Episode 101

Allard de Boer on Impact of integrating AI into hiring processes and Career Development

TIM: We are live on the Objective Hiring Show today. We're joined by Allard. Welcome. Thank you, Tim. . It's great to have you with us. And where I'd love to start is just to learn a little bit more about yourself. Who are we speaking to today? Who are you listening to?

ALLARD: Sure. Like so, I'm based in the beautiful country of the Netherlands, like just outside of Amsterdam. And this is me dialing in. Yeah, no. So I've been in the space of data analytics for the last 15 to 20 years. And like for the most part, I've been part of eBay, having different analytics roles, different senior leadership, product analytics, and experimentation. I did that for many years. And before that I was in consultancy where we helped brick and mortar stores go online with e commerce solutions. So yeah, quite, quite lengthy. like a journey over all the different stages of data analytics over the last few years. Yeah.

TIM: And as someone with that, let's say, slightly longer perspective, do you buy into the LLM hype? Is this a real changing point, a transformational point, or is it a little bit of overhype?

ALLARD: So, like, I think it's super interesting to see what's happening. You can see some parallels, you see, on things happening in the past. So where a new technology is, it is assumed to be the big game changer. And then what people try to do is they try to fit in. The, this technology into their existing processes. And where you see is that then, like, there's a lot of disappointment around that. And I feel that it's also happening with the AI at the moment. So people have their fixed processes, and they see AI as a huge game changer. And then they, they chuck it into their existing processes, and it doesn't really work. So redesigning your process, starting with AI first, is super important. Yeah, and I, I, I think it's. It has the ability to supercharge and, like companies, supercharge all individuals and leaders and teams in many different areas. But again, you need to think in a different way.

TIM: Yeah, I think that is a subtle and really important point. And I think it's so true in hiring. I feel like we're going to see this new wave of AI-native HR tech that's probably emerging right now that's going to become very popular because I don't think the solution to hiring is all every single thing we were doing manually; do it the same way. But with a, I think that's going to lead down the wrong path, which we've already started to see. Bits off. And it sounds like you've also seen that almost like a hammer-based approach where I just get my AI hammer out and just try to automate the thing that I'm currently doing.

ALLARD: Yeah, I think so. What I see and what I've also heard from colleagues and past colleagues over the last year is that. So people just apply for so many different roles. It's so easy to, to apply. Now, you just ask your GPT to modify your resume to include certain things. I even read the stats on the number of job applications in Workday. In three, there are three times more than the number of jobs on Workday. So it's really interesting to see the inflation going on that AI brings for candidates just applying, and companies react by just chucking AI against it. But yeah, for some reason it doesn't really work. I remember the story of one of my former team leads, and she was telling me that they, she, in her new role in a different company, had a performance interview, and, like, the people in our team were creating their performance reviews with ChatGPT. And then she was reviewing it with ChatGPT. And then, like, it was, it was a very, like, a very, yeah, stupid process, to be honest, right? So, like, where we're keeping each other busy and just chasing our own tails constantly and not getting to the core. So, yeah, I think it's an interesting time, and I also think that the people who really are ahead of this curve and really understand the value that these tools and data-driven decision-making can bring will be ahead. It's as simple as that.

TIM: What about in your personal life? Have you started to adopt AI in any meaningful way? Any specific use cases you've had yourself?

ALLARD: Well, first of all, I'm super interested, so I try every AI that I can find and try to understand, like, okay, what can I do with it? How can it work? How does it work? How does it benefit me? But I, well, I've seen that, that using AI as sort of your coach on the side where you can ask questions like, Hey, what's this? Eh, like I, I, I created this, this content, or I, I made this decision. Like, was this like a smart thing to do or not? Or like, how can this be perceived? And, and. And I found it very, very enlightening and powerful for using that I now do, like I do a lot of journaling, but journaling together with your AI is even more powerful. You get into layers of depth. You ask yourself different questions, and it helps you to get a much broader perspective. So for me, it helps a lot. Also quite dangerous because, like, the moment you start relying on AI making decisions for you or coming to conclusions that you didn't drive yourself. Like, I've also had some interesting experiences there, but yeah, so like, and, but we're still on this discovery journey. I feel I'm not sure, like, well, how you see this, but like what you mentioned, it's, it's been a huge hype, and, and you see good practices going out, but also there were many, many bad practices. Yeah,

TIM: Yeah. And it's created new problems, as you said, in hiring; it's broken the hiring process, at least in the short run, but I think we're just in this intermediate period where it's like candidates have adopted it en masse, are using it at scale, and applying to, I don't know, a hundred roles when previously they couldn't apply even if they wanted to do more than 20; now they've applied to five times as many. And we're in this intervening period where we haven't properly solved that problem yet. So it's a little bit broken at the moment.

