Talent Talks with Tom Hacquoil

Welcome to Talent Talks, quick fire questions to get to know leaders in recruitment. I'm Tom Hacquoil, Founder and CEO here at Pinpoint, and today I'm joined by Markellos Diorinos, co-founder and CEO of Bryq. Bryq's a talent intelligence platform designed to help organisations hire the right people fast and to enable existing talent to realise their full potential. We love Bryq at Pinpoint and I personally love Markellos and what they're doing with data science. I'm super excited to have him here for this episode today. Markellos are you ready for some questions?

Oh, yes. I am.

Thank you very much. Cool, right? So look, obviously we work together. We're an integration partner with one another and love everything you're doing. I think one of the things that struck me when I first met you is you've got this really interesting background in products and operations and so on. Would love a 60 second summary of what brought you to found Bryq.

That's a great question Tom. And here's my short story. I've been working with my co founders for the past 15 years or so. And we used to work together at a small multinational and those things existed. It was a, 400 person company, but we had people anywhere from Argentina to the Philippines and everything in between. So we were hiring lots of people. And when you hire in Vietnam and you hire in Argentina and the US, these are different people. So some of them we got right. Some of them we got invariably wrong. And the worst part of getting someone wrong is that you're wasting your time. You're wasting their time. It's a bad impact on both. But then we're looking back at all our process and we're saying, There was no way to predict that and that's what struck us and luckily one of our co-founders has an IO psychology background and he came in and said. You're being idiots. And we said, why are you saying this? There's a whole science that's about how do we pick the right people? Let's go and use some of it. He knew it, I didn't. And we went and we looked at psychology and said, Ah, assessments, this and that, let's go and do it. And we looked at all the different tools out there and he said, it should be really easy, they should tell us directly what to do. And, We didn't like what we found. We found a lot of solutions that were cumbersome for candidates, really hard on recruiting managers and hiring managers, and there was no clear outcome. So instead of helping you and giving you data that will help you make better decisions, it actually obfuscated things more and would give you tons of information that you didn't know what to do with. And this is where we saw the gap in the market. And we said, Hey, Can we get science that works, things that we know have worked for the past 50 years? And can we package it in a way that your average hiring manager, your average recruiter, can actually make heads and tails of it and make better decisions so then when they go back and they say, Hey, we hired Markellos and he didn't work out, they can say, okay, it was a reasonable gamble. We knew what's good and what's bad about him and it played out or it didn't play out. And at the end of the day, this is what we do for our customers when they come back and they say that things work for me with Bryq. Usually the feedback is not, Oh, we hire great people and they stay and they, have great retention performance. And most of the time. We hire people that we wouldn't necessarily have hired before. That's the point, right? Can we surface the data so that people can truly and objectively evaluate candidates and employers alike.

Yeah. Love that. Makes a huge amount of sense. I want to pick up on one of the things you said as we talked through, right? So there was this repetitive theme of like data and science, right? And helping the average recruiter kind of make heads or tails of all of that stuff. I think one of the things that we try and make a big part of our mission here at Pinpoint is giving recruiters that sort of strategic seat at the table that they don't, historically known to have had. And I guess I'm interested in your perspective on what it is you think's stopping recruiters using data effectively today?

The first thing that we have to consider is how do we hire people? And for me, candidates and employee are like icebergs. There's a small 20, 30 percent that's up top that is usually their hard skills and their degrees and all that stuff that they can put into a resume. And that's great because you can see it. And by the way, because you can see it. This is the data that you usually go off of. The problem with that is that this 30 percent of hard skills is actually stuff that's necessary to do the job. But it's not sufficient to do the job. And then once you start looking underwater and you go to that 70%, which is their attitude, their personality traits, their growth mindset, these are the things that are determining for their success in the role. And these are very difficult to change. So what we're doing is that we hire people that look good and we ignore all the rest. So once you hire with that for the 30%, invariably some of the time you have very limited data. And you can't make great decisions. It's exactly the thing I was describing in my opening, right? I didn't have the data to say that this would be a bad hire. Once you expand and you start looking at the whole iceberg, looking at the whole person, then all of a sudden you're saying, oh, okay, that makes sense. And once I give that data to the recruiter can actually make better sense of the candidates. He can make faster selection, their interview to hire is going to improve radically because they no longer have to give 15 people to the hiring manager, right? They're going to give him three or four and they're all going to be good candidates. And then it's all about weighing. There's never a solution for hiring. It's always a compromise. I'll hire Tom and he's going to give me this and lose that. I'll hire Markellos and I'm going to compromise on something different. You're always going to do that. As long as you have some reasonable data to do it on. That's when you're more successful.

