Illustrative sample
What an AI Opportunity Map looks like
This example shows the shape and decision standard of an audit deliverable. It uses a fictional distribution business and is not presented as a client result.
Do now
Invoice reconciliation
- Current friction
- Finance staff match transfer alerts, invoice references, and customer messages manually.
- Why it is first
- The work is repetitive, the inputs already exist, and every proposed match can be reviewed before records change.
- Evidence to collect
- Weekly transaction volume, minutes per match, exception rate, and cost of delayed reconciliation.
- Safety condition
- Keep approval with finance staff and restrict access to the minimum customer and payment data required.
- 30-day test
- Run on one account. Continue only if review time falls without increasing reconciliation errors.
Validate first
Customer-enquiry triage
- Potential value
- Group incoming questions by urgency and draft responses for staff review.
- Unknown
- Whether message volume is high enough to justify another system.
- Next evidence
- Sample two weeks of enquiries, classify them manually, and measure response delay before selecting a tool.
Ignore for now
A general-purpose company chatbot
No defined user, no named workflow, and no measurable result. Buying it now creates training, data, and maintenance work before the business has identified a problem worth solving.
The first 90 days
- Days 1 to 30: establish the reconciliation baseline and run the controlled test.
- Days 31 to 60: compare time, error, and exception data against the agreed gate.
- Days 61 to 90: expand only if the result clears the gate; otherwise close it and document why.
Need this decision for your own business?
The AI Opportunity Audit applies this standard to your workflows, numbers, risks, and team.
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