Strategic AI Adoption for Asset Managers and Enterprise Decision Makers - with Robert Kubin of Amundi

Welcome, everyone, to the AI in Business podcast. I'm Matthew DeMello, Editorial Director here at Emerge AI Research. Today's guest is Robert Kubin, Head of Sales for Central Europe at Amundi. Amundi is a European asset manager ranked among the top 10 globally by assets under management. It provides savings and investment solutions across active and passive management in both traditional and real assets. Its offering includes IT tools and services like Amundi technology that cover the savings value chain. Amundi is also listed on the stock exchange and manages more than 2.3 trillion euros in assets. Robert joins us on today's show to unpack how AI is moving beyond legacy technology patches in asset and wealth management. He explains how it can drive measurable business outcomes from transforming decision-making and portfolio management to automating time-intensive processes. AI is helping firms overcome long-standing scalability challenges. Our conversation also highlights practical ways to capture ROI, including automating KYC and onboarding, streamlining marketing and client reporting, and helping smaller managers compete at a global scale by embedding AI into core operations. Today's episode is part of a special series on AI and wealth and asset management sponsored by FE Fund Info. But first, interested in putting your AI product in front of household names in the Fortune 500? Connect directly with enterprise leaders at market-leading companies. Emerge can position your brand where enterprise decision makers turn for insight, research, and guidance. Visit Emerge.com slash sponsor for more information. Again, that's Emerge.com slash S-P-O-N-S-O-R. Also, are you driving AI transformation at your organization? Or maybe you're guiding critical decisions on AI investment strategy or deployment? Without further ado, Here's our conversation with Robert. Robert, welcome to the program. It's a great pleasure to have you. Thank you very much, Matthew. Looking forward. Absolutely. We've talked so much about the challenges in the asset and wealth management spaces throughout this series. But we were having some preliminary conversations and I only bring these up in these episodes for the audience on special occasions when I think it can really pull back the curtain for them and really help them get a really distinct view of the challenges in these industries. But we spoke in those preliminary conversations leading up to this interview, you mentioned that there are characteristics of the asset and wealth management space that make these industries particularly well suited for AI deployment. I think that's a great framing before we start talking maybe about the challenges. Tell us about challenges that you're seeing in how asset and wealth management spaces really reflect these technologies and what makes these capabilities so applicable to this industry. Of course, Matthew. And one more time, thank you very much for having me here. It's my pleasure. So, you know, asset management, I would say is rather specific industry. Obviously, we don't sell goods, we sell services and these services are very easily scalable. Second, it's a very immature industry with a lot of competition out there, big hunger for customers and a lot of fights within the players. Third, this is very specific to asset management. You cannot really increase the prices of your products, unlike many other industries, for many reasons. One is the competition, just the competition doesn't allow you. but second is the regulation because if you wanted to increase your fees that you charge to the customers you need to have a consent by the regulator and in most cases the regulator is not gonna give you the consent so you need to keep your prices flat or falling and if you are you know in the ceo level uh and you try to grow the company have a lot of push by the shareholders you know how to grow it then you can cut the cost obviously but that's pretty difficult right you You cannot do it over the long run. You cannot increase the prices. What you can do is you can grow the business, you can scale it, you can grow the assessment management, and that's how you become bigger and profitable. But in order to do that, you need to do a lot of investments into technology. And if you look at industry reports, if you look at the financial statements of the various players, you realize that this is very often the case, that many of the industry leaders, they invest a lot into technology. but these investments are not really reflected by the corresponding increases of productivity. And there are several reasons for that. Maybe the most important one is basically these investments into technology. What they mean is mainly plugging in the holes, covering the gaps and fixing up systems, which are by definition quite obsolete. So the situation is not an easy one, but there are hopes that AI might be a game changer for this. Absolutely. Now, you're talking about a problem that maybe a little bit more in the AI adoption space. We call it legacy tech stacks and, you know, modernizing those tech stacks. But you're describing how traditionally within the asset and wealth management spaces, you're only really plugging little holes where you've gone about this traditionally. And now you have all these new products more from the FinServe space or FinTech space rather that promise you know what I call and I heard this term a couple of times out there but still technology of okay you can keep most of your tech stack but we going to put it on stilts and then it going to be able to modernize There be kind of like like something like a wheelchair You know, you sit in the wheelchair and then the A.I. is actually helping