Josh Zywien (00:03):

Well, hey Madeline, how are you?

Madeline Laurano (00:05):

Hey, Josh. It's good to see you.

Josh Zywien (00:07):

Yeah, same. I feel like it's been forever. Well, thanks for joining us today. I know you just published a new report and every time you publish something new, it usually gives me something to read on the weekends or evenings at a hockey practice or a track meet or something. But this one in particular I got excited about because I know we've had a ton of conversations over the years about what AI is and isn't in the confusion in the market. This one kind of digs into a lot of that. So it's either adoption for practitioners what candidates like and don't like about it or why it's a good thing or it helps them, and then the impact that it's actually making on employers. So what was kind of the driver for you in doing this research and what surprised you?

Madeline Laurano (00:52):

Yeah, it's so interesting because it's not the first AI report that we've ever published or that we've covered in some of our research, but I think we always talk about AI in terms of our companies using ai. Are they increasing their investment? We weren't getting into really what the challenges were or why and where companies are at with it. And actually we had a conversation about a year ago about a company you had been talking to and they were just kind of coming to you and it's something that you heard quite a bit. It's something I hear quite a bit where they were saying, okay, we're going to buy ai. We got the green light to buy ai. And when you kind of just hear that, it makes you wonder, okay, what are you buying and where are you using it in the process? And most companies can't answer that question.

(01:41):

They can't get beyond the, okay, we're going to start using AI or we're excited about ai. They don't really know what that means. They don't know that they're probably even already using AI to some degree in some of the solutions they have. And there's a lot of confusion around how AI is being used, I think, in HR technology. So from that conversation, I know we've had several more, and we hear that quote quite a bit from companies. So this was really the idea was to put a stake in the ground and say, okay, this is how companies are using ai. These are the different types and modalities of ai. These are the different areas that are high risk, low risk, medium risk for ai. And then with all of that, the real kind of hypothesis for this is can AI improve the candidate experience? There's so much fear around ai, introducing bias into talent acquisition, into taking away that human element of talent acquisition. And we know based on all the work that you do at Paradox is that actually it can reduce bias and it can significantly improve communication, consistency, even humanity within talent acquisition. So we wanted to take a deeper dive into AI in talent acquisition, but through that candidate experience lens.

Josh Zywien (03:04):

Yeah, I think it's brilliant. I think you talked about this employer that we won't name here, but wanting to just buy AI just as a blanket term. There's also I think just a trend in the market of we're not going to buy ai, we're afraid of buying ai. And I think that's the wrong way to look at it as well, because you use the word modalities. I love that. But I sometimes say flavors. There's a spectrum here of different types and different risk tolerances and different use cases. So let's talk through some of the data specifically. What were some of the findings, either improvements, the candidate experience, time to hire, cost per hire? What were you trying to really dig into here and what did you find?

Madeline Laurano (03:48):

Yeah, I think it was two things. And we had talked a lot about this. Josh Seacrest and I talked a lot about this and we really wanted to accomplish two things in the research. The first is to understand where we're at with candidate experience. We've been talking about candidate experience now for two decades and we know a lot of companies haven't really made any movement there. The reality is with candidate experience is if you don't get the job, then how can you create a good experience? The best experience for a candidate is that they get the job. That's the best situation. That's a great experience. You can't argue that, but for 90 whatever percentage of candidates, they're not going to get the job. And if they don't get the job, how can you still create a good experience? And you can do that by making sure it's fair, it's transparent, it's consistent that there's feedback involved.

(04:43):

So the first thing we wanted to do is really kind of dive into that experience. So if you're not going to get the job, how do we create fairness and transparency in the process? And then the second goal for the report was how can we do that through ai? We know humans and recruiters and managers are completely overwhelmed. Everyone's work has quadrupled in the past four years and we know they don't have time, they don't have time to reach out to every single person that applied for a job, especially if you're talking about frontline workers, especially if you're talking about high volume industries. So how can we scale experience and make sure that it's fair and equitable and transparent and consistent? And we can do that through technology and we can do that through AI and we know that. But we wanted to really see if companies realize that if candidates realize that we surveyed a lot of candidates.

(05:38):

And then to your point, not all AI is the same. So what are the different types of and flavors of AI that will improve that candidate experience? And then what areas of talent acquisition presents low risk, high risk, medium risk? We don't want AI making the final decision on talent. That's not how it should be used. That's risky. Candidates don't want that, employers don't want that. But there are lots of different ways that AI can support that experience to get companies to a point where they're making the best decision and can also give candidates that confidence and trust to say, I feel like I was given a fair go. I feel like I was given an opportunity. And I don't know if that's the case with a human.