Yeah, that makes sense. It's hard. I guess it's hard to be data literate and it's hard to leverage data when you don't have it in the first place. And I guess that's 70 percent of the iceberg that's underneath the water is you equipping recruiters with the tools they need to do that which makes a great deal of sense.

It's really interesting. I was talking to Gary Crispin the other day and I was reading an interview he did yesterday with someone and he was saying, Hey, what do you think has changed in hiring? And he was saying, Hey. We've got to start using assessments. We've been reluctant to do so before because they were so bad. But now assessments are transparency and you have to have transparency. You have to use things that are scientifically validated and do the right thing, but then you have the data to make your decisions on. It's not like people didn't do that, but they didn't have the right tool. So now we give these right tools to recruiters. We give them to hiring managers. And all of a sudden we shift the way that people hire. It's really important that there is a platform behind that can actually help surface that. Because if I do the assessment is just in a note field that you have to click 25 times to find. Yeah. You know how that works.

It doesn't.

Exactly, which is exactly the point, right?

But no, okay. That makes loads of sense. I think if we talk about challenges, right? Like challenges around understanding the candidate, understanding that kind of bit below the water that you just talked about with your iceberg analogy, like there's a clear challenge there, data literacy is a challenge. Have you got any examples at front of mind of other big challenges you think talent leaders are facing in 2024?

There are two major trends that we're seeing. The first one is that sourcing is a challenge for everyone. It's not only a challenge because there's a low unemployment, you can't find enough people, but you go and you want to hire people who have, two years worth of AI experience. And there are probably like a thousand of those in the world. We are hiring for new skills. The pace at which skills are outdated is tremendous. I was reading a study the other day that said that the average skill goes away in two and a half years. So how do you find these people? We have to invest more into, training and development. So instead of trying to hire for something that's going to be outdated fast and fish from a tiny pool of maybe a few candidates worldwide that do this, you have to expand your horizon say, Hey, I want people to have experience in data and programming and this and that. This is what's important for me. These are the skills. I also want people who are able to learn and who are going to work. And, they're open to change and do all of those things and then bring those people in and make them successful. So this is how it's a very big shift in who do I hire? I'm no longer looking to hire someone who used to do this. I'm looking to hire someone for potential. People who can do this. And that's a really big part that we see in hiring. And the other part that we're seeing with our customers is that we're starting to realise the value of human resources. And I'm saying people have always been integral to companies, but we're quick to let people go. Now this, all this difficulty in sourcing new people and hiring people makes us say, why would I ever let Tom go? He already has been with my organisation for five years, and maybe he wants to do something completely different, but let me talk to him again. So there is a focus of expanding talent and it used to be talent management here and there, but now it's all getting joined.