you move and you're using maybe your arms to really drive the technology because your legs don't work. Right. You know, I'll do I'll do a respect to to to our disabled folks. out there for this metaphor, but for where asset and wealth management firms need to change their mentality around this technology that's far more over-encompassing than just plugging in holes. So far in this series, we've talked about how asset and wealth management leaders are feeling the inevitable mandate to modernize, given that backdrop. There are a lot of challenges getting in the way, especially around harmonizing fragmented data, which has evolved from the earlier problem of just, as we said, updating tech stacks. And firms are trying to see the real value in AI often through experimentation and open-ended policies on shadow AI that offer greater efficiencies. That's what we've touched on so far in the series. Is there any of that that's resonating with your experience or where are you seeing the most opportunities for AI and asset and wealth management today as opposed to these kind of like pothole plugging approaches to technology? Yeah, very good question, Matthew. And I mean, basically, I pretty much agree with all you said. You know, the upgrades of the IT infrastructure, they tend to be, I mean, you invest into a new system which helps asset managers to make decisions then you have a different system for hr recruitment then whatever right then you have another system for finance and accounting then you have another system for an ev calculation and basically you end up with you know different systems based on which the company runs they are very often not talking to each other right and you know as soon as you upgrade them they become obsolete right because the technology improves so fast. And then there is a second aspect where, as I was saying a couple of minutes ago, there is a lot of push on growing profitability. The result of that is that there is very strict policies on increasing headcount. And in reality, what you see is many of the asset managers are understaffed and still they have a lot of manual processes, right? And that creates a lot of friction and maybe creates you know small problems small mistakes but then if you you know add them together you see that i mean as a whole you have pretty severe deficiencies right and pretty severe bugs that you need to plug and i think most of them you know if you look at ai you can use it for i mean for many reasons but i would say probably for the purposes of our conversations there are two that come to my mind the first one is to improve your decision making And second is to, you know, improve the processes and, you know, create efficiencies and increase productivity. Basically, that's how I see it. Right, right. So just for trying to keep in mind that ultimately, especially with a lot of legacy tech stacks and legacy industries overall, the first advice or the first mentality might go to buying. Even if at this point, especially so far after the generative AI explosion, we used to call it build versus buy. And now when you really look at it, everybody does some kind of ratio of both. The audience is very sick of me saying this. I say this. I've been saying this for most of the episodes over the years, over the past year. But you're basically everyone is now really going about this question of, OK, I know I'm going to need to build something. And especially for these legacy industries, they know they're going to need a buy actually quite a bit. given how much they're dependent on, as you said, manual processes in the older technology. Given that backdrop, how should they think maybe first about building to prepare to buy, especially if they have to kind of take the stilt wheelchair approach that I was making a metaphor for before? You know, I mean, the answer that I'm going to give you sounds very trivial, but in reality, it's the case. Right, right. Very much easier said than done. Exactly. Like, you know, the first important decision that, you know, the C-suite needs to make is that there is something like AI, and it's not a threat, but it's actually, you know, it's gonna help us, right? And it sounds really easy to do. But in reality, this is not for several reasons, you know, the very top of the organizations, they might not be too friendly with new technology, for example, and then the middle parts or the bottom of the organizations, they are very busy with, you know, doing the daily tasks. and they don't even have time to think of how to improve it. Right. So I think this is very often the case. And the way that companies solve this or try to solve this is that first they need to make a decision that this is something that they want to do and they see the value at it. Second, they set up a department sitting somewhere in the home office who is dedicated to AI. And then the work cannot stop there. but ultimately what you want to do is you want to make AI as part of your DNA basically. So it's not a kind of a separate thing, but this is a thing that you use daily. And so you set up the department that looks at AI, what can it do, etc. And then you need to have, let's say, local AI champions. If you have a global company that sits in different places, or you need to have an AI champion for each and every line of business and each and every, you know, geography function, etc. And all together, they should make sure that actually the whole thing is, you know, becoming your DNA, as I said, right? And then the department at the top of the organization, basically, what they should do is they should look at you know where AI or what AI is nowadays because it evolves very fast and what would be the use cases of that within the organization and then you know you mentioned then they need to make decision