The one where Kim Bryant returns

Speaker A: Welcome to episode 41 of the Talent Intelligence Collective podcast, where returning champions bring fresh perspectives on the evolving world of talent insights. I'm joined once again by my brilliant co hosts Alison Etrude and Toby Kulshaw from Lightcast for what turned out to be a masterclass in how TI has matured from mystical promise to business critical capability. In Toby's news roundup, we dug into Sanford's Canary in the coal mine study on AI's employment effects, though Toby rightly questioned whether it's correlation or causation when entry level jobs disappear. We also covered the ongoing saga of the UK Labour force survey with response rates that would make any researcher, uh, weep and Deloitte's warning that the war for talent continues, particularly in those same old GPS hotspots. But the real treat was welcoming back Kim Bryan from AMS. Nearly three years after her first appearance in June 22nd. Now global head of research, leading AMS's new research lab, Kim brought fascinating insights from analyzing 400,000 hiring records across 100 countries. Among the revelations we uncovered, uh, why sales roles are the most volatile for offer declines, but project management was the surprise outlier. How compensation isn't the main reason people reject offers. It's everything from flexibility to hiring process experience. The evolution from TI being, uh, an expensive mystical add on to an expected core capability. Why, despite all the AI and automation, the recruiter candidate relationship still makes or breaks the hiring process. Plus Kim's top tips for TA leaders. There's a mouthful including the importance of creative space and finding joy outside work. Refreshingly human advice in our tech obsessed world. So settle in for a conversation that spans continents, decades of experience and the fundamental truth that people still matter most. So stay curious, stay data driven and most importantly, stay intelligent folks.

Speaker B: Bye.

Speaker A: Before we get on with the main event, I wanted to remind you that Lightcast kindly sponsors this podcast. Here's a very well spoken chat to tell you more about them.

Speaker C: Um, Lightcast is the global authority on labor market data. By collecting, analyzing and refining profiles, job postings and market data globally, Lightcast provides actionable data driven talent intelligence, giving HR leaders the context to make better decisions. The world's top companies trust Lightcast to deliver, ah, clarity in a complex world of work. For more, visit Lightcast IO.

Speaker A: What happens when you combine global talent insights, cutting edge research and nearly a decade of industry leadership? You get a talent Intelligence superstar. Uh, this is episode 41 of the Talent Intelligence Productive podcast and we've got a returning champion with us today.

Speaker B: Yay.

Speaker A: Yay indeed. In the virtual studio. With me are, um, my brilliant co hosts Alison Ettrich and Toby Cauldshaw from Nightcast. Give us a noisy wave team. Hello, Toby, are you there?

Speaker D: Oh, I've made the dogs bark now. Sorry.

Speaker C: Set them on.

Speaker A: That's a form of. A form of welcome. It'll do. Now, it's been nearly three years since we had today's guest on, on the show back in June 2022, episode 18. For those that are keeping track. Um, she brings decades of experience. Sorry, Kim, that makes sound really old. She brings lots of experience from the front line of talent intelligence. Please welcome back to the show, Kim M. Bryan.

Speaker B: Thank you very much for having me, Alan.

Speaker A: Well, you're more than welcome. So for those that might not know you, just in case, um, and because three years have passed since our last episode, and for anyone who didn't catch your first appearance in it, could you give us a bit of an update? Tell us who you are and what you're up to nowadays and how your career has moved on since we last chatted.

Speaker B: Absolutely. So, hi everyone. I'm Kim Brian. I'm the, uh, global head of research here at, um, ams. Um, I have been with, uh, AMS for. Gosh, it's nearly coming up to, to 10 years this time. I'm a bit of a boomeranger. I was with them for, for many years before that too. And it's, um, definitely coming up to nearly the second decade when you add it all up together. Um, this time I am heading up a new think tank for AMS in terms of the research lab. Um, but before that I looked after talent intelligence for AMS and a global team of 120, I think, at its peak. Um, and then before that I had a bit of a varied career across, um, insurance and ehr, A really odd mix of both, uh, kind of numbers and people, which is probably why I've ended up, um, in all things intelligence and insights. I think it's a happy marriage of those two things.

Speaker A: Excellent. That's an amazing introduction. Thank you very much. Uh, for those that are new to the podcast, here's how we'll roll for the next hour or so. First up, Toby's going to give us his trademark market intelligence roundup. So now, um, sorry, then Alison will be leading our chat with Kim and I suspect we'll have some amazing discussions around what she's doing in the world of research. Toby, after one of the most bumbling intros I think I've ever done, I'm going to hand over to you for your usual professionalism with the news.

Speaker C: I like the fact you're saying the usual professionalism. You're not, you're not setting up for anything. Great. We're run with that. We're run with that. It's an intro isn't it? Um, so one of the bits I've been reading this week was the Stanford Digital Economy Lab did a study on AI's effect on um, employment and they called it the Canaries in the Coal Mine Six Facts about the Recent Important Effects of Artificial Intelligence. It's quite a long title. Um, some interesting bits in there, but also bits I'm not sure I agree with. Um, and we've discussed elements of it in the past in the pod. But uh, the main findings were young workers, uh, in areas exposed roles see a significant 13% relative employment decline while older workers experience growth. Uh, the effect is concentrated in jobs with AI automated tasks. So it's the whole task automation rather than role replacement. Um, big hit on entry level hiring and it's not really a piece on um, salary. It's more the actual jobs aren't there. All very interesting, all very valid. I'm just not, I'm not 100% convinced that it's not um, a correlation versus causation piece. I think there's a lot of research has been done around this at the moment and it's all coming to a similar piece around AI is replacing entry level workers, there's less entry level jobs. But I think that the contextual piece that a lot of this misses is the economy. And every time the economy tightens the first piece that gets hit is entry level workers. And you see graduate hiring drop on every single time the economy tightens and none of them ever really seem to address this. Um, now don't get me wrong, do I think certain roles are being impacted with this stuff more than others? Are certain tasks being impacted? For sure. I'm just not convinced that it's in the way we think it is. Um, I think ultimately the way that this will play through is that the design of entry level roles will change and we might expect more from people in entry level roles because they are going to have AI assistance or whatever. Um, but I just don't think it's going to have this full on reduction in entry level requirements that people are professing.

Speaker A: Whereas if there has got to be an entry level of a form, otherwise nobody's entering the workplace are they? Exactly. It's just the type of roles that they'll be doing be different Yeah, I

Speaker D: think that's the point. It's all about how, how are you using AI to augment a role, not to automate a role. I think, I think, you know, you, you're right Toby. In so many ways I think this is probably just causation rather than anything else due to the economy. But um, equally businesses don't really know what's going on and they don't really understand the impact of AI and they don't know where to look in any of their functions. So when you look at the confusion that's going on in businesses around the, the impact of AI and then you combine that with the titan economy, the, you know, it kind of, it's almost naturally the first thing we're going to do is stop hiring young people. We don't know what we're going to do with them. You can kind of see how that, that could become a cycle. It's worth, um, for those people who haven't read it, it's worth reading the beyond the Buzz report, um, that's out on AI skills. And Kim I know has got one as well that we can talk about a bit later. But there's lots of research in, in this space. I think this is probably. Don't read just one report and believe it read lots, particularly because this one is 57 pages long.

