Quality Issue With AI Boom

Speaker A: Like it doesn't matter who you're speaking to. It could be a colleague, it could be a peer, it could be an interview, it could be a nan, sometimes agentic AI, Claude, Gemini. All these tools are coming up in conversation. And yes, my N does talk about it. But the thing is, when AI becomes so good, what does good really look like? Here is a little bit about today's conversation.

Speaker B: I basically introduced and sharpened my pre screening questions, right. In the tool that I'm working with now, we have included more concrete pre screening questions, uh, in that sense to understand if there is actually more than just passwords and the very stylish resume that looks amazing on paper. But in fact even if it would be relevant, maybe there would still be missing what we truly need for the candidates to be successful. And that has improved uh, my ability to screen resumes, right, Because I can much more easier, uh, prioritize the candidates who actually break the wheel deal, uh, but also improved our conversion rate in yet my focus is kicking off my calls with helping them understand more about what we do and what the product can provide at join. In that sense, that's a very good way to also understand if they actually did some research beforehand or they're actually intentional about it. But I don't do this from a place of checking them. I do this from a place of empowering them with the ability to be successful and really have a solid understanding of what they are signing up for. And I try to, even if they tend to not go in that direction, I would still try to empower them for success by during the interview already explaining them why it's important for them to use the STAR method, for example. Right? Because my job is not to set them to fail or to put them through fire. My job is to really evaluate their potential and if I see it there then to really support people to be successful.

Speaker A: Definitely one of those conversations where you need to grab a notepad and pen start to finish. Our guest has been phenomenal, very articulate in focusing on not just hey, AI is amazing, let's implement it everywhere. But how can humans be accountable? How can we look what good looks like when everyone's CVs now look great and how can we not over rely on AI but instead utilize it as a tool to create a better human experience process. And I'm so glad that she was able to join us for this conversation. So please, please do me a massive favor. Listen patiently, have a look into how you and your team exercises and systems that her team have done. And I Hope you get just as much out of this conversation as I did when we recorded. Thank you. Welcome to Teatime with Talent Acquisition, the podcast aimed at fostering knowledge and sharing stories within the vibrant talent acquisition community. I'm your host, Eden, and for next hour or so I'm going to be joined by someone who spent the last decade shaping hiring experiences across Europe. From leading high volume recruitment teams, hiring hundreds of people each month, to building talent functions inside fast growing tech companies from the ground up. Currently a senior talent partner, she's helped scale her current team's tech hub while partnering closely with leadership on global strategies, workforce planning and creating hiring processes that actually put people first. What really stands out is a balance between the data driven recruitment approach and the genuine human connection. And at the core of her work is a simple belief that hiring isn't just about filling positions, it's about creating an environment where people can truly thrive. So when you're ready, grab your favorite tea or coffee if you prefer, and join me and welcome today's guest, the senior talent partner at Join Ariadna, uh, Razeshanu. So thank you so much for joining.

Speaker B: Hi Eden, lovely to be here.

Speaker A: No, I appreciate it, but from my side I'm very curious because of course the conversation that's going to unfold is one of those which I think many people are still exploring. Obviously it's the AI topic still, but obviously we're bringing it from a slightly different angle today. But we'll get into all of that in a moment. What I would love to understand, first of all, because you've been in recruitment for a very long time, lots of things have changed throughout your career, uh, in terms of how the markets have been perceived, the tools we're using. But what do you think is going to be the biggest challenge us as the individual contributor recruiters are going to face in the next three years?

Speaker B: I think that one of the biggest challenges, uh, that we could face is, is essentially ending up in a scenario in which we will have agents communicating with agents. Unless, um, we actually focus on keeping the focus on the human touch. I think that's, that's a challenge that could come and I'm not sure yet if it's going to be something wrong or good. It could also be good. Uh, but I think definitely that's where we could head for.

Speaker A: Do you think that will, do you think that will remove the need of what our job actually is?