Yeah. Look, that's super interesting. Markellos, I think there's two pieces that I heard there that I think are really topical. One is recruiting on hard skills and experience and it's so obvious that people get this stuff. I like, I was looking at a job description the other day for a government AI research role that asked for 10 years of experience working with ChatGPT and it's like stuff like that. It's just like laughable. And a surefire way to turn off the right candidates. I think the second piece is this kind of thematic stuff you're referencing. So I, I have a. I don't know how to describe it. Like a mentor type figure, someone I speak to very frequently and ask for advice on interesting, bigger picture, macro HR stuff called Marilyn Miller, who's a Chief People Officer. And I think one of the things that she said to me the other day that I found super interesting is really playing into what you're talking about. She called it the buy versus build analogy. And I think these CPOs are these very large organisations, 10,000 headcount plus enterprise orgs typically that we work with. We speak to some of these CPOs and what they're talking about is, yeah, looking at a multi year timeline and thinking about buying versus building, right? Their historic approach was let's buy in experienced talent that's ready to deploy and make an impact today. But I think in a market where sourcing is increasingly more difficult and cost expectations and other overheads are increasingly high, actually she's saying now we could hire X number of people at Y salary. We could also hire z number of people a bit earlier at a salary and build them basically right train them, take them younger, take them less experienced and take them on a journey and teach them to be effective within our organisation and give them the skills provided. And again, massive emphasis on provided that the foundational parts are right, the personality, the attitude, the underlying competencies. And I think it's easy to say buy versus build, it's bloody hard to do. And I think making sure people are equipped with the skills to actually assess the sort of softer stuff and assess the propensity for somebody to do well in a build timeline. It's really hard. And I think, yeah, like everything you're saying really strongly plays into that. And so great feedback. Thank you. I think I'm going to move on. I'm going to spice it up a bit with a wildcard question. So I think we're going to get back to recruitment in a second, but I'm interested selfishly on, on the business side, like what's the biggest mistake you think you've ever made in business so far?

You know how they say that if you don't fail, you're not trying hard enough. We definitely try hard enough.

Me too. Me too.

I'll tell you the most recent mistake that we did, and I don't know if it's going to qualify as the biggest, but it's a mistake I've seen over and over where companies overhire. And then they don't take the time to integrate the people. And the result is that you're going from a well oiled machine that, fires on all cylinders, on a larger machine that kind of sputters along and tries to get ahead. And that was a challenge. We've seen it before. I've seen it like at least 10 times in companies I've worked for. I swear that wouldn't do it. And then I did it. Yeah, that's a challenge. You have to be very strategic on how you bring people. And once you bring too many people on board, we were lucky enough to bring people who were at least a good fit for the company. And what I mean by that is that a really important part when you're hiring someone is, A, can this person do the job? B. Is this person going to actually come and bring something to this company? Are they going to swim with the current or are they going to be fighting it every step of the way? And, there are many ways to talk about this. People talk about culture fit. I don't like culture fit. We don't hire an army of clones. I like to think about adheres to cultural values or culture add if you like. What's this person going to bring, that's going to make my company greater than it already is. And we've been really great at that, but we brought a lot of people where, you know, organically, we couldn't digest them fast enough. So even if you bring the right people on, you have to give them time to become a part of the machine and then start producing.

Yeah, that's a great answer And I think something a lot of us can relate to because I'm definitely trying hard enough and definitely failing very frequently as well. Cool. I want to move back to talking shop. And I think the one thing I know we agree on is the importance of candidate experience, right? And for anybody who's ever used Bryq as a product, I think that shines through very clearly. I guess what I will say is lots of people talk about the importance of candidate experience. Not that many people actually deliver a great candidate experience end to end. And I guess I'm interested in your take on what the most important pieces of the candidate experience puzzle are.

