The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
Welcome to The Breakthrough Hiring Show! We are on a mission to help leaders make hiring a competitive advantage.
Join our host, James Mackey, and guests as they discuss various topics, with episodes ranging from high-level thought leadership to the tactical implementation of process and technology.
You will learn how to:
- Shift your team’s culture to a talent-first organization.
- Develop a step-by-step guide to hiring and empowering top talent.
- Leverage data, process, and technology to achieve hiring success.
Thank you to our sponsor, SecureVision, for making this show possible!
The Breakthrough Hiring Show: Recruiting and Talent Acquisition Conversations
EP 149: The evolution of recruitment software with Workable's CEO, Nikos Moraitakis
James Mackey, CEO of a leading RPO provider, SecureVision, and Elijah Elkins, CEO of Avoda, a highly rated global recruiting firm, co-host Workable’s CEO, Nikos Moraitakis, in our special series on AI for Hiring.
Nikos outlines the company's evolution from an ATS to a full-scale HR software solution. Discover how AI-driven tools are reshaping hiring processes—from resume screening to interview scheduling—empowering recruiters to work smarter.
Thank you to our sponsor, SecureVision, for making this show possible!
Our host James Mackey
Follow us:
https://www.linkedin.com/company/82436841/
#1 Rated Embedded Recruitment Firm on G2!
https://www.g2.com/products/securevision/reviews
Thanks for listening!
Welcome to the Breakthrough Hiring Show. I'm your host, james Mackey. Today, we are joined by my co-host, elijah Elkins. Elijah, what's up? How are you doing? Hey, I'm all good, good, and our guest today is Nikos Moriartakis. Nikos is the founder and CEO of Workable and he's going to be here today to talk to us about Workable, telling us more insights into the primary value proposition, how they're serving customers, and we're going to be dialing in on AI use cases, specifically about how Workable is thinking about AI and feedback they're hearing from customers, and what Nikos thinks about the recruiting technology space right now in the direction we're heading. So, nikos, thanks so much for joining us today. How are you doing?
Speaker 2:I'm doing well. Thanks for having me. It's a pleasure to be here.
Speaker 1:Yeah, Nikos, why don't you just start off telling us a little bit about yourself and what you're doing over at Workable right now?
Speaker 2:Workable is an HR software vendor. Primarily we cater to small and medium-sized businesses up to maybe a few thousand employees. We've been around for over a decade. Everybody knows us for recruiting because that's where we started, although in the last few years we've expanded and now we do all things HR. But we've served just to give you a sense of proportion, over 30,000 customers over the years and we've seen 400 million candidates for jobs, several million hires. So it's been around for a while. We can talk more, a little bit about what's going to be in 400 million candidates for jobs, several million hires. So it's been around for a while. We can talk more, a little bit about what's going to be in the future and what we're building, what the customers are doing. But yeah, we're how can I say mid-size software vendor for HR, mostly in Europe and the United States.
Speaker 1:Workable? Did your team start in the HRIS side or the applicant tracking system side? Where was the initial product? And then how did you build upon that and expand?
Speaker 2:Initially it was a straightforward applicant tracking system. We started 12 years ago. Back then, the main proposition of SaaS software is I'm going to give you better UIs, prioritize the user, give you better experience and also make technology that previously was available to the big enterprise accessible from price and utility and ease of use to smaller companies. So Workable. Started like that, like many other companies of that generation. Over time, in recruiting, you realize that one of the most important things is sourcing is to be able to build a target pipeline. So we spent a lot of time building one of the top partners for Indeed and one of the top partners for LinkedIn. We move around millions of candidates every year. We have our own sourcing tool, so we made an investment in that and over the last three years we've expanded into HR management, employee profiles, documents, time off, that sort of thing.
Speaker 1:Yeah, I like the strategy and so it sounds like you. When did you expand into sourcing? I'm just curious from a timeline perspective.
Speaker 2:It was around 15, 16, when we put a lot of emphasis in sourcing. Oh, that's awesome, and also that happens to be the genesis of all our AI technology that we're going to talk later, because when you go into sourcing, it's one of the places where this is even earlier and, let's say, less strong technology than the one we have right now, where applicable.
Speaker 1:That's great couple.
Speaker 1:That's great because the reason I asked that question is I'm curious.
