The one with Tony Holly (Magnit)

Speaker A: Welcome to episode 42 of the Talent Intelligence Collective podcast, where we shine a light on the massive, often invisible world of contingent workforce intelligence. I'm joined by my brilliant co hosts Alison Etridge and Toby Caulshaw from Lightcast for what turned out to be a masterclass in understanding a workforce that touches 800,000 workers every single week, yet flies completely under most people's radar. In Toby's news roundup, we dug into Ron's LinkedIn bombshell about AI not creating any net jobs yet Danone's impressive redeployment of 90 employees through strategic workforce planning and Workday's top five HR challenges for, uh, 2026, with compliance and regulatory complexity finally making the list. But the real treat was our conversation with Tony Holley from Magnet Global. Leading a team of 40 plus consultants advising 600 Fortune 500 clients, Tony brought fascinating insights from the contingent workforce world, a space so complex that when he started in 2013, even looking at a company's website couldn't explain what they actually did. Among the revelations we uncovered why job titles are meaningless when pricing rolls. It's about the actual work, not whether you're called Supreme Commander of the World. How many organizations are paying 70% markup instead of 40% just to hide headcount in different budget lines? The power of redeployment pools that include everyone from silver medalist candidates to retirees wanting short assignments. Why? Despite all the tech, those light bulb moments when clients finally see their complete workforce mapped are both exciting and terrifying. Plus, Tony's audio recommendations. The Nvidia Way easier for some to say at two and a half speed. And his refreshingly straightforward advice. Just give me everything dirty and unclean and we'll make sense of it. So settle in for a conversation that reveals the hidden workforce powering many of the world's biggest companies. Stay curious, stay data driven, and most importantly, stay intelligent. Folks, Before we get on M 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 B: 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 clarity in a complex world of work. For more, visit Lightcast IO

Speaker A: hey there talent intelligence pros, enthusiastic, uh, amateurs, and everyone in between. Welcome back to what I promised was going to be a, uh, fascinating episode. You're tuned into episode 42 of the Talent Intelligence Collective podcast. Yay. Yay, indeed. In the virtual studio with me today, as always, are my brilliant co hosts Petridge and Toby Coolshaw from Lancast. Give us a hello and a virtual wave. You too.

Speaker C: Hello everybody.

Speaker A: Hey. The magic works. And joining us today is a strategic leader who's been driving data led growth and optimized workforce strategies for over 15 years. He leads global BI teams at Magnet, advising hundreds of Fortune 500 clients. We're absolutely thrilled to have him. Please welcome Tony Holley. Hi, Tony.

Speaker D: Hi everyone. Wow, great intro. I don't know that I could have done it better myself, but, um, thank you for that. I'll add on to it a little bit. Um, I'm Tony Hawley. I am a senior director with Magnet's strategic Advisory team. We're a team of global, uh, experts in America's APAC and emea, um, spread all over the world, uh, hundreds of us. And we focus on supporting many of the world's biggest companies and clients and help make sense of their data and provide them insights and analytics and proactive understanding of what's going on with their workforces.

Speaker A: But you said you didn't think you could do any better. You were wrong. You could. That was a great intro. Thank you, Tony. For those that are, um, new to our podcast, here's how we're going to roll. So first up, Toby's going to be giving his, his trademark TI market roundup. And then Alison will be leading our, um, chat with Tony. The bit why you're always here, um, with me and Toby chiming in where appropriate. So as they say, without further ado, Toby, what's caught your eye in the world of talent intelligence over couple of weeks or so?

Speaker B: Well, the first thing that comes up is, uh, it's not even an article or a note. It's a, uh, LinkedIn post by a colleague of mine, Ron, who is super smart. He's one of the principal economists over here and he did a really interesting LinkedIn post that was basically looking at how AI hasn't really created any net jobs. And there's obviously off the back of a load of research recently that AI isn't really proving the uh, return on investment that a lot of people think thought you've got the coupling effect at the moment of this week. We've seen lots of layoffs, uh, we've talked ad uh, nauseam on this podcast about layoffs. So I don't want to stick on the layoffs this week, but, um, it's quite a pertinent piece around the fact that AI isn't really creating anything valuable. Uh, ah, back to the economy at the moment. And when you look at the overall spending, um, and it's the real advanced retail sales by employment is how he cut it. But it's essentially that we're making things much, much tighter. We're not giving the jobs out there and we're not giving people the ability to spend out there. So from an economic perspective it really isn't giving us the benefits back. It's a kind of a one way system at the moment where yes, it's propping up the um, stock market etc. And you're seeing huge amounts of money being pumped into it. AI this is. But you're not really seeing that whole kind of trickle down piece. You're not really seeing the fluidity of the spend going through. It's which is fascinating. It was a really interesting take that it's kind of sets the tone of all right, are we as a consumer base and are we as a workforce and are we as a population really seeing what we would hope to see off the back of it all?

Speaker A: Do you think it's too soon? Do you think eventually we might see it or.

Speaker C: Ron was a bit more doom and gloom than that and true. Which was unlike him actually because. Yeah. So I mean he was basically just saying the world has changed. Investors haven't got the slightest clue what's happening. I need to get this off my chest. Have a great weekend. Which was kind of like a little bit of a bomb exploding. But I think the, I think the word yet is the key word and that's not me putting that word into Ron's mouth. But I think that's the key word that comes out of this is. You know the Internet arrived at a time when we were already and we had lots of disposable income. Um, and what's happening is we don't have as much disposable income. And this is based on us. I should, I should um, say don't have as much disposable income. And then if AI is going to come along and be quote a jobs destroying money, devouring technology on close quotes, um then where is the money that people are spending going to come from and what impact does that have on our labor force? I think it ties into everything that we've, we've talked about in many ways before which is there's still a shortage of people um, because of the rising storm and demographic changes. It'll just be which jobs do people end up doing and which jobs will be impacted most? I think that's still a bit of an up in the air. Nobody knows.