Speaker B: I'm personally not concerned because I think that we anyhow, as humans need to evolve constantly. So I'm Confident that there are going to be other things that we can add value to, um, through our expertise. Uh, so it's really not my concern. My, my concern is not knowing exactly how that will evolve and what would imply. Right. Because as I said, I'm not sure yet if it's a good or a bad thing. And I think it's quite, quite close to us in the end. I definitely think we're not there yet. Right.

Speaker A: So, yeah, I mean, I see the, I see some of these search and match tools coming out. Um, I can't remember the name of the company they're backed by. YC literally got announced, I think yesterday. They received some funding. And not to throw shade at the company at all. Right. There's a place in recruitment for everybody. But they're taking the traditional recruitment. What I do, the agency side, they're automating the entire process. So the company sends a spec. Um, I might be upselling them here now, I don't know. The company sends a spec, they don't talk to them, they don't understand anything about the culture. It's just purely what that spec says. Then they go to a database, typically they're sourcing from LinkedIn and then they just send it back. And it's all automated, no human involvement whatsoever. Which begs the question, like I said from outside, is that first initial agent is already talking directly to the customer. It's already talking to the candidate. It's started to remove us. And that's backed by a very, very influential vc. So we're starting that path, I think already.

Speaker B: I think many companies or many organizations are trying to do this. I think not many know what they're actually doing. And what I see around me, at least in my bubble, is that everybody's jumping on building agents and everybody's actually able to build agents until one point, but nobody's actually focusing on understanding how to overall optimize the architecture in order for these agents to actually make sense. Right. Because it's easy to make an agent in the first step, maybe then the second step. But how do you connect also internally these agents, how do they in the end actually bring value to it? Right. Because in this phase of research, at least on my end, I also like to explore, of course, but it's still time consuming rather than saving capacity because I need to understand what I'm using this agent for, how I should properly use this agent for. Right. So I think it's a process and I think that obviously it depends on the stage of the company, it depends on the values that your company has. Um, for me personally, that doesn't sit well and it doesn't work because I'm human focused and I like directing with humans. M. But, yeah, I think, uh, it's definitely worth exploring. So just because I don't use it per se, also because right now I have my, uh, needs covered by the tool that I'm actually working with, and I feel that the agents I have the capacity and knowledge to already build, for example, are already, um, covered in that area through the ats. However, uh, I still like to explore, and I still think it's important for everybody to stay up to date and try things. But the question is, do we also have to implement what we're testing, or should we just stick to testing?

Speaker A: No, I mean, we can. That's the purpose of the testing, is to validate. Do we need this tool? I mean, I've tried a fair few of them and a lot of the work I do is like, you know, that really early stage, 0 to 1. And the problem I face is that people are writing better cvs.

Speaker B: Right?

Speaker A: Let's just say how it is. The CVs are getting better. Um, so there's nuances that you have to really understand, especially in that. So the world I'm in, that really early stage side, it's not about the cv, it's about the person behind that cv. The CV is purely just other core skills there that the companies, you know, not necessarily coming from, you know, like the big tech or, you know, the new age of AI companies, but other companies, somewhat familiar. Do I see traits in that profile? And I think I'm still yet to see can an AI spot that? Because I'm not looking at one individual component. And they always say, yeah, they can. But, you know, there's. You speak to engineers who, on paper look absolutely shocking for this type of company. When you get on the phone, you're like, oh my God, you've got like a YouTube channel, you've got all these GitHub projects you haven't even shared, and they obviously get missed. But when everyone is now writing better CVs than they were five years ago, have we had to change the narrative about what a good CV looks like?

