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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 Expect real-world case studies, tactical frameworks you can actually use, and moments that challenge outdated norms.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.
Welcome to The Agentic Allocator. Today, we're joined by Shaun Meng, who has built a thirty year career at the heart of capital allocation. Most recently, he served as managing director at the Cleveland Clinic Investment Office, and before that held a senior leadership role at the World Bank Pension and Endowment Group.
Shaun Ng:Consequential innovation of our lifetime. So it's gonna change everything that we do. Right? How we learn, how we teach, how we communicate, how we invest, how our managers invest. And what the CIOs essentially have been tasked to do is to navigate the investment office from a pre AI world to a post AI world and be able to thrive in this new world.
Victoria Sienczewski:Since leaving his investment role, Shaun founded AI for Allocators, an independent newsletter dedicated to helping LPs navigate the complexities of AI adoption.
Shaun Ng:After talking to so many different LPs and allocators, after I launched AI for Allocators blog post, I came to realize a lot of these so called mistakes or missteps all stem from one foundational misdiagnosis of what AI challenges.
Victoria Sienczewski:In today's conversation, we cover the pain points LP organizations were already dealing with before AI arrived, how to build institutional rather than just individual value from these tools, and what a well run investment office looks like on the other side of this transition.
Shaun Ng:I would really challenge CIOs to not look at it as a product problem, but look at it as how do I create an environment so that my team can adopt AI in the most effective way in areas that matters the most for the organization.
Victoria Sienczewski:I hope you enjoy the conversation. Shaun, thank you so much for taking the time to meet with me today.
Shaun Ng:Great to be here. Great to be here in person with you, Victoria, and London.
Victoria Sienczewski:Shaun, before we talk about AI, which is our topic today, wanted to dive in and ask you to maybe paint the picture for our audience. LPs have had, you know, and allocators have had numerous challenges, and so would love to understand what are LP pain points today before the advent of AI?
Shaun Ng:Yeah. You know, even before AI, there are quite a number of challenges that we faced. Like, number one is somehow the demands from stakeholders is getting higher and higher. What does that mean? More reporting.
Shaun Ng:You know, when I talk to my peers, the icy slide deck seems to be getting thicker and thicker. But at the same time, the size of our team do not change. Right? So so essentially, you are asking to do more with with the same. So that's that's the first kind of pressure point that my colleagues and I have been facing over the last ten years.
Shaun Ng:And then there's a data complexity. Right? So so on one hand, we have the quantitative data, meaning the performance, the exposures, and we've been spending a lot of time designing and implementing data warehouses or engaging with portfolio management systems like Cohesa, Solovis. So that there has been quite a lot of time and effort on that. And then you also have the unstructured data.
Shaun Ng:The the the qualitative, the narrative, the the PDF if you like. Right? So we are talking about investment memos, the the manager on quarterly letters, meeting notes, capital call statements, and we have been spending time to engage and hire CRMs and data management system like Bipsing and Dynamo. Right? So so data complexity is another one that keep has kept us really busy.
Shaun Ng:And the third one, which I think not many people has talked about is talent. So at the end of the day, capital allocation is a people business. Right? So so talent recruitment, talent development, onboarding so that they can contribute as as quickly as as possible, making sure that people potential reached. So those are those are also very, very time consuming.
Shaun Ng:And AI came along and and it affect every single one of those.
Victoria Sienczewski:Thank you for painting that picture. Extremely helpful. And would love to now dive into AI. You've built an incredible platform with AI for allocators. Over 50 newsletters published so far, exploring different AI topics and challenges and implementation strategies for LPs.
Victoria Sienczewski:Would love to ask you, where do you think LPs are going wrong today? What are some of the, you know, common mistakes you think LPs are making?
Shaun Ng:After talking to so many different LPs and allocators, after I launched this AI for allocators blog post, and only the last few weeks, I came to realize that a lot of these so called mistakes or missteps all stem from one foundational misdiagnosis of what AI challenges. Right? So so if you can recognize this core error, you can pretty much prevent 80% of of these downstream errors. Right? And before I tell you what the misdiagnosis is, I think we need to take a step back and and and describe what we are really going through, this this AI transition.
Shaun Ng:AI is by far the most consequential innovation of our lifetime. So it's gonna change everything that we do. Right? How we learn, how we teach, how we communicate, how we invest, how our managers invest. Right?
Shaun Ng:It's gonna be ubiquitous. And what the CIOs essentially have been tasked to do is to navigate the investment office from a pre AI world essentially to a post AI world and be able to thrive in this new world. Right? So so think about electricity. Right?
Shaun Ng:So so we are we are going through a historic time when the factories are being redesigned, right, to take full advantage of the power of of of electricity and that's what the CIOs have been tasked to do. But when we are when I talk to the allocators and the business the the the missed diagnosis is this. Right? So so they look at it as a technology problem, as the IT problem as opposed to a historic strategic transformation challenge which is really a leadership challenge, a change management challenge, a cultural challenge. It's it's huge.
