I'm gonna go ahead and get things going because we've got a lot to cover. This is a a super hot topic, fixing hiring's front door. We're gonna be talking about resumes. We're gonna talk about applicant volume. Hard not to talk about AI, but, we're gonna be talking about in a way that hopefully feels meaningful and actionable for you all. I'm Jam Khan. I'm the chief marketing officer, at Criteria. I'm gonna be your host today. And as we get through the different sections, I will introduce my guest host, Ben and Rachel. Just a little bit, if you are unfamiliar with Criteria, Hopefully, you get familiar with this real soon. You know, our our mission is always been to reveal the real potential behind every good candidate, help you make a more informed and predictive hiring decision, And we're lean heavily on our background in science, you know, a wealth of data built up over close to twenty years, and we've layered, you know, AI responsibly over that. You'll find out a lot more about that today as we dive into a little bit of what we're seeing in the current hiring environment and how it makes every bit of what this company has founded for almost two decades ago just as relevant, if not more so today. We've got plenty of time for q and a, so, you know, we will address all the questions you have, at the end. I would ask that, you use the q and a section there rather than just put it in chat. We'll go through the chat as well, but sometimes, things get lost in the chat. So if you can find the q and a section and drop your questions in there, we'll make sure we get to them, along the way. Do keep this interactive. So if you've got some opinions and perspectives that go beyond these questions, feel free to share them in the chat as well. And in case you're wondering, yes, we will be sharing, this deck as well as the recording with everyone, both registered and attending. So we've gone through the agenda, so I'm happy to say we're tracking well on time for our five minute intro. I'm gonna give a little bit of an update on what we're seeing in the market, and then we're gonna jump into a a really interesting q and a section, with Ben Eubanks, the chief research officer at Lighthouse Research and Advisory. We have some, you know, very interesting research that we'll be sharing with everybody here. You know, some highlights in this webinar as well as the full research report will be available to to everyone. So we'll direct you to that, and, we've got a a great q and a section with Ben. I would also encourage you to drop questions you have for Ben in the q and a, tab. And then we're gonna give you a a product update, from our head of product marketing, Rachel Zerilla. So I'll set the stage here by, you know, you know, framing us a little bit of the current state. And the reason that, right, for us having a webinar on fixing hiring's front door is that we're starting to see employer confidence in the resume in in quite measurable decline. And there's sort of three reasons for this. One is the rise of AI generated applications. We've also seen balloon in applicant volume, and we're seeing an inability to verify properly. These two are not, you know, completely disconnected. AI generated resumes make it harder to verify based on, you know, the artifact we've always relied on, the resume. So we're gonna be diving a little bit deeper into that. I do wanna set the stage here that this is not an anti resume like webinar. As you'll see in the research, the resume is still, you know, a a pretty foundational aspect of the hiring process. But what we'll be diving, a bit into here is, you know, how that's weakened as a signal, some alternatives to build around it, and just what we're seeing from a survey that was conducted, with over a thousand employers and just attitudes in general shifting because of the tremendous impact that AI has had on resumes. Now the biggest issue here we're seeing is, you know, I I said it was gonna be hard to avoid AI. Well, generative AI has sort of flooded the resume layer. And if you look at these, numbers, I don't know if these are surprising to folks or if you're already starting to, to see this. This would be a good time to drop in some of your observations in the chat. But seventy percent of candidates tailor their resume for a role, and at least one in three have used AI somewhere in the application process. Now on the surface, tailoring a resume sounds like a good thing. It shows effort. It shows interest. It shows that a candidate is trying to connect their experience, to the job. Almost impossible not to do it these days. But I think the rise of generative AI has sort of changed what tailoring means. The resume written may not even have been written by the person who sent it. They are tools now that can apply at scale. The cover letter may have been generated. Bullet points can be rewritten and tailored to an ATS system. Now it's important to know this is not about blaming the candidates for doing this. The candidates are responding very rationally to the system that's in front of them. They know that resumes are being screened by ATS systems. They understand about keyword matching and AI based tools that are screening them, so they're optimizing for the screen. And the problem with that is that the signal we used to get from a resume, which is, you know, how someone communicates, how they frame their experience, what they actually did, is pretty obscured by AI polish. And that's sort of at the root of what's happening over here. I think we all sort of have observed that and feel it in varying degrees. But as we dig in a little bit more to this, we'll see how this is really manifesting itself. But it isn't just about AI generated content. It's about the ease that AI has created in actually applying at scale. We're seeing eleven thousand job applications submitted on LinkedIn every minute. That's up forty five percent in the last two years. Now that is not reflective of the job market. We don't have forty five percent more like candidates. It's also not reflective of what you might be seeing in terms of job losses. This is people applying at scale using tools that are allowing them to do that very efficiently. And so the current hire system that we're all used to filtering through was built for a very different scale and size, completely different volumes, you know, a very different pre AI world. And so the issue is we're now filtering candidates using a system that was never built built for this sort of environment. And this sort of trust collapses is running both ways. Candidates are struggling as well. Employers are overwhelmed, but fifty three percent of candidates report being ghosted, by an employer. Now I think what we refer to as ghosting has sort of changed now. Right? Candidate sentiment was ghosting is never gonna be positive, but it's almost impossible now for employers to extend themselves to every candidate when you're seeing applicants' volumes go up by forty five percent. So from the candidate's perspective, the system feels opaque. You apply to dozens, sometimes hundreds of jobs. They hear nothing back. They don't know whether the person filtered them or whether it was an algorithm doing the work. Did they ever even get, you know, a a fair shot? And then forty nine percent say the competition for jobs is too high. These two are also interrelated. When you're applying at scale, when employers can handle the volumes, what end up happening is a lot more candidates for the same roles. We're seeing dramatic increases in orders of magnitudes, ten x more applicants for the same job that would have had a much lower applicant volume just two years ago, like, just even a year ago. And there are sort