How Private Equity Deal Teams Are Using AI (and Why Most Are Doing It Wrong)

AI Has Already Entered the Deal Room

If you manage or work on a private equity deal team, AI is already part of your workflow. Maybe not officially. But somewhere in your firm, an associate is pasting sections of an offering memorandum into ChatGPT to get a faster read on key terms. An analyst is feeding financial data into an AI tool to accelerate comp analysis. A principal is using it to draft the first pass of an IC memo.

The productivity gains are real. What used to take a junior team member an entire weekend — reading a 300-page OM, extracting the relevant financial metrics, benchmarking against comparable transactions — can now be reduced to hours. But there is a problem that most PE firms have not fully reckoned with: the way they are using AI is exposing their most sensitive data to third parties.

The Use Cases Are Compelling

AI adoption in PE is accelerating because the use cases map directly to a deal team's core activities.

OM Summarization and Analysis. A deal team reviewing 50-100 opportunities per year spends enormous time on initial screening. AI can read an OM in seconds, extract key financial metrics, flag concerns, and produce a structured summary that lets a partner decide in minutes whether a deal warrants deeper diligence.

Comparable Transaction Analysis. AI can pull together relevant transactions, normalize the financial metrics, and surface the patterns that inform valuation — compressing hours of spreadsheet work into a focused conversation with an AI assistant that understands the context.

IC Memo Drafting. Investment committee memos follow a predictable structure: thesis, market overview, financial summary, risks, terms. AI excels at producing first drafts when given the right inputs, letting deal team members refine rather than write from scratch.

Portfolio Reporting. Post-acquisition, AI can synthesize financial statements, operational KPIs, and management commentary into standardized reports that give the investment team a quick read on portfolio health.

The Risk Most Firms Are Ignoring

Every one of these use cases involves feeding proprietary, market-sensitive information into an AI tool. And for most PE firms, that tool is a consumer product running on someone else's infrastructure.

When an analyst pastes an OM into ChatGPT, that document contains the target company's financial statements, growth projections, customer concentration data, and proposed transaction terms — exactly the information covered by the NDA your firm signed. When that data enters a consumer AI platform, it leaves your firm's control. Depending on the terms and account type, it may be retained or used to improve future models.

Multiply this across every deal your team evaluates. Every OM summarized. Every comp analysis generated. Every IC memo drafted. The aggregate data exposure is substantial — and it includes information that is both proprietary to your firm and confidential under NDAs with sellers and intermediaries.

Shadow AI Is the Real Problem

Most PE firms that have considered AI risk have addressed it with a policy: do not upload confidential deal materials to external AI tools. But the productivity gains are too significant. An associate who can process an OM in 30 minutes instead of 8 hours will not stop because of a policy memo. The incentives overwhelmingly favor using the tool, and enforcement is essentially nonexistent.

This is shadow AI — unauthorized use of consumer AI tools within the firm. It is happening at nearly every PE firm, and leadership either does not know or has tacitly accepted it.

The Private AI Alternative

The solution is not to ban AI. It is to give your deal team the same capabilities on infrastructure you control.

Private AI means running open-source models — like Llama, Mistral, or GLM — on cloud infrastructure that belongs to your firm. The interface looks and feels like ChatGPT. The capabilities are comparable for deal team use cases. But no data leaves your environment. Every query, every uploaded document, every generated output stays within your firm's secure perimeter.

Your deal team keeps the speed advantage. Your firm keeps its proprietary data. NDA obligations are honored not because of a policy memo, but because the infrastructure makes it impossible for deal data to leave your control.

The real estate investment teams within PE firms face the same dynamic — property financials, tenant data, and development projections benefit from AI analysis but cannot safely pass through consumer tools.

The Bottom Line

AI is transforming private equity whether individual firms choose to participate or not. The question is not whether your deal team will use AI — they almost certainly already are. The question is whether they are using it in a way that protects your firm's proprietary data, honors your NDA obligations, and maintains the confidentiality that your LPs and portfolio companies expect.

To see how firms are implementing private AI for deal teams, visit our case studies or book a conversation about your firm's specific needs.