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The "manual era" of capital allocation is in its final chapter. The firms still relying on manual data extraction and analysis aren’t failing overnight, but they are falling behind one week at a time. While most of the industry continues to "white-knuckle" through 200-page documents and legacy databases, and manual Excel extraction, a new breed of Agentic Allocators is quietly rewriting the rules. They aren’t just using AI to summarize emails; they are leveraging AI-augmented workflows that intelligently automate parts of their investment and operational processes that were previously impossible to automate.
Hosted by Victoria Sienczewski, CEO and Founder of AuumAI, The Agentic Allocator is the "behind-closed-doors" look at how the world's most sophisticated Limited Partners (LPs), allocators and General Partners (GPs) are actually deploying AI, and the hard-won lessons from those building the systems.
This isn't a series about high-level theory or technical gibberish. Each conversation features industry leaders, forward-thinking LPs, GPs and experts who are rewriting the rules of capital allocation through agentic AI. Expect real-world case studies, tactical frameworks you can actually use, and moments that challenge outdated norms. You'll come away with a clearer understanding of the critical questions every allocator must ask - about data privacy, team adoption, integration, and governance - before investing in any AI solution. If you're tired of the "black box" and ready to evolve your investment office for what comes next, you're in the right place.
The sooner that you start experimenting, the sooner you'll start learning and the sooner you'll become educated. I don't think that that you can fully AI your process now, but if you don't start now, you are gonna get left behind.
Victoria:Welcome to The Agentic Allocator. On today's episode, we're joined by Chris Hartnell, the CEO and managing director of HEICO Investment Group. Chris operates at the unique intersection of institutional investing and industrial technology and innovation.
Chris:I don't necessarily see a world where there's not a human in the loop. I view it as the combination of the human and the AI.
Victoria:At HEICO, he built and oversees a global multibillion dollar platform that includes a sophisticated multi asset class endowment style portfolio paired with direct investment and incubation of new ventures in sectors like maritime logistics and energy transition.
Chris:During rapid sort of changes in the market, the teams that have adopted AI, their ability to respond to the market will be much faster, more responsive, but I think also making better decisions.
Victoria:Today, we're exploring Chris's blueprint for the modern investment office. How he balances an endowment style portfolio with a company builder approach to direct investments and incubation. Why he believes the investment industry is ripe for AI disruption and where the most significant leverage exists for LPs.
Chris:There's just these real ground truths that are just absolutely critical to answer, but they're not necessarily sexy tasks that the team are are having to do, and it's not necessarily what jumps to mind when you think about adopting AI.
Victoria:I hope you enjoy the conversation. Chris, you've navigated some of the highest stakes environments possible, from aerospace engineering at Rolls Royce to Morgan Stanley M and A advisory, and you were also a Royal Marine. What did those experiences teach you about risk and how you invest today?
Chris:That's a great question. Those are three, quite diverse experiences in my life. So I'll I'll try and pull them together on one thread, but I think operating in those high risk, high stake sort of environments, I think there's really two sides to it. I think one of which is being prepared. So doing the work in advance, trying to understand the risks, trying to to think through what will happen after the action and sort of how you might respond.
Chris:I think, actually, the Royal Marines was the one that taught me that you need to think through both the downside, but also if opportunity presents itself, to take advantage of it. So I think that preparation is is key. But the simple truth is is the only thing that you can guarantee is that it won't go to plan, and that something will pop up that's unexpected. And I think I think that then going into that experience and knowing that the sort of field will open up, that things won't go your way, I think having done that preparation, you're much more prepared to confront those opportunities and those setbacks and respond to them because you've already done the thinking upfront, and therefore you can be much more focused on execution rather than being blindsided by some of those setbacks when they come. That's incredible.
Chris:And it's very topical
Victoria:for a conversation today around capital allocation and AI. And HEICO has done a lot to prepare for adoption of AI internally. But before we touch on that, we'd love to also speak about some of the cultural elements that HEICO has uniquely. There are two specific elements of the organization. You have an endowment style portfolio balanced against incubating and investing in high velocity technology companies like AI.
Victoria:How do you balance those two elements internally?
Chris:Yeah. That's a great question, and it's something that we grappled with a lot at the start of building HEICO. So we had an aspiration to combine both of those fields and both of those teams and really have both disciplines operating alongside and sort of mingled together. And I think what I learned through that journey is that they actually are two very different processes. The capital allocation process is a is a very different process to running and and managing direct deals, and I think that trying to put those together and have have those processes too close together can create a lot of challenges in the organization, a lot of noise.