of three forces, right, that are quietly making resume screening less reliable. And we've talked about this, you know, a little bit. One is AI generated applications. It's now the most cited resume concern masking real ability behind, you know, sort of polished pros. So what do you do when every application looks amazing? There's a lot of volume, but it doesn't have the same sort of signal. When every every resume looks the same and when the resume volumes have doubled, tripled, quadrupled, it's much harder to make a high quality decision. And then resumes have always been self reported snapshots, but now they're really, really good self reported snapshots. So you're dealing with a system that suddenly makes a resume based screening much, much less reliable. So let's talk a little bit about just the math behind some of this. Right? You know, when we talk about fake profiles, there is what I call small letter fake and capital letter fake. And I think we've always lived in a world where, you know, you're gonna embellish your resume. That's sort of expected. But, you know, research by Gartner revealed that twenty five percent of resumes will be entirely fake by twenty twenty eight. So now you're dealing with a different sort of dynamic. You're trying to filter out who is embellishing, who's maybe pushing the envelope, and who's completely altered, you know, a resume. It isn't it's not representative of anything they've ever done. Almost half of newly hired employees fail within eighteen months, which tells us that we're just really not very good at screening and hiring. And then, you know, as mentioned, there's a forty five percent increase in applicant volume across major job platforms. This signifies, you know, a real collapse in the system. So if you don't change the way you start to, you know, filter for talent, right, this isn't getting better. These numbers are only gonna go up, and we're seeing we're still, you know, at the relatively early stages of what AI can do, and we're already seeing some pretty dramatic results. So if you project this this problem out in six months, twelve months, one can't continue to hire and screen based on the same systems we've already used. And now here's where things get really interesting and and almost a little bit dystopian. Candidates are using AI to write. Employers, in return, are using AI to read the resume, and some recent research, revealed that the model actually prefers itself. So let's walk through this a little bit. A candidate polishes a resume using AI. They use generative tools to tailor their resumes. Employers are forced to use AI tools to screen and filter through these resumes, but the AI model actually prefers an AI generated application. So now a candidate is actually at a disadvantage if they're writing their own, like, resume. What would have been considered an act of authenticity and integrity is actually now gonna, like, backfire. Not only does AI prefer an AI generated resume, but the model actually prefers itself. So if there's a screening tool that's been built on Claude, it will prefer a resume that's written using Claude. That's literally rewarding a system that we've already shown is starting to break under the pressures of AI generated resumes. This is pretty striking. And if you look at the results of this, you know, the AI self preference bias, you know, sixty seven to eighty two percent, you know, of the resumes that were generated by the same model that was screened were were preferred over a human written, like, resume. You're more likely to get shortlisted. So if the AI tool matches the screeners, you are much more likely, although the the research range anywhere from as low as twenty three percent to as high as sixty percent, to be shortlisted if your resume was written by the same model that was used to be screened. So can this is sort of rewarding a system now. It's only gonna continue this resume collapse. But, you know, more than half of the bias is fixable. There are interventions. There is system prompting. There are ways to cut this, but the current systems right now aren't built for this. So with all this said, right, I mean, if the resume as it existed can't be, like, trusted, what else can we, like, rely on? And that's where the notion of a talent signal, like, comes in. So what's a talent signal? Well, a talent signal is something that shows you a person's potential. What's the best indicator of future impact? What are a person's skills? Can they do the job that, you know, they claim to do? How can you screen for that? How can you actually measure true competency versus a self reported claim? And then how likely are they to can, deliver consistent results? What is their performance on the job gonna, like, look like? These signals can be measured and, if measurable, are a much better indicator and a much better predictor of success. What do these things reveal? Talent signals reveal those durable skills that have always been better predictors, of of on job performance, conflict resolution, critical thinking, problem solving, emotional intelligence. You see a small snapshot of these here. But these are signals that will tell you how somebody performs, how likely they are to adapt, and are proven to be much stronger signals than what you're only getting out of a resume. I mentioned a a research report. I'll I'll share a few of the takeaways here, but, you know, I've shared just a few sort of highlights of where resumes are failing. But this is a pretty significant research. Ben's gonna be here to talk about this in a little more, like, detail. But we surveyed close to a thousand, employers, and the results are quite, like, striking. If you throw a comment report in the chat, we'll make sure we share this research with you. It's on the criteria website as well, under our resources section. You can go ahead and grab a a copy. I'll share just a couple I like takeaways, but there's some really, really interesting research that indicates just shaking confidence in the resume. One is, you know, as I mentioned, you know, the purpose of today was not to be just to outrightly bash the resume, but just to indicate that as it's existed and as it has been used and relied on, it's becoming increasingly unreliable. So thirty three percent of employees, you know, are confident that resumes accurately reflect candidate skills. I expect that number to keep going down, but that means, you know, right, sixty seven of employers don't feel very confident. Only two thirds about you know, two thirds of employers still use a resume screen. It still is a primary artifact, which means, you know, the dynamic has changed, but our systems still haven't caught up. There still is a huge overreliance, which is why, you know, we see levels of turnover that we do. So, you know, the result that we're seeing is is a widening trust versus usage gap. People are trusting resumes less and less, but they haven't yet shifted their systems to lower a little bit of that resume dependence. Takeaway number two, volume and AI are flooding the front end and distorting merit. So seventy two percent of employers report higher application volume. Many roles now requiring, you know, fifty one plus resumes before initial cuts. That's a massive volume for sort of small talent acquisition teams to to handle, and and talent acquisition teams are typically not an over resource function in an organization. Ninety two percent said AI generated resumes now are commonplace. So the the top concern is AI masking truability. And, you know, when we talk about these sort of AI washing of resumes, this is really interesting. The top quintile candidates hired, right, nineteen percent less often, and the