Fund AI — how the fund industry actually uses AI
Every fund conference now has an AI panel; almost none of it tells you what anyone has actually deployed. This library answers the question underneath the noise, one job at a time: what the AI actually does, how honest the evidence really is, what it can't do, and what you would otherwise do instead.
Use-case library
One page per job: what the AI actually does, how honest the evidence is, what it can't do — and what you would otherwise do instead.
Fund operations10Deal sourcing2Portfolio management2Every assessment carries the date it was verified — read it as true at that date, not as a standing promise.
Each page covers one job — across fund operations, deal sourcing and portfolio management — setting out what the AI does, how honest the evidence really is (graded on every page: production-verified, vendor-claimed, or our own operator assessment), what you would otherwise do (your administrator, the law firm, a specialist platform, more people), what it can't do, and what standing it up takes in effort and cost. For what firms have actually put into production — deployment by deployment, sourced to the trade press — see AI adoption in the fund industry. For the small, concrete ways AI already helps on an ordinary desk — graduated wins, industry practice, anonymised — see Field Notes.
Use-case library — job by job, where AI earns its place
Adoption news tells you what a few large names have deployed. This library answers the question underneath it: for a specific job, what does the AI actually do, what's the proof (every page grades its own evidence — production-verified, vendor-claimed, or operator assessment), and what are the real alternatives — because the honest competitor is rarely a robot; it's your administrator's ops team, the law firm's associates, a specialist platform, or simply keeping the analyst. Every page is built the same way: the pain in operator words, the hard limit and where the human sign-off sits, what you need in place before any tool helps, and effort with an order-of-magnitude cost — including the below-this-volume-don't-bother line. The limit is the point: a tool sold as autonomous that isn't is worse than no tool, and plenty of "AI projects" are really deterministic-automation problems in disguise. Click any card for the full assessment.
Two honesty notes before you read. Freshness: AI capability moves faster than any library — every page carries the date it was verified, so read an assessment as true as at its date, not as a standing promise; newer real deployments in the adoption news are what refresh an entry. Choice: in a regulated group the tool decision is often not the desk's to make — vendor whitelists, group IT strategy, or a centrally-built AI capability you're expected to use can settle the question before any comparison does. The pages still matter then, just differently: they tell you what to ask of whatever you're handed.
AI in fund operations
- Capital-call & distribution notice processing
Notices in any format become cash-flow data — but a human still owns the wire.
- Investor onboarding & AML/KYC chasing
Classify documents, chase gaps, triage screening hits — the AML sign-off stays a named person's call.
- Annex IV & regulatory report assembly
Mostly a deterministic rules job, not AI — the honest exception to the hype.
- NAV break & reconciliation triage
Cluster, rank and hypothesise on exceptions — but a model can't clear its own breaks.
- Side-letter obligation extraction
Prose promises become a deadline register — a genuine unstructured-to-structured AI job.
- DDQ & RFP answering
Draft from your approved answer bank — but a human signs off every regulated answer.
- Board packs & minutes first drafts
Summarise the pack, draft the minutes — the board still adopts the record.
- Fee & carry verification vs the LPA
AI reads the terms; a deterministic model computes the number. Emerging, honestly flagged.
- Investor-services inbox triage
Classify, route and draft TA queries — but nothing that moves money acts on its own.
- Closing-set consistency review
Cross-read PPM vs LPA vs subs for mismatches — the legal call stays human.
AI in deal sourcing
- Deal sourcing & pipeline screening
Surface and rank candidates from signals no database holds — conviction stays human.
- Data-room & due-diligence first pass
A first pass across thousands of files in minutes — the verdict on the deal stays human.
AI in portfolio management
- Portfolio-company reporting roll-up
Harmonise mismatched portco packs into common KPIs — the step Power BI can't do alone.
- Covenant & portfolio monitoring (credit)
Extract terms with AI, test with rules — and keep the compliance test deterministic.