TA leaders usually know what needs to improve in their function; the hard part is deciding what to do first. AI is adding to the confusion, with an ever expanding vendor landscape and technology that is getting stronger for many use cases while remaining weak in others. So what do the teams that keep improving do differently?
My guest this week is Tony de Graaf, Co-Founder of the Recruiting Excellence Foundation, whose global benchmark measures the maturity of TA functions around the world against a single framework. In our conversation, Tony shares what the benchmark says TA does well and where it falls short, why no region is as far behind as it thinks, how to make sense of the AI vendor landscape, and the one question high-performing teams use to decide what to improve next.
https://www.linkedin.com/in/graaftony/
https://recruiting-excellence.org
In the interview, we discuss:
- Building a global benchmark of TA maturity
- What TA functions already do well, and where the gaps are
- Why so many regions believe they are behind the others
- Narrow AI tools and the time they do and don’t save
- Do AI tools pay for themselves?
- Making sense of a confusing vendor landscape
- Personal AI agents and what they really cost
- The one test high-performing TA teams apply to every improvement
- Why improvement is continuous, not a project
- What does the future look like?
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Key takeaways
- Global benchmark data shows TA functions score highest on the core of the recruiting process (sourcing, reviewing, interviewing and assessment) and lowest on the strategic work around it, including job intake, workforce planning and reporting the right metrics.
- Regional differences in TA maturity are not large enough to call out; the real differences are cultural, in how a TA function can challenge the business, and shared access to technology is shrinking them further.
- Narrow AI tools often move time around rather than save it, because instructing the tool and processing its output takes effort; roles may move faster without the recruiter’s day getting any lighter.
- An AI tool only belongs in the stack once the team can explain how it earns back its subscription; until then, demos, pilots and curiosity are the right level of commitment.
- High-performing TA teams prioritize improvements by business impact, asking what most increases their ability to consistently deliver the right person exactly on time, rather than by what irritates the team most.
- In high-performing teams improvement is continuous rather than a project; nothing in the process is set in stone and anything can change if it improves the ability to deliver.
Transcript
Matt Alder 0:00
You probably already know the three or four things your TA function most needs to improve. The difficulty is rarely knowing what’s wrong. It’s deciding what to tackle first. And a market offering an AI tool for every item on your to-do list only makes that choice harder. So how do the best teams move forward? Keep listening to find out.
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Matt Alder 1:37
Hi there, welcome to episode 836 of Recruiting Future with me, Matt Alder. TA leaders usually know what’s needed to improve their function. The hard part is deciding what to do first. AI is just adding to the confusion, with an ever-expanding vendor landscape and technology that’s getting stronger for many use cases by the day. So what are teams that keep improving do differently? My guest this week is Tony de Graaf, co-founder of the Recruiting Excellence Foundation, whose global benchmark measures the maturity of TA functions around the world against a single framework. In our conversation, Tony shares what the benchmark says TA does really well and where it falls short, how to make sense of the AI vendor landscape, and the one question high-performing teams use to decide what to improve next.
Matt Alder 2:33
Hi, Tony, and welcome back to the podcast.
Tony de Graaf 2:36
Thank you. Thanks for having me again.
Matt Alder 2:38
A pleasure. According to my records, this is the fifth time you’ve been on the podcast.
Tony de Graaf 2:44
The fifth time already?
Matt Alder 2:45
Yeah, yeah, yeah, over the years. So it seems a bit strange to ask you to introduce yourself, but do that anyway, in case someone’s listening to you for the first time. Tell us who you are and what you do.
Tony de Graaf 2:54
Yeah, so I’m Tony, Tony de Graaf, that really Dutch last name. So I am Dutch, living all over Europe, to say it like that. And I am the owner of the Recruiting Excellence Foundation and we work with TA teams around the world to help them improve their TA capability, so to become a high-performing TA function. And that stretches from simple workshops, hiring manager trainings, hiring process design, all the way to full-blown transformations and reassessing the operating model that some TA teams actually use, and any type of work in between.
