Episode 250: AI in HR Tech: What Investors and Leaders Need to Know (with Thomas Otter)
The HR technology market is booming - but with so much innovation (and noise), how can HR leaders and investors tell what’s truly transformative from what’s just trendy?
In this episode of the Digital HR Leaders Podcast, host David Green sits down with Thomas Otter, General Partner and Venture Capitalist at Acadian Ventures - a firm dedicated to investing in groundbreaking companies that make work better.
With decades of experience spanning SAP, Gartner, and now venture capital, Thomas brings a rare 360-degree view of the HR tech ecosystem - from building and leading product teams to backing the next generation of innovators.
Together, David and Thomas explore:
Whether HR tech is going through a true transformation or simply evolving
Where AI is actually making a difference, and where the hype is getting ahead of reality
Why AI adoption remains slow for many organisations, and what leaders can do about it
The traits and technologies that make HR tech startups stand out to investors
The trends and breakthroughs shaping the next five years of HR technology and the future of work
If you’re an HR or people analytics leader, tech founder, or investor looking to cut through the noise and understand where HR tech is really headed, this is a conversation you won’t want to miss.
This episode is sponsored by TechWolf.
TechWolf helps enterprises get fast, accurate, and actionable skills data—without surveys. From identifying the skills your workforce has to mapping what they need, TechWolf’s AI integrates seamlessly with your existing systems to turn messy data into strategic advantage.
Learn more at techwolf.com
This episode of the Digital HR Leaders podcast is brought to you by TechWolf.
[0:00:00] David Green: If you've been watching the HR tech market recently, you'll know it's been buzzing. In fact, according to recent market research, the global HR technology market was valued at about US$37 billion in 2024 and is projected to reach roughly US$82.8 billion by 2033, growing at a compound annual growth rate of 9.3%. Some people say we're in the middle of a complete HR tech transformation; others believe it's more evolution than revolution. But one thing's for sure, never before has there been so much opportunity or confusion in the world of work technology. So, who better to help us separate signal from noise than Thomas Otter, General Partner and Venture Capitalist at Acadian Ventures, an organisation dedicated to investing in groundbreaking companies that make work better.
I'm especially excited for today's conversation because quite honestly, we haven't had someone like Thomas on the show before. He's been on all sides of the HR tech ecosystem, from building a leading tech development at SAP, to being a market analyst at Gartner, and now to advising and investing in the next generation of HR technologies that are shaping the way we work. He's seen what works, what doesn't, and what still needs to change if HR tech or work tech is really going to deliver on its promise. So, today, I'm taking this opportunity to get Thomas' perspective on where HR technology really stands right now. We'll talk about where AI is genuinely making a difference, where the hype might be getting ahead of reality, and why adoption is proving so tricky for many organisations. And if you're an HR tech founder, aspiring investor, or someone responsible for driving HR digital transformation in your organisation, this is an episode you'll want to pay close attention to, as Thomas shares what he looks for in the companies he invests in, the innovations that make him sit up and take notice, and his predictions for what's next in the world of HR technology and the future of work. So, grab a coffee and let's get the conversation started.
Thomas, welcome to the show. To kick things off, could you share a little bit about yourself and your journey that led you to now becoming a venture capitalist in the HR technology and future of Workspace?
[0:02:33] Thomas Otter: Yeah, thanks David. Firstly, thanks for being on the show. I'm a regular listener and very much enjoy your podcasts and newsletters and stuff. They're super for the industry, so cool with that. Thanks for having me on the show. Yeah, so I've lurked in HR technology for all of my working career. I started as a consultant back in South Africa in the '90s, ended up working with SAP, gradually climbed the ladder up SAP, ended up at Gartner, led the HR tech research at Gartner for a while, and then returned to SAP, led Product Success Factors, did a bit of advisory work after that, and sort of I fell into venture. Most people actually do it the other way around. They go into venture capital quite early in their career. And for me, it's kind of turned out the other way around, which is kind of an unusual path, but it's quite an interesting one because I'm now able to take the sort of experience that I've gleaned over the years and apply it now through an investing lens. So, that's kind of our differentiation as a firm, in that we really understand the space in which we invest.