Speaker D: Yeah, I think I would agree with that. Right. One of the ways that we're speaking about it with the clients that I work with is it's not necessarily to replace people or get rid of jobs. The better approach is to identify where AI can free up those workers and that manpower and those hours to do something else, something more valuable, more strategic for the organization. So rather than lose those folks to rifts and layoffs and lost jobs, redeploy them into somewhere else where you can get value for the organization. Right. They're already trained, they already know your culture, they speak your language, they've got your systems access and all that. Why lose them down the street to a competitor when you can get them doing something else more meaningful for your organization? So that's the guidance, the approach that I've been taking with clients.

Speaker A: Yeah, we've been having similar conversations.

Speaker C: Yeah, it's the mix of the two, it's the mix of the human and the technology. But also at the moment there are not really solid links between the existing layoffs that we're seeing and, and AI as a technology. It's just that we're, we're just always seeing headlines that go AI is m making people lose their jobs. And actually that link is not necessarily showing through the data yet.

Speaker A: No, I am, although anecdotally I think we're starting to hear it at uh, UDA definitely. But what organizations are doing, and I think it's back to your point, Tony shows it's so short sighted, it's unreal. If you had a table of 100 people, AI can't replace a single job there. Uh, but it can replace in theory, 20 or 30% of everybody's job. So instead of going, hey, let's free up 20 or 30% of everyone's time and get them to your point doing some really cool stuff or in fact just creating nicer jobs so people aren't having to work 60 hour weeks or whatever crazy stuff might be going on. Um, they're going, let's just shed our headcount by 30% instead. And everyone still has exactly the same job as they had before, how much effort they have to put into it. We just need less of them. And it's so, so, so short sighted. It's unreal and it will eventually start to bite, uh, these organizations on the uh, proverbial backside. I think, um, those that are being a little bit more forward thinking are going, let's create better jobs and free up people's headspace to create a better company and deliver better work. They will have it tough in the short term, but I think they'll come out, come out up in the long term.

Speaker C: Completely agree, completely agree. And I think Ron was, um, particularly cross about the number of investment dollars that are heading in the direction of AI. And I think that that's what's potentially skewing the views of everybody.

Speaker A: It's insane. The amount of money that's being pumped in is bonkers. And also there's this, I'm going to use the phrase circle jerk going on, um, amongst the larger firms who are almost. They're investing in each other. It's insane. They generally are pumping money into each other. Nvidia are investing money into organizations that are buying their GPUs. Bonkers.

Speaker C: It is bonkers. It is completely bonkers. But then that's the world, isn't it? Right. We've always lived in a bonkers world. You know, when the Internet first arrived, everyone said, oh, that's rubbish. It's never going to take off and people are putting loads of money in. And then we saw the boom and, you know, now it's a staple. Right. I think it's just, that's where the word yet comes in. We're just in this place of yet.

Speaker A: Let's hope the yet happens.

Speaker C: Oh, hold on, I'm going to. Oh, God. Bernard Marr. I follow a guy called Bernard Marr. He does great.

Speaker A: I know him.

Speaker C: Come on. Um, and Bernard Marr did a really interesting, um, video on LinkedIn a couple of days ago about the difference between large language models and reasoning models, um, and the difference that that is going to have on AI, which I just, I thought it was the first time anybody put it into English. I thought it was quite interesting. It's worth it. It's worth a dig out. And look at.

Speaker A: I know him. I was on stage with him at an event in London. He's a very nice man, I suppose. Very nice man and a very smart.

Speaker C: We should have him on the podcast too.

Speaker A: Definitely. Right. What else is happening, Toby?

Speaker B: One thing that comes. It's kind of in line. What we were just saying was a case study I saw from Denome and how they redeployed, um, 90 employees affected by workforce changes. And, um, more importantly, it was how they integrated strategic workforce planning and workforce planning into their business, uh, rhythm of business, um, which I really liked. And as part of that there's a whole load of kind of structural changes that happened, but one of the bits they did was really simplifying the structure and getting the focus away from positions and headcount and into roles. Um, so that they reduced the company structure from 90,000 positions down to 1,300 roles, which is huge. And I think every company I've ever been at the job, family architecture has been an absolute mess, um, because people can call themselves whatever they want and they end up with positions and it's a nightmare. I really like both the mindset of people first and trying to redeploy and trying to redesign, um, but also starting with the business problems and getting embedded with the business. And I actually came off a call earlier today where we were talking about, um, people analytics and talent intelligence, et cetera. And we said it's business intelligence and business analytics that happen to be about people and talent. And I think that for me was really how I saw this Denone piece was they were doing, uh, business planning that happened to resonate around the workforce, but it primarily was a business planning piece. Um, so I really liked it. I thought it was quite a nice foil to everything we've seen recently, everything we just mentioned around the real knee jerk, short term mentality of let's just cut because it's going to pump our numbers for a quarter and make the shareholders happy. Um, I thought this was a much nicer approach and I wanted to applaud Denone for it.

Speaker C: Go on, Tony. Sorry. Okay.

Speaker D: Yeah, I alluded to that. And when I talked about Ron's article just a few moments ago in that redeployment is something that I've been talking about probably since 2015, so probably 10 years now, in that why lose good folks down the street to a competitor? Why not redeploy them? Why not find them somewhere else in the organization where their skills are a better fit? Or if there truly isn't a place for them in your organization. Maybe what you were talking about with the, you know, the GPUs being purchased by the people that are, you know, the, the round and round funding. Why not find partner organizations where you can redeploy your workers into where, hey, I'm, um, I'm organization A, I don't have a place for this person, but I know organizations B, C and D that I work closely with do. Let's redeploy them over there.

Speaker C: Yeah, I think that's the bit that I like from this is, well, uh, I guess there's two Things So they talk about org view and Lightcast is also a supplier into Genome. Right. And it's important to point that out from an architecture point of view. But the bit that's most exciting about this is that it's an always on approach and in fact they've lost the word strategic which implies kind of this planning cycle and just use the word rhythm as part of the business. So you're really aligning growth productivity transformation goals with what we actually need, um, and then grabbing the data to do that in order to figure out the skills you need in order to figure out where you could redeploy people. And so uh, it's a top down approach that we all talk about and I love Tony, the fact you're talking about it, we all talk about it as if it's common sense but when we're in front of our clients it still appears to be a bit like oh, okay, right, that's where we start. And I think that's just an interesting concept that we need to continue to break.