Chris:And so what we what we realized was that what we wanted was the sharing of information. We were seeing deals in companies and technology and opportunities that were then feeding back to the allocators, and the allocators were being exposed to some of the smartest people in the world and and sort of gathering their ideas. And so what we've really focused on much more is trying to create a much better flow of information between the two teams, building sort of one culture across those teams where they share that, and that's been very successful. We see associates on both both teams, networking, spending time together, and swapping stories. But the lesson learned for me is that those sort of businesses and and what they're trying to achieve operate very different processes and for good reason.
Chris:And actually keeping those separate is is a good thing.
Victoria:Super interesting. And you recently described OMAI as addressing a transformative yet overlooked opportunity within the ecosystem. So bringing intelligent automation to LPs and allocators. Why do you think the industry has been okay with their manual processes for so long?
Chris:You know, was thinking about that on on my way over here, and I think it's that thing that we love we love to sort of give other people advice, and especially, you know, whether you're a direct manager investing into into businesses and you're sitting on their boards and trying to point out where they can improve the business or or whether you're an allocator talking to a manager and sort of pointing out where where you think they can tighten their processes and and and, you know, their alignment with their own thesis. I think it's just human nature that we're outward looking, and it's very easy to give other people advice. But it it's it's rarer that we sort of take a look at our own house and, and reflect on that. And I think, you know, we've had, ChatGPT OpenAI has sort of released that, I think, three and a half years ago now, And it's amazing to me that, you know, the Allocator community still is is is still quite reticent to adopt it. But I think it says a lot about both human nature, but also about just investment firms in general.
Chris:I think a lot of a lot of CIOs have built their process through through years of experience. And, the idea of changing that in in a sort of traumatic way through the instruction of AI, I think, runs contrary to the traditional wisdom of of being conservative with how you develop and build your investment process.
Victoria:And HEICO obviously has taken a look at its internal processes and is far along the path of AI adoption. You've started off by addressing the kind of foundation from a data perspective. You're extremely digitalized which is very unique for the industry. And so right now when you're looking at specific use cases like LPA analysis or DDQ analysis, how do you think about implementing that internally?
Chris:Yeah. So we we've done a lot of work upfront and and I think that's a critical thing to do because, you know, you you you have to take security seriously. You have to set up the guardrails, the privacy. That there's a lot of groundwork that you need to do, and it's not sexy. It's it's really operational, and and and that that's real work.
Chris:But once you've got that, I think it's I think it's quite liberating, you know, to go back to the analogy of realizing that you need to sort of just adopt AI in in some way, shape, or form and just start to experiment. And how we thought through that was that trying to apply AI in some of the more most discrete process steps that we have, where we have a clear understanding of the input and the output, and where we can, you know, really apply it in a in a measured way, measure that output, measure that that that, the impact on the organization, and start to experiment, but also build comfort and, you know, comfort in the team and in the organization with applying AI. So we've taken very much a applying it to our building blocks and our process and with the idea that this is gonna be a multi year process for us to learn and adapt and integrate. And I'm sure it won't go right every time, but it's only through doing that that we're gonna learn where it's really valuable.
Victoria:Absolutely. And what are some of those early signs of value in terms of how you're measuring success internally?
Chris:I think I think what we're seeing is it's the productivity gains that you can get really first. That's the first bit that you can access. The ability to summarize, you know, LPA documents, you know, they're they're long meaty documents, but actually a lot of times there are some really key clauses in there that you can start to understand sort of, you know, how market is this for for this sector that you're that you're investing in. I think also a lot of the manager interviewing notes. You know, running a running an interview process is it's a time consuming process, but also requires a lot of focus.
Chris:And giving the interviewer the the and and the the the team member the confidence that's that sort of AI is working in the background to make sure they're capturing the data that's happening frees that individual up, I think, to really lean into that conversation on another level. So we're definitely seeing the the sort of productivity boost. And what I'm really excited for is what we're gonna discover a little bit later, I think, in the journey, which is once we see the productivity boost, I think it's then we're gonna start to see, new areas that starts to add value, which is really around the process and thinking through an investment. One of our direct investments, we started running some of those through some AI models, and we are seeing it identify perhaps slightly different reads on risk Mhmm. That we maybe hadn't identified so much or maybe prioritized as a team.
Chris:And I think that's really valuable, almost having a sense check that you can add, you know, to the as as sort of another team member.
Victoria:Yeah. Absolutely. And it it's interesting you mentioned obviously historically the process of learning the trade or the role of an allocator. And when I think about customizing AI agents it follows that same pathway in terms of almost like training an intern or training an analyst over time with your data, processes and with that perspective. So it'll be very interesting to see the outcomes that HEICO gets from a performance perspective over time.
