The Agentic Allocator

AuumAI
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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 Expect real-world case studies, tactical frameworks you can actually use, and moments that challenge outdated norms.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.

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  1. Alex Harstrick on Why You Are Leaving Money on the Table by Not Implementing AI Right Now

    2 NGÀY TRƯỚC

    Alex Harstrick on Why You Are Leaving Money on the Table by Not Implementing AI Right Now

    Alex Harstrick, Managing Partner and Co-Founder of J2 Ventures, joins The Agentic Allocator to share how one of the most focused early-stage funds in the US is building AI into every aspect of how it operates. J2 invests exclusively in deep technology for the US government, and Alex brings a sharp view of what AI adoption requires from GPs and LPs alike. Alex's position is unambiguous: you are leaving money on the table by not implementing AI right now. Cybersecurity questions, accuracy concerns and open questions of all kinds remain unanswered. But waiting for all the answers, he argues, means waiting for the industry to pass you by before you have them. In this episode, Alex walks through how J2 is using AI to improve its internal operations, why the entire VC SaaS stack is ripe for disruption, and how AI is reshaping the sourcing game in ways that go far beyond automation. He also shares a pointed view on how LPs are thinking about AI adoption, why the largest and most sophisticated institutions are often the most afraid, and how AI can surface the kind of manager evaluation information that social relationships and slick pitch decks were designed to obscure. What You'll Learn: Why Alex calls AI 'advanced computing' and what that signals about how J2 thinks about technology investmentWhy the entire VC SaaS stack is overpriced, under-delivering, and vulnerable to being replaced by tools you can build yourself over a weekendThe time cost of manual CRM notes: five minutes per meeting, five to ten meetings a day, 120 hours lost per year, and what that means for deal flowHow AI changes the sourcing game not just by finding founders faster but by helping VCs actually connect with them as human beingsWhy the largest and most sophisticated LPs are often the most afraid of AI, and why Alex thinks they are asking the wrong questionHow AI can evaluate managers against 56,000 alternatives and surface performance information that social relationships and polished track records were designed to hideWhy AI could perpetuate backward-looking biases in manager selection and what LPs need to understand about that risk The retrospective analysis opportunity: tracking the nine managers you passed on and using their subsequent performance to improve your own decision-makingWhat the LP/GP ecosystem looks like in five to ten years: automated DDQs, blind ranking mechanisms, algorithmic portfolio company reporting, and the parts that will always need a human in the loopWhy if you do not leverage AI, someone else will, and they will out-compete youAbout Alex Harstrick:  Alex Harstrick is Managing Partner and Co-Founder of J2 Ventures. J2 invests exclusively in deep technology for the US government. Before founding J2, Alex had a career that spans healthcare venture capital, service as an Army intelligence officer with special operations deployments to Afghanistan and Iraq, and a role at the Defense Innovation Unit where he helped deploy nearly $2 billion into defense technology startups. That combination of operational experience, government service, and investment expertise is what shapes J2's uniquely focused approach to backing founders building at the intersection of national security and advanced computing. Episode Highlights: [01:51] Why J2 Calls It Advanced Computing, Not AI Alex believes the word AI has lost a lot of meaning. Alex explains why J2 uses the term advanced computing instead, and why an early-stage investor who only looks for AI in the broad definition will miss a lot of what is interesting   [03:12] Why the Entire VC SaaS Stack Is Ripe for Disruption VC is a niche industry served by expensive software that ratchets up pricing once it has you, without meaningfully improving. The best CRM most VCs have ever used is a shared Google Sheet. You can now build something better, bespoke for your workflows, over a weekend.   [06:02] The Real Cost of Manual Note-Taking: 120 Hours a Year Five minutes per meeting, five to ten meetings a day, 120 hours lost per year. Once you frame it that way, the urgency of fixing it becomes impossible to ignore. [09:19] LPs and the AI Question: Why the Biggest Institutions Are the Most Afraid The largest multi-asset managers are mostly asking about cybersecurity. Alex thinks that is the wrong question unless you are simultaneously asking how you are implementing AI across your workflows. [15:04] How AI Changes Sourcing: Finding Founders and Actually Connecting With Them Everyone is looking for expectational founders. What is differentiated is getting there first and walking into the room knowing the things about that person that create a real connection. AI helps with both. [21:11] How AI Evaluates Managers Against 56,000 Alternatives LPs often rely on relationship signals: impressive annual meetings, famous co-investors, high-profile connections. AI can evaluate a manager against the full universe of alternatives and surface what the polished materials were designed to obscure. [22:44] The Retrospective Analysis Opportunity When an LP meets ten managers and invests in one, the nine they passed on are almost never revisited. AI makes it possible to track those managers, compare their subsequent performance, and use that data to improve your own decision-making. [23:19] The Bias Problem: Why AI Can Perpetuate Historical Heuristics Train AI on historical manager data and it will tell you the best managers look like the ones who succeeded historically. LPs need to understand that risk explicitly and understand what to do about it.  [24:40] The Five to Ten Year View DDQs processed algorithmically, managers ranked against blind benchmarks, portfolio company reporting automated. The part that will not change: people want to meet the person they are giving up a meaningful part of their lives to work with. Episode Resources: Alex Harstrick on LinkedIn J2 Ventures Website Victoria Sienczewski on LinkedIn AuumAI Website