Josh Zywien (06:27):

Yeah, I think that's the secret, right? To your point 90, it's probably close to 98% are never going to get the job. And so you're going to have people who are disappointed, but that doesn't mean that they can't feel to use words that my daughters use, seen. Everybody wants to feel seen and heard and have been given a fair shake at the job. I think one of the interesting things here is this stat on the right for time to fill is typically like an internal TA operation slash financial metric. It's something that TA teams track to understand are we putting people in seats fast enough? Are we filling roles fast enough? There's correlation here to cost per hire and to total cost of recruiting. I actually think this is a candidate experience metric too because when this is longer, when time to fill is longer, that usually just means there's candidates sitting around waiting to hear what the next step is. Are they going to be considered? How long does it take to schedule their interview? When are they going to get their offer? When's their start date? So when you can truncate that and kind of remove some of the waiting time, it's not just good for the employer. This is the golden metric where it's good for both sides.

Madeline Laurano (07:35):

Yeah, a hundred percent. And I think we really saw that during the pandemic is that efficiency and time to fill became critical. I think for a long time we thought these were not the metrics to talk about because they're not strategic enough, but they actually are. I think to your point, and we've talked a lot about this time to fill is a candidate experience metric. Nobody wants to wait four months before they're going to hear back on a job or anything else. I mean, you really want to make sure that we're kind of moving this along and it's even more so for the candidate than it is for the employer. Our good friend Tyler Weeks who we both have love and have done tons of work with when he was at Intel, I love his story about metrics they would track and time to fill was obviously a huge priority for them.

(08:28):

And I forget their average time to fill, but it was standard around somewhere between 30 and 40 days. And what Tyler thought about very strategically was we also need to think about time to respond. And what they found is that time to respond was often 90 days. So they're filling positions within 30 to 40 days and then they're not responding to all those people that did not get the job until 90 days. And that's not uncommon. That's probably actually good. 90 days is probably better than a lot of companies, but time to respond is also a candidate experience metric that lets somebody know, okay, thank you for applying. This wasn't the right opportunity, but go keep looking. We're not going to hold you up for the next three months.

Josh Zywien (09:17):

Yeah, I think you're dead on. I think there's a needle to thread there where it shouldn't be 90 days and it shouldn't be two hours because what we've seen in the past is employers will set up triggers and their at s to automatically disposition candidates who aren't a good fit. And we've heard these horror stories in the past where a candidate applies, they spend two hours applying to a job, they put a lot of thought into it, a lot of care into it, and then they get an email two hours after they apply that says, unfortunately you weren't a fit for the job. That's an instant notification to the candidate. Nobody looked at their application, nobody reviewed it. Oftentimes it's 24 hours so that you can set rules around this. But I think interview scheduling is a great example here where oftentimes because recruiters on the backend are chasing hiring managers or maybe it's a hiring manager in a restaurant or retail store who has to schedule these candidates, it's days and it can be minutes.

(10:11):

And so this is back to your point, and I think we can kind of talk through the use cases or the places where you're seeing technology make an impact, but this is both great again for the hiring manager or the recruiter because they're not having to do that work anymore. And then for the candidates instead of waiting days, it's now minutes and they're getting a text message instead. So I think the impact there is just dual-sided and I think it's this panacea of how do we help the company be more efficient, save money, operationalize this in a better way, but you can do that while you're making it a better experience with a candidate too.

Madeline Laurano (10:45):

Absolutely. And these one point I'll make here too is these are all improvements to these metrics. So it's not that the cost per hire has gone up with ai, it's that you're actually improving the cost per hire, you're improving time to fill. And I think when you talk to companies that are strategically using ai, they're using technology, this comes across so it's really changing the game.

Josh Zywien (11:11):

So let's move on to the next one. I think one of the things that is always interesting to me is even back to this conversation around I'm going to buy AI or I'm going to run an RFP for ai, or you and I have heard people say, I'm never going to buy AI when someone is setting out on a project to transform ta and you spend a lot of time with TA leaders. I do too. What's your advice or how does this research, how should it inform leaders are thinking and what their priorities are? This ties back to some of the metrics and findings of the report, but talk me through this framework here of the goals that folks should think about as they're setting out on new projects.