ALLARD: Yeah. And I'm also not sure, like, if that problem will be solvable, like, because the barriers to entry have gone down so much. And like the online job postings have already accelerated, like the number of jobs you can apply for. And then, like, the ease of applying.

TIM: Yes.

ALLARD: Also, the funny thing I was thinking about, like when I started at eBay, I started with doing SEO product management, and the whole thing was around making sure that. That we understood the algorithm of Google and that we could tailor our content towards that. I feel it's very much similar to this. Like, if you understand how the AIs evaluate your resume, you can start tailoring your resume towards it. I even read one example where people were adding, like, white text to their resumes so that, like, the AI would pick it up, but the humans wouldn't see it. Like, they would just add, like, keywords in there and these kinds of things.

TIM: Yeah, and that was what the sort of 1998 Google SEO hacks were. I feel like if people just want to know the AI hacks of 2025 for their CV, just go and look at whatever SEO was happening about 25 years ago.

ALLARD: Yeah. I think that's true, to be honest.

TIM: Yeah, I think it's, I think part of it is because it's obviously such a powerful tool; naturally, people are going to apply it to anything and everything, at least try to do it. And I've heard from a few fairly substantial tech companies who recently, this is maybe October or November, had said to their entire workforce of sometimes like several thousand people. Stop your day-to-day jobs. Stop doing what you're doing. Don't work on any tasks. Just figure out how to implement Claude or Chachapiti in your day-to-day job. Which I feel like normally that would be a bad way to think about it, because that's like a solution looking for a problem, which is a bit perverse. But maybe if the technology is so transformative, we need to have that time to sit down and think, Hang on, what are the hundred things I'm doing? Oh my God, 50 of these are now stupid, given AI could do them. I just need to have enough time to sit down and think, What am I doing every day? Is it worth it? And then outsource the ones that I feel have low value to AI or AI could do. At least on an individual basis, maybe at an overall process level, like you were saying before, that mentality is a bit wrong because you almost need to reinvent the process from scratch rather than just automate the manual bit. But yeah, I feel like we're in this state where just, yeah, people are looking for. Any problem to solve with AI, and maybe it'll turn out only 20 percent of those problems AI is a good solution for. Like programming, for example. Probably, AI is going to be the programmer in a year. Maybe the idea of writing Python from scratch is going to be ludicrous. I don't know.

ALLARD: On these companies, right? So like what you mentioned is that they're, they're, they're using AI; they're forcing their employees to use AI. And what I do think is that AI won't replace people instantly. But like what they say a lot is that people who are savvy and really good at AI, they will replace the people who are not. So, so facilitating at least people getting into it and learning and seeing what they can do. I think that's a good thing. Yeah. My experience has been like, I also look a bit at the analogy of data-driven companies where, where, where. I've seen so many teams that are telling me, like, Give me more data, give me more data, give me more data, and they can, and they would then just chuck it in their head and then hopefully come up with a good solution. And, but the teams who knew upfront, like, hey, this is really where, what kind of data I need, what I'm going to do with it, and really think through this whole process. Those were the successful teams. So and, and Many of my like my interactions with with different business leaders in the past have been that the number of questions exceed the capacity of us to deliver on this head there. So, having a clear, clear understanding of what you want to do with it helps to accelerate it. And the other thing I've found over the years is that. The opposite, where we would provide insights or data to teams, but because they didn't really need to use it, they were not evaluated on that, or they never asked about it. They would just accept the numbers, but then do their own thing anyway, right? So, so people will start using the numbers if they. If they think through it, and if they really understand what the benefits will be for them along the way, and if they don't see any benefits, they won't use it. And I think AI is very much the same. If people see a benefit, they will use it. If they don't, they won't.

