SELLEST OSAST
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 data
- Why being able to say 'no' quickly, to managers, to co-investments, is just as strategically valuable as identifying the right opportunities
- How 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 classes
- Why Aksia is actively tracking diligence report length as a KPI and what it signals when reports get longer
- How 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 development
- The 'blank sheet of paper' trap: why starting with a specific recurring task beats starting with a grand AI strategy
- Where the edge in alternatives investing will come from next: differentiated data, combined with experienced human judgment
About 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:
Victoria Sienczewski on LinkedIn
AuumAI Website
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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.
