, to help you make better decisions, to save you time so that you could focus on other impactful initiatives. It’s not here to replace the decisions you make, but it’s here to help you make the better decisions quicker, more efficiently, so you could get to the best candidates before anybody else can.

Highlighting what matters.

So everyone in the hiring process is AI enabled and that includes the candidates as well. And now’s not the time to fight against the current, but to swim with it. We have to understand our candidates are going to use it. We’re using it right in talent acquisition. It’s only fair we expect them to use it.

But then it’s just about how do we use it wisely? How do we use it in a way that is as impactful as possible where we’re giving our candidates the fairest and best shake as possible, but we’re doing work that also enables us to be as successful and find the best candidates that we can. And then problem first, not feature first. So this is a really important point. And I always encourage anyone here regardless of the solution you’re using or looking at, whether it’s payroll or ATS or anything else.

Look for where AI is impacting a problem that you have and is not asking you to change everything that you’re doing to put a square peg in a round hole to leverage AI for something that may be bright and shiny, but isn’t solving a real problem for you. Be focused on the real problems and where AI can seamlessly work into your existing workflows to make things run quicker, easier and smoother for you.

So our strategy is compounding. It’s how does AI in one solution of ours impact another component of that solution, which impacts another solution. So you’ll see using AI in your screening and selection will feed into how you can successfully collaborate with hiring managers and other team members and using AI in that regard to help you use other aspects of our ATS to hire the best candidates. And you’ll just see it’s virtuous circle of leveraging AI to enhance your entire experience with using Spark Hire. And as you see here, no random acts of AI. Every time we add a new feature that focuses on AI, it really is about what are our customers telling us they’re struggling with the most and how can AI just make it easier for them to get the job done and to do it quicker and more efficiently.

All right. So what you’ll see here, I don’t even wanna talk AI for a second. I just want to talk about what you see because everybody’s recruiting workflow is different, but it boils down to the same components. Writing that job description, looking at resumes and comparing it to the job description, interview questions, then scoring the candidate, then summarizing it and handing it off to the hiring manager or having them summarize and hand it to you.

Well, easier said than done with the hiring managers. But imagine leveraging AI in all the right ways here. So using AI to help you write better, more inclusive job descriptions, which leads to screening resumes, leveraging AI that surfaces the best candidates first. And then using that to ask great interview questions because AI helped you generate job specific interview questions.

Then using that to score candidates with scorecards that are again are specific to the role that you’re hiring for with the use of AI. And then again, leaning on AI to help you build that summary so that you could share it with other team members so that they can work more efficiently as well.

Alright, so that’s the compounding value. All right. I’m going to go into a very quick demo and show you just a few of our AI features and again how they work seamlessly into your existing workflows. I’m going to stop sharing for a moment and now I’m going to Right, I’m doing everything right. So we’re still we’re still going in the right direction here. Let me share my screen again.

And now we’re gonna go into recruit, which again is our ATS. So here is recruit. We’re now in our ATS and what I wanna do first is I wanna go to a job that I’m currently building. Right?

And in a lot of jobs that you’re building or or refreshing, sometimes you have a stale job description or just needs a bit of an upgrade, especially as we see the market and technology changing dramatically. Our job descriptions need to keep up with it. So I’ve got my workflow here and we’re gonna talk a little bit about this workflow in a minute, but I wanna go over to my job description. And you’ll see here, I’m just gonna use AI to write my job description.

And you’re maybe saying, Adam, this kinda goes against everything we’ve been talking about. You’re using AI to write your job description for you. But actually what I’m doing is using AI just to help me get started so it’s quicker and more efficient. I know when I have to write a job description, I go on Google and I look at 10 different samples and I kind of Frankenstein together a job description that works for me.

But using AI, it takes away that work and you then still have the ability, you know, must travel. Like, you then have the ability to customize it. Let’s say I don’t need the candidate to analyze market trends. I can delete that.