Madeline Laurano (11:54):

Yeah, I think identifying priorities is so important. I mean that's why we always ask this question in all our surveys and companies should be doing that work themselves too. But I think to your point earlier, these are not isolated priorities. They're all actually intertwined. And the candidate experience is really the thread throughout quality of hire, candidate experience, efficiency, all improve that candidate experience. The goal is that we're actually giving candidates again, that fair and equitable experience, one kind of priority that's not on this list. And it's been interesting doing this research and asking this question for years is we saw reducing bias and improving de and I as a top priority in 2020 and 2021, and we're not seeing that on the top three list here. That's definitely disheartening when we look at the research and you see that that's not coming across. And I think there's lots of different reasons for lots of different organizations as to why that is, but it needs to be a priority and it doesn't mean you're sacrificing one thing for the other. Reducing bias, improving diversity, equity and inclusion should be something that you're prioritizing when you're looking at technology, when you're looking at your strategy for talent acquisition and it's improving the candidate experience, it's improving quality of hire, it's improving efficiency. So that also needs to be a consideration and we need to talk about it more than we have been talking about it, but it's a very big topic within AI and talent acquisition and it's one I think we need to be more vocal about,

Josh Zywien (13:34):

Vocal about, transparent about. I think when I saw this slide and read your report, it's like three legs of a stool. It's these three things are not independent of each other. I think you made the point that they're all kind of connected. If you improve efficiency, that means you're going to hire faster, which means you're probably going to improve quality of hire, which means candidate experience is better. So you can start in any order here and you can tie these three together. I think on your point around de and I and bias, sadly, I think you could almost see this story coming a few years ago where everybody raced into it and some with real rigor and dedication and they meant it. And then other companies, it was something that they posted about on social media, but you could tell they didn't care about. What I think is interesting is as this evolution has happened and DEI has become deprioritized, which is interesting to me, what you're actually finding is who are the companies who really care about this? And I think you're going to see more candidates go there because they're attracted to the mission. They can see a company actually is committed to this. And in that world, to your point, if you can hire faster, if you deliver a better experience, if you're communicating to these candidates that you care about them, you're going to hire more of those people. So those organizations are naturally going to be more diverse and inclusive because they're going to attract a more diverse and kind of inclusive candidate base too.

Madeline Laurano (15:04):

Absolutely. And even for those like 98% of candidates that do not get the job, they're going to truly believe that this organization that's committed to this gave them a fair opportunity. And I think when you look at a negative experience, what makes an experience negative in talent acquisition is I was not given a fair opportunity. I was either given that email an hour after I applied for a job, which made me completely understand that nobody even looked at my name, let alone my resume or anything else or my application. And it's also to say I was completely ignored through this process and I have no idea if I was even considered. It doesn't feel fair. It doesn't feel fair. And recruitment for a long time for candidates has not felt fair. And that's not just for external hires, it's for internal hires as well. I mean you have your employees that are working for you and have worked for you for years or months, whatever it is, and have dedicated their time, have made sacrifices, and they're not feeling like they're given a fair opportunity when they look to move within your organization. And it has crazy ramifications on overall productivity, performance, everything else.

Josh Zywien (16:29):

Yeah, literally. I think it's just so closely tied to culture. I think it's easy. I'm a marketing person, so it's really easy for me to create a beautiful website with nice copy and messaging and I can say things that sound great, but actions speak louder than words and the companies that are living this stuff, not just from a de and I and bias mitigation perspective, but that are truly making an effort to make the candidate experience better or make the recruiters lives better. I know you've done research around that. Those are the ones that I think in five, 10 years are just going to have such a massive advantage. They started before everybody else, so I dunno. It's super interesting. All right, so we'll jump into the third finding that I thought was really interesting here. 62% of candidates believe AI will make the recruitment process more human.

(17:13):

This is interesting because the trope around AI is this is bad for candidates, it's great for companies, makes them more efficient, makes the process faster, saves the recruiters time. What you're actually finding here is that this is from the voice of the candidate that they actually believe this is making the process more meaningfully connected to the people, which is always kind of the knock against ai, that it's actually creating distance or creating a canyon between. So kind of dig into this for me. Tell me what you found and what you think is the driver of this.

Madeline Laurano (17:48):

Yeah, I think this will surprise a lot of people because to your point, the belief is that AI is not human. It's not a human. So it's taking a human element out of talent acquisition. And we know that's not the case because we know first of all, candidates have not been pleased with not being considered for jobs, not receiving any response whatsoever, not given a consistent experience, and that doesn't feel very fair or inclusive or anything like that. So the human experience hasn't been great, but to see that the AI can actually make it more human, to me it's almost humanity, human's one thing, but then humanity's the next level of that and it's putting some humanity back in talent acquisition. And this is not a knock on managers, this is not a knock on recruiters. I mean recruiters and managers are completely overwhelmed. There are so many things that you have to do in talent acquisition, whether that's your job or whether you're a store manager and you have to recruit people.

