AI for private equity is no longer a question of whether to use it but where it earns its keep. Deal teams are using AI to screen more companies, read data rooms faster, structure diligence findings and track portfolio performance. At the same time, nearly every target now claims to be "AI-enabled," which creates a new diligence problem: separating real capability from marketing.
We work on both sides of this. We use AI in our own diligence and operating work, and we assess AI and technology claims in targets for PE clients. This article walks through the deal cycle stage by stage, with practical examples and the limits that matter.
Sourcing and screening
The top of the funnel is where AI saves the most raw hours. A deal team that used to screen a few dozen companies a week by hand can now process far more, as long as the criteria are clear.
Useful applications:
- Building target lists from public business data, industry directories, association member lists and company websites, then classifying each company against the fund's thesis.
- Summarizing a company's website, product pages, job postings and press coverage into a one-page profile.
- Flagging signals of interest: founder age and tenure, hiring patterns, new locations, product launches.
- Drafting tailored first-touch outreach to owners, with a person reviewing every message before it goes out.
The limit here is data quality. AI will confidently classify a company based on a stale website. Treat AI screening as a way to rank and prioritize, not as a final filter, and check the top of the list by hand.
Pre-LOI analysis
Before a letter of intent, the deal team needs a fast, defensible view of the business. AI helps by turning a CIM, a management presentation and a few years of financials into structured notes: revenue by segment, customer concentration, stated growth drivers and open questions.
It is also useful for building the first version of a market map. Given a defined buyer and category, AI can list competitors, pull their public positioning and pricing pages, and organize them into a comparison table that analysts then verify and refine.
The rule we follow: AI drafts, people verify. Every figure that goes into an investment memo should trace back to a source document, and someone on the team should have checked it.
Diligence
This is where AI has changed the most day to day.
Data room review
Data rooms routinely hold thousands of files. AI can index them, summarize contracts, extract key terms (change-of-control clauses, termination rights, auto-renewals, exclusivity, pricing escalators) and flag documents that do not match what management presented. A reviewer still reads the critical contracts in full, but they know which ones to read first.
Commercial diligence
Customer and industry interviews remain the core of commercial due diligence. AI helps with the surrounding work: transcribing and structuring interview notes, coding themes across dozens of conversations, summarizing public reviews, and building competitive benchmarking tables from competitor websites and documentation. The interviews themselves, and the judgment about what they mean, stay with people.
Technology diligence
Technology due diligence asks whether the systems can support the business plan: whether the product scales, whether security and data practices are sound, whether key-person risk is manageable, and what it will cost to fix what is broken. AI helps review code repositories, architecture documents and security policies faster, and can draft first-pass findings for experienced reviewers to confirm.
Assessing a target's AI claims
This is now a standard part of our technology due diligence work. When a target says it uses AI, we ask:
- What exactly does the AI do, and what share of revenue depends on it?
- Is it built in-house, or is it a third-party model accessed through an API? If third-party, what happens to margins if pricing changes, and what happens if the provider changes terms?
- What customer data goes into it, and do customer contracts allow that use?
- Is there human review of outputs that reach customers?
- Can the team demonstrate it working on real data, not a scripted demo?
Many "AI-powered" products are a thin layer over a general-purpose model. That is not necessarily bad, but it changes the defensibility story and should change how the deal team thinks about competitive risk.
The 100-day plan
After close, AI shows up in the value creation plan in two ways.
First, as an efficiency lever inside the portfolio company. Common early wins include automated reporting, faster month-end close, AI-assisted customer support, sales follow-up and proposal drafting. The AI readiness assessment framework we use helps sort which of these a given company can absorb in the first 100 days and which need foundational work first.
Second, as a reporting lever for the sponsor. Connecting AI to the company's accounting, CRM and operational systems makes it possible to produce board packs and weekly KPI summaries with far less manual assembly. We build these with connections to systems like QuickBooks, Shopify and Amazon, and the same approach applies to most mid-market software stacks. Our article on AI dashboards goes into the mechanics.
A caution: do not load a newly acquired company with five AI projects in the first quarter. Management is already absorbing a change of ownership. Pick one or two projects with clear owners and measurable results.
Portfolio monitoring
Across a portfolio, the hardest problem is usually inconsistent data. Each company reports on its own schedule, in its own format, with its own definitions. AI does not fix bad definitions, but it can do much of the work of mapping each company's reports into a common format, flagging variances and drafting commentary for the operating partner to review.
Firms that get the most from this tend to agree on a short list of standard metrics first, then connect AI to the source systems rather than to emailed spreadsheets.
Exit preparation
At exit, AI helps assemble the data room, check that documents are complete and consistent, and draft sections of the CIM from existing materials. It is also useful for running a buyer's likely diligence questions against the company's own documents, so gaps are found and fixed before a buyer finds them.
Where AI does not replace judgment
It is worth being plain about the limits:
- AI does not know whether a management team is credible. That comes from meetings, references and track record.
- AI summaries can miss a single clause that matters more than everything else in a contract. Critical documents need a human read.
- AI tools can produce plausible but wrong figures. Every number in an investment memo needs a source.
- Confidentiality matters. Data room contents and interview notes should only go into AI tools on business plans with appropriate data-handling terms, and in line with the NDA and the fund's own policies.
Getting started inside a fund
For a deal team starting out, the practical sequence is:
- Approve a small set of AI tools on business plans and write a short policy on what deal data can go into them.
- Pick one repetitive task, such as first-pass CIM summaries or target screening, and standardize how it is done.
- Build a reusable process (a prompt, template or packaged skill) so results are consistent across analysts.
- Add data room review and interview analysis once the team trusts the first workflow.
We help funds and their portfolio companies with both sides: using AI in the deal process, and assessing AI and technology in the companies they buy.
Frequently asked questions
Is it safe to put data room documents into AI tools?
It can be, if the tool is on a business plan with data-handling terms that fit your NDA and fund policy, and access is controlled. Personal accounts and free consumer tools are not appropriate for confidential deal data. Check with counsel if the NDA is restrictive.
Can AI replace analysts on a deal team?
No. It changes what analysts spend time on. Less time goes into reading and formatting, more into verification, interviews and judgment. Teams still need people who understand the business and can tell when an output is wrong.
How do you tell if a target's AI is real?
Ask for a live demonstration on real data, find out which models it relies on and on what terms, check how much revenue depends on it, and talk to customers about whether the AI features matter to them. Technology due diligence should cover this directly.
Where should a PE firm start with AI?
Start with a narrow, repetitive task in the deal process, such as screening targets or summarizing CIMs, on an approved business-grade tool. Build trust in one workflow before expanding to diligence review and portfolio reporting.