Or if there are other requirements I wanna add. So again, it’s not writing the job description and as we were talking about earlier, it can’t replace culture. It can’t figure out your company’s culture. But again, it could set the foundation so that you could put in the missing pieces and get that done quicker.

Alright. So we’ve done it to create our job description. Now we’ve put that out in the world and candidates are starting to apply. So I just again wanna go to our workflow here.

And you’ll see here our first step is AI resume review. So what that means is when candidates apply, our AI is going to review their resume and let us know how much of a fit they are with the role that we’re hiring for. So you’ll see here strong, good, partial, and low fit. And I can determine from here whether I wanna still add a review step or advance the candidate to the next step.

Now I did see a question in chat that was something to the effect of, well, why use AI to rank a candidate if you still want that human touch?

Well, it’s not there to replace the ranking of the candidate. What it’s doing is it’s saying, if I were you, I would look at this candidate first because they’re really good and you don’t wanna wait three hours of reviewing 400 resumes before you get to this candidate. You wanna review their resume now, reach out to them and book that interview quick. So that’s what AI resume review does for you is it it’s going to surface the best candidates.

Now I wanna go to a role where we’ve already done this. Our software engineer role. And you’ll see that it has candidates in strong fit, good, partial and low. So all I’m doing is I’m saying, look, my time is precious to me.

I got a lot to do. So I wanna go to my best fits first, look at their resume and see if I wanna reach out to them. So I’m going to click on Jessica. And when I scroll down, I could see that Jessica hits the nail on the head with all of my requirements for the role.

But if I wanna dig even deeper again, because I wanna take that next step as a human, I’m going to click into the evaluation and I’m going to scroll down and I’ll look our requirements, see why that candidate hits the mark, and then I can even expand and get more context. And now I feel so confident about Jessica that I am gonna move her to the next step, will be a phone screen or an interview. So what AI did here was rather than go through three to 400 resumes before I found somebody great like Jessica, put her at the top of my screen, I’m reaching out to her before my competitors do, and I’m getting her in the door.

Because what our candidates like more than anything else is quick, efficient recruiters to work with.

Now, just as an example, let’s say we have a candidate that’s a low fit.

If I don’t trust it, I can again go in deeper and look at that candidate and see, well, why aren’t they a good fit? What are they missing? And again, expand to learn more. So these aren’t making the decisions for me, but empowering me to make the better decisions. The very last thing I’ll highlight is again, now using AI for the future.

So what I also have the ability to do is now in my database, let’s say I’m hiring for a software engineer six months down the road. Well, I just wanna go to all the software engineers that were a stronger or a good fit to begin with.

And it’ll surface those candidates so I can reach out to Becca and say, hey, Becca, you were great the first time around, we did hire somebody else, but if you’re still interested, love to meet with you again.

So as you could see here, human involved in the whole process, but again, using AI to get you better results and to make sure that you’re focusing your time and efforts on other impactful opportunities, while the AI is just doing a little bit of that heavy lifting for you.

I’m gonna stop sharing. I’m gonna turn it back over to Jen in the crowd, but thank you very much.

That was awesome. Thanks, Adam.

And a lot of questions in the chat. We’re gonna try to get to all those questions during the q and a, so we’ll try to like parse out some from the chat too. But thank you. So we’re gonna get into talking about how to safely and effectively incorporate AI in hiring and truly embrace the AI enabled candidate. So first, from an employer use perspective, how do you maintain transparency when using AI in evaluation so that candidates understand what’s happening behind the scenes?

Yeah. It’s such a good question. I mean, to be honest, I think that we should do more of this. We should do more of it at Zapier.

I think companies should do more of it because I actually think it would mitigate a lot of the fears that candidates have. That, like, it’s just AI making a decision on them and not understanding context. Right? It would help people understand that humans are making decisions on their candidacy.

For example, at Zapier, we still do review every application, and now we certainly use tools that help us prioritize and find best fits, Adam was sharing. But we still have that human in the loop there. So I think explaining that more, and we’ve been talking about even at Zapier, maybe we should put out a blog showing exactly how we use AI. And this is like how we use it, why we use it, what it helps us do, can actually just really help candidates feel a little bit more comfortable with it.

