Ep 805: Hiring's Signal Problem

Speaker A: Employers say they can't find the talent they need. Strong candidates say they're applying into a black hole and hear nothing back. Both are, uh, describing the same system. So which side is actually right? Keep listening to find out. Support for this podcast comes from Mackie. Mackie 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, uh, consistent, fair experience at scale. See how the end to end picture comes together by going to mackypeople.com that's mackypeople.com and Maki is spelled M A K. I, uh, M.

Speaker B: There's been more of scientific discovery, more of technical advancement and material progress in your lifetime and mine than in all the ages of history.

Speaker A: Hi there. Welcome to episode 805 of Recruiting Future with me, Matt Alder. The talent market is sending mixed signals. Employers insist they can't find the people they need, while experienced, capable candidates say they're applying into a void and hear nothing back. Both are, uh, describing the same market. So something in the middle is failing. A lot of recruiting technology was built to handle volume, to move large numbers of applicants through a process quickly.

Speaker C: What it struggles to do is read

Speaker A: signal to interpret whether someone actually has the judgment and context to solve the problem a business has. So how do we fix this problem? And will AI give us the solution? My guest this week is James Gardner, a talent acquisition leader who spent over 20 years building and scaling talent functions. In our conversation he shares what his own data driven job search reveals about the market, why volume systems and signal systems pull in opposite directions, and how AI could either fix the problem or make it considerably worse. Hi James, and welcome to the podcast.

Speaker D: Hi Matt. Many thanks for inviting me on. Looking forward to it.

Speaker E: Pleasure to have you on the show.

Speaker A: Please, could you introduce yourself to everyone?

Speaker D: Yes, yes indeed. So my name is James Gardner. Um, I'm a, uh, talent and acquisition and transformation leader with uh, over 20 years of experience now, which seems like a long time. I've been building and scaling talent functions across technology, software, health, tech, retail, in pe, VC backed and also enterprise organizations. Um, my work has typically sat at the point where business ambition meets workforce reality. For want of a better phrase, um, I help organizations build a talent strategy, build the operating model, understand data, process and ah, really leadership and deliver their capability to scale um, so I've built talent functions from scratch. I've led executive hiring. I built, um, Dixon's car phone's internal executive search capability. I've scaled engineering and product teams, uh, reduced agency dependency and really improved hiring quality using data and technology to make talent a much more commercial function. So I suppose in simple terms, um, I help companies move talent acquisition from being a, uh, reactive hiring service to become a, uh, becoming a strategic commercial lever.

Speaker E: It's a very disruptive time at the moment. I mean, what are you seeing in the talent market? I suppose both from employers who are trying to hire, but also candidates who are trying to get noticed.

Speaker D: Yeah, I mean, strangely, I was on a panel yesterday, uh, discussing this, uh, the TA disruptors. But I think, um, the best way to put is both sides of the market are struggling, uh, for slightly different reasons. But for employers at the moment, there is still a need for high quality talent, especially in leadership, engineering, product, um, AI, data and transformation roles. But there's also increased cost pressure. There's more scrutiny and I think more hesitation around decision making and hiring. Businesses want better people, but they are taking longer to commit. I think there is a lack of clarity. There is a, what you might call an arms race at the moment that everyone is. All organizations are striving to say, well, we must have AI, we must use AI. But I don't think there's a strong understanding of what they're looking for the outcome to be. They don't understand return on investment on it, and there is a lack of objectivity to it. And it's very clear that just the fundamental use of AI, if there's a lack of clarity, process and structure to the hiring function, all AI is going to do is speed up bad decision making. Um, for candidates, I think the market feels noisy. Uh, there are a lot of applications per role. There's more automation, uh, there's more, far more generic rejection and often very little feedback. Um, as candidates, we're being told to personalized network, demonstrate value. But the feeling is there's a level of distrust because the feeling is it's going into processes that are designed for volume and don't read signal properly. So as a result, um, you've got quite a strange contradiction. Um, employers say they can't find the right talent, while strong candidates are feeling invisible and feeling as though they're going into a black hole. And it's very much the case, uh, for myself at the moment. Um, I think the organizations that will win in this case are ones that are very clear on what good like got good looks like have a strong clarity in terms of understanding what and why they're trying to hire. They're building better assessment processes and using technology to improve automation of repetitive tasks, but are uh, leaving the bandwidth for the human capability to drive judgment. And we've got a huge increase in volume from both points of view of um, candidates applying. And as a result of that the, the drive and use of AI which was very much focused I think for organizations to reduce cost is actually now creating a situation where there's a loss of signal understanding. And the expectation from the organization is that they can reduce costs, but it's at the cost of candidate experience. And my big worry for organizations at the moment, given the current market dynamics, is their response is well it's good enough, we don't need to worry too much about it at the moment. So the market's not short of talent, it's short of clarity, it's short of signal and it's short of decision discipline.

