The talent market is sending mixed signals. Employers insist they can’t find the people they need, while experienced, capable candidates say they are applying into a void and hearing nothing back. Both are 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. What it struggles to do is read 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 and transformation leader who has spent over twenty years building and scaling talent functions. In our conversation, he shares what his own data-driven job search revealed 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.
In the interview, we discuss:
• What’s really happening on both sides of the talent market
• Why the market isn’t short of talent; it’s short of signal.
• Running a job search as a data funnel
• Why silence, not rejection, is the real problem
• Why volume systems and signal systems contradict each other
• Where AI screening still can’t read potential
• Applying AI to a broken process just makes it fail faster.
• Moving TA from a service function to a commercial lever
• Owning the outcome, not just the shortlist.
• What does the future look like?
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A full transcript will appear here shortly.
Matt Alder 0:00
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 describing the same system. So, which side is actually right? Keep listening to find out. 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.
Archive Audio 1:08
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.
Matt Alder 1:22
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 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. What it struggles to do is read 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 has 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.
James Gardner 2:33
Hi Matt, many thanks for inviting me on. Looking forward to it.
Matt Alder 2:36
Pleasure to have you on the show. Please, could you introduce yourself to everyone?
James Gardner 2:40
Yes, indeed. So my name is James Gardner. I’m a talent acquisition and transformation leader with 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, and retail, in PE and VC-backed, and also enterprise organizations. My work has typically sat at the point where business ambition meets workforce reality, for want of a better phrase. I help organizations build a talent strategy, build the operating model, understand data, process, and really leadership, and deliver their capability to scale. So I’ve built talent functions from scratch, I’ve led executive hiring, I built Dixons Carphone’s internal executive search capability, I’ve scaled engineering and product teams, 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, I help companies move talent acquisition from being a reactive hiring service to becoming a strategic commercial lever.
Matt Alder 4:06
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.
James Gardner 4:16
Yeah, I mean, strangely, I was on a panel yesterday discussing this at TA Disruptors, but I think the best way to put it is both sides of the market are struggling for slightly different reasons. For employers at the moment there is still a need for high quality talent, especially in leadership, engineering, product, 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 what you might call an arms race at the moment, in that 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. For candidates, I think the market feels noisy. There are a lot of applications per role. There’s more automation. There’s far more generic rejection, and often very little feedback. As candidates, we’re being told to personalize, network, demonstrate value, but 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 you’ve got quite a strange contradiction. 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 for myself at the moment. I think the organizations that will win in this case are ones that are very clear on what good looks like, have a strong clarity in terms of understanding what and why they’re trying to hire, are building better assessment processes and using technology to improve automation of repetitive tasks, but are leaving the bandwidth for the human capability to drive judgment. We’ve got a huge increase in volume from both points of view of candidates applying, and as a result of that, 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. 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.
Matt Alder 7:58
Let’s dig into some of that in 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.
James Gardner 8:13
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 touch points, understand metrics, understand the data. If we look at the numbers, 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. From that, we generated 16 meaningful conversations. It’s very clear that there was more meaningful conversation conversion from direct outreach, but the reality is obviously there’s not a specific role that you’re discussing. What you’re trying to do in those discussions is unearth an issue and provide a solution, and as a result of that ask 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. 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. I’ve designed hiring processes and systems for years, yes, 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, 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, what is our hiring process illustrating? 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. I think the process didn’t seem capable of distinguishing between relevant seniority, context, and 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.
Matt Alder 11:16
To sort of really pick up on that, because it’s interesting, 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, 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. What is that issue, and how do you think it might be able to be fixed?