So, my partner is a guy called Jason Corsello, who's followed a similar path. Some of you may know him. He was the Head of Strategy at Cornerstone. And so, together we have a deep understanding of the age of HR tech space. I've got a bit of an academic background as well in the HR space too.
[0:03:50] David Green: When you look at the space now, Thomas, do you think we're in a real transformation in HR technology, or is it more of an incremental evolution?
[0:04:00] Thomas Otter: I don't think we're in a revolution yet. There's a lot of talk of revolution, every press release is a revolution, or a game change, or whatever. And I have a simple definition of revolution. It involves that someone loses their head. And if we apply that definition, Louis XVI, the Romanoffs, etc, there's a certain amount of bloodletting and head removal in any kind of revolution. And when I last checked, the incumbents in our space are still very much the incumbents, their share prices continue to tick up, they continue to do okay. So, we haven't had any kind of wholesale revolution in HR tech yet. There are skirmishes around the edges, let's say, but we don't have a revolution yet. What we're having at the moment is that the incumbents are co-opting some of the would-be revolutionaries. So, we're seeing quite a lot of acquisition going on at a relatively small scale. These aren't big acquisitions, they aren't world-changing, game-changing acquisitions. These are, putting the football terminology here, these are relatively small transfer market manoeuvres, these aren't changing any rules or making any massive differences in the game. It doesn't mean they're not smart acquisitions, but we're not in a phase of revolution yet. The potential for a revolution is clearly there, but we're not in it yet.
[0:05:30] David Green: And whilst, again, I mean maybe putting one of your former hats on as when you were at SAP running product there, you can understand the vendors making a big noise about it, and their marketing teams maybe putting a bit of hype behind some of these acquisitions. But it seems that that's replicated in the analysts as well, and maybe they'd be doing a better service to the industry if they're a little bit more sober in their analysis of some of these acquisitions. That's just my comment, and I'm not talking about any particular analyst in particular, and I know some are more sober with it.
[0:06:06] Thomas Otter: Yeah, I would like to see a little less exuberance from the analyst community. When a vendor that's got a market capitalisation of several hundred billion buys a company for a billion, it's not a big deal. It may seem a big deal to some people, but it's not really a big deal. It's just sensible business as usual. Established vendors are sitting on cash piles, and it's beholden for one's investor to do something with that cash. You can either give it back to them, or you can spend it. There are a couple of ways of spending it, and one of them is through acquisition. So, I expect that we will see more and more acquisitions over the next few months. Partly what happens in these big companies is that once you fire up the acquisition machine, it's quite hard to stop it, because you've then got teams of people that are incentivised to do acquisitions. You have an M&A department, and so these things tend to have a life of their own. And I expect to see that the acquisition momentum will continue from the usual suspects. And it makes tactical and strategic sense for those companies to be doing acquisitions.
[0:07:29] David Green: And obviously, now in your role as a venture capitalist at Acadian, obviously one of the outcomes that you're probably looking for, for some of the companies that you invest in, is an acquisition down the line by one of the bigger firms. Maybe again, can you tell us a little bit about what's the role of a VC in the HR tech area, because I'm guessing a lot of our listeners probably don't necessarily know? And then, maybe how you found it, having been on the other side of the fence, working at a big vendor or working at an analyst, kind of providing finding analysis on the space, what's different from being an investor to those two areas as well? And then, a little bit about what you do and what Jason does at Acadian.
[0:08:15] Thomas Otter: Well, we could probably go on all day about that, but let me start by -- because I didn't really understand a whole lot about venture capital until I became one. In fact, I'm still learning every day after being one full time for four years or so. But basically, what venture capitalists do is they provide funding to early-stage companies. The way this works is we will invest in any given fund. We'll invest in roughly 30 companies, and our investors have an expectation that we will give them a good return, that otherwise they wouldn't give us the money. That's one of the things that people forget about, about venture capital from our side, is that there are two parts of this job. The one job that you know about is investing, and the other job that we have is a more regulated one, which is where we have to find the people that invest in us. And so, our work involves both. Everyone has a boss at the end of the day, and our boss is our LPs, our limited partners, the people and the investors, the strategic investors that invest in our company. So, for instance, in our last fund, ServiceNow is one of our anchors.