Speaker B: I would like to start by saying first and foremost that I'm actually very proud, because I think that in the past years, at least from my perspective, how things evolved was that organizations started to first leverage AI in this sense. And then I'm really proud to say candidates have adapted really fast. So I'm Honestly really proud of the candidates because most of them got this um, fact straight that they need to adapt in order to be able to be successful in the current job market. Right. That's important to give credit to. However this is a very common problem that I think we are all facing and indeed it's um, consuming a lot of capacity. Especially if you are compliant and you're trying to really screen those resumes uh, as opposed to just uh, run through them really superficial or use any tools in that sense. So in that uh, in that direction, personally what I have done in order to save some capacity and to also make sure that I prioritize the candidates who don't bring only the skills in that sense, but also the real hands on experience that it requires to be successful in the role. I basically introduced and sharpened my pre screening questions. Right. In the tool that I'm working with in our ATS join you have this possibility to add pre screening questions uh before or during the application process, which is extremely useful in my experience. But if until maybe a year ago my pre screening questions were uh, related to when would your start date be? Or uh, what would your preference be in that sense? And so on. Now we have included more concrete pre screening questions uh, in that sense to understand if there is actually more than just buzzwords and the very very stylish resume that looks amazing on paper. But in fact even if it would be relevant maybe there would still be missing what we truly need for the candidates to be successful. And that has improved uh my ab screen resumes. Right. Because I can much more easier, much uh, easier um, prioritize the candidates who actually bring the real deal but also improve their conversion rate in the end.

Speaker A: So those pre screen questions, are they different role to role I'm assuming?

Speaker B: Absolutely. Yes, yes. I can maybe give one example for the engineering roles.

Speaker A: Yeah, if you can.

Speaker B: Yeah, for sure. Uh, and I'm going to stick to the engineering roles uh, in this sense. Uh, as I said until now, now I was focusing on very general questions that are more logistical in that sense. And as of uh, the new uh recalibration that we have done, uh, we included concrete questions such as do you actually have experience in production using rstack, uh, which has made a big difference. This is just one example. But uh, it was obviously much more concrete. It was still not as an AI uh self served interview where you go and talk on your own or some question that you have to um, answer very um, lengthy and to, to invest much more effort than you should at that stage in the process. But I think it really made a difference in our case because that's important for the candidate to succeed in the role. And I think it's a, it's a good way to help um, with prioritizing those who can actually be set for success.

Speaker A: So you built those pre screen questions before you obviously speak to them. So they're applying, they'll get those pre screen questions in an email or however how many.

Speaker B: It's everything included in the ats so they don't really have to do any uh, extra effort in that.

Speaker A: Okay, so same same screen moment, their application's going to happen.

Speaker B: Exactly.

Speaker A: And typically how many questions are you asking at that pre screen phase?

Speaker B: On average? Between three to four, I would say.

Speaker A: Do you see much of a drop off from people saying actually solved this? I'm not answering the questions.

Speaker B: I think that's a very, um, useless questions these days because in the current market situation there are so many applicants that um, maybe it's worth evaluating time and see if the quality improves or not. And in our case it has actually because people are intentional. And combined with these pre screening questions. I have to say I also recalibrated our job ads because I think it's very important to bring clarity, especially in the AI era where it's very easy to just write uh, job descriptions. Right. Um, I also use our tool in that regards because that's actually one of the use cases where Join has already introduced AI for the public. But my focus was to really extra share. Right. Um, what you need to be successful at Join. What can we offer you? What do we expect from you? And one thing that has also improved the quality of my applicants in that sense, uh, was introducing a part in the job ads of why you should not apply for Join.

Speaker A: Did you. Have you seen any, I guess more like surprising patterns from having these questions because obviously if you. The whole idea was to create better quality, but is there anything that you've seen that just you didn't expect, uh, or has been a, also an added benefit, um, from adding these questions in,

Speaker B: I would say that, uh, I noticed that uh, candidates I have the opportunity to talk to, um, are more intentional than have been before because I guess this extra step, although it's a minor effort from their end, it does make you think before going into an interview. Right? It makes you think, hey, should I really apply for this job? Would I really have a shot? Is it worth that I put time into it? And I think that this pre screening questions combined with the job at that it's clearly not uh, written exclusively by an AI, but very personal and, or at least I hope. And that's the feedback I got. I think it, it was a good signal and I have got this feedback from my candidates and I'm very grateful that it actually made the difference in their decision. Right. Because not only they came more empowered with more information and know how in terms of how to connect with me and with the company and with the product, but uh, it also made them more intentional about it saying that already there has been an effort from our end to make this beyond the standard job template.

Speaker A: Do you think because of these types of AI toolings that's happening pre screen questions that are coming forward, do you think the, the need of a CV is becoming less important as a result?

