AI has been applied to almost every step of the hiring process. Sourcing, screening, assessments, interviews; each has its own tools, and many of them are effective. For many organizations, though, the gains from optimizing individual stages are flattening out. Hiring quality is shaped by the entire journey, not by any single step, and most hiring technology was never built to connect those steps. The focus is shifting toward connecting the whole process so that each stage learns from the others and improves over time.
So what does it take to move from optimizing separate steps to building connected intelligence across the hiring process?
My guest this week is Ben Chino, Co-founder and CPO of Maki. In our conversation, Ben explains why improving hiring one step at a time has hit its limits, what end-to-end hiring intelligence looks like in practice, and what it means for recruiters and candidates.
In the interview, we discuss:
- Why optimizing individual hiring steps with AI has hit diminishing returns
- The difference between a system of record and a system of intelligence
- How a connected hiring process improves decision-making at every stage
- Where the ATS fits in the next generation of hiring technology
- Why human judgment in hiring is less consistent than most people think
- Freeing recruiters for better judgment and more time with candidates
- Turning 800,000 applications into a real candidate experience
- Why adopting AI in hiring is an organizational change challenge, not a technology decision
- What does the future of hiring look like?
https://www.linkedin.com/in/ben-chino/
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Transcript
Matt Alder 0:00
Recruiting technology can be very good at making individual hiring steps faster and smarter. What it hasn’t done is connect those steps into something that learns and improves over time. What does it take to build intelligence across the entire hiring journey? Keep listening to find out.
Advert 0:20
Support for this podcast comes from Maki. Maki began by replacing the resume screen with a fair, structured voice interview that assesses real skills before anyone formally applies. Now that same intelligence is extending across the whole funnel, from the first conversation to the final decision. They recently launched Tomo, an AI interview assistant for hiring managers, the next step towards one connected system that screens, interviews, and gives every candidate a consistent, fair experience at scale. See how the end-to-end picture comes together by going to makipeople.com. That’s makipeople.com. And Maki is spelled M-A-K-I.
Matt Alder 1:25
Hi there, welcome to episode 810 of Recruiting Future with me, Matt Alder. AI has been applied to multiple steps of the hiring process. Sourcing, screening, assessments, interviews, each has its own tools and many of them are very effective. For a lot of organisations though, the gains from optimising individual stages are starting to flatten out. Hiring quality is shaped by the entire journey, not by any single step. And most hiring technology was never built to connect these steps together. The focus is shifting towards connecting the whole process so each stage learns from the others and improves over time. So what does it take to move from optimising separate steps to building connected intelligence across the hiring process? My guest this week is Ben Chino, co-founder and CPO at Maki. In our conversation, Ben explains why improving hiring one step at a time has hit its limits, what end-to-end hiring intelligence looks like in practice and what it means both for recruiters and for candidates. Hi, Ben, and welcome back to the podcast.
Ben Chino 2:42
Hi, Matt. Thanks for having me again.
Matt Alder 2:42
An absolute pleasure. You were my guest co-host on the June Roundup edition. For people who may not be familiar with your work or seen that particular episode, could you just introduce yourself again and tell everyone what you do?
Ben Chino 2:56
So my name is Ben. I am one of the co-founders of a company called Maki. And what we do is that we provide a platform to run hiring end-to-end, obviously using AI to do that.
Matt Alder 3:09
And I really want to kind of dig into that end-to-end aspect to this because I think sort of thus far in the evolution of AI and automation in recruiting, there’s been a real sort of focus on specific use cases and automating, almost automating individual parts of the hiring process. What’s changing and what are the sort of the big companies that you work for now asking for when it comes to AI and automation?
Ben Chino 3:39
So it’s funny because we actually come from that, right? So we tried to really nail one specific step, which was assessments. And so what we’ve seen is that that phase of really like improving specific steps in the process is really mature. And so there’s not much more that we can do. It’s not going to save more time. It’s not going to fundamentally change the quality of the hiring outcome if you only focus on optimising, you know, several steps. And so what we hear from our customers is that we basically need to reinvent the way we do things. And the great news is that AI is actually an opportunity to connect the dots and make sure that those different steps actually work together, learn from each other, right? So that the actual quality of hiring increases in an era where intelligence becomes abundant. At least this is how we view things. And I think this is also the conversation that we’re having with most of our customers because they really want to switch the focus from optimising one single step in the process to actually reinventing the entire process.
Matt Alder 4:50
What does that look like in practice? How does that end-to-end flow actually work? What’s different from what we’ve seen before?
Ben Chino 4:57
You know, if you think about the way hiring is traditionally run in companies, right, they’re going to use an ATS, which, by the way, remains an essential part of the hiring stack, right? But it’s going to be basically what we call a Kanban board, where you move candidates from, you know, one box to another, and there is no real intelligence powering that move, right? And so what we’re seeing is that storing information, right, and improving hiring are fundamentally two different things. And so whilst an ATS can actually, you know, show you where a candidate is in the process, it cannot really like elevate the quality of the judgement being applied to that candidate, right? And so what we’re seeing and what we’re trying to build by the way is actually doing that, which is, you know, helping people create roles in a very effective manner, understand the skills that you need to assess within that role and then distribute those assessments in different touchpoints that range from screening to assessment to interviews and obviously helping each step learn from another so that the process becomes super complete for recruiters, it becomes super seamless for candidates. And so this is what we’re seeing where ATS has struggled to reinvent that model and you have newcomers, mostly agentic platforms, that are doing that very, very well. Obviously, we try to be one of them, but there are others that are doing that very, very well.