Speaker 1:My assumption is that your company weathered the storm of market consolidation a little bit stronger than some of your competitors, due to the fact that you built out a product suite more so proactively, prior to COVID and the market crash in 2022.
Speaker 1:I've spoken with a lot of recruiting technology leaders that are essentially scrambling to develop a product suite to make their products stickier right, like maybe they were just only doing sourcing and now they're scrambling like, okay, we really need an ATS, because companies aren't they're not going to have several technologies in their stack, particularly if they're SMB and mid, lower, mid-market focused. And so then I talked to others like who are ATS CEOs that are like, okay, we really want to get into potentially more of the onboarding HRS side or we want to get more into the sourcing side, and they're essentially just trying to make decisions based around, likeactively, try to expand your product suite, and probably something that I would again, this is a total assumption. I don't know, but I would say, compared to other recruiting tech companies, probably enabled you to have a little bit more consistency and minimize churn as much as possible, which, of course, I'm sure for everybody it's still increased, but weathered the storm a little bit more than some of the other CEOs that I've spoken with over the past few years.
Speaker 2:Look, you touched on something a little bit more fundamental about how the market moves. I think we are at the end of a cycle of innovation, so usually you have a decade. There was a big decade for B2B SaaS software. It became easier and easier to make it. Players played well with each other, there was a lot of financing, there was a lot of appetite in the market. The market was going up, companies were willing to spend for point solution, for a small edge on competitiveness and there was a lot to be gained there and this resulted in a proliferation of a lot of software and a lot of software for very, very specific use cases, from point solution down to what would be features of a point solution.
Speaker 2:Now, obviously, this clears up at some point. So naturally, the market is going to consolidate. I think it's driven more both from the industry, as you said. For each one of those companies it's a stronger play to be able to expand into more things, to sell more things, to grow their footprint on the customer, to maybe consolidate through M&A and wicked by the financing with others and, on the other hand, the customers as well. I don't know about you guys, but I hear the story very often. I am the new HR manager. Previously, the company was growing fast and somebody bought expensive software for everything. Now cleaning up this mess, I want an affordable solution that does everything. I bet it's happening everywhere it is.
Speaker 2:Now, in our case specifically, I think we got a bit lucky. We were one of the first to come into this space, so we built some scale. We already had numbers that we were a profitable company with strong economics. So, even though the 22 and 23 were completely terrible years, it wasn't a huge setback for us. And we were lucky as well because, for our own reasons, we ended up becoming profitable right at the year of COVID, before we knew that this was going to happen. So we didn't have to retrace our steps like many companies had to do during these years, and we said since we're not going to sell a lot over the next two or three years, we might as well make a new product to improve our retention.
Speaker 1:That's pretty smart. Yeah, lucky or not, I'm really happy for you guys. It's a strategy that seems to it just makes sense. It makes a lot of sense and your team was able to pull it off, it seems, a few years before a lot of recruiting tech companies. I know recruiting tech companies are just rolling out additional products. In the past year or so they probably already churned a lot of customers that they might've been able to hold onto if they had done this proactively.
Speaker 2:I think through M&A or product expansion, you're going to see this market start consolidating a little bit over the next few years.
Speaker 1:Yeah, for sure, for sure, Elijah, you have any follow-up thoughts on that topic for?
Speaker 3:years. Yeah, for sure, for sure, elijah, you have any follow-up thoughts on that topic? I've actually implemented Workable a couple of times at a few different companies. I didn't mention that to Nikos in the beginning, but so very familiar with the product. I haven't used it for a couple of years but it was super interesting to see you all had sourcing before anyone else did, before the other ATSs, before Ashby came out with kind of their sourcing tool with that kind of all-in-one workable was had that TA product suite before. I'm curious you don't see too many companies right, start with the ATS, have a very robust solution and then build out an HRIS or an HRMS system. You've seen HiBob BambooHR Rippling right try to add like recruiting modules, like an ATS-like functionality to their HRIS. What does that kind of look like for you? Is the ATS almost a standalone product side by side and you switch from one user experience into the other, or is it holistically integrated to one or just yeah? How does that kind of look to build out an HRIS at workable?