Matt Alder 3:24
So you do some really cool stuff but I think amongst the coolest thing that you do is the benchmarking exercise that sort of runs continually through the year and it’s something that we’ve collaborated on a few times and I’ve sort of sent listeners in that direction to look at various aspects of their TA function. Tell us a little bit about the report and how it works and what it measures.
Tony de Graaf 3:43
Yeah, it’s actually started from maybe a personal frustration, is that the right English word for it, is that it is really hard to compare TA teams across the world. And we do get questions if we work with customers in Australia like how are we actually comparing to Europe, or when you are in America they want to know how Europe is doing or the other way around. So interestingly enough a lot of regions always feel that they are behind other regions for some reasons. And there is not a fantastic global data set. There are some data sets and people that do benchmark but they’re very regional heavy, so very Europe heavy, very United States heavy, or somewhere else. And between them, because they measure in a different way, it’s actually not difficult, I would almost say impossible, to compare them or paint a global picture. So what we set out to do is we standardise the way that we actually measure the maturity of a recruiting function, and we are running this benchmark globally. So we got results in from Southeast Asia, all the way to China and Japan and the Middle East, Africa, North South America, and all over Europe, of course. And all those TA teams, big and small, sometimes it’s a company of 600 people, sometimes it’s a company of more than half a million people. They are measuring the way that they’re doing recruiting following the exact same framework, the Recruiting Excellence Framework. So this gives us not only a picture for them individually so they know where they stand, but we can actually offer them a proper global benchmark, so you know exactly where you stand versus the globe, to say it like that.
Matt Alder 5:20
When we spoke a few weeks ago we were focusing on the areas that you sort of have found TA teams have reported that they struggle the most with, and it was the intake meeting and the hiring decision, which we were kind of discussing is almost kind of strange because that’s the, you know, they’re the kind of the biggest two aspects of it. Just to change the focus slightly, what is it that you found from the research that TA teams actually do really well? What is it that, you know, where are they the strongest? What do the sort of highest scoring TA functions have in common?
Tony de Graaf 5:52
Yeah, I like the question. It’s not the typical doom and gloom, what are we doing wrong type of question, right? So I like to flip it on its head because TA is doing very well, I think, in general. It’s more, of course, there are always areas to improve and we always like to focus on that, like the entire AI discussion that we’ll touch upon later as well, right? There is always something happening that we need to do. But looking at what we do very well, we do a lot of things very well. And that really centres around, if we look at the benchmark results, so just for the listeners, we break down the TA function into 21 individual components. And the components that score strongest — nice English sentence — are the ones in the core of the process. So everything from sourcing, the first reviewing, the interviewing and assessment type of things, that at the core of the function is where you see that recruiters are just really good at their job in general, right? That’s where you see the highest maturity. And that also makes sense because it’s the core of a job. So I think that is where they are really well and good. And it is more around, so I think it’s that ever-going question from being an operational recruiter to becoming more a talent advisor or that strategic thing, whatever that elusive strategic thing is. That is where the areas of improvement usually show up. So really becoming more advisory role in terms of who to actually hire and who you’re actually looking for, the workforce planning bit, being able to report on how you’re actually performing with the right metrics. So I think the core of the recruiting function is very well, and it speaks also to that, that the areas where we can improve are the ones that I just named, like becoming more strategic things. It’s not so much about the core of the actual recruiter profession that we struggle with. I think that’s a positive thing for sure.
Matt Alder 7:41
Yeah, I think that makes a lot of sense as well. It’s kind of like where the sort of the core skills of the industry have been for a really, really long time. And, you know, as things change, things have to adjust. I want to go back to what you were saying right at the beginning about people asking you how they benchmark against different countries and things like that, because I get asked that all the time as well. It’s like, oh, this place is ahead of this place and this industry is ahead of this place. And the picture is always much more nuanced than people think. Well, what do you find? How do different countries benchmark against each other? Are there any learnings or interesting insights there, or is it as random and as nuanced as perhaps I think it might be?