Matt Alder 6:34
I think that’s really interesting because if you kind of look back at the history of the ATS, it was designed almost as a… Over time, it’s sort of tried to sort of twist and turn into various things. But really kind of what you’re saying is it’s just a very different centre of things now rather than basing things on filing cabinets.
Ben Chino 6:57
Yeah, exactly. I think, you know, the way to describe it is that an ATS is a system of record. But what we’re seeing from customers is that they need a system of intelligence. And I think the next generation of hiring technology will be those systems of intelligence. It doesn’t mean that we’re replacing the ATS. It means that we’re building a layer around that helps people interpret information, apply better judgement, and continuously improve also the quality of the process. And I think that’s super, super important, which is you want a system that learns automatically and that improves over time.
Matt Alder 7:30
And I think it’s really interesting because I think anyone who’s sort of seriously kind of experimenting with some of the capabilities that these AI models have, it’s just the integrations, their ability to look at different sets of data and go into different systems and create that intelligence, isn’t it?
Ben Chino 7:53
Absolutely. And again, what’s fascinating is that it can be applied all the way from, you know, job description, right, to assessment and deep assessment, but also interviewing and potentially also everything that happens around that. So keeping the candidates engaged, helping them understand where they’re in the process, what happens next, maybe reengaging with them when all of a sudden we lose them. So I think what’s fascinating with that is that it can be applied to multiple use cases. But what matters is that how do you coordinate the different steps and make the entire journey super seamless and extremely transparent for candidates?
Matt Alder 8:31
I suppose bring this to life for us a little bit more. So, the companies that you’re working with, the other companies who are making genuinely better hiring decisions, what are they doing in practice that’s different from everyone else? And, you know, have you got any examples of that?
Ben Chino 8:49
So I think, you know, if we look at our customers, what we’re seeing is that better hiring outcomes don’t come from only improving one stage in the process, right? So you can have excellent sourcing, but weak screening. You can have strong assessments and yet inconsistent interviews, right? So I think hiring quality, as I said, is shaped by the entire journey. And so what we’re seeing is that the companies that have the best processes are the ones that utilise intelligence in every step of their process. Because it helps them define what good looks like before they begin hiring. They know exactly what type of skills they want to assess, what type of profiles they want to go after. And so sourcing becomes easier, but once you’ve sourced candidates, then you know exactly what are going to be the first steps, what are the skills that you’re going to be looking after, and so on. And throughout the process, everything is more structured, everything becomes more useful, and everything supports better decision-making. Ultimately also with a speed ingredient because since everything is done automatically you already have the output as soon as a candidate is done with an experience and so you can literally make an offer in just a minute instead of, you know, having to spend days collecting the information, reviewing the information, maybe pushing back because you feel that the output from a human is not objective enough and so on. So this is what we’re seeing and again I think what’s very, very important is that the best companies that we work with are the ones that utilise intelligence in every step of the process and not just in individual steps.
Matt Alder 10:31
From the human judgement perspective, I mean, I think every single sort of podcast conversation I have has a question like this, you know, where, what’s the future for recruiters? Where does human judgement sit in? What are we, what are humans doing? Where’s the automation sit? And what’s your perspective on where human judgement should sit in that process, where it’s essential? And how might that be different from the way people might be thinking about it at the moment?
Ben Chino 10:58
One very important thing is that, you know, this is not about removing people from hiring. It is absolutely not about that. I think when you think about it from a legal perspective, but also from a candidate experience, having people is quite central in the entire process, right? What we want to do, on the other hand, is help people operate at a level they could not consistently reach on their own for many reasons. We’re asking recruiters and hiring managers to make complex judgements, often under time pressure with incomplete information. And so this is why intelligence should be there to support them. And honestly, even with super seasoned and experienced people, the output can be inconsistent. And we see that firsthand from our science studies, which is, you know, whenever we release a scale model, we compare that with human reviewers and sometimes certified human reviewers. You would not believe the type of differences we see in reviewing exactly the same candidate input from two different people, right? Even though they’re seasoned, even though they’re experienced. So we know that human judgement can also be inconsistent. What matters to us is how can we equip them with the data signals that they need in order to make a good judgement. And the second thing is that if you free time from mundane tasks for people, you also allow them to spend much more quality time with candidates. So it’s not only about simply judging candidates, it’s also about convincing them, seducing them, making sure that you can actually close an offer with a candidate. And so we want people to be spending much more time on, one, making quality judgement, but also, two, spending time with candidates so that eventually you convince them to join the company.
Matt Alder 12:58
And I want to talk about candidates a little bit more because a lot of the conversation around AI and hiring focuses on the role of the recruiter, efficiencies, speed, all these kind of things. What does this kind of approach, what does it give to the candidate on the other side of the process?