Because I also think candidate sentiment will evolve over time. What may feel really wild today to be doing in a recruiting process may feel very normal six months from now. And so by having that transparency, you’re also taking candidates with you along that journey too.

Couldn’t agree more. I think most negative candidate sentiment about anything in the hiring process usually stems from the lack of communication or transparency about what’s going on behind closed doors. The reality is the hiring process itself is kind of just like this black box process. Like, you have all these stakeholders involved, and, you know, there’s just a lot of mystery about what’s going on as decisions are being made. And so I think the more you can shed light on how you’re running your process and how you’re using AI as part of that process, the better. We could probably send it in the follow-up.

But I had a conversation with a talent acquisition leader at a company called Merit America, and they did a really good job of putting out a page on their website which not only talks about, like, Tracy, what you just mentioned, how they’re using AI in the hiring process, but it was also a guide for, how candidates should use AI in the hiring process.

So I think, again, the more you can, like, invest in transparency, the better.

You mentioned in your article, Tracy, that not every AI tool is created equal. What red flags do you watch for when evaluating hiring tech vendors?

No. That’s a really good question. So personally, I mean, baseline, I look at have they considered all of the ethical considerations in their design? How do they think about mitigating bias, data privacy, local laws around that?

All the questions that my legal team would ask, that’s probably the most baseline thing. The second thing, though, I do is I really like to understand their roadmap and see if it’s, one, aspirational, inspirational, and feasible, but also aligned with the roadmap of where I want my team to go in terms of our vision for any technology use, AI or something else. So it’s important to me that with technology changing so quickly, I’m not just working with vendors that are really good right now and might be a market leader right now, but I have some belief that they’re gonna continue to evolve and be a market leader in the future.

So that’s one. And then the last thing is like, I just this is maybe a personal preference. I will never do anything without a pilot, preferably a free one. I need to see the evidence that the tool does what you say it can do, and they and that it works in our existing systems and workflows, that it can be integrated with all of the things that we already do.

It can be seamless. I want to have real use cases to be able to get my team invested in and then be able to say so they’re saying, like, I wanna use this tool. We have to have this tool, Tracy. So those are, like, my my three things, I would say.

Thanks.

And I I I I mean, we’re a vendor, so, you know, I can speak from our perspective, but it definitely aligns with, you know, what Tracy’s saying. The way that we think about it and, hopefully, what shined through at Adam’s presentation today, is is you’re evaluating a vendor. We think about it, like Tracy said, in three, you know, core pillars. There’s, like, the compliance, ethical, legal, you know, core principles side of it, which is, the price of admission.

Right? You either pass that test as a vendor or you don’t. Because if you don’t, it’s like yeah. There’s there’s a lot of downstream ramifications of that.

So that’s, you know, everything from security, privacy, but also, like, ethical, you know, the way that you’re approaching AI and, how you’re testing things and how you’re auditing things. That’s all gotta be part of the consideration.

Then, you know, number two is does the vendor actually have a perspective? I think the challenge that a lot of folks are having on the market right now, in practitioners with vendors in the space is there’s just so much noise. Similar to what we talk about with applicants, there’s so much AI noise, AI hype. Everybody’s releasing a new feature every single day that has AI incorporated into it.

LinkedIn feeds are flooded with AI feature announcements. But it’s really hard to find a vendor’s perspective on, like, what are they actually trying to solve for? And so for us, you heard it through in our presentation, like, we’ve aligned on these two core challenges that majority of customers that we work are facing every single day in their hiring process with screening and selection and hiring collaboration. So everything we do is aligned with those two core challenges and compounds across our solution.

So like Tracy mentioned, she wants to see their road map. I totally agree. I think it’s like they have to have a plan. They have to have a perspective.

And then the third side of it, Tracy mentioned, she said, you know, a pilot, I think, you know, I would call the pillar evidence. Right? So it’s like whether it’s a pilot and you see it for yourself or whether there’s some form of social proof and customers or references that you can talk to, I think that’s, like, the third pillar, you know, and select software. Obviously, I can advise you guys as well on software to give them a shout out.

