Episode 18

Sevinj Aliyeva on transforming Hiring Practices: The Rise of AI and Skills-Based Assessments

TIM: you know, AI is changing so many things at the moment. Uh, I think it's going to change all knowledge workers jobs for sure. What about in terms of the data roles? You know, the data analyst, the data scientist, the data engineer. How do you think AI is going to change those roles? What can you imagine those roles even doing in five years? Will they even exist, do you think?

SEVINJ: Yeah, absolutely. I think those roles will definitely exist, but, uh, the way I see it is that they will probably evolve and become a more strategic, uh, for example, in five years, uh, data engineers will become rather architects of insights building. Uh, connected, um, data ecosystems that power business intelligence. And for example, when it comes to data analyst, the way I see that role to transform is that probably the role will transform from, um, crunching data and crunching the numbers into. Rather becoming a business navigator and guiding the decisions in real time by turning the raw data into actionable insights. So yeah, definitely. I feel like those, those roles will still have a place. But of course they will evolve in a way that they will bring more benefits to the organization.

TIM: And could you imagine that AI might end up taking care of maybe some of the more mundane, repetitive tasks that these jobs have. So it's like they're almost unburdened to do more of that higher value stuff you're talking about.

SEVINJ: Exactly. They, uh, as I mentioned, I believe that those roles will probably hand over, uh, more repetitive tasks to AI and rather focus on, uh, business intelligence and really understanding what the data is, is telling us and really becoming, um, the, um, the people in the organization. Who are guiding all of the decisions that are being made in the organization, uh, with the, with the backup of the data.

TIM: I wonder then thinking about it now, if the profile of those roles, almost like the expected skills and experience might adjust because it sounds like they're becoming a bit more kind of higher business acumen, a bit more strategic, as you mentioned, maybe a little bit less technical, what do you reckon?

SEVINJ: Yeah, yeah, definitely. So again, uh, from like, they, they will definitely move from, uh, just, uh, being the people who are doing the, um, the work in the background and really, you know, just, uh, bringing the numbers and putting them in the tables into the roles who are going to be the business navigators and who are going to be the visionaries who, for example, craft the. Models predict future outcomes and again become the people who are able to read the story that is the data saying

TIM: Um, what about thinking a little bit now about the hiring process? So, uh, we work in data. We use data to make all kinds of decisions about product, marketing, sales, et cetera. I often find that a lot of companies don't tend to make data driven decisions in hiring, though, which I find quite interesting. Um, if you've got, have you seen any examples of, uh, data being used in hiring in an interesting way?

SEVINJ: I think in in traditional hiring candidates are often screened based on, you know, their resumes, especially what is listed on the resumes, which is education, the past job titles, and also the years of experience. And of course, those details provide a lot about the background, but I think that those details do not always predict how well someone will be performing on the job. And, um, in these, um, data driven hiring process. Um, I would say one, one of the companies that I worked for before, they decided to take a bold step by removing the, the personal details like name, education, the past roles, and really, uh, instead kind of move the candidates, uh, to the stage of completing skill based uh, assessment, which was quite interesting, but I think it was also. bringing a lot of, uh, nuances that, you know, uh, we are not really seeing in the traditional hiring process. And, um, again, by focusing on those specific markers, uh, which are assessing the skills, uh, of the candidate, not, and rather not looking into the markers on the resume is actually a very good idea because this is the way that the company actually found, I would say, hidden gems. the talented individuals whose skills, um, outgrew the traditional criteria. And I believe that this is, this is the way to go in the future for, uh, for organizations.

TIM: And what do you think it is about, uh, sort of the fixation that typical hiring processes have on a CV? That doesn't work or doesn't work as well as having this more measured approach where you've actually directly measured people's skills and select the people on that basis.

SEVINJ: I think it's again, um, because what we see on paper on, on resume specifically, it's not always telling us a lot about, uh, how good the, the skills of the person or how good they're going to be fitting the role exactly in this specific company. Um, the reason can be different. Um, it, it really can, can be that, you know, the, the people are coming from, let's say, um, from a background from another industry, and maybe they were performing very well in that environment, but they would not be a good fit for this specific company. Um, and again, I feel like there are a lot of talents out there who are skilled and who have a lot of, um, you know, uh, skills working with the data, but maybe They were not lucky enough to, to get the best titles or, um, yeah, maybe they were not again, lucky enough to have, uh, an education from, you know, top universities and, um, I think we, we should be able to give a kind of opportunity to those people as well, and maybe they will actually become a better choice.