Speaker A: Let's dig into some of that in

Speaker E: a bit more detail. So starting with the candidate side, you've taken a very data driven approach to your own recent job search. Tell us what you did, but also what the numbers tell you.

Speaker D: Well, I mean very simply because of my background and what I've done, I decided to look at my job search as a classic funnel. Really understand touchpoint, understand metrics, understand the data. And if we look at the numbers, um, we look at applications, direct outreach responses, conversions, progression and sadly silence. But in total there's about 1,417 touch points, which is about 817 applications and roughly 600 direct messages or approaches. Um, from that we generated 16 meaningful conversations. It's very clear that there were more meaningful conversation conversion from direct outreach. Um, but uh, the reality is obviously there's not a specific role that you're discussing. What you're trying to do in those discussions in is unearth an issue and provide a solution. And as a result of that, um, ask uh, the organization to think how they could come to a suitable solution utilizing my capability. And what I did find in that was I ended up doing a lot of FOC consulting which was quite frustrating. But I think what's most frightening is not the rejection because in any process you're going to get rejection. Um, I think what was frightening is the silence because if you look at the numbers, there's around 44% where there was absolutely no meaningful response at all. And with today's technology that we've got, that shouldn't be the case. So I think the experience gave me a very different perspective. Um, I've designed hiring processes and systems for years, but when you experience that from the candidate side and you see how much of that signal is lost and how my level of capability and expertise seems to be being lost in a black hole, um, I think that leads me to think, okay, we've designed systems over the years that are based on volume, not based on understanding capability. And that I think is a fundamental issue and a pause for thought where organizations should be understanding, okay, how are uh, we, what is our hiring process illustrating? And I mean I wasn't applying randomly to a certain extent I was because I've been applying for roles that are definitely below my level of seniority, but I was targeting roles where there was suitable alignment. Um, but I think the process didn't seem capable of distinguishing between relevance, seniority, context, potential value. And so for me, the data showed that the hiring process has become very efficient in dealing with volume and applications, but highly ineffective at interpreting that information

Speaker A: to sort of really pick up on that.

Speaker E: Because it's interesting, because is that the fault of the technology or is it the process or is it a bit of both? Because there's obviously a serious issue here, uh, and companies are missing out on the talent that they might need because the process and the technology isn't serving the way that the market is now working. The kind of reality in which we're living.

Speaker A: What is that issue and how do

Speaker E: you think it might be able to be fixed?