bottom quintile fourteen percent more often. The reason for that is it's just becoming harder and harder to distinguish, you know, who's really good versus who's not if you're relying on, like, the resume. When everybody has a great resume, how do you make that distinction? You're bound to make mistakes along the way. And the third takeaway, employers are starting to trust demonstrated ability more, but they just haven't rebuilt the process yet. Again, this goes to a lot of what we shared leading up to this discussion. The the numbers are showing that the system isn't working, but people have not yet rebuilt their process around it. So ninety eight percent of TA leaders say alternate signals, like assessments, structured interviews, work samples are more dependent than resumes. The most trusted alternatives, skill based and work based assessments, fifty eight percent, structured interviews, fifty percent, yet those aren't always well baked into the hiring process. The resume still ends up often being the first screen. So that filtered pool of candidates working on is not necessarily the most qualified or the highly skilled base, which is why you see people from the bottom percentile being hired more often, top candidates being hired less often. So this momentum is real, but the barriers remain forty four percent cited the need for clear internal standards and thirty seven percent cite the lack of effective tools. So there's a real recognition that something needs to change, but the systems around it, you know, aren't supporting them. People are looking for for better ways to to adjust their hiring process, but we also need to recognize that the shift has just come about very, very, like, quickly. And I think we're so surrounded by so much, like, AI, like, impact, that it's hard to recognize that the landscape just in the last six months has changed so dramatically. So I think that is a perfect segue for me to introduce, Ben Eubanks. He's the chief research officer of Lighthouse Research and Advisory. Ben, you wanna just introduce yourself and let our our audience know a little bit more about Lighthouse? Sure. Absolutely. Hey, everybody. Glad to be here with you. So as Jam said, I am a researcher by trade, but before that, I was a recruiter and HR leader myself. So all the things that we're we're going through already, I'm taking notes myself, Jam, today, like, learning some things. I'm I'm reliving some of the conversations we had internally with our leadership, with our hiring managers, and all those sorts of things. So this has been already good takeaways before we get to some of the stuff that I'm excited to talk about. So I'm a researcher. I spend my days. We do survey work with candidates, with employees, employers to try to figure out what's happening, what's changing, and try to convey those things back to all of you in a practical and tangible way. And we'll do that in a little bit. I'll be sharing some of the some of the data we have, some of the stories and examples, some case studies and things like that to try to make it as tangible as possible for you because the best research tells you what to start doing, stop doing, keep doing. And, again, I've already got some notes and some ideas from that from the bit I I saw from the the criteria research, but we'll give you some more of those as we're going through here so you have lots of takeaways. Unfortunately, that means homework for you, but it's the kind of homework that'll make your job hopefully more more easy long term. So there is that. So excited to be here. Well, I'm gonna put you in the hot seat now, Ben. We've got a few questions lined up for you, and I think your audience would love to hear your thoughts on this. I know you you you travel around the country quite extensively. You speak to a variety of industries. Let's talk about the trust gap. Thirty three percent of employers are confident resumes are reflect real skills, yet most still use resumes as the first screen. So from your research, from what you've observed, you know, speaking to your clients, what's the most effective way for executives to realign their hiring process, you know, with what they already believe without slowing down hiring or increasing cost? To me, this sounds like the quality triangle. If any of you have seen that before, it's like, it can be good, fast, and cheap, but it can never be all three. It can only be two of those. So you're like, do it better. Don't slow anything down. Make it super fast, but also make sure that the cost stays where it is. And it's almost it feels impossible. Like, you can't get those. In all seriousness, we've got to think differently about the process. If you keep doing the same thing you've been doing, we've all been told you get the same thing you've been getting, but in this environment, you keep doing the same thing you've been doing. You actually get worse results than you were getting in the past because of the increase in volume, because of the muddy waters around picking the right candidates. The signal's not there in the resume. So you already threw this stat out, but when we see in the data that only one out of three leaders, like all of you listening right now, are very confident that a resume is an accurate picture of that candidate's skills, we see in the research that two out of three companies say, you know what? We actually admit that we've hired people that misrepresented themselves on their resume. So we know and have known for a long time that they're not always the best source of truth. One of the the funny way I put that to people is how many times do you see a resume come across your desk that says, listen. I'm not really a team player. I don't work well with others. I don't put in extra effort. I'm not a very hard worker. I'm not no one talks about those things. No one admits all their faults. They only paint this pretty picture that they are wonderful and great and the best hire you haven't made yet. And we all know that everyone has flawless challenges, and so resumes can't tell us that whole story. So from a practical perspective, we have to get down to what are the key tangible things we're looking for. We're trying to hire for someone in this role. Sure. But they need to be able to do a, b, and c very well. These are the results and outcomes we're looking for, or we're always gonna be a little bit frustrated with the outcomes in that. There'll be friction with hiring managers. There'll be issues with candidate satisfaction where they feel like they got a bait and switch. So we gotta be clear on the front end. We get to measure that in the process, not just with a resume, but with conversations, with structured interviews, with other assessments that we saw. We'll talk more about those in a minute. But using these other tools we have in our tool belt to try to get to, can that person do that thing that we really need that this person's role to do? And if you right now are hiring for positions and you don't know specifically what are the outcomes or results that person's supposed to get in those jobs, then maybe time to pause for a conversation, realign with your hiring manager to make sure you get that answer because that's otherwise, you're gonna end up in a place you didn't mean mean to end up in, and, your GPS takes you to the wrong place. So long answer to that one, Jam, but there's there's some of the things that are top of mind for me on that. Yeah. It's super interesting because, you know, one of the things we look for in a resume, you know, myself as a as a as somebody who's made done a lot of hiring, you first look at, you know, a relevant work experience. You know, you look you look at some places they've they've