TIM: I think when it comes to hiring, candidates have certainly perceived the benefit of AI because they've adopted it very quickly, en masse, much quicker than companies have in response, which is only natural because they're individuals. They can just start using a tool. They don't need approval. They don't need to wait for a bit of software to be built on top of the AI. It's just, here's this amazing tool. I'm going to use it. And so the candidates have started using it too. Optimize their CV, maybe write their CV from scratch to match it against a job description, and maybe apply at scale to all these jobs, which superficially is solving a problem for them because, like my problem, I need to apply to a lot of jobs because there are so many other people applying to lots of jobs. I need a better chance of getting a job. Therefore, it's like a numbers game. But is that approach almost going to backfire on a candidate? Because now From a company's perspective, you're like getting inundated with all these applicants. They all seem to look similar to each other. They all have the same AI-sounding tone. Should candidates almost do the opposite? And go, actually, I'd rather abandon AI completely. Go for a pure manual approach. I'm going to try to get a job by sending a customized video directly to the hiring manager. Where it's definitely me, it's not a deep fake; it's 100 percent personalized. Including what's, and all including my imperfect English and all the weird things that make me human, but at least I'm going to stand out and not be in the mess of everyone else.

ALLARD: I saw this approach from one company that they were scanning all the content a candidate had posted online. Like, I have social content and also professional content. And they would, they would aggregate that and look at that addition to the resume just to get a much more complete look at that. Well, so. I have never liked, so with all the tenant acquisition teams I've worked with, like, and like, well, it goes with waves, right? So like when you're, it's always this case when you're allowed to hire, like then everybody's sort of allowed to hire, like in the other way around. So either these tenant acquisitions have all the time in the world, or they're super swamped. And when you need to hire, they're, they're, they're like, I found that they're, they're swamped a lot. And I think that. It's very difficult to get through that as a candidate. So this is where I believe this whole process needs to be redesigned. And of course, a TA will use any tool at their disposal to reduce, like, to work divergent through all the clutter of these resumes, but then also the resumes, they are like, they're, they're. They are tailored by the people applying for the roles to those roles. And what I think is very challenging, like if you go on LinkedIn, for example, and you see a role coming by. So then the AI of LinkedIn will say, like, Okay, check if I'm a good match for this, for this role. And then you, you say, sort of say, Check and. I already know I'm not a good match for this role, but then LinkedIn would come up as well. Like, these are the strong, strong areas, and these are areas that might be a little tweaking. So then it's so easy to say, like, okay, let's tweak these areas and see if I can make it more fit. And this is the area where you really want to get through, because if you have AI looking at these resumes up front and, like, you really want to vet the quality of those resumes, and I think AI is not really capable of doing that at the moment just yet, and this is where human intervention or skill vetting is super important for making sure that you just know the people. I'm pretty sure that people who are just very technology savvy will just get ahead even though their skill sets, the hard skill sets, might not be like what they need or their leadership or their way of processing information is just not at the right, at the level that you need it to be.

TIM: Yeah. And maybe another way that another segment of candidates might get ahead is instead of, let's say, I don't know, the equivalent of SEO optimizing their CV for an ATS, which let's say is one game plan. The other game plan, like if I were a candidate right now and I was applying for jobs. I would probably not even bother applying through LinkedIn and Indeed and Seek and what have you. I would just sit down and think, Okay, who do I know? Who's on my network? What are the strongest 30 connections I have? Who do they work for? Who do they know? Maybe do an almost account-based marketing approach. What are the top 10 companies I'd love to work for? Do I have a first or second-degree connection there? I would just like to completely bypass any traditional process. And try to backdoor it basically. So maybe what we might see if these online platforms and processes are so broken is that it'll almost be a diversion back to the old boys club. It's not what you know, it's who you know, and that will become more relevant. What do you think?