But that’s that’s the way I look at it.

And I’ll I’ll I’ll say, of any feature we’ve ever released or any theme of features we were we’ve released, trials have never been asked for more than with AI. There’s still so much skepticism about the promises that organizations make and and what AI can accomplish. And even though companies are coming out with new features all the time, it still is a relatively new technology. So you have to lean into that skepticism a little bit, which also means you’ve gotta make sure that what you’re rolling out really does help solve a problem because your customers, the market, they will sniff that out pretty quickly if it’s just a shiny tool that really isn’t doing much for you.

Yeah. It’s a good segue into our our last question in this section, and I wanna jump on your candidate use. But what guidance would you give to HR and business leaders who are hesitant to trust AI in high stakes activities like hiring?

For me, I think it goes back to, like, you delegate the work but not the accountability. Like, we have a clear role at Zapier that humans make decisions. I also do really just think that, like, the question sort of implies that AI is more biased than humans, which I don’t necessarily believe to be true. Like, now we know AI is trained on data and bad bad data in, bad data out.

Right? If it’s trained on human data with bad judgment, it will have bad outcomes. And so I think that’s the piece where HR leaders and vendors and everyone has to be really aware of, like, it’s a design choice. How you think about equity and fairness and inclusivity in your process, those are all design choices.

And so that’s just one piece that I would ensure that people think about. But then I think it’s also about trying the tools. You only get better and you only get more trusting the more you use it. Try it, AB test it, have an AI tool do something for you and have a human completely manually do it for you.

What are the differences in the outcomes? Maybe the AI tool is better, maybe the human is better, but try it and find out. So that’s why I would tell people to start. That’s a great point.

Agreed. Yeah. I mean, I I think, you know, we talked about it earlier as well. Your stakeholders hesitant because of the value of it. Focus on the outcomes that you’re hoping to drive with it. If they’re concerned, you know, or they’re hesitant because they have some objection for how you plan on use it, perhaps then start with a lower stakes use case in the hiring process until you can show evidence of the outcomes, and then you can build up your business case from there.

And and don’t penalize the candidate if you feel like they’ve used it as well. Like I said, you know, everyone’s using it, and you don’t wanna miss out on a great candidate because you’re working under an assumption that they even if they did use it, you know, everyone tailors their resume for or should tailor their resume for all the different jobs that they apply for. So you don’t wanna miss out on a candidate. So if you are skeptical, go to their LinkedIn, look them up, take those deeper steps that Tracy’s talking about that that I have accountability there and take those extra steps to, like, vet out that candidate that has that, you know, potentially perfect AI resume.

And on the candidate side, Tracy, at Zapier, your team has publicly embraced AI and made AI fluency a requirement to work at Zapier. So why the shift, and how are how are you evaluating AI fluency? You talked a little bit about it, at the start. And how are you making this transparent for candidates?

Yeah. So I I think I talked a little bit about why the shift and and sort of how we’re evaluating it. But just to go a little bit deeper, so we do evaluate it at multiple parts in our application process. We have an optional question on the application.

We vet it in recruiter screen. We have a technical assessment that vets AI strategic acumen and builder skills, and that’s very differentiated by role. But even nontechnical roles, like a tax accountant or a recruiter or an ops person in EA, they have an assessment as well where we’re evaluating AI fluency. And then we evaluate it once more at the executive interview at the end.

So again, we do it through all those places. But throughout the way, as as soon as somebody applies, we’re saying, hey, AI fluency is really important to us, and here’s how we’re going to be asking you about it in the future. And here’s transparently how you can brush up on your skills before you even get to that skills interview. So we want to, again, pair the expectation with support so that candidates, if they’re coming from a regulated industry like health financial services, or maybe they just, like, haven’t had the opportunity to use AI in their workflows, they’re not automatically knocked out.