worked in the past, but then you start to look at the results that they've driven there. Right? And those things always leap out at you. If a person the ideas of a person knew the job well, they will be able to cite the right impact, the right metrics, the right effect they've had. And and that at least felt like it was a somewhat reliable screen. You don't know how to talk effectively about something you haven't done. That's the part that AI has completely sort of made irrelevant because it will tailor your resume to highlight the things that hiring managers look for. They'll promote the right, like, stat. They'll talk they'll speak in the right language. And that's where I think that that's this thirty three percent is only gonna go down now because it's gonna become indistinguishable to see, like, who really did the things that they actually claimed they did. You you talked about the big letter, the little letter. Right, Faye? Know, like, in the past, it was very easy to to just slightly massage one word and one bullet point on your entire resume and make yourself look better. Right? From I participated in a team to I'll say I led the team. Right? Just like one little change there, and suddenly you look more appealing as a candidate. And the problem is the data show in every piece of research I've seen so far that when we start letting AI do some of the writing for us that we quickly stop checking what it says. We don't fact check it because it looks pretty polished. And because of that, it's gonna start adding things in there that are untrue. If I said, hey. Would you eye to eye, would you lie to me, Jamie? Like, well, no. I wouldn't do that, but that resume will do it for you. And if we're not checking, we're allowing that to get out there. So, yeah, it's a it's a weird dynamic because it puts employers in a position where they're trying to pick how is suddenly every candidate above average. Statistically, it's not possible, but they are all above average when you look at the resumes. That's right. And and, you know, based on on that research showing that the, you know, AI screeners prefer AI resumes Oh, gosh. We're we're actually telling candidates, if you're not using AI, you're putting yourself at a disadvantage. Yes. Absolutely. Let's talk about application volume and other impact of of AI. It's super easy now to apply at scale. So seventy two percent of employers seeing application increases. What process changes are the highest leverage for reducing that noise while still protecting your access to truly high ability candidates? And what do you think leaders should stop doing? So wildly, I talked to a recruiting leader at a technology company recently, and he said we're doing more in person interviews than we've done in over a decade As he said, because we're still trying to figure out how we're gonna change our processes just like we talked about in that first question a minute ago. But in the interim, we're getting people back in front of us where we can look at them. We can get a sense for their their personality. Are they gonna treat our customers well? Right? Do their resume look great, but they're not they're not gonna fit because they don't have a personality. They have a personality of of a tree stump, and they're not gonna be able to interact with that customer and give them a good experience. So doing that sort of thing, bringing that bringing that back to try to get to the signal, again, filtering out some of the noise that might show up there. One of the things that stuck out to me is in our TA research just recently, we're asking leaders about have the metrics that matter changed? See, there's a company that told us in one of our case studies, they said, you know, two, three years ago, we would get about twenty thousand resumes a month on average. Big global company, biotech. They said we would get twenty thousand resumes a month. They said now we're getting twenty thousand resumes every three days. And as you said a minute ago, it's not that there are more candidates. It's just easier to apply and that all the automated applies are going everywhere. And so they said for a while at the the earlier stage, it was easy to lean on that quantity, that volume as a metric for success. We are doing a good job because we have lots of people interested in our And increasingly, they're getting pushback from their leaders and having to change that conversation from we have lots of applications for our jobs. That's a good thing to we actually are digging in and saying, we have this many qualified applicants in our funnel. And they're looking to change the conversation from just the quantity measurement measurements to the quality measurements on how many people are in there that we'd actually hire. I don't need more people in my funnel I'm not gonna hire anyway. That's just more people to say no to and reject. I want more people in there that are qualified, that are good, that are capable, and that's that's one of the things I think employers are being pressed on, talent leaders being pressed on is how do we get to the quality metrics, how do we make sure we're measuring the right things. I'm gonna probably reiterate that a couple times as we're having our conversation because I've been guilty of it. When I I remember hiring for a job years ago. To this day, I can't tell you what the person did in the job or what their metrics for success were. I have no clue. Okay? A provisioner still to this day. Don't know what they did. And I tried to meet with a hiring manager, tried to get and I just didn't ask good enough questions. I didn't get enough clarity. And so I was not a good partner for that hiring leader on that requisition because I didn't know enough, and I didn't do my homework well enough to really be a good partner there. And so if we are trying to get to this, our our friends on the hiring manager side are dealing with these same things we're thinking about. We may be thinking about it at a higher level across the jobs, but they don't wanna hire the wrong person because their resume look good. They don't wanna let someone who's unqualified come in the door and try to take care of their project, their their safety issue, their customer issue. They wanna get to the right ones. So from a tactical perspective, getting to the heart of what good lacks for the for that role specifically is a key thing. Not the list of accomplishments, not the twenty seven required skills that are on that requisition that came out, but specifically, good looks like this, this, and this from an outcome perspective. And if everyone on the hiring team is aligned around that, then you have your marching orders. You're able to go and accomplish that. You're able to be a true partner on it in a way, again, that I confessed that I wasn't all those years ago. So getting getting clear around those things is critical. Yeah. The the the cost of a or the the opportunity to make a bad hire was much less when your surface area of exposure was a lot smaller. So, you know, there is a little bit of a vicious cycle now where if you feel like you can get screened more often, as a candidate, you're gonna cast a wider net and apply to more places. And so just the the problem seems to compound itself, which also means you've got that many more unqualified candidates that could slip through the cracks if you're using, you know, your traditional ways of hiring. I remember I was talking to a a gentleman. He was a recruiting leader for a technology company. They were fast growth, and he told me, listen. The ability for us to hire quickly was the limit for how fast the company could grow, period. If we were slow at hiring, the company was slow at