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24 turns · 23 minWelcome to the Egentic Allocator. Today, we're joined by Shawn Eng, who has built a 30-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.
AI is by far the most consequential innovation of our lifetime. It's going to change everything that we do, 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.
Since leaving his investment role, Sean founded AI for Allocators, an independent newsletter dedicated to helping LPs navigate the complexities of AI adoption.
After talking to so many different LPs and allocators, after I launched the AI for Allocators blog post, I came to realize that a lot of these so-called mistakes or missteps all stem from one foundational misdiagnosis of what AI challenges.
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.
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.
I hope you enjoy the conversation. Sean, thank you so much for taking the time to meet with me today.
Great to be here. Great to be here in person with you, Victoria, in London.
Sean, 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 we'd love to understand what are LP pain points today before the advent of AI?
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. You know, when I talk to my peers, the IC slide deck seems to be getting thicker and thicker. But at the same time, the size of our team do not change, right? So essentially you are asking to do more with the same. So that's the first kind of pressure point that my colleagues and I have been facing for the last 10 years. And then there's a data complexity, right? 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 Carissa, Solovus. So there has been quite a lot of time and effort on that. And then you also have the unstructured data, the qualitative, the narrative, the PDF, if you like. So we are talking about investment memos, the manager and quarterly letters, meeting notes, capital call statements. And we have been Spending time to engage and hire CRMs and data
Episode notes
Shaun Ng, founder of AI for Allocators and former Managing Director at the Cleveland Clinic Investment Office, joins The Agentic Allocator to share what three decades of capital allocation experience and over 50 newsletters on AI adoption have taught him about where LP organisations are going wrong and what they need to do differently.
Shaun's diagnosis is clear: the single biggest mistake allocators are making is misidentifying the AI challenge as a technology problem. It is not. It is the most consequential strategic transformation of their careers: a leadership challenge, a change management challenge, a cultural challenge. Every downstream mistake, from delegating AI to IT project managers to setting fixed start and end dates for implementation, flows from that one misdiagnosis.
In this episode, Shaun walks through the pre-AI pressures that were already straining investment offices: stakeholder demands, data complexity, talent, and explains how AI implementation maps onto each one. He makes the case for why CIOs who are not personally using AI are making a critical error, why creating the right environment matters more than choosing the right tools, and what the AI flywheel looks like when it is spinning properly. He also offers a vivid picture of what a genuinely AI native investment office looks like in four to five years. The edge will belong to organisations that