ALLARD: What I would find amazing is that you could just start a conversation with the company, just saying, like, Hey, I see this job posting. I think I have quite a bit of relevant experience on this one, but let's work through this and see if there's a good match. Like, this is always the, like, if I do this within my network, this is the conversation I have with, like, Hey, like, I don't know if this fits, but let's find out. And it would be amazing if that could be done at scale in such a way that you can rely on this so that people can ask the AI maybe a question or, like, an upfront, like, okay, what's in the, what's in the role, what's not in, because the job description is the same way, right? It's like, you never, like, companies put something in, but there's so much more depth behind any, any role. And my experience has been any, any. Concept of, like, the moment you really built this human connection and you spent time having a conversation and understanding. Okay. So where are you at? Where are you in your career? Like, what are the things you're looking for? Like, oh, and then these are the best predictors for me of having a good hire later on. So I haven't seen the company doing this just yet, but I know there's, of course, like, development in this area, but that would be amazing, and I think there's so much opportunity still in this whole process. I've heard from people that they are applying for jobs, and I think one in every five jobs, they get a response, and four out of five, they just get nothing or maybe an automatic generic email, like, Hey, thanks for your interest. But like we didn't see much point. So I think that's, that's terrible for your hiring brand, to be honest. There's so much opportunity there to be gained. So I'm hoping that there's development this year in this area. I'm, like, not sure what you see happening, but it needs to happen. Right.

TIM: it does, and call me biased and call me putting my data hat on to this problem, but I feel like the solution is better data on both sides, because especially those kind of early stages, typically, you just have that LinkedIn profile at CV, just like you, if I went on to LinkedIn now, I'd probably get recommended as a great transcriber. You know, a candidate for parliament of Australia and a farmer, and I don't know what technical business analyst in Canada Like, what the hell? Because I've got, you know, maybe 30 overlapping skills, like, it's so irrelevant, a matching system at the moment. I think the unlock is going to be more data on both sides. So you mentioned that other products you'd seen were aggregating extra data points from a candidate around the web. Yeah, something like that on the other side. We need more information about the job. I always think about what the questions are. You can just aggregate answers to all the most common questions a candidate is going to ask and have that as nice content. A company we used to work with was really good at this. They were very proactive in how much information they gave out to candidates. Instead of just a job description, they would have, Here's a day in the life of the role. Here's exactly who your teammates are going to be. Here are the LinkedIn profiles. Here are the metrics of our team. Here's the product we're working on and why. Here's the share option plan. Here's how much they're worth. They put in a lot of effort to compile this content. But then, crucially, they shared it at the application stage. So the candidates already had it. Now, I would have thought building that data and collecting that information is the hard bit. But once you've got it, then if you had that kind of AI in between that's answering all the questions on the basis of that, that could help a lot. And that's just about having more data points about the candidate. Is it almost that data you'd normally get in the interview? Maybe like all those questions you'd ask. Most companies are going to ask similar questions. Could there be, like, an intermediate product that's presenting and matching those candidates?

ALLARD: Let me ask you a question on this because this is one thing I'm a bit curious about, and I like it at one, and you want to be as transparent on the process as possible, right? So, because you want people to have a good experience, and the better they know what to expect, the better experience they have from the hiring journey, but then the easier it is to manipulate or hack, like you can, so you can ask, like. Yeah, you can ask an AI just to prompt all kinds of nice answers for you if you're up front. So, like, how do you see that? Because, like, you want to get to know the real person and not the scripted answers.

TIM: That is a great question. And yeah, that is such a fundamental challenge in an interview. Are you getting the real person, or are you getting some facade? And you're right; if they've been almost prompted with all these data points around the team or what have you, they could almost set themselves up for success to tell you what they think you want to hear. I wonder if it's if I think back to some people I've interviewed recently, their interview style. One got people to almost go through case studies. So there's almost like a live problem-solving element where someone was thinking on their feet, and they didn't have forewarning of what the case was. And so they couldn't necessarily prepare for it. And I remember another person; their interview style was that they had what they called three levels of a question. So they were mainly interviewing for macroeconomists and econometricians, and the way they thought about it was they gave just a general problem to the candidate. So, what is the impact of changing interest rates? That was their example. And so normally the candidates would have some kind of economics background; others didn't. So their expectations of the answer were slightly different depending on the candidate. But they'd almost go deeper and deeper in the layers of the answer and keep digging and digging until they'd almost exhausted the candidates knowledge in the area. I feel like something like that would be hard to gain or prepare for because you just have to be able to deal with it in the real interview; it does require some kind of technical type of interview. But I feel like that would be hard to prepare for. What do you think?