Speaker B: I still think we're not yet there. I do think that one of the other challenges TA will have on a short term, not necessarily long term is definitely to filter out all the resumes that are not legit. Yeah, that's increasingly a problem. It's, it's, I wouldn't say fraud, but also in some cases it can become fraud. And I think that beyond the resume, we are already at the point where you need to understand if you're actually talking to a human or you are talking to an agent that is replicating a human, that has happened as well. So in the way where I see or how I see things right now essentially is that you need to invest extra effort also in the screening, uh, beyond the pre screening questions that can help to some extent. Um, and it's very, very important to also have a very structured uh, interview process in that regards that helps you understand if the skills or the abilities of the candidate go beyond the buzzwords that are included in the resume. Because I think this is the most common thing that we see these days, people uh, creating hundreds of resumes, adapting each resume based on the job ad. And then obviously as you said as well, they might seem great on paper, but uh, when they get into the interviewing process, if you're only uh, interviewing for skills, they might even have great answers. Right. Because they're going to be again synced with the requirements that you have already made um, clear in the job ad. So um, we're trying to also focus on a very structured approach, having not too many steps, but each step have, has clear expectations and each stakeholder has clear focus on one area that they would have to look into it. And I think this is something that also helps helped in our case

Speaker A: by the way, just to go back a second on the element of fraud, Um, I had one literally yesterday. Um, it was an absolute mental conversation that lasted or wasted 15 minutes of my life. Person kept on dropping in, out, and I was asking a very simple question about are, uh, you actively looking for a job? Just to ease the conversation, start the conversation and then it was like always a 20 second delay. And then I didn't hear you. Can you repeat the question? I'm like, okay, maybe it is technical. So I'm like, I'm sitting there thinking, okay, maybe it was Internet. I'm like five times I'm asking the same questions, pause delays and then just goes into like a pitch. And I'm like, this is the wildest conversation I've had in a very long time. However, saying that I think now I'm starting to recognize patterns slightly in people that apply. Then when you get them on the call, then you see who they are. And I know this sounds mental my side, but there is a group of individuals from a particular area that seem to be doing this far more than others. AI is this element of we want to try and remove bias from the process, but the adoption of AI has made some. And I put myself into this category, um, um, I don't like it but I put myself there. I now become more biased to certain situations because I've wasted hours and hours of my life giving benefit the doubt. And then you get the person on the phone and they're absolutely not who they say they are and it keeps on happening. What's kind of your thoughts around the whole bias topic with AI in that case, do you think it's helping to remove or do you think it's now adding more in?

Speaker B: Well, I mean this is as old as me, uh, that uh, even if you go on the, on the search engines there has always been bias there. And I think AI is not yet at the point where they can or it can eliminate this bias. And that's why in our experience we find very useful to have several stakeholders involved in the process. And I go back to the idea that each stakeholder needs clarity on what's the area they should focus on. Right. Because in the end we don't want to just put the candidates on the spotlight through the same questions again and again. So we have the strong strategy in terms of okay, who's asking what and what are the expectations in this regards. Um, I personally understand your point of view and I do understand that it's quite hard to not uh, become biased.

Speaker A: It frustrates me. No not the fact that the time's wasted. It frustrates me that now I can see biases seeping into my process because it's not the right way you do recruitment. And it frustrates me because I'm like, that's, that's, that's not who we are as a, as individuals, as recruiters, we shouldn't. But it's started to see patterns of, okay, that particular conversation has led to that and that and that.

Speaker B: But I have to tell you, I think it's normal that it happens. And the question is, is it really a bias or is it a, uh, a, uh, safety net in the end? Because we also need to protect our capacity and our time. And if people misuse this, then I think they should be disqualified. It's not really a bias because if you're not ethical about it, I would disqualify myself as a recruiter if I would not be ethical in the way I use it. Right. So the point is, of course we need to stay strong and we cannot step into an interview assuming the worst. We always need to give this benefit of doubt, which I understand how it becomes more and more, more difficult. But in my experience, I actually call people out when I feel that there is this possibility that they might be using an AI to transcript their answers. And I feel like the conversation is really not where it should be. I honestly really call them out because I think it's only fair, otherwise we make a, um, um, the worst use of our time. So I'm like, hey, yeah, I'm going to ask you very straightforward, or I'm going to ask indirectly, what is your take on using AI? Non ethical. Then they will blush. Uh, nonetheless, I think it's okay to

