Ep 800: Will AI Break Recruiting?

Recruiting was never deliberately designed. We inherited it. And it's barely changed in 200 years. AI has taken us to a fork in the road. Will it add more value than we've ever seen? Or will it be the thing that finally breaks recruiting as we know it? Keep listening to find out. Support for this podcast comes from Paradox. You know, I've recently heard some crazy success stories when it comes to hiring with AI. FedEx is sending offers to candidates within 10 minutes. General Motors saved $2 million in recruiting costs in a year. Chipotle reduced time to hire from 12 days to 4 and 7. Eleven is saving their store leaders 40,000 hours per week. The craziest thing is all of these companies did it by leveraging the same technology. Paradox acquired by Workday in 2025, Paradox pioneered conversational hiring. Whether acting as your ATS or automating the hiring process on top of your existing hcm, Paradox streamlines the entire experience. And if you're a workday customer, Paradox now operates seamlessly within their Talent Acquisition suite as the Workday Paradox Candidate Experience Agent Talent. Driven by a 247 AI assistant, Paradox can handle up to 95% of the hiring process for deskless hiring teams or just automate specific time consuming tasks like screening, interview scheduling and onboarding to allow recruiters to focus on recruiting. Paradox continues to help hundreds of the world's top employers simplify hiring and save money while creating great candidate experiences. Spend more time with people, not software. You can find out more by going to Paradox AI. That's Paradox AI. 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. Foreign. Hi there. Welcome to episode 800 of Recruiting Future with me, Matt Alder. This is a very special episode for a couple of reasons. It's episode 800, which is a milestone I'm really proud to get to. And it's also a solo episode. Just me and I don't do that very often. I want to tell you a story that I first told at the ERE Recruiting Innovation Summit in Atlanta last It's a story about legacy, inheritance, recruiting and AI. It's the story of my cousin Jack. Our story opens in 1824 in a place called Real de Monte in Mexico. It's a place that's famous for its silver mine. And there was a real issue with the silver mines in Mexico back in 1824 in that they kept flooding and to fix that they needed specialist skills and technologies that weren't available locally. So what would you do if you needed specialist skills and you couldn't get them locally? You would source them, you'd recruit and you'd look at various places in the world that had those skills. And that's exactly what the mine owners of the 1820s in Mexico did. Now it happens that the talent they needed was 3,000 miles away in Cornwall in the UK. For those of you who are not familiar with where Cornwall is, it's the bit that sticks out in the kind of the far bottom left hand corner of uk. Now these days it's better known for its beaches and its tourism and its ice cream. But back in the Industrial Revolution, Cornish miners were the most skilled miners in the world. So tin has been mined in Cornwall for thousands of years. And during the Industrial Revolution, the Cornish invented a high pressure steam pump that pumps out flooded mines. And their expertise was in huge demand over the period of about 50 years. From this point, thousands of Cornish miners emigrated. They went to Australia, New Zealand, South America, the usa, California, Wisconsin and of course to Mexico. So in this particular story, 125 of them took an epic journey by ship across the Atlantic to Mexico. Now it's a very hazardous trip. A number of people contracted yellow fever and didn't make it. But also they had to drag their steam pump engine, which is absolutely massive, 250 miles across jungles, swamps and mountains to get there. So a totally epic relocation journey. Now, one of the reasons I'm telling you this story is I'm actually Cornish originally, and six generations ago, members of my family were some of the miners who made these incredible trips all around the world. And I've always wondered about this and it's a question that's, that's always nagged me and it's how do you recruit people 3,000 miles away 200 years ago? So there's no Internet, there's no email, there's no job boards, there's no AI, there's no social, there isn't even a telephone. How did you do it? And I've kind of thought about it for years and the best thing about giving this presentation a few weeks ago was I just got into deep research mode to find out how it happened. You know, how Was recruiting done 200 years ago? Guess what? Pretty much exactly the same way that it's done now. Very little has actually changed. The way that the miners were recruited was through job adverts. They were in newspapers and on leaflets and pamphlets, but they were still job adverts, agents, staffing agents who were on the ground you know, whipping up interest, persuading people to come. There was compensation benchmarking because obviously the Mexican mine had to pay more than the Cornish mines were paying them. There was employment branding, how do you know which country or which mine to choose? And relocation packages. But also, really interestingly, there was a fantastic referral scheme. So the reason that this story is called My Cousin Jack is once these miners got in situ in the countries that they were going to, they'd always ask if there was a job going for their cousin Jack at home, and that was a nickname that stuck. So that's what they were called, cousin Jack's. So 200 years later, we are still doing the same thing. We are still recruiting in exactly the same way. Now, is that a good thing or is that a bad thing? It's a little bit of both. In some ways, humans don't actually change much. The human brain takes tens