growing, and that didn't make anybody happy. So we had to make sure we were keeping pace with this. And when we started looking at he started looking at some their data, he said, you know what? We know something about a lot of people that are in our database, in our funnel. Right? We know people who are referrals. We know people who are silver medalists, who are a great candidate the first time around. They just didn't get picked because they were this close. They didn't get selected because of one reason or another or people who are internal that are raising their hand, for opportunities. Like we know some of those things, So they use that as their signal. That's easier because you know something about those people. This other audience that you and I are talking about, we've got to go beyond just the resume because that's never gonna tell their full story and get into the other ways to get signal from them, whether it's assessments. I wanna have you sit down and, Jane, I need you to write me a marketing plan. Give me a sample for how you do that. How'd you allocate this budget? It's not hard for us to think about those things. We stop and do it, but if we just say, no. Our process is we open the job. We screen a bunch of resumes. We call them back to make sure they're human. Then we do the interview, virtual or in person. If we keep those processes in place, again, that's gonna cost us long term. Yep. With these with these applicant volumes we're seeing here, that math doesn't work. No. No. So I'm not talking about AI changing the signal. So, you know, part of the research cited AI generated content masking truability is the top resume concern. And, you know, how do you recommend organizations redesign the interview and screen process to separate polished answers from real capability, you know, still allowing, you know, sort of appropriate and ethical AI use? I think we can't go to a world where we just say, hey. No one's gonna use AI anymore. Well, let me touch on the last part first, then I'll back up to the first bit. So if we all got together as a group, we could probably come up with a pretty comprehensive, this seems like a fair and ethical use of AI for candidates. The the problem is there are some weird dynamics to our employers who are like, no. You can't use any AI in the process, but we're gonna use it to screen you. Or you can't use any AI in the process, but make sure you're good at it when you get a job here because you're going to use it every day. So we're sending these mixed signals. What we find in the data we actually asked this question last year in our research, and we found that employers like all of you said, okay. If someone's using this to practice an interview using, you know, chat or something like that, no big deal. Good use of AI. If you're using it to help you because you're maybe you're not a native English speaker and it helps you write your resume and it looks more polished, you're not adding in a bunch of things that aren't true, we're okay with that. When it gets over into the the dark side is when you're like, hey. You've got an assessment, and we can tell that you are putting all the the questions in and come back with these very polished answers, or we're doing a video interview, and we can tell you're looking off the screen every time you're looking for an answer somewhere else or getting an answer from somebody. Those things we would agree are not ethical uses of AI. So setting that part aside, this first part is the same as it's always been. In every interview, the very first time I interviewed and, had my my manager over here was like, I'm gonna coach you afterwards. Like don't take it personally. I'm gonna tell you the things you gotta fix. She's like, the person said this, this, and this, and you just said okay, and you wrote it all down and didn't ask any follow ups, didn't dig in any deeper. You missed a chance to clarify because they could be overblowing that. They could be, like, way down in the weeds and have really great experience that you didn't get at because you didn't stop and and go deeper when the moment presented itself. And so in this, it's that polished high level answer. Hopefully, candidates are prepared and rehearsed for that, but then go deeper and say, okay. Tell me about that. Okay. Tell me more about that. If they're giving you these high level answers, try to dig deeper, deeper, deeper. There's a there's this old quality concepts. It's like the five whys or whatever. Like, when something goes wrong, we ask why, and then we ask why again. We keep going deeper until we get to the root of the actual problem instead of trying to treat the surface level issue. If we only try to treat the surface level issue right now, we're gonna say, well, how do I get fewer resumes in? That doesn't fix the problem of making hiring easier. Right? We get to figure out how do I make sure that we're screening well. And I'll throw one more thing out there, then I'll I'll pause for a second. The the data we have on interview screening. We actually work with the team at Criteria a few years ago to look at some of the practices to help do this better. And what we found is if you are trying to help prepare someone to be an interviewer for the very first time, we're saying this is what interviewers do here. The best companies with the best results don't just do one thing. They do some combination. So maybe you have to shadow two interviews before you can run your own. Or maybe you get an interviewer some feedback like I mentioned a minute ago as part of that. Or we give you some training on it on this is these are the key things that we do in every interview regardless, or you're always five minutes early, you're never five minutes late, or whatever it is. But giving them a couple different things to hook into so they know what good interviewing looks like, that leads to better and more consistent interviews overall. And regardless of all the wacky AI stuff, those things still need to be happening if we want our interviewers to be able to suss things out in those conversations instead of leaving it a chance they're gonna run across the right conversation point. I'll keep it moving along here. Yeah. Yeah. I'm sorry. I'm I'm out of pocket here. A good one. So we we know we want alternatives. I mean, you know, execs certainly want alternatives, although it seems like the the top down approach often tends to be, you know, use more AI. But, right, ninety eight percent of, talent acquisition leaders trust alternatives more, yet we still see a large reliance. We saw two thirds of folks still using resumes as their primary sort of entry point. Where should leadership start first? Like, how do you start to bring in different signals at the top of the funnel, you know, sort of sort of have a better, more positive impact? So I'd say, like, this will be super practical. Start with a specific job, not just any job. You don't need to pick one that you hire for twice a year. Pick one you hire for pretty regularly. Define what good looks like, as I mentioned earlier, so not a broken record, but define what good looks like for that. And then I would recommend deploying some sort of assessment around that. It could be an actual structure assessment. It could be something as simple as a a job simulation or exercise, but you put that around it, and then you measure and you measure and you measure. Then come back and look and see, did that person succeed on the job? That sort of thing is how assessments have been built and used for decades and it still works today. There was a company I worked with recently that started using an assessment for their nurses because they had high turnover. They're trying to