What the findings can look like
Fictional example. Not a client result.
A small cleaning company wants to review its enquiry handling. This is what one page of the findings could look like. Names and numbers are invented for illustration.
The reviewed process, in short
- Enquiries arrive by phone, WhatsApp and email, and land wherever the owner happens to see them
- Quotes are written by hand, one at a time, in the evenings
- If a quote is sent, its status lives in the owner's head
1. Record every enquiry in one place
Why it matters: with enquiries scattered across phone, WhatsApp and email, some are answered late and a few are missed entirely. A shared enquiry record with a simple status and a responsible person makes the backlog visible at a glance.
Next step: agree a single enquiry record with the team and a short daily check to keep it current. (This is a process improvement, not an AI feature.)
2. Prepare quotes from a price list
Why it matters: hand-writing each quote makes the evening workload unpredictable and the quotes inconsistent. A one-page price list that states what is included in each type of job makes quotes consistent and faster to prepare.
Next step: draft a one-page price list from the last twenty quotes and review it with the team.
3. Track the status of sent quotes
Why it matters: while the status of sent quotes lives in one head, follow-up depends on memory. A simple status field with a weekly review makes follow-up deliberate rather than accidental.
Next step: choose a simple status field for quotes and review the list once a week.
The diagnostic does not assume AI is the answer; where AI genuinely helps with the first reply or with drafting, the findings say so. In this example, the improvements above are process work, not AI.
In this example, the written findings would summarise observations like these for the agreed processes and close with a prioritised list. Everything would be phrased factually; nothing would be promised about results.