Speaker B: Yeah, I think it's really easy to let the blame at the foot of AI too. I completely agree with you Toby. I think it's so much more complex than that. Um, with all of the variations and changes and evolution in skills hiring, um, and um, the economy, all of it put together, it's a little bit of a perfect storm. And also I think there's a huge pressure on businesses to make associated savings with AI to say, okay, okay, you know, this is coming in so we can either reduce our headcount or claim efficiencies with it when they're not really sure exactly where AI can benefit. And that's this is the easiest place for them to make that change.

Speaker A: Definitely. It's interesting, isn't it? Because whatever AI is doing in terms of replacing work that is being done, so the tasks etc. That people used to, used to do that now can potentially be automated with AI. And then that's different subject as to whether it does it any well does it well at all. But whatever it's replacing, the decision to make layoffs is still very much a human one.

Speaker D: Yeah.

Speaker A: Um, often organizations are uh, being, being forced to make these efficiency savings for no other reason than Greed and profiteering. And that's why they're being forced to do it ultimately. Not because they have to, to survive or anything like that. They're doing it because they want to increase the shareholder value. And I'm not saying that's necessarily wrong either. Uh, but we shouldn't be blaming AI for layoffs. We should be blaming. Humans are using AI as an excuse for the layoffs.

Speaker D: Yeah, and, and there are, the report says there are places where it is most obvious, like software development and customer support. Well, you would expect those to be areas actually, regardless of age, where AI is going to have a bigger impact because you can definitely automate code and you can definitely automate a level of customer support. Right. So it's just a, you know, that it doesn't feel like that's a young person thing, that just feels like that's a role thing.

Speaker A: Well, interestingly, I was, I was on a thread last night on um, um, Reddit where it was, it was software engineering thread and there was a lead developer talking about AI's impact on his team. So he, he led a team, I think like 10 developers all. Everybody reported to him he referred to as junior developers. And he said that the kind of latest iterations of uh, tools like Cursor that are powered by Gemini or ChatGPT or um, one of the other large language models have enabled him to basically. Whereas he was hiring almost every two or three months to keep his team at 10 people. Yeah, because he had to and because people were often poached, they went elsewhere etc, so he was always in a hiring cycle of some form. Um, he hasn't hired anybody new for 12 months. And um, his productivity, his work, that what their team are actually pushing out as, as maintains, as maintained what it was before, even though his team has shrunk from this 10 down to I think he said 7. So he's lost 30% of his headcount and is delivering the same amount of work still. So that's obviously in a microcosm, in a very small sample size, but it's probably relatively reflective. And he was saying he's gone from. He has to spend typically with a, uh, per sprint with each of his junior developers. He probably gives them three or four hours of his time, uh, before they kind of head off and do things. And then he checks in with them every so often, see how they're going on. He's saying now when he spends time with a tool like Cursor that's doing some of the heavy lifting around the coding, he's spending 10, 15 minutes with it for the same task, the same deliverable. So who would have thought, Alison, that any of you here. But you mentioned customer service and developers side by side. Who would have thought five years ago would have been putting those two groups of folk in the same pool until being impacted by tech? Bonkers, isn't it?

Speaker D: Yeah, it is, completely. I don't think anybody knows. Right. We can do all of the research. It's. Yeah, we can do all the research in the world, but it's all historical looking and this is moving. The pace of change is so fast that, you know, it's kind of like. Well, I'm not, I'm not sure there's any point in looking backwards. Right. It's just because we have no idea what's going to come around the corner. We've just got to go with it.

Speaker A: You've got. Exactly. It's got to be agile, haven't you? And adjust being able to be able to course correct really quickly because weird shit is happening and it's happening at a rapid rate of pace.

Speaker D: Exactly.

Speaker C: I think the thing that I noticed, we did a study here, um, it was an AI study on jobs beyond tech, um, which I've forgotten. The beyond the buzz. Look at me getting all corporate and getting the right title and everything. Um, but the thing that really stood out to me, the whole really interesting report, I really liked it. But the bit that stood out for me was across all functions, when you look at kind of which areas people are using AI and related terms in, the one that's completely devoid is anything to do with governance or risk or control. And honestly, I just feel like we're in a period of absolute shiny magpies where everyone's like, oh, look at all these amazing things we can do and we're all cracking into it and everyone's just using it and everyone's just kind of cracking on, but there's no control in it. There's no control or stability or structure. We had chats with Antonio and Bob years back on human centered AI design and I just feel like we're, we're just so far from that at the moment. It's like the Wild west of everyone just going and playing and doing stuff and, you know, saying, look how much I've saved, look at the efficiency gain. And it's like, well, yeah, but what's the true cost of this? What are you actually doing? Um, yeah, it's slightly concerning for me.

Speaker D: I think that might be the new world, though. I think. Yeah, I think that's, that is the new world. We're going to have to accept it and be agile, as Alan said, and just, you know, course correct the whole time and ah, as you course correct, you're going to learn your rules.

Speaker A: Absolutely. Let's remember when the, when, when the Internet, we're all old enough here to remember the Internet property came around. Ash Jeeves was looked on as being incredible. We were blown away by its capabilities.

Speaker D: Flis said to me the other day, she said, um, she said, what's Yahoo, Mummy? And I went, oh, wow. That was like a bit of a, you know, kind of blast. It was just weird. I went, that was really when mommy was young.

Speaker C: And to any listeners out there, I'm sure you're still very big. I'm very relevant still.

Speaker D: Well, still relevant because she still saw it. Right, so still here and still a brand.

Speaker C: Exactly.

Speaker A: So what else is happening, Toby?