figure some things out and when they did this, they had an outcome that surprised them. They did not expect. They just started doing the assessment to figure out who was capable of doing the job. What they found were they were hiring people and putting them into roles just assuming that nurses are interchangeable and they're not. And so some of them are process people and some of them are people people. And they started using the data they got from this assessment actually to split them up into those two different types of roles. If you're a people person, we're gonna put you with the the family with young kids who's a little scared about this surgery coming up. And if you're a process person, you'll be doing meds or you'll be doing something else where you get to be the the focused detailed person you are without having to worry about all that smiling at humans kind of thing. And people the the nurses in both roles were more satisfied and they dropped their turnover doing something like that. But they started with one key role, and your job may not be a nerd, maybe something else, but start with that one key role you hire for consistently, define good, put some things around that for extra signal, and measure, measure, measure. I like that, Ben. I think, you know, a lot of times when you start looking at alternatives, it can get over can get overwhelmed. But if you focus just on a couple of signals that can already start to create a filter, that's already a significant step forward from from where folks currently are. So the other part about this is, like, this tends to still be a lot of our hiring still, the the instinct is always to be sort of gut based. You know, almost half of HR professionals are saying they need clear internal, like, standards. What does good standardization look like in practice? What have you seen out there? Do you have some examples you could, share? Because it does feel like this varies organization to organization, and the default is still to rely on the on the instinct of the hiring manager. So a couple different things. One of my good friends used to lead talent acquisition for Wyndham Resorts, the the hospitality organization. And he said one of their things they taught every single recruiter or HR business partner who was supporting recruiting, anyone who was touching that process from the people side is they taught them this thing they're called contracting. And just like a if you ever have hired someone for a project to to fix something in your home, for example, you get down in the weeds of it to understand exactly what's gonna happen and and everything else. And so he said, an example of that is on the hire manager brings you this rack that says, hey. They must have four years four years of experience to go do this job. And the immediate thing you turn around and said, okay. So if someone has three years of experience, they're an absolute flat no. Well, three years ought to be okay. Oh, let's change it then because three years is now the threshold, not four. But going back and forth and getting clear before you skip over those resumes, before you skip over those candidates is a key part of that and helps to to make that a more equitable relationship and more of a give and take instead of just take, take, take, which is what it feels like sometimes working with our hiring managers across the aisle. The on the rubrics and scorecards piece, there are three things that have to happen there, and I won't even give you an example. I'm gonna tell you these three things have to be in place because if you have a good one that you'd like, great. Run with it. It doesn't have to be magic, but you have to do three things for it to work. Okay? Whether you have three questions or ten questions or fifty questions, bless you if you do and your people who are doing interviews, but the three things. Number one, it has to be consistent. Everyone that goes through and is part of the hiring team has to do it. Has to has to has to. No one gets the excuse because they're, oh, well, I I do this other work, I can't do that. I'm too busy. Or, well, you know, I sit on the interviews, but I don't want to give feedback. No. If you come, you must give that feedback. Number two, it has to be fast. They cannot bring that back to you as feedback two weeks after the interview because they've already forgotten most of what happened. You have to do that within twenty four hours of the interview happening so that all that is fresh on your mind and you're able to pick those things back out about that candidate. The longer you delay, the more it starts to fade in and get muddy in your head. So doing it quickly. So consistent. It needs to be quick, and it needs to be independent. We had a process in the past where we changed it actually because we realized it was changing the results where we would allow people to just throw their stuff into a shared drive so that you could actually pop it open and see. I could look at what jam Jam had already scored someone or Rachel had scored someone before I put my scores in and realized that was changing how I scored people. And so it was you submit it blind, and afterwards, you can see what everybody else put in there. So it wasn't biasing my responses. So it needs to be consistent, needs to be quick, and it needs to be independent. And whatever you're using as a rubric or scorecard will be better overall for those things. Last question, Ben. This has been amazing. Before I let you go, what are some ways you're seeing the candidates have been taking advantage of AI in the hiring process that you think our audience may not yet be aware of? I'll give you a quick score story that scares the pants off me every time I tell it. So a friend of mine is a manager for an aerospace company. He's on the data analytics team, and recently, they went through the hiring process. They got to the final interview, did a virtual screen because she was remote and or located elsewhere. They're gonna bring her in for the job, but she was elsewhere. And the lady did an amazing job. Very smart, very sharp on it. So they hire her. They bring her in, and from the get go, she's only able to produce about ten percent of the actual value in the job that she was supposed to be producing. And they thought maybe she's nervous. Maybe she's some coaching, and they spent three whole months putting people around her, having the team pick up the slack, trying to help her get going. Finally, they said, what's the deal? In the interview, you knew all of this, and now you can't do the work. And it turns out she had used one of the AI tools that puts the answers right here. So I'm reading it. I look very sincere, and I'm just getting all the answers to the questions being asked from an algorithm that's telling me what to say to look compelling for the interview. And so I that story to me is powerful because I said, well, what are you gonna do about it? And he said, exactly what you've all heard for the last thirty minutes from me and Jam. We're gonna have to do something different. So he said, my my first step is next time we do this, we're gonna have that technical screen in person. They're gonna produce a work product for me there live so that I can see it happen instead of them being able to do something virtually remotely and just send it in because I can't trust that anymore. So they got their lesson learned from that, and they're gonna hopefully do better next time. But for any of us, that sort of thing is happening all the time. It's happening on a consistent basis. It's hard to flag when that happens. But when you say that big mismatch in performance and the actual performance of the interview, you can tell that's a that's