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The Agentic Allocator

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by AuumAI

5.0(3 reviews)

13 episodes

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Podcast Overview

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 Expect real-world case studies, tactical frameworks you can actually use, and moments that challenge outdated norms.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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3/23/2026

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Recent Episodes

June 30, 2026

Inside SITFO's Skynet Group: Ryan Kulig on Building AI Into a $4.7B Permanent Fund

Ryan Kulig, Finance and Operations Officer at SITFO, the Utah School and Institutional Trust Funds Office, joins The Agentic Allocator to share how a $4.7 billion-dollar permanent fund for Utah's public education programs became one of the earliest institutional allocators to systematically build AI into the way it operates.For the past 16 months, Ryan has led a rigorous AI landscaping exercise at SITFO, evaluating vendors, building agentic workflows, and integrating AI into the operational fabric of the agency. As a member of several industry networks of leading endowments, foundations, and health systems, Ryan found that almost no one else in those communities was actively implementing AI, which only deepened his conviction that SITFO needed to lead.Ryan walks through why SITFO moved past using AI to generate investment memos, which it found to be a commoditized solution, toward a cross functional platform that serves finance and operations, strategy and risk, and manager research. He explains the integration work required to connect AI to the CRM, benchmarking systems, performance vendors, email, and shared document storage, and why that unglamorous plumbing work is what makes the AI powerful. He also shares his view that institutional investors have been slow to adopt AI because they treat it as a productivity tool rather than a transformational technology, and why SITFO concluded that the cost of moving early outweighed the risk of waiting.What You'll Learn:<ul><li>How SITFO grew from three people and an inherited portfolio of Vanguard mutual funds into a sophisticated allocator over its first ten years</li><li>Why SITFO moved past using AI for investment memos, which it found to be a commoditized solution, toward a single cross functional platform serving finance and operations, strategy and risk, and manager research</li><li>Why SITFO prioritized integrating AI with its existing CRM, benchmarking, and performance systems</li><li>Why Ryan believes institutional investors have been slow to adopt AI because they treat it as a productivity tool rather than a transformational technology</li><li>Why SITFO concluded that the cost of being early outweighed the risk of waiting, and what that meant in practice</li><li>How SITFO built a pipeline tool that pulls documents from its CRM daily and screens managers against desirable metrics across asset classes</li><li>Why low hanging fruit such as reviewing limited partnership agreements, populating subscription documents, and redlining NDAs frees the team for higher value work</li><li>Why Ryan believes manager research is shifting back to a people business, with more time spent on reference checks and relationship building and less on memo writing</li><li>Why SITFO's two most recent hires were chosen for technical skill rather than manager research background, and what that signals about where the team is headed</li><li>Ryan's advice for peers earlier in their AI adoption journey: assess your resources and objectives first, then run a structured landscaping process</li><li>How SITFO secured board and management support years in advance, including through an internal working group it calls the Skynet group</li><li>The data integrity and integration challenges SITFO worked through, including