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  2. The OCIO That Keeps Raising the Bar on AI: Inside TIFF's Culture of Continuous Improvement

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    The OCIO That Keeps Raising the Bar on AI: Inside TIFF's Culture of Continuous Improvement

    Brad Calder, Managing Director, Head of Equities at TIFF Investment Management, joins The Agentic Allocator to walk through what two years of AI adoption looks like inside the OCIO. Brad leads TIFF’s public equity investments, including select arbitrage and crypto strategies, and plays a central role in manager selection and portfolio construction, and has spearheaded the firm's AI implementation. TIFF is an OCIO serving endowments, foundations, and other mission-driven organizations. In this episode, Brad walks through what AI adoption looks like on the ground: the use cases that are delivering tangible ROI, the ones that are still works in progress, the infrastructure decisions that turned out to matter, and the cultural conditions that made it all possible. Brad describes one of the most ambitious projects on TIFF's roadmap: a tool for the investment committee that connects every memo the firm has ever written to structured returns and exposure data, automatically surfacing the three most comparable historical investments whenever a new decision is being discussed. For a 35-year-old organization where no current IC member has been there since inception, this tool would give the IC access to the full weight of every decision TIFF has ever made. He also makes a sharp point about what AI adoption requires that most allocators underestimate: the willingness to keep re-experimenting, because the underlying models improve fast enough that something that failed six months ago may now be entirely viable. Think of it like high school chemistry, he says: if the experiment doesn't work, you alter it and try again. What You'll Learn: How TIFF's decade of backing machine learning oriented hedge funds gave them a head start on understanding and trusting AI before the rest of the industry caught upWhy TIFF's legacy research management system became the catalyst for their AI build-out and what the 'SaaSpocalypse' means for allocators still locked into incumbent technology providersHow TIFF is using AI across unstructured data pitch books, quarterly letters, capital call notices The collective intelligence tool TIFF is building for its investment committee: surfacing comparable historical investments, tied to returns data, going back to the firm's foundingWhy the ROI of AI for allocators is not just efficiency and where Brad believes the real value will ultimately be feltHow TIFF combines offshore BPO with AI tools, and why the combination is complementary rather than substitutiveWhat an AI-enabled allocator looks like in five to ten years: instant probability estimates on prospective managers, AI-shaped due diligence agendas, and humans focused on the relationships and control rights that resist disintermediationWhy subscale endowments and foundations face a real risk of being left behind and why that is part of TIFF's mission to solveThe high school chemistry framework for AI experimentation: alter the experiment, keep trying, and never conclude something can't work based on a model that's already six months out of dateAbout Brad Calder: Brad Calder is Managing Director, Head of Equities at TIFF Investment Management, where he leads TIFF’s public equity investments and has spearheaded its AI adoption and implementation. TIFF is an OCIO serving of endowments, foundations, and other mission-driven organizations. Brad has been at TIFF for 11 years. Before joining TIFF, he built expertise in systematic and machine learning-oriented investment strategies that has since informed the firm's approach to integrating AI across its investment and operational process. Episode Highlights: [02:12] How TIFF Got a Head Start on AI Brad traces TIFF's AI journey back a decade, to when the firm began backing machine learning oriented systematic hedge funds. That early exposure, learning how the technology worked from the inside, meant TIFF was ready to move fast when ChatGPT changed the landscape. The culture of continuous improvement that governs how TIFF evaluates external managers, Brad explains, is the same standard they've applied to their own organization. [04:39] The SaaSpocalypse and the Case for AI-Native Vendors TIFF's AI build-out was triggered, in part, by frustration with a legacy research management system that lacked the API capabilities needed to run LLM models over their database. Brad frames this as a broader market dynamic the 'SaaSpocalypse', in which allocators are waking up to the fact that incumbent SaaS providers that aren't investing in AI are creating white space for AI-native competitors to step in. [06:14] What AI Is Actually Doing for TIFF’s Investment Teams Brad walks through specific use cases: rapidly summarising incoming manager pitches against a structured template, comparing quarterly letters across managers over multiple periods to surface trends and outliers, and helping write investment memos. The shift from an analyst manually reading and cross-referencing multiple letters to an AI that can instantly identify what one manager is doing differently from the rest is, Brad argues, qualitatively different, not just faster. [07:39] Building TIFF’s Collective Intelligence Layer The most ambitious project on TIFF's roadmap is a tool for the investment committee that connects every memo the firm has ever written to structured returns and exposure data and automatically identifies the three most comparable historical investments. [11:09] Where the Real ROI Lives and Where It Doesn’t Brad is clear that AI adoption at TIFF is not primarily a headcount reduction exercise. The offshore BPO team in India has full access to TIFF's AI systems, and the combination is complementary. The true ROI, he argues, will be felt most in decision quality: the tools being built for the investment committee are designed specifically to help the team make better decisions and avoid the kind of mistakes that result in large losses.  [14:08] The Challenge of Building Confidence in AI Output Brad is candid about the adoption friction inside TIFF: even when validation processes are in place, some team members still feel the instinct to double-check, follow up, or verify. Building the confidence to trust a validated AI output takes time and deliberate cultural work. His advice: keep re-experimenting, because the models are improving fast enough that a use case that failed six months ago may be entirely viable today. [15:56] The Five-to-Ten Year Vision: Humans Focused on What AI Cannot Do Brad's picture of the AI-enabled allocator is specific: systems that can take textual data and make forecasts from it, instant probability estimates on prospective managers drawn from decades of comparable data, and AI-shaped due diligence agendas that surface questions humans wouldn't have thought to ask. Where he believes humans remain essential for longer is in the relationship and control dimension: negotiating GP terms, engaging company management, exercising the kind of judgment that equity ownership demands. [20:08] The Scale Risk for Smaller Allocators Brad closes with a note of concern for subscale endowments and foundations that lack the budget to make these investments. AI, he argues, is not a rising tide that lifts all ships. It only lifts the ones that invest in making it work. That gap is precisely why TIFF exists: to help smaller mission-driven organisations access the sam...