Speaker C: Um, the joys of the UK Labor Force Survey and the Office of National Statistics is bubbling away as always. There's been a couple of articles around, um, its effectiveness. Once again, um, it's a really interesting topic. I get kind of torn on this one. As we know, the ex chief and former head of the ons, um, said that essentially they're hamstrung by treasury funding constraints and they just don't have enough resources so they're getting drawn into different areas. Um, and he goes into the difference between kind of a statistical office versus an economist office. I find this just wild both in terms of when you compare the closest comparison at a national level would be the US Current Population Survey. And for comparison figures, back in 2015, the US CPS was uh, 80 something percent, essentially a 90% response rate and the UK Labor Force Survey was 47% or something. And they've both declined. And the US CPS is down 68%, something like that. Now, um, UK Labor Force Survey actually went right down to about 12, 13% and it's back up to 20%. So we've always struggled, we've always struggled to get, get the population engaged on this labor force survey. And they've made efforts to try and brute force it, they've made efforts to kind of give better incentives around it, et cetera, et cetera. But for me it's, I find it fascinating that it's such a big important piece that we're just not funding to get the results. Uh, when you've got the head of the service saying we're not funding this sufficiently to get what you need the work from this is Absolutely. Core to everything from kind of the bank of England, the treasury, the uh, Office of Budget Responsibility. It's like core to the financial planning of the government and the interest rate setting and everything else. When you've got the head of the ONS saying you're not funding us enough to do this properly so you're getting bad data out and yet we just continue and they're not. I find it a fascinating one. I'm like, I don't know what the right solution is but it seems to be a really strange, almost like political hot potato where people uh, almost don't want it to work. I don't know, it feels very strange to me.

Speaker A: Are people scared of the answers?

Speaker B: I think that's a big part of it too and I think it's how much money is it going to cost to fix? Because I think there's a kind of tale of two cities here where you know the ONS has had to use the word old fashioned but it has had methodologies and ways of working for a long time. And have they been working as they should for the time that they've been operating? Uh, I think it's, that's debatable. But even if you look at um, whenever they did the, the COVID 19 infection survey there was, I think the latest figures was something like 988 million that they spent on that it be could and that was how they were able to very quickly get all of those numbers and get all of that data. I actually took part in that survey and they paid, I think it started off they paid 50 pounds per person for every single um, response that they give. So it was, that was how they very quickly got to that data. So I think when they're able to put money in they're able to get the appropriate data out. I just think whoever's doing the calculations around how much it's going to cost effects, they obviously got a number that's just too scary for anyone to contemplate.

Speaker A: My question to hopefully one of you three who are way smarter than I about this kind of stuff was where is the cost? And you kind of answered a bit of that, Kim. It's actually been gathering the data itself. Is it? Rather than the, rather than the analysis?

Speaker B: Yeah, I think a lot of it they spend, they still um, hire individuals to go around door to door to take some of those services. Still a very manual process and I think a lot of that needs to be looked at too.

Speaker D: I think what's interesting though when I read this article because I'M with you Toby. It just, it feels like this has been bubbling away and there's really important decisions that happen as a result of this. But the bit that shocked me was there was a figure in it that showed the response rate and it had the US UM Current Population Survey and it had the UK Labor Force Survey and in 2016 we'd still got less than a 50% response rate and it's just been diving ever since. But I think that means that for the last 10 years we've been making decisions based on less than a 50% response rate. I mean it's just that for me was just mind boggling. Never mind the fact that it's dropped and we're still doing it in an old fashioned way. Um, Whereas in the US it was 90 or nearly 90%. You do start to question the validity of both the approach and the stats and the very like all of these things. I'm going to sound like a broken record. It's historic data because it has to be because of the way that you're gathering it and then analyzing it and doing it. And that just means that it's out of date so fast.

Speaker B: Um.

Speaker D: I don't, I don't think there is an answer and I'm not sure money is the answer actually.

Speaker C: I'm not sure.

Speaker B: Doesn't it for external companies to come in like that they have in. In the US Yeah but I don't

Speaker C: understand why they don't tie in closer with other government bodies. So if you think about like labor force force surveys all we're looking at kind of obviously what the labor force is made up of and how many people are in it, et cetera, et cetera. We all pay tax. Well why can't they just tie in? And this is me being oversimplification as always but why can't they just tie in with like HMRC or just to look at the numbers there and it's a triangulation thing like tie that in. I know they use external labour market data to look at job count in the UK for example and that's all published and publicly available. So they've got an idea of the number of jobs being advertised. If they can't do HMIC time are like the payroll providers or like people have got to be paid somehow it doesn't. If it feels like they've got all the data just in different government bodies and they just don't want to talk to each other with it. I don't know. It feels baffling to me.

Speaker D: I Think it's really easy for us, having spent a long time in this space, all of us, to go. I don't know why they just don't join the dots on this. It's, you know, but actually if you, if you take a step back, we know this data is really scrappy, we know it's really messy, we know it's in thousands of places and trying to, and we know it's all in a different language and I mean, you know, title skills, employed, not employed, permanent part time consultant gig. Yeah, it's actually, it is really messy data to try to sort out. So I think it's very easy for us, having been in the industry for a long time, to go, well, why don't they just do this? Um, they need a proper ti person in there leading it and talking about it. I think that's the reality.

Speaker C: Uh, do you know what? Uh, the geek in me now wants to try and work out how many people they actually have working on this study and if we were running it as an equivalency, what sort of headcount we'd look to have on that sort of resource, on that sort of study. Because my gut feel, I think you're probably right. I think they probably don't have enough people working on it is the short answer as well.

Speaker B: And enough of the right people. Uh, it's probably a skills mismatch there too.

Speaker D: Yeah, I think that's the point. This is not, forgive me, just data gathering. It's understanding where all of that data is, all of the different data points and how to make it all talk to each other, um, to become relevant and to draw the insight from it.

Speaker C: Yeah,

Speaker A: five and a half thousand people apparently the Office for National Statistics employees, I don't know how many of those are working on, on this specifically.

Speaker D: I was gonna say they do other stuff as well.

Speaker A: Yeah, of course, yeah, yeah. All national statistics kind of thing, but still a big old business. Big old, sorry, big old organization. Clearly not a business, a big old organization. But yeah.

Speaker D: Well, I'm sure that the brains on this, on this call this pod could help them out. So if anybody from the ONS is listening, we're more than happy to talk.

Speaker A: Yeah, absolutely. Okay. Uh, Toby, have we got time for one or maybe even two more bits of news?

Speaker C: We've got as much news as you got time for, but let's kick off with another one. Um, a couple of bits I wanted to talk about. One was once again about AI, um, and the AI sector, uh, is prioritizing skills over Formal education. So this is a piece of research by Oxford Internet Institute and the um, University of Oxford. Dr. Fabian. Stephanie, who's the departmental, uh, research leader for AI owned work, uh, said our research suggests that the UK's labor market is undergoing a fundamental shift. The traditional path of university education leading to higher pay is no longer the default for AI professionals who are now being rewarded for practical skills and industry specific know how. Um, which is interesting. We've spoke ad nauseum on this pod over the years around how the education system isn't rapid enough in development, it's not keeping up with the industry needs. And uh, I think uh, what this paper is suggesting to me at least is what we're seeing in AI is kind of the turbocharge of that. The market just moves so much faster than traditional education that it's a bit of a, uh, this is ironically considering the name of the previous paper, but this is probably a canary in the coal mine for the education system in a lot of ways in my mind.