of thousands of years to develop. So it kind of makes sense that some of the tools that worked 200 years ago still work now. But everything else has changed. The world has changed, communication has changed, technology has changed, and recruiting has stayed stubbornly the same. And that cannot be a good thing. Now, I've thought really deeply about why this is, and I know it's something that people have been wrestling with for decades, if not hundreds of years. You know, why is it so difficult to change things in recruiting, even when the technology and all the things that surround it have changed so much? And really, I think it boils down to the fact that recruiting has an innovation problem, a massive innovation problem, and always has. Now, the key question here is why? And the reason is not the reason that we all assume. So if you think about all the change that you've tried to make, you know, in your career in recruiting, you know, whether. Whether that's in the way that you recruit or the way that a company recruits or an agency recruits or an industry recruits, whatever it might be. Change is difficult. And the reason for that is that there is a huge amount of entrenched thinking, and there's actually a model. There's a pattern behind this. So it's kind of like an iceberg. Imagine an iceberg with lots of things going on underneath the surface. So at the top level, we have recruiting that goes on, and sitting just below the surface, we have the patterns of behavior that define how recruiting works. And obviously, people are trying to change people's behavior all the time. I'm constantly hearing about, we're doing hiring, manager training, we're trying to change people's behavior, the way that they think about recruiting, and it doesn't really work. So I'm sure many of you listening will have dug a level deeper, gone down a little bit further beneath the surface, and looked at the system structure that sits behind recruiting. So let's change the system because that will change the patterns of behavior, that will change the way that we do recruiting. But as we know, that doesn't work either. And why is that? And the reason is that there's an even deeper layer sitting below systems, which is the mental models that we all hold about recruiting and jobs. If you think about it, everyone in the workforce has been through a recruiting process and with that comes a mental model about how recruiting should work. We have to have resumes, we have to have interviews, we have to do things in a, in a certain way. And we never really address those mental models, which is why we never get the change that we want, which is why we have an innovation problem. And we inherited recruiting. No one actually designed this. Recruiting was kind of formed by various things that happened over the centuries. And when you look at it, very often it's really not the best way of doing things, even back then, let alone now. So I want to dig into this a little bit. So let's do a little bit of recruiting archaeology. Let's start with the resume. So I don't know about you, but I've seen so many presentations where people stand up and say that Leonardo da Vinci invented the resume. And people tend to kind of nod and go, yes, absolutely, he did. And I was like, really? Is that really true? So I looked into it and where this comes from was back in 1482. That's right, 1482. He wrote a persuasive letter to the Duke of Milan to that he wanted to come and help with the war effort that was going on at the time. Now he totally tailored his application to the job. He basically lists sort of 10 inventions that he can bring that are going to help. He offers to meet in person. So he offers, he offers a face to face interview. And the crazy bit is he doesn't actually mention that he's a painter until one sentence right at the very end, when I'm paraphrasing this a bit because I don't speak Italian. But it was something like, when it comes to painting, I'm as good as everyone else, whoever that might be. Which I think is probably the biggest understatement anyone has written on a resume ever. You know, this is the, this is the man who painted two of the most valuable paintings that the world has ever seen. So that was a resume, it was a persuasive letter. And, you know, things evolved from there. First job advert. This takes us forward to 1657. And there is. Newspapers haven't quite been invented, but there are regular pamphlets or periodicals in England that are being distributed. And there's one called the Public Advertiser. And in 1657 it runs what is regarded to be the world's first recruitment advert. And it's an advert for a tutor, for a rich family. First. First job advert. 1657, gonna jump forward quite a long way to 1921. And Thomas Edison has always been renowned as the person who invented the structured interview. Again, looking into this, what was happening was he was getting too many qualified applicants to work in his lab, which sounds like a very familiar problem. I'm sure that many of you are having that problem right now. He needed a way to assess them other than using the obviously qualifications and hard skills that they all had. So he built a test designed to look at curiosity and breadth of thinking, two of the soft skills that we value very, very highly today. There are some very strange questions on this. If you look into it, to be honest with you, they're no stranger than some of the questions that hiring managers ask today. So has the interview really evolved in that 100 year period? So jumping forward a little bit to 1939 to talk about job descriptions. So in 1939, the US Department of Labour published a dictionary of occupational titles which was basically designed for wage benchmarking across different occupations. And these job descriptions were not designed for the way that we use them, the way that