How are private equity firms using AI in due diligence?

It depends on the stage of the deal and the type of work. AI is already useful for document-heavy tasks: reviewing data rooms, extracting key terms from contracts, summarizing expert calls and management meetings, and producing first drafts of memos and question lists. It is much weaker at the parts that decide the deal: judging management, building conviction on a thesis and setting the price. If your team’s time goes into reading and organizing, AI can help quickly. If the problem is judgment, it will not replace your team.

Who this is for

Operating partners, deal team leads, associates and COOs at private equity firms, and anyone at an investment firm who spends a lot of diligence time reading, summarizing and organizing documents.

The short version

The best early uses are reading-heavy: data rooms, contracts, call notes and memo drafts.
Every output that informs an investment decision needs to be checked against the source.
Searchable past deals and investment memos are one of the most valuable things AI can give a firm.

Where AI helps today

Data room triage. Data rooms can hold hundreds or thousands of files. An AI assistant can help sort documents by topic, summarize what each contains and flag what is missing against your standard diligence checklist. Associates still read what matters, but they start with a map instead of a folder tree.

Contract review and extraction. AI can pull key terms from customer, supplier and employment contracts into a table: change of control clauses, termination rights, exclusivity, renewal dates and pricing terms. Legal advisers still own the conclusions, but the deal team gets a faster first view of where the risks may be.

Expert calls and management meetings. Turning call transcripts or notes into structured summaries, themes and follow-up questions is a simple, reliable use. Comparing what several experts said on the same topic is especially helpful.

Building question lists. AI can draft diligence request lists and management questions from the deal thesis, the data room contents and your firm’s past lists. A senior person edits and prioritizes.

First drafts of memos. From the team’s notes and analysis, AI can produce a first draft of sections of an investment committee memo, such as the company overview or market summary, in your firm’s format. The deal team rewrites the parts that carry the argument.

Searching past deals. Many firms have years of memos, models and diligence findings spread across drives. A well-built search assistant lets a deal team ask how a similar business or risk was handled before. This depends on organizing and connecting the documents, not just buying a chat tool.

Where AI is weaker

Financial models. AI can help write formulas, check a model’s structure or summarize outputs, but the model itself, and every number in it, needs to be built and verified by people.
Quality of earnings and accounting judgment. These remain the job of your advisers and finance team. AI can help organize their findings.
Judging people and fit. Assessing management, culture and alignment is not something to hand to a tool.
The final call. AI can make a team faster and more thorough. The investment decision stays with the partners.

Data and confidentiality

Diligence material is usually covered by NDAs and often includes sensitive information about the target, its customers and its employees. Before anyone uploads a document:

Use AI tools on business plans with clear data protections, not personal accounts.
Check that your NDA and data room terms allow the use you have in mind. Some restrict where material can be copied or processed.
Agree internally on what can go into which tool, and write it down. Our guides on keeping client data safe with AI and writing an AI acceptable use policy cover the basics.

How to start

Pick one reading-heavy task. Contract term extraction or expert call summaries are good first choices because the output is easy to check against the source.
Test it on a closed or past deal. You know the answers, so you can judge the quality without deal pressure.
Train the deal team on real material. Associates and VPs need to learn how to give AI the right documents, ask precise questions and verify outputs. See how to train non-technical employees to use AI.
Measure one thing. For example, hours to produce a contract summary table, before and after. Our guide on running an AI pilot that doesn’t stall explains how to set it up.
Then decide whether to build. If the task repeats on every deal, a custom agent connected to your data rooms, document storage and templates may be worth it. See build vs buy AI tools.

Off-the-shelf tools or something custom?

A general AI assistant on a business plan, with good training, can handle call summaries, memo drafts and much contract review. Some data room and deal software providers are adding AI features, so check what your current tools already offer.

Custom work makes sense when you want AI to work across your own systems: searching every past memo, applying your diligence checklist automatically, or filling your memo template from the team’s work in one step. Our page on custom AI agents for your business explains what that involves.

Common mistakes

Trusting summaries without checking. A missed clause in a summary is still a missed clause.
Uploading data room material to personal accounts. It can breach NDAs and your own policies.
Training only associates. Partners set the standard for what is acceptable. See how to get partners to use AI.

When you do not need us

If your firm already has an approved AI tool, clear data rules and someone keen to build good prompts for diligence work, you can get real value in-house with the steps above. Outside help is more useful when you want structured training for deal teams, or custom agents that work across your past deals and systems.

How Ruby helps

AI training: hands-on sessions for deal teams using real diligence tasks from closed deals.
AI strategy: choosing the first uses, setting data rules that respect NDAs and running a measured pilot.
AI engineering: building agents that search past deals, extract contract terms and prepare memo drafts in your format.

If diligence work is stretching your team, book a call with Ruby.