TIM: Yeah, it's interesting thinking also about it from like the bias perspective. I always think that, you know, CV has some information on it that you can help. That can help you to make a decision, but it's got so much noise. Like, you know, why do I need to know their gender, their ethnicity? Their religion, sometimes what they look like. This has got nothing to do with who the best candidate is. And I think we're all biased in different ways. We can't really help it. And so any process that starts with giving people all this noise is almost doomed to fail in my view anyway.

SEVINJ: Yeah, yeah, definitely. I agree with that.

TIM: In fact, I can even share a particular example of where I knew I was biased. So, you know, we think about like conscious and unconscious bias. This was like a conscious bias that I had. I can remember trying to hire a product analyst about six years ago and, you know, looking through hundreds of CVS, you might glance at it for 10 or 20 seconds and CVS sometimes have that hobby section at the bottom. And I remember one of these candidates was from Brazil. So already he's got a tick for me. Cause I have this thing with about Brazil. I like the football, I like the Samba. And then I saw at the bottom, it said he was a professional footballer in Brazil. And we had a five aside team at work where we needed just like one more good player to win the competition. And so straight away, I was even telling my, my colleagues about him. Like, Oh, cool. This guy's applied. Let's get him in for an interview. So that person 100 percent got an interview with me for a product analyst job because he was a footballer, which is of course stupid and incredibly unfair for all the other 499 candidates who weren't footballers. Um, in the end we didn't hire him. So fair enough sanity prevailed, but it's just like, that's one of, I feel like a million ways that. If you give someone a CV, they could be biased against a candidate.

SEVINJ: Yeah, yeah, definitely. I agree with that. I also, um, definitely agree with, you know, uh, what I call the, the resume effect, basically hiring the people who look impressive on the paper. Um, and I feel like without a structured and, you know, skill, skill based approach, companies really pay, uh, risk picking people who, who fit. Um, but maybe they're really lacking the edge, um, that is needed to, to tackle the challenges of a specific company. And I believe that, uh, the candidates who actually break the mold are often the ones who are bringing in fresh ideas and also, um, innovative solutions.

TIM: So from your perspective, then it's almost like we could have a lack of diversity of thought if we're fixated on getting someone who fits into a certain box that ticks the right boxes then.

SEVINJ: I definitely agree with that. And I've actually experienced it in, in one of the previous companies that I worked for. Basically, uh, we had this like, uh, team event where we decided all to taste, uh, to take a test, uh, which was, uh, in the end giving us the results of What kind of a personality we are at work, you know, what is that we are preferring, what kind of work environment we like, and it was very funny to see that all of the team members and in our team were basically having the same outcome. Of course, uh, it meant that, uh, it was easier for us to, to work with each other, but also it kind of meant that we were lacking, uh, people who were thinking different than us. And of course, uh, it also means that we're lacking people who would bring a different perspective.

TIM: So yeah, similar story for us actually. So we had our team of analysts, all were like almost maximum blue, like the logical, rational, and very low red, the emotional,

SEVINJ: Yeah. Yeah. Yeah. and what was also, I guess, funny was our stakeholders were often the opposite.

TIM: So the people who we would tend to maybe have a bit of friction with would be very high red, very low blue. And so there's something to be said for learning a little bit from people who are different to you.

SEVINJ: Yeah. Definitely. Definitely agree with that.

TIM: So what about once we actually get into the interview stage of the hiring process? Um, I know a lot of companies, a lot of HR teams have really almost, I'd say obsessed with the idea of cultural fit, which on one hand I get. Uh, but on the other hand, I feel like some of them. overemphasizing that maybe almost at the expense of just, is this the right person for the job? Can they do the job to have the right skills? Um, have you seen that happen? Do you agree? Do you disagree? What are your thoughts on that?

SEVINJ: Um, maybe we can think about the data team specifically. Um, I, first of all, I absolutely, um, agree that culture fit matters. But for me, over emphasis on it, um, to be honest can lead to hiring a team member, um, that is perfectly aligned yet ineffective. And I would probably compare it to, you know, uh, well oiled machine, but without fuel. And for the data teams, fuel is. their technical skills and kind of analytical thinking. And, um, again, I would, I would definitely agree that cultural fit is important. It's, it's important to make sure that, you know, everyone is aligned in the company. Everyone is kind of bringing in their perspective, um, from, from a professional, uh, you know, uh, from a professional side of the things. But again, Um, over emphasizing it, it's, it's not something rational and it's not something that I would go with.

