Zero-Admin Recruiting with Marcus Sawyerr

The distinction is the ones that are getting the most out of AI have a problem that they want to solve, and they have clarity around the problem. So the problem might be as a recruitment firm, I don't have enough clients. The problem might be my team is overwhelmed with applications and I can't get back to enough of them. Those that are not working so well are trying to find the AI and then take it and retrofit with a problem. Today we're joined by Marcus Sawyer, CEO of the EQ app and former president of ADECO's Group X. Five years ago, Marcus predicted how AI would transform recruiting and now he's building that future himself. From his mission to create zero admin recruiting to the rise of AI agents, we get insights from a pioneer building at the front lines. Marcus, welcome to the show. Thanks for having me, Paul. Always good to reconnect. We met a long time ago back when it was, the buzzword was the gig economy and staffing and people were trying to figure out how to get the right talent to do the right jobs because everything was hard. And now fast forward seven years and people are still trying to figure out how to get the right talent because things are moving fast and things are hard. You have a rich history in staffing. Tell me a little bit about how you've transformed your passion and where you spend your time from traditional staffing to the new world of AI. Yeah, so as you mentioned, it's an evolution and it's never a revolution actually as many people think because you have to bring people along the journey. But my background started from everything from convincing people that you should advertise on this thing called the internet instead of newspapers for job ads. Fast forward when we met, I was buying companies, investing in organizations in HR Tech and recruiting on behalf of the ADECO group and operating summer CEO. Got to spend some time on the Microsoft services board also where we interacted in the halls. And the evolution has really been how can you make a very people driven business more effective and more efficient to get to what the people were actually built to do, which is build relationships and develop a meaningful trust between one another instead of doing all of the busy work. So I've just constantly been on that quest and as the technology has got better and there's been more opportunities, we've just been chipping away at the same problem, which is you want to connect people to the right opportunity at the right time more effectively and efficiently as possible. So not much has changed from that standpoint, but the opportunities on how you can do it now are immeasurable at this point. Before we get to AI and the EQ app, what are the challenges? Why is it so hard? Yeah, so I think about this a lot and I think that everybody has a view on how to do things. So it's a very subjective versus objective industry in terms of connecting people to jobs. And this is defined by the niche that you're in, if you're in healthcare, or if you're in financial services, or if you're in sale, everyone has a slightly different process. And then what happens is there's been technologies that have been created to apparently help you streamline this process, but then there's more that goes on top. And then what you start to do is you kind of create this environment where you're spending time on figuring out what's the best tool to use. And I always like into a hammer, right? And sometimes a knife depending on what I'm talking to, but like it into a hammer is like, well, do you want the hammer? And we know this well, right? It's the job to be done. Do you want the hammer? Do you want the screwdriver? Or do you just want the IKEA furniture to be set up? And I think that everybody who's in recruiting has a different way of doing it. Then you have new leadership that comes into a new company. They have their own spin on things. And so it just creates this kind of monumental mess of a workflow and a process. So I think it just gets it compounds. The difficulty starts to compound versus the other way round. And I think companies that are starting from scratch or the blank sheet of paper have a huge opportunity. Now let's talk about the zero admin revolution and your perspective on the EQ app. You wake up every day, super passionate. You're all over LinkedIn. You're all over podcasts. And thank you for joining. But like what gets you motivated? And what do you think you've figured out in that compounding complex human problem of matching people with opportunity with the EQ app? Yeah. And so I'll take a few steps back, like kind of from a personal standpoint, I think that the best business is done through relationships. And I think building your relationships, a lot of that comes with one to one connection, whether that's in person, or like what we're doing now, right? Like we're going to learn something about one another that we'll probably remember. Like we went for a walk, right? A couple of years ago. And we were just talking. But in order to get to that, there has to be some kind of planning, some kind of scheduling, some kind of arrangement. We have to maybe and now you and I, we just talk generally so we don't need the full documentation to say, okay, this is what the podcast is going to be and this has going to operate. But there's some of that. And I've always been someone that's not been great at the administration side. I've had to learn to deal with it as you get into bigger organizations you look and then I realized I don't love it either. So I'm not going to be great at it. There must be a way away. So how can you connect people to building that relationship as quickly and effectively as possible by removing all of the busy work? And a lot of