Speaker A: And it's nice of the education system themselves to confirm it to us.

Speaker C: Yes, exactly that.

Speaker D: I think that we're definitely starting to see a shift in education establishments going. Right. We have got to figure out what are the skills that we actually teach in a course, um, and what do those skills mean for a career path for individuals and then what does that mean for the education and the formal education as they leave? Is it going to be a degree that covers all things or actually is it going to be lots of mini certificates that when they add up become a thing? I think there's a, there's a massive transformation. Um, and I have a feeling this will be led by the us because we know that US organizations are jumping into skills more quickly than potentially European businesses. Albeit that feels like it's changing slightly. But I think this is leading to a major overhaul of the education system, which is a good thing.

Speaker A: Definitely. And also all of those skills that are learned, are they really skills until, until they've been tested in anger?

Speaker D: Yes.

Speaker A: Um, you know what I mean? Just because your certificate, you have a certificate so you can do something, then you can do it until you've actually proven you can do it.

Speaker D: It depends on that.

Speaker A: Have they been validated?

Speaker D: Yeah, I mean that's just. So that's exactly the point, isn't it? Because it depends entirely on the course. If you're going to operate on a dog and you're a vet, you'd like to think you've practiced that a bit through rather than there you Are, there's your certificate crack on.

Speaker A: Right, exactly.

Speaker B: Yeah. And our education system really hasn't changed fundamentally in, in a very, very long time. Yes, what we call the, the qualifications has changed, but fundamentally it hasn't changed really since you know, post industrial revolution. And I think there is a bit of a. We're actually doing a piece on this right now. I think we're hoping to publish it next month. Um, and I, for me it is very much an education revolution. I thought that's a great headline. Then realized it's been used before so uh, I can't use that. But uh, you know it definitely is something that is, is coming. And I think this all feeds into what we talked about right at the beginning about um, the drop in the graduate hiring and entry hiring. It's all connected to the fact that employers aren't necessarily looking for those same um, educational um, qualifications or even topics that we've been so heavily relying on for the past, you know, 50 years.

Speaker D: Well maybe they are and that's what's causing them their problems. Yeah, I think it's.

Speaker C: To listen to your point earlier though, it's the, the relevancy of the course. So I'm, I don't want to come across as. I hate education. Um, I was, I was a terrible student personally and you know I've spoken on the POD before around the fact I home educate my kids but I think education in the right, in the right way is absolutely vital and I think one of the bits of work I know one of the teams here do is looking at the um, kind of the economic impact study of different courses or qualifications or um, the future career trajectories of certain qualifications over others. And I think that's what I really like. It's the, what's the value and the return of this work Because I think the problem is there's just too much education happening where it's for the sake of the education uh, provider rather than the student. And students are coming out with a set of skill and qualifications that just aren't relevant. And that's what frustrates me is if they were coming out and there was a bang on return on investment at this time and effort, um, and it's going to improve your life. Brilliant. I'm all for it. It's where they're almost being sold a dream and then having the rug pulled under them. That's, that's what frustrates me with the

Speaker D: system and it's, it's, it's going to change and keep changing um, at speed as more organizations become skills based just, I mean it just has to because people are hiring for, for ability to move people around and create a flexible workforce and yeah, that is going to involve a whole bunch of soft skills that aren't necessarily taught in the education system today.

Speaker B: Yeah, it's like a return to the old, you know, kind of apprenticeships and um, more schemes like that and um, those are seeing a little bit of a resurgence and I do think we'll see more of them in the future.

Speaker D: Yeah. As someone who spends an absolute blinking fortune on their child's education, I'm still kind of hoping I'm going to get some sort of ROI on it.

Speaker A: It's a form of them caring once they leave home. It's like the ROI you're looking for, Alison.

Speaker D: I don't think I care just anything.

Speaker B: I think the attitude though of students themselves is changing too because I know certainly in my generation it was very much the thing of you know, go to university and um, you get a job and that's know there, there's quite a defined path for you to follow. Um, whereas I know certainly my kids are you know, 19 and 16 and uh, my 16 year old is really interested in a career in hairdressing because she feels that that's a more sustainable career for her than anything else. Um, I'm absolutely happy to support that with her and I think we are seeing. And that's, that's not ah, you know, it's not a unique kind of mindset. There's quite a few of her peers that are, they're looking for much more um, practical um, sources of employment rather than necessarily going down the, the education

Speaker D: which is, which is awesome, isn't it? Right, what we always going to need. Plumbers, electricians, hairdressers. Right. You're not going to have a robot come in and cut your hair just like. No way.

Speaker C: Perfect. Alan would have no issues with that at all. Just laser up.

Speaker A: As a bold one, I'm the least the last person should be commenting on this, but even I would not be so um, welcoming of risk to let a robot cut my hair when I

Speaker D: cut Dave's hair during COVID the first time. And I am not a robot and I'm not a hairdresser either. Um, Fliss called him Acorn head and I think he looked a bit like Beaker out of the quite some time. So I wasn't allowed to do it again.

Speaker A: He also only has one ear now.

Speaker D: I didn't, I didn't wound him luckily bang off, Dave.

Speaker A: Oh, let's do one more bit of news then, Toby. Only because I've uh, read the title and I've got the rage over it. The old term that they use, well

Speaker C: it must be the Deloitte continue in warning for the war for talent will continue.

Speaker A: The war for talent.

Speaker C: War for talent.

Speaker A: It's not the war on talent, which is exactly that.

Speaker C: Um, so this is off the back of the 2025 Deloitte Global Business Services survey. So it is specifically tied to GBS, um, and 50% of companies with GBS. So global business services operations plan to expand their presence and face major challenges in finding the right people. Um, I'm not hugely surprised by this in the sense of if you look at most GBS hubs, they are being very traditional. They're all going into the very traditional low cost country centers generally. Um, so there is going to be a squeeze on the availability of talent just because you're all putting all your global business service centers in the same places. It feels, and this is the TI geek to me, it feels like a lot of these decisions are made on the equivalence of just asking chat, GPT or Claude, where should I put my GPS center? And it goes, oh, these are the good locations and just spits out the same locations for everyone. Whereas I uh, don't think people are really looking contextually as to, okay, what's the growth rate of these, these service centers? What's the sustainability of them? What are the other options? What are the. There's, there's very little inventiveness and um, creativity around this at the moment. It's you know, the same Bangalore, Pune, Manila, ah, you know, Krakow, whatever. Like it's a very traditional path for gbs. And I think it's um. Yeah, the whole study made me chuckle a little bit if I'm gonna be

Speaker D: honest because they're still not, you know, broadly speaking that these decisions are ah, made based on foreign direct investment incentives, um, and cost and ah, not availability of talent. Right. And m. Yeah, and I think that's just the real um, and those, those organizations that are then including the availability of talent are the ones that are managing to find the kind of next spots. So they're getting, they're still getting the investment but they're managing to you know, get in first. But the reality is that the minute somewhere becomes a next spot for a GPS center, it's going to become a hotspot over the course of the year. That's just the reality of the way that this market Works. Um, so I'm with you. I think it's a. I was gonna say it's a bit of a non story but yeah, nobody is saying that the labor market is going to get any easier. Organizations, we've got a big demographic crunch coming. We've got big shifts in uh, attitudes to migration and mobility around the world and then we've got AI you know, it's just all of those things combined mean that the labor market is not going to get any easier. The fact that they've focused on GBS centers, I'm. I'm not sure is that exciting. Sorry, Deloitte.