you know in what part of the process they would source and what they will build in-house that very much depends on the size of the organization the larger the company is probably the more of these things should be done internally but if you are relatively a small asset manager right who doesn't and have the capabilities, you often acquire something which is out there. Right. And this makes the asset and wealth management space much more akin to other industries and how they'll be affected, as we've seen. I came from a couple of months ago, Alibaba, who's in retail, invited me to an expo where it became very, very obvious. You're going to start to see procurement teams in retail of maybe five or 10 in number be able to do the scale and scope of very large corporate teams. And it sounds like this is basically coming to this space of, you know, yeah, yeah. If you're a small asset manager, you can buy the technology to compete at the volume of big asset managers. And that, you know, as much as asset and wealth management might be a special space suited for AI in that way, they have much more in common, even with industries like retail, et cetera. Yeah, very much. Absolutely. So keeping that in mind, and I think even listeners at home in the asset and wealth management space can go, okay, well, I'm in a big organization. I know we're going to have to build more than buy. I'm in a small organization. I know I'm going to have to buy more than it really makes sense to build. What else can we add to the list for what to keep in mind, especially as these technologies become more commonplace? Of course, competitive factors are a lot different. I always make the comparison to early internet adoption. There's a big difference in how you're going about your life when, you know, you're the only house on the block with dial-up internet versus everyone on the block has Wi-Fi. It's just a different world. How should leaders think about that world where they're going to start to see small asset managers have the technology to compete at that level? I think, as I was a little bit touched upon that, AI could be used for improvement of the processes or to drive the decision making. The improvement of the processes, that's probably a relatively easier place to start because it's just easier to implement. And if I was a leader of any asset manager, I would think, OK, I need to scale. I have several processes. They pretty much very well know what is falling apart. right and they should look at it and they should say okay so we have a pretty manual process let's start with a small portion of it let's you know test it let's see how ai actually can you know solve the task once that is successful you know they can grow it and they can scale it and uh for asset managers maybe i will give you you know some examples how this is uh typically applied but for example you know when you do a kyc process you know know your customer process i mean every asset manager when they deal with the client's money they need to know you know where the money comes from right where this is legitimate you know uh etc and the process in reality what that means is you need to get a different source of sources or different information some of them coming from some external databases you know some registers the others come from you know the client directly sends it to you you know in different formats by emails etc and you have you know a bunch of information uh first of all you need to ensure that i mean there is nothing missing and second you need to find a way how to extract the relevant information from all of that right so ai is actually a very good use case for this because you know you can really uh improve the process and just structure it way better than you know you can cut the manual requirement on the people second very good example for example is marketing materials for every you know fund every you know portfolio every product to every client asset managers they provide uh you know on a monthly quarterly or annual basis some marketing materials and in order to provide those you need to have inputs on what is going on on the markets right you need to know what is going on in the geopolitics you need to know what is going on when the products what does asset manager feel about you know this product what uh he's gonna do about it then you need to know uh some documentation of the fund etc so you have again you know a lot of different sources uh you know with unstructured information so it takes you a lot of time to put this together then you do it on a regular basis right and then kind of you know using ai you can substantially improve the system the process i mean and you can really make it very much more straightforward than before without using it absolutely and talk so much about really being able to judge the system that you that you might be buying um i think that's a Maybe even a little bit different than maybe assessing the vendor that you're working with. And as we've kind of said in this spectrum, whether you're big or you're small, you're going to have to buy at some point. What factors do you find are most important when vetting AI vendors for enterprise use? You know, probably as with any other procurement process like this, you look who is out there, who provides the technology, right? But then you have your technology guys making sure that the whole technology side of things works well. I mean, I'm not an expert on that, so I would leave it to the people who are familiar with this. You would look at the use cases, what the company, who is the customer, where they have experience with this, how many customers they served in the past, obviously what is the price of the technology, whether the whole thing makes sense, whether the result. is justifying the cost that you put