Ben Chino 13:17
I think there are a couple of components to that. And maybe to start, there’s one example that I love, which is a big customer that we work with. They have around 800,000 applications per year, right? Before using our platform, they used to interview 20,000 candidates. So the very vast majority of candidates would apply. And by the way, it was a very long, boring process. I think it lasted 15 minutes. And it was just an ATS format, but it was still a very painful experience. So after having gone through that experience, they never heard back from the company. And I think there’s not, you know, this is a completely terrible experience from a candidate perspective, especially from a B2C aspect where those candidates might also be customers, right? So what the bank was telling us is, you know, we’re afraid that we’re not only losing candidates, we’re also losing customers. And to them, that was a big, big issue. So I think, you know, when it comes to candidate perspective and experience, it is very, very important to provide a good experience. And the first thing that we can do is, you know, have them be valued for what they’re worth. And so, you know, going after the skills, asking questions, and, you know, hearing their answers and so on is already a first step. And so this is why a lot of companies that we work with start with our AI interview because candidates feel valued. And by the way, the metric that we get is that the average satisfaction rate of candidates on that experience is around 9.3 out of 10. So genuinely candidates like the experience. The second point is that they can also ask any questions they have about the company, about the role, and so that feels very bi-directional, where also candidates can understand that maybe this role is not for them, so they value that transparency and they value that experience. The last thing is that they’re assessed on capabilities that actually matter for that role. And so they’re not asked to repeat the same information at every stage. Since the process is better distributed, interviews are better prepared. They’re more focused. And so the entire journey feels just more relevant, more consistent, and more respectful towards candidates. And again, I think collecting metrics around candidate experience is super important. And we see that firsthand from those metrics, which is candidates value the process, and they actually have a better perception of the companies they apply at than they used to have for that company in the past.
Matt Alder 15:53
This is really one of the biggest shifts that we’ve seen in recruiting, certainly since the internet was invented, potentially even in a couple of hundred years, in terms of how it’s changing the process and how we’re thinking about things. That’s a lot to take in for employers. And there are some really big things that TA leaders need to think about. So what advice would you give to the TA leaders listening who fully appreciate that they need to rethink how hiring works in their organisation, but they just don’t know where to start? What would your advice be?
Ben Chino 16:26
And that’s a great question. I think the way I see it is that, you know, from our perspective, we’re not selling software. We’re actually selling change. And so the first thing to do is really embrace that change opportunity and accept that, you know, we’re gonna have to change the way we think about hiring overall. And I think this is super, super important in terms of distinction because it’s not about, you know, technicality. Yes, you can buy software and you can probably, you know, change part of your process, right? But I think the biggest thing that they should have in mind is we need organisational change. We need to accept it, we need to embrace it, and then obviously we need to find the right partners in order to help us support that transition, right? But I think ultimately this is much bigger than just adopting a new tool. This is really about understanding, okay, where should AI be used in my process? Where should it not? Which decisions should remain fully human? Which ones should be partly automated? How should my recruiters and hiring managers work moving forward? How do I build trust internally? How do I build trust externally with candidates as well? How do I measure quality? So again, this is a vast, vast change process. And this is also why what we’re seeing from our customer deployments is that we take it step by step, even though ultimately the entire hiring end-to-end is something that needs to happen, but it takes time. And again, I think there are people and companies and vendors that are here to support as well.
Matt Alder 18:12
Final question for you. What does the future look like? If we were having this conversation again in two or three years’ time, what would have changed? What would hiring be like?
Ben Chino 18:22
That’s a great question. So, fundamentally, if you ask me, and this is something that I often discuss with my business partners is, we believe that CV is going to be dead. So there will be no hiring process that starts with collecting CV. We believe that most of those processes will start with an interaction. And that interaction between an agent and a candidate can be very skill-focused, and perhaps can even be seamless in the sense that maybe in the near future, there’s a platform that knows you enough because it’s assessed your skills and maybe it’s assessed your skills in a continuous manner, not only during hiring processes, but also in your work. And so this is where talent management can become very, very interesting. And so if that platform knows you enough, it can accelerate your hiring processes, meaning that it can help you find opportunities and tailored opportunities. It can help you understand also where you could go in an adjacent manner. So maybe you’re missing one or two skills and it can help you upskill or reskill in order to access those opportunities. And so maybe the entire hiring journey becomes extremely, extremely seamless and automated because, you know, we know so much about you that you don’t even need to apply. We can literally just help you access those opportunities in a more seamless manner. This may sound a little bit futuristic, but I think this is where things are going. And so obviously we want to be a part of that journey because there’s a lot of things that, you know, can be built on top of that vision.
Matt Alder 20:15
Ben, thank you very much for talking to me.
Ben Chino 20:17
Yeah, it was a pleasure. Thank you so much, Matt, for having us.
Matt Alder 20:21
My thanks to Ben. You can follow this podcast on Apple Podcasts, on Spotify, or wherever you listen to your podcasts. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our weekly newsletter, Recruiting Future Feast, and get the inside track on everything that’s coming up on the show. Thanks very much for listening. I’ll be back next time, and I hope you’ll join me.