TIM: so we've spoken a little bit about using data to make hiring decisions better. We spoke about having this kind of skills based approach rather than a fixation on fitting people into a box. Are there any other things you've seen, any other kind of interesting ways that companies. Uh, make the hiring process a bit more objective either with or without data.

SEVINJ: I would say probably one of the interesting ways to, uh, transform the hiring process is, uh, to look at the resumes from different perspective, like really stripping away, uh, the names, education, and also background, and really solely focusing on, on the skills, uh, and also, you know, on, on skill base assessment to, to let candidates to actually, uh, you know, shine with their abilities rather than with their background. Um, and I think, yeah, definitely, uh, imagine not having names and, um, no past titles. I think that would be, um, that would be the, the environment where we would probably see the pure talents shine.

TIM: Yeah, I mean, exactly. At least anonymizing or stripping out those irrelevant details. Like, why do you need to know someone's name at the initial stages anyway? It doesn't really matter, does it? Um, that is why I'm, uh, in terms of AI, I'm a little bit excited because I think at the screening stage, in theory, some kind of accurate AI screening tool that runs automatically, immediately. Uh, deliberately does not, uh, look at any of these irrelevant factors that could in theory give feedback straight away to the candidates kind of in real time about here you go. Here's how we scored your resume compared to the job description. Um, I feel like that's got a lot of potential above. the kind of traditional human approach. Um, in fact, I've heard from quite a few companies recently who've kind of hacked out their own version of this with either Claude or Chachapiti, um, because they seem to be getting inundated with CVs at the moment. And so it's like almost a necessity that they would automate that screening layer. Um, but on the flip side, uh, I've also heard from a lot of companies who've said that they feel like a lot of the CVs are now, you know, Even more bullshit than they used to be, basically, that a lot of candidates are using chat GPT to augment or exaggerate the CV. Have you seen any of that yourself?

SEVINJ: Um, I think it's, it's becoming very usual. I've seen a lot of it. And I've also seen actually those things happening, uh, specifically in take home assessment tests, uh, as well. And sometimes I would even like really put the question in Chat GPT and try to see what the answer is coming back and then compare it to the answer from the candidate. And There were cases that it was like matching a hundred percent. And, uh, for me, of course, um, in those cases, during, during the interviews and, or during the sessions where the candidate was running us through their solution, I would really try to give some specific questions and really try to assess like the skills, which I potentially could not really assess because it was purely generated by, uh, by AI.

TIM: And what's your view on that? Just philosophically? Like, do you think all candidates are going to use this tool anyway, in the real job? Let's let them use it in the hiring process? Or is it more like, well, I want to get a true picture of their skills. I don't I don't care what AI can do. I want to know what they can do.

SEVINJ: Um, I agree with both of the perspectives. Uh, so for me, it's not something that we can avoid. And I'm sure that, you know, we will see AI probably transforming and getting better with time. And this is gonna become, it is already a part of our work life. And it's gonna, you know, uh, probably get deeper and deeper in it. So it is okay for me, definitely. Okay. That candidates know about it. Candidates, you know, know how to use it and they actually refer to it. It is a good sign, but for me, it's also important that, um, the person has enough skills because it's, it's not about like getting the specific information from an AI, but it's also knowing. Um, what should be the questions to ask, like, what is the good approach, uh, to, to build the question so that AI is giving the, um, the relevant, uh, answer, because there are also cases when you would ask a question and you would get a very random answer, which is not really matching with the, with the question. Um, so yeah, I definitely agree with both of the perspectives for me. I think it's important that the person, that the candidate. Uh, has the relevant skills and they understand their work, but on the side, if they're using AI in order to, you know, become efficient, uh, use time better, then of course, um, that's not something that I would try to avoid.

TIM: Yeah, I was thinking about this recently because we've been doing some hiring and we noticed some candidates using Chat GPT for various bits of the process. Um, and what I was struck by was that they would use it sometimes for things that I didn't think they would. So, for example, in our process, we had a question that was something like, you know, imagine this day one at Alooba What are the three things you need from us to be successful? They would use even for that, which I found quite strange. Cause it's a very personal question. Like I really care what they think I'm like, I'm going to do these things so they can be successful yet. They still outsource that to Chat GPT I found that quite odd personally.