we use them now, which in many cases is effectively exactly the same way as job adverts. So all four of these artifacts, if you like the resume, job advert, interview and job description, we've kind of assimilated them without actually thinking about what they're for. They're just there. They've always been there, They've been there for hundreds of years. In some cases, they're part of the recruitment process and they're massively part of the problem. If we look back over the last 20 years, as technology has moved on massively, we can just see a string of missed opportunities to make recruiting better. So starting in 1989, 1989 was when the first ATS system was launched by a company called Resmix, who launched it with their client, Sun Microsystems. And effectively what they were doing was reinventing the filing cabinet. They were Making the filing cabinet into a piece of technology. That is what the ATS was when it was invented. So as we've moved forward, we've tried to make the ATS a CRM, we've tried to do different things with it, but fundamentally it's a filing cabinet designed for compliance. And, you know, there's a real missed opportunity there to actually, you know, use this technology to change the way that recruiting works. Likewise with job boards. So there's a big debate about when the first job board was actually launched, but I'm going to pick 1994 and I'm going to pick Monster because it was the biggest mass market job board at the time. So we'll take that as the starting point. Now, again, there was a chance to change the world, but instead what happened? Because the people who set the job boards up wanted to get traction. They wanted to, they wanted things to seem familiar to both employers and job seekers. So they just took how the newspapers work and put that on a screen. You could access this on a, on a global scale. You'd have to buy the particular newspaper on the particular day. But really, really the only kind of improvement was there was no space limit on the amount of text that you could put in, which meant we could fit a whole 1939 style job description in it. Again, a massively missed opportunity. 2004 Facebook launches, or rather the Facebook launches at Harvard University. Now, interestingly, I was working in recruitment marketing at the time and we ran one of the first ever recruitment campaigns on Facebook for students. But at the time, only students could access Facebook, so we couldn't even see it and the client couldn't see it. So maybe it didn't run. But anyway, it was an interesting, it was interesting to sort of discover the Facebook back then. Obviously the Facebook became Facebook and there was a kind of explosion in the discussion around social recruiting. How can we use social media to recruit people in a different way? And unfortunately, where we got to with that is we've just got a tool for spamming people faster. I don't think much of the promise and the potential that we thought social media had for recruiting really kind of came to fruition in terms of radically changing the way that we recruit. It's just become another channel and in, in a lot of cases, a very, very effective channel, but just another channel. So Moving forward to 2022, the first public LLM is launched. So AI is here, and that's what we've been wrestling with for the last four years. And we're doing the same thing that we have always done, we're not changing anything. When I talk to groups of employers about what they're doing with AI, the two things that come up time and time again are scheduling interviews and rewriting job descriptions to make them into job adverts. Now, job descriptions do need rewriting, so this is good. And scheduling interviews is also great. But the technology to do automated interview scheduling existed sort of four or five years before this wave of AI came along. So we really are in danger of missing a massive, massive opportunity here. I think that we're at a critical fork in the road. I truly believe that AI could add value to recruiting at a scale that we haven't seen in 200 years, or it could be the point at which, recruiting, pieced together from all these artifacts for centuries, finally breaks. Now, I'm actually pretty optimistic about this. The reason why I'm optimistic is I've been doing this podcast now for 11 years and 800 episodes, and I've spent a lot of the last two years talking to people about what's really happening with AI. And some of the use cases are absolutely extraordinary. Now we're still at an early stage and they're very unevenly distributed. And the reason I do the podcast is so I can share these stories with with you all, so you can make informed decisions about what you do moving forward. I think one of the big issues is the way that a lot of people think about AI is actually 18 months, two years out of date, because things are moving on so incredibly quickly. So I just want to talk about two podcast episodes that I did, which I think are great case studies for what's possible with AI. Now, neither of them are the most sophisticated or advanced use of technology. In fact, one of them arguably barely uses AI at all. But it doesn't matter because both of them address issues with the recruiting process and change things to create a win for candidates, a win for recruiters, and a win for hiring managers. So the first one I want to draw your attention to, you can listen to on episode 711 of the podcast, and it's called How AI Agents are Driving Recruiting Results. It's a case study from ADECO in Belgium, and my interview is with Simon, who's their marketing manager, and Ritu, who's the CEO of Vonk, who provided the technology and the know how for ADECO to do this. What's really interesting about this story is there is instant candidate engagement here. So when anyone applies for a job, they get the opportunity to interact