I think it starts with how you think about the candidate experience. It used to be let's make it easy for people to move along the process so that we can select the best. And now most people, if not everybody already in the market is recognising that this is no longer the one way street it used to be. The hiring process now has to work both ways. The candidate has to wow you and explain to you why they'd be great into the role, but you also have to explain to them why the company is great. And is this really the place that they're going to flourish? Is this a place where they can, give their best self and be successful? So when we created Bryq, we said, oh, okay, we'll do psychometrics. So let's start by telling people why we're doing psychometrics. And why we're making them go through the test. It's not, okay here's a hoop for you to jump. It's more about, Hey, you want us to make the best decision about you. Give us data about yourself. Let us understand who you are and don't let my bias or the recruiters bias, or even the hiring manager bias stand in the way. We are all people from different backgrounds, we have different notions of how things work and, we've lived different lives. We're bound to judge people based on their own personal filter. So once you start talking about this and say, hey, if you're all asking for equity, diversity and inclusion, if you want to have equity, you start by jumping through a process that's very standardised for everyone so that we can have data that compares and then you go from there. One of our customers recently we were talking with him and they early careers program and they were saying, you know what we've forbidden people to talk about interviews. Now forbidden is a strong word. You can never forbid anything from your employees. He said, but we went back to our employees, said, don't think about it as an interview. Think about it as a hiring discussion. They need to learn as much about you as you learn about them. And that's the kind of shift perspective that's happening here. It's two ways, you're shopping, they're shopping, and then it goes well. You do the assessment, you give them back data. We're spending a lot of effort with talent reuse or what we call talent rediscovery. Once someone comes through the hoop, and you've spent so much time and effort to attract talent, right? Everybody knows that it's not easy. You look at them for a position, and then you're saying, eh, Markellos is not a great fit, so let's throw him out of the window. What we're trying to say is that, Hey, you're opening a new position. Have a look at your existing candidate pool. See if there's anybody who fits and then reach out. See if they're still available. This is such a huge gain because if a company comes, a week, two weeks, three weeks, a month after and says, Hey, we're now having this opening. Would you like to consider it? This is such a huge win for me as a candidate, because all of a sudden I know that this company actually values people. They're practicing what they're preaching. That's what I'd like to see from everyone.

Instead of diving in with a specific example of Oh, make it to be a scheduling better or do this differently. I think you talked at the macro level, which I really appreciate. And I think we try and do the same thing when we talk to customers. We just say hiring is selling, not buying. And I think that perspective shift makes such a huge difference. And if you do that, every other kind of little micro part in the process ends up feeling and looking and operating more efficiently. And yeah, I couldn't agree with you more on everything you said there. Continuing to drive that forward though, we look at maturity of organisations when we think about hiring and lots of organisations finally understand the importance of candidate experience, and I think quite a lot are starting to perceive it in the same way you and I are, which is a good thing in my opinion. I think what we see organisations who get that right move to is this conversation around hiring manager experience and internal stakeholder management and sort of bureau speak it a bit. And I guess, can you think of examples and stuff you think is best practice for recruiters when they're working with folks internally, right? When they're talking to hiring managers, when they're talking to other folks in HR to get more effective at recruitment.

One of the challenges we see a lot with customers is What does a successful candidate look like? And that's really hard. And, a lot of times, unfortunately, the best practice still is ask the hiring manager or look at your top performers, which is if you don't have any other options, it's a way to do it. But we like to think about this in a much more data driven approach. So think about it this way. You have salespeople at Pinpoint. I have salespeople at Bryq. And I don't know. An oil and gas company also has salespeople. They all have probably the same title and maybe they're enterprise account managers or whatever. They may even end up having the same job descriptions, but these are completely different jobs. Part of our challenge was, How do we help these people find what they need for their role? And, the usual practice of let's have the hiring manager answer 10 questions and we'll know what they want. It's fine. If you want to hire more people that the hiring manager likes. But your goal is to hire people who are going to perform well into the role. So we've come up with this whole new system, which is really a rehash on the old system. There always have been criteria validity studies and what the criteria validity study tries to do is they're trying to figure out what's the relation between the inputs that you have, psychometrics perform and whatnot, and the outputs, performance, tenure and so on. They have been around forever, but they usually take, six figures to run for a single role. We've built into Bryq a whole mechanism that it's about. Can I do profile validation as we call it or run a criteria validity study if you want to be fancy or really can I understand what makes people tick into that role and we do this by looking at existing people. We're assessing them, we're taking up the metric that we want to improve and that metric isn't always necessarily performance it could be tenure it could be safety behaviour it could be whatever it is important whatever is challenging you as an employer and then we look at great and not so great people at it. And we find what is it that drives the difference. Now, once you do this, you get a profile that's a lot of times unexpected. And we were doing this for a customer the other day and we're saying, Hey, you need to hire SDRs that are hesitant. And they were like no, you're crazy. All my SDRs need to be flamboyant and they need to go out and win everyone over. And we're like, you can have your opinion, but it turns out that the way that you're running SDRs, and by the way, this is not a global truth for SDRs. All SDRs in the world have to be hesitant. The way that you want your SDRs to behave and the way that the people who today do it well are successful, is that they're hesitant, which means that they're following the process. They're doing every little step of the way that you're giving them, and they're happy to do that, and they're not going to go and mess it up. And that makes them successful. So that's the kind of thing where big data can actually help you because it's no longer my opinion. It's no longer your opinion. It's what actually works. And the thing that people need to realise is that what worked last year isn't necessarily going to work this year. So you need to keep updating this. You need to keep rerunning this so that you know what's working for your organisation.