Speaker B: I wonder also how much of it's uh, I completely agree with everything by the way but um, I wonder how much is a capacity piece. And I have no idea about the Danone swp, uh, team or how they operate or function. But if I think about companies I've been in the past, if you wanted to break into that rhythm of business, the amount of resource allocation to do that kind of rhythm of business intelligence, agile, uh, intelligence that we're in there every single month with every single business leader, every single business unit, we just weren't resourced up to do that. So we had to kind of almost pick our battles and pick our times because of that. But I think it really is what's needed. And I mentioned to IX communities I presented to last week, um, about where I see the future of TI moving and it's having that central TI capability and data set but very much decentralized intelligence where it needs to be on the ground. And I think this is a great example of if you actually embed the capabilities much, much closer, much more directly with the business, you can actually impact things in a much more fluid way.

Speaker C: Yeah, I think what's also interesting is that Vincent, who was the guy that Vincent Farrer who drove the change here, kind um, of went in house and then he went externally. Um, and I think he was Deloitte's other consultancies are available but I think he was with Deloitt, um, but also was in Latam. So when you take that experience of working with a consultancy, seeing lots and lots of different human capital projects across lots of organizations. Plus the fact that his experience was latam and therefore in a high growth market for many organizations I think brings a different level of insight to the HR and OD role than you would perhaps always see.

Speaker B: Yeah, absolutely. And I think the results are there. I know they um, had 800 roles impacted by the transformation and um, 90% of employees were offered new roles internally, 70% accepted. And the bit that really stood out from that was um, employee engagement increased and HR's net promoter score jumped from 4 to 8 out of 10. And that's really telling in a period where we've discussed before around the fact that most employees are utterly miserable in their current employment. You know they've been through the last 18 months, two years where not.

Speaker C: You're talking generally, just to be clear.

Speaker B: Generally. Sorry, yeah, yes, the workforce, um, you know they're sitting there at companies where you've got no job security, you're being told there's uh, no route for promotion, there's no salary increases, but the, you've got record profit margins and you've got record, et cetera. That general disconnect has been absolutely massive. So um, to see Danone really looking at their workforce so positively, it's huge.

Speaker A: Awesome. Toby, one more news article.

Speaker B: I think one more. So we've got Workday did uh, the top five human resource management challenges for 2026 which is that time of the year we always like to look ahead and see what people are thinking about. Uh, number one they said was attracting and retaining top talent. Uh, up there was upskilling and reskilling of your workforce and population compliance and regulatory complexity which uh, boring some may find it. I was very happy to see that one up there particularly off the back of our uh, beyond the buzz report that had so few roles within that regulatory space around AI usage. So I was very happy to see that um, adapting to technology and AI was number four on their list and then number five was strategic workforce planning. So I was quite pleasantly surprised with some of these topics were in there. Um, I think there's some really pertinent bits for our space and what we're doing, whether it's upskilling, reskilling skills, intelligence, the buy, build, borrow, bot type piece that ties into workforce planning. Uh, and then obviously you got the attracting and retaining. I thought some really interesting bits in that Workday report generally.

Speaker A: Any surprises?

Speaker B: Um, I think there's you know, some hot topics that historically would have been up there that aren't anymore and we all kind of know the reason why. But like De and I, obviously it's been a very hot topic for a decade plus now and obviously it's not um, so much on the radar at the moment given the changes that have happened in the last couple of years. Um, so I'd say that's probably the one that stands out for me. I was surprised how important we're seeing. Honestly seeing swp. Uh, not from a. I don't think it's important but I'm still seeing a lot of teams being cut, a lot of teams being made redundant, um, and a lot of short term thinking. So I was surprised that we're. The rhetoric is this is a really big issue. We need to focus on it, we need to do something. But I'm um, not necessarily seeing that on the ground as much.

Speaker A: And um, attracting and retaining top talent almost always comes at the top two or three of these things, doesn't it? Whenever you see it. I think I'm m more surprised that the adapting to tech and AI pieces and further up the ranking so to speak because it's.

Speaker C: But I think that ties into the

Speaker A: upskilling reason about it.

Speaker C: M. Yeah, but I think that tight. That ties in Alan to upskilling and reskilling being higher up. So I think. I think the two are inextricably linked, aren't they? It's just that's meant that organizations are starting to look at their workforces in a different way, um, as per the competition.

Speaker A: So maybe the voting has split between those two and actually there's a lot of overlap and a Venn diagram of that would probably bump it up even further.

Speaker C: Yeah.

Speaker B: To be clear, I don't think the five were in order. Uh, I think it was just top five.

Speaker A: Okay. Okay. Oh, it's. I'm a sports fan. I like to imagine a table being in a very defined order based on performance.

Speaker B: Got it.

Speaker C: You didn't talk about Unleash. Can I talk about Unleash? Just quickly?

Speaker A: I was going to ask you about Unleash. I know you've been at Unleash, so I wasn't able to go. So that would be amazing. What can you tell us? What were your highlights? Alison?

Speaker C: So I think it's interesting because I look at those top five, um, and there were two of those that I think definitely came out. Um, Unleash. One was looking at learning. Um, and this again ties back to the skills conversation but it was really saying that actually people want learning and not training. And that there is a fundamental difference between an employee learning and HR giving them training. Um, and so I thought that was just quite interesting. It was. And again, that ties back to any. We'll talk about this in. In more detail in a minute. It ties back to that redeployment piece that not let me give somebody something to train them to make them better at their job, but how do I support people to learn the skills that they want to learn? So I think that was an underlying current of everything. Um, and then the second piece was all about how do. Well, there was a conversation about workplace of the future, which was a workplace of the future being about technology and humanity working together and being aligned. Um, and actually how do we focus on keeping people at the center of transformation? Ah, I think it was the best. It's certainly the best conference we've done this year, but I think it was the best Unleash I've been to ever.

Speaker A: Wow, there you are.