with an AI agent that gets more information from them and gives them that kind of instant engagement. The AI agent does the screening and then the recruiters come in later in the process. And some of the interesting benefits that they've seen from this, the first one is with an AI screener. It has the ability to read every single word on a resume, but also ask questions to kind of really enhance that. So it creates a dossier for the candidate with a very transparent scoring mechanism. What this did was it helped to identify transferable skills that some of the candidates have that would never have come out or been uncovered in the recruitment process as it stood. It also meant that the recruiters could have much higher value conversations with the candidates. So rather than having to ask them if they were qualified to work, had the right language skills, they had a driving license, whatever it was, they could get straight into the bit where they build the relationship. And it transformed the quality of candidates that they had to present to their clients. It also transformed the candidate experience. The candidates have absolutely loved it and they offer people the opportunity to speak to a human straight away rather than doing the AI screening. But the uptake level on the AI screening is something like 90%. It's very, very, very high. So really interesting example of AI enhancing the candidate experience, enhancing the way that recruiters work, increasing the quality of the candidate pool. The second example is on episode 722 and it's called Soft Skills Hard Making Predictive Hiring Work. Interview with Vera Nique from Bell Canada and Stefan, who is the CEO of Hiring Branch, who provided the technology for this particular case study. What they've done here is they've really almost kind of inverted the hiring funnel. So the first thing that happens when someone applies to this is call center recruitment. When someone applies is they do an assessment on their soft skills. So they have a huge amount of information over and above their resume about whether they're able to do the job. It also has stripped a lot of bias out of the process. What it hasn't done is strip recruiters out the process. The recruiters still make the make all the decisions about who gets hired. What's really interesting is onboarding and learning and development are also linked to this. So they have the ability to look at how people performed, who was retained, and relate that back to the hiring decisions that they made. So the recruiters can get that kind of end to end view of the eventual performance and tenure of the people that they made decisions about. Compare that back to the assessment that everyone did about their soft skills and their resume and they get smarter with every hire. They can basically see the consequences of the decisions that they make and how they made them. And I just think it's a fantastic example of technology being used to make recruiting better for everyone. So that's the optimistic stuff. Now, it would be ridiculous to say that there are no risks that come with this AI revolution. And I think there are three real risks that employers and vendors need to be aware of. The first one is what I've been talking about for the whole episode so far, which is automating dysfunction. What I'm seeing is that people are plugging AI into processes that already don't work, that are already broken, that are inherited, that were never designed, that are made up of these ancient artifacts. And this mental model about how we think we should do recruiting, it doesn't fix broken processes, it just makes broken processes go faster and do more damage. So really we have to ask ourselves, have we got the right process? And to do that we have to ask questions that never get asked about these processes. For example, do we actually need resumes anymore? What's the point of the interview? And there are loads more really kind of interrogating and asking deeper and deeper questions about why you do recruiting. The way that you do is really important. And the answer to some of the questions might be positive. Yes, it does make sense for us to do this, but perhaps it doesn't make sense for us to do this other thing. We don't question the fundamentals of recruiting enough. In fact, we don't question them at all. Things might be okay, but we don't know unless we ask. And that's really important. The second risk is dehumanising hiring. Now, ultimately, and we're talking here about hiring humans. So providing we're still hiring humans, that is a deeply human thing to do. Changing jobs is a massive decision for people. And it's a decision that isn't just about logic and career. It's about emotion, it's about family, it's about everything else that's going along. Recruitment isn't E commerce, as they say in a lot of staffing agencies. We're selling a product that can change its mind about being sold and that doesn't happen anywhere else. So we need to think carefully about where the humans go in the process. And I'm going to come back to this a little bit later in the episode because there is some really important things to consider here and I want to spend time digging into those properly. And also, this isn't just a one sided Thing, it's not about employers, it's also about what the candidates are doing. And we've seen an explosion of candidates using AI in lots of different ways to apply for jobs. That's why so many of you are getting unmanageable amounts of applications and resumes that all just look the same. Now, I think we're getting our thinking wrong here because I think that there is still a temptation to think that this is cheating, that a lot of the companies selling this technology to candidates are just grifting and it's all slightly murky and should just go away. And it's not going to go away. And I