We have to offer a return to our investors. And the way we do this is we assess hundreds of companies, literally hundreds of companies a month, and we pick one every six to eight weeks or so to invest in. And we do that over a period of four years, so roughly it ends up that we have about 30 investments after four years. And what we assume is that when we go into every investment, we have the expectation or the belief, we have the belief that it will do what we call 'return the fund'. And what that means is, let's imagine for a moment we've raised 30 million, let's assume for a moment, and we make an investment, and let's say we make an investment for a million, we expect that that investment that investment will 30x and return the fund. And our hope is that we will have, from the 30 investments we do, that we'll have a couple that will do that 'return the fund', will have that return-the-fund moment. So, that means we're looking for companies that will grow big, because to return the fund, you have to become a sizable companies. So, we're not looking at funding nice companies who are solving nice little problems who want to be reasonably successful. We want to find those companies that are going to grow really, really big. The goal of VC is to help accelerate these next generation, give them the capital so that they can really grow and build quickly.
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Where is AI actually making a difference? I remember the 2010s and there was lots of talk about AI in HR then. Now, our research at Insight222 says that companies are now actually investing in AI and HR, even if it's in small numbers at the moment. Is the market potentially getting carried away with the hype?
[0:12:49] Thomas Otter: There's two thoughts in the AI world. The one thought is that we are close to something called AGI, and there's another group of people who think that we're miles away from AGI. And I fall into the latter, I think we're miles away from AGI. And if you look at the academic thinking, it's probably split half-half between, "We're going to reach AGI very soon", and, "We're not going to reach AGI in a long time". My belief is we won't reach AGI in my lifetime. I believe we'll meet some measures that people set for AGI, but genuine AGI, we won't reach in my lifetime. It doesn't mean to say that there won't be a lot of exciting stuff done with AI, but I think when we overhype and we think that we anthropomorphise and we add these human qualities to AI, I don't think it does us any good. AI is a fantastic technology, and I think there are fantastic ways that we can deploy it in work, and I think it's already starting to have disruptions and impact on work in both positive and negative ways. But I think we need to we need to stop glamorising it, if you keep saying that AI is there to replace people.
So, what I'd like to see more of is, how can we use AI to grow new markets, to grow new ideas, to do things that we were completely unable to do before, rather than simply seeing AI as, "We have this current process. How can we inject AI into this current process and save money and take humans out of the process?" I think that's a rather blinkered and a limiting view of what AI can do. So, as you can tell, I'm probably a little bit confused, these two thoughts in my head about, "AI is overhyped", yet "We're not genuinely taking advantage of AI, because we're not really letting our imaginations use AI in a positive way. We're using it in this way of thinking of it in terms of replacing rather than augmenting human behaviour".
[0:14:42] David Green: Yeah, that's right. And your mention of Potemkin made me remember that like me, you're a big history buff as well. And if we look at previous industrial revolutions, each industrial revolution has always created more jobs than it's discarded effectively or eliminated. I'm an optimist and I hope that's the case this time as well.
[0:15:12] Thomas Otter: Yeah, it's not to say that there aren't areas which are challenging at the moment. So, graduate recruitment, for instance, is pretty brutal at the moment, but I do wonder how much of that is genuinely AI, or how much of it is just general macro conditions and a cynical use of AI as a reason for that lack of hiring. But some of the research is pointing to impacts on particular jobs and graduate jobs seems to be, at least in some areas of white-collar consulting and so on, seems to be genuinely impacted by AI. There's an interesting economics phenomenon called the Engels' pause, which is named after Friedrich Engels, Marx's mate. And that looks at the impact of, when you have these changes, there is a period of wage depression. So, there are elements of that when you look at the economics. But on the other hand, there are certain jobs where the opposite effect, where the wages are going through the roof.
So, there's one side of it is AI as a driver of inequality. So, will we see AI driving greater inequality or will we see AI used to drive more equality in work? I think those are big social questions that we've yet to find answers for.