Shaun Ng:It's it's not easy and fifty years from today, people will look back and say, wow. You you helped guide the investment of this through this time period. Right? So so once they know that, I believe they will prevent quite a lot of common missteps that that that that we see. What are some of the common missteps?
Shaun Ng:You know? Like, one of the ones that I that I get quite a lot is CIOs and the senior management don't actually use AI. Right? They they may encourage us to use AI but they don't actually know the pitfalls, the power of of using AIs and we are talking about a historic transformation that is not acceptable or that that will not work. Right?
Shaun Ng:And and I understand why certain senior management would do that. Right? If you if you think about all the complicated risk systems, I I know many investment officers actually run a very effective portfolio without the CIO knowing how to use Aladdin. Right? Because they know how to ask great questions and so the the the kind of strategy they are that they are familiar with is not gonna work this this time around.
Shaun Ng:Some of the other common missteps, they delegate it to a IT project manager, they give it a budget with a start date and end date, and if you look through what I just how I framed the AI transformation, there's really no end date, right? You are really building AI capability here to to help guide it to the next era. So, yeah, so I think if people can understand that this is not a technology problem, this is more of a strategic transformation problem, I think a lot of mistakes can be avoided.
Victoria Sienczewski:Incredibly powerful framing, Sean, in terms of viewing it as that transformation. And for me that means, as you mentioned, cultural transformation, governance processes that need to be put in place, but also the way work has been done in the LP and Allocator context will have to change. And so you need both senior leadership and more junior or mid career professionals to be part of that transformation, extremely important. So you talked about the challenges and some of the missteps. Would love to understand if you were to guide an LP or allocator organization that is early on in AI adoption, what would you say they should do?
Shaun Ng:Yeah. Yeah. The what to do with AI is a question that I get most and my answer always surprises the allocators. Right? So which is don't start with technology.
Shaun Ng:It's it's very tempting to ask what tool should I use. Right? To come up with a vendor checklist, start talking with your peers to see what they are using, to see what is a good fit for your organization and it's really, it's it's it's it may feel like progress but it's actually will stop you from reaping the long term reward of of of AI. I would really challenge CIOs to not look at it as a product problem but look at it as how do I create an environment so that my team can adopt AI in the most effective way in areas that matters the most for the organization. Alright.
Shaun Ng:So so let let let me repeat. So focus on creating that environment. Right? Not a product, but the environment so that your team members can actually thrive in areas that matters to you, to the organization. Right?
Shaun Ng:So that they don't use AI on police stuff that that doesn't really matter. So what what does that look like? I talk about the CIO senior leadership should be ready to have a communication with the team members. I call it the communicate your AI stance. And and this may sound as simple as, k, guys.
Shaun Ng:AI is here to stay. Ten years later, our nature of work will look very, very, very different from how it is like today. How do we gonna get from here to there? I don't know, but I need your help to figure it out together because the technology is so new. So it can't be as simple as that, but what it gives the team members is number one, a sense of urgency that this is the way to go, and number two, they're free to then explore and experiment this new tool.
Shaun Ng:Your team members are also reading a lot of news about AI and and job losses, so when you have the communication, do be ready to talk about kind of your your opinion or whether or not you will impact jobs. Right? So so I I I've written about it at least a couple of times. In my view, I don't think it will impact the number of jobs. You'll completely change how we do things, you know, that the maxim that everybody use I think also works for the allocators which is AI will not take your job but the people who knows how to use AI might.
Shaun Ng:Right? So I think that also applies to to allocators. But the CIO I know will have their own opinion and feel free and just be ready to answer that question. You really want to remove that fear so that people can experiment freely. And second thing before you can encourage them to experiment creatively, you also need to give them very high level kind of guidelines and guideposts, right, so what kind of tools can they use, what kind of use cases can they experiment with, so so people call it the AI policy if you like.
Shaun Ng:Right? So so it sounds formal but what you really need is a living document that changes very frequently and people know where to find that document. The the goal is so that they know that they will not get into trouble when they try to play around with certain tools or certain kind of use cases. Right? And then the third one, the the third critical element is based on your investment office own culture, come up with a way that have carrots and sticks, right, to see this through, to to continue to push the, what I call the AI flywheel.
Shaun Ng:Right? So sometimes that may mean as formal as putting it into the OKR of your team member's annual evaluation. Sometimes it's just a celebration when people have shared their prom and other people have benefited from it. Right? So so you you need to come up with the effective way that this is not just a one week, one month process, but the AI flywheel should continue to accelerate with time.
Victoria Sienczewski:Shaun, you mentioned creating the environment, putting in place the guardrails, putting in place incentives for teams, extremely important. What I've noticed is that there is a difference obviously with having individuals within an organization, let's say quietly or individually using tools or experimenting. How do we then get to the place where we're seeing real institutional value? Where you have an organization, you have the environment, you have the cultural elements, the governance in place where the entire organization is benefiting. How do you navigate that transition?