Speaker A: call people out because to be fair, I do now. Um, and then my, my philosophy to that element. And actually I give them the benefit of the doubt. But my philosophy too is if I'm calling you out and saying you're not who you say you are, and someone said it to me, I would take that personally and be like, then I have to try and convince you that no, I am who I am. And I've got a really easy to convince somebody and I will, I'll explain it. Um, so I had it with a senior engineering manager the other day and I wasn't on my video. I, uh, was at home. My little one was a little bit of a pain, so it wasn't the right place. And he was asking me all these questions and I said, look, the easiest way I can show you that I'm not AI and again, pardon my French, to anyone. You. It was the easiest way I could say it, right? Because I said, like, honestly, the LLM isn't going to swear to you. So that's my exact, exact, easy way. And he was like, all right, you've convinced me. I will put my camera on next time when my little one isn't around because she had, like, chocolate all over her face. It's just not the time or place to have the camera on. So I get it. Um, but I do. I do the calling out part and then I'll stop the interview just because it's a time guzzler.

Speaker B: I think it's insane, though, that we have come to a point in time where we have to say cursing words or we have to write with, um, typos in our outreach messages just to convince the candidates that, hey, this is not an AI generated, uh, message. I'm actually reaching out to you. Uh, but yet that's where we are right now. And I think it's insane if you ask me.

Speaker A: I agree. You mentioned about having that structured evaluation framework. Um, so who creates that structure in the first place? Is it all. The whole hiring team would sit down at the beginning and say, okay, Eden, you're responsible for this. Ariana, you're responsible for this. Simon, that whatever the case is, Right? Um, or is it driven by specifically the hire manager and then you implement it?

Speaker B: How does it look in our case? I propose, um, the structure before we actually kick off the new role. And then we sit down and we align, uh, individually and as a group. Uh, so I come up with the strategy, my colleagues bring their input, because I think it varies from hiring team to hiring team. And of course, it always has to be very personalized based on their requirements. Um, one thing that has been very easy for us is the fact that we do everything through our ats, from the moment that we publish the job at until the point where we process the candidates, we complete the scorecards, even the scheduling is fully automized. So in that sense, I don't need any agent because guess what? Everything is working smooth. Um, but I really think this is extremely, extremely useful in that sense because communication is extremely easy for us and everybody is in the loop at all times. Right? So of course, then we need to discuss and agree on the details in terms of how do we complete the scorecard, how fast do we need the scorecard to be completed? Because we want to provide the good experience only to keep pace. Uh, who should read the scorecard and which scorecard, whether they should read it before. What. Um, we, we really try to organize internally and uh, in that sense we are developing also internal manuals because of course it's sometimes easier if you have a guideline, but it's a lot of calibration and when needed recalibration. I think I'm really blessed because I work with an amazing team that is actually very open to maintain the high quality of the hiring process. They are also very human centered and in regards, I understand this might not be the experience everybody has. So I would like to give credit to, to them because they are really truly amazing. But I uh, think that our joint effort in the end has reflected in the success that we have in the hiring process because we do take pride in our new colleagues and we do fill up the roles quite well.

Speaker A: So I guess I love the phrase you said human centric. Um, do you feel that, that, that human centric approach that the team have created or fostered, do you feel that that bleeds into the hiring process? Even in the situation where, you know as a talent team there's going to be peaks of pressure. All this automated AI resume responses, you know, lots, you know, sometimes it can get overwhelming. So do you feel that human centric approach has allowed you to bring that into that process? I guess more to connect with the applicants, not make it for like a cold automated process, if that makes sense. Still keep that human side.