[0:16:27] David Green: What would your advice be to maybe Chief People Officers, or senior HR leaders listening, to help them approach AI a little bit more pragmatically, and maybe trying to get beneath the hype and partner with their leaders so HR can actually help lead this transformation rather than being a hostage to fortune?
[0:16:52] Thomas Otter: I'd say two things. It's a great opportunity for you to catch up on the tech side in your own competence. And this is going to sound insulting to a few HR leaders, and I'm sorry if it does, it's not meant that way. But if I go back 25 years or so when I was doing pre-sales at SAP, I was the guy driving the mouse, doing the demos. And one of the jobs you do is that companies would come and they'd bring, like, ten of their C-level execs to go over to SAP in Germany. I used to sit through these sessions, but everyone else was doing the demo and HR was always the one after lunch, or whatever. But I used to watch how CFOs would interrogate SAP, "How does the product do this? How does the product do that?" And the CFO was deeply, deeply, deeply invested in the impact of technology on their function and on the organisation. And so, CFOs got tech-smart, and so too did marketing leaders, they also got tech-smart. When tools like Marketo, and whatever, came about, they embraced them, and the leaders of those companies embraced them and became tech-savvy.
You have an opportunity, HR leaders, because the playing field is now level; the CFO doesn't know very much about AI either. So, you have a chance now to actually learn something when you're at the same starting level. So, I'd say, learn what it is, learn what supervised learning is, learn what unsupervised learning is, understand what a decision tree is, understand the advantages and disadvantages of an LLM, the difference between probabilistic and deterministic AI. You don't need a PhD in maths to do this. I nearly failed maths at school, and I hadn't touched it in 30 years, and I've learned about AI, so it can be done. And put the time aside to actually learn a little bit about the technology. I'm not asking you to become AI gurus, but I'm asking you to be able to have a coherent conversation with your IT leaders about AI.
If you can do that, you'll be in a really strong point to help shape that discussion, because essentially there's two sides to the equation. There is human work and there is AI work, and these two are going to collide. And if you've got to be the people that shape work, well then you need to understand both of those dimensions. And the criticism of IT is that they don't understand the human angle; but the criticism of HR would be they would not understand the IT angle. So, I think if you can have an understanding of the tech, and then also you need to be learning a little bit about the fundamentals, so you can have that discussion with IT about things like, "Well, what about bias in recruitment tech? How are we minimising that?" Understand the weaknesses of generative AI, because there are many weaknesses of generative AI and technology. And be able to participate in that discussion, because that will enable you then to have the discussion about the impact of AI on work.
[0:19:59] David Green: I want to take a short break from this episode to introduce the Insight222 People Analytics Programme, designed for senior leaders to connect, grow, and lead in the evolving world of people analytics. The programme brings together top HR professionals with extensive experience from global companies, offering a unique platform to expand your influence, gain invaluable industry insight and tackle real-world business challenges. As a member, you'll gain access to over 40 in-person and virtual events a year, advisory sessions with seasoned practitioners, as well as insights, ideas and learning to stay up-to-date with best practices and new thinking. Every connection made brings new possibilities to elevate your impact and drive meaningful change. To learn more, head over to insight222.com/program and join our group of global leaders.
At Acadian, when you evaluate HR tech startups for potential investment, what are the kinds of innovations or approaches that are making you sit up and take notice?
[0:21:21] Thomas Otter: Well, so interestingly, it's normally the people, because we're investing at a stage where there isn't that much product yet. So, we're really investing in the founders and the way that they're thinking about the market. So, I'll pick out maybe a couple. All my portfolio companies are my favourites. But if I think about a couple, for instance, Charlie Franklin at Compa, they do a lot of AI stuff in compensation, the company's really going gangbusters. What we found about him is he was a former comp leader himself, and then he got the startup itch. And that was super-interesting for us, because one of the things we sometimes look for in founders is you have deep functional knowledge of a problem, and then you have a burning desire to solve that problem. That's super-exciting for us. And we felt like enterprise compensation was an area where there's a lot of spreadsheets, there's a lot of opportunity to rethink how compensation is done. And we had a very clear view of how they could disrupt the compensation space. And I think that they're well on the path for doing that.