    23 phút
  3. The CIO Who Codes: Al Hemmingsen on Weaving AI Into the Fabric of a Multi-Generational Single Family Office

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    The CIO Who Codes: Al Hemmingsen on Weaving AI Into the Fabric of a Multi-Generational Single Family Office

    Al Hemmingsen, CIO of a prominent US-based single family office, joins The Agentic Allocator to share what hands-on AI adoption looks like from inside an institutional family office. Al is the only CIO, we know of, in the LP world actively publishing open-source code on GitHub for use by fellow allocators. In this episode, Al walks through how vibe coding with Claude Code became a serious tool for unlocking legacy data and automating workflows, how he is building a culture of accountability across his team, and why the LP that gets to the best GPs first has a competitive edge that only widens over time. He also makes one of the sharpest points we have heard on the competitive stakes: some aspects of this work should be shared freely with peers, but the ability to process information faster than other LPs, build higher-quality manager meeting lists, and get to the best GPs before they fill their calendars at conferences, is a genuine edge. If you do not build it, someone else will. What You'll Learn: How a background in SQL and Visual Basic at business school laid the foundation for a career of hobbyist coding and eventually full vibe coding with Claude CodeWhy the single best measure of AI ROI at a family office is not efficiency gains but tasks that were previously too expensive or simply not feasibleHow $59.32 in API credits unlocked 50-plus years of legacy transaction data and saved $10,000 a year in perpetuity, in one afternoonHow Al thinks about the boundary between enterprise systems of record and the custom scripts that live in between themThe psychological dimension of AI adoption: how to take existential fear off the table so your team can actually engage with the technologyThe autopilot accountability principle: why the person who creates an AI tool is always responsible for what it does, regardless of what the machine decidedHow a standing Monday meeting agenda item and a shared Microsoft environment for corporate-owned apps is building a firm-wide culture of AI experimentationWhy the investment case for AI comes down to the widest top-of-funnel possible: seeing more managers, filtering more efficiently, and getting to the best GPs faster than peers who are not augmentedWhy sharing freely with peers and competing are not contradictory, and how to think about the line between the twoAbout Al Hemmingsen: Al Hemmingsen is the CIO of a prominent US-based single family office with a history going back to the mid-20th century. He has been at the helm for the past decade, managing a multi-generational pool of capital with a quasi-perpetual mandate. Al is one of the most hands-on AI adopters in the institutional family office world and is the only CIO, that we’re aware, of actively publishing open-source code on GitHub for use by fellow allocators. Episode Highlights: [05:56] The $59.32 Moment $59.32 in API credits unlocked 50-plus years of legacy transaction data in a single afternoon. That moment set off a systematic search for every legacy problem in the organisation AI could solve. [09:18] Building in the Seams What is worth building are the tools that live between enterprise systems: workflows too specific for any software company to build a product for. [11:23] Taking Fear Off the Table The goal is to eliminate the 30% of every job that is tedious and repetitive, not the people doing it. Remove that fear first and the rest follows. [19:09] The Investment Case There are 56,000 alternative managers globally. The LP that processes information faster and gets to the best GPs have a genuine edge. [20:55] Collaboration and Competitive Advantage Al's view is that sharing tools and ideas freely with peers in the family office world makes everyone better, which is why he publishes on GitHub. But that openness has a natural boundary: the capability to see more managers, filter faster, and reach the best GPs before their schedules fill is a genuine investment edge worth building and protecting. Episode Resources: Al Hemmingsen on LinkedInAl Hemmingsen on GitHubVictoria Sienczewski on LinkedInAuumAI WebsiteDisclaimer: The views and opinions expressed by Al Hemmingsen in this interview are his own and do not necessarily reflect the views, positions, or policies of any organization with which he is or has been affiliated.

