--- "title": "How should a private equity firm train its team on AI?" "description": "How private equity firms should train deal teams, operating partners, finance and portfolio leaders to use AI well and safely, what to teach each role, in what order, and how to measure whether it worked." "section": "By industry" "sidebarTitle": "Private equity: AI training" "published": "2026-10-30T13:04:15.975Z" "updated": "2026-10-30T13:04:15.975Z" --- ## How should a private equity deal team learn to use AI? It depends on who you are training and what rules you already have. Deal teams get the fastest return from hands-on training on real, sanitized deal documents such as CIMs, data room files and memo drafts. Operating partners need training aimed at portfolio work and board preparation. Finance and investor relations teams need it on reporting and routine questions. If your firm has no clear rules on confidential data, set those first, because uncertainty about NDAs is the main reason careful professionals avoid AI altogether. ## Who this is for COOs, operating partners and deal leads at private equity firms who have AI tools in place, or are about to, and want people to use them well and safely. The approach also applies to other investment and professional services firms that handle confidential documents under time pressure. ## The short version - Train by role. Deal teams, operating partners, back office and portfolio leaders do different jobs. - Use real, sanitized firm work in sessions, not generic public examples. - Set data rules before training, so people know what they can use and where. - Teach checking as a core skill. A fluent summary can still be wrong. - Measure use on live deal work, not attendance or seat counts. ## Why private equity needs its own approach Private equity work is full of long documents, tight deadlines and high-stakes judgment. That makes it well suited to AI help, and it raises the cost of careless mistakes. Generic AI training tends to miss three things: 1. **Confidentiality.** Deal materials are under NDA. People need to know which tools and settings are approved for which information. See [keeping client data safe with AI](/docs/keeping-client-data-safe-with-ai). 2. **Verification.** AI can misread a figure or blend two documents. Teams need the habit of checking claims against source pages. 3. **Judgment stays with people.** AI speeds up screening and first drafts. The investment view, and accountability for it, stays with the team. ## What to teach, by role **Deal team (associates, VPs, principals)** - First-pass review of CIMs and data room documents, asking for page references to check against - Building question lists for management meetings and expert calls - Turning call notes and transcripts into structured diligence notes - Drafting sections of investment committee memos from notes and models - Comparing a company's claims across documents to spot inconsistencies For more on where AI fits in diligence itself, see [how private equity firms are using AI in due diligence](/docs/ai-for-private-equity-due-diligence). **Operating partners** - Quickly assessing where AI could matter inside a portfolio company - Summarizing monthly reporting packs and flagging changes before board meetings - Building repeatable playbooks to reuse across the portfolio **Finance, operations and investor relations** - Drafting investor updates and answering routine questions from fund documents - Spreadsheet, reconciliation and reporting support. See [how AI agents can help automate LP reporting](/docs/automate-lp-reporting-with-ai-agents). **Partners and senior leaders** - Short, practical sessions on reviewing AI-assisted work and setting expectations for the team. See [getting partners to actually use AI](/docs/get-partners-to-use-ai). **Portfolio company leadership** - What AI can and cannot change in their business - Picking the first two or three workflows to improve - Knowing when they need training, outside strategy help or engineering ## A sensible order 1. **Set the rules.** A one-page guide covering approved tools, what data may be used and how to check output. Our guide to [writing an AI acceptable use policy](/docs/write-an-ai-acceptable-use-policy) walks through it. 2. **Start with the deal team.** They have the most document-heavy work and the clearest early wins. 3. **Capture what works.** Turn good prompts and checklists into shared assets, such as a CIM review checklist or a memo-drafting guide. See [how to create prompting standards](/docs/create-prompting-standards). 4. **Extend to operating partners and finance.** Reuse what the deal team built. 5. **Bring in portfolio companies.** Offer the same structured start, adapted to each business. 6. **Spot work worth building.** Recurring tasks, like weekly pipeline summaries or monthly portfolio reporting, may deserve an agent instead of repeated prompting. ## Formats that work Short, hands-on sessions on real work tend to stick better than one long lecture. Many firms combine a kickoff workshop with follow-up coaching over several weeks, so people bring questions from live deals. Compare the options in [one-off workshop vs ongoing coaching](/docs/ai-workshop-vs-ongoing-coaching) and [how long it takes to train a team on AI](/docs/how-long-to-train-a-team-on-ai). ## What to measure Skip vanity metrics like licenses issued. Track instead: - Time from receiving a CIM to a first screening view - Time to a first draft of memo sections - Share of the team using AI weekly on real deal work - Number of reusable firm assets created, such as checklists and prompts - Portfolio companies with an active AI plan Our guide to [measuring whether AI training worked](/docs/measure-ai-training-results) explains how to set a baseline. ## Common mistakes - **One firm-wide lecture.** Partners, associates and back office need different content. - **Public examples only.** People learn faster on work that looks like theirs. - **No confidentiality guidance.** This leads to either risky use or no use. - **Assuming the portfolio will figure it out.** Most portfolio companies need the same structured start. - **Expecting AI to make the call.** It shortens the path to a view. It does not replace one. ## How Ruby helps - **AI training:** hands-on, role-based sessions for deal teams, operating partners and operations, built around your real workflows. - **AI strategy:** finding where AI belongs across the firm and portfolio, with data rules and measured pilots. - **AI engineering:** secure agents and integrations for recurring deal and portfolio work once the team is ready. To plan training for your deal team, [book a call with Ruby](https://rubyadvisory.com/book-a-call). ## Related pages - [How are private equity firms using AI in due diligence?](/docs/ai-for-private-equity-due-diligence) - [How do I choose the best AI training company for my firm?](/docs/how-to-choose-an-ai-training-company) - [What AI skills should analysts have in 2026?](/docs/ai-skills-for-analysts) - [How to keep client data safe when using AI](/docs/keeping-client-data-safe-with-ai)