(18:57):

There are so many aspects of it, it's completely changed as a profession. To be able to reach out to every single individual and give them humanity is impossible. That's a completely impossible task. It's unfair that we would even expect recruiters to do that, to be able to have AI do that, create consistency and communication so that people are receiving some type of human response humanity through the process to say, I see you. I love the ICU comment. I think that's what AI does. This was interesting because this is a survey to candidates, so we're surveying candidates and 62% are saying it's making it more human. When we ask the same question to employers about a third of employers said it's more human, which is more than I thought would say that. But it is a disconnect, right? It's actually we don't realize that candidates would much rather receive some type of consistent response than to be completely ignored in the process. And we know that as buyers, I would much rather get an automated email to say, we got your order. It can be friendly, it can be human. We got your order so excited to send you this, you're awesome. And then to actually hear nothing and have absolutely no idea if what I ordered is ever going to arrive.

Josh Zywien (20:24):

And I think this is back to the question of what I think this is a quintessential question that people need to ask about ai. There's a lot of stuff that can be automated. AI has become very powerful. But I think the question that needs to be asked is of the things that could be automated, what should be and what shouldn't be. And the thing that shouldn't be is when you make an offer to somebody, let's not that when you let somebody down who went through three interviews with you, who went deep into the process, who invested a lot of their time, let's not automate that. Let's make sure the recruiter has enough time to schedule 10 minutes with someone to let 'em know why they didn't get selected. When it comes to making sure a nurse has the right license or certification that they can work a certain shift, why not automate that?

(21:15):

A candidate doesn't care that they're talking to a human to gather basic information about them or to schedule an interview or to make sure they have questions answered about what they should wear to the interview or where they should park. So I think it's just that question of as we design the process in the future, what parts of that process makes sense to automate and what parts just are should never be, frankly, no matter how powerful technology is, it scares me to think that somebody someday will try to completely automate or use technology for the full interview process. That feels like a bad idea, but yeah,

Madeline Laurano (21:53):

And candidates don't want that. Employers don't want it. I mean, your whole process should be flexible enough that humans can also step in and intervene at any time. So even for those kind of low risk areas, if a recruiter or a hiring manager wants to step in and say to that nurse for that certification, thanks so much, actually was wondering if you had this, they can do it. And I think to your point, you really have to look at interview to offer. It is higher risk for using ai. So you really have to look at how you're using that. And then I think what we talked about earlier with the flavors of AI and the different modalities, they can be very human, even more human than I think a lot of people recognize. We found that in the research, conversational no surprise and voice really came out as being having significant impact on the candidate experience, and that's what candidates would like as well. And it doesn't mean a voice is going to be like, Josh, please come to. It can be like, Hey, Josh. And conversational is the same way. I mean, you give the great example that I use all the time, which is people write back with emojis and exclamation points and smiley faces all the time. When they're using paradox, they know it's not a human, but the conversation and the engagement feels so human that the natural response is to be like, you're the best exclamation point.

Josh Zywien (23:30):

Totally. I think I'll probably butcher this stat here, but I think it's 93% of candidates still say thank you at the end of the conversation. Yeah, right. You don't say thank you when you've had a really crappy experience unless you're horribly sarcastic. Alright, we'll move to the last one here. So this, that was super interesting to me. 69% of companies are increasing investments in AI for talent acquisition. Obviously I'm on the vendor side, this is good news for me. What's your kind of take from this and what would your, this is maybe not in your report, but what would your advice be to employers as they start to evaluate the market? I think this goes back to don't put out an RFP for ai. Instead think about what is the problem to be solved? What's the job to be done? What's the use case here as investment increases? I think the risk here is, and I'll say this as a vendor, the risk here is that a lot of money gets spent on AI projects, they don't work, and then all of a sudden the market turns in two years and everybody's perspective is that we bought ai, it didn't work, it didn't solve anything, so we're just going to stay away from it.

Madeline Laurano (24:40):

Yeah, I think I have similar fears. I love to see the stat and it's exciting because a lot of the fears and the misperceptions around AI that we've been talking about, it's not human or that it's going to introduce bias or hurt. The process seem to have gone away. I mean, we know AI is not going anywhere. It's here to stay. We use it all the time in our personal lives. I think the concern here to your point, is that either this kind of turns people away eventually because they're not using the right solutions, but I also see it as they still, there's just a lack of awareness of all the things we've been talking about, about where to use it, how it's being used, how to evaluate providers. I mean, all AI is not the same different flavors of ai, but also there's different ways vendors are using AI and some are not using it ethically.