    26 phút
  4. Professor Ludovic Phalippou on the Promise and the Peril of AI for LP Decision-Making in Private Markets

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    Professor Ludovic Phalippou on the Promise and the Peril of AI for LP Decision-Making in Private Markets

    Professor Ludovic Phalippou of Oxford Saïd Business School joins The Agentic Allocator to deliver a rigorous, unsparing look at what AI can and cannot do for Limited Partners in private markets. Drawing on his research paper, 'Limited Partner Versus Unlimited Technologies,' and hands-on experiments applying machine learning to real LP datasets, Professor Phalippou maps both the transformative potential and the structural risks of AI adoption in private markets.  Private markets are not data-poor - they are data-overwhelmed. A single fund investment can generate thousands of pages of PDFs and countless spreadsheets. A large LP like CalPERS monitors 200 active private equity funds and receives quarterly reports on 3,000 underlying portfolio companies. No human can absorb that. The promise of AI is that it can. But Phalippou warns that the same tools capable of unlocking hidden signals in qualitative data can just as easily be gamed, misused, or deployed in ways that amplify the industry's existing distortions. Professor Phalippou shares research showing how AI sentiment analysis of GP quarterly reports can predict future portfolio company returns with real predictive power, far beyond what the reported marks alone convey. He also lays out in precise detail how GPs could exploit AI-reliant LPs, from burying critical fee terms ever deeper in footnotes to inserting invisible prompt injections into documents. He outlines the governance framework LPs need to build before they trust any AI output with a real decision. What You'll Learn: Why private markets have too much information, not too little, and why that is precisely where AI's power liesHow AI sentiment analysis of GP quarterly reports can predict portfolio company returns beyond what reported marks revealWhy extracting a headline EBITDA figure with AI is a recipe for catastrophe, and what to ask for insteadHow GPs could exploit AI-reliant LPs: from burying critical terms in footnotes to inserting invisible prompt injections into fund documentsThe legal frontier: why AI provisions will soon appear in LP/GP confidentiality agreementsA governance blueprint for LP organisations adopting AI: who is responsible when an AI-extracted number turns out to be wrong?Why the right question to ask AI is never 'give me the EBITDA,’ and what high-quality AI prompting for private markets actually looks likeWhy LPs must avoid both naive enthusiasm and reflexive dismissal, and what a mature, risk-aware approach to AI adoption actually looks likeAbout Professor Ludovic Phalippou: Ludovic Phalippou is a Professor of Financial Economics at Oxford’s Saïd Business School and specializes in Asset Management, with a special focus on investments in private equity funds. He is one of the world's leading academic authorities on private equity performance, fees, and transparency. His research on private markets has been widely cited by LPs, regulators, and policymakers. He sits on the investment committee of Oxford’s Queen’s College, has collaborated with institutional LPs on applied machine learning research, and authored the paper 'Limited Partner Versus Unlimited Technologies,' which examines how generative AI and machine learning are poised to reshape LP decision-making, and the risks that come with it. Episode Highlights: [00:03:21] The Real Data Problem in Private Markets Phalippou dismantles the assumption that private markets suffer from a lack of data. The problem, he argues, is the opposite: thousands of pages per fund, quarterly reports on thousands of portfolio companies, and none of it structured or comparable. AI's promise is to swallow that volume and surface what matters. [00:04:31] Sentiment Analysis as a Return Predictor Drawing on original research, Phalippou explains how running AI sentiment analysis on GP quarterly report text produces a powerful predictor of future portfolio company returns - one that adds significant information beyond the reported mark. Two companies both marked at 1.2x can have dramatically different outcomes depending on how positively or neutrally the accompanying text is written. [00:09:19] Why EBITDA Extraction Is the Dangerous Use Case The most common instinct for LPs, using AI to automatically extract and aggregate financial metrics across hundreds of portfolio companies, is also the most dangerous. EBITDA figures in private markets are loaded with definitional choices, add backs, and adjustments buried in footnotes. Asking AI for the headline number without the context around it can produce false confidence. [00:11:40] How GPs Can Game AI-Reliant LPs Once GPs know their documents are being processed by AI rather than read by humans, the incentive structure shifts. Critical fee terms and performance qualifications will be buried even deeper in footnotes. More alarmingly, Phalippou explains how invisible prompt injections, text hidden in white-on-white font, can manipulate AI summaries to generate favourable assessments. Academics have already been caught doing this with peer review systems. [00:13:29] What Good AI Prompting Looks Like for LPs Rather than asking AI for a single extracted number, Phalippou recommends asking it to surface all relevant passages in a document on a given topic, including definitions, footnotes, and any conflicting disclosures. The absence of a clear definition is itself an information signal. Over time, analysts can train AI on their own judgment: flagging specific types of add backs, identifying patterns that have historically indicated problems. [00:19:29] The Legal Frontier: AI Provisions in LP/GP Agreements Some GPs are already attempting to prohibit LPs from processing their documents with AI. Phalippou argues the logic does not hold: when an LP hired Cambridge Associates, they were paying for judgments informed by thousands of comparable documents. Giving a document to an AI does the same thing. The conversation predicts LP/GP confidentiality agreements will soon include explicit AI provisions, a structural shift in how the industry governs information. [00:21:00] The Machine-to-Machine Future Phalippou reflects on an emerging world in which GPs produce documents with AI assistance, LPs process them with AI, and the analysis in between is conducted by AI. He uses the analogy of education: when exams are written, answered, and graded by machine, what is the human layer actually doing? His answer: setting the values, defining the questions worth asking, and retaining genuine accountability for the answers. [00:23:29] A Governance Blueprint for LP Organisations Phalippou's closing framework centres on responsibility. When an AI-extracted number turns out to be wrong and a real investment decision follows from it, who is accountable? Organisations must resolve this before deploying AI, not after. He also stresses the importance of avoiding both naive adoption ('this will simplify everything') and reflexive dismissal ('it just generates noise'). The mature position is to use these tools seriously, with clear governance and a clear-eyed understanding of the risks. Episode Resources: Professor Ludovic Phalippou on LinkedIn Oxford Saïd B...

