AI & Diligence
How LPs Should Use AI in Investment Due Diligence
AI should surface patterns, not make decisions. Here is a practical framework for using machine intelligence in alternative investment diligence without compromising fiduciary judgment.
Matthew S. · Founder, Allocator Desk · January 2026 · 6 min read
The AI conversation in institutional investment management has been dominated by two camps. The optimists say AI will transform due diligence — processing documents faster, flagging anomalies, generating insights. The sceptics say alternative investing is fundamentally a judgment business and AI has no place in it. Both are wrong in the way that extreme positions usually are.
The more useful question is not "should we use AI?" but "where, specifically, does AI add value in our process — and where does it introduce risk?" The answer to that question is operational, not philosophical.
Where AI genuinely helps
In our view, AI earns its place in LP diligence in three areas.
Pattern detection across large document sets. An LP team running active diligence on fifteen managers simultaneously is reading hundreds of documents per quarter — DDQs, audited financials, quarterly letters, side letters, LP agreements. AI can read across all of these simultaneously, flag inconsistencies, surface changes from prior periods, and identify language that warrants attention. A human analyst could do this, but not at the same speed or without the fatigue-induced blind spots that come from reading the fifteenth 80-page DDQ in two weeks.
Synthesis across your own private signal history. This is the use case we find most compelling and most underappreciated. If your team has been logging signals — call notes, meeting summaries, reference call observations — for several years, you have a rich private dataset. AI can surface connections across that dataset that a human would struggle to find: three conversations where a manager expressed uncertainty about succession planning; two reference calls that flagged similar operational concerns; a thesis statement from the original IC memo that hasn't been revisited. These connections exist in your data. AI makes them visible.
Drafting structured starting points. IC memos, coverage summaries, risk flags — these take time to write from scratch. AI can generate a structured draft that pulls together the relevant signals, documents, and prior observations, giving an analyst a starting point to refine rather than a blank page to fill. The analyst still does the intellectual work. They do it faster.
> "AI should be the analyst that never sleeps and never forgets — not the principal that decides."
Where AI does not belong
The areas where AI should not have a role are equally clear.
Final investment decisions. A recommendation to commit $75M to a fund is a fiduciary act. It requires judgment that integrates relationship history, market context, portfolio construction considerations, and qualitative factors that no AI system can fully capture. The IC exists precisely to apply that judgment. AI can inform the IC. It cannot replace it.
Reference call interpretation. A reference call is a conversation between two humans in which enormous amounts of meaning are communicated non-verbally — through hesitation, word choice, what is said versus what is not said. AI can transcribe and summarise. It cannot interpret. The analyst on the call has to do that work.
GP relationship management. The relationship with a GP is built over years through direct human interaction. How you treat a manager when you're passing, how you communicate concerns, how you engage with their team across the cycle — these define your reputation in a small community. Delegating any of this to automation is both ineffective and, in the long run, reputationally costly.
<aside><strong>Our principle at Allocator Desk</strong><p>Every AI inference in our system is explicitly labelled as such. AI surfaces. Principals decide. Every final action in the system is attributed to a human team member. The audit trail is always clean.</p></aside>
The fiduciary frame
The cleanest way to think about this is through the fiduciary lens. A fiduciary is personally accountable for the decisions they make on behalf of beneficiaries. That accountability cannot be delegated — not to a consultant, not to a committee, and not to an AI system.
What a fiduciary can do is use better tools to make better-informed decisions. A Bloomberg terminal doesn't make decisions; it surfaces information that helps a portfolio manager make better ones. AI in LP diligence is the same category of tool — powerful, high-leverage, and entirely compatible with fiduciary duty, as long as the decision itself stays with the human.
Teams that get this right will move faster, see more, and make fewer errors of omission — the quiet mistakes that come from not having read something, not having connected two data points, not having surfaced a concern that was sitting in the notes from three calls ago. That is where AI earns its place.
Topics
- Artificial Intelligence
- Diligence
- Fiduciary Duty
- LP Operations