Speaker B: And it's quite cyclical isn't it with the centres. They tend to go to certain locations and then often come back to nearshore and then back again and I think it's. We're starting to see history repeating itself.

Speaker D: Yes, absolutely. Speaking of history repeating itself. Alan, please can I talk to Kim?

Speaker A: You can. Time to put Kim in the hot seat. Or back in the hot seat anyway. So Alison, over to you. Let's crack on with the. The main reason we're here.

Speaker D: Cool, thank you. Um, Kim, I've been really looking forward to this one. Uh, can you. So start off UK you gave a summary of your background before and Alan summarized it beautifully as I think you were a ti Rock star or a superstar, something. Anyway, not wearing. Can. Can you talk about the latest move? So what does your head of Global research at AMS say mean? Do? Why?

Speaker B: Good question. Um, and obviously I think we're quite unique certainly in the role that I've been in previously where you know, looking at the impact of what's happening across hundreds of different organizations and different locations in different industries. Um, and one of the things that we noticed is that there's a lot of, there's a lot of stuff out there in the market. Um, but there's a lack of real strong reliable data LED research that actually points to um, some strategic thinking around what to do next and we m. See some organizations are starting to do that. I think my class included in terms of what you're able to. The data that you're able to provide and the. So what that you're able to get out of it. Um, and m. What we really wanted to do with the research lab was to create um, some content that's based on our proprietary data that we have that's been you know, gathering globally over the last six uh, years plus um, that and we can put that, bring that to the market and really help to inform thinking of TA leaders and CHROs are out there because there's just so much at their fingertips. And I think it's almost, it's not that there isn't the information that's available, it's that there's too much uh, to kind of wade through and to really do some of that for them and point to how they can make some decisions without going down a rabbit hole of you know, 50 or 60 reports every week.

Speaker D: So kind of a trusted source or go to. For your clients. For TA leaders. For who?

Speaker B: Yeah, for all of the above all the market first and foremost. Um, but also our clients as well. Um, but also to give them the practical um, next steps as well. Um, because we often find reading through a lot of the research that's out there, it is quite high level. Um, and to really bring it down to what it means for them in terms of immediate next steps and long

Speaker D: term, that's really cool. So actually here is the research, here is the insights. This is what you need to do about it.

Speaker B: Yeah.

Speaker D: Okay, so maybe at the end of this conversation when we're talking about top tips, maybe that would be really interesting. Let's make it, let's make it practical tips rather than kind of top tips for thinking. That would be brilliant. Can you talk a bit about how um, TI has evolved ams. So I know we're kind of moving out of the research role but going back into ti, how's it evolved? Because the business, you know, had a TI team reasonably early for staffing organization organizations, forgive me for that, you know, that kind of generic statement. But yeah, so 2020 was recently early and then. Yeah, just interested to see how it's evolved in the last five years.

Speaker B: Yeah, it's very much become part and parcel of our, our day to day and in terms of what we offer to our clients, but what we do for ourselves too. So I think when we started off it was very much a kind of an add on, it was a kind of an extra piece that, that our clients could use and it was, it was very new to a lot of our clients. Um, and fast forward to today and it is connected to every part of what we, what we're trying to do. Um, so whether it's right at the very beginning of a process of having um, that TI information available before uh, a requisition is even raised right the way through uh, the entire process. So it really has altered in that respect. It's also moved much more to self service um, in the early days whenever that information was um, not as freely available. It was obviously my team that was creating that content and explaining it um, and showing individuals how to apply it. Fast forward to now. We've done a lot of education throughout the organization to uh, give individuals some of those skills themselves so that they are um, using that lens for the advertisement. They're doing a little bit like tech used to be very much a standalone bit of most businesses. And whereas now it's part of everybody's job in terms of um, we're expected to go to help desks ourselves and sort those things out. And it's the same with ti. We really want individuals to be involved with gathering that information and using it at as many points of the process they can. There, there are obviously, you know, challenges with that too. Uh, not data literacy is one. I think there's a huge amount of people that have a, I don't know, a little bit of an inbuilt fear of data. A little bit like, you know, some kids just naturally don't, or feel like they don't like you know, maths at school because maybe they've had a bad teacher or they've just had a bad experience. And um, certainly from what I've seen a lot of individuals um, in different roles have a kind of innate fear of data because they're not quite sure what to do with it or how to read it. And I think once you've gone through that process of really helping them to understand for many of them it is game changing.

Speaker D: So there's been, you're kind of disseminating the data now to your embedded TA leads on client sites rather than the TI team centrally delivering them. Is that right?

Speaker B: To some degree, yes, certainly. For the much more, the much more transactional pieces of TI that we would have done, um, there's still, obviously we have still got a core team. Um, and actually we've introduced new roles which are, we call them insights and intelligence partners that face off to every one of our clients and every part of our internal business, um, that actually work with them to really build TI into every bit of what they do and also to combine all of the other bits of data that are available. So if there's beehive reports or different bits of MI M that's kind of dotted about the place that all of that is pulled together by one individual. There may be teams working in the background that are actually producing the reports, but that one individual is the face of all things data. And we find that has really elevated um, what we're able to do because Suddenly you're not just talking about the kind of the ti element, you're talking about all of the available data sources. And that gives us a much more panoramic view of what's happening.

Speaker D: Really interesting how. Have you seen the demands of clients change?

Speaker B: Absolutely. I think if I even think back, you know, six, seven years ago, clients were, it was, it was a little bit mystical to them in terms of, uh, you know. Yeah, it was like, we'll give you some money and you give us back a report which will solve all our problems. I think now clients are much more savvy, and rightly so, with what's available. But there's also an expectation that that should be very much part and parcel of what they get from anyone that they're working with. It shouldn't be just something that is, um, wildly expensive or added on to what they do. So there's a much higher expectation that really good quality insights and intelligence will be part and parcel.

Speaker D: Are you seeing the things that they're asking change?

Speaker B: I think there's still some at the very core that are just, where can we find the right people with the right skills at the right time? I think that's almost always at the crux of what their ask is. I think it's the, the context around how they're asking it is changing. You know, it's the speed at which they want that information, um, and, um, and um, the application of it that's changing more than the, than the actual ask itself.