Speaker B: I think that even though it might not always end up with hiring the candidate, um, it's still important to provide a good experience because it's a very difficult process when you are in uh, on the lookout for a new job. And in our case I think it goes well with the values that we have and it has been not only helpful, but it's been monumental in maintaining the values and the alignment in values, uh, that we want to have. Right. Because that's who we are, that's what we stand for. Even as a tool, even as a product, we have the capacity and uh, know how to develop new features that involve even more AI. But we're taking our time in understanding where those features can actually bring the most value and how we can implement those ethically before jumping onto it. So in that sense this was something important for us. I understand that it might not be the focus that every team will have and that's also okay. They have to adapt to what their needs are. But for us there, there is no other way. We do not see things working in any other way. It's important to work with humans that are very capable. But, uh, that, um, have a solid foundation.

Speaker A: When it starts with that, what would you say from. A typical process that you run would be the standout touch point? That really adds value,

Speaker B: I think, really that starts with the job at. It starts very basic with the job ads. I have received positive feedback that I'm very grateful for that. It has been very helpful to tell people, hey, you should not apply for this role if you like this, this and that, because that's not who we are. And you're not going to find that here. And then we can save time on both ends. Right. Of course there will be candidates who don't read it and that still apply. But that's. Yeah, no judgment there. It's a number of games or a game of numbers. I'm sorry, um, but I think that's a very small detail that already made a difference. And then as I said, um, the way I approach the screening in that sense that I'm responsible for, I really try to make things as easy as possible. And I don't like talking about myself. So I'm personally not a fan of going into a meeting and let me introduce myself. I don't think the candidate should care who I am and what I do. Respectfully, it's not about me, it's about them. So my focus is kicking off my calls with helping them understand more about what we do and what the product can provide and join. In that sense, that's a very good way to also understand if they actually did some research beforehand or they're actually intentional about it. But, uh, I don't do this from a place of checking them. I do this from a place of empowering them with the ability to be successful and really have a solid understanding of what they are signing up for. Right. And then, um, I, um, have obviously read your resume by the time we're at that point. So I honestly kick it off or continue with the questions that need to be very factual. And I try to focus on concrete examples rather than theoretical, um, questions that sound very good but might, uh, end up as in a TV show or something. So I really, I really want to hear examples, I want to hear outcomes. Um, and I try to. Even if they tend to not go in that direction, I would still try to empower them for success by, during the interview, already explaining them why it's important for them to use the star method, for example. Right.

Speaker A: Yeah.

Speaker B: Because, uh, my job is not to set them to fail or to put them through fire. My job is to really evaluate their potential. And if I see it there, then to really switch support people to be successful in the process. That's. That's how I see it and that's how I run my job.

Speaker A: I think the m moment you make an applicant feel that your job is to get them that job. And that is literally the phrase that I use because it's true. Right. I only get paid when I hire people. So my job is to get you this job and I will get you the job. They, there's, there's like a switch in how they respond to you. And even if they don't go the distance, you know, they. People just value having someone there who will fight a corner for them. Um, I love the fact that you've. You mentioned job descriptions and on the spec you put reasons why you don't shouldn't apply. I don't do it on the job spec. I'll do it in the first call. I'd say like 95 of all the highs I do are headhunted anyway. So I'm specifically wanting to speak to you for that reason. And I'll approach mine slightly differently where I've got a job in mind. But the very first thing I'm asking was like, ignore this job. It doesn't exist. What do you actually want? Then they'll spend 10, 15 minutes telling me everything they're actually interested in. And then I can say, this job is right for you or this job isn't right for you, or on the fence, spend 15 minutes pitching the company. Okay, you're hooked. Then we spend 15, 20 minutes going through, you know, the specifics of what I actually need to know for that particular customer, that particular job. But it gives them like this get out of jail free card. 15 minutes of your life is gone. But we know this process definitely isn't right. And then I'll explain it to them as to why straight away reject him on the call or, you know, decide to not move forward straight away. So there's no this waiting around. Aiden's going to come back to you like, that's done. We move on. When I speak to you next, it's, uh, remember me as the recruiter. Didn't waste your time. I've got something for you. So we started go.