SEVINJ: But maybe it's also a problem of, of the hiring process because maybe the, the candidates actually think that there is a right and wrong answer to those questions. Maybe they have experienced it. Maybe in the past of interviews, someone reached out back to them saying that, Hey, I'm not We're not going to move forward with you because of your answers to the questions, X, Y, Z, you know, and maybe they, they just had bad experience with that. And they learn that there is always, uh, you know, the, the best right answer to the question. And that's why they're trying to, to get to that, uh, answer using AI.

TIM: Yeah, no, I think you're absolutely right, actually. And if I think of all the stupid questions that people have been asked and been rejected on the basis of including like, Oh, If you're an animal, what animal would you be? Sorry. No, you said mouse you're out then. Yes, that's good. It's good reason for them to be worried. So that's a good point. Actually. I hadn't thought of that. Um, one analogy I was thinking about also with like using some of these AI tools is like, Let's say I'm going to use Chachapiti for some analytical work. I'm already analytical. I already have a base knowledge of how analytics works and the skill set. I feel like there's a very big difference between me using that to make my life easier versus my six year old nephew. Plugging in something to Chachipiti and getting something out because he would have no idea about the context or what to ask it or what have you. So I feel like it's, it's like a leverage tool to help make people who already have a baseline of skills better, rather than helping someone who knows nothing, if you see what I mean.

SEVINJ: Yeah. Yeah, definitely. Uh, again, I, I feel like it is a tool that should be used in order to become more efficient, uh, at your work. But, uh, again, for, for that, in order to get the right answers and what you need from the tool, you actually have to have the skills and you have to have the knowledge to know how, how to structure the questions and so on.

TIM: Yeah, and definitely to make sure you catch the hallucinations, uh, as they come up. Um, What about this? So from all the literature I've read, this is like academic studies from universities over years, looking at what ends up predicting whether or not someone's going to be successful in a job tends to show that things like unstructured, like Let's grab a coffee. Let's go down to the pub type of interviews. Um, years of experience and age are variables that don't seem to predict whether or not someone's going to be successful at all yet. Things that are a bit more structured, like a structured interview, um, an IQ test, certain bits of personality, skills based assessment seem to predict who's going to do well, quite well yet. Most companies don't use that approach. Most companies use something more like the first approach, which is a, you know, a vibe check, a cultural fit, this, a pub test, that a coffee chat, this. Why, in your opinion, do you think there's a gap between what the literature says works and what companies actually do?

SEVINJ: that's a very good question. Um, and the answer is very simple. To be honest, it's that old habits die hard. And I feel like a lot of companies, uh, they're just used to working and in such a way, and, uh, they're just used to, you know, uh, checking, uh, kind of the, the boxes and, um, making sure that it's rather a safe bet because Let's be honest, um, just by looking at the cv, if you see the background, if you see the education, and in the end, if, if the candidate is not the best match, uh, the, for example, the HR person can always refer back to the CV saying that, but hey, look at this. The, the person has a very good background and, uh, we had no way of, you know, knowing upfront that the person would not be a good fit. And instead, uh, you know, assessing candidates based on the skills and really not looking at anything on, on their background and education, it is kind of a risky approach. And, um, a lot of companies for sure, they do not want to, take that risk, um, but I, I believe that, um, as they say, uh, the best view comes after the hardest climb and, um, updating hiring practices is a climb well worth making.

TIM: That reminds me of the saying, I think it's something like nobody ever gets IBM. So maybe there's a certain risk aversion built in that if you just keep doing it the same way everyone else does, as you say, you've got something to point to, um, In, in your defense of it all goes haywire.

SEVINJ: Yeah, that is, to be honest, something that I'm, I'm trying to change at least in the companies that I'm at, or at least for the roles that I'm personally hiring. It's not that easy again, of course, because I am in an environment where the traditional way of hiring is. Um, has been there for years. And, uh, of course, uh, let's say that people team wants to still implement, um, those strategies that they had in place for years. And it's not that easy to convince them to change, uh, those, but again, uh, I'm, I'm trying to do whatever I can do because I've also been on the, uh, other kind of end of the things when I was, uh, being interviewed and, uh, And there were, I would say, more than what you think, like the number of the interviews where I've been, uh, where at the end of the interview, the person interviewing me would ask about my age and that would be very surprising to me. Yeah, so, um, this is happening. This is happening a lot. Um, but I also understand why it's happening because, you know, it's, it's kind of a safe bet to go for a person who has much more years of experience, maybe older, and maybe they're just checking all of the boxes on paper.