We want to make it possible for people to start playing around with these tool tools and trying them out so that by the time they get to, say, the skills interview, they’re a bit more comfortable, and they could actually pass it. But at Zapier, we also just assume that everyone is using AI. Like, we are not concerned about cheating. We do have application questions that, you know, go beyond that.

We have a candidate statement where we ask people to to agree and say with integrity, like, I will not use AI on live interviews. And like all of the things that I do say, regardless if they’re polished by AI or not, are my own real experiences.

Could people lie about that? Sure. But I think that it really, really differentiates the serious candidates, because if we do find you out, which we have in many cases, we then remove you from consideration for applying to Zapier ever in the future. So I think it really does help candidates understand that we’re serious about it, and we expect you to use AI when you’re here, so we expect you you’ll use it in the application process. Why not?

Is there I wonder, is there a particular moment in the hiring process where it becomes clear, whether a candidate’s use of AI is enhancing or masking their abilities?

I mean, I think it comes out in discussion.

Whether That’s, again, why we have live interviews. We get deeper. We we have the skills assessment. I think it comes out in multiple different ways.

And I would also kind of say, like, just again, this is again, my personal view and taking a little bit of a step back is, like, if you expect people to use it while they’re here in really smart ways, then why would you not expect people to use it in the hiring process in smart ways? Because if they can use it really well, either so much that you can’t even tell in the hiring process, they’re probably gonna use those same tools when they’re here. And as long as they can still do their job and their function and they have that level of expertise, I don’t see the the big risk of them using tools to do something.

I think of, like, a simple example like writing. I use AI with my writing all the time. If I use in the hiring process, maybe I’m presenting myself as a slightly better writer than I really am. But guess what?

In my job, I’m still gonna use those same tools. And so I’ll always be that level of writer for you. So I think the hiring process is supposed to mirror how people perform in the job. And so that’s why we embrace it.

But in discussion, that’s where we get into how did you use it? Why did you use it? How would you tweak it? Where would you not use it?

Things like that. Like, we can get really into how people are using AI and why, but we can also get into the in-depth technical things and their function through discussion.

Yeah. I love that, and it it goes back to highlighting what matters. Like, I don’t care if a candidate uses AI in the hiring process to help better present what actually matters to me and what I’m trying to understand as the employer. And to your point, Tracy, like, would want them to do that in the workplace to enhance and enrich the work output, that they’re putting out at our company.

So I think, yeah, it just comes back to highlighting what matters. And then to your point, you can discover that throughout the hiring process by just better interview questions, better probing questions, behavioral based questions that draw on real world experiences. Because that’s kind of the line to me is, like, I don’t care. Use AI all day to better present yourself, but don’t use it in an inauthentic way that is either, like, cheating, lying about your skills, embellishing some experience.

And so there’s kinda this balance as the employer of, like, you have to set the proper expectations for how candidates can use it, but also expect they’re gonna use it. And as the employer, you kinda have to suss out if they’re using it in a way that’s highlighting what really matters or if they’re using it in an inauthentic way, which eventually, you know, is gonna be found out, and it doesn’t really serve anybody. So, yeah, couldn’t agree more with your take.

Think you’re leaning into the part, Josh, is, like, there’s a difference between using AI to help put your best foot forward and, like, fraud and, like, literally just, like, flying. You know? So that I I consider that something very different. That is why we have number of things in place to prevent fraud, including we do live reference checks because we think external signal’s really important. Even though it takes a little bit more time, it really helps us understand the skills of the candidate and get that sort of external signal from people who know them best. So I do think those are slightly different things. But, yeah, I think you have to adjust your hiring process believing that everybody will use AI.

This is great.

We should launch our networking minute, and we have about ten minutes left for q and a. But this was a fascinating discussion and and looking forward to to getting some questions from the audience. So, first, please drop in the chat, your LinkedIn URL. You’ll also find, all relevant links, in the doc.

We’re about to share our session takeaways doc along with a bit more about our panelists. You can connect with them on LinkedIn, and we have a couple of resources by SSR that would be useful for HR leaders. We have our latest resources, info about our jobs board, and more. See the LinkedIn.

