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AI & Finance Checklist for Business Buyers
AdminForce · September 2026
Use alongside your financial, legal and transaction advisers. This is an operational discussion guide, not a quality-of-earnings report, valuation, legal opinion or cybersecurity certification.
1. Reporting quality
- Can management reconcile recent reporting to the ledger?
- What is manually adjusted, by whom, and with what evidence?
- Are reporting definitions consistent across periods?
2. Cash and working capital
- Is receivables aging reconciled? Which balances are disputed?
- Where do expected receipts and payment commitments come from?
- Who owns cash assumptions and approvals?
3. Owner-dependent work
- Which customer, supplier and operating decisions rely on the seller's memory?
- What is documented and transferable at close?
- Who can review exceptions when the owner is unavailable?
4. Systems and data
- Which systems are used, and can data be exported lawfully?
- Are customer and supplier identifiers consistent?
- What access, permissions or third-party contracts would a new workflow need?
5. Recurring manual work
- Which reports, collections tasks or input requests repeat?
- What is the measured baseline: hours, errors and cycle time?
- Would improving the process be more useful than adding AI?
6. Controls and accountability
- Which outputs require human approval?
- Who owns exceptions, access changes and incident response?
- Can the proposed workflow be stopped and reverted?
7. Implementation readiness
- Is there a named process owner and a known-good reference output?
- What data cleanup, integration work or training is required?
- Can one bounded workflow be tested before expanding?
8. First 100 days
- Days 1–30: establish baseline, owners, prerequisites and pilot scope.
- Days 31–60: test sample cases and supervise a live cycle.
- Days 61–100: document, train, measure and prioritize the next change.
For each opportunity record: business problem; proposed change; sources; human reviewer; complexity; risks; further diligence; timing. Treat benefits as hypotheses until measured. Avoid assuming automation improves EBITDA without evidence.