this work, unfortunately, has been created by the technical evolution and revolution, right? So more computers equal more data. More data means more busy work and more jobs. And it used to be the filing cabinet, but now it's the internet. So you've got to sift through this whole thing to get to what you want to in the end. And so I'm just really about clarity, where's the direction that you want to go? And that's why it's really important when we talk about AI or as we go into that, it's about figuring out what's the problem you want to solve? Or what's your purpose? And then working back into that instead of starting with the tech. Technology is the problem. There's this idea that, hi, I'm from a company, sas.com, and I can solve all your problems, right? And then you kind of get in there, you sign up, and there's a couple of features, and it ends up creating, like you said, more compounding problems when you layer it over an organization of people that's, again, constantly evolving. When you say get to zero admin recruiting, take me through a story of a client that used the EQ app, met Marcus at a conference, I'm sure, where he was giving a great presentation on the future of staffing, you got off the stage, talk to the person, they're like, I get it, help me with that story. Okay, I'll give a very specific example of, there's a client, they're in the recruiting space, but the way that they recruit is very much by community. So they like to organize dinners for mid to senior level executives, because they're clients, they're candidates, and they're candidates to clients. Now they do dinners all across the US in different markets. They have a certain archetype of the type of person that they want to connect with. So we gave them a form, fit in your form of who you want, within two or three minutes, we send them a list of these are the people that we've identified in the location, the jobs that you're interested in, you hit approve, we outreach to them, we send them an invite, they turn up at the dinner, and then you're going to have your dinner. Like that's like a very specific kind of use case, which is quite niche, but you can use it for all different types of people connections. Does that make sense, Paul? It does make sense. You posted something the other day that I want to build on around data. The companies aren't short on data. You have all these systems, you have your ATS system, your CRM system, there's this ecosystem and community around whatever industry or whatever your company does. How do you think about companies utilizing that data as we move into using technology like AI? Because it's different. It is different. The way that I think about and going back to the relationship piece on the business side, I was at a conference recently and I asked everybody, what's their superpower? So maybe I'll ask you actually, Paul, what's your superpower? It was funny. I was talking to a client that I worked for this morning and I think in systems. I think in systems and I try to make them more efficient. So I've had jobs as a chief of staff or an operator in organizations and I focus on the system. And so my superpower is trying to create the world's most efficient system to get done whatever it is that I need to get done. And then the Bill Gates quote would say that you want to get something done efficiently, hire a lazy person, because they'll find the easiest way to get it done. Right. And so let's say this to the audience. So everyone will have a few seconds. Think of your superpower. I don't care how many people this has gone out to, whether it's a billion or nine billion, the whole population. I can guarantee not one person is going to be saying updating databases. And so every time I go to conference, I ask the same question. So going back to your point around like where do we leverage the AI? Like how do we do why? And the AI's, the technology, the infrastructure, all of these computers, all of this code, these ones and zeros have actually created all of this data in the first place to your point before what we were talking about. So they are best placed to sit through that. So really it's about collecting all of the data from the disparate sources and bringing them into one area and being able to pull on that at the right time for the right opportunity. And that's what the internet, that's what Google's promised us. Right. For so long, it's a search engine, but you have to know what you want. And where the AI's are kind of moving a little bit further is they're going to provide you what you need before you know what you need. And we've seen this in recommendation engines, whether it's Netflix or it's Amazon or what have you, a lot of people still think I was my phone listening to me. And like, you're never really sure because the next time you go in, you see something you're like, ah, you've had that moment, right? Yeah, I was about to say you're not going to convince me that my phone is not listening to me. Right. Exactly. Exactly. I can't convince you of that. I was talking to somebody about this the other day in person. I was in Dallas. I was with a group of a staff and a recruit group and they, we had the same discussion about like listening. So I said, okay, well, you've got your phone and your phone can tell someone where you are. Tell you what you recently searched for. And did you search for maybe some black sneakers, right? And then maybe with those black sneakers, you might want some shorts, right? So there's a prediction. And if you do that over and over again, and you do that along your journey to and from work, it starts to understand who you are. So I think the kind of end promise is that AI understands what you need before you know what you need provides you with a recommendation