Tony de Graaf 8:19
Exactly, the funny thing is we ran all the comparisons that we could but we could not identify — there are differences but not significant enough to call them out in the benchmark. So funny enough it’s not that Europe is behind America on A, B and C, or the other way around, or Australia or the Middle East. It’s more that you see the differences more a little bit in the culture. So for example in the Middle East it’s more a classic and formal culture, so you will see this also in how they organise recruitment. It’s way more a formal relationship with the business and recruiting, and how you’re also treated as a candidate. Where Europe of course is an extreme example of everybody is equal, doesn’t matter if you’re an intern or a CEO. It’s a little bit exaggerated but you do see that in how they organise their TA functions and how collaboration comes to fruition. I think in Europe it’s sometimes easier to call out, hey, we should improve this area. Where in the more classic and formal type of organisations and cultures, it’s not always expected from you or accepted from you that as a TA function, you call something out that should be changed. But that doesn’t mean that it doesn’t happen. It just requires a different cultural lens and a different approach to achieve the same goal. So that is more what we see. So the issues are not fundamentally different, but how do we call out the business or point to the mistake or find a way to collaborate with the important stakeholders? Sometimes it’s leadership. Yeah, you fundamentally need to find another way to get leadership involved and find this important if you’re working with a company that is mainly Indian in culture versus an American company. So that’s more what we see in the differences, so how do we actually solve these things and get it moving. But the funny thing is that the more technology grows faster and faster, like it goes in the last decade, the differences really start to shrink between the regions. Like every region basically has access to basically the same type of technology, yeah, the same type of tools. So those differences start to disappear more and more.
Matt Alder 10:30
Yeah, I think that’s the thing. You know, what I see is any differences are cultural or down to individual legislation or regulations in specific countries or sectors. And yeah, the technology thing’s changed massively. I think if you go back 20 years, it was a real differentiating factor. But these days, we’ve all got the same tools pretty much at the same time. So yeah, less of a differentiator these days.
Tony de Graaf 10:54
Yeah, I think that’s a good thing.
Matt Alder 10:56
Yeah, no, it is definitely. I think it, and also I think that there is the way that we communicate these days has meant sharing across borders is a lot easier than it used to be. Sticking with technology, let’s just talk about AI. I almost need like some sort of jingle or music to come up when we have the AI section.
Tony de Graaf 11:12
Yeah, exactly.
Matt Alder 11:12
It kind of has to happen. So the way that AI is spoken about by the people who develop it at the moment is they talk about this sort of, it’s got a jagged edge, a jagged frontier, which kind of means it’s good at some things and not good at other things. And I’m kind of seeing that jaggedness happening in the way it’s being implemented in our sector. So some companies are doing lots of stuff, some companies are still experimenting, and some companies are not touching it. So what are you seeing? What’s kind of going on out there? How is it developed maybe over the course of this year?
Tony de Graaf 11:44
Yeah, that’s also funny. Over the course of last week. Yeah, exactly.
Matt Alder 11:50
We last spoke in June, but let’s go with this year.