Speaker 2:that's a very good question. It's a different problem which direction we go to, and a lot can be said about this, but let me try to focus on a couple of interesting bits. I think what we're doing commercially is more difficult, because you may have a bunch of customers who are even excited about your product but they're not the buyers of the other product. So you're typically not. You may be talking to the HR department but you're not talking to the same buyer. So essentially, commercially, you need to rebuild a lot of the relationships and marketing and brand that you need to build. And it's also usually the recruiting is a sub-department of whoever buys the HRIS. So nobody's going to buy software for their boss or for some of the people who have something very separate.
Speaker 2:So expanding in that manner is not tremendously easy. On the other hand, you have another force into it that most companies, as I said, would realize. It's better to have one software where everything from the org chart down to the terminations, to the backfields, to the acquisitions, where it understands my people, it understands who performs well. Now, with AI, you can believe that understanding performance is going to link to better recruitment. So when customers go for this becomes a question what is better to buy if you assume, let's say for simplicity, that the customer says workables HRIS is the newest weakest and bamboos recruiting is the newest weakest, which one do you hurt more by accepting a lower end?
Speaker 3:solution.
Speaker 2:The argument there, and my hope is that recruiting is the one that is a performance application. It does make a big difference in results. The other one is a database with a good UI, let's be honest. So would you rather have more candidates or a different signature? The signatures are the same.
Speaker 1:When it comes to incorporating AI at Workable, I'm curious to see how aggressive Workable is with implementing AI functionality. My assumption I think you touched on this is are you starting more on the sourcing side? But I'd love to learn more about that. But I think some of the more established players I've seen are moving maybe a little bit slower, not necessarily because they have to, but just because they have a full product suite. They're taking their time to be a little bit more thoughtful about what they're rolling out. I'm curious are you pushing AI aggressively or are you just trying to roll it out in very small little phases that you're more confident, or high impact? What's your kind of AI strategy? How do you think about incorporating AI into a product or pushing out a feature or like where to start? I'm just curious about, like, your thought process and then that leading into where did you actually start? Can you walk us through your process?
Speaker 2:The short answer is that we are mega aggressive with AI. We have seen fantastic results. We already have a ton of things in production available to all our users, and thousands of users daily, of many different things we do with AI. We're extremely enthusiastic about the results. Yes, it's a new technology. Yes, harnessing it and managing all this is not as simple as it sounds, but I think everything is going to move in that direction and I think applications are going to change in shape as well. This is going to change UIs. It's going to change a lot of things, and there's absolutely no way we're waiting on this game. We want to take every problem that there is in there and try to crack it with AI dooms and crack the code of how it's going to be represented, interacted with, et cetera, in the future. Let me give you a sense of where we are right now. So today in Workable, we'll write your job descriptions with AI, and already 30% of the jobs created in Workable today are written by AI. This is not a small. This is a big use case. We publish 700,000 jobs a year through our customers and a third of them are already done with AI.
Speaker 2:We have salary calculators based on our data. Ai helps you identify what salary you need to do. It creates interview kits, it creates scorecards, it creates video interviews for every job. It functions like a junior sourcer and it's going to go and find you 50 passive candidates like a recruiter would, at least as a target who are matching with the job. It's going to write the emails, but highly personalized, based on their experience and everything that he knows on the internet about them, to recruit them. Those emails have twice the response rate of personalized emails from recruiters and almost four times the response rate of templates.
Speaker 2:And then, for all the candidates that come into the system, anything that you put in that you see on your interface we scan all of them. For each job description, we have an AI that essentially determines what should be the criteria for screening for that job and then it marks every one of those criteria. So, basically, on top of every profile you see a little scorecard what are the five bullets you want and which ones are green. So it it does all the reading of the resumes and interpretation and screening for you, and all of these things are live use cases like they're now on the system. You can go get a trial account and use them, and thousands of people are using them every day. So there's an endless discussion about where this is going, how good it is, and all that good stuff, but it's clear to us that this is going to be a productivity bomb.
Speaker 1:Okay, so we covered a lot of ground there too. I actually had to write some stuff down because I want to slow down on it. I wasn't, honestly. I thought you were just going to tell me like, oh, we were going to, we can write emails for your sourcing team, like a lot of companies.
Speaker 1:That's where they're at. Oh, we generate a sourcing messages for you. Okay, we talked about, of course, like the AI generated, jd, which is, honestly, that's. A lot of companies are doing that one Interview kits, scorecards. Now for the interview kits. Can you provide us a little bit more detail on what that includes?