Speaker C: That's a big tick for unleash isn't.

Speaker B: Um, seemed to be that human centered element was coming through on M when I was looking at all the post was gutted. I wasn't there, if I'm going to be honest. But the, um, Obviously tech was a big, big piece, but it seemed to be really focusing on the human centered moments that matter. Uh, that seems to be coming through as quite a big thematic. Across most posts I saw.

Speaker C: 100%. Yeah. And it really was just an interesting one. Yeah, it was an interesting one all around. Right. The right level of speaker. Ah, talking about the right level of things and not in the weeds. Um, and then underlying all of that was all about skills and job architecture. So from a vendor perspective, it worked really well for us, as you can imagine.

Speaker B: Yeah, I love it. Love it.

Speaker A: Should we get on to the real reason we're here?

Speaker B: Yeah. Yes, let's do it.

Speaker A: We're chatting about other things and other people. Let's chat about the most important reason we're here, which is not chatting about. Chatting with Tony. Alison, I think this is where we say over to you. Cool.

Speaker C: Um, Tony, great to meet you, albeit virtually. And I'm gutted that I wasn't at the Talent Intelligence Conference in Amsterdam where you met Toby and first spoke. So I guess I've been there three years in a row and then wasn't there this year. So tell me, let's start with what was that like? And then we'll go back to a bit more about Tony.

Speaker D: Yeah, the one word answer is great. So to expand on that. It was a fantastic conference. Um, it was my first year. Uh, I found out about it through uh, the giant tool that Intelligence Group offers. Um, they provide market intelligence for m. Part of the tool provides it for Europe. And my organization wanted to get some more market intelligence insights into uh, the European talent market. So we subscribed to the tool and I uh, met with the leaders of that uh, organization, gave them some feedback on how it might apply differently to contingent labor which is the area that I live in the most, that non employee workforce. And one thing led to another and they asked me to come and speak. So I was one of the presenters. Um, was there for the full ah, conference, saw lots of great sessions, met lots of great people, learned lots of interesting things and different global perspectives that I don't necessarily get to see all of the time being, um, in the US West Coast. Um, so yeah, back to the one word answer is a great conference, very cool.

Speaker C: I'm really sorry to have missed it and meeting you there this year. Um, for those people. Go on then.

Speaker B: Sorry. I was just going to say for me, Tony's session was a real breath of fresh air. Um, and the reason being was we hear so little about contingent labor market data. Um, and in our space we do so much around skills or competitor intelligence, location strat but permanent workforce. But we equally talk so often about the fact that the workforce is becoming more fluid and you are seeing this kind of. We've been talking about the rise of the gig workforce for um, too many years now. But to see Tony Zeshen and uh, the amount of data on that contingent workforce was fascinating. So for me it was, it was a real breath of fresh air. I loved it.

Speaker C: Yeah. Cool. And that would be great if we can explore that a bit Tony, because we haven't met before, um, can you give us the kind of the summary of your career, um, and then how you ended up at Magnet and with a focus on contingent. That would be cool.

Speaker D: Yeah. Yeah. So, um, going back to undergrad. Undergrad in psychology. You can't do a whole lot with a psychology degree. Uh, undergraduate. Um, didn't necessarily love all of the uh, uh, counseling and different focus areas like that. But I always think back to. There was one sentence in my Intro to Psych textbook around um, organizational development. And the sentence was something along the lines of organizational development is the application of psychology in a business setting. And that was about all that intro, uh, class taught me about it. And my undergrad had no classes on it at all. Uh, but I continued to explore that further, got a master's in industrial organizational psychology, a Ph.D. in organizational development, which is a heavy focus on consulting. I'm not necessarily the most entrepreneurial person in that I didn't want to open my own consulting firm and hang up my name on a building somewhere and try to be that one employee organization. So the closest fit for somebody with a ton of degrees and no experience is human resources. So I started in staffing and recruiting and compensation and um, rate intelligence. Did uh, that for a couple years and then moved over to the contingent space not knowing anything about it at all. Right back when I started in that space was 2013. And you look at some of these organizations, m my companies and our competitors, and you look at the websites and you still have no idea what it is. It is often a very forgotten or afterthought of businesses. Uh, as you just mentioned the conference in Amsterdam, I think I might have been the only person that mentioned contingent workforces in any of the sessions. If I missed one that did, and I'm stealing that from somebody, then I apologize. But I don't think anybody else talked about contingent labor at all. And there were staffing agencies there, right? There are folks that, that is one of their core areas of their business and you know, they didn't mention it. So I kind of fell into it. Right. Um, finished my PhD and was looking for related areas in the areas that I liked. Business intelligence, insights, analytics, uh, statistics, metrics, those very quantitative number focused things. And the company, it's now Magnet Global, it was Pro Unlimited at the time. They were hiring for a business analyst. So I started with uh, the company as an entry level business analyst. Moved up through business consultants, global consultant manager, director, up to my current role as a senior director, uh, leading folks across multiple countries in providing insights to. Right now I support about 600 of our 700 clients in some way. So you know, it's, I like that approach in working for a company that works for a ton of other companies in that I'm not, I'm not doing the same thing every day, right? It's not that I'm working for Magnet, it's that well, today I might work for client A, in the morning, client B, 20 minutes later client C comes in with something urgent that has to happen today. And you know, I'm working on three or four different companies and it's like I have three or four different jobs every single day. Um, so it's very different than that. You know, knowing what you're doing every day, nine to Five doing the same thing. So that's how I got into it. That's my interest area.

Speaker C: Really cool. And I love that background. I love the idea that you just read a sentence about organizational psychology, um, and that has steered your career path. I just think that's. That's really. It's really interesting. Can you talk a little bit about the comps and bends? Right. Intelligence. Because that's an area from a talent intelligence perspective, that's always really kind of tetchy because historically we find that comp and Benz want robust historical data and talent acquisition 1. What's the market paying? And somewhere in the middle, I guess, is a happy medium.

Speaker A: Yeah.

