Short answer
AI helps private equity (PE) firms most at the stages where analysts spend hours reading and sorting, which covers sourcing, screening against a mandate, diligence document review, memo drafting, and portfolio and limited partner (LP) reporting. Investment judgment stays with the partners. Results get much better once the AI is connected to the firm's own CRM and data providers. Lattice Partners is an AI implementation partner based in Los Angeles and New York that helps companies build an AI strategy, trains their teams on AI tools, and builds custom agents and software, including for PE and finance teams.
- Screening is the clearest early win. AI can sort companies against a written investment mandate, and an analyst reviews the ones that pass.
- General assistants like ChatGPT Enterprise, Claude, and Microsoft 365 Copilot handle drafting and document Q&A with little setup.
- Custom builds make sense once the work depends on firm-specific pieces like your CRM or your mandate rules.
- PE-specific software (deal CRMs, data platforms, portfolio monitoring tools) is adding AI features, which helps if your team already lives in those systems.
Where does AI help at each stage of the PE deal lifecycle?
AI has a job at every stage of the PE deal lifecycle, but the job in sourcing looks nothing like the job in LP reporting. Before a deal exists, AI is mostly sorting long lists of companies. During a live deal it reads data rooms and drafts, and after close the work becomes a monthly chore of pulling portfolio numbers into reports.
| Stage | What AI does | What it needs | Build vs buy |
|---|---|---|---|
| Sourcing | Finds companies that fit a thesis, enriches records, flags new targets from news and databases | CRM (such as DealCloud), data providers (PitchBook, Crunchbase), your thesis notes | Buy data; build the enrichment and matching layer if your thesis is specific |
| Screening | Classifies companies against the mandate (sector, size, geography, ownership, exclusions) with a reason for each call | Clear written mandate rules, company descriptions and financials, a review queue | Build; screening logic is firm-specific |
| Diligence | Summarizes data room documents, extracts terms, builds question lists, compares management claims to the numbers | Data room exports, financial models, expert call notes | Buy a general assistant for reading; build for repeatable extraction |
| Investment committee | Drafts memo sections, pulls comps and market data with citations, checks the memo against the model | Memo template, model outputs, approved data sources | General assistant plus a firm template; build if pulling from many systems |
| Portfolio monitoring | Collects monthly KPIs, flags variances, drafts commentary | Portfolio company reporting files, accounting exports, KPI definitions | Buy a monitoring tool or build a light pipeline into your own data |
| Board and LP reporting | Assembles board packs and quarterly updates, drafts narrative, answers LP questions from approved material | KPI data, prior decks, fund documents | General assistant with templates; build for recurring packs |
Drafting is covered in AI tools for investment memos and board materials. Training for portfolio teams is covered in AI training for portfolio company operating teams.
What is deterministic screening against a mandate?
Deterministic screening means the AI applies the same written mandate rules to every company and records why each one passed or failed. A deal team gets a consistent, auditable list instead of a pile of chat answers that change with each prompt.
In practice, someone writes the mandate down as explicit criteria, such as revenue band, sector definitions, geography, ownership type, and hard exclusions. The AI scores each company's description and available data against every criterion and writes a short reason for each score. Edge cases go to a human queue. Since the rules and reasons are stored, an analyst who disagrees with a call can tweak the rule behind it and rerun the whole list.
Lattice Partners builds this kind of screening. Analysts then spend their time on the companies that pass, where their judgment matters more.
The glossary defines deterministic screening and board pack in plain terms.
Should a PE firm use ChatGPT Enterprise or Claude, or build something custom?
Most PE firms should start with a general AI assistant and add custom builds only where the work depends on firm-specific data or rules. Reading and drafting are well covered by general tools, so a custom build usually earns its cost when a task needs your CRM or licensed data.
The options fall into three groups.
- General assistants. ChatGPT Enterprise, Claude for Work, and Microsoft 365 Copilot are good at summarizing documents, drafting memo sections, and answering questions about files a user provides. OpenAI launched ChatGPT Enterprise in August 2023 with a commitment that it doesn't use business conversations for training (TechRepublic). Microsoft says Microsoft 365 Copilot grounds answers in organizational data through Microsoft Graph, with access scoped by user permissions (Microsoft Learn).
- PE-specific software. Deal CRMs, market data platforms, and portfolio monitoring tools are adding AI features inside their own products. These fit well when the data you need already sits in that product.
- Custom builds. A custom agent or integration connects AI to several systems at once, like the CRM, a data provider's API, shared drives, and portfolio KPI files. A common way to do this today is Model Context Protocol (MCP), an open standard for connecting AI assistants to business systems.
"Pick one repeatable workflow that's costing you deals and improve it. A firm-wide AI rollout with no first win usually goes nowhere."
Colin McDonnell, Co-Founder and Managing Partner, Lattice Partners
How long does it take to get AI working at a PE firm?
A focused first version, such as mandate screening or a memo drafting workflow, can usually ship in weeks. Firm-wide adoption is slower. It depends on training and data access, and mostly on how fast deal teams change habits.
Training and building tend to go together. Analysts who've already practiced prompting and checking AI output on general tools get much more from a custom build when it arrives, while firms that skip training often stall at the pilot.
Pilots that stall usually lack an owner or a changed workflow. Why AI pilots stall lists the common causes.
How Lattice Partners does this
Lattice Partners works with mid-market PE firms, finance teams, and the operating teams at their portfolio companies. Engagements follow four steps. We start with Learn, mapping the deal process to see where analyst time goes, and if AI isn't the right answer for a workflow, we say so. Tailor is the design step, built around the firm's CRM, data licenses, mandate, and team skill level. During Build and Ship we aim to have a first version live within a few weeks, often a screening tool, a memo drafting workflow, or an MCP integration that connects Claude to systems like DealCloud, and the hands-on training uses the firm's own deals and documents. Iterate is where real usage shapes the tool; most products go through two to three iterations.
Custom software is a one-time build and the firm owns the code. There's no recurring per-seat or per-module license from us. Many firms start with training and move into building, and we can stay on after launch to support and improve it.
Frequently asked questions
Can AI make investment decisions for a private equity firm?
No. Investment decisions stay with the partners and the investment committee. The setups that work best hand AI the repetitive classification and drafting, then route every recommendation to a named human reviewer.
Is it safe to put deal data into ChatGPT or Claude?
Enterprise versions of these tools are designed for business data, with admin controls and commitments about training. A PE firm should still review each vendor's data terms, use single sign-on, and limit what confidential data goes into which tool. Data licensed from third parties may carry its own restrictions on AI use.
What is the best first AI project for a PE firm?
Mandate screening is usually the best first project. The rules are already written down somewhere, there are thousands of companies to run through, and an analyst can check each call quickly. Memo drafting from a fixed template is a close second.
Do we need a data science team to use AI in private equity?
Most mid-market PE firms don't need an in-house data science team. A partner who builds and trains can ship the first workflows, and trained analysts can run them after that.
Related reading
About Lattice Partners
Lattice Partners is an AI implementation partner based in Los Angeles and New York. We help companies build an AI strategy, train their employees on AI tools like ChatGPT and Claude, and build custom AI agents and software. Clients own the code we build. Lattice Partners was founded by Colin McDonnell, Martin Anquetil, and Christian Yeghnazar. Contact us to talk about your use case.