    25 phút
  5. From Digitally Native to AI-Driven: How Moonfare Is Rethinking Private Markets Access

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    From Digitally Native to AI-Driven: How Moonfare Is Rethinking Private Markets Access

    Marine Eugene, Chief Commercial Officer at Moonfare, explains how one of the private markets' most recognised digital platforms is now embedding AI across its investment process, commercial operations, and client experience, without compromising on the high-touch service that sophisticated family offices and investors demand. Moonfare has built its reputation democratising access to top-tier private market funds by removing friction from the investment process. Now, with a team of 15 investment professionals monitoring over 250+ alternative funds annually and a client base spanning 23 markets, Marine explains how Moonfare is deploying AI to handle the scale of data processing, lead triage, and compliance monitoring that simply wasn’t possible manually. In this episode, Marine traces the full arc: from the early days of digitising paperwork and onboarding flows, to deploying AI for legal document extraction, Excel automation, and IC memo generation, and into the next wave - using AI to surface fund managers, track live portfolio performance, and deliver the kind of interactive, real-time data experience that family offices are now expecting as standard. What You’ll Learn: How Moonfare’s investment team uses AI to extract key terms from 200-page fund documents in seconds, replacing manual page-by-page reviewWhy AI-driven lead scoring and triage is allowing Moonfare to serve 10,000-15,000 new platform registrations per year across 23 markets with a fraction of the junior sales headcount previously requiredHow Moonfare trained an internal AI agent on a new brand tone-of-voice book to keep all client-facing teams, from operations to investor relations, communicating consistentlyWhy Marine is cautious about fully automating client interactions, and where the human touch remains non-negotiable for high-net-worth and family office investorsWhat Moonfare’s investment team could look like in five to ten years: AI surfacing unique fund managers, tracking live portfolio performance, and eliminating the lag in quarterly reportingThe cultural playbook for AI adoption: AI working groups, team champions, and a clear message that AI is a tool to free talent, not a substitute for itMarine’s candid advice to any CCO or CIO who hasn’t yet started: you’re already behind, get curious, learn to prompt, and be very precise about where you expect the returnAbout Marine Eugene:  Marine Eugene is Chief Commercial Officer at Moonfare, the digital private markets platform recognised for democratising access to institutional-quality funds. Prior to Moonfare, Marine was an executive in the private aviation industry, including at NetJets and FlexJet. Over the past three years at Moonfare, she has led the firm’s commercial growth across 23 markets, with a focus on serving family offices and sophisticated institutional investors through a high-quality, digitally-native experience. Episode Highlights: [00:01:36] From Private Aviation to Private Markets: The Digital-First Shift Marine reflects on her transition from private aviation to private markets and how family office expectations have evolved, from digital onboarding as innovation, to interactive live data and portfolio-level analytics as the new baseline. [00:04:14] Inside the Investment Team: AI Meets 250 Funds Per Year With 15 investment professionals monitoring over 250 alternative funds annually, Marine explains how AI is already accelerating document review, Excel-based financial modelling, and IC memo generation, and what the shift from efficiency to value creation will look like. [00:05:50] When AI Became a Commercial Priority at Moonfare Marine traces the timeline: from deploying basic AI tools nine to twelve months ago to improve team workflows, to now fielding client expectations that AI will enhance their own user experience with more interactive, live data. [00:07:20] Lead Triage at Scale: 10,000–15,000 Registrations, 23 Markets Marine explains how AI-powered lead scoring and triage is replacing the archaic five-to-ten-person junior sales teams that previously managed lead qualification, enabling Moonfare to focus investor relations talent on the most mature, high-value opportunities. [00:10:00] The High-Net-Worth Line: Where AI Stops and Humans Begin Marine draws a sharp distinction between using AI in the back office for efficiency and using it in direct client interactions. Family offices and HNW investors don’t want to talk to a bot and the human relationship remains the firm’s commercial edge. [00:11:00] Building an AI Culture: Working Groups, Champions, and Brand Training Marine describes Moonfare’s internal AI rollout: cross-functional working groups, team champions, and a practical project using AI to enforce brand tone-of-voice consistency across every client touchpoint. [00:11:57] Human-in-the-Loop Is Non-Negotiable Marine is direct: AI is a formidable shortcut, but you cannot rely on the output without human review. The investment and commercial teams treat AI as a tool to go faster, not a replacement for judgment. [00:14:15] Compliance at 100%: AI Listening to Every Call Previously, compliance teams could only sample a fraction of recorded calls. Marine highlights how AI now enables 100% call coverage, a step-change in risk management that was simply not achievable with human resource alone. [00:15:20] The 5–10 Year Vision: AI Surfacing Unique Managers and Live Portfolio Tracking Marine paints a picture of an AI-enabled investment office that can surface niche funds, track every underlying asset in live time, and eliminate the lag of quarterly reporting, replacing tribal knowledge with something close to perfection. [00:18:00] Advice for CCOs and CIOs Who Haven’t Yet Started Marine’s message is unambiguous: if you haven’t started, you’re behind. Get curious, register for alerts on LLM prompting, be precise about the ROI you’re looking for, and don’t just buy the most talked-about tool. Episode Resources: Marine Eugene on LinkedIn Moonfare Website Victoria Sienczewski on LinkedInAuumAI Website If you enjoyed this conversation, make sure to subscribe, rate, and review on Apple Podcasts, Spotify, and YouTube.