Speaker D: Yeah. From the approach that we take as an organization. And I like the approach, so I think it's probably one of the right approaches. I don't know if it's the only right approach, but we always look at it from internal and external. So internal is so important to us. And what I mean by internal is our clients. We work with 600 plus, um, some of the biggest clients in the world, 20% of the Fortune 500. So it's looking at their data, and we'll never share one client's data with another. It's always anonymized and, um, aggregated data. But looking at the actual line level, transactional level, details of this worker for this client billed eight hours at this. And their skills. So what components are in the job description or in their resume? What are they doing? What is the actual work? I don't care about job titles. Job titles are meaningless when you price out a role. You can call somebody the supreme Commander of the world, but, um, if they're working in Excel all day, doing equal sum on every single column, then the title was meaningless there. So it's looking at the work that they're doing, finding others that are doing similar work regardless of title, and understanding what the market will bear from a pricing component, comparing that across clients and then across those data sets that are available. Whether it's the tool that I mentioned earlier, Giant, or when it wasn't, uh, shut down, the U.S. bureau of Labor Statistics, or a tool that I just heard about recently at the, uh, Amsterdam conference was Revelio Labs. So pulling all of that data together and there's tons of other sources, um, pulling it together to give our clients a picture of if you're sourcing for these required skills, what will the market bear? What's the appropriate pricing? You don't necessarily want to pay so little that you're. The person doesn't want to work for you and they're already looking for the next job when they start. And you don't want to pay so much that the company's overpaying and losing the opportunity to hire other workers. So it's finding that happy median, that happy middle in what is the appropriate rates for that worker.

Speaker C: And this is on contingent's workforce. Right.

Speaker D: So we have a tool, uh, at my company, pay intelligence that not only has non employee labor, but it does offer full, uh, time rates. So we can price out full time and then we can price out contingent. And for those that might not know that are listening, there's lots of different ways that you can source a contingent worker. Uh, through EOR payroll, employer of record, through staff augmentation, through professional services procurement, sow as an independent contractor. Globally, there's things like broker services and there's all different ways and pricing structures to look at it. And it really does vary globally by company, by location, by the laws that are at play in those individual regions. Um, but yeah, we price all of that out and we have tools to do so. Uh, and that's part of what I build into the stories and the intelligence that we bring back to clients is how are they doing? Right? Are they pricing appropriately, are they saving money? What does that look like? And are they making smart decisions? That's what business intelligence to me comes down to is, are we helping our clients make the smartest decisions for their business?

Speaker C: Yeah, really, really interesting. Can you, can you talk a little bit about who the internal client is when you're talking about rates for contingent workers?

Speaker D: So the, so like I said, we work with hundreds of clients and typically you'll have a program, stakeholder program sponsor that might be the one looking for guidance or the individual hiring manager. So those are folks that are looking for, hey, I need eight software engineers in the next two months. What should I pay for them so I can get that budget and ensure that I'm going to have enough, uh, budget approved that I can get the eight folks that I need. Um, so those are the typical client approaches not limited to as a team. We will of course help our internal magnet employees and human resources and talent acquisition with that type of information. But that's not the most common or m most important focus for us. It's always the client.

Speaker C: Really interesting. Um, a bit like you. I fell into contingency labor, but only for about three years, um, with a vendor neutral managed service business here. But their take was all about the number of invoices that get processed through lots of different agencies at lots of different rates, um, across the board. And actually the way that they consolidated that into one individual invoice for the client meant that their internal stakeholder was financed and it wasn't a hiring manager at all. So their play was all about, we can save you money on invoice processing and we can help you to become more compliant. Um, does that play to anything that Magnet is doing currently?

Speaker D: Yeah, yeah, definitely. Um, that is one of what I would call our cost savings levers. It's to simplify it. Consolidated invoicing. So rather than have a client's finance accounting, uh, accounts payable team pay hundreds of workers, thousands of workers in some cases, or hundreds of vendors, you have them pay one managed services provider. Um, so think of, you know, if every week you're paying magnet with one bank transfer, it's a lot simpler than paying 2,000 workers with an individual, um, you know, payment to their, to their bank account every week.

Speaker C: And can you bring that to life in terms of the numbers? Because I don't think, I don't think people who are not familiar with the contingent workforce have really got an idea of what that relates to in terms of potential number of workers in a large organization who are contingent, what that means in terms of invoicing suppliers. You kind of alluded a little bit to it there, but what's the typical split of workforce, split of spend, how fragmented is it? Help to bring it to life for our listeners?

Speaker D: Yeah, uh, some of our biggest accounts could have as many as 100 plus agencies that are supporting them. So staffing suppliers, that's probably too many. I'd focus on more of an optimized approach. Um, but you could have hundreds of vendors that each have hundreds of workers. So we have clients that will have non employee labor. So contingent workers in the 2, 3, 4,000 workers that uh, they're paying every week. Um, you know, that's not to say that there aren't smaller organizations that have a handful, 10, 20, 30 contingent workers. But um, it can vary greatly. So to put it into scope as a, ah, full organization, we will in some way touch hundreds of thousands, you think in excess of 5, 6, 7, 800,000 workers every week.

Speaker A: Yeah.

Speaker C: So it's just, it's a massive market, isn't it, that people really are, uh, not naive about. That's the wrong way of putting it. But you know, just, just don't understand. Right. It's just like, okay, where has this come from and how does it operate? And m. Um, you know, unless you're in a big retail or a big warehousing operation or any of those, it can kind of sit under the, under the radar a bit. Um, you said when you were, um, uh, giving a much better introduction than Alan did on yourself, that you help organizations make sense of their data and understand their workforces. Can you bring that to life for us a bit, yeah.