Speaker 2:I'd like to dial into that a little bit more Basically. For an interview kit you are pretty much the best way to describe it. You're pretty much a structured interview. Okay, you have some areas that you want to touch and for each one of them you have a few questions and next to it, if you have a scorecard, you also have a way to note how well you felt that person responded to that question on your notes. Now, this is a great practice.
Speaker 2:The problem is to construct this set of questions and to have a comprehensive interview that answers the right points and have imaginative ideas for questions. That's hard. That's what the AI does. It basically writes that for you. So, let's say you were hiring a finance director, it will create a section about. It says it sees director. It will create a section about leadership, managing teams, et cetera, and have some questions about this. But he will also ask questions about the specific jobs and if you do a video interview, he will create the video interview questions for you. So he will prepare the video interview, what should be asked, etc. Which is a slightly shorter form version of a scorecard. In fact, the thing.
Speaker 2:Okay, this is new. We have a way as well to for this, to listen in on the interview and transcribe the notes. But the funkiest part that we're going to do soon is that essentially it's going to go to the scorecards and evaluate the candidates based on what it heard on the interview. It's going to write the evaluations for you, so it drafts it for you, you check it, that it's fine, you make any corrections and you're done. And then the other thing that for next year is that obviously, if you're looking at a lot of people, you would like to see an inference report, somebody like a recruiter telling you we've looked at these five candidates, this one was stronger. They're going to write you a bit of an essay where it synthesizes for every area who's the strongest and et cetera. So basically the other thing that we're making now we have shown a little video of a demo of it. We haven't made it public yet.
Speaker 2:We have an agent. He has an email in a Slack. You talk to it on Slack like you would with an employee and essentially it goes and does these things I just said for you. So you say schedule the next ones for interviews, move them along, tell me what was the result of all the interviews, start to make the job and basically you talk to it like you would an employee and it goes and accomplishes tasks and it also comes back to you and say I got three emails from those candidates. One is not interested, I will reject him. The other two are interested. I prepared a response for you to review. Click here and go on. This is how you should imagine it in the future. So anyway, I'm getting excited with these things and I just go off on a tangent, so I'm sorry.
Speaker 1:Oh yeah, the tangents are the best part. Let's just go down like the random stops where we just go down these random rabbit holes, which is like the whole point here. I think that's again the value out of this podcast in general is the hosts, as well as the guests, are experts in the space, so we can get a little bit more technical, which is, I think, what we like to do and what our buyers and what Workable's buyers they want to hear. They want to understand the differentiators, how and even it's not only just like your current functionality, it's like how you think about AI, because that's going to inform your product development and roadmap, right, if they feel like, okay, this guy, nikos, really has a solid pulse on this stuff. He's doing some innovative stuff in the space that we're not hearing from other well-known recruiting tech providers. Like, maybe we should bet on this guy and workable versus some of the other ones.
Speaker 1:So it's going down these rabbit holes is actually, I think, really important. Yeah, it's fun for us, first of all, selfishly, but it's also something that I think people need to hear. So I guess, like I and Elijah just cut me off. Man Like I'm, I have so many questions, questions, but I I like one one thing real fast is now you're talking about, like the video interviews you talked about. Essentially, are you guys also doing like the ai note taking, where you're plugging into interviews and it sounds like you said you're going to start to put together evaluations of the transcript.
Speaker 2:so you're doing that, okay. But the interesting thing is then when you read the transcript Okay, so you're doing that, okay. But the interesting thing is then when you read the transcript and you also interpret it and help the interpretation. But that is for later.
Speaker 1:So for the interpretation piece like what's interesting is that we've now spoken with the CEO of BrightHire, ceo of Pillar and then a couple of CEOs of smaller companies a little less established in the space, and what was interesting about Pillar and BrightHire is neither of them are actually providing. It's like they're providing summaries and they're packaging the data in a consumable way so that hiring managers can compare and contrast candidates and then also evaluate. Okay, you have three stages of interviews, you've covered 75% of the things you need to, but we didn't ask this one question in the screening call. We need to double back on that. Or they're providing some kind of have, we asked all the evaluation criteria, but they're not necessarily scoring candidates. It doesn't sound like the products are saying, hey, here are your best candidates, or they're not really doing an evaluation piece or a scoring piece.