ALLARD: I think that the case study is one of the most powerful tools you can use to really get in there under the hood to understand. It depends a bit on what kind of role, like if you have a leadership role, you probably want somebody to have put down a vision on how certain things work. And if you have, like, an individual contributor, you want, like, a hands-on case study on this one. I've seen this work. really, really well, like where my question is. And I think this is, this will change more this year, like with all the advances on the, on data and AI for also the hiring process is that because it's, it's a very time-consuming process. So doing a case study, and because there's such an increase in the volume of people applying for the roles, and, and, and it's, it's difficult to get down to the, like, who are you going to, who are you going to select and who are you going to, like, think at the first round? And, and this is where steps need to be made, right? So just as an example, if people put in all kinds of things in their resumes, like vetting them upfront, we'll assess, like, okay, how truthful have people been on the resume? If they haven't been truthful on the resume, you can ship them immediately. Right. So because then you know that you're getting a lot of BS down the line. Yeah, but, and I think more needs to happen here because. If you need to do this manually, it's not going to fly. And if you rely solely on AI to make the distinction, you also miss out. So it's something that needs to give there.

TIM: Have you gotten the sense that the CVs that you're receiving now are less representative of the truth of who the candidate is than what they were a few years ago?

ALLARD: And well, like, I don't, I don't have data on that, but what I see and like, and also like fiddling around with all these AIs, is that it's. It's super easy to identify gaps and to step out of your own. So if I write a piece of content, if I do a presentation, or if I need to do something for a certain audience, like the authentic part, you write it yourself, but then you do check, like, Hey, okay, this is the audience. This is the content that I'm giving. What will they perceive? What will they remember, and what will they not remember? And like, that coaching will help you to make the content better. But it can also, like, eh, coach you maybe to put in content in such a way or, like, have just. Making it much thicker on the things that are not your strength. And just to fit the audience. And this is where I think there's a potential danger, that people either do this like consciously or unconsciously, but it will happen that there's just a mismatch between what you read on the resume and what people can actually do.

TIM: I wonder if part of the maturity of our use of a tool like AI is the language we use because when an AI lies, we say it hallucinates, which is a very Orwellian euphemism that I feel like is not really true. Helping us because the way it probably should be viewed is if I'm a candidate and I've used AI to augment my CV and what's on there is no longer a fact. I have lied as me. It's not another tool doing it. Or if it is, it's a lie. I feel like there's going to be some level of acceptance over what we're doing, and we can't just outsource the morality of our bullshit to an AI.

ALLARD: Yeah, you should, yeah, exactly. But then, like. It's the same with this analogy that I put on the data-driven process, right? So, like, if you say, Hey, I'm going to be data-driven. And then you make decisions based upon data, but you don't afterwards check if you were right on, like, making those decisions, like, you will never learn. So it might pan out well; it might not pan out well, but if you are looking at certain metrics and you see them going up and your plan was for them to stay stable, you need to look at your plan again. Like, okay, did I do the right things here? And I think with the speed the companies are working with that I'm familiar with, right? So this time of reflection is not taken into account as much as it should. And the same goes for candidates; they're like, Hey, what I put in did it, like, does it really stand out to what I am? Yes, so you're always accountable, but will you be held accountable for the results of that? That, that's sort of a question that I think it should, and I think it will make the process much better, but then, and like, are you taking the time for it? And, and, and. And I do have the right tools to do so.

TIM: And then yeah, we're probably going to get into some kind of gray area where it's if I say I cause Chachapiti would have a tendency to inflate things and put them in almost like glossy, flowery language. I pioneered. And I led this function where you're an intern and you work there for four weeks.

ALLARD: Exactly.

TIM: So there's a spectrum of lying, from complete lies to exaggeration.

ALLARD: Yeah. And I, and I, and I genuinely believe that there's, like, so on your resume, you always try to show the best version of yourself. Right. So, but it's your own evaluation of that. So if an AI constantly tells you, like, Hey, this is important. This is important. This is important. Like, there will be some personal hallucination as well. Right. So, like, helping the candidates to vet is also helpful for them in their careers, like, if you, if you. And like people put in skills, for example, hey, these are my skill sets, right? So I can add, like, for example, and, like, I can do SQL, I can do Python, I can do R, right? So, like, if I had, like, from for myself speaking, right? So my SQL is way better than my Python, right? So, but they're on the same level, right? So, and this is, and if the role requires being much more proficient in Python than in SQL, this should be caught early for both the candidate and the employer. Else, like, you will run into problems later on. I've seen this happen.