cleaning its CRM and controlling permissions across email and shared drives</li></ul>About Ryan Kulig:Ryan Kulig is the Finance and Operations Officer at SITFO, the Utah School and Institutional Trust Funds Office, a $4.7 billion-dollar permanent fund to support Utah’s public education programs. Ryan joined SITFO in 2016 to manage office operations, portfolio administration, and investment analysis, and has spent the past 16 months leading the agency's AI landscaping and implementation effort. Before joining SITFO, he worked at Sax Angle Partners, specializing in fundamental and technical analysis of equity investments. Ryan holds a Bachelor of Business Administration in Global Business from the University of Portland and an MBA from the University of Southern California. Episode Highlights: [01:57] From Three People to a Sophisticated AllocatorRyan traces SITFO's growth from three founding employees and an inherited portfolio of Vanguard mutual funds to a fully built out institutional allocator, and what it took to establish the foundational governing documents early on.[03:12] Beyond Investment Memos: One Solution Across Three VerticalsSITFO's early attempt to use AI for investment memos quickly proved commoditized. Ryan explains how that pushed the team toward a broader search for a solution that could serve finance and operations, strategy and risk, and manager research, and why integration with existing systems was a key priority. [04:44] Why the Industry Is Holding BackRyan's view on why institutional investors, as risk conscious fiduciaries, have been slow to adopt AI, and why many are still treating it as a productivity tool rather than a transformational one.[06:42] Why SITFO Chose to Move EarlyRyan explains SITFO's calculation that the cost of being early outweighed the risk of waiting, and how the team built a cross functional case for AI that could benefit every vertical in the agency rather than a single department.[07:39] Low Hanging Fruit: LPAs, Subscription Documents, and NDAsDocument intensive work is the clearest early win. Ryan explains why automating the baseline redlines of an NDA does not replace the attorney, it frees the attorney to focus on a more thorough review.[08:37] Building the Pipeline Engine: Top of Funnel to Bottom of FunnelRyan describes the tool SITFO built that pulls documents daily from its CRM and files them into active and prospective manager hubs, allowing the team to landscape its entire network and screen managers against asset class specific metrics.[10:31] The Cultural Shift: Back to a People BusinessRyan explains how AI is moving the manager research role away from quantitative screening and memo writing and back toward reference checks, relationship building, and firsthand observation of how managers operate.[11:25] SITFO in Three to Five Years: Hiring for Technical SkillSITFO's two most recent hires were chosen for technical aptitude rather than manager research background. Ryan explains why he expects the team to spend significant time coding and developing prompts to build out the agency's AI framework.[12:16] Why Manager Research Is Becoming a People Business AgainRyan's view on why AI, by making content easier to produce, will push allocators back toward firsthand experience and direct relationships as the basis for conviction.[13:46] Advice for Peers: Resources, Objectives, and a Structured ProcessRyan's framework for allocators evaluating an AI solution: assess your team's resources and skill set, define what you are trying to achieve, and run a structured landscaping process. [15:11] Governance: Board Buy In and the Skynet GroupRyan describes how SITFO secured support from its board and CIO years in advance, including through an internal working group established roughly three years ago to explore AI implementation across the organization.[16:05] Challenges: Data Integrity and Integration PermissionsRyan walks through the unglamorous work behind the AI bu...