Speaker D: And are you seeing them asking? Because that sounds like it's still, forgive me, quite, um, transactional AI. Are you seeing a move towards the more strategic questions over do we, do we buy or do we build or what do we do from skills perspective? And how are you feeding into that?

Speaker B: Yeah, absolutely. There's a lot more asks coming to us at an earlier stage, um, which is obviously definitely what we like to see because no one likes to get an ask after decisions been made, you know, just so they can back up a decision. Um, but getting it at that early stage and we're seeing more of that feeding into, um, the forecasts, kind of the business plans for the next, you know, five plus years. Um, and I think it's, for us, it's really important about having the conversation with them around what they're trying to solve for, with, with the asks that they have and what they want to get out of it. At the end of the day, I think that is at the heart of it, in terms of how we're feeding into it.

Speaker D: There's a difference. When you first start working with a client, have they typically had an Insights team beforehand or is the fact that they're then moving into this space. Oh, hold on a minute. We didn't know this existed.

Speaker B: I think a few years ago that might have been the case. I think now they've either had, um, some type of an Insights, um, facility. Um, but more likely it's that they've had access to the tools and the licenses. Uh, what we see often is that, you know, sometimes clients will have purchased quite a lot. Um, and we see this more, uh, in global clients where they'll purchase a lot of, um, uh, licenses and make them freely available. And then they find that because they haven't necessarily, you know, provided the same training or again, that whole data literacy piece, then the tools aren't used as effectively as they could be. Yeah, um, and then, you know, there's an assumption then that there's something wrong with the tool or that there's, you know, there's something wrong with TI when that's just not the case at all. It's, it's. No, it's the misapplication of those tools.

Speaker D: Do you see any correlation? I've been trying to find for years, so if you say yes, I'd be really pleased. A correlation between the size of a business or a vertical that they are in and how strategically they are using ti, or is it still completely random?

Speaker B: Oh, I think this is a really interesting one because we see it across different industries. There's some industries that are much, they have a much bigger appetite for it, uh, and they use it, you know, much more heavily. If I look at industries like investment banking technology, pharmaceutical industries, you know, they are, they are really, really switched on when it comes to things like that. I think when you look at some of the industries like even retail banking, not that they're not, they don't have the appetite for it, but the uptake is less so. So there's an interesting kind of dynamic across different industries and, um, even sometimes within skill sets within those industries. It's like you. I've been looking for a correlation, and I hate to use the word that I feel because I feel like that's not then, uh, quantifiable. But I feel that there is. I just haven't found the data to back it up yet.

Speaker D: I completely agree. There must be something to do with the size of business, the shape of that business and what they do. And um, you know, and I, I keep landing at the moment on. I Think it depends on how commercial the individuals are within that organization when it comes to human resources. And that feels like the wrong, that feels like the wrong output. There needs to be a better answer than that because I'm people bashing hr.

Speaker B: I think you're absolutely right. I think it depends who's champion. If you have someone who firmly believes in it, um, and is willing to invest in it, then you, you know, you see a much bigger uptake.

Speaker D: It's really interesting. I did, I'm not sure I can mention the company, um, so, but I did um, two sessions last week on how to tell stories with data to a Fortune 100 business. And they were having a global people and culture development day. And this was all about how do you use data? What does labor market data do? How can you tell stories with it? What are the user cases? This is a massive organization in one of the sectors that we've just talked about. But I loved the fact that they were doing this as part of their development day. There were 80 HR leaders on two of those. You know, over those two sessions. You just think it's. Yeah, it's fascinating that you know, again, what we take as kind of day to day knowledge because we been so immersed in this industry for such a long time is still seen as a development tool within, within a large organization. So yeah, that's got to be a tick. Can you talk about the new research that um, that has just been published so I'd be really keen. So the, the one about TA landscape offer declines and dropout rates. Yeah, thanks.

Speaker B: Absolutely. So this was a, a bit of a labor of love for us. It was one of our, our first pieces that we have published under the new research lab. And it was I think around 400,000, um, Heron records that we'd aggregated over just under 100 countries. Um, I mean we spanned it from 2020 right through to 2025 so that we can get kind of as broad as reach, uh, as possible, um, to be able to really pull out some of the trends that were, were seeing that were happening. We tried to cover as many job families and seniority levels as uh, we could within that. And I think we find, I mean there were some very obvious findings that we find within it. I mean of course it doesn't, things don't finish whenever candidates accept an offer. I think that's, that's very obvious. But some of the kind of the rationale that we find behind it was fascinating to see how that changed across different, different regions, different industries and then different skill sets within those industries. And I think being able to tie it through to the cost in terms of, of the time to hire, that we saw that whenever, uh, offers were declined, whenever that they significantly declined, that it was a 15% increase in time to hire. And being able to link that through was really, was really valuable because I think sometimes it's one of those things, it's, it's a very obvious statement say, of course, people saying, no, it's going to cost you money, but actually seeing it in, in the kind of cold, hard data fact makes it, you know, it really helps people to say, okay, I need to do something about it.

Speaker D: Yeah. I thought what was interesting was this is. It was, um, clearly compensation played a role. But if you had asked me before beforehand, I would have said that people would not start having accepted an offer because they either got a better offer for more money and that that would be 70 or 80% of them. That would have been completely wrong. Based on this research and actually personal reasons, hiring process, lack of flexible working, all of the sorts of things that we inherently know are more important, not more important, but more important than I thought they would be. Yeah, that's probably a better way of putting it.

Speaker B: Yeah, absolutely. And that's one of the reasons why we pulled out. Obviously compensation was pulled out as a separate reason, but getting a more competitive offer and the competitive bit was covered so many different factors, uh, and that just, you know, depending. And it was very difficult actually to pull it out any, any further than that because it was so individualized in terms of, um, those different reasons. But from everything you mentioned, from, you know, flexibility through to other different benefits through to, you know, um, the projects that they'd be working on, all of that was often covered under the umbrella of, you know, having a more competitive offer.

Speaker D: Can you talk about. You, you put a spotlight on the great flattening in there. Talk to us about what that is, um, and what you saw.

Speaker B: It's a good question and actually I'm going to do that. I think not a political answer of not answering, but we're going to do a bigger piece on that over the next week or two which expands on it. So I don't want to steal the thunder on that, but watch this space. I'm going bring it through. It was a term that we saw that we thought was really interesting. We wanted to pull it through into the research, um, because we felt that it really did play a part. So I will say, because I read

Speaker D: it went, oh, there's much more in there. Um, for those people that haven't had a chance to read this report yet, um, which is the research lab offer declines and dropouts. Um, uh, it's available clearly, uh, online, um, from the AMS website. But actually it talks about the. The declining demand for management and leadership layers, which links back to what we were talking about earlier. Earlier as businesses prioritize agility. Right. Um, in response to what's happening in the economy. So I just, you know, I think that's, that's. It's really, really interesting that you're seeing that and then picking up and saying, okay, are we seeing this and are we seeing it at all levels? So, yeah. Okay.