Speaker B: I can't disclose my interview questions, but it's the same setup in the sense that obviously I want to hear what is important for them. And yeah, yeah, definitely I hear something that I know I can't give them. Of course, giving feedback is always challenging. Right. And I try to understand how open they actually are, but then I try to be very honest. I do give them the possibility to continue, if they are willing to, of course. But I do try to make them aware also, as early as after this first question, that, hey, that's not something you're gonna find here.

Speaker A: Exactly. And I think that's important. Say, look, just, again, it might not be right. You might want to hear it and it might change your perception. That's fine, we can go through that. But I'd rather not waste people's time just straight up. Um, the other thing as well is that, given the feedback, one thing that has worked for me personally, again, I'm quite direct. Um, I basically say to someone, I'm like, Marmite, you're either going to love working with me or you're going to hate working with me. And when you hate working with me, you're probably going to block me on LinkedIn. Right. That's just the way it's going to end up being. It's fine because I'm very direct with it, but I'll give them the option and say, look, how do you want me to give you feedback? Straight, remove the ego, we'll just go for it. Or do you want me to fluff it up? And then obviously, everyone says, go straight with it. And I am straight. How I'm reading the cv, what I'm hearing, and that's when the Marmite comment comes in, because they're like, whoa, I didn't think it was like that. But I have to go on what I'm seeing, what I'm hearing, what I'm reading. Um, fortunately, most people don't block me, by the way, like, it is. No, no, it is a more positive outcome. But I really, really value what you're sharing there about your process and join and reasons why you shouldn't join the company in the first place. More companies should do that because that, that's where you get better commitment, because they're actually looking and going, yeah, that's not for me.

Speaker B: In my opinion, I think honesty is the utmost form of respect. And I have so much respect for the candidates that I interact with every day. Uh, nobody's perfect. So of course there are going to be hiccups and mistakes or delays on our end as well. I luckily focus on also maintaining these timelines. And if I have feedback before, then I would rather send it to you before than just waiting the particular amount of time. Just so you feel we carefully review things. No, Yeah, I, uh, do Take the time, and I like the pace. And again, I'm lucky and I'm in a good situation in which my, my stakeholders support this and they like the same things. Um, but of course, it's not always possible. But I do think honesty, it's the utmost form of respect. And I think many recruiters, uh, in that sense, don't understand the value of making the candidate feel valued, that even if you don't end up hiring somebody. I have one too many success stories in which I had the opportunity to maybe reach to the candidate that I didn't manage to hire in one project and hired in a different project, or, uh, as simple as that. I had candidates who maybe were not yet where they have to be to be able to start with us at that point in time. And I reached out months later and they were still eager and they still accepted an offer.

Speaker A: The biggest. It's the biggest compliment you can get as a recruiter. If someone refers you to someone who's unbelievable and you end up hiring that person if they're willing to speak to you again in the future, if they write a recommendation for you and they never. You never hired them, you know, that's the best thing that you can get out of it. I think the barometer I have is whenever someone says to me, oh, do you work for the company? I know I'm an external partner, but that to me is like, I've done a great job. Like, I've convinced them that I'm internal enough, that I know enough that I'm excited about the product. And that to me that's like, okay, that's a great candid experience where they don't feel like it's just, all right, quick pre screen, move on. You know, that that's our job is to be, you know, really, you, uh, know, put ourselves in the position of them and help them back. Back to the AI part. Um, do you think that companies, hiring companies are getting the balance right today on AI, on how much adoption they should do? For anyone who's listening, by the way, not watching, we're shaking heads.