Tony de Graaf 11:50
But yeah, a lot happened between we spoke in June and now, because now everything is about bots. I will circle back to that in a moment. But I love the jaggedness that you explained. Thank you also for explaining it. I also would have asked you the question what it actually means, because the real English people who speak native English always have very beautiful words to explain a lot of things and nuance that us non-native speakers just don’t have. So thank you for explaining that. Yeah, exactly. Yeah, the jaggedness. I still think this is a big issue. So a lot of the AI tools are very, very narrow. So they can do one thing, good or okay depending on your personal opinion, let’s just say good for the argument of the discussion. So they can do one thing very well. So for example, you see some of those sourcing tools, right? So that you then can find candidates, so you give it some information and it gives you back like 20 people or 25 people and where it pulls that from the internet, we don’t exactly know. They basically have a big-ass library that they actually go to, they try to engage these people, okay, fantastic. But that is just what it does. And I think the issue is, how much time does that actually save us? Because we do need to set it up. We do need to feed it. All the time you spent feeding and instructing it before it goes out, that was time you didn’t spend before because you just opened LinkedIn Recruiter and off you go. So there was a percentage of time that you actually spent more instructing the AI. Then the AI is saving you some time, but then it gives you that list of 20–25 people or random example that you then also have to process and pick up again. So I think we need to become better, and that’s just learning by doing I guess, figuring out if we apply AI to something, what is the actual benefit. Because sometimes it’s speed but we don’t really gain a lot of time personally, because I don’t have extra free time because I now instruct all those AIs on all those searches and I’m processing all those lists. So my day doesn’t particularly change, but I can move those roles faster, to say it like that. So the game of applying AI is still, I think, the most difficult because all of them also cost money. So if you work with such a sourcing tool and it costs you a couple hundred a month, depending on, or you have that individually or as a company, does it actually save that money? Do you earn that back? Because if it doesn’t, you shouldn’t apply it. And that is a little bit where I guess the difficulty now is. A lot of AI tools are out there, but if we would assume that all of them are very good and at very nice what they do, so we probably need to go out today and buy 10 or 15 of those AI tools, but that means that we have 10 to 15 new subscription models of a thousand to a few thousand a year. Does it actually save us that money? Do you actually get that back? If you can’t explain that, then it’s not time to actually implement that. Then I would say, yeah, keep experimenting and keep trying. And it’s fantastic. Just call some of those AI companies and say, we’re very curious. We don’t know where we’re ready to buy, but can you show us the product? Convince us why we should care about this and why we should potentially make a buying decision in this field. They’re more than happy to show you. They also understand that not everybody is ready to adopt it. So I like this type of mindset around the AI. If you see something cool, you work in-house, call them, just send them a message. Be informed and learn. And because it helps you internally I think with building the business case on, okay, this is the issue that we’re trying to solve, this is what we actually gain versus the cost. So I think it’s a fantastic reason to try this and pilot this to see if it actually gives us this back. But I would not wait. I just go and do some demos and take a look at it, because everybody is figuring this out. It’s not a set field. It’s not very easy and straightforward to have, this is the exact business case and this is how AI replaces humans or will be cheaper or will be faster. We’re figuring this out as we’re going. So also approaching it with that mindset I think helps.
Matt Alder 16:09
Yeah, I think it’s an interesting time because I think if we sort of fast forward a couple of years, and this is another conversation, we can’t get into this now because we don’t have time, but I think if we fast forward a few years, we’ll be in this kind of, you know, one agent to rule everything and things will look very different. In the meantime, I think we’re at a difficult place because all the vendors out there want to offer everything now across their recruiting lifecycle. So you can technically go to one person. They’ve got all these areas mapped. They’ve got AI, everything, agentic this, agentic that. But, you know, some of them are better than others at certain parts of this. You know, some of it is just an add-on that they put on because they’re losing a bit of traction to some competition. And then there are people that are really, really good at doing one thing. And I think it’s just a confusing landscape to kind of get your head around. And I like the approach that you sort of talk about there, because I think being curious, asking questions, and just understanding the breadth of what’s going on is just probably the most important thing to do at the moment. Definitely.
Tony de Graaf 17:08
Yeah, exactly. Yeah. And the problem also is I don’t think all the AI companies know what the value of their proposition exactly is. Because I have an example. I am experimenting now with Grok bots. I would advise everybody to do that. It’s the big next thing. If it’s OpenAI’s or Meta Muse or Grok bots doesn’t particularly matter, go play with bots, personal agents, personal agents, army of bots that can do different things for you. Go experiment that. That would definitely advise. But an example, I was trying to figure out if I could build a bot. I was like, wouldn’t it be amazing if one of my bots could call my candidates and schedule interviews, reschedule interviews in the completely fluent and normal way you can talk to a Grok bot, because that is really human-like and it’s super fast. So I tried to set that up and there is a vendor that does that. But they are asking a whopping, I won’t call them out of course, but they’re asking a whopping $200 a month just for the phone number and the technology. And that is not including the unlimited use of the phone. So you still need to pay for the phone usage. And this is the exact problem because you are on a bot plan for $60, $100, or $200 a month. And then for one plugin, you pay another $200.