Speaker D: The example that I always give is anyone can tell somebody a number, right? If I tell you your spend this week was $20 million, well, what does that mean? There's no context to it. If you don't have context, you don't know if that's good, bad or otherwise. But if your goal as an organization is, uh, growth, and you need to add 50 new software engineers to accomplish whatever project you're working on, and you had millions budgeted every week for that, and your spend went down from the prior week, maybe it was 30 million announced. 20 million. That's probably not a good number. That's not the number that you wanted to hear. You were expecting it to go up. Uh, on the flip side, if you're looking to drive cost savings and if your spend continues to tick up week over week, you might not necessarily be achieving that. So just giving somebody a number doesn't really mean a whole lot. Giving them the number today and the number year over year, quarter over quarter, month over month, week over week, that might also not mean a whole lot if they don't have a comparative benchmark. Now, thinking about something like time to fill, how long does it take to find talent? If the benchmark is 15 days, the client is performing at, uh, 27. What's going on there? So you've identified now that red flag, that anomaly or that area of concern that you want them to dig into. So those are the things that, as a team, uh, the team that I work for, the Magna Strategic Advisory Team, that's what we're trying to call out, is let's find those anomalies in the data, let's see where things might be moving in the wrong direction before it becomes a problem that causes concern for you, and take some action on that, help advise you in some way. What's the benchmark? What is a best practice scenario? What is a policy that your hiring managers might not be following that they should be, that, uh, you need to step in on and correct? Um, but it's that telling a story with the data. You can't just give folks a ton of numbers and expect them to know what those mean.

Speaker C: Yeah, I love, I love that we talk a lot about, um, bringing context to, to your plans. Right. There's absolutely no point in having in our world a strategic workforce plan that says we're going to hire 1500 people in, you know, um, San Francisco if there aren't 1500 people with the skills you need there. Right. Just like. Yeah. And, and in no other business function would you make the decisions that we make over our workforces without the context of what's going on the, in the external market. And I guess the same applies to what you're talking about. When you talk about a benchmark. What are you benchmarking against?

Speaker D: So the most typical would be against either all of our clients. So an aggregated anonymized number. The average time to fill is X. The average percent cost savings is Y. Um, or it might be at the sector or industry. So all banking and financial services clients typically see this. Whereas if you're a tech sector client, you might expect it to be 20% higher than that or 50% lower. Um, you might also want to see it by location. Right. So I know a lot of organizations are moving towards return to office in very specific physical locations. But you know, regardless of if it's a physical location or folks can work anywhere, what is appropriate for where they're working? So is the time to fill in San Francisco 20 days on average, but in New York City it's 53. Well, if you need somebody quickly, even if you have a physical office in New York City, you're not going to get them quickly. If the average is 53, can you consider San Francisco and 20? And I'm just making up a random example, I don't know the real numbers in those sectors and cities, but we can look into that and help you identify what to expect when you're hiring. Um, but then it's not just speed, it's not just cost, it's how many candidates can you anticipate being submitted to your requisition? Uh, how many interviews does it typically take to hire that person? How long after the selection of that candidate to be a temporary worker for you does it take to get those background checks completed? And when can they actually start? And then once they have started, how successful are you at, ah, hiring? Are those workers working the full assignment, whatever that is, whether it's 10 weeks or 52 weeks or longer? Or are they always leaving in five days? And then are they always leaving because it's a manager, because it's a pricing problem? Uh, again, finding those, those anomalies or those red flags and helping the client address that before it turns into a larger problem. So if you see five back to back negative terminations where a worker left on their own choice and they all blamed the attitude of the manager, that's a place where you'd want the MSP or the program sponsorship at the client to step in and talk to that manager and say, hey, this is the feedback we're getting. What can we do to help you improve in this area?

Speaker B: Such interesting data sets. Oh, I don't know if we've lost Alison. I was just excited. Uh,

Speaker A: I had a whole heap of questions lined up as well.

Speaker C: So rude. So, uh, I've just let me ask this question, then you guys can go and then, and then I can, um. Tony, I'm just going to give you a warning that I'm going to ask you about top tips for people when managing their contingent workforce in a minute. So that's a warning for a few minutes time. But I guess there's a, ah, part of me that, and this is probably naivety on my behalf. You talked a little bit about return to office then, um, and where to put a particular worker based on, based on criteria. And could you look at doing things in a different way, which is very similar to the permanent workforce? In my head, a contingent worker is always working from a place and that place is normally set. Is that just completely naive?

Speaker D: I wouldn't say it's naive. Uh,

Speaker C: if you like Tony, it's all right. The others are just as rude.

Speaker D: Not at all. No, no. But I think it comes down again to some of the other stuff that we talked about is what work is actually being done. So if you are a pharmaceutical client and you're working on labeling vials of, uh, vaccines, then that probably has to happen in person in a laboratory somewhere. I don't know too many people that have clean rooms set up in their houses. Um, so there's not a lot of choice sometimes. But if you're a software engineer typing away code on some program, do you really care as an organization where that work is being done? Maybe you do, maybe you don't. As a managed service provider, we'll take your guidance.

Speaker C: Right.

Speaker D: If the client is looking for folks to be in person in a specific physical location, we will help them find and advise on how you find the best workers the quickest, at the most competitive rates, with the highest quality.

Speaker A: Right.

Speaker D: You can, you can do that. There's data out there, um, and we will help identify that. But if you don't care where that person is Right. If they're, uh, probably like all of us sitting on a laptop all day in front of some monitors, typing away, um, it doesn't really matter where you do that. As long as you've got, uh, a power outlet and some WI fi, you're usually pretty good to do your job. So do you really want to source that person to sit in a physical office in, let's say, high cost Silicon Valley, when you might be able to source them in? I'll use where I live as an example. Sacramento county, not the cheapest place in the country to live, but it's a lot cheaper than San Francisco. So you can get a more competitive rate for workers that are just as qualified as somebody living in Silicon Valley.

Speaker C: Okay, um, I've got a question then. I know Toby and Alan have got a couple, but, um, uh, you talked earlier about redeployment of people. Are you seeing organizations do that with their contingent workforce as much as they're doing with their permanent workforce workforce?