And we’re you’re also gonna get the the chat after too with everybody’s LinkedIns.

Have a couple of upcoming events. On July 23, we have the business of fractional HR, and on July 29, preventing HR burnout.

We will jump into the q and a, and then we’re also gonna launch a feedback poll. See if I can launch this. What feedback do you have for us? Please, please let us know how we did and how we can be better.

And, so now we’ll address some questions. First question, came in ahead of time. What are some of the easiest ways to incorporate AI into your hiring workflow for an immediate positive impact?

I I can give an example. So I mean, I also feel like I should say, like, use Zapier. It’s, like, so easier to use. I mean, just, like, in terms of in terms of guidance, like, I would say start with your biggest personal pain point.

Like, what do you hate doing and what takes you hours? Again, maybe for me, that’s writing memos. And I’m so much better at editing them than writing them from scratch. So I would just say, like, start with what your personal biggest pain point is.

Yeah. Agreed. I think also a low, you know, low effort, low risk, way to use AI is just as a thought partner. As you’re kicking off the hiring process and maybe you’re trying to work with a hiring manager during an intake to figure out what you’re really looking for.

I find that often even outside the hiring process, just like having almost a dialogue with AI, to help extract better thoughts, is really, really helpful. So that’s something that you’re not necessarily incorporating into your process. And so I see it as, like, a very low stakes thing, but it exposes you to AI, and exposes hiring managers to it. So maybe a good place to start for school out there.

Oh, can I give a good example? Because this is one I I feel like my team uses all the time. So I I do really believe that with AI assistance, you can increase the quality of the candidate experience too and the quality of the human connection. So just as an example, at Zapier, we’re we aim to give feedback to all candidates who we don’t advance after, like, a certain stage. Like, if you pass the recruiter screen or job fit, if we don’t move you forward, we will give you feedback.

But given the volume of candidates, the feedback used to have to be pretty high level. So, our recruiters would say more of like a general, well, here’s generally why you didn’t move forward. But now we use AI insights to surface up all the feedback from the scorecards. It’s just a summarization tool, really.

It’s like so instead of recruiters having to read through every single scorecard from every single interviewer, we can utilize AI just to summarize that. And there, we can deliver tailored specific feedback to candidates that is ultimately so much more meaningful for them. Obviously, the recruiter is looking at it and being like, is this right? Yes.

And, you know, I’m double checking it, of course. But I think that’s a higher quality experience for the candidate, and that has saved our recruiters so much time of hours that they can now pour into actual relationship building with candidates and prepping them for interviews and things like that. So I think, again, that was a big pain point for us because we wanna do right by candidates, but we weren’t able to dedicate all the time to do that. So utilizing AI for a pain point that can really benefit the candidate and your team’s efficiency is, like, where I would focus to.

And where do you see AI going next in the hiring process is our next question from participant.

It’s a really good question.

I’d be curious what you let’s see Josh and Adam because you talked to a lot more TA teams. I I definitely see more AI enabled processes at the beginning of the funnel, kind of like I said earlier, because there is so much volume and there is increasingly more fraud.

If you can weed that out prior to recruiters, especially the fraud, if you can read that out prior to recruiters spending their precious human hours talking to less qualified candidates or, in some cases, like actual bad actors, I think that’s the place where I would like to see AI going next. Because then once you have your pool of serious candidates, again, you want to spend time with them. Recruiters want to spend time getting to know them, vetting them, prepping them for interviews, supporting them in the process. So I’d like to see more of that upfront AI support in in weeding out some of the bad actors and keeping the good humans in, as we like to say.

Yeah. I mean, for us, we’re definitely as you saw in Adam’s presentation, we’re definitely focused on screening and selection. We’re really leaning into the fact that customers and TA teams, and we were at SHRM, like, everybody’s telling us their biggest pain pain points. And so we’re really leaning in to address those pain points that are pretty fundamental to the hiring process.