Matt Alder 18:27
Oh, yeah.
Tony de Graaf 18:28
You need another one for $100, or $50, or $10, or even if it’s a $10. You build everything up and then you’re already at a thousand or fifteen hundred euros a month for one person just for those plugins. You can almost hire a personal assistant fractional for that to actually…
Matt Alder 18:41
Well, yeah, that’s the thing. It’s just like, I’ll get a human to do it, and in fact they can bring their own AI and pay for the AI themselves and do it. And something…
Tony de Graaf 18:48
Yeah, exactly.
Matt Alder 18:48
Yeah, yeah. It’s going to be a really interesting conversation as this sort of stuff kind of comes over into recruiting, so I definitely want to pick up with and talk about in depth as it sort of happens. Coming back to sort of TA function improvement, during the benchmarking, you’ve got all this data, you know, really granular stuff that TA leaders can really dig into to see where they stand. What do the best TA teams and TA leaders do with this kind of information? How do people actually turn data and benchmarks into real change that creates value?
Tony de Graaf 19:23
Yes, exactly. It’s a good question because this is where a lot of the potential stalls, the right word to say it like that. Because for me, it is not so much about knowing what to improve because in my experience, 99% of every TA leader I ever speak to, they know what the big topics are. They can give you the top three or top five issues that they think they should improve or where the biggest delays exist or where they lose the most candidates, whatever it is. In general, TA leaders know it. This next step is usually what is most difficult. What should you improve first? And TA teams that seem to be on that track to really be high-performing TA teams, they don’t see it as, okay, the topics that I need to improve, what annoys me most, but they change the mindset: what has the highest business impact. So they don’t say, like one of the examples, if you talk to TA teams that are not in a full continuous improvement mode, there are always things in the process they want to fix on the career site, in the email templates and the candidate experience stuff. Great, important, but not urgent. It never is. Because even if we improve all those things, there is usually very negligible impact on your ability to consistently deliver the right person exactly on time. And that’s exactly what should be in your mind. You are delivering for your customer, which is the business. So the mindset should be, whatever I prioritise to improve, what has the biggest impact on my ability to consistently deliver the right person exactly on time? And that’s it. That’s the literal only sentence that high-performing TA teams are focused on. So sometimes an improvement can be beneficial for us because we are clicking around, the system is annoying. But we’ll just leave that for now because it doesn’t have the highest impact on our ability to consistently deliver the right person exactly on time. So if you constantly focus on that, then it takes away that discussion of the five or top 10 topics that you have. Because you can, in general, without any training or any strategy consultant that you need, from your top 10 list you can say, okay, these two or three probably have the highest impact, if we would fix it, on our ability to deliver the right person exactly on time in a cost-effective manner, right? So that is the mindset that you see with high-performing TA teams. They really focus what they improve on their ability to deliver. I guess that is the key. And the second biggest thing is that there is not an improvement mindset that is a project. An improvement mindset is continuous. Nothing is set in stone. And high-performing TA teams will probably not have a process — 80% of the entire function isn’t the same two years ago or three years ago. Everything is up for discussion. It’s not the goal to map a process, train everybody on it, standardise it, and we never touch it again. No, we can touch everything if it actually helps us improve our ability to deliver. So those two are I guess the biggest mindsets we see with the high-performing TA teams, where you see that they are always running faster and getting further ahead versus other teams.