Speaker D: I couldn't agree more. I think the factors of where we are at the moment is managing volume isn't about processing, it's about moving large numbers of candidates to a system quickly. Um, reading signal is about interpretation and it's about understanding whether someone has the experience, the judgment, um, the context, the potential to actually solve the problem the business actually has. And those are two very different things. Um, a volume LED system is asking, I suppose is the best way to put it, how do we screen more people faster? A, ah, signal LED system is asking what evidence tells us this person could succeed here. And I think the systems that we have designed are contradicting each other. So if technology is designed mainly to manage volume, it's over relying on keywords, um, previous job titles, rigid criteria. And I think also with the onset of AI, especially from a screening perspective, there's a huge level of distrust from a candidate perspective as to how effective that screening process is. It should be effective, but is it fundamentally not much more than a Keyword search. So um, if you've got kind of rigid criteria that can work for some high volume hiring, but it's much weaker when you're hiring for leadership transformation because um, ambiguity comes into it, trade offs comes into it. I can't understand for example complexity versus scalability versus revenue opportunity that they can't distinguish between those trade offs and potential. So where the person needs to drive significant transformation or build something the capability to comprehend that doesn't yet exist. So what gets lost is context. And I think we're moving into quite a dangerous position because so many of the best hires, and I've hired hundreds of people over my career or and myself, um, and my teams that sometimes the best hires aren't actually the most obvious pattern match. They're they're actually individuals who can make sense of ambiguity, create value. And to do that, AIs can't screen that potential capability effectively. So volume technology helps you move candidates to a funnel. Signal technology should help you understand who's going to actually create value. And that's the important aspect.

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Speaker E: And on that AI issue because you know, a few of the things that you've, that you've said so far about it are kind of, you know, really resonating with me. Applying it to processes that don't just makes them not work faster. Uh, I think that there is a huge potential that AI has to fix some of the issues that we are talking about. And if it can't do it right now, then it certainly should be able to do it in the future.

Speaker A: But at the moment it is just

Speaker E: being implemented to make things go, to make things go faster or be allegedly more efficient. Where's the thinking falling down here? What's the bigger opportunity here that people are missing and how should they be thinking about, about it?

Speaker D: So I'm by no means anti AI. I'm a great believer that it is a superb enabler in the market. Um, I would use the analogy of how a traditional recruitment team works. Um, if you imagine a Talent partner previously 95% of their time is based on um, driving negative outcomes. Because it doesn't, because there's only, let's say there's one role to fill and only 5% of that time is maybe focused on that individual. The rest of the time is actually focused on repetitive tasks that if you can automate them, that creates so much more bandwidth for the talent partner. And I mean I'm a great believer, if I go back to my start in recruitment at Harvey Nash many, many years ago, we spent huge amounts of time on the phone. We were salespeople, we were, we were project managers. We built fantastic relationships. And I think um, talent partners recruiters have a plethora of skill sets that are really ignored by most organizations. So one of the things I did in Dixon's car phone, um, when I first came into the organization, I could very clearly see that all my team had phones, but they didn't use them. They just emailed everybody. And my question was, okay, so how are you building a relationship? How do you influence. And if you go and create that bandwidth, it leaves space for that individual to create far greater, uh, relationships with their hiring manager to become a real advisory council to the, not just a process driven, service orientated ideal. So it enables the recruitment function, the talent acquisition function to move upstream. And to move upstream we then need to Become more responsible for outcome. The usual response from a talent partner is, oh well it was the hiring managed decision. Yes, but we're there. We should be advising them and giving them information. We should be joining in. I mean one of my KPIs for my team at Dickson's car phone was very much. You should be in the strategic meetings of your function twice a month. I want you to understand what the next six to 12 months, um, strategy for that function are so you then can come armed with information about scarcity of um, capability, about what they are trying to do in the future. So you actually advise them from a talent perspective as opposed to just going, yeah, here's five CVs, we'll find your guy. Uh, because that immediately changes the conversation. And I go back to what I mentioned kind of in my quick synopsis. We then are treated as a function that is a commercial lever, not just a service orientated organization. So uh, as I said, I think AI is inevitable. It's coming on board at the moment. TA is the perfect guinea pig for it because we have specific kind of incoming, specific outcomes and lots of data. So organizations are using ourselves as a guinea pig. But I think we need to embrace it, but we need to have guidelines. I uh, go back to what I said previously. If there's not clarity, if there's not strength of process, strength of framework, if there's not a true understanding of exactly what, what, why you are trying to hire AI can surface, uh, lots of interesting data but all it's going to do is speed up bad decision making if you're not ready for it.