Speaker B: Yeah, definitely.

Speaker D: Was there anything, um, on. And um, forgive me, I haven't read the report in depth, which is why I'm asking you questions about it. Um, was there anything that came across globally that made. That was really obvious? Yeah. Or something surprising that we wouldn't have seen in the office starter ratio?

Speaker B: I think probably the most obvious thing was, you know, sales is most volatile. I think that probably comes as no surprise. I think probably one of the big surprising things that we saw was around project management. Um, I think that that came left field for all of us as we were doing the research. So much so that we checked, we checked that out at least three times to make sure that we, uh, weren't listening anything with the data. Um, but I think it really, what we're seeing there is. Can be explained a lot by the shift to much more change management that we're seeing across different organizations where the appetite for people to manage those change projects is unlike anything we've ever seen before. Um, and so that really, it was a surprising data point that we saw. But, uh, it definitely can be explained by, you know, the, the rate of change projects that we're seeing across different organizations.

Speaker D: I think the bit for me that was interesting then when also when you looked at the global data was that, um, APAC clearly has remained pretty strong and consistent, which ties in culturally in many ways to Navy there. But Japan showed a decreasing offer to start a ratio which felt, you know, again, that was another piece where you. Oh, that feels weird given how that labor market has performed.

Speaker B: Yeah. And there were some, uh, not anomalies, I would say, but some interesting kind of quirks that we saw in some of that research. And I think those are definitely, um, areas to watch in future because they definitely were reacting differently to the rest of the trends that we saw across the report.

Speaker D: Yeah. Okay. Really interesting. What, um, advice. So there's lots of recommendations at the end of this. What advice would you give to an in house recruiter on this basis?

Speaker B: I think more than anything and certainly some of the, the other research that we've done subsequently, um, we did an interesting piece on um, CSAT and hiring manager satisfaction and one of the things that we uncovered was that even if a process has been, been really bumpy and it's, you know, things have gone up and down or on the flip side if it's gone phenomenally well what impacts satisfaction um, and the outcome more than anything else is the recruiter and sourcer relationship uh, with the candidate in terms of how successful that's been. And I think that for me would be the you know, the biggest recommendation that comes out of uh, particularly the offer decline piece. But some of the other bits that we've done too, that despite all of the you know, the tech advances and all of the different strategies that you can apply, the biggest difference that you can make to your process is still through your people, um, they still having that post offer engagement um, can be the difference between an offer being accepted and seen through and are being declined.

Speaker D: Which is amazing if we think about what's happening in the marketplace and how much we're automating the recruitment process. Actually it's all. I took the words out of my mouth that's about making the process better.

Speaker B: Right.

Speaker D: Don't make me wait for a response. Make sure that I get a response really fast. Make sure that you're telling me what the next step is. All of that piece is just process, just forgive me process related. And that's what the automation is doing. Well but at the end of the, the day it's still about do I like you? Yeah, yeah, that's, that's just really big. Toby, have you got anything to comment on on that? Because I just, it uh, uh, feels like you should have. You and Alan feels like you should have.

Speaker A: I've just come in the chat though. I think we're both just sitting back and listening because it's really, really interesting.

Speaker C: I think my fear is that most TA leaders are getting squeezed on costs so they look at AI and automation and tech and go oh, I can improve my efficiency here. And so instead of a recruiter carrying, well we've known for years that recruiters over 15 Rex, the service level goes down through the floor and yet we still ramp recruiters up at 40, 50, 60, 70 Rex a pop. Um, and my fear is that we use the tech to give that efficiency gain and just ramp recruiters up even more. Whereas this research is really clearly showing use the tech to give time back to recruiters so they can spend the time to build the relationship and have the value add. I'm just terrified that TA leaders are going to take the wrong end of the stick and run um, with it.

Speaker B: Yeah, and I think you're absolutely right, Toby. It's about making, making the processes much more slick and efficient so that recruiters and sources are freed up to do this. You know, the important bit which is building that relationship and maintaining that relationship with it, with the candidate. That's what the candidates want. That's what the recruiters and sources want. But you're right, I think that that pressure and that's for me what's important to get, you know, the output of this type of research into the right hands.

Speaker D: Very interesting. Let me uh, let me bring it back a little bit back to broader talent intelligence. Um, Kim, you are a thought leader in this space. Uh, what is next for ti?

Speaker B: Oh, good question. I think for me it's more of the, more of the self service piece. I think in some cases we still shy a little bit away from that. Um, that uh, can be a little bit trial and error because there's always that risk. And when you give uh, people access to more information that you know, there's mistakes that happen along the way. But I think that has to be part of the process. Um, but I also think we need to do much more for TI in terms of centralizing all of the data that we have available and somehow speeding up the access to the final piece of output. I think there will always be a need for time um, because it's never going to be necessarily the push of a button and sometimes nor should it be because I think part of understanding what people need within TI and what they get at the end of it is an important part of the process. But we do need to find ways to be more creative with how we operate with all of that data and getting the right sets of data to the right people at the right time. Because I think more and more, we have just had more and more data at our disposal and there's more that we could put into all of the TI reports that we produce. But is anybody actually reading that at the end of the day? So I think it's, that's what's next for me. It's about um, centralizing it all and making it much more, um, you know, much more digestible, um, and maybe looking at different formats in terms of how we do that, that's my thought.

Speaker D: What you were going to say more of the same to start offering. I was like, oh, you went somewhere else with that? I was like, no, more the same. That's like. But I, yeah, I think centralizing more of the data for me is, that's, that's where it's going. And I've talked for donkeys years about you need to be able to bring internal data and external data together in order to make really sensible decisions about your workforce. And yeah, I think, you know, we've talked about people analytics and talent intelligence and how they all sit together and where there are differences and similarities. So I think that's. I'm glad it's not more of the same. Can I ask you the same question? So if you were a smaller staffing firm right now you've got big challenges going on in the world of work. You must be looking up at some of the thought leadership that AMS is putting out and like us is putting out and others and going, ah, how do we play in that world?

Speaker B: Right.

Speaker D: What should we be doing? You got any tips for them? So, sort of advice for smaller staffing firms.

Speaker B: I think for, for them it's really about focusing on the basics. Um, gosh, if I had, you know, if I had my time all over again to do things, I'm sure I probably would have done things slightly differently. Um, it's very easy to kind of look at the end product and you know, kind of throw everything at it. It's by choosing one thing or two things that you can focus on and focus on. Well and I think that would be my recommendation. Look at the, you know, the basics of what you need and focus um, on that and then build on it and expand it out. I um, think trying to solve it all in one go. End up creating more problems uh, for yourself and you will have very little engagement with what, what you're producing. I think that organic growth is, is really important when it comes to ti.