Matt Alder 22:45
Yeah, that’s really interesting. When you were talking there about focusing on that, you know, that kind of one purpose, there’s a brilliant book I read a few years ago, I know it’s one that does around so people talk about it a lot, called Will It Make the Boat Go Faster by a guy called — I just looked up — by a guy called Ben Hunt-Davis, which is a story of the kind of the British Olympic rowing eight from, I can’t remember which Olympics or which World Championships. And it’s just kind of a diary of how they became the best in the world. And they just focused on any changes, anything they did, they just asked that question, does it make the boat go faster? And if it did, they did it. If it didn’t, they didn’t do it. It’s just a really great philosophy. And you just reminded me of it there. So you are now actively collecting data for the next kind of annual report. So people can benchmark themselves. I would urge all the TA leaders listening to this to go and take the survey. It doesn’t take very long to do. The data that you get is fascinating, and obviously you’re contributing to the overall picture. If you head over to mattalder.me slash 2027, and that’s in numbers 2027, that’ll take you to a page where you can start filling this in. Tony, tell us a little bit more about what people can expect from, you know, sort of taking part in this.
Tony de Graaf 23:57
Yeah, exactly. I think the biggest difference between a lot of surveys and benchmarks that are out there versus ours is that the moment you complete it you get a personalised report instantly. So you don’t have to wait until we have completed the benchmark. So that you do the survey and say, well, we contributed, you have to wait for five months to hear something back and get some insights. No, we already have global benchmarking data so that’s already in there, plus the report is automatically generated and you get the full version the moment you click and answer the last question. So if you actually do it you get something in return instantly. And a second thing is that because I said it goes across the 21 individual components of the TA function that we’ve broken our function down into, say it like that, it takes you around 15 to 20 minutes. You can continue at any moment because we’re always busy, we always get this phone call, we always get distracted. You can just start, you directly receive a link and you can continue whenever you want. And there is even a mini assessment version that only takes less than two minutes if you want to get a feel with the format. If you’re like, I’m not sure I actually want to invest already like 15 minutes, you can do like a two-minute assessment, you get a mini report to get a little bit of feel of the format, and if you decide that you like it you can continue.
Matt Alder 25:16
Fantastic. So we’ll put the link in the show notes. But if you want to take note of it now, mattalder.me slash 2027. So final question for you, what are you expecting to see in this new set of data? Where do you think things have shifted over the last sort of year or so? What would not surprise you if you saw it in the results?
Tony de Graaf 25:35
It’s a good question. I think, I hope to see that the general maturity, so the average, has moved a little bit because we measure the maturity over four levels, and where you get to level three and level four, AI is more part of your TA function and how you do your work. So I hope to see a shift that we see a total of companies that are more functioning at that level three. That will be fantastic because that means that we move forward. The weakest point last year was indeed the job kickoff meeting or the job intake. I hope to actually see that we improve there as well, but I also think that it could very well be the weakest again this year, because that one particularly is not solved by any form of technology. It’s a token to see where you actually are on that journey from being an operational recruiter to becoming a strategic talent advisor. So that really requires a transformation from your team and how you actually engage with the business to achieve higher maturity there. So it could still be maybe lowest, I don’t know, because it takes maybe a little bit more time and effort to actually make that change. So that could be a multi-year transition. But those are my two predictions I will be definitely looking at first when the data actually comes in. Maybe we catch something else.
Matt Alder 26:56
Yeah, I’ll see. Well, looking forward to talking to you about the results when they’re done. In the meantime, everyone, make sure you take the survey. Tony, thank you very much for talking to me.
Tony de Graaf 27:05
Thank you for having me. The fifth time was — that’s fantastic. Thank you.
Matt Alder 27:08
My thanks to Tony. You can follow this podcast on Apple Podcasts, on Spotify or wherever you listen to your podcasts. You can search all the past episodes at recruitingfuture.com. On that site, you can also subscribe to our weekly newsletter, Recruiting Future Feast, and get the inside track on everything that’s coming up on the show. Thanks very much for listening. I’ll be back next time and I hope you’ll join me.
