Sample
What an AI Opportunity Map looks like
This shows what an audit report looks like and how firm its recommendations are. The business is made up, a distribution company, so this isn't a real client's result.
Do now
Invoice reconciliation
- Current friction
- Finance staff match transfer alerts, invoice references, and customer messages manually.
- Why it's first
- The work is repetitive, the information is already there, and someone can check every suggested match before any record changes.
- Evidence to collect
- Weekly transaction volume, minutes per match, exception rate, and cost of delayed reconciliation.
- Safety condition
- Finance staff still approve every match, and the tool only sees the customer and payment data it needs.
- 30-day test
- Try it on one account. Keep going only if checking takes less time and mistakes don't go up.
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
- Take two weeks of enquiries, sort them by hand and measure how long replies take before choosing any tool.
Ignore for now
A general-purpose company chatbot
Nobody in particular would use it, it isn't tied to any task, and there's no way to measure if it worked. Buying one now means training, data and upkeep before the business has even found a problem worth solving.
The first 90 days
- Days 1 to 30: measure how reconciliation works today, then run the test.
- Days 31 to 60: compare time, mistakes and odd cases against the target we agreed.
- Days 61 to 90: roll it out further only if it hit the target. If not, stop and write down why.
Need this decision for your own business?
The AI Opportunity Audit does the same for your business, using your own work, numbers, risks and team.
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