at the right time to connect with a person. So I think this goes back to the data piece like how do you bring all of that data in one place so it can have access to it? And with the AI, it's not that hard to do. It just needs to be fed into the AI through different work streams. And it almost becomes an orchestration layer that understands what you're trying to achieve and provides you with those recommendations. I was helping a startup that was starting to scale. And so we were kind of doing budget forecasting like, hey, we get from here and in two years, we end up at this place and how many people are we going to need? And we did an exercise where everyone guessed. Everybody kind of put their guesses and stuff. And then we asked the new AI reasoning engine saying, hey, we're this kind of business. This is where we are today. This is where we'd like to go tomorrow. How should I think about the resources I need as I scale? And the most important part, ask me any questions that you need to complete this task. And it asked like some super intelligent questions that somebody probably would have paid a consultant a bunch of money to ask. And so it asked the questions. And it came out with a model that was pretty spot on. How are people taking that kind of capability and then putting it into the recruiting system? In my experience, and you spoke to this, going from I want to hire people to actually hiring people as a hiring manager. Yeah, it is just daunting. Yeah. Yeah, I will be talking about early offline. It's early, right? So not everybody's doing that just yet. But I've seen some signs of some examples of how it can be done. So one client, they've got 68 recruiters, they get 250,000 applications per month. They placed 18,000 people last year. Now, they're just trying to keep their head above water and respond to people in a thoughtful way. Now, that doesn't necessarily say that they're going to predict who's going to be the best and how it works. But one of the things we've been thinking about with them, but a few others in particular, has been how do you rank those applications in a strategic way, respond, and provide a summary so the recruiters can prioritize. So you almost have to predict based on your criteria. So we don't set the criteria, we give some templates to say, okay, like it might be based on experience, a number of years, but you can change the dial for what you're looking for. Because all the jobs things are predictions. Like who do you think is going to be the best fit for the role? Like that's a prediction, actually, because you don't know. So you've got some historical data, and then you've got some maybe forward looking data, or you have to create that forward looking data, which is a judgment on how you think people are going to perform. And to your point, with your example, you had, I don't know, let's say four or five people in a room, AI had access to the internet. And then there's like, okay, out of the internet, there's 99.999% of it is irrelevant for this question. And let's find that 0.001% piece and start to like figure out what has worked, what the businesses that are doing well, the ones that have gone public, and how did they scale, where did they get to, and let's infer what we think the likelihood of this is. So a lot of it, it's all weights and biases, and it's all there's biases in this as well. And if you understand that, like you obviously do pull and even with the question, like what are we missing, you can then impact the biases with the input that you're providing, which is all context. And that's what we talk about context and the knowledge basis. And the more knowledge you can give it around your situation, the more accurate the result is going to be. So I think companies have to work hand in hand with the AI to program them and have system thinking in order to get the output that they want. You've been around resumes for a long time. Yeah, I've seen a lot of resumes. A friend of mine is looking for a job and we did an experiment yesterday. Yeah. And I want to ask you, because I want you to be able to give whoever out there is listening some advice, because they're really interesting experiment. So he's looking for a job and he's got three different resume formats. It's very stressful formatting a resume. One that a professional resume person did, one that he had from five years ago that he kind of did some updating on, and then another one that another friend who was a professional did. So we have three different resumes, same person. And we put into one of the LLMs. In this case, we use ChatGPT, the reasoning model. Hey, you're a hiring manager. Here's three resumes. Which one would you choose based on the information as provided? And it was really interesting because the one that he thought was like the best example of who he was was the one at the bottom of the stack. And so as you have people working to try to, you said, 250,000 resumes in a system, and there's part that I need the opportunity to at least start building a relationship with you. But before I do that, I've got to get through a gauntlet of systems and now a gauntlet of AI to get to a recruiter. What is the advice in the industry as this all changes? What are you hearing? There's probably three things. There's two that I know of, and there's probably a third one that's going to come after I say the first two things. The first thing is there's one data point that's key to that, which is the job. So you've got the three resumes, but what you should also do is input the job and then say, okay, what is the likelihood that there is a fit for these three resumes against this job? Okay, and that it's going to be seen and read. So it's like almost what you were doing before is like kind of