Sometimes you meet three super-smart people who are just straight out of university, which an example would be TechWolf. There's no cookie cutter for the founder, but for us, it's about the desire of the founder to be successful. Because we can't necessarily predict where the technology is going to go, but we look for a couple of things. So, is this somebody creating a new category? So, this is like a whole new area. So, if you think back like 10, 15 years ago, people like Peakon, they created the category of employee listening. It wasn't really a category before. They created a new category of tech and those companies grew for a few years, and they were then eventually acquired very, very successfully by Workday. But that created a new category. And so, for instance, some of the stuff going in frontline work at the moment, you could argue that's a new category.
Another thing could be where you have a product that augments existing technology. So, for instance, somebody might do a candidate relationship management on top of an ATS. That's an example of a product that augments the existing world, but doesn't necessarily disrupt it. It sits comfortably with the existing tech stack. And then you might have something that is disruptive, which looks to replace elements of the incumbent stack. So, we see those three buckets, if you like, of changes in HR tech. So, is it something that creates a whole new category where we didn't have a market before? Is it something that augments existing environments? Or is it something that is fundamentally replacing an existing play? So, something like TechWolf, for instance, it's got a little bit of both in the sense that the skill stuff is, on one hand, a new category, but not completely a new category; but it also augments. It doesn't seek to replace your workday, it augments your workday or your success factors.
So, we kind of think in terms of these ecosystems and we look a lot for what could the synergies be between what they're trying to build and the existing landscape, because we understand that existing landscape very well.
[0:24:57] David Green: With AI changing how work is done, how should organisations think differently about preparing their employees for the future? That might link on quite nicely with the TechWolf investment, for example.
[0:25:11] Thomas Otter: Yeah, I'd argue that you need to have an understanding of what your employees know, and most organisations don't have a clue what their employees know. So, understanding what your employees know, of course, you can ask them, but employees are kind of tired of that, "I've been working here for 20 years and you still don't know what I can do". So, the concept of TechWolf, without trying to pitch them, is that they're able to use quite clever AI to actually infer from your current work what your skills are. So, if you're a programmer, they can look at what you contribute in GitHub; if you're a medical researcher, they can look at your patent filings or your other medical research and they can derive from your work, with your consent, what you actually know; if you've been running a team for ten years, we can derive from that that you're a manager. We don't have to ask you, "Do you know how to manage a team?" If you've been writing emails coherently in French, we can assume that you speak French, and we shouldn't have to ask you, "Do you speak French?" So, you're able to derive quite a lot of information from just analysing what you are.
Then, if you have, like TechWolf have done, analysed billions of jobs, literally billions of jobs, you're able to make quite a lot of predictions about where the job market is going and jobs are changing. So, the combination of these two dimensions starts to provide quite a lot of insight for the individual, but also insight for the organisation in terms of the direction and the requirements for change in the organisation. So, I think it's very good to have an analytical basis for thinking about change. And understanding where your organisation is likely to be impacted by AI, I think is a very good starting point. So, the product that TechWolf has just released, the Workforce Intelligence product, helps you I think get to some of that.
[0:27:17] David Green: You wrote something recently about an article that was published in HBR around AI-generated work slot, which I must admit I read as well and enjoyed, because like you said it did with you, it kind of resonated with your own confirmation bias that you're reading a lot of stuff at the moment and some of it maybe isn't that good. And after you've read it, you think, "Oh, God, that's ten minutes of my life I'll never get back". But I think what you unpick there, as well as something about the quality of maybe some of the articles that are on HBR now, you pick something about research, because I think this is an area where it is a challenge, because we all read these very well-written articles, and it is a very well-written article, and everything in it as well. No criticism necessary of the people that wrote the article, but the research is a little bit wonky, shall we say. And you actually got some comments. I don't know if you want to tell the story, because I think it's a great story which might resonate with some of our listeners that have maybe seen the HBR article, but not read your one?