a big indicator or red flag that that's that happened. So staying on top of trends and what's changing the space like the session today that that the teams put together is a great way for you to stay on top of what that what's possible, what's changing, and to keep your keep yourself plugged in in the loop because it's it's Wild West out there. It feels like jam. It it sure does. And I think the the issue of con candidate authenticity is a perfect segue over to to Rachel's section. Thank you so much, Ben. Yeah. Folks, I highly encourage following Ben on LinkedIn. Check out Lighthouse Research to put out a lot of really good information that I think is very, very relevant to what we're seeing in this very fast changing environment right now. Appreciate the time, Ben. Rachel, talking about your authenticity. Welcome to the party. Thank you. Happy to be here. So thank you both, Jim and Ben, and we're excited to get the lighthouse research that you've worked on, Ben, into everyone's hands. So you can expect to see that in a follow-up from this webinar. We're gonna start out you know, we just heard, how authenticity is a huge concern. And our main goal is to help you with the selection process, which has as we've heard plenty of examples of, become so strained with AI. And to be honest, there's just more outright fraud now than there was even a few years ago. So with the massive surge in applications, we hear a lot of you concerns that these aren't even legitimate humans seeking employment. And then out of the ones who are legitimately human, it's hard to tell who's actually interested in the job and who actually has the ability to do the job, in the example that Ben just shared. And then finally, the fact that you're missing really good people who are legitimate humans, are legitimately interested, and qualified is the ultimate price that you're paying. So here we have a stat. Sixty four percent of employers that we surveyed said that they've made a bad hire where performance didn't match the resume. Misrepresentation is just normal now. AI is hiding ability, and great candidates are getting overlooked in the noise. Go on to the next slide here. So we wanna move from telling to showing. So as we look at how difficult the selection process is, it's important that we're moving to a more predictive talent signals, which you've heard us mention quite a bit. We're gonna talk about assessments today as being the right move for candidates to show, not tell. We also have interviewing selections and post hire development solutions, which can mitigate a lot of this too, but we're gonna kinda start with that first step. Next slide. So it's a two way street. Right? And you heard Jam talk about this a little bit. We're seeing more and more that candidates wanna be evaluated this way too. Sixty eight percent, actually, from the ones that we surveyed, feel that the assessment is a much more positive way for them to put their best foot forward. They're getting ghosted. They're not hearing back. So it gives them the signal that they're being taken seriously and gains their trust. So, again, like I mentioned, from a product lens and as far as criteria is concerned, today, we're gonna focus on assessments and authenticity protocols that we're developing and have developed since that's where we're seeing the most trouble screening and prioritizing. So I am a huge fan of analogies, and I'm gonna start with a few for you here. So how many people do you think are inclined to speed when there's a speed limit sign posted? Or how many are inclined to speed if there's a trooper with a speed monitor pointed at you or a digital sign that's posting your speed in real time? Or how many people are inclined to steal if they see a large security tag on something versus when something just has a hidden sensor and a price tag that they can't see. So point here that I think is probably obvious is that visual deterrents make a huge difference in setting expectations, which is what proctoring does. We're gonna talk a little bit about that. But we're also gonna talk a little bit about the safeguards that we build into the assessments themselves and then just how using proctoring is an added layer of protection there for you. Okay. I think we missed a slide. Sorry, Jan. Yeah. Sorry. We're gonna go back through this a little bit. So in order to maintain, a more equitable hiring process in this era of AI and fraud that we've talked about. We already take extensive measures to safeguard assessments. So we're gonna kinda go over a few of the things that we do. So we have dynamic item delivery, which means that no two candidates are gonna get the same test. We have validity scales that are gonna flag inconsistencies. We block copy paste functionality, and we lock out tab switching. We have notifications that AI assistance is not allowed, so candidates know that they cannot use them when taking an assessment. And we have data security measures, including IPGolocation verification and fingerprint ID. All of this is already built into assessments without proctoring. So you can think of these as that speed limit sign or hidden sensor in the price tag. But as cheating and fraud become more sophisticated, it's good to have an added layer to deter. So in addition to these safeguards we build into the assessments, then we also have proctoring, which is more of the visible speed monitor, hard tag sensor per our analogy. So the statistics that we just saw on the last screen, fifty nine percent of hiring managers do suspect use of AI tools. So people using them to not only enhance resumes, but also in Ben's example, coach them through a live interview. One in six remote applicants show signs of identity fraud. So either having someone take a test for them or having someone interview for them altogether. And then sixty seven percent of HR leaders like yourself say AI resumes are just slowing down the hiring process altogether. And that last stat is really important because we just don't want you to have to waste your time. So here, we're gonna talk about what we do in terms of proctoring. So we have candidate verification. So we ensure that the same person is taking the test for the duration of that, assessment. We also mitigate AI generated answers and other assistance with screen sampling that captures browser activity, that captures second devices, outside assistance such as multiple people in the room. So you can have more trust in remote hiring, especially when on-site evaluations aren't scalable. We did hear Ben mention mention they, like, come back, and we've heard a lot about the comeback of on-site evaluations, which is a good deterrent, but you can't necessarily scale that across every type of organization. So we're gonna go through a little bit of, an example here with a customer. And, also, we note here, it's really easy to set up, and proctoring is, just standard across all of our assessments just with one click setup. So we have a health care employer that's a current customer that was, like, seeing an an enormous amount of candidates paying people to complete their tests, which was leading to unqualified hires, high turnover, all the things you can expect at one, and they couldn't in person test. It wasn't scalable for them. So they turned on proctoring, and immediately, the fraud stopped. They are able to scale this. They have conducted assessments with proctoring across thirty thousand candidates, and they have ten thousand more projected next year. So if you were go to the next slide, Jim. If you were concerned at all about the use of AI, because we do use AI in our proctoring feature, we take ethical and responsible use of AI very seriously, which is why we are