    21 phút
  6. The Data Behind the Edge: How Aksia Is Building AI on 20 Years of Private Markets Intelligence

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    The Data Behind the Edge: How Aksia Is Building AI on 20 Years of Private Markets Intelligence

    Harry Seplowitz of Aksia explains how the $360B alternatives specialist has embedded AI across its investment process, from initial manager screening to LPA benchmarking, without losing sight of what makes its research truly differentiated. This conversation is a candid account of what AI adoption looks like inside a firm where proprietary data, and the experienced team who know how to act on it, is a real edge. Aksia manages relationships with over 100 clients, monitors 14,000-plus distinct holdings, and reviews hundreds of investment opportunities annually. In this episode, Harry Seplowitz, Managing Director on the Pan-Alternatives team, walks through how Aksia has systematically embedded AI across investment screening, co-investment underwriting, cross-team knowledge sharing, middle office operations, and LPA review. Harry describes how Aksia's proprietary database of 150,000+ private markets transactions spanning more than 20 years has become the fuel for AI-driven analysis. With that foundation in place, the firm can rapidly benchmark any new manager against its entire universe, identify headline risks in a manager's history before committing to full diligence, and surface the full breadth of cross-team intelligence. But perhaps the most important takeaway is what Aksia is choosing not to do with AI. Harry makes a sharp distinction between using AI to generate longer diligence reports and using it to produce shorter, sharper, more data-driven ones. In his view, firms that let AI inflate their output are eroding, not enhancing, their value proposition. What You'll Learn: How Aksia uses its 150,000+ deal database to benchmark any new manager or co-investment opportunity against 20 years of private markets dataWhy being able to say 'no' quickly, to managers, to co-investments, is just as strategically valuable as identifying the right opportunitiesHow AI has dissolved the silos between Aksia's hedge fund, private equity, private credit, and real assets teams, creating a unified intelligence layer across asset classesWhy Aksia is actively tracking diligence report length as a KPI and what it signals when reports get longerHow Aksia structures AI governance: what's centralised (vendor selection, compliance, data privacy) and what's deliberately distributed (team champions, use case development)Why early-career employees have become the most valuable drivers of internal AI adoption, and what that means for hiring and career developmentThe 'blank sheet of paper' trap: why starting with a specific recurring task beats starting with a grand AI strategyWhere the edge in alternatives investing will come from next: differentiated data, combined with experienced human judgmentAbout the Guest: Harry Seplowitz is a Managing Director on the Pan-Alternatives team at Aksia, based in London. He focuses on implementing customised portfolio solutions for Aksia's European and Middle Eastern clients across private equity, private credit, real assets, and hedge funds. Aksia is a leading alternative investment specialist with over $360 billion in assets under supervision and serves as a partner to many of the world's largest and most sophisticated allocators. Episode Highlights: [00:01:51] When AI Became Strategic at Aksia Harry traces how Aksia's early adoption of public AI tools, driven by a tech-savvy team, quickly became a firm-wide priority. [00:02:49] Deal Vault: Turning 150,000+ Deals into a Screening Engine Harry describes how Aksia uses its proprietary private markets database to map every deal a manager has ever done, and benchmark it against the full universe in seconds. The result: faster conviction and faster 'no.' [00:04:19] Co-Investments: AI as a Speed and Precision Advantage On co-invest opportunities, Aksia can immediately surface every deal a manager has done in a specific sector and geography and compare it against all relevant deals in their proprietary database. [00:05:16] Dissolving Team Silos: One Company Name, Full Firm Intelligence Harry explains how typing a single ticker or company name now surfaces the complete Aksia view across hedge funds, private equity, and private credit, including every manager's long or short position, thesis, and historical context.    [00:07:42] LPA Benchmarking at Scale With tens of thousands of LPAs reviewed across the firm's history, AI has shifted the LPA review process from summarisation to benchmarking - identifying what's off-market, what's non-standard, and how terms have evolved over time. [00:09:45] The Middle and Back Office Dividend Aksia's clients invest across 14,000 holdings, generating tens of thousands of annual transactions. AI-driven data extraction from capital account statements, valuations, and investor calls is cutting input errors and freeing senior time for higher-value work.  [00:10:28] Reordering the Diligence Process AI has enabled Aksia to front-load background and company checks, identifying potential headline risks before committing weeks of cross-team resources.  [00:11:58] Measuring ROI: Report Length as a KPI Harry describes Aksia's unconventional AI metric: they want diligence reports to get shorter, not longer. Firms letting AI inflate report length are substituting volume for insight.   [00:14:14] How Aksia Organised Its AI Rollout Centralised for vendor selection, compliance, and data privacy. Distributed for everything else. Team champions across asset classes drive use case development and AI adoption is now embedded in end-of-year performance reviews. [00:15:47] Junior Staff as Innovation Leaders Harry pushes back on the narrative that AI threatens junior hiring. At Aksia, junior and mid-level employees have been the most effective AI champions, and the firm is leaning into that, giving them ownership of a strategically critical initiative early in their careers. [00:17:32] What AI Looks Like at Aksia in 3–5 Years The vision: producing initial insights on every institutional fund in the market at scale, driven by proprietary data. Full diligence effort reserved for what actually requires human judgment. The edge increasingly comes from pairing proprietary data with experienced human judgment to act on it. [00:19:39] Advice for Allocators Just Getting Started Don't start with a blank sheet. Start with the recurring task you did last week and ask how AI could have made it faster. Then ask the AI itself, it will usually suggest better applications than you would. Episode Resources: Harry Seplowitz on LinkedIn Aksia Website Victoria Sienczewski on LinkedIn AuumAI WebsiteIf you enjoyed this conversation, make sure to subscribe, rate, and review on Apple Podcasts, Spotify, and YouTube. Disclaimer: This podcast is for informational purposes only. The views expressed are those of the speakers as of the recording date and may change over time.