TIM: I wonder if one short run change might be just the length of the CV because. It's often said, maybe it depends a bit on the geography, but it's often said, Oh, like two pages max; don't try to make it too long. Even if you've got 20 years of experience, keep it nice and short. Some other markets may be a little bit different, but that's what I'd hear here. But if an AI is going to be screening the CV soon, if it isn't already, then it's not really any limitation on the length. You may as well be a bit more verbose and explain your experience in more detail and get into some depth of what you've actually done. Rather than having to hit an audience of a human who's got six seconds to review it. I wonder if that's what we'll see.

ALLARD: I think that's a great point that you're making. Yeah. So if I would not put up a CV in a, like, I would focus on two pages max, but I have almost 20 years of experience in there. Like, yeah, like how, how do you fit? And every piece of this experience I still use today. So yes, the more recent is more relevant, but still, like, everything is relevant, and it's also your journey that, that sort of. Defines like why you're a good fit for this role in my view, and this is why I like the conversational part a lot. But yeah, so like having a lengthy CV if you're just getting AI to summarize it anyway. But yeah, then the question is like either you do the summary or like the company receiving the CV does the summary, and this is also like, yeah, but yeah, then the company at least can decide like, okay, which ingredients do I think are the most important for this candidate in this role to be successful and then get the much broader view. So I think it's a good idea, but I also saw that every company has a different approach. Sometimes I hear people telling me in my network, like, Hey, well, like, so, like, I had a two-page CV, and they wanted, like, they said, like, Hey, you need to provide much more detail so that I understand. And, and, and the other way around, and that, and so it's so specific for the company.

TIM: Yeah. And I've often thought in the past one underappreciated difficulty of getting a job for a candidate, particularly in a field like data, is the drastically different audiences that their CV has to speak to. And even their interview. So you apply, and you would typically have, let's say, a TA HR recruiter as the first screen. normally would have no background in analytics, really, like they're not going to evaluate from a technical perspective, but they're trying to do some kind of at least first pass of the CV. So they've got to get past that type of persona and then almost the polar opposite if they're then dealing with a hiring manager or future colleague. In the domain. So now maybe they've got like a third audience, which is the AI bot. So the CV is going to have to somehow be optimized for all three. Again, maybe there's some lessons in SEO and optimizing websites for Google bots as well as human readership, meaning almost like two versions, an HTML version and a whatever version; I don't know.

ALLARD: And I think that, and so we're now double-clicking a lot at the first part of, like, an employee journey. But like the employee journey is so much broader, and thinking of the bigger picture here, like every employee wants to join a company for certain reasons, right? So, like, to become better, to have an exciting time, maybe to make a nice, like, make some money. There's, there's, yeah. All kinds of different reasons for the company and looking at the holistic way. Like, for example, if you do a skill assessment in your hiring process, but you don't have your overview on, like, what skills do I need in general in my team? And where are the gaps? And if you don't make an assessment on that, like, Hey, then this whole process at the beginning also doesn't make sense. So thinking about this, this, in a much broader perspective, is super important as well. Because that also can help you with your branding. It can help you in so many different areas. And this is what I try to do. I try to focus on the whole employee experience, not only the hiring part, which needs to be great because it's sort of like your first interaction with the company. But then if the things after that do not fit and do not match, like it's, it's. It's quite like there's a mismatch. I, I have the same when it comes to, like, filtering out biases. Yes, you can filter out biases in the process of hiring, or at least make a very strong attempt to filter out those, right? But then if the company doesn't filter out biases in day-to-day interactions, like then, then there's a mismatch there. I remember this like I had this. I have many examples in this area. For example, I had a rockstar team in China, and they were, like, super, super, super smart people, or they are super smart people. And, but like what you see with Chinese is that they tend to use their eyes and their ears more than their mouths. Right. So, so then in interactions. Like when Europeans, like, for example, we had more Southern European teams, and they are used to doing a lot of, like, vibrant discussions in meetings. And, and, and, and you need to sort of jump in and pitch in and make your points, and like, it's, it's a very different dynamic, and this just clashes. So, like, then you have your bias figured out, but then I spoke to one of the leaders, like that, that were engaging with. This Chinese team was asking, like, do they speak English? Like, do they actually, like, like, there was such a mismatch there, and you can, you can cover all these biases in the hiring process, but if they don't align with your company culture and don't align with how you as a company look at diversity in general, like, then you have a mismatch, and I think it's so important to make sure that these systems are fully aligned.