Episode thumbnail for Professor Emmanuel Yimfor on Capital Allocation Bias in Private Markets and the Choices That Will Determine Whether AI Fixes or Entrenches Them

June 23, 2026

Professor Emmanuel Yimfor on Capital Allocation Bias in Private Markets and the Choices That Will Determine Whether AI Fixes or Entrenches Them

Professor Emmanuel Yimfor, Assistant Professor of Finance at Columbia Business School, joins The Agentic Allocator to share his research that should sit at the centre of every conversation about AI in private markets. His work documents the core friction driving racial and gender disparities in access to capital: not quality, not track record, but networks. Who you can reach, not how good you are.That finding has direct and urgent implications for how AI gets deployed across the LP/GP ecosystem. Used thoughtfully, AI has the potential to widen the top of the funnel dramatically, reducing the cost of due diligence enough that LPs can evaluate managers far beyond their existing networks. Used carelessly, the same tools will automate and entrench the same exclusions, encoding past decisions into future ones in ways that are subtle, hard to detect, and difficult to reverse.Professor Yimfor walks through the mechanics of embedding-based matching and why it is a black box that can pick up on signals of race, gender, and network affiliation even when no one intended it to. He explains what the research on accelerators and structured access programmes shows about what happens when the top of the funnel is genuinely open. He makes a clear, practical case for what LPs, GPs, and technology developers should each be doing differently right now. What You'll Learn:<ul><li>Why the core friction driving racial and gender disparities in private markets is networks and what the research evidence shows</li><li>Why Black and Hispanic founders raise around 40% less capital than peers with identical patent holdings, educational backgrounds, and track records</li><li>Why the gap in funding disappears entirely when access is structured, as in accelerators and grant programmes, and what that tells us about where the problem lies</li><li>How embedding-based matching works, why it is a black box, and how it can encode biases in allocation decisions even when no one intended it to</li><li>Why asking AI how similar a new manager is to managers you have backed before is not objective, and what the alternative looks like</li><li>How structured, standardised due diligence processes enabled by AI can reduce the role of network signals and subjective impression in manager evaluation</li><li>What GPs should do differently when preparing pitch materials and identifying which LPs to approach in an AI-enabled world</li><li>What LPs should ask any technology developers and providers about how their existing AI tools are sourcing and filtering the managers they evaluate</li><li>Why the industry is at a fork in the road and what Professor Yimfor’s research will be tracking to understand which path it is taking</li></ul>About Professor YimforProfessor Emmanuel Yimfor is an Assistant Professor of Finance at Columbia Business School. His research focuses on the core frictions driving disparities in access to capital in private markets, with a particular focus on race, gender, and the role of networks in determining which founders and fund managers receive funding. His work has direct implications for how AI systems are designed and deployed across the LP/GP ecosystem, and he is currently researching how AI adoption is reshaping the equilibrium dynamics of capital allocation across the industry. Before joining Columbia Business School, he was an Assistant Professor of Finance at the University of Michigan Ross School of Business.Episode Highlights: [00:30] The Core Friction: Networks, Not QualityThe racial gap in access to funding disappears in structured settings like accelerators and grant programmes where anyone can apply. It shows up most sharply in relationship-driven contexts. Black and Hispanic founders raise around 40% less than peers with identical credentials and track records. The mechanism is the same for gender. Who you can reach matters more than how good you are. [03:55] How AI Can Fix or Entrench the ProblemWhether AI amplifies or reduces existing disparities depends entirely on how the system is trained and what input data it uses. Ask AI how similar a new manager is to managers you have backed before, and the model will automate the same exclusions that drove the original gap, because the past portfolio was built through the same narrow networks. Use AI to expand the set of pitch decks you evaluate and the dynamic flips. [09:10] The Embedding-Based Matching RiskEmbedding-based matching converts pitch materials into numbers and compares them to past allocations. Behind the hood, even if no one has consciously made a decision based on race or gender, the model may be picking up on those signals. The past decisions pollute future decisions in ways that are subtle and hard to detect. Opening the black box and auditing what features the model is learning from is not optional, but essential. [14:20] What the Research on Structured Access ShowsWhere the top of the funnel is as wide as possible, with an Apply Here button and a structured evaluation process, the gap in access to funding for underrepresented founders disappears. That finding is the clearest signal in the research about where AI holds the greatest promise: using time savings from processing more materials to have more in-person meetings with people outside your existing network, rather than fewer. [14:20] Practical Advice for GPsUse AI to identify which LPs are most likely to be a fit for your strategy based on publicly available mandate information, rather than relying entirely on network referrals. Resist the urge to generate pitch materials using the same AI systems that LPs are using to evaluate them. The GP that has great ideas but historically lacked the resources to present them well now has a opportunity to close that gap.[16:20] Practical Advice for LPsAsk how your existing pipeline of managers came to you. Are there opportunities to expand the top of the funnel using this technology? Are managers running similar strategies being evaluated with the same questions, regardless of their background? Those are the questions any AI implementation consultant should be helping you answer. [21:15] The Fork in the Road: What the Research Will TrackAI adoption in private markets will resolve one of two ways. If LPs use it to widen their search, more traditionally underrepresented GPs enter the market and the research will show whether they deliver. If LPs use past data and past networks to train their systems, disparities in capital allocation will widen. Professor Yimfor is using big data to track exactly which path the industry is taking.Episode Resources:<a href="https://www.linkedin.com/in/emmanuel-yimfor-19b01486/">Professor Emmanuel Yimfor on LinkedIn</a><a href="https://www.linkedin.com/in/emmanuel-yimfor-19b01486/">Columbia Business School Faculty Profile</a><a href="https://www.linkedin.com/in/vsienczewski/">Victoria Sienczewski on LinkedIn</a><a href="https://auumai.com/">AuumAI Website</a>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.

Episode thumbnail for Shaun Ng on the One Misdiagnosis That Explains Most LP AI Implementation Mistakes and How to Build an Investment Office That Thrives in the Post-AI World

June 16, 2026

Shaun Ng on the One Misdiagnosis That Explains Most LP AI Implementation Mistakes and How to Build an Investment Office That Thrives in the Post-AI World