into it. So, and then you select, I don't know, 10 providers, you shortlist three, you speak with them, right? You probably ask them to provide you some sample for improvement on the smoke process Then if that works you select them and you let them run you know bigger thing basically But I think this is similar to anything else right? It's just, it's another service that, you know, we acquire by, you know, from someone else. Absolutely. And when I've spoken to a lot of folks across FinServe spaces, especially more on the legacy side, it tends to be that the conversations around ROI with the C-suite tend to be really the hardest just because the C-suite so quarterly focused, they want hard numbers, they want metrics. And often the ROI from these systems can start much more ideologically. And we talk about kind of a pearl of strings approach. You want to be able to show early wins for a broader vision that kind of encompasses that more existential ROI that can't really be boiled down to metrics. How can especially asset and wealth management leaders best determine ROI from AI? investments? Yeah, you know, you need to quantify, right? I mean, the cost that's known, that's what you basically purchase. And then you need to quantify to what you are going to get out of it. And then you would need to measure, I mean, if you are improving a process, which turns out to, you know, decrease the cost, then you basically need to calculate what is the decrease of cost, you know, coming from deployment of AI, right? If it is, you need less people to scale your business, then you can calculate, you know, how few people you need to in order to grow. On the other hand, you would say, OK, so if I need to expand to this business, I need a hundred more people. But if I use AI, you know, I need only 50. So you can cost and then you have it. I mean, many things are about, you know, improving the speed. So you also try to quite quantify, right? If this particular task takes me two days and I can, you know, stream it to half an hour, Obviously, you can cut the cost and on and on and on. So obviously, you don't know exactly the end result, but I think you can calculate it pretty accurately, I would say. Right. There's a few trade-offs that are obvious from the offset that translate, maybe even in a zero-sum way as you're presenting that. Although I think across industries, it gets a lot more existential, especially as we start to see agentic AI really get put in the mix. Also, that even for all so many of the deterministic use cases, the simple applications of AI that don't look like chat GPT don't involve, you know, crazy models or anything like that. We don't see enough of that in any of these any of these spaces just yet. And that's the stuff that's been around for 10, 15 years, whether people know about it or not necessarily. But lots of food for thought here, Robert. Really, really appreciate this perspective, especially as those asset and wealth managers are taking their first bold steps into a much broader world, as they say in Star Wars. Anyway, Robert, really, really appreciate you being with us this week. Thanks so much. Thank you very much, Matthew. My pleasure. here. Wrapping up today's episode, I think there were three critical takeaways for enterprise leaders in asset and wealth management from today's conversation with Robert. First, AI can move beyond legacy technology, quote unquote, patches to deliver measurable business outcomes, transforming decision making and automating time intensive processes that have historically limited scalability. Second, embedding AI into core operations can reduce manual workload, improve efficiency, and allow smaller teams to operate at the scale of larger competitors. Finally, successful AI adoption starts with leadership commitment and structured deployment. Establishing dedicated teams, local AI champions, and clear build versus buy strategies ensures that AI becomes part of the organization's DNA rather than a standalone initiative. Are you driving AI transformation at your organization, or maybe you're guiding critical decisions on AI investments, strategy, or deployment? If so, the AI in Business podcast wants to hear from you. Each year, Emerge AI Research features hundreds of executive thought leaders, everyone from the CIO of Goldman Sachs to the head of AI at Raytheon and AI pioneers like Yoshua Bengio. With nearly a million annual listeners, AI in business is the go-to destination for enterprise leaders navigating real-world AI adoption. You don't need to be an engineer or a technical expert to be on the show. If you're involved in AI implementation, decision-making, or strategy within your company, this is your opportunity to share your insights with a global audience of your peers. If you believe you can help other leaders move the needle on AI ROI, visit Emerge.com and fill out our Thought Leader submission form. That's Emerge.com and click on Be an Expert. You can also click the link in the description of today's show on your preferred podcast platform. That's Emerge.com slash ExpertOne. Again, that's Emerge.com slash ExpertOne. We look forward to featuring your story. If you enjoyed or benefited from the insights of today's episode, consider leaving us a review on Apple Podcasts and let us know what you learned, found helpful, or just liked most about the show. Also, don't forget to follow us on X, formerly known as Twitter, at Emerge, and that's spelled, again, E-M-E-R-J, as well as our LinkedIn page. I'm your host, at least for today, Matthew DeMello, Editorial Director here at Emerge AI Research. On behalf of Daniel Fagella, our CEO and Head of Research, as well as the rest of the team here at Emerge, Thanks so much for joining us today, and we'll catch you next time on the AI in Business Podcast. Bye.