(25:34):

We know that and some crazy examples, but we really have to be laser focused on how we're evaluating providers. If anyone follows Commissioner Keith Sonder Ling, he speaks sort of all over the place, but he really has been an amazing voice and a resource for our industry. He's a resource for me in my research and he gives great advice on how to think about this and how to embrace it. He's definitely a proponent of ai, but we have to think about it in a very ethical way and not every provider does. And that can be scary. And I think the more that you can be a champion of technology and a champion of ai, connect with people in the industry, build your own support system to be able to evaluate providers and understand this market, it will help you so much in the long run. You don't want to be replacing systems because they actually couldn't do what you wanted to or they're posing great risk to your organization, but there's great technology out there and there's great ways to use AI that will do all of the exciting things that we've been talking about.

Josh Zywien (26:47):

I love that. I think it really comes down to not to oversimplify, map out the problems you're trying to solve and don't think about the technology that solves 'em first. Don't fall in love with a particular category of tech. Don't fall in love with a vendor. What are the things that are problematic to your business right now? What is slowing your process down? What's making your candidate experience worse? What's making your recruiters inefficient? Really dig into those pieces of the process and then try and map the technology to that. It's this old saying, I'm going to butcher this quote, but it's bad software products, try to find a problem to solve. Good software products have identified a huge problem and then build a solution for that. I think to your point, it's identification of those problems, mapping the technology to that and then finding a vendor and a partner really that's been there and done that.

(27:42):

Talk to their clients and if they won't let you talk to their clients or you can't find any of their clients, then that's probably a red flag. And then find somebody that's willing to work with you because this stuff's going to change quite a bit. It's going to continue to evolve. You've been in this industry for a while for me in almost 10 years. It's transformative where we are today versus where we were in 2014. So I think it's just great advice as more budgets open up for this. More companies try to put an AI halo over everyth, everything they do, the diligence around this stuff is just more important.

Madeline Laurano (28:17):

Yeah, a hundred percent. And I think when you're evaluating providers, it is very easy to just get excited for cool technology or to get excited around what might seem like it's innovative at the time, but you really want to evaluate the full picture. You want to look at the full company. You want to see what's the heritage of this company, what are they committed to? Are they able to share a breakdown of the percentage of their team focused on sales and marketing versus focused on product versus focused on customer support. You want to understand that full picture a company, and then you want to understand the product and the roadmap and where they're going. And when you look at it in that full picture, you really can differentiate providers, we know this, we know all these players, and you're like, there are some providers that are just in this for a very short period of time looking for an exit or don't understand the work that goes into talent acquisition. So when you're kind of evaluating, I would definitely think full picture

Josh Zywien (29:18):

When we get RFPs, sometimes they dig into the financials and I think it's actually a good question to ask. You've seen a lot of these companies in our space that raise tens of millions if not hundreds of millions of dollars and spoiler alert we're one of 'em. But I think look at how that money has been spent. Are they raising every single year? Well, that probably means they're not very efficient with their capital. It probably means they're spending a ton of money on marketing. To your point, go on LinkedIn, look at the breakdown. There's beautiful charts that you can look at to see the distribution of people inside of that company. Because what I'd like to see is I want to see a product company. I want to see a company that's over invested in product and engineering, client support, client success, and then this is coming from the marketing guy. I like to see less money invested into marketing. It's an important part of this. Obviously my job is to get a message out there and for people to know who we are, but more importantly, I want them to love the product when they use it. So I think that's great. Well, one

Madeline Laurano (30:18):

Thing I'll say too, I'm not making this an ad for paradox, but I think what you've done with marketing that's very different than what I've seen from any other vendor in our industry is a lot of the marketing you're doing is coming through the customers, and that's very genuine. It's not that you're begging customers to do this. Everyone's trying to get what you're doing and you've been so successful at it. And you have customers that are talking so positively and actively about paradox that the marketing is not coming from just you. It's literally coming from the companies and the people that are using your product. And that is the most genuine messaging that you can ever get across.

Josh Zywien (31:03):

What makes my job so easy, I would rather nobody know who I am and everybody know who our clients are. To your point, when I go buy any product, whether it's something off the shelves at a grocery store or something on Instagram, I'd rather hear from people who actually use it and are seeing some value from it than the slick marketing person who can spin a nice story. So I'm glad you picked up on that, but it's obviously intentional and it's really hard to force. You just can't go. None of your clients are obligated to do a case study, and so they have to actually see some results and want to talk to people about it. So we're incredibly fortunate. I think it's just maybe the thing that makes my job about as simple as it gets.

Madeline Laurano (31:52):

Well, you do a great job, and the case studies that I've done with you are case studies I've reference all the time too. People are really willing to share their experience. So

Josh Zywien (32:01):

Awesome.

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