among the first in the world to receive this ISO four two zero zero one certification. We're in good company, with other organizations such as AWS, Google, IBM, Microsoft, and it covers a hundred percent of our AI products. So we do have AI based scoring for interviewing transcript summaries for our interview products and then AI that monitors our proctoring product as well. And with all of that, yeah. So just to wrap everything up here, as Ben said, really, like, you can start simple. You can start with one role. Again, make it a high volume role, then measure and expand. Replace your first screen with that assessment, and then see how this works for that particular role and then scale it out across your organization. Ad proctoring in is an added layer of verification, and we'll just, and and then we can continue into other parts of your hiring workflow with interviewing and interview intelligence. So if you wanna see AI proctoring in action, you can drop proctor, the word proctor, into the chat. You can also reply to the webinar follow-up and, or talk to your current criteria team. So with all that being said, we do still have a little time for q and a. So in the q and a chat, you can drop your questions there, and we love to take a stab at answering those for you. Can I throw the other creepy story in real quick? Because I've mentioned the chat, but I was gonna make sure we had some Okay. So I was doing a session a training session for the team at Milo's Tea recently, their entire HR team. And so we were talking about some of the the creepy things. I told them that story with the aerospace company, and one of their leaders said, you know what? I didn't know that what this was, but now I know you've talked about this deep fake video thing where they where you train a video model basically on your upper body and your and your face. And she said, was doing an interview with someone and they felt very stiff and very wooden. Like the person was didn't move their entire time. They were like this the whole time. There was no real expressions. And at some point, she's like, hey. You seem really tense. Like, this is just a fun interview. Would you just stand up and kinda shake it out? They're like, no. She's like, okay. Well, give me a virtual high five. And the person's like, no. I don't want to. Let's continue the interview. And she's like, okay. This is now you're making it weird. Like, hey. Hand up. And the person would not do it, and when she pressed one more time, they just hung up and disconnected from the thing. And the reason for that is if you are ever you're seeing a deep fake, you train it with this and that's all it can do. It can't do this. It can't do this. It can't stand up and move around yet. And so the person realized that they were, you know, they were caught and they had to had to bail out. But if you haven't want that going, it looks like you're attentive, and you can be on another computer looking up answers, using other tools, whatever else. And the person on the other end can't tell if they're not picking up the signals that you're you're looking very stiff. So they were telling that story for one of their leadership role for an executive role they're hiring for, and that that, again, weirded me out because I would like to think people are are not doing that kind of stuff, and yet it's popping up in all these different industries across the board that we have to be vigilant. We have to be smart. And as I said earlier, we can't keep doing the same things, or we'll get even worse results than we've gotten in the past. So now that I've probably scared the pants off everybody, there's your your little bit for me. Ben, there's a question here. Are there are there roles that you would focus on first? If you're starting to think about shifting away from resumes, you don't wanna just go all in and you're thinking, like, let me layer in some some assessments. Is there a certain type or certain characteristics of roles, that you see more likely to be successful? Yes. So I I pointed out earlier really quickly that it depends partly on the volume. So if you're hiring this person this role once a year, it doesn't really make sense. But if you're hiring this role a couple times a month or you're it's a high volume thing. Right? We're filling these every week. In that case, that's a great candidate for that. I've also had success in the past where I looked at roles that were highly connected to revenue for the business. So if I try to position this for like an admin, leadership may or may not go for it. But if I position it for someone on our sales team or one of our key engineers who builds product for customers, those types of roles would have been more likely to get approved for that because they're tied to how the business makes money. So the more closely we can connect those dots, if I go back to the example for my friend in aerospace company, how we position our data affects how the company sees us, how our customers see us, and so we have to do something else to solve for this to get better at this selection process, the screening process because our old approaches that work, they could get buy in and budget if they need it to do it that way. I hesitate to say, we'll go do it for these three roles because all of you are gonna be a little different. But if you don't know the most important roles when these positions open up and the leadership team is focused on those those requisitions who are closed, then you I'll give you that as a homework to go and speak with some of your leaders and stakeholders to figure out what those are for your business because those that they have their eye on are the ones that are the highest risk for you getting it wrong and the highest reward for you getting it right, and that's where I would start. Amazing. We have one last question that we have time for with about a minute left. You mentioned that company transitioning from using twenty k applicants a month as a quality metric to reporting number of quality candidates in the pipeline with twenty k applicants now every three days. So the Yeah. How do they transition? So they're that's over time they realized we have more applicants, and so that can't be our thing anymore. And so they began looking at the number of the percent of qualified people in their hiring funnels. And part of the way they did that was asking more specific knockouts in the front end, more specific screener questions so they could see, hey. Half the people applying don't meet these three basic criteria for the job. So it doesn't matter if we have a hundred thousand, half of those people were no good for the jobs we're trying to fill. So they started adding more specific screeners in there to help them narrow that pool down a little bit because their leadership team didn't care about the big numbers. They cared about the quality numbers in that. So they they had to make that transition once the world changed. And, again, they maybe at some point, if candidates become scarce again, that's gonna happen. But for the moment, that's that's how they've shifted, and that's what the data we're seeing say other companies are doing as well. I think quality will remain scarce. Volume will not Which is why we're in this continuous cat and mouse game, why we need to explore other alternatives, why tools like proctoring are really, you know, a way to surface those high quality integrity candidates that deserve a fair shot. Yes. We're at the one hour mark. This has been incredible. Thank you again, Ben. Thanks, Rachel. Anyone have any more questions, please contact your CSM or get in touch with Criteria. If you're not already a customer, we'd love to continue this conversation.