Speaker D: Uh, I don't necessarily have insights into how often they're doing it with their permanent workforce, but I do see it being done regularly in the contingent space. That's one of the interesting, um, I like to think that m my team, uh, pioneered the tool that we have built for redeployment. We have it built in two different ways. One is client specific, so you have your own pool of talent, whether that is existing workers that are coming up on the end of their assignment. It might be workers that have just ended a contingent assignment. It might also be silver medalists that, you know, TA didn't necessarily choose for an FTE role. You might be able to put them into a pool of people to consider. Um, it might be people who have just retired and they don't want to sit at home all day and watch movies or play golf. I know I would like to do that, but, you know, I'd love to have all day, every day to watch movies and go running and ride my bike. I don't have that luxury. But if you're a retiree and you want to come back for short assignments, you might want to get in that redeployment pool as well. So wherever the candidates are coming from, clients can look at that and pair what skills and openings they have today with who's available. If you have prior quality scores, if you've done, um, ongoing manager assessments of that worker, you can pair the highest quality workers that are available. If you've got pricing history, if you've engaged them on three prior uh, contingent assignments. And you know, their average bill rate is $100 an hour and you're looking for somebody that's under $120 an hour. Then you've got a candidate who fits your pricing. They've worked with you before. If they're coming up on the end of their assignment and they're still working, you don't even need need to work with systems and badge access anymore. Just put them over into the next requisition. Um, we have other clients that might consider saying, hey, I don't necessarily have a need for this person for another role. Let's put them into a shared pool where it is multiple clients that we work with that will say, hey, we've got this software engineer. We don't need them ourselves at client A, but clients B, C and D might have a need for them. So let's redeploy them within the magnet ecosystem as opposed to just within a single client. So there's a number of different ways to approach it and we always take our clients lead on how do they want to leverage their talent pools.

Speaker C: Really interesting. Um, you talked a little bit about. Oh, no, sorry, Alan. You wanted to ask something. Alan and Toby. Sorry, I'll let you go first.

Speaker A: Toby, you go first.

Speaker B: Okay. Um, I love all this stuff, Tony. I know we talk often here about total workforce management. Um, and friend of the pub, Martin Hansen, is a huge fan of it. I'm still, from my experience, I look across most organizations I've been in and contingent and permanent workforce is still managed fairly separately and recruiters on the ground, they'll either be permanent recruiters or they'll be handling contingent stuff. And they're not really having that fully blended total workforce conversation. It's the decision is made by the hiring manager. Are you going contingent or perm? Um, and silos off. What's your perspective on it? Are, ah, things becoming more, um, consolidated? Are you seeing people looking at things as a total workforce option or is it still fairly, fairly split into silos?

Speaker D: Yeah, I think it's still fairly split. The terminology we use, uh, at my company is total talent intelligence. So it's the idea of seeing all of your workforce regardless of where they're coming from, full time or non full time, seeing them all in one place. So we have tools that can do that. Right. We've built, uh, business intelligence suites that for a selection of our clients and it's available to all of them if they wanted to do so. But for those clients that want to see everything in one place, they can share pieces of their FTE data with us and we'll map it into the contingent suite to say, hey, you have this many workers in this city and this city and this city and here are the basic stats about them. Whether it is pricing or time to fill or quality or whatever data you're looking for, whatever business imperative exists that the client cares about, we will uh, help build those views for them to see that. But that's what we always like to view as kind of our stage 5 visionary clients on a scale of uh, how advanced a contingent workforce program is, is when you see all of the organization's talent in one place to make a decision, not just siloed into FTE or contingent, but as that total talent pool.

Speaker B: Makes sense. Makes sense. Thank you.

Speaker A: I'm m going to ask my question now then. Um, this one is quite selfish actually Tony. So I run a consulting firm and I know you lead a team of uh, 40 plus consultants or consultant type people across the globe, really us, EMEA and APAC and that's complicated. What's your approach to maintaining a high quality, consistent delivery globally?

Speaker D: Yeah, um, the approach is, I think it comes down to training and knowledge sharing and good hiring practices. Right. So looking for folks that have the necessary skills that you need. Right. And you know, we're not always looking for the exact same person. Right. We don't need a carbon copy. Um, we've had clients that are in high fashion and we've hired folks to the team that have backgrounds in fashion design. So not necessarily somebody that you would think would be very quantitative and having skills like um, you know, SQL and Python and Power BI and, and all of those associated data intelligence and data science type skills. Um, so it's a blend of that. Right? It's the, you know, hiring not just carbon copies of the same type of talent and then it's knowledge sharing and regularly meeting with the team and highlighting what works well at um, you know, what has worked well at one client and then identifying where that might benefit another client. Whether it's same industry or same hiring philosophy, same location or no relation at all. Just an interesting approach that they care about. Um, but yeah, it's one of those challenges that I think every company and every team worries about is making sure that regardless of who you're working with at the organization, are you getting the service that you expect. Something that we're always focused on and trying to improve. And we run regular trainings, record them so future uh, joiners of the team can learn from that and I think all of the, uh, leaders and my colleagues on the team will take the same approach as to, we're there to answer questions, we're there to advise and teach and guide, and just pushing our folks, our team members, to come to us when they're running into a problem and saying, hey, I've never seen this before. Do you have any ideas? Or. I'm, um, you know, hitting a brick wall here. How do you think I should solve it?

Speaker A: Great. Awesome. Alison, should we go back to you and, uh, you can wrap up the questions?

Speaker C: Yeah. So I've got two questions. So the first is, um, in our little preamble and we're all kind of getting to know each other a little bit. Um, uh, you talked about Tony, about A, having never listened to a podcast, which is going to change now. Um, but B, you talked about audiobooks, um, and listening to them back at two and a half times, which I love. I love that concept because my brain and my mouth work very fast. Hopefully my ears do too. What's. What's the most recent one that is memorable to you that you think our viewers should listen to?

Speaker D: Yeah. The book that I had just finished, um, a couple days ago was the Nvidia Way. So, ah, about Jensen and his approach with that organization. And it had tons of great ideas from just a management perspective. How to approach teams, uh, how to approach problems, how to approach, um, business. So I really liked that one. I don't have a review yet of the one I'm listening to that I just started yesterday. Uh, Amazon Unbound. Uh, it's a. I don't know what the sub. The secondary title is there, but it's a story about Jeff Bezos and how he brought that company to the behemoth that they are today. Um, so those two were, Were really good, or, uh, at least, um, so far the one I'm listening to is really good. Um, lots of nonfiction, lots of. Lots of business books that I've listened to.