James Gardner 11:45
I couldn’t agree more. I think the fact of where we are at the moment is managing volume is about processing, it’s about moving large numbers of candidates through a system quickly. Reading signal is about interpretation, and it’s about understanding whether someone has the experience, the judgment, the context, the potential to actually solve the problem the business actually has, and those are two very different things. A volume-led system is asking, I suppose the best way to put it, how do we screen more people faster? A 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, 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, 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 ambiguity comes into it, trade-offs come into it. AI can’t understand, for example, complexity versus scalability versus revenue opportunity. It 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, myself and my teams, sometimes the best hires aren’t actually the most obvious pattern match. They’re actually individuals who can make sense of ambiguity, create value, and AI can’t screen that potential capability effectively. So volume technology helps you move candidates through a funnel, signal technology should help you understand who’s going to actually create value, and that’s the important aspect.
Matt Alder 14:44
On that AI issue, because you know a few of the things that you’ve said so far about it are really resonating with me. Applying it to processes that don’t work just makes them not work faster. I think that there is a huge potential that AI has to fix some of the issues that we’re talking about, and if it can’t do it right now, then it certainly should be able to do it in the future, but at the moment it is just being implemented 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 it?
James Gardner 15:24
So, I’m by no means anti-AI. I’m a great believer that it is a superb enabler in the market. I would use the analogy of how a traditional recruitment team works. If you imagine a talent partner, previously 95% of their time is based on driving negative outcomes, because there’s only, let’s say, 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. 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 sales people, we were project managers, we built fantastic relationships, and I think talent partners, recruiters, have a plethora of skill sets that are really ignored by most organizations. So one of the things I did at Dixons Carphone 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? If you go and create that bandwidth, it leaves space for that individual to create far greater relationships with their hiring manager, to become a real advisory counsel, 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, it was a hiring manager’s 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 Dixons Carphone 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’ strategy for that function is, so you then can come armed with information about scarcity of 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. That immediately changes the conversation, and I go back to what I mentioned in my quick synopsis, we then are treated as a function that is a commercial lever, not just a service-orientated organization. So 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 incomings, specific outcomes, and lots of data. So organizations are using ourselves as the guinea pig, but I think we need to embrace it, but we need to have guidelines. I 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 why you are trying to hire, AI can surface lots of interesting data, but all it’s going to do is speed up bad decision making if you’re not ready for it.
Matt Alder 19:10
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?
James Gardner 19:22
Yes, completely. And again, I go back to, if we look at how TA has operated historically, we have provided metrics that are backward looking, time to hire, cost per hire. 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 what inventory costs, bench, how long someone might be on the bench. We suddenly become a real predictor and impact maker on revenue and cost, and we suddenly then can move upstream and become part of the conversation. 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 commercial remit, where we are actually directly impacting commercial decision making. Because 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. My view, 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.
Matt Alder 21:07
So, a final question for you. 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 talent acquisition will have changed, or maybe how do you hope they’ll have changed?
James Gardner 21:20
I go to the commercial lever piece. I go to the point that I think talent and TA will become far more integrated with workforce strategy. The best teams won’t just ask who do we need to hire, they will ask what capability does the business need? Why are we hiring this, and what is the result of this hire? And then they will provide the best way to access it. So, if I take on board a distributor’s approach in terms of our offering to market, this might mean permanent talent, it might mean contractors, fractional leaders, internal mobility, 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, so TA becomes less about requisition fulfillment and more about capability orchestration, if that’s a phrase I can use. The role is going to become far more data led, which I think is very important. As talent leaders, we need to speak the language of cost, capacity, 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. I think going forward, candidate experience will become an enormous differentiator. As more businesses automate, the ones that use technology well but retain that human judgment and communication will stand out from the rest. I think in two or three years, average TA teams will become more automated, but great TA teams will be more strategic, more commercial, and I go back to the point I made previously, more human where it matters most, and I think that’s the key. There is still a human requirement in what we do, whether it’s judgment, whether it’s interaction, whether it’s influence, whether it’s relationship building. I think one thing AI is going to do is surface what I call talent individuals in our function, who are going to be successful in the new age. Historically, we’ve looked at TA people, and we’ve gone, the great TA people are people who can get through volume really quickly, are detailed, and 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, 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.
Matt Alder 24:53
James, thank you very much for talking to me. 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 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.