Speaker B: No, thank you, Aidan. I'm gonna go back to the point, uh, we had in the beginning. Um, I think, ah, at least in my bubble, what I'm seeing is that, uh, the tendency now is to just build agents without understanding what these agents actually need to do in the end. So we know the first stage that, okay, it will, I don't know, filter out this or it will filter out that or it will optimize that. But we don't really have a solid way in most cases. Again, in my bubble of, um, understanding what the solid architecture to actually make these agents work together would be, and where do you draw the line in what the agent can do as opposed to what you actually have to do directly, uh, by the human touch. So in that sense, I see a lot of situations in which people are either very against AI and they are terrified and they were like, no, please, no AI. Or people are so eager and so hyped about AI that they're just starting to implement it without having this solid foundation of understanding what it actually involves and how they can actually make the most of it. So we end up with tons of organizations that implement AI agents and tons of people that come up with AI agents that they are reselling per se. Um, because everybody can do it these days, so it makes sense. Why not? I think we should not do things just because we can. But that's a different story. Uh, so we end up in this situation where a lot of organizations are very AI driven and very cool when it comes to the marketing per se. But in the end, if you look into what are the agents that they manage to build and to actually use, those tasks are already covered, likely by an applicant tracking system, or those tasks are very likely already covered by a different, um, uh, different tool that already exists. And as opposed to the LLM, agents that we are building are actually compliant. Right. So, uh, this is what I see around me, and I think this is the tendency right now. Uh, nonetheless, um, I think it's very important to really understand before jumping into the, uh, AI agent topic and before building up your entire process around AI agents, what a good foundation for that is, where do you want to get with that, what do you want to obtain from the overall experience? And how can you make that in a way that actually adds value, not just, uh, you know, sparkles into the, into the topic.

Speaker A: So if anyone listening, where do you want to get to? What do you want to achieve out of it? What's the value really being added? And if then it makes sense to introduce an AI agent, but once you understand those two elements, that's, that's the right time to start implementing it. Not just going straight away, let's create an agent. Because we can.

Speaker B: Yeah, and I would really ask, and I do ask myself this question, like, okay, I have the know how and the ability to build an agent that can do this, but do I not already have a tool that actually does that? So if I do, why would I go through the process other than testing and experimenting, which, it's cool, do it. But, uh, why would I invest all the effort into coming up up with the solution to something that is already covered and in the end just uses extra capacity? But also the point in my perspective is that AI doesn't yet have accountability. So when I use a tool that is compliant, that takes accountability, if, for example, I set it correctly but something goes wrong, as opposed to using an agent that if things will go wrong and it's just a matter of time until something does go wrong, I still have to take the accountability, but I don't really have real control. Because in my experience, at least when you're working with this AI vibe coding, right, um, it does the trick. It works, it's great. But often when there's a bug that comes around and you look into it, you're like, so, what should we do here? Uh, and you go back to the AI. But in reality, if this has an impact on your business, it's not going to be the AI that gets fired, right? It's going to be you. So, I mean, be mindful and be careful because I think human accountability is super important. So ask yourself, do I not already have a tool that I can use that covers that? Uh, am I really in need for this agent? Is this really going to bring value into my work? Or do I just do it because it's cool and I can, which is still fine? And I understand it's a great feeling once you get it done and it works. But the point is to actually, uh, make a use of it and have a use case that actually makes sense.

Speaker A: I appreciate you hopping on and sharing your thoughts and obviously sharing a little bit more about, you know, join as well and kind of what you guys are doing. Um, if someone were to pick this up, learn a little bit more about yourself, learn a bit more about the team. Where is the best place that they can find you?

Speaker B: I would say connect on LinkedIn. Um, I'll respond as fast as I can. As I said, nobody's perfect. So sometimes I'm quite, uh, delayed myself. But also visit our career page. And our ATS also has this cool feature in which candidates can drop a message to the hiring manager. So if you see my face, feel free to reach out. If you see my colleagues, you feel there's anything we can support you with, feel free to reach out. We're going to do our best to get back. Please just be intentional about it and, uh, come as yourself, because I really value people who are authentic as opposed to very, uh, polished messages written by AI. Mistakes in which I might not reply. By the way, if something is truly not to the point, I do need to protect in this sense the capacity and reply to people who are actually authentic. Which, by the way, I have had many, and I'm really grateful because my experience overall when it comes to this has been positive. And, uh, again, I would like to applaud the candidates that adapted to the new situation of the market and particularly the ones that are really using it in an ethical way. I think that's also important to say.

Speaker A: Perfect. Uh, well, thank you so much for joining and I really appreciate you taking the time. Enjoy the rest of your day.

Speaker B: Thank you, Eden. And, uh, it was great, uh, chatting with you. So till next time.

Speaker A: Till next time. Bye. Bye.

Speaker B: Bye.