like further inquiring. So if you get that data point, you probably got a higher chance of matching against that. That depends on the level that you're going for. Now, the other thing is how do you reach the hiring manager? Right? If you're really looking for a job, what you want to do is you want to reach the hiring manager. Now that the resume, parsing or processing might be a formality, but you always want to go that extra line and say, hey, look, I've applied for the job. I think I'm a really great fit. Here's my profile. And so you could use AI to build a shortlist of all the target companies with all the hiring managers, all the people that you want to reach out to, send them a personalized message, and then send them the resume or let them dictate to you to go through the ATS. So I just think that it's evolving because everybody's receiving more applications. And so how do you get to the top one match against the job to find a person that you can connect with inside of that organization, reach out to them? Better yet, the third thing would be find someone that works there that you know that could then refer you in and strategically plan your job. I'm speaking to someone today who's thinking about leaving. They haven't left. They're thinking about leaving because they've noticed what's going on in the market. So we were talking about some stuff that they could do with us as a freelancer. And I was like, what are your intentions of that? So if it goes well, I'd like to come full time. Okay, that's an interesting way for us to kind of test and learn, right? So I think that there's opportunities like for a job seeker, but depends on the level that you're at. But find the hiring manager, I think it's key. There's one thing that's in the news, at least the AI news every day. So those that are following the news. And it's AI agents. And the deeper down the rabbit hole you go with with AI agents, I think sometimes the more you get confused between and use this word traditional AI, which I would call chat GPT, the basic chat bot. Yep. AI workflows, which is, hey, read this maybe this item in a Google sheet, think about it because you're using chat GPT and then spit out an answer. And then there's agents. And so I know you're giving a talk next month down in Florida on AI agents. And can you just help people understand what an AI agent is, like how to think about it in a pretty simple way. And then one example that illustrates, you know, how powerful it could be. Sure. Yeah. So the way that I think about AI agents is a kind of a three pronged framework. I'll give you a framework OTT. So the difference between an agent and traditional AI is OTT. So the agent needs an objective tasks and tools. So the objective could be a job, something you want it to get done tasks is how is it going to get it done tools could be the CRM, the ATS system, or another AI. Okay. So objective tasks and tools, the way to think about that and why that some are great and some are not so good. If you don't give the agent the right objective and the right tasks like hiring a person, and in the right tools, it has a hyper-pensity to fail. We as human beings are less forgiving when machine fails than when a human fails because we can talk to them and say, Hey, and we've got reprogram this thing. Okay. How to get it right. Focus on a very specific use case. We have an agent that is there just for ranking candidates or just for enriching your database. So your objective is to make sure that my database has enough information where I can hire a candidate based on the information that's there. Task view the database and have a look on where the gaps are. Second task, find that information from these sources, these tools that I've given you to enrich and upload. And then final, present it to me. So you give it these kind of three elements. Now, when you start to say just generally make my database better, it's not clear enough. So the briefing is really getting the same thing pulled as freelancers. As you know, if you don't give them a clear objective, you don't give them clear timelines, don't give them clear, it's not going to work well. But I think what we've done with AI agents as they've started, and we were subject to this and we've learned a lot, we've modularized all of them so they're like Legos. And then you can connect one AI agent, the next one will then reach out. So you want to really have them focus on specific tasks and get really, really good at something that's very, very particular instead of too general. Otherwise, it won't work well. One of the things organizations in my experience have struggled with is identifying objectives of what they're trying to accomplish. How are you seeing, you know, those that are adopting AI or agents and those that aren't because there's people that are all in. They're like, I know this is where the future is going. I'm watching the videos, attending the conferences. I'm curious. And then there's others that are like, I'm busy. It's not going to impact whatever the reasons are, but there seem to be two distinct camps. Yep. I think that's a fantastic observation. And I believe it to be true also. The distinction is the ones that are getting the most out of AI have a problem that they want to solve. And they have clarity around the problem. So the problem might be as a recruitment firm, I don't have enough clients. The problem might be my team is overwhelmed with applications and I can't get back to enough of them. Those that are not working so well are trying to find the AI and then take it and in retrofit with a problem. There's another category of people that just like to test stuff and they're kind of starting from scratch. And they'll figure it out and figure out different use cases and