Speaker 1:We spoke to a CEO of a smaller company called Qual and they're, I think, a very early stage company. They do scoring. They do scoring. Now it's different. They're working with high volume, light industrial workers where apparently, he said, the resumes are typically very incomplete so it's very difficult to tell who's worth speaking to, and so the scoring aspect is important because there's thousands of applicants and there has to be some kind of way. That's not doing a keyword match. That's not effective. So maybe because Bright Iron Pillar are more in the corporate type of space, if you only have five candidates in the interview process, do you really need a stack rank or like a scoring mechanism in place? I'm curious how you look at that topic, like when you're thinking about the note-taking aspect, packaging the analysis essentially, or summarizing. Do you see Workable going in the direction where you're actually going to be scoring candidates and saying hey, or do you see yourself staying away from that? And how important do you think it is? Why would you do it or not? What do you think?
Speaker 2:No, because I don't think that's what you need in recruiting. I think most people, especially people outside the space, imagine that recruiting is a process that involves a great degree of judgment and ranking people and making some evaluative assessment on them. In reality, it's not so much. It's more of a filtering process. It's a process where you try to arrive at a few people for whom you have collected the appropriate information and you have verified that they are an appropriate fit. Let me put it in simpler words If you give me a short list of five resumes of people who work this job, have the right qualifications and we have verified a few things around them, my job is easy. That's not what I want the software to do. That's an easy task.
Speaker 2:The computing part in recruiting comes into the fact that you have to process a lot of things from many sources and you have to go through many steps. It involves many channels, from job boards to emails, to this to that. So it's more of an information organization process, as you suggested. We see, as you said before, many vendors say I'm going to organize the information for you, I'm going to present it to you, transform it, process it. Take something that was said in an interview, write it in words, put it in the right box so you can see it next to the right things and make a decision. The decision is neither the most important thing for the software to do, and I think it's the thing that the software is least qualified to do, because it's the most idiosyncratic as well. Think about it Honestly at the end of the day, people do have companies, departments, teams, whatever it is. They have different view of what is important to them. The point is to get them to the point where they have a meaningful choice between a few very qualified candidates for which they have done their due diligence, so to speak. They've got the right information.
Speaker 2:When you hire an executive recruiter and you pay top dollar for that job, what do you get? You get all the resumes, you get all the information, but you also get some organized data and comparative things around them and you get a little bit of a narrative. Somebody, essentially, has prepared a dossier with you and has checked everything, and he knows that you could hire any one of those people. Just pick the one you like. That's what the software should do. The other thing about scoring and ranking it's also that it's touchy. You can really go wrong there. Do you want a world where any vendor can make a tweak on the algorithm and that changes who gets hired? We don't want to get into that. I don't know if I'm willing to believe that sometime in the future AI might be better and more objective than humans, because humans are flawed for things like that. Maybe sometime in AI is going to be better, but not today, not next year.
Speaker 1:What about top of funnel? So okay, not hiring decision scoring and I don't mean scoring necessarily like 98%, I just meant okay, of the criteria that you're looking for. There's a group of folks that seem to be more aligned with that. Whatever like loose, right? Okay, so not. Maybe not at the end of the process. It's more packaging, the data, the narrative, as you put it, organizing, I think, the slack aspect of what you talked about. Hey, reach out to these next folks. That's awesome. I'm really excited to see that product. That's going to be so cool.
Speaker 1:But what about top of funnel? Right, so you get 1,000 applicants? Do you see value in any kind of automated screenings? So AI can help package, saying, okay, out of these 1, these thousand people, you're essentially allowing them to engage in a bit of the screening process upfront so that you can have a better way of identifying a short list than just do a resume keyword matching situation that, either done by a person or an application, is not necessarily the best way to figure out who to actually jump on a call with, like with a person. So do you see any kind of scoring benefit on top of funnel? Or still you're just you don't like it? Curious there.
Speaker 2:Even on the top of the funnel, where indeed there is more motivation, because you really need to do a first filter and, to be honest with you also, we're not the government. We're not giving people benefits to the extent that it's reasonably objective, even if there was someone who was good and you didn't manage to capture them, it's survivable and humans do it too. However, I think it is extremely hard to create a scoring mechanism that isn't going to go wrong. It's such a complex problem like. Even if you have five criteria, we all know they do not all have the same weight. The job description doesn't tell it either. It's just a document. It's not in your head, so I don't. I would be extremely reluctant to make a system. We could make a system like that with Merka, but it would be good many of the times. It could probably beat some not very good recruiters. That's not what you need, what you really need.
