AI for Investment Banking

Introduction

Investment banking is a documents business. Pitch books, comps, CIMs, diligence trackers and valuation models are all built from large volumes of information under punishing deadlines. AI compresses the assembly; it does not change the standard every client-facing page is held to. This guide covers where it helps across the deal toolkit.

Pitch Books

AI accelerates the raw material behind a pitch — company overviews, market context, precedent situations, first-draft narratives — so the team spends its time on the argument and the story rather than the assembly.

The bar for a client page is exactness. Every figure and claim the AI drafts must be traced to a source and confirmed before it reaches the deck.

Comparable Companies

Building a comp set means identifying the right peers and pulling consistent metrics for each. AI can propose a peer universe with a rationale for every name and assemble the first-pass table, flagging where definitions differ between companies.

The judgement — which peers truly belong, which to trim — stays with the banker, and every multiple is verified against the source before it drives a valuation.

Industry Research

For sector coverage, AI reads across filings, transcripts and news to assemble a structured view of a market — size, drivers, competitive dynamics, recent activity — far faster than doing it by hand, and in the shape you specify.

CIM Analysis

On the buy-side of a process, a CIM is a dense, persuasive document that has to be read critically. With retrieval, a team can interrogate it directly — customer concentration, margin bridge, the assumptions behind the projections — and get cited answers to work from.

A CIM is a sell document; AI helps you read it sceptically, but the scepticism is yours. Verify the claims that matter against independent sources.

Due Diligence

Diligence over a data room is a natural fit: ask questions of hundreds of documents at once, surface every instance of a clause or risk, and build a findings tracker with a citation behind each entry.

The citation is what makes it usable — it points a human to the exact document to confirm before a finding is relied upon.

Valuation Support

Use AI for the narrative and assumptions around a valuation — laying out drivers, sensitivities and the comparable context — not for the arithmetic inside the model. The math stays in an auditable spreadsheet where every input can be checked.

Client Presentations

AI is a capable drafting and editing partner for client materials: structuring an argument, tightening language, adapting a narrative to an audience. What it produces is a draft to a professional standard, not a final page — the review discipline is unchanged.

Best Practices

• Verify every client-facing number against its source before it ships.
• Keep comp-set and peer selection a human judgement, informed by AI, not made by it.
• Read sell-side documents like a CIM sceptically; use AI to interrogate, not to trust.
• Keep valuation arithmetic in an auditable model, never inside the language model.
• Treat AI output as a first draft held to the same standard as any junior's work.

Further Reading

Continue with AI for Hedge Funds for the deep single-name research workflow, or AI for Private Equity for the sourcing-to-monitoring lifecycle in private markets.

Frequently asked questions about AI for investment banking