The top of the funnel screening and selection, and hiring manager collaboration. We talked about a lot of the things that are causing the screening and selection challenges with easy apply, AI generate resumes, all at the top of the funnel. Hiring collaboration has always been a challenge. A lot of recruiters have complaints about collaboration with hiring managers, but it’s so more so much more prevalent now because because of all that stuff that’s happening at the top of the funnel and because all TA teams are being asked just generally to do more with less, the reliance on hiring managers as active participants in the hiring process is so much more.

You need them not only for their capacity, but you need them to just run a better hiring process in general. So for us, a lot of our road map is aligned around those two fundamental challenges that, you know, the customers we speak with are are still dealing with every day as as, you know, basic and fundamental to the hiring process, as as they seem. I think there’s obviously still a lot of work to to do in those two areas. So that’s where we’re focused.

Yeah. And I I agree with Josh. I think, you know, when I talk to a lot of our customers, no matter what technology is implemented, no matter what solution you’re using, every talent acquisition person is chasing down their hiring manager for feedback on their candidate. That is like the never ending like journey that a talent acquisition manager goes on.

So I think the collab, yes, top of funnel is tremendous. And I think collaboration, how do we use AI to make it as easy as possible for hiring managers to get educated on candidates so that they can provide their feedback? And I think things like the AI summaries and summarizing scorecards and summarizing one way video interviews and all of the different things that we do so that hiring managers can digest it quicker and easier and then provide you with their feedback or make that determination whether or not they wanna interview that candidate based on the data they’ve received. That’s gonna be a really powerful area that I feel like AI is gonna be leaning into.

We have time for about two more questions. The the next question is, how do you think AI changes the skills hiring teams need today? Are we entering an era of AI literacy in TA roles?

A 100%. I cannot say that enough. Like, if recruiters or anyone on a TA team I would say a talent team, a people team. I would expand this actually to every function. But since you’re asked about hiring teams, like, if recruiters, hiring managers, and interviewers are not utilizing AI enabled workflows, if they’re not learning AI skills, for recruiters, I tell my recruiters, I don’t see how it would be easy for you to get a a next job. Like, this is the opportunity where you’re gonna start to separate those who are embracing and adopting AI versus those that aren’t. And so I just I don’t see a world where this is going away.

No comment. That that question was, like, tailor made for Tracy.

What would we do with this last question? If you could give every HR leader one mindset shift to adopt in this new era of hiring, what would it be?

I think mine will be, I use this analogy with my team. Like, aim to think about AI the way you think about the Internet. AI isn’t a thing that you, like, now add on to your process. Like, you could do it manually, or you could do it with AI.

Now that might be the case. In the future, how I’m trying to build my team is that my team should literally not be able to run without AI. My team could not run without the Internet. We could not do our jobs without the Internet.

In the future, that’s how I believe it will be with AI. And so start thinking about that now. And that’s a mindset shift that I think really unlocked a lot for me and my team.

Yeah. I’ll I’ll just I I couldn’t agree more, and I’ll just go back to what I’ve said here before, and it’s focused on outcomes. Of course, AI is gonna be deeply embedded in everything we do now and and going forward, but focus on the problems that actually need to be solved. Focus on the outcomes you’re trying to achieve.

Don’t just use AI because you can. As as Adam said on that one slide, no random acts of AI. So I would say focus on the outcome.

Yeah.

And to what I said earlier, just set the right expectations of what you wanted to accomplish for you and what you what you needed to accomplish for you. Having lofty expectations where it’s replacing a lot of the work you’re doing, that’s not realistic and it’s gonna come back to bite you. If you end up giving too much responsibility to the AI, you have to continue to own some of it. So setting that right expectations for yourself and your team.

Awesome.

This was great. Thank you so much everyone for attending. Thank you, Tracy, Josh, and Adam for for for sharing. This has been really insightful. Hrci and SHRM recertification credits will be sent via email soon. Thanks again, everyone.

Tracy, you rock. You crushed it. Thank you so much.

Thanks for inviting me. This was fun. Appreciate you all.