they're constantly renovating and redesigning. But the ones that are solving the problems with AI don't care if AI solves the problem. Actually, they just want the problem solved. And so it's going back to vitamins versus pain killers. I've got headache. I just want that problem solved, right? Obviously, I don't want all the terms and conditions that come with the ad at the end. It's like, hey, you might die in five minutes or something. You don't want that bit, right? But you're willing to do most things to solve that problem. So I think that's a key differentiator, identifying the problems that you want to solve. Now, some people say, I don't have problems. I look at opportunities like it challenges. And I'm kind of a bit more optimistic. So I think in that way as well, having said that, you might have a purpose or a goal that you want to achieve. What are the barriers that are stopping you from achieving that goal? So you have to have some clarity of thought on what you're trying to get done. When you talk to staffing companies, because I've been on some of those same stages, I was always surprised at how slow staffing was to adapt to technology because to your point, there's an important relationship aspect that people shouldn't minimize. It is critical to be able to talk to someone and have a face-to-face call and really understand if they're the right person for an opportunity. Are you seeing an acceleration of the staffing industry or recruiting an HR adopting technology with AI? So in the last couple of years, since 2021, the staffing industry has been down year over year. I think last year was down about 11%. And it goes back to what we were talking about. That's a problem. So as you have the problem, some of those companies are like, what can I do about this problem to solve? And it's almost like COVID, right? You knew before COVID, hey, video calls are actually really efficient. Other people, well, I can't get to my meetings, I can't sell, so I'm going to now start using video conferencing calls. So I think there's been adoption where people have had challenges. And then there are other, like maybe growing the business or getting clients, the other ones like, let's take healthcare, for instance, there's a surplus of healthcare jobs, in particular nurses, there's like 60 million global nurse jobs, and there's only 30 million people that can do it. So the thing that you're, this is a very supply driven market. So the way that you care for those nurses and you communicate to them, that sometimes takes a lot longer for a human to consistently do that. But we've seen examples where the AIs will reach out, send personalized messages and updates. So the adoption has really been determined by the problem they're trying to solve. And everyone's already heard this, but I think as Einstein who said this, is that necessity is the mother of all invention. So when you have a problem that you need to solve, you're going to figure out a way to get it done. And those constraints are breeding new opportunity and new innovation. But if you're not constrained and you're doing well and you've got high margins, you're kind of like, hey, like, I'll just continue. There's no problem. Yeah. What would you tell someone who is, you know, listening because they were curious about AI and they get it. They're like, Hey, I understand that it's a thing, it's a technology that's going to be structurally impactful. And I'm curious, I want to get started. What's something they could do today? So play is the first thing I always say. So play around and get familiar. If you're more serious, we're going to be to be side, figure out the problems that you're trying to get solved and just write them in a structural format. We've got this format called the 10Ps, which I can send over, which is we'll put a link in the, yeah, we'll put a link. Yeah, exactly. But what's the purpose? What's the problem? How do you make profit? Who are the partners, who are the people and so on? And you can change some of the piece if you're like in film or media, you want to put it in production, but put it out, write it out, figure out what you're trying to solve and then start searching. Remember the time when everyone said there's an app for that? There's probably an AI for that, but have clarity of thought on the problem. And if you haven't started playing around, send your first message and get some feedback and see how it feels. And then also the other thing as well is find a group and there are many groups online. We have a newsletter that you could sign up to as well as free and you can get access and talk. You're obviously doing great work in the space as well, Paul. So like keep educating yourself and think about it like the internet. Think, could you do your job without the internet? That's going to be the same for AI in the future. Marcus, thank you as always for your time. I know you're busy and I know you have to run to another event. If somebody wants to get in touch with you or reach out to you to learn more about the EQ app or anything else that you're talking about, what's the best way to reach out? So if you're on LinkedIn, you can just find me first name, last name on LinkedIn. Also, if you're interested in learning more about AI and you're in the space and recruiting space, you can follow me on my sub stack. It's just first name, last name and sub stack. And then we've got a four letter word domain, which is ecu.app. Check it out. Get your free AI action plan. You can go on the site and get access to that and it'll give you some indication of what you can do. Sounds great. Marcus, thank you as always for your time and everyone out there. Keep experimenting, keep learning and most importantly, stay curious. Thank you. Thanks Paul.