Speaker D: Really interesting. Um, you alluded earlier to um. The great flattening has a new piece of research that's coming up. What else is, what else can we expect out of the research? The new year kind of research stood up function within AMS this year.

Speaker B: So a couple of different things that we're doing and some again talking about going back to basics, some basic things that we're covering off simply because of the demand, um, and some of the asks that we're getting through. So um, we are focusing on some things like you know, labour market overviews or employment kind of situation updates, but also then some industry kind, uh, of deep dives and newsletters to focus on the impact of what's happening in each industry. And obviously taking, um, our perspective in terms of some of the data that we have that focuses on that, um, and then some more big pieces like the offer to clients piece that we did, we're going to look at, um, the skills mismatch that I alluded to there in terms of our, uh, educations and universities preparing students for tomorrow's jobs. We're going to look at, um, kind of the stores to supply chains and how holiday hiring is changing because we've seen quite a few changes over the last couple of years on that front. Um, we're also going to look at, um, some of the employment law changes that are coming up towards the end of the year and the good old EU Pay Transparency Directive that's in there too. Um, and then going into next year, ah, we're hoping to do some big pieces based on our proprietary data. So really start to, um, get out there and share some of the, the key metrics, I guess, that matter to most, um, TA professionals in terms of what their, you know, the metrics that they would be measured on and really give them something to benchmark based on those metrics. Because there is a, There's a big space, I feel, in the industry and not in terms of, you know, real deep, um, benchmarks that are based on a broad data set. So we're hoping to bring some of those to the market next year.

Speaker D: That's so interesting. And blimey, how busy you guys gonna be? Uh, I remember when I first set up Stratagens, um, one of our investors was going, well, you just create benchmarks, don't you? And I remember sort of shivering a bit and going, that's a bit of a boring word. Um, but actually that's where the traction comes within a business, isn't it? Uh, how do we compare and are we doing well or aren't we doing well? And therefore you start to inform the decisions of where to invest rather than it just being a. Let me just compare myself to Bob and see how he's doing.

Speaker B: Uh, yeah, it helps him understand where the true challenges are. Because I think so often, um, you know, a lot of, A lot of businesses, you know, focus on a metric, whether it's usually it's time to hire or it may be another metric, uh, you know, a cost per hire, and they go, you know, this is a problem for us and we've got to, we've got to sort this out. But actually if they were able to have some benchmarks to show how they were performing not just against their competitors, but against, you know, the regions that they operating, are operating in and the skill sets that they're hiring for, that would give them a much clearer indication of is that actually a challenge or actually, is that absolutely fine, but their challenge is over here in, you know, in their CSAT or wherever else it might be. So I think it would be much more beneficial, um, for organizations out there to see what the true context is that they're operating under.

Speaker D: Yeah, it's interesting. We're doing some, um, increasingly more things where organizations are going, ah, our whole workforce is going to be disrupted and we don't know what to do. And, and instead of going, yes, it is, you know, let's, let's try and eat the elephant. We're going, okay, let's just take a little bit and, and look at that bit and see how much that bit is going to be impacted and see whether, you know, what, what are the areas where your workforce is most likely to be disruptive based on what's going on in the outside world. Yeah, now let's focus on that area because that's, that's what our big challenge is and not on the kind of bigger, you know, let's not try and do the whole lot. Forgive me anyone who hasn't heard the term eat the elephant before. Um, uh, last question from me, unless the guys have got anything else, um, can you give us even in the new report that, um, we read at the end, there's recommendations, there's practical insights, there's top tips. Can you give us your top X. I'm not going to limit you to, um, practical tips for an TA leader in a global business right now.

Speaker B: Well, I think probably one of the most important things they need to do is give themselves some creative space because I think there is so much happening, um, right now, there's so much change, there's so much expectation in terms of delivering against budgets and costs that they very much get caught up in all of the doing, um, rather than actually planning ahead. So I think despite how difficult that will be, actually giving themselves some creative space to think and to plan will pay them dividends in return. I think if they're always, and it's such a, probably such a trite thing to say, but I think there's all. You're always firefighting. You don't accomplish anything at the end of the day. So I think that Creative space is really important. I also think, um, my second tip would be to find ways to give back to your team. Because I've certainly noticed over the course of the past four to five years, we take so much from our people, um, and there's so many demands that are put on them. Um, and, um, there's less and less time to actually spend developing people and giving them opportunities that I know I certainly was very fortunate to benefit from. So I think find some time to give back to the people within your functions, um, so that, you know, you're able to not just develop them into and help them to be the reach their potential, but also so that it's an enjoyable place for them to work. Um, and then my third tip would be a little bit of an off the wall one, but I think it would be find something outside of your professional work that makes you happy. Um, because again, so much of our work consumes so much of our life. And I think in TA more than any other, um, industry that it's very true. So I think find something that makes you happy outside of work and that will make you a much better, much

Speaker D: better professional and find the time to do it.

Speaker B: Exactly.

Speaker D: Awesome. Absolutely awesome. Thank you.

Speaker B: Very welcome.

Speaker A: 100%. I can't believe we're out of time already.

Speaker D: I know. I was like, oh, oh, I've gone over the hour. Sorry.

Speaker A: That's all right. It's always a good sign, though. Kim, it's been brilliant having you back. How was it for you?

Speaker B: It was brilliant. I felt like I was, uh, you know, sitting down for a drink with old friends. So, uh, it was great. Thank you for having me.

Speaker A: Less of the old.

Speaker D: We're talking about decades and old.

Speaker A: I'm like, whatever, lad. Uh, that was fantastic, Insight. Thank you very much to all of our, uh, lovely listeners out there. Uh, whether you're in hr, talent acquisition, or just a business bod who's fascinated by how data and research are revolutionizing the way we think about people and work. Share this episode with your friends, with your peers, with anybody you think needs to hear it. Um, whether you're building your first insights team or transforming your talent strategy, I think there's genuine gold in today's conversational hills. And as ever, stay curious, stay brilliant, and, um, more importantly, stay intelligent, folks.

Speaker C: Bye.

Speaker D: Bye.

Speaker A: Um, thanks for listening. Before you go, I want to remind you about our generous sponsor, Lightcast. Here's that well spoken fellow again to tell you about their amazing product.

Speaker C: Lightcast is the global authority on labor market data. By collecting analyzing and refining profiles, job postings and market data. Globally, Lightcast provides actionable, data driven talent intelligence, giving HR leaders the context to make better decisions. The world's top companies trust Lightcast to deliver clarity in a complex world of work. For more, visit Lightcast IO.