would actually go as far as saying that I think all the innovation in AI in recruiting is actually going to be driven by the candidates. It's coming from the candidate side. There are some really interesting companies building technology for candidates, genuine companies who are trying to make recruiting better from that side of things. And we could very quickly get to the point where the candidate has an AI agent that looks for jobs for them, the employer has an AI agent, and the agents talk to each other and handle like most of the recruiting process. And we need to make sure that we're using that kind of leap in technology to actually enable better human connections, not no human connections. So if a lot of the funnel is automated, we need to be very clear about where the humans sit and the value that they add to the process. The risk here is total automation, no humans at all. There's obviously some regulation at the moment that, that prevents that. More about that in a second. But really, if we go down that route, then it's very, very counterproductive. And I think the companies that do this, that over Automate, actually won't realize the damage that they've done until a long time afterwards. So it's a real, real risk. The third risk I want to talk about is drowning in AI slop. So I don't know about you, but more and more every time I go onto LinkedIn, which is obviously every day because I work in this industry, I. I'm just, oh, despairing of every single post and every single comment sounding the same, you know, written by an LLM, no attempt made to make it sound like the human who's actually allegedly sitting behind it. If there indeed is a human sitting behind it, it's got so bad. I'm starting to talk like an LLM now. I'm going around saying it's not this, it's this. It's just awful and it's just got to Stop. I think there's a real marketing lesson here, which is it's not enough just to think about the message you're delivering, it's also how you deliver it. And AI is undoubtedly going to be an incredibly powerful tool for employer branding and recruitment marketing, but not when everything looks and sounds the same. So differentiation is absolutely critical here, and at the moment we are drowning in AI slope. We need to think about that. Use AI differently to support the messages that we put out there. And please make it go away and change. This connects to the resume, because resumes are words. They're just words. And LLMs love words, which is why we are already at the point where all resumes are now looking identical. So finally we can explicitly see the shortcomings of that tool. The problem here isn't the AI or even how people are using it, it's the tool itself. It's the resume itself. And I'm fascinated to see how things develop and how we move forward from here. 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Okay, so what do we actually do about this? We've seen that recruiting is inherited, we've seen the promise that AI has and we've also seen how it can all go wrong. I want to talk about AI readiness now. One of the brilliant things about running this podcast is the amount of data that's been accrued. So. So 800 episodes, there are over 4 million words of transcripts, there are something like 320 hours of content. And I've been saying for years, I wish someone would invent a bit of technology that allows you to look at huge amounts of data and quickly spot patterns. And brilliantly, that's exactly what this generation of AI does. So I'm able to go back and interrogate and, and find out the patterns, find out what the employers who are being successful with AI, what is it that they're doing? How can we kind of break that down? I've created a readiness framework and I want to talk about four areas of that. The first one is process architecture. So getting back to that whole concept of automating dysfunction. So really first and foremost it's important to understand how recruiting processes are built because what we need to be doing is redesigning them first and then automating them, not automating them, seeing how they break and redesigning them. It's doing the other way around. Now one of the other key decisions here is where do the humans go. So as I said earlier, humans in the process here is completely non negotiable, partly because of regulation, but also partly because it's recruitment and it's a human activity. But this is not quite as simple as you might think it is because what we've really got to do is really think about what AI can do better than humans, what humans can do better than AI, and also what's non negotiable from a human perspective, even if AI could do that better. And there's some really complicated thinking here and I was kind of struck. As I said earlier, I sort of first presented this story at the ERE Recruiting Innovation Summit and there was a presentation the day before mine from Marriott, the hotel group. And they were talking about change management and they were talking about how one of the big issues they had was people like holding on to tasks that they like. So it can be very difficult sometimes to get people to change because they really wanted to do the thing that they really liked doing. And unfortunately a lot of the things that we really like doing aren't necessarily the best way to use human expertise in the recruiting process anymore. For example, screening resumes. I could do some, I'll do some rough maths here so you can, you can fundamentally disagree with my numbers, but I think that this will get the context across. So I thought about it and in a 20 year career, a recruiter may be able to review around a quarter of a billion resumes. You know, throughout that career they've seen a quarter million resumes. Their judgment and their experience comes from seeing that many resumes looking at the patterns also critically