Speaker C: Yeah. And that's what I like about it. Right. Is I, you know, I kind of. I think like many people, I buy lots of business books and some of them I get around to reading, but actually to be able to consume them in a faster way. Um, and audiobooks, when driving or whatever it is, I just think is, you know, or traveling. I don't travel would be very cool. Um, it would be remiss because we've talked about other suppliers. It would be remiss of the fact that Lightcast is also a key supplier into Magnet. Um, so I must make sure that, that that message also lands. And then my final question, if you don't mind, Tony, is, and I did give you a bit of a heads up over this. If, if you're speaking to an audience who have done nothing to look at the data in their contingent workforce, what are your top tips for where they start now?

Speaker D: Yeah, so I think that you did

Speaker C: a really big sign end, Tony. Oh, God.

Speaker D: It's something that we run into a lot is just get everything in one place. Um, we always tell prospects that want us to either look at their data and tell them what's possible, or companies that have become clients that say, okay, well here it is, let's get to work. I want to see all of the data and I don't necessarily want the client or the prospect or somebody at that organization to put any spin or work into it. I just want to get everything dirty, um, unclean, for lack of a better term on the data. Just give me everything, whether it's spreadsheets, whether it's access to internal tools, it's stuff in emails, I don't care. Get it all over to us and we'll start making sense of it. And then we'll work together to identify and understand do we have everything. And often the best approach is to see what we have and then point out what's missing. And that's usually what's eye opening for clients is, well, hey, you gave me all of this data and you told me it was complete, but I know clients of similar size would have had X, Y and Z and you didn't give us any of that. And it's the light bulb moment of, oh, no, I totally forgot those three teams. I've got to go talk to eight other leaders and get a whole bunch of other stuff before we can make any guidance or insights to you. Um, once we have everything in one place, the funniest reaction that I always get, especially when it's something like a map and putting all of the workers in one place, is it goes through a pattern of, wow, I can finally see everything in one place. And then, wow, I can finally see everything in one place. I didn't know I had people there. And then I didn't know I had people there. Should we, can we, uh. And then you start to get into, especially for the large global organizations, are you managing them appropriately? Do you understand pay parity laws? Do you understand, um, length of stay policies and how after a certain amount of time those workers might need to become a full time employee or you might need to roll them off of their assignment. And that's usually the kind of one of those exciting moments for me and my team is seeing those light bulb moments go off with clients. We've made sense of a whole mess of data and we've made a whole lot of work for ourselves because now we've got to work through that issue or problem with them. But that's the fun and interesting thing.

Speaker C: And just to follow on from that, because I think you talk about this. This is a data set that is so fragmented and in thousands of places within an organization. And you said, okay, now let's look for what's missing. And that might be xyz. Can you just give us examples of what the XYZ could, could be? Or typically you'll kind of hear from an organization where they go, oh, God, yeah, I didn't realize that I hadn't grabbed that data or gathered that data.

Speaker D: Yeah. I'll look at a recent example. Big pharmaceutical company, billions of dollars in professional services procurement spend. They did not have a way to tie what sows they've engaged with with a quality score and a spend amount. So imagine an organization saying, hey, we'll create this computer, uh, program for you and we're going to charge you a million dollars. Well, as an organization, you need to know, did that company do that? Was that work good? And did it end up costing you more or less or at a million dollars? And if you don't have that data, that's really concerning. Um, so that's one of the things that we often see missing is the tie of data between what you expected or wanted to happen and what actually happened. The other that, um, I spoke about in, uh, Amsterdam is the fatal migration to sow. And we've seen a lot of organizations and a worker's contingent assignment and then bring that same worker back under the same supplier, um, often at the same pay rate for that worker under a much higher, a significantly higher SOW markup, provisional services procurement markup. So they're paying for the same work under a different service line due to friction in the organization from a headcount perspective. So a lot of organizations will look at contingent workers as headcount, but they won't look at consulting firms and professional services agreements as headcount. They'll just look at it as budget spend. So we've seen a lot of organizations hide that spend within a different service line to get around headcount restraints. But the average markup on staff fog in the US is about 40% give or take. And the very conservative markup on sow is 70%. So you ask somebody a very simple question, do you want to pay 70% more or 40% more? And that's what's happening. And they're paying the 70% more to get around artificial policies that are hurting the organization, costing more money. Why do that? So those are kind of my two most recent examples that we've been focused on.

Speaker C: I love those. It's so interesting, isn't it? Because it ties back to some of the news articles that we were talking about earlier, which is start with the business problem and, um, work backwards and stop starting with how do I get around a policy that's in front of me to do with my people.

Speaker D: Yep, yep.

Speaker C: Love it. Thank you.

Speaker D: Absolutely.

Speaker C: That's it from me, Mr. Warcraft.

Speaker A: I hope if I unmuted that. Is it from you? Any more Questions from you, Mr. Sure.

Speaker B: That's it from me too, coming from

Speaker A: now on, Mr. And Mrs. And everything like that, I think very formal.

Speaker C: So marriage made in heaven right there. Mr.

Speaker A: Yes. I can't believe we're out out of time already. Um, Mr. Holly, this has been fantastic.

Speaker D: Uh, yeah, yeah, definitely enjoyed it.

Speaker A: How was it for you?

Speaker D: Yeah, it was good. Yeah. No, I, I think we probably could have kept talking for another hour if we had more time.

Speaker A: I think so. Definitely. Definitely. Thank you so much for your time. And, um, is this where we reveal to the audience that this, this will be your, the first podcast you've ever listened to?

Speaker D: It will be. So I'm, I'm excited. Looking forward to it. Can't wait, uh, for you guys to release it.

Speaker A: Cool. Brilliant. Um, thank you, Alison. Thank you, Toby.

Speaker B: Thank you, Alan. Thank you, Alison.

Speaker A: You're welcome. And um, thank you to all our listeners out there. So if you've enjoyed today's conversation half as much as we have, do us a favor, share it with someone who needs to hear it. Um, and as ever, stay curious, stay brilliant, and most importantly, stay intelligent, folks. Bye. 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 B: 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.