TIM: And this I assume is in an environment where that question is actually illegal to ask, and they're asking it anyway.

SEVINJ: I, I've never questioned, to be honest, that's a good point, I never questioned the legality of the question, because it would be usually asked in a way that, hey, I'm sorry to ask, but just to understand, I'm curious, how old are you? Uh, so, I wouldn't take it bad. I would be like, okay, fine, you're, you know, I, I, I can speak about it. I have no problem. And if you will assess me based on that, then that's your loss.

TIM: So they sort of sugar coat it or make it sound like it's almost friendly chitchat, but you know that they're recording it for some reason. And, and so you're, you, I'm interested in this. So from your perspective, you're assuming that, uh, they are using that as like a proxy for experience. And so if you sort of older is better or something like that, or is, you know, what, what do you think?

SEVINJ: To be honest, because they can also see my experience, um, based on my CV, because I'm, I'm trying to show like where I worked and for which periods. So they know it, but I feel like they're still curious to know. Just to make sure that maybe I am matching, uh, the environment that I'm, I'm going to be put in and yeah, maybe they, they really want to hire people in this specific age range. I don't know, but that could be the case.

TIM: uh, I hear from a lot of hiring managers even in data, uh, that. Or almost slightly dismissive of technical skills. And they'll say something to the effect of, uh, you know, technical skills can be taught or learnt. I really care about the soft skills, care about the cultural fit of the candidates. And part of me understands this. Um, we've already kind of discussed how maybe AI is going to make some of the jobs a bit less technical. Maybe they're kind of thinking into the future. Um, but I also think that. Some managers probably forget how useless they were when they were graduates in terms of their lack of technical ability to do anything and how, how much of an art it is to become a data professional. And it's not just something you can pick up in a few weeks in a, in a Udemy course. So. If, if hiring managers overemphasize that and go for someone with almost no actual technical skill, but are a great fit and a great communicator and blah, blah, blah, are they going to deeply regret that when they put that person in front of the computer and they can't actually do anything?

SEVINJ: think focusing too much on soft skills over technical abilities, it's, it's definitely a very risky approach and it's a very risky assumption that technical skills can be taught in the job. Because, um, let's be honest, like learning, let's say, uh, how to use SQL or mastering. Data science isn't like learning to ride a bike. Uh, and of course it takes a lot of time and on top of that, it, it takes a lot of efforts. And maybe the person could be a very good fit, but, um, it can be the, also a risk that the, the hired like employee will, you know, at some point realize that it's, it's not a good fit for them personally, and they wouldn't want to stay with the company. Maybe they will find it too hard. Maybe they were not actually ready for it because that was never questioned or assessed during the interview process. And again, yeah, I definitely think that it's, it's not a good approach, definitely. And it's, it's also not a safe way to go with when it comes to the hiring process.

TIM: And it's certainly not a good approach if the reason you're hiring this person is because you yourself are so ridiculously busy and you want them to quickly take over some of your workload, because that's certainly going to backfire because you'd spend probably more time teaching them how to do the things you want them to do, rather than if you just hired someone who could come in and, you know, with some onboarding, more or less hit the ground running, because they've got that foundational skill set and you don't have to teach them SQL or what have you.

SEVINJ: yeah, yeah, definitely. No, I, I, I definitely agree. And, um, I've also had experiences where, for example, someone was hired for my team. Um, where we knew that the person was junior, of course, and we had a thinking that, you know, things can be taught, uh, during the process, but unfortunately that that wasn't the case. And then after realizing that as a team, we're spending actually a lot more time to teach the person how to do a specific task. And there were the cases when we would actually not ask that person, but we do it. on our own and then that would bring to the situation that Okay, why why did we hire the person in the first place then?

TIM: Yeah, I've experienced exactly the same thing myself, hiring people for a particular role and realizing, Oh, they probably can't do 50 percent of this, then taking that on myself. And that's just, that's a spiral to failure. Basically, that's never going to end well, I don't think. Um, and I was recently trying to reflect on like, what, what were my skills like when I first started working? And so I can vividly remember one moment, this is my pretty much my first job as an intern at PWC. Um, Way back, 13 years ago, and I had some spreadsheet of data, uh, can't remember what it was about, and I didn't know the concept of freezing panes. I didn't know you could freeze like the top column or the, or the leftmost row or vice versa. Sorry, top row or left column. And so I was scrolling down like 20, 000 rows and then back up 20, 000 rows to see the column edit again and again. And my buddy came across and said, Oh, like, let me show you something. Bang. Like, so that's, that's the start of my career. I'm pretty sure I can remember how to freeze pains now, thankfully. And that's one micro example of like a technical skill that without that, I would suddenly be 99 percent less efficient at a basic task. So yeah from my view, I think completely dismissing technical skills is, Oh, you can just pick that up and magical skills can appear is, um, Yeah, misguided, I think, deeply.