    23 phút
  7. Data Readiness: The Critical First Step for AI Adoption at Makena Capital Management

    31 THG 3

    Data Readiness: The Critical First Step for AI Adoption at Makena Capital Management

    Breanna Genecov and Kunal Koppula of Makena Capital Management discuss how the firm is rebuilding its data architecture as a prerequisite to AI integration. They detail the transition from fragmented, Excel-dependent workflows to a unified, cloud-based data stack, and explain why institutional investors who skip this foundation will struggle to extract reliable value from AI. Practical guidance on change management, leadership buy-in, and phased execution makes this essential listening for LPs & allocators at the early stages of their own transformation. Makena Capital Management is nearly two decades into its institutional investing history and one year into a deliberate, firmwide data transformation. In this episode, Breanna Genecov (Portfolio Solutions & OCIO) and Kunal Koppula (Data Engineering) explain the technical shift from managing "Keyman risk" in isolated Excel files to building a unified cloud infrastructure. They discuss the practicalities of porting tools into a modern data stack, the necessity of firm-wide upskilling, and the phased roadmap from structured data consolidation toward AI-enabled unstructured data analysis.  The conversation cuts through AI noise: before any meaningful automation or intelligence layer can be built, your organizational data foundation must be sound. The guests detail how siloed Excel workflows, manual data pulls, and keyman risk have constrained the firm's analytical capacity, and how a centralized data stack is changing that. What You'll Learn: -        Why Makena treats data infrastructure as the non-negotiable prerequisite for AI adoption -        How a $22B OCIO moved from fragmented, Excel-based workflows to a centralized, cloud-native data architecture -        What "one source of truth" means operationally and why inconsistent data across teams leads to incongruous decision-making -        How to manage the cultural and skills gap when moving analysts from Excel to SQL and BI tooling, without making data engineering their day job -        The two-phased roadmap: structured data consolidation first, then unstructured data contextualization via AI -        Why "garbage in, garbage out" is the most important AI principle institutional LPs & alloactors aren't taking seriously enough -        What leadership buy-in actually looks like in practice and why without it, transformation stalls   If you enjoyed this conversation make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube.  About the guests: Breanna Genecov leads the Portfolio Solutions and OCIO team at Makena Capital Management. Over more than a decade at the firm, she has held roles across manager research, portfolio strategy, and risk management, with a current focus on the design and implementation of multi-asset class portfolios. Kunal Koppula leads Makena's data engineering team. He previously worked as a trader and quantitative developer before joining Makena, where he now oversees all data-related workflows across the organization and is spearheading the firm's transition to a centralized, scalable data architecture. Episode highlights: [00:02:05] The Catalyst for Change  Breanna traces Makena's data evolution from a 2016 platform overhaul with uneven adoption, to the current firmwide initiative driven by leadership. [00:03:16] What the Data Transformation Entails  Kunal outlines the scope: consolidating all data into a single source, replacing keyman-dependent Excel processes with automated cloud workflows, and building a unified data lineage across every team. [00:05:29] The Manual Tax Described in Detail  The guests describe the true cost of legacy workflows: manual PDF and portal extraction, repeated Excel downloads, and the compounding risk of human error across every reporting cycle. [00:08:44] Breaking Out of Silos Across Investment, Operations, and Client Teams  Kunal explains how his role as “connective tissue” across teams surfaces redundant workflows and enforces a single source of truth. [00:14:53] Phase One vs. Phase Two: Structured and Unstructured Data  Kunal distinguishes between the current phase of structured data consolidation, and the emerging phase: making unstructured data (PDFs, manager notes, etc.) accessible and contextualizable via AI. [00:17:20] Advice for Peers Jumping Straight to AI  Breanna and Kunal make the case against skipping the foundation which they say includes covering garbage-in/garbage-out risk, the need for human validation, the importance of the right project lead, and why patience is a strategic requirement. Episode resources: -        Breanna Genecov on LinkedIn -        Kunal Koppula on LinkedIn -        Makena Capital Management Website -        Victoria Sienczewski on LinkedIn -        AuumAI Website