with in house recruiters, seeing how those people then performed. Now, I don't have the benchmarks for this, but I suspect that the latest AI models could read a quarter of a billion CVs in about 10 minutes, you know, maybe an hour tops. But very, very, very, very, very quickly. And also the AI, as I said earlier, reads all of them, every single word. It does things that recruiters can't do. It takes away the capacity limitations that we worked under for hundreds of years. So it makes perfect sense that actually that's what the AI should be doing. However much the recruiters want to do it, or however good they think they are at doing it, that's what the AI should be doing. But the recruiters are still the people who see how the team performed. They see the consequences of those decisions. Really important that that is where they are slotted into the process. So really is don't use humans where tech is better, but don't lean on tech where the human is the point of all of this. So some really hard decisions here. I think you need to ask yourself, does a human really need to do this? Is this step of the process only here to cover up for everything else being broken? So really, really big questions to work through. So the second part of the AI readiness framework is very much linked to this and it's about team capability. And with things changing so much, the skills that your TA team were hired for are not the skills that they need moving forward. So exactly what those skills are that are needed in this AI world is very much up for debate. And it really varies from organization to organization. The good news though is that we're a really early stage of this, so there is a really big opportunity to upskill. And this is really important. It's really important to define what your team are actually going to be doing, because I'm seeing so many TA leaders fall into this trap. So when you ask them about AI, it's always greeted as a good thing. It's going to free our recruiters up to do other things. And when I ask people what those other things are, I really get a good answer. It tends to be things like things they should be doing, the valuable stuff. And there's no definition behind that. And we've got to do better. We've got to be very specific about what our teams are going to be doing with this extra capacity, the skills that are needed, and how it adds value to the organization. And it really matters, because every business is chasing efficiency and productivity at the moment. AI is really a reason why they're opening those conversations. And if you can't describe how you add value, then any capacity in a team that's freed up just means that people's jobs get cut. So you create capacity and then the capacity immediately gets removed. Whereas it could be adding significant value to the organization. So it's really important to. To really kind of think that through. So what is it that that AI is going to enable our team to do differently that's going to bring value to the organization? The next part of AI readiness is data and measurement. And this is a really, really big topic. And there are two parts to this. One of the big questions is all about how data is structured and connected across different systems. So you can get the sort of the true advantages of agentic AI and orchestration. And that's a very, very big topic. And I've had some other PODC on that topic, and I'm looking to do more in the future. So I'm not going to get into the technicalities of that right now. What I am going to say, though, is when it comes to measurement, what are you measuring and does it actually matter? Because I think a lot of the time we are measuring things in talent acquisition because they're easy and because the system lets us do it or the system does it for us. And again, it comes back to that value creation for the business. What is it? Where are we going to create that value and how are we going to measure it and prove it's there? So it's a difficult area. You can easily end up with more questions than answers. But it's a really important thing to be thinking about in terms of what is that value and how are we proving that value? The next one, the final one I'm going to talk about in this episode is Building Trust. Now, there are huge amounts of legal and regulatory and ethical implications around AI. AI and again covered those extensively. In lots of other episodes of the podcast. And I'm sure they will be a talking point, certainly for the months, if not years to come. What I want to talk about here with trust is something that is just, I think is incredibly important, which is building trust with the candidates. This is really frustrating because when I have case studies on the podcast, like I did with the Adeco case study and the. The Bell case study and other ones that I've recorded since then, and a couple that I've record that haven't even published yet, the message always comes back as the same, which is the candidates really, really like this. There was a really high level of people opting in for AI screening and it improved our candidate experience and everyone was happy. But when you look at the external data. So, for example, a company called ThriveMap recently did some research into trust that candidates have in the AI process and. And 49% of people thought that AI and recruiting was unfair, versus only 22% who thought it was fair. And 41% want humans to make decisions without AI involvement, showing a strong preference for human judgment over automation. And this really follows through. Whenever you see articles in the media about AI recruiting, particularly AI screening, the pattern's always the same. It starts with a massive negative. This man applied for 9 million jobs, a robot rejected him in 5 milliseconds. It's all biased. It's terrible. This AI thing is just not very good. And