Makes perfect sense. And to be honest, I was going to ask you later, examples of interesting stuff you uncovered from your data that others might learn from. But I think actually, that's a fantastic example of exactly that. And I think the one thing I'll also say is, we speak to lots of people in the HR tech ecosystem, and we do it ourselves, and we care a great deal about data analytics and reporting and insights and so on. I think one of the things that continues to surprise me every time I talk to you is this kind of not fallback but this consistent reference to science, and you know you do things like criteria validation studies and all these sorts of things like I think it just brings a level of maturity to the discussion that is sometimes lacking elsewhere and so massive commendation for that. Two final questions, right? One a bit left of field, but interested in your perspective on HR tech arena, and then one, we have to touch on AI because it plays into a lot of this stuff in a lot of ways. And so I guess final wildcard question is obviously you're building Bryq for a reason, and we're all glad you are, I think if you were leaving Bryq and going and building another business in the HR tech arena. What would you be building and why.

My first choice would be to build a solution for video conferencing, but every time I go into a meeting, I spent the first five minutes saying, Hey, can you hear me now? So you'd think that after so many years and after COVID, we have something that works there. Actually, we have pretty decent tools. There's still ways to go. But if I was going back to the HR tech arena, I think there are so many areas that are ripe for disruption. We love the hiring part. We're spending more, and it's quasi building a different business in talent management. And I think over time, we're going to see more companies thinking about those two things as a continuum. So it's no longer we hire and we manage talent. It's. Oh, we have talent and we have to make the most out of talent, so that's a great way to think about talent, but that's a shift that's obviously going to take a very long time. And then the area that gets us most excited is learning and development. Always on our plans, we have very few things, very few compared to what we want to bring. And sometimes learning and development is not so much about content. There are great providers of content out there. It's about currently L&D takes a kind of spray and pray approach. Oh, let's go train everyone on blah. They're carpet bombing people with training and hopefully some of that sticks. And that's fine when you don't know better, but as we go and we understand employees, situations, teams better. What we realise is that there is a very big gap between understanding what you have, where you want them to go, and that gap can be breached with learning and development. So I don't necessarily feel that we need to provide the content, but if we can just go and point people and say, Hey, to make this team more effective, to turn this person to a great manager, here are the kinds of things that you need to teach them. We absolutely love that and we're integrating more and more into our offering, but it's such an interesting field. I'd do a whole business based just on that.

Yeah, I agree with everything that you said, but particularly the piece around organisations doing sort of recruitment or net new hiring and this kind of talent management or talent mobility piece is one consolidated problem set. Couldn't agree with you more on that specifically, right? And we're seeing that increasingly happen, especially for larger organisations. It's almost like talent sits on the balance sheet and they're working out how to deploy it most effectively, right? Yeah, look, super, super interesting. And thank you for that. I think final question, and I apologise because it's super cliche, but I am very interested in your opinion on it. AI is gradually finding its way into our space. And I think I speak to lots of recruiters and to be honest, most of the time when they talk to me about AI, what they actually are talking about is automation. But I'm really interested in your perspective on the role AI is going to play, both in the arena you play in specifically around talent intelligence, but also more broadly in our segment, how do you think that's going to play out in the near future?