Victoria:And curious, you mentioned obviously there were challenges to adopting AI internally. Can you walk us through some of the challenges and how you've overcome them?
Chris:I think one of the one of the biggest areas really was was on a I'll call it IT, but I I view IT as not just the hardware or the software. I think it's also the behaviors and and the processes around that. So something as simple as actually structuring your your digital folder structure and making sure that AI has access to the right folders and that it's not going into the compensation folder, to pull data from there, which just sounds so small and simple, but that's not necessarily something that you can do overnight. That that takes, a lot of thought, and it again, this is these are quite operational detailed processes that you have to think through upfront and go through that and do the hard work upfront. You know, we've also had to go to our lawyers and ask them the question of what is you know, what are the data protections of these documents, you know, from our our GPs?
Chris:And so there's just these real ground truths that are just absolutely critical to answer, but they're not necessarily sexy, you know, tasks that the team are are having to do, and it's not necessarily what jumps to mind when you think about adopting AI.
Victoria:Absolutely. And there are no easy answers. So when you're going to your legal team, in many cases, you may be the first one or the second one asking them these questions so they're also going through their own internal process of adopting AI within also a field that can really benefit from it tremendously. So would love to fast forward five or ten years. How do you think an AI enabled investment office will look like?
Chris:I think to be clear, I don't necessarily see a world where there's not a human in the loop. I think I think the art of science of of allocation, I think it's a bit of both. I think it's investing in managers, and that is an inherently human approach. Right? Like I said, there's a there's there's a huge part of that, is understanding human beings, understanding their motivations.
Chris:Yes. I think in in the future there may be quant funds where actually maybe there's not such a human in the loop, but I I think especially around privates, for example, huge hugely important part of any portfolio, that will continue to be a very human human sort of industry. And I think I view it as the combination of the human and the allocator. And what I talked about earlier of, you know, we've seen investment teams make a judgment on the on the risk reward profile of an opportunity, and the AI was actually able to help bring in a new read on risk and actually identify risks that we perhaps hadn't weighted so highly. And I think it's that sort of dynamic that that we're gonna start to see.
Chris:What I can't tell you is how the investment process will change. I think that is gonna be really interesting to watch, maybe where AI can lean in more, but and maybe other areas where the human will very much remain in the loop. But the other area that I'm excited about, and we talk about this, is during rapid sort of changes in the market, our teams you know, allocating teams are often smaller, and they have a lot of information to digest. They have a lot of decisions to make if they're gonna make any decision. And that that decision making window can be small, very time constrained, and the team can be under a huge amount of pressure to, run through their process to make a decision, whether it's rebalancing, leaning in, leaning out, responding to what's going on in the market.
Chris:And I think that's gonna be really fascinating because the teams that have adopted AI will be able to operate that process so much quicker than teams that haven't. And their ability to respond to the market will be much faster, more responsive, but I think also making better decisions and and capitalizing on those those shifts in sentiment and also working with their managers to to respond as best as they can.
Victoria:And so if you were to advise a MD at a peer organization who may still be on the fence about AI adoption, what is the one piece of advice that you would give them or maybe the first step you would suggest that they take?
Chris:Well, I think the first question the the first piece of advice I'd say is, you know, be prepared. Before you jump in, don't just let AI sort of dive in and and and start, you know, trawling through all your data and because things may happen that you don't want to happen. So I think first off is be be prepared. But I I think you the sooner that you start experimenting, the sooner you'll start learning and the sooner you'll become educated. And and this is gonna take years, but if you don't start now, you're gonna be behind that curve.
Chris:And, you know, the technology will become more sophisticated. There are some brilliant entrepreneurs out there right now that are creating this technology, and they're looking for partners, and they're willing to spend a lot of time with organizations developing that technology. But as those as those institutions go up the maturity curve, as those solutions go up the maturity curve, you know, you you will be left behind, and and and you will be playing catch up with developing your processes. So I don't think that that you can fully AI, you know, your your process now. But if you don't start now, you you are gonna get left behind.
Victoria:Absolutely. Chris, thank you so much for taking the time to join me.
Chris:Thank you.
Victoria:Was a pleasure speaking. That's a wrap for this episode of The Agentic Allocator. If today's conversation gave you a clearer vision of where the industry is headed or helped you pinpoint exactly where your own process is stuck, go ahead and follow or subscribe wherever you get your podcasts. And if you're curious what Agentic AI might actually look like inside your investment office and how to get there without compromising on security or control, visit auumai.com for demos and resources on AI native LP and allocator workflows. Until next time.