only when you scroll down in that article and you get sort of towards the bottom do you get positive case studies and people from the industry coming and defending, you know, some of the things that are happening. And it's just really important that we flip that around. We have to build the trust with candidates. And it's a. It's a really hard message because it actually means that, that we have to be very honest about what's happening at the moment. So millions of applications are not being looked at at all. And is it better to have your application not looked at at all or have it screened by an AI and then potentially get through and talk to a human? So it comes back to those mental models. It's a massive, massive shift. There's a long way to go. But we really need to take this seriously about how we build trust, how we get those great stories out there. And, you know, and this is where, what this podcast is all about, it's finding. Finding the stories and sharing them, because this is so important and building trust is such an important part of it. Onto the last few thoughts here. So how do we plan the way, the way forward? So I think the first thing to say is this is a really challenging road ahead. It's not a straight line. In my presentation, I had a picture of a very, very kind of bendy road. It's going forward, but it's meandering a lot. So this is windy and difficult, but it's only going in one direction, and I'm really confident with that. There are lots of variables out there. There's legislation, there are legal cases that we don't know the outcome of. There's the issue with data centers and powers, There's a token pricing issue. So many complications and things that are going to affect how we move forward. But I think it's very fair to say that the technology is out there now. We can't unsee it, it, we can't put it back in the box. And even if it didn't develop any further, the tech that we already have in this AI revolution is enough to fundamentally change recruiting. So this is only going in one direction. What's also difficult about it is what Ethan Mollick refers to as the jagged frontier. If you've not come across Ethan Mollick's work before, he is a business school professor and he's one of the leading thinkers around the, the implications and impact and implementation of AI in business. So he's the kind of real deal. And I would definitely check out the stuff that he writes and he calls AI the jagged frontier because it's very good at some things and not very good at other things. And it can be very difficult sometimes to know which is which because it's very confident when it's not very good. So it's very difficult to sometimes really understand where it can add value and to complicate that. Things keep changing all the time. New models come out. Generally it gets better, sometimes it gets worse at things that it was good at. It keeps changing and you kind of really have to be using it and experiencing it to really understand what's going on there. So it's complicated, but also it is moving forward. And I think sometimes I hear people talking about things that AI isn't good at that are just out of date, that you're going off information from a year ago, six months ago, even six weeks ago, is changing that quickly. So keeping up to speed with what's happening, very important. And then finally, innovation is really hard work. Going back to my cousin Jack and the Cornish with their steam pump, they had to drag that steam engine 250 miles across the jungle and across mountains. And that's not easy. Innovation is really hard work. So we need to think about how we design the future. And that comes from asking the unaskable questions that I refer to to earlier. Why do we do this? Do we really need a resume? Is our recruiting process actually fit for purpose? Is it? Do we just do this because this other thing is broken? It's really important to understand where you are. It's really important to also build a vision of what TA in your organization looks. Looks like, what is transformational, what is going to happen. And very few people can actually do that. But the ones who can, I've noticed, are the ones who are driving the most change. Then you have a measurable target around that. Then you get to the work. What's the current state? What are the jobs to be done? How do we move forward? So having a really structured approach to innovation is a way of helping with recruiting's innovation problem. So what happened to the Cornish miners in the end? What happened to them in the end is what's actually happening happening to us now. The skills that made them the best in the world went out of the day. New technology came in, new ways of working came in, and they had to start from scratch. So they had to make a choice. Some of them adapted, thrived, carried on and stayed where they were. Some of them went home and just gave up. And that's our choice right now. Do we take hold of this and make recruiting amazing, even though it's hard, even though we don't quite know how to do it? Or do we go home and think about how things used to be and all the what ifs? I know which one I'm doing. What about you? If you want to know more about AI readiness, I'm just about to publish the framework which I've talked about in this episode. It lists the five priorities that determine whether a TA function will get real value from AI or not. To get your copy, head over to matalder Me. Reddit ready. That's Matalder Me ready, and I'll send it over to you. Thank you so much for listening to this episode and thank you for the many of you who've been listening to the podcast for years. And thank you also to all the new listeners who've only just discovered it. I absolutely love doing this show and you all make that possible for me. I'll be back next time and I hope you'll join me. This is my show.