June 29 – July 2, 2026 · San Francisco, CA · imported from ai.engineer's public schedule feed

AI Engineer World's Fair 2026 — unofficial import demo

Unofficial demo. This programme was imported from the AI Engineer World's Fair's own public schedule feed to show vibeboard at real conference scale. Not affiliated with, or endorsed by, the organisers.

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AI in FinanceSession

Build for the Memo, Not the Demo — Notes from 200 Investment Committees

Shawn Chan

When
Thursday, July 21:30 PM – 1:50 PM · 20 min
Where
Track 3San Francisco, CA · imported from ai.engineer's public schedule feed
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About this session

By the end of this talk you will have a buyer-side specification for AI investment agents, the exact artifacts, evidence formats, and trust gates a senior finance team will require before letting an AI system touch a $100M+ capital allocation decision. Drawn from fifteen years and roughly 200 investment committees at CK Hutchison (A.S. Watson Group) and China Resources Holdings, on the side of the table the AI engineering audience almost never hears from. Most enterprise AI in finance is still being built by engineers who have never sat in an investment committee. I have spent fifteen years on the other side of that demo, cross-border M&A, IPO execution and strategic investment, as a buyer on deals including Oatly (Series B through Nasdaq IPO), Airbnb (Series F), SenseTime, Moore Threads, Leapmotor and EVE Energy, and on the A.S. Watson tri-market IPO and Temasek's strategic stake. I have watched analyst memos get torn apart, and signed off on decisions where being wrong meant being wrong by nine figures. From that seat, almost every AI finance demo I have seen has the same problem: it optimizes for the demo, not for the memo. This talk walks through the specific failure modes that kill AI agents at the IC door: Source hierarchy is not retrieval. A footnote in an audited 10-K outweighs a sell-side note, which outweighs a transcript, which outweighs an internal email. Most RAG systems flatten this. Numerical consistency is non-negotiable. A memo that says "revenue grew 18%" in paragraph one and "17.4%" in the sensitivity table is dead on arrival. Contradiction is a feature. Real diligence surfaces conflicts between sources; AI agents tend to silently resolve them. Every assumption must be separable from every fact. Investment committees do not approve assumptions hidden inside prose. Audit trail is the deliverable. If a regulator, an auditor, or a board member cannot trace a claim back to evidence in under thirty seconds, the system is unusable. Accountability cannot be delegated to a model. Someone has to sign the memo. The architecture has to reflect that. The session closes with a concrete buyer-side specification, what an AI investment agent must produce, in what form, with what evidence, before a senior finance team will let it touch a live deal. Not a framework slide.

Speaker

Shawn Chan

Vice President, China Resources Holdings

Shawn Chan is Vice President at China Resources Holdings, leading a consumer-sector fund, and was previously Head of Investment at A.S. Watson Group (CK Hutchison). Across fifteen years he has executed cross-border M&A, IPOs and strategic investments in companies including Oatly, Airbnb, SenseTime, Moore Threads, Leapmotor and EVE Energy, across Hong Kong, mainland China, the UK and the US. MSc Finance from the University of Manchester. His current focus is what it takes for AI agents to earn trust inside real investment committee workflows.

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