    21 phút
  8. The Blueprint for AI-Ready Investment Offices: Lessons from HICO Investment Group

    24 THG 3

    The Blueprint for AI-Ready Investment Offices: Lessons from HICO Investment Group

    Chris Hartnoll, CEO and Managing Director of HICO Investment Group, discusses how the firm is approaching AI adoption across its multi-asset class endowment style portfolio and direct investment platform. He explains why preparation, security, and operational groundwork must come before any meaningful AI deployment, and shares early signs of productivity gains and improved risk identification. Practical guidance on experimentation, leadership mindset, and the urgency of starting now makes this essential listening for LPs and allocators considering their own AI journey. Chris Hartnoll operates at the intersection of institutional investing and industrial technology. At HICO Investment Group, he has built a global multi-billion dollar platform that pairs a sophisticated multi-asset class endowment style portfolio with direct investment and incubation of new ventures in sectors like maritime logistics and energy transition. In this episode, Chris unpacks what it means to prepare an investment office for AI: the security considerations, the folder structure decisions, the legal questions around data protection, and the cultural shift required to move from manual processes to AI-assisted workflows. The conversation is grounded in Chris’s background navigating high-stakes environments, from aerospace engineering at Rolls-Royce to Morgan Stanley M&A Advisory to the Royal Marines. He draws a direct line between the discipline of preparation in those fields and the approach HICO has taken to AI adoption: do the unglamorous operational work first, then experiment in discrete, measurable process steps. He argues that the allocator community has been slow to look inward and apply AI to its own processes, despite readily advising GPs and portfolio companies to do the same. What You’ll Learn: Why preparation and operational groundwork are the non-negotiable prerequisites for AI adoption in investment offices How HICO balances an endowment style portfolio with a company builder approach to direct investments and incubation, and what that means for information flow internally Why the allocator community has been slow to adopt AI despite advising portfolio companies and managers adopt AI How to start AI experimentation in discrete, measurable process steps Where early productivity gains are showing up: LPA summarization, manager interviews, and AI-assisted risk identification Why AI-enabled teams will respond faster and make better decisions during periods of rapid market change Why starting now is critical, even if full end-to-end AI integration is years away, and the cost of waiting If you enjoyed this conversation make sure to subscribe, rate, and review it on Apple Podcasts, Spotify, and YouTube.  About the guest: Chris Hartnoll is the CEO and Managing Director of HICO Investment Group, a global investment firm focused on maritime, logistics, and energy transition. Chris built and oversees a multi-billion dollar platform that includes a multi-asset class endowment style portfolio paired with direct investment and incubation of new ventures. Before founding HICO, Chris held roles in aerospace engineering at Rolls-Royce, M&A advisory at Morgan Stanley, and served as a Royal Marine. He holds an MBA from Harvard Business School.Episode highlights: [00:01:46] Background and Approach to Risk Chris traces the thread connecting his Royal Marines experience, aerospace engineering at Rolls-Royce, and Morgan Stanley M&A to how he thinks about preparation and risk management in investing. [00:03:31] Balancing Endowment Style Asset Allocation with Direct Investment Chris explains how HICO balances its capital allocation process and its direct deal-making and incubation activities, and why keeping the processes separate while sharing information has been key. [00:05:17] Why the Allocator Community Has Been Slow to Adopt AI Chris reflects on why investment professionals are quick to advise others on improvement but slow to apply the same lens to their own processes, and why CIOs have been reluctant to disrupt what they’ve built. [00:07:27] The Operational Groundwork for AI Adoption Chris details the unglamorous but critical preparatory work: security guardrails, data privacy, folder structure, legal review of GP document protections, and building the right digital infrastructure. [00:08:55] Early Signs of Value: Productivity and Risk Identification Chris shares where AI is already delivering: summarizing LPA documents, capturing manager interview data, and surfacing new ways of viewing risk. [00:12:57] The Future of the AI-Enabled Investment Office Chris outlines his vision for human-AI collaboration in investing, why private markets will continue to have a human-in-the-loop, and how AI-enabled teams will gain a decisive speed advantage during periods of market volatility. [00:15:39] Advice for Peers on the Fence Chris delivers his core message: be prepared first, then start experimenting immediately, because the learning curve is long and the cost of waiting is compounding. Episode resources: Chris Hartnoll on LinkedInHICO Investment Group WebsiteVictoria Sienczewski on LinkedInAuumAI Website

    18 phút
  9. The Agentic Allocator Trailer

    TẬP 1

    The Agentic Allocator Trailer

    Today, we are introducing The Agentic Allocator, an exclusive conversation series that pulls back the curtain on how the world's most forward-thinking LPs & allocators are actually deploying AI in their investment offices.  For many LPs & allocators, implementing AI still feels like a "black box". In this series we dig into what it really looks like to move beyond legacy workflows toward agentic AI, and the real obstacles allocators are navigating along the way. If you’re assessing how AI might look like inside your investment office and where to start, you can find more resources here.

    1 phút

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Giới Thiệu

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.

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