Shaun Ng:That's a great question and we are lucky as as allocators because as a community, we tend to be later adopters of of technology and for for good reasons. Right? And now the PWC of the world, E and Y, McKinsey, Stanford, MIT has come up with very, good research papers looking at how certain industries, not the allocators industry or certain industries have adopted AI, the lesson that we can learn from that. Right? So so I do write quite a lot about that because certain things are just not relevant but other things you can see that happening to to our world as well.
Shaun Ng:It's very common to hear comments when I talk to allocators that the workflow for junior analysts have changed completely. The junior analyst knows how to use the modern AI tools to come up with the investment memo, organize their meeting notes, and it's it's much more efficient. Like, check the box. Good. But the MD's level, their work has not changed that much.
Shaun Ng:And if you're looking at AI transformation in the long term, that is just not gonna work. Right? So so McKinsey especially have talked a lot about making sure that the people who are leading the charge are the people who are responsible for the decision making. The other lessons that we learn is getting a IT person to lead the charge who then learn about your business workflows is not the way to go. You need to get somebody who is a domain expert, who is interested to learn or have to learn about AI and that kind of person will be much better of a higher probability of success to for for that person to lead a charge.
Shaun Ng:Another example is make making sure that you have like brown bags, meetings regularly, have a workflow, the AI workflow day where not only do you ask how can I improve my existing workflow, but also what are the kinds of activities that we'll not be able to do in the past? And now, with AI, we can do it. Right? So remember that second question as well. So that's also kind of the tangible action you can do to help move from just being an individual doing AI properly to institutional institutional benefit.
Shaun Ng:Benefit. One person's breakthrough should be the team's kind of default going forward, right? So that's the kind of goal that you want to have.
Victoria Sienczewski:Fantastic. It is it is difficult to get there, right? And it's a journey getting from individual adoption to full enterprise, not easy at all. Shaun, what do you view AI native LP and allocator organisations like in four or five years? What do you think they will look like?
Victoria Sienczewski:Where will how will the edge evolve? What will work look like?
Shaun Ng:Yeah. No. And I think that's a fun fun part of of the question where you can imagine freely. I think that the the true answer is nobody knows, you know, we can have educated guesses. I think the near term, you you said five years, at least the first year or two, agents would definitely play a more critical role than than than they do now today.
Shaun Ng:Right? So so what I can envision is people coming into the office, and then the agents has already looked at your emails, have already drafted responses to your emails, have already digested the letters from managers that that arrived last night based on the criteria that you gave the agents has able to identify some inconsistencies to what we believe the managers is doing, essentially flagging things that you should then take it to to the next level. So a lot of the recurring tasks that can be documented step by step, you know, those those should be able to done to to be able to carry out by by agents and then allocate a lot of the time to, okay, how do I address this challenge? The the one though that they involve human judgment. If you look out three to five years, I know when when agents start talking to agents, so GPs will have their own agents.
Shaun Ng:LP will be communicating with that agent. Right? How will it look like? You know, that's a fun fun bit that we can continue to to explore. And also the technique.
Shaun Ng:Right? So so I've been also thinking a lot about if you look at the what are the frontier labs doing, there's a lot of discussions about auto researcher. Like, can we use the same technique in helping allocators do some of our investment theme thematic work? I I I don't know. Or or or to select managers, to help us select managers.
Shaun Ng:You know, I I don't know, but those are the kind of questions that you and I, I'm sure, will be discussing in the future.
Victoria Sienczewski:Wonderful. Shaun, last question to wrap up. Through your 50 newsletters and dozens, if not hundreds, of conversations with LPs and allocators navigating this cultural technological transformation their organizations. What has surprised you the most about how people are approaching this?
Shaun Ng:What has been most satisfying as well as surprising is how much being proficient in AI in one small area actually helps you a lot in a completely different area. Let let me give you an example. I was talking with this allocator that was very proud that he was able to come up with a brand new way of coming up with the first draft of investment memo. Right? So so not only is just a productivity play, but he was also able to prompt AI to sharpen his his thinking.
Shaun Ng:And then he's saying that, oh gosh, Shaun, you wouldn't believe it. You know, I have the manager's meeting, and he he was able to identify AI slog more easily, able to identify AI washing, you know, managers that really use AI in the right way, in the right spirit versus the the all all the GPs are pressured to say that they use AI. Right? And those who say they use AI, but they don't really use AI in the right way. Right?
Shaun Ng:So so he was able to be in a better position to underwrite the new set of managers based on this new criteria. So, yeah. So so, you know, and and you see examples happening all all the time. It's a powerful and yet very generalized tool that once people use it, it's quite amazing what other use cases they can come up with to take full advantage of this power.
Victoria Sienczewski:Shaun, thank you so much for taking the time to join me and for sharing your insights.
Shaun Ng:Thank you.
Victoria Sienczewski: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. Your firm's judgment took years to build. Stop letting it reset every time a team member walks out the door. Visit auumai.com to discover the structured digital intelligence foundation your team needs to leverage Agentic Allocator to its fullest potential.
Victoria Sienczewski:Until next time.