I can make a few predictions. I think I'm going to be terribly wrong about all of them, but that's not going to stop me. Here's how I like to think about AI these days. Do you remember when computers got introduced? It was all of a sudden was oh computers are going to take all our jobs and people are going to do nothing. It's going to be all computers. Yeah. That didn't happen. But what did happen is that all the areas where people suck, I was trying to find a better word than sucking, but things that people don't do well. Computers can now take it and they can handle vast amounts of data and they're never going to make a mistake and they'll do tedious processes and so on. And that's fantastic and we have that and now AI is going to come in and it's going to say, Oh, what are the things that people are not consistently executing?Think about the HR space. The infamous statistic about how long does a recruiter look at the resume. Five seconds, seven seconds depends on who you ask. It's definitely not the right approach. So if instead of burdening the recruiter with looking at, a pile of, I wanted to say thousands, but nowadays 50 resumes is already great if you're getting them. A pile of resumes instead of looking them and trying to make heads on tails. If I can have an AI and break this to. How are these resumes fitted for the specific job? And that's something that an AI can do, quite repeatable. It has nothing to do with who they are and what they are. And, if you do it right, you can always implement things wrong, but you can always implement software that loses data. So that's not the point here. If you do it right, it can help you. So if we can lift from recruiters, hiring manager, all the tasks that are hard for them to do, tasks that are sometimes boring, they're biased in performing them, and instead arm them with information. We're actually elevating their decision making. We're taking away all the tactical stuff and we're giving them access to information so that they can make the strategic decisions, strategic hiring, strategic movements, and so on. That's what AI should enable us to do. And this is the vision of how we're seeing it. And every time we're thinking about, are we integrating some AI here or there? The question is, can we do this in a way that people will understand what it's done? I hate the black box AI. So I would never trust an AI where I say, here are a hundred candidates and the AI says, okay, here are the three guys that you should look at. I want to be able to understand, give me data, say that these people are better in this, these people are better in that, and here's the difference and so on. And then I can make my decision that much faster. That's what I'm seeing in the future. A lot of those things, the tedious things should leave us and we can elevate our decision making. And by the way, this is going to be so great. I look at my daily life and I'm not happy about it, but more than half of my time goes to things that are not so challenging. These are things that I would be very happy to leave to an AI and then focus on things about an AI can help me with thinking about the future, thinking about problems in ways that haven't been thought before.

Yeah, look really resonates. I Again, I won't really add anything to what you've already said, other than to say that I think the analogy rings true here, right? Like a lot of ATSs are integrating AI, and I think they're doing that in interesting ways. My view is if you're the artist, you don't want your AI to do your art for you, you want your AI to do the paperwork and to help you sell the piece you created, right? And I think a lot of people on the ATS side are building tools to help with candidate selection and sort of the soft skills and the touchy feely bit and the sell side. I think like we view. Recruitment in the same analogy as with the artist, right? We want to make the recruiter's life focus more on high value things around selection and interacting with candidates and kind of the sell side of recruitment. And we want to take away the pain and the admin and the form filling and the comms and some of the friction there. And so I think it's just interesting looking at how people perceive things because, yeah we want AI to remove the tedium. Not the high value stuff. And we really actually value the human decision making stuff. And I think we're here to support that and not replace it. But look, Markellos it's been super, super helpful. Thank you so much for your time. We kind of made it through 10 questions and appreciate your super detailed answers on everything. Massive thank you for joining me. And everybody else, I think it really is worth following Markellos on LinkedIn, checking out what Bryq are doing. There'll be links to both his profile and to Bryqs stuff in the show notes. And if you want to join me on Talent Talks, please do get in touch. Thanks again, Markellos.

Thank you for having me, Tom. It was a real pleasure being with you and it's a great pleasure working with Pinpoint in general. I think that we're seeing a whole new generation of solutions that are going well in our humble opinion in exactly the right way and these are the things that are going to revolutionize the way we work with talent in the near future.

Thanks Markellos.