SEVINJ: Yeah, yeah definitely

TIM: Uh, what about this, uh, if you learnt a lot from any particular person when it comes to hiring, or can you sort of imagine any kind of hiring hero, like imagine a person with a cape, uh, a sort of a heroic figure who does hiring in an amazing way, you know, how, how would you describe someone like that?

SEVINJ: that's a very interesting question. For me, uh, probably a hiring hero is someone, um, who is setting, I would say, a very, uh, good standard for fair and also objective hiring. And again, it would be a leader, uh, who is, uh, rather, you know, um, rather focusing on the data driven hiring and really, you know, structuring the interviews in a way that it's minimizing the bias in hiring. And again, um, it would be the, the leader, um, that do not solely rely on gut feeling or what the paper in this case, the CV is telling us, but rather is focusing on the data driven methods to make the best choices.

TIM: Yeah, and I think in some sense that would be heroic in two ways. One is, it's more objective, you're Trying to make sure the best person objectively gets the role rather than your mate or someone who looks like you or someone who plays football, in my case. Uh, but it's also going against the grain, as you point out, like the safest thing in the world would be to do what everyone else does, which isn't that. So there's some level of kind of putting your head above the parapet as well, isn't there?

SEVINJ: Yeah, yeah, definitely. And I definitely agree that, um, in reality, we see a lot of biases. It can be not even about being relevant or feeling relevant to the candidate. It can also be that you see something on their CV and it just sounds cool or it looks cool and you're like, oh, it's probably a cool person. Uh, let's, let's move forward with that person. Um, and again, um, in most of the cases, it doesn't say much about how the person is going to be performing on their So,

TIM: Yeah, I think that's the fundamental problem with a CV is whatever way you look at it. It really just doesn't predict the quality of the candidate at all. Um, on both sides. So there's all these candidates who probably are amazing, but their CV looks terrible according to someone's opinion and vice versa. And then also just what is, or is not a good CV is so subjective. Um, it reminds me actually of a really interesting experiment we did a few years ago where we got about 500 CVs and we hired 10 different recruiters all to work independently of each other. We gave them all the same 500 CVs. the same job description. And we said to them, shortlist us candidates for this role based on this job description. They all chose different candidates, like flipping a coin. None of them had, I think only one candidate got selected by more than two recruiters. Uh, so it's just pure chaos. And then behind the scenes, what we didn't share with the recruiters was that we had skills assessments from these candidates already. So we already kind of had a better picture of their skills. And again, it was almost like a negative correlation between how strong the skills were and who was selected. So the CV screening stage just must be so inaccurate for so many companies.

SEVINJ: Yeah. Yeah. That's, that's actually a very interesting, uh, you know, experiment that, that you run. And I think it says a lot, it definitely says a lot about what's happening right now and like generally in the traditional hiring process. And um, yeah, it's, it's interesting. it's definitely a reason for changing things and for shifting things around. And I think the more we hear about it, and the more, you know, we talk about it, there is probably going to be a time when a lot more organizations will start thinking about it and really implementing, uh, you know, strategies in order to change their hiring process.

TIM: Yeah, for sure. And there's just going to be some tipping point. I don't know when it will be, but if I think about football, you know, until only, I reckon, Six or seven years ago, most recruitment of football teams was still very traditional, you know, not releasing any data at all. Then the teams that started using data started being a bit more scientific and smarter about it started winning the teams that didn't were losing. So then that was like, The straw that broke the camel's back was seeing clear evidence that this is a better way to do it. It is, it is going to make you more successful in the football pitch, I guess, more successful in business. Maybe the, the connection in normal companies isn't going to be as obvious. So it might kind of take longer to play out, but I feel like eventually this is going to be some clear evidence that one way is better. And then every company will have to do it. Otherwise they, they won't survive.

SEVINJ: Yeah, yeah, definitely agree with that and looking forward to that.