[0:28:14] Thomas Otter: Yeah. So, I read the HBR article and this concept of work slot, the idea that AI-generated work that looks brilliant, but actually when you dig into it, it's actually not very good. And we've seen that. We've all experienced that anecdotally, where we've spent some time reading something, and then we realised afterwards that we're not quite sure, does this actually really stand up? This author was saying, "This is a problem in organisations because what it does is it undermines trust with the person that sent it to you. It's a productivity destroyer", and it's quite a refreshing anecdote to the sort of AI's awesome school to say, actually, there are problems with AI-generated content in that AI doesn't help us read faster, it just helps us to make things faster. So, there's this cognitive imbalance. Also, the way I describe it is that there's this kind of a heuristic challenge, because we used to dismiss poor work by spelling mistakes or not nicely formatted, or we say, "Okay, well, this is sloppy work. The person hasn't put much effort into it". But when we receive something that's beautiful slides, what seems well-thought-through, we give it immediate respect. And that heuristic is broken now. So, this sounds pretty realistic, right?
The challenge I had was that the researchers that did the research, it was essentially vendor-driven research with a bit of academic backing, is that they made some assumptions in the research that weren't very robust. And I didn't realise that when I first read it. And then some guy that I know, which is a professor at another business school, had a look at it and said, "Well, hang on a minute, there's some problems with the actual research". And so, I dug a bit deeper and I found a couple of things were wrong with it. And you guys have done surveys and stuff. So, an example would be that they were they were calling within the paper for more contributions to the research. So, they were describing the output of the survey so far, so let's say 1,000 people had answered the survey. But then, within the research note itself, they were calling for more people to answer the research.
So, that's the research equivalent I would call it of leading the witness, right? You've read the report, sounds like it makes sense, "Okay, let me ask you the same questions that I ask the people". It's not academically robust. And this is a problem that we have with a lot of work that you need to take as a listener, is when you see something that's published by a leading consulting firm or by a little semblance of academic rigor to it by Professor ABC, or whatever, you've got to actually take a step back and understand the purpose of why the vendor did that research and assess it for its research validity. And you'll find often a lot of the stuff is a white paper. So, it's a well-thought-through opinion and you may well agree with opinion, but they're often not really based in deep analytics, because no vendor is going to publish something that undermines their position. A large consulting firm is not going to publish something that says, "You need to stop doing A, B, and C, which is driving 30% of our revenues". And the same when you listen to people like me. As an investor, I'm going to be telling you, "Startup ABC is great", because I have an interest in telling you the startup ABC is great. And I think just to be a little bit more critical. And AI demands from us greater levels of critical thinking, not lower levels of critical thinking. So, just tip up your critical thinking whenever you read any research about AI and you understand where it's coming from.
[0:32:15] David Green: Right, a couple more questions, Thomas. So, I'm going to ask you to look into your crystal ball now. If you look at the next five years, don't worry, I won't hold you to it, what are the trends or technologies in HR or work technology that you are most excited about and why?
[0:32:31] Thomas Otter: Okay. So, I'll pick out a couple of things. I think we will start doing a lot better job at recruiting. I think we the opportunity, if it's done right, to not only deal with some of the elements of AI bias that we see, but also help solve for human bias. I think we can make tremendous breakthroughs in recruitment. There's a lot that is broken in the recruitment process. I don't like saying processes are broken, but there's a lot that can be fixed in recruitment. And some of that I think is relatively low hanging fruit, given the state of where we are with technologies. I think, at the same time, I think we can do a lot better job in in the areas around learning, how we teach people, how we prepare people for work, both before they start work and engaging them in the work process. Just to plug another portfolio company there, we have an investment company called Arist, and when I look at what they're able to do in terms of creating meaningful learning product very, very quickly, the future of learning, I think, is bright. There's a lot of challenging learning, but I think there's areas where we can make big breakthroughs in the next couple of years.
Then, a couple of other areas which I'm interested in is where we can do things with AI that we couldn't do before. And so, one example I'll talk about now is a company we have in a portfolio called Origin Benefits. And I want to imagine for a moment that you're Head of Benefits for a large, multinational company with, say, 150,000 employees in 80 countries. You've probably got 30 or 40 benefits policies per country and you have no idea what's going on. What are you paying for them? Are they competitive? Are they providing good value for your employees? And what Origin does is it soaks up all your policies that you have globally in benefits, compares them against a really robust, well-thought-through database of good policies, and tells you how you stand. And if you think about what that does, to do that, it would have been impossible to do that as humans. It would have just been too much work to do that. You wouldn't have said, "Okay, I want to consolidate all my global policies into a single searchable, understandable database in a single language". You would just not know how to do that. It would have just been impossible. So, this is a long way around of saying there'll be a whole lot of things in HR tech that we'll do that will seem simple in five years' time, that feel impossible today. And those are the ones that I'm most excited about.