TIM: And in that scenario, in a typical hiring process, I would feel like. Introverted, a bit more reserved, a bit less arrogant, in some sense. Candidates would often get screened out, especially in the early stages, because I feel like traditional interviews are almost optimized for confident, extroverted, handsome, tall, blah, blah, blah people. That, in a way, is almost quite hard to undo, I think. Like we're certainly with human interviews anyway. Like you, you can't unsee things, and it's yeah. So easy to confuse confidence with competence. Maybe especially if the person doing the interviewing isn't a domain expert and they can't, like. Dig and sniff out the bullshit like I can. I can think of the first salespeople I ever hired, who are obviously very good at selling themselves, and I didn't have enough experience in sales myself or hiring salespeople to really know the difference between a lot of talk and any action, so I feel like maybe that's where you really need an expert interviewer. But even with that, we're all just humans with biases. Maybe this is where I could help. Could AI be curing us of some of these human biases?

ALLARD: Well, I hope so. So I hope so. Because I have two thoughts on this. So. The first one is around making sure that you're looking at the data network and that that's where AI helps. And, but the second one is about building in feedback loops and just making sure that. And all your assumptions that you did at the beginning, that you apply those later on a check, like, Hey, was I right on these assumptions? And I made decisions on a certain level, making sure that you journal those and, like, capture those, and that you keep looking into what I mentioned; I journal a lot. And I, I read back my journals. Many, many times. Like, what was my thinking back then? Like, hey, why did I think certain things? What were my expectations? Are they still meeting those? And I think that's super important. So AI can help structure this thinking in this process. But in the end, it's also about being disciplined and, and, and, and, and looking, trying to look at this objectively and with enough curiosity.

TIM: One really interesting use case I think of for AI in hiring that is a little bit more nuanced and complicated than just screening this CV or interviewing this candidate is almost back to what you're getting to before, which is like a higher-level thinking of, are we even pursuing the right skill set in the first place? Is this even a gap? Are these skills available in one candidate, or is this like a one in a billion person because there's just no one in the world who has these things? Are we being realistic? Is the salary total anywhere near what we'd expect it to? What are the trade-offs we're making? Like this one, I think that comes up a lot, but we don't think about it as okay, we're going to hire people in just this one city. Okay, fine. We could have hired people remotely within these countries, though. Like, how much have we decreased our talent pool as a result of this one filter? Other things around, like just interview feedback, like you could almost imagine an AI interview helper giving coaching to the interviewer around the questions they're asking, how they're framing them, how they perceive the candidates answers, like, Oh, you mentioned the candidate was like, I don't know, shifty and untrustworthy. Why did you say that? What was it that made you think that? Did you realize you asked them this and it made them uncomfortable? There's just so much room for improvement. I think. In making the whole process a lot fairer.

ALLARD: I like it if we could get there, and hopefully we will get there soon. That will be amazing. Like now, like in the interviewing process, it's not, it's not done having the AI be part of that interview because of all the privacy, and you never know what people will do with it. But yeah, it's like finding a way that you are secure and like, and, and have respect for people's privacy and like their data, but having this coaching function is, is like that, that will be a good situation if we, if we get there, not sure if that's already like common practice, but here in the Netherlands, like I haven't seen it. I haven't seen it done.

TIM: I haven't seen it either, but from my own personal experience in using AI, I found it, that there was, Similar to you, like I would ask it lots of different things about different stuff that I was thinking and decisions and I was getting it to help me with my plants last week about a fungal infection that it could see on the soil and I managed to figure that out. So that was helpful. But what I've noticed is my receptiveness to its recommendations and feedback is very high. Like, it's not like asking a colleague or your boss or your whatever partner where there's a little bit of. You feel no matter how receptive you are, there's always a little bit of, Is this person judging me, or do they have a bias, or what have you? with AI. It's, you don't have that at all, at least I don't. So my willingness to even listen to it is super high, which even if it's advice is imperfect, sometimes wrong, sometimes whatever, it's better than not getting any advice.