Speaker 2:In screening. You have a big task. You have to go through a lot of resumes and you have to impartially, objectively and consistently evaluate the facts on the resumes versus a list of things you're looking for. Nobody really does this. It would take you a few minutes per resume. Nobody really does this.
Speaker 2:In fact, most people, actual recruiters what they do they operate more like keyword matches. They look for the resume for a few things that seem to light a bell on them and then they move them forward. And let me tell you the business reality of the thing. A few more resumes come a few days later. Do you think they get the same attention from the same person with the same criteria? So there's a very inconsistent process from getting to the big amount of information that's a big chunk, which is all the resumes to the summary of that, which is all these people on the five things we need will do the sit. That is what AI can do for you, and then AI doesn't need to be involved in the decision because it has condensed the information, does the legwork so much for you that now it's easy for you to just look at them and apply your own criteria on top of that.
Speaker 1:It's like a data organization presentation.
Speaker 2:I wouldn't do scoring, because scoring suggests that you take all that information to a formula and say this guy is a 10, is a 20, is a 50. That is what does it mean. It sounds silly even when you say it. What does this mean? But what it can really do is to show you a bunch of cards of people with some information that makes you easy to make a decision. Let's think of it realistically. You see, someone who meets all five criteria, move them on, someone in between? Okay, I can still look and verify, but it really makes it easy to pick up things that are completely off or completely on. Which is the majority? Okay, when you get the pipeline, the majority. If you take the ones that are bang on and the ones that are white in the sky, apply here you have 80% of it.
Speaker 1:Yeah, that's okay, that's super helpful. Thank you, elijah. What do you got, man? What questions you got in your head?
Speaker 3:Yeah, two quick questions. One's a comment. Thank you, elijah. What do you got, man? What questions you got in your head? Yeah, two quick questions. One's a comment, the other is a question. Have you guys considered or are you working on anything that analyzes all of the scorecards and the feedback and provides almost like a state of the role and kind of trends that seem to be things going well with candidates being passed through, or things not going well, because I've never seen that, but that's often right. What a more strategic kind of recruiter is trying to figure out is what are these trends with the candidates being passed through? Why are candidates getting rejected? And you don't always have the right rejection reason categorized appropriately in the ATS. It may actually be within data or the scorecard notes or something. Does that make sense?
Speaker 2:Yes, but not with AI. Workable allows you to do this, both to classify the reasons of rejection and obviously when you get the scorecard. There is a little bit more how can I say? Informal enforcement. It's very hard for someone who meets all five criteria to say it wasn't good enough on the first screening. So it keeps you honest a little bit because it surfaces more information more deliberately in front of you, but automatically, no, we don't do it. Now, the other thing you said to try to even create a profile for the role. I haven't thought of it this way. What I think, what I would like as a user, I would like the AI to get to the point where, in one way or another, it tells me remove this requirement. It's stupid, you're not selecting for it. You said five years of experience and you didn't honor it.
Speaker 1:stop I wish I could talk to my customers that way as a like secure vision by companies rpo provider. I wish I could get on the phone with a demanding ceo of a growth stage sass company and be like remove this requirement. It's stupid, I end up doing it, but it takes a lot more tact and time and data. To summarize, but if I could have a software do it for me, that'd be great. I could say, hey, it's all right here.
Speaker 2:What we are hoping at Workable and I'm not going to say that we really do this in any meaningful way, okay, only indirectly. Is that eventually Workable when it writes the job description which, as you said, to get a GPT or something similar to write? It is not very impressive on its own. It's to the point where it starts writing the job descriptions for better success for you knowing that stuff. But right now you will see applications using AI technology let's say, black boxes for the moment, using this new technology, plugging it into specific places and improving a part of the experience and then making it informed by more information. And when we collapse the dam of information and start sharing between for your account, where one can inform its decisions from things that happen elsewhere in the application that you might see the really big steps change, where this thing makes things better for you because it has also observed the way you work. We're not there yet, but we will be there in a few years.
Speaker 3:Is that part of the building? The HRIS side of things is really so you get that whole picture of, like talent management, who's being promoted, who's at the company, their performance, and that can actually feed back into the recruiting side of things.