So, in a sense, I'm not answering your question, because there will be a chunk of things that we'll be doing in five years from now that are completely impossible today; and there'll be a bunch of things that we do today that we will be doing significantly better. So, the time to hire has not moved, no matter what brochures you read, what are the success stories you read, the time to hire has not significantly moved in 40 years. So, I haven't really answered you, but that's what I think.
[0:35:33] David Green: Well, you have. And I think what you showed with that tool, Origin, I think you mentioned it was, that helps a company consolidate all its benefit policies across all the countries that they're operating in, which you said would be impossible otherwise, I mean to me, it's not the most exciting idea, but hell, it doesn't half provide a lot of value. And I remember someone saying, it's not always the most sophisticated analytics that gets you the biggest business benefit. It could be that it's not even the most sophisticated or ingenious solution that gets you the biggest benefit, but this is one that would get you a huge benefit.
[0:36:08] Thomas Otter: Yeah. I mean, if you think about it, 30% of your HR costs are benefits, right, and you just do not have those under control. You just do not know globally what the hell is going on, because you outsourced your HR in France, and so they're running the shared service centre. And one company that they spoke to, they had a policy that was set for 2,000 employees. And basically, it was the company's responsibility to say when the headcount changed so that they could reduce the policy. And the policy you've been running for ten years, they now have 20 employees in that country, and they've been paying for years for several thousand employees. And those kind of cost-saving opportunities are massive, yet it requires sophisticated technology to do the analysis correctly and to expose that. So, it does in a sense take a form of analytics, but it combines various AI techniques together with a certain amount of gen AI to do that.
[0:37:15] David Green: So, Thomas, let's get to the question of series, and then I've got one last question afterwards as well. So, this is a question we're asking everyone on this series of the podcast. We've touched on it a little bit, so you might summarise stuff that you've said as well, and that's fine. How can HR lead the shift to skills-powered workforce planning?
[0:37:33] Thomas Otter: Well, firstly, you should buy TechWolf, obviously, but putting that aside for a moment! We need to understand why skills have become important, and the way I describe it is, I remember going to a conference about 20 years ago, and I stood up and I held up a bolt. And there was a bunch of engineers in the room, and I held up a bolt like this, and I passed it on to the people in the front row, and they said, "Oh, it's a left-threaded, 14-centimetre bolt, and it's built with XYZ steel and 17.4 quality steel", or whatever. And they told me, "And it's got a hex dot", you know, told me what that nut was on the top. And within a few minutes, we'd completely described this bolt. And I said, "What does it cost?" And they could tell me, okay, what the landed cost was and whatever cost. And I said, "Okay, so this bolt costs you less than a cent, right? And you can describe it perfectly, yet you can't describe your colleague who's costing you €150,000 a year. Part of me says, "What's taken so long in HR?" The currency of understanding your workforce is the skills that they have, and you should be ashamed that you don't know what your people know.
So, you've spent all this money all these years on HR technology, you say you're the partner of the business. But if you don't know what the business needs and you don't know the skills your people have, what are you doing? Get on with it. Figure out what your organisation requires. And the currency, the best currency we have, it's not perfect, but the best currency we have or the best mechanism we have to do that measurement is skills. So, I don't know why you're debating this. I went back and I'll go back, like way back, when I think it was the US Navy or the army came out with their kind of competency models before the Second World War, and they mapped out how their workers got the technology. The technology is there today, whether it's TechWolf's technology, whether it's anybody else's technology, to actually understand what your people can do. And you understand what they want to do, what they can do, and understand what your organisation requires of them both today and in the future, and just get on with it. You have the opportunity to use reliable data to massively impact the success of your business, and I'm puzzled about why it's taken so long.