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 start building that environment now. What You'll Learn:<ul><li>The three core pressures LP organisations were already facing before AI arrived: stakeholder demands, data complexity, and talent</li><li>Why every common AI mistake allocators make flows from one foundational misdiagnosis and what that misdiagnosis is</li><li>Why CIOs who encourage their teams to use AI without using it themselves are repeating a strategy that will not work this time</li><li>Why starting with tools is the wrong first step, and what to focus on instead</li><li>What 'communicating your AI stance' means in practice </li><li>How to build the AI flywheel: the combination of communication, guidelines, and incentives that sustains institutional adoption over time</li><li>Why moving from individual AI use to institutional value requires the decision makers, not just the junior analysts, to lead the charge</li><li>What an AI native investment office looks like in four to five years: agents digesting manager letters, flagging inconsistencies, and routing human judgment to where it matters most</li><li>Why getting proficient in AI in one small area produces unexpected benefits across completely different parts of the investment process</li><li>How one allocator's AI fluency helped him identify AI slop and AI washing in manager meetings. A use case nobody predicted</li></ul>About Shaun Ng:Shaun Ng is the founder of AI for Allocators, an independent newsletter with over 50 editions dedicated to helping LPs navigate the complexities of AI adoption. He brings a 30 year career at the heart of capital allocation, most recently as Managing Director at the Cleveland Clinic Investment Office and previously in a senior leadership role at the World Bank Pension and Endowment Group. His work sits at the intersection of institutional investing, and the strategic transformation challenge that AI represents for the allocator community. Episode Highlights: [02:20] The Three Pre-AI Pressures LP Organisations Are Already FacingBefore AI entered the conversation, investment offices were already under pressure. Stakeholder demands were rising, IC decks were getting thicker, and team sizes were not growing. Managing data complexity, across both quantitative performance data and unstructured qualitative material, was consuming enormous time and resources. And talent remained a constant challenge: recruiting the right people, developing them, onboarding them quickly, and ensuring they could contribute at their potential. AI arrived and immediately touched all three. [04:55] The One Misdiagnosis That Explains Every Downstream MistakeShaun identifies a single root cause behind the most common LP AI mistakes: treating AI as a technology problem rather than a historic strategic transformation. CIOs have been tasked with navigating their investment offices from a pre-AI to a post-AI world. The analogy is electricity. Factories had to be fundamentally redesigned to take full advantage of it. Delegating that task to IT, setting a project timeline, or skipping personal engagement with the tools: all of these are symptoms of the same misdiagnosis.[08:30] Why CIOs Who Do Not Use AI Are Making a Critical ErrorOne of the most common missteps Shaun sees: senior leaders who encourage AI adoption without personally using the tools. In previous technology cycles, it was possible for a CIO to run an effective portfolio without knowing how to use Aladdin. That model will not work for AI. This is not a risk system. It is an infrastructure level transformation, and leaders who do not understand it from the inside cannot guide their organisations through it.[10:15] Start With the Environment, Not the ToolsWhen allocators ask Shaun what AI tools to use, his answer consistently surprises them: do not start with tools. The temptation to build vendor shortlists and compare peer approaches feels like progress but stops organisations from building the long term capability they need. The real question for any CIO is how to create an environment in which the team can adopt AI effectively, in the areas that matter most. Not on low value tasks that do not move the needle.[12:40] Communicate Your AI Stance, Build the FlywheelShaun outlines three elements that turn a one-off initiative into a sustained institutional capability. First: communicate your AI stance. Even a simple acknowledgement that AI is here to stay and the team needs to figure it out together removes the fear that stops people from experimenting. Second: give the team high level guidelines so they know they will not get into trouble exploring new tools. Third: build the AI flywheel using incentives: formal OKRs, informal celebrations of shared breakthroughs, so that adoption accelerates over time rather than fading after the first month. [17:00] From Individual Use to Institutional ValueThe gap between a junior analyst using AI to write investment memos and an organisation extracting genuine institutional value is significant. Shaun draws on research from McKinsey, PwC, and Stanford to explain what it takes to close it: the people leading AI adoption must be domain experts who understand the business, not IT professionals learning the workflows as they go. Decision makers, not just junior staff, need to be driving the change. And the mechanism for sharing breakthroughs: brown bag sessions, AI workflow days needs to be built deliberately.[20:30] What an AI Native Investment Office Looks Like in Four to Five YearsIn the near term, agents will handle the recurring, documentable tasks: reviewing emails, drafting responses, digesting manager letters, flagging inconsistencies against known mandates. Human judgment gets directed to the genuinely hard questions. Further out, GP agents and LP agents will begin communicating directly, and the frontier research techniques being developed by AI labs: auto researcher capabilities, autonomous investment thematic work, may reshape how allocators think about manager selection and portfolio construction entirely.[23:45] The Unexpected Cross Pollination of AI Proficiency

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What is The Agentic Allocator?: 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.
How often does this podcast release new episodes?: This podcast updates daily.
Where can I listen to this podcast?: This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
Does this podcast accept guests?: Yes, this podcast regularly features guests.

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