ALLARD: Yeah. And I, like, recently so like these things that they're looking at asking those things around the house, like, how should I do it? Like, I've been using ChatGPT on this a lot. And then recently I switched to Gemini just to get a bit of a different flavor. And I noticed that I started getting annoyed, right? It's like, okay, so, like, these are not. Like, I need to ask the questions in a different way. And like this is also something to be really conscious of. You got acquainted with one tool, and that, and that you get locked in. But then zooming out and making sure that you stay to agnostic and that you keep using your own like mind. I think it's, it's, it's It's a danger that we need to watch out for, like, if we start over-relying on one AI, like, then you miss out on a lot.

TIM: Yeah, particularly, I don't know about you, but I feel like Chachapiti's. Very friendly and nice to you. Like, it's not going to, unless you really ask it, it's not going to give you really direct feedback. It's not going to give you Dutch-style feedback, is one way to put it.

ALLARD: Yeah. And, and, and like, and for some people, if it's like, I remember that like we were talking about like introverts, extroverts just now, like, so like for the introverted people working remotely. It's amazing, right? So they, they had, they, they are in a meeting; they had, like, and they can switch off outside of a meeting, which fits them really well. So this, this specific way of working fits certain people much better than the other people. Like, for example, I'm somebody who loves to be. In a room full of people, I love to be interacting. I can sense the room way better. So for me, it's a disadvantage, and the same goes with that, like all these different tools, or some, some will work really well for you, and like, and the others might, might not. And, and it's, we'll see how this evolves, but it's, it's good to be conscious of that as well.

TIM: How, what would be like the main difference for you between using Gemini and ChatGPT? Is there anything that stands out?

ALLARD: No, so like, it's. There are limitations on any of these AIs. And I know the limitations of JetGPT. And I wasn't aware of all the limitations of Gemini. Yes, which are different, right? So like in general, of course, I understand the limitation, but like the operational nuance. So then, for example, Gemini, I was asking Gemini to put something in my calendar, and then it puts it in my calendar. And then I was asking, okay, so can you tell me like the 10 things I need to focus on for next week? And then it started like, Hey, but I cannot read all your calendars. And like, so I started to get annoyed. And if I were to make a screenshot and put it in a chat GPT, I would get the answers, right? So, like, it's just getting to know a bit the tools and, like, how to work around this. That just gives you a better way. But because it supercharges you on certain things, it just helps me to become a better cook at home. For example, I have all these ingredients; like, what can I make of this? And like, or like this, like my dish was overcooked or like undercooked, and like, and what are the things I could look at? So, like these, these are the things that. You get used to the answers of, like, your questions you can ask and answers you can get. But then if you switch tools and you don't get it, and like you would go back, reverting to the old tooling, but maybe, and like, so there's a learning curve. You just, you need, you constantly need to go through, and I think the people who are able to do this over and over and over again in the changing environment that is currently happening will be ahead, I would say.

TIM: Yeah, 100%. And if I were to summarize all the conversations I've had on this podcast, the singular theme would probably be the willingness to learn adaptability and the ability to learn new things quickly. That is what pretty much everyone in the world is agreeing on. Yeah, that is, of course, always been important. Maybe it's more important now than ever, given how quickly AI is developing. A lot, if you could ask our next guest on the show one question, what question would that be?

ALLARD: So, like, one thing I'm curious about, like how other leaders look at this, is are you either higher for skills or are you higher for the person? Like, where's your emphasis, and why? So, like, that will be an interesting one. And the context behind the question is that nowadays this adaptability means that maybe personality is more important, even though if you look at the data currently, there's more emphasis on hiring for skills. I'm curious how the next person would look at this dilemma.

TIM: I look forward to hearing their answer when I level that question at them sometime next week. And a lot, it's been a great conversation today. We've covered off a lot of ground, a lot of different areas. Thank you so much for joining us and sharing all your insights with our audience.

ALLARD: Tim, thank you so much for your time.