Speaker E: What you say there about being more responsible for the outcome is just so critical because if you're going to be a strategic function, then you have to align yourself into business value in that way, don't you?

Speaker D: Yes, completely. Um, and again I go back to that. If we look at how TA has operated historically, we have provided metrics that are backward looking, time to hire, cost per hire. Um, what we want to do is become predictable. We want to be able to forecast like a revenue commercial function does because we then become important in workforce planning in understanding um, what inventory costs, bench, how long someone might be on the bench. We suddenly become a real dictator and impact maker on revenue and cost and we suddenly then can move upstream and become part of the conversation. And I think talent leaders especially need to change the way that we operate within an organization and take on board what I call a wider remit, but a more um, commercial remit where we are actually directly impacting commercial decision making. Because if you, I mean if you think about it Matt, every company in the world will sit there and go, we're only as good as our people. And the reality is that is talent acquisition. But talent acquisition is not treated in that light unless the organization is particularly enlightened. Um, and my view and the way I talk to CEOs is always, you can have the best product in the world, but if you can't take it to market effectively, you've got a failing business. It is people who take it to market effectively and deliver it. So they are the absolute fulcrum of how successful your business is.

Speaker E: So final question for you. I mean, how do you think this is all going to play out? If we look ahead sort of three or four years into the future, how do you think hiring and the role of TAT acquisition will have changed? Or maybe how do you hope they'll have changed?

Speaker D: I go to the commercial lever piece. I go to the point that I think talent and TA will become far more integrated with work workforce strategy. The best teams won't just ask who do we need to hire? They will ask what capability does a business need? Why are we hiring this and what is the result of this hire? And then they will provide, this is the best way to access it. So if I take on board distributors approach, in terms of our offering to market, this might mean permanent talent, it might mean contractors, fractional leaders, internal mobility, um, automation, AI agents, outsourcing, upskilling existing workers. So I think the whole talent and talent acquisition function gets far more amalgamated and we look at total workforce strategy. Um, so TA becomes less about requisition fulfillment and more about, um, capability, orchestration, if that's a phrase I can use. The role's going to become far more data led, um, which I think is very important. As talent leaders we need to speak the language of cost, capacity, um, productivity, margin, risk and ultimately workforce planning. And the absolute principle about this is we will need to show how talent decisions and talent acquisition connect to business outcome. Um, I think going forward, candidate experience will become an enormous differentiator. Um, I think as more business automate, business automate, the ones that use technology well but retain that human judgment and communication will stand out from the rest. Um, I think in two or three years, I think average TA teams will become more automated. Um, but great TA teams I think will be more strategic, more commercial. And I go back to the point I made previously. More human, more human where it matters most. And I think that's the key where matters. And that is still understanding that there is a human requirement in what we do, whether it's judgment, whether it's interaction, whether it's influence, whether it's relationship building. Um, I think one thing AI is going to do is surface, uh, what I call talent, individuals in our function who are going to be successful in the new age. Historically, we've looked at, uh, TA people and we've gone the great TA people are people who can get through volume really quickly detailed, are really efficient. That's going to change because the efficiency is going to be delivered by technology. If those individuals can't advise, build relationships, create influence, um, operate with the right level of gravitas, they're suddenly going to find themselves falling down the pecking order because the skill sets are going to become different.

Speaker E: James, thank you very much for talking to me.

Speaker A: My thanks to James. You can follow this podcast on Apple Podcasts on Spotify or wherever you listen to your podcasts. You can search all the past episodes@, uh, 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

Speaker E: hope you'll join me.

Speaker B: Foreign.

Speaker A: This is my show.