AI Operational Due Diligence Checklist for PE Operating Partners

How to Use the Checklist

Run this checklist alongside commercial and operational diligence. The goal is not to decide whether the target is "AI ready" in the abstract. The goal is to identify specific workflows where data quality, system access, and operating pain create a clear post-close value-creation path.

For the longer assessment model, see the full AI operational due diligence framework.

The AI Diligence Checklist

Area	Question	Evidence
Data	Are customer, job, invoice, inventory, and labor records structured enough to model?	Data export, field dictionary, sample records, data-quality report
Workflow	Where do employees re-key, copy, reconcile, chase, or wait for approvals?	Process maps, SOPs, queue exports, screenshots of handoff points
Systems	Which systems have APIs, webhooks, audit logs, and stable IDs?	Application inventory, integration map, API docs, vendor contracts
Governance	Who owns customer data, model outputs, approvals, and exception handling?	Security policy, access matrix, escalation policy, compliance obligations
Adoption	Which teams already use automation, and where has tooling failed?	Tool usage reports, rollout retrospectives, training materials
Value creation	Which AI plays can change revenue, gross margin, working capital, or SG&A?	Baseline KPIs, backlog, lead sources, service-level reports

Evidence to Request

Trailing 12-month lead source, response, booking, and close-rate exports.
CRM, ERP, accounting, field-service, BI, and ticketing system inventory.
Three examples of reports leadership uses to run weekly operating meetings.
Screenshots or exports from manual queues where work waits on humans.
Data retention, privacy, security, and customer-communication policies.
Current automation inventory, including scripts, Zapier flows, macros, and vendor automations.

Scoring the Opportunity

Score	Meaning	Interpretation
1	High friction	No clean data, undocumented workflows, resistant team, or closed systems.
2	Fixable foundation	Core workflows are visible but need cleanup before AI can scale.
3	Near-term opportunity	One or two workflows can be automated within the first 100 days.
4	Platformable value	Repeatable AI plays can improve margin or revenue across locations.
5	Portfolio pattern	The company can become the reference implementation for the fund.

First 100 Days

Convert the checklist into a short post-close roadmap. Pick one revenue workflow, one margin workflow, and one reporting workflow. Baseline each one before implementation, assign an operating owner, and define the first board-reportable KPI before vendors are selected.

Operator rule

If diligence cannot name the workflow, owner, baseline, system of record, and KPI, the AI opportunity is still a hypothesis.

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