There's been a lot of hype and whatever about it, but to me, it just seems so basic, obvious business requirement to know what your people know. Too many of the skills projects that organisations have done in the past have been for HR's sake. They haven't taken them and put them into a context that is useful to the line manager or useful to the executive, right? It's been an HR project for HR's sake, and HR's patted itself on the back when it's done the project. And I think the piece that misses there, how do we take this and move it into the business? And I'm a big believer that HR exists to make the business better. And it's not a necessary short-term, every day make the business better, but it's a long-term thing that HR is there to help drive the long-term sustainability of the business. And so, the dialogue that's been missing to date has been the dialogue back with, I think it's an executive failure, where there hasn't been a dialogue with the other executive is why this is important. And that, I think, needs to change.
So, if I have one request for the leadership of HR is, go into a board meeting and fire up some of your HR technology and demo it. HR technology should not be something that is 15 layers down in the organisation. It's something that should be in the kit bag and in the weaponry of a CHRO. And so, use these technologies to make your point in the boardroom, and then things will change.
[0:41:40] David Green: Last question, Thomas. This one's for a friend, shall we say. If someone is currently an HR practitioner, an analyst, works for a technology firm or a consultant, and they want to explore moving into become a venture capitalist, what advice would you give them?
[0:41:59] Thomas Otter: The financial part of it is actually not as difficult as you think it might be. The jargon and whatever, there is a jargon, there is a code in finance that you need to learn. It'll take you a couple of years to get the hang of it, but that shouldn't put you off. If you don't have a finance background, it shouldn't put you off. You can build one. You can go on courses and you can learn enough about finance. But what you have to remember is that your job is investing, and that's the change. And I'd say that if you want to become a venture capitalist, what it means is you're becoming an investor, and your goals are to provide a return to the people that are trusting you with their money. So, I have a big fiduciary duty, both legally and personally, to the people who've trusted us with their money. And so, it seems glamorous on the outside, whatever, and it is a very cool, glamorous job, no doubt about that. But you are, at the end of the day, responsible for other people's money. So, it can't be a part-time thing. You can't say, oh, I want to dabble. There are roles where you can dabble. So, you can do advisory work with portfolio companies, you can be a scout. There's various ways you can dabble in venture capital.
But if you want to do it, if you want to really be an investor, then you have to commit to it full-time, and it's a job that takes more than six hours a day. You're busy and you don't determine when you're busy. It's when you find an opportunity, or whatever, then you might be free one day and then the next day, you're working 20 hours. So, you just never know. But I'd say that if you're curious about the future of the space and you want to have massive influence on where HR tech goes, do get in touch, I'd love to discuss it further.
[0:43:52] David Green: Can you share with listeners how they can follow the work you're doing at Acadian and also, how they can read your musings on the work in progress?
[0:44:01] Thomas Otter: Yeah, I'm on Substack. So, if you just look up Thomas Otter on Substack, you'll find me. We have a website called Acadian Ventures, so www.acadianventures.com. The legacy of Acadian is that it's actually the name of a national park in the US where Jason used to hang out. So, that's where we got the name from. It's also A, so it ends up first on the list of things. But yeah, if you just stick Thomas Otter into Google, normally I pop up. There's a professor in Frankfurt called Thomas Otter. That's not me. But yeah, look me up. You'll find me on the interwebs.
[0:44:38] David Green: That's great. Thomas, thanks so much. And thanks as well for reintroducing me to Echo & the Bunnymen. Thank you.
A huge thank you to Thomas Otter for joining me today and for bringing such a refreshing, honest perspective on what's really happening in the world of HR tech. And of course, thank you to you, our listeners, for being part of this community each week. If today's episode gave you something new to think about, or helped you see the HR tech landscape in a different light, please do subscribe, rate, and share the episode with a colleague. It really helps us keep bringing these kinds of thoughtful, forward-thinking conversations to HR and people analytics leaders around the world. To stay connected with us at Insight222, follow us on LinkedIn, visit insight222.com, and sign up for our fortnightly newsletter at myHRfuture.com for the latest research tools and trends shaping the future of HR and people analytics. That's all for now. Thank you for tuning in and we'll be back next week with another episode of the Digital HR Leaders podcast. Until then, take care and stay well.