Speaker 2:Yeah, let me tell you futuristic features I want. When somebody quits to have a clone button and this thing understands this employee, their role, who they work with. It probably already knows their personality tests etc. And then goes out to source and recruit people against that and even tells you how their personalities will fit Remove the personality part, which sounds a little bit dodgy. Something that understands your company, your needs, something that gets your hiring plan and knows how much time it's going to need to fulfill those positions, because it takes us I don't know three months to get a batch of HDRs. So I want all my orgs to be able to do my future planning and my acquisition planning and my succession planning. And this thing understands the people. I want a referee on the performance review, somebody who has gone into salesforce and to application and has read things about the employee and knows the one-on-ones and tells me and helps me do the performance evaluation and compares people who's the best in the department of this or that.
Speaker 2:There's so many things you can do. This is futuristic. I'm not promising something like that now, but it is totally within the realm of the possible and I think the most interesting thing with AI is not going to be that it's going to give like one absolutely wow up that stuff. Like that are going to start sounding. Ai is going to get into everywhere and eventually smart companies are going to start sounding. Ai is going to get into everywhere and eventually smart companies are going to create interesting UIs that do these things. Imagine we're in 1997 and say I would like a place where it has all the videos and all the movies in the world and I can watch them by connecting to the internet. It would happen.
Speaker 1:Similar things would happen with that when we were talking about some of the initial ai applications, there was a follow-up question I had on sourcing. But before I switch over to that topic, elijah, do you have any other questions you want to dial in on here?
Speaker 3:I'm just curious quickly, nikos, how do you feel like workable and other but?
Speaker 3:what I wouldn't call legacy ATSs right, because it was only 10 years ago. How do you feel like you'll compete and what the dynamic will look like? Competing against more like AI native maybe you could say recruitment software companies or HR software companies that are coming out of places like Y Combinator today, where they're starting with Gen AI day one and architecting everything around that? Do you think companies like Workable still have the advantage because of the expertise and knowledge and team size and customers and all of that, or that they actually may have an advantage in some ways? I'm just curious.
Speaker 2:Look, any new company has the advantage that they're starting from a blank slate. Okay, this in some years, most years is not an advantage, it's a liability when there's a new technology coming into place and everybody needs to adapt. What did I say in the beginning? This is going to change UIs and how we think about how this happens and all that stuff.
Speaker 2:Obviously, if there's a new optimal state, there's a new best design for that thing with a new technology, the one who starts from a black slate has a chance of getting there faster than the one who has to redesign things there faster than the one who has to redesign things.
Speaker 2:So the question becomes where is the change going to happen? Right now there is. I don't want to strawman it, but there is a little bit of a how can I say? Simplistic view that, oh, since AI can do everything, we can write the code. We can do everything with AI. Basically, we replace a lot of the business logic, functionality, workflows, processes, right, yellow that the software had with just an AI making a decision or making it. It's not good enough to get that point. You can't just get an AI and say, oh, you be my recruiter and now we have no UI. I just talk to you and you do everything. We might get there one day, but I'm not even sure that would be the ideal interface for a big company.
Speaker 2:But I do not fear that somebody is going to use AI to replace what a big business software like Workable does. I'm not afraid of that at all. I think the technology is going to come and slot in. Just because you made a new plane, you can't throw away your airports. Somebody's going to post this joke to LinkedIn. There's so many things that need to happen.
Speaker 2:What I think will be disruptive is the second wave of those companies that come in with different premises. Sooner or later we will start to see an emerging pattern on how new software is going to be organized and new UIs, somebody coming with a good intuition and vision about how that would apply to the particular domain, like we did 12 years ago A different design of how the application should be. And they use AI technology to get there much faster. And because they built this with the assumption it's going to be done with AI, they are not incubates from the requirements of our users, who would not be willing to put up with such a dramatic change, I'm guessing I'll tell you in five years. All right, sounds good, but we're not willing to put up with such a dramatic change.
Speaker 3:I'm guessing I'll tell you in five years.
Speaker 2:All right, sounds good, but we're both heading in the same direction, and that's the important thing.
Speaker 1:Good. So I have one question about sourcing, and then I was hoping I could ask you about your LinkedIn profile photo. We're eating the watermelon. I want to make sure we have plenty of time for that. But when it comes to the sourcing functionality, we had plenty of time for that. But when it comes to the sourcing functionality, you're saying it's like the AI can go out and pull candidates right, like I think you said that right, the AI could say, hey, here's some great candidates for you to consider, right, okay. So if that's the case, then like, where does it go? Where is it pulling this data from finding the candidates? How's it doing that?
Speaker 2:Earlier, I mentioned that for a few years we spent a lot of time developing sourcing technology. Essentially, what we've built is through a network of partnerships with job sites, with data providers, etc. We've built a pretty decent database for looking for talent. It's not as big as LinkedIn. It's not as accurate. It's basically a composition of many sources. However, for the purposes of an AI identifying people that would be suitable or a good match for the job, it's perfectly good because it's much you're going for. So, essentially, we have our own proprietary database that we've built over the years. Just to be clear, this doesn't involve our customers, right? Yeah, obviously, I'm assuming you guys understand that. There's no way this could be the same thing.
Speaker 3:Yeah, yeah, yeah, different data. Data is completely separate.
Speaker 2:Obviously otherwise, if we could, if the data of our customers was our own which it isn't it would be one of the biggest in the world by far. It would be similar in size to LinkedIn, but that's not our data. However, our investment in sourcing was essentially building the technology to put this together in the partnerships so that we have a way for us to identify individuals that you're later going to go and research further Got it. This creates a very interesting dynamic because essentially, you can power both recruiters with this.
Speaker 1:That's great, Elijah. Do you have any remaining questions before we jump into a watermelon photo?
Speaker 3:I'm good. I'm excited to learn about the watermelon.
Speaker 1:This is actually one of my favorite questions I'm asking today. So, everybody, in the episode description there's going to be a link to Anikas' LinkedIn profile. So when you click on that, you're going to notice that he's taking a huge bite from a watermelon. So I just want to know what that's all about and the backstory to that photo.
Speaker 2:Remember how the you know why I picked the photo to put on LinkedIn. It was like a joke or something, but this is one I was using internally. But the funny story is this you remember about? Was it a year or so ago when SVB collapsed? Oh yeah, I do remember Workable, had a lot of money on SVB and I was one of the people who rushed to take the money out successfully.
Speaker 2:And but you remember, I'm greek as well, which means about 10 years ago I did the opposite I rushed to take the money out of greece when greece was collapsing oh man so I made the tweet, like guys, I was never expecting I'd be sending my money to greece to be safe, because for 10 years I've lived in a situation where this would be a ludicrous thing to say which got retweeted by something like the Minister of Economics or anything, and there was a big discussion and then articles were written and some journalist wrote an article about the topic in which he introduced me in the article like Nikos Varaitakis, CEO of Workable, a guy who likes watermelons, literally on the newspaper, like the equivalent of the Financial Times. So officially for the Greek government, I'm the guy who eats watermelon.
Speaker 1:It's not a very exciting story, but it's a great story.
Speaker 2:That's just a bit random, but if I told you I could bring SBB into that, you wouldn't believe me, would you?
Speaker 1:That's awesome. Yeah, it's definitely. It's a memorable photo. I'm not going to forget that, that's for sure. But yeah, look, this has been a very insightful episode.
Speaker 1:We covered a lot of ground that we haven't yet in this AI for Hiring series. I think this is the fifth episode we've done thus far. We probably have at least 15 more to go and because just so you know is thus far, we've had four pretty good episodes if you want to check them out. But yeah, we're going to have a lot more.
Speaker 1:We're doing a combination. We're talking to some category leading companies and very small kind of startup companies as well that we think are doing disruptive things in the space. So it's a good combination there of CEOs that are coming from different levels of scale and product market fit, so to speak, or customer base, and essentially different ways of architecting these types of solutions. So it's a pretty cool mix right, Like we're speaking to you one day and then we got a CEO of a three-person company coming on the next day. So it's a pretty cool series that we're running right now and I really appreciate you being willing to contribute to the community, to talent acquisition leaders across several industries. We got listeners in essentially every continent, every country. I know everybody tuning in globally is really thankful for you joining us today.
Speaker 2:Thank you, guys. I really enjoyed the conversation. I really do think it's a very cool thing you're doing here, so thank you for including me.
Speaker 1:Yeah, of course, of course. Hey, look for everybody tuning in. We've got a lot of great episodes coming up here in the near term and we got I think we're trying to push out basically like two episodes a week for this series. It's going to be a lot of fun. Thank you so much. We'll talk to you next time, take care.
Speaker 2:Thank you.
