Fast replies need workable promises
A custom packing business must send quotes before customers book elsewhere. Its owner is weighing AI drafting software: faster replies look like growth. Yet a quote promising an unavailable slot can win an order the team cannot deliver. Customers pay for finished packing work.
Assume a hypothetical week with 40 enquiries and 13 staff hours for quoting. The following times are illustrative, not reported results.
One week, two workflows
The current workflow takes 12 minutes to prepare each quote and 6 to check it: 40 × (12 + 6) ÷ 60 = 12 staff hours.
With AI, preparation takes 3 minutes but checking takes 18: 40 × (3 + 18) ÷ 60 = 14 staff hours. Keep the current workflow: it fits the allowance. This compares labour capacity; software fees and profit are not assessed.
Friday is already full
The assistant reads the enquiry but cannot see the live packing schedule. An enquiry requests Friday; the draft sets dispatch_date to Friday, although no slot is free. A reviewer must recover the booking context, reconstruct the schedule check and correct the promised date. Manual preparation already includes that work. AI is an amplifier for drafts; it does not create packing capacity.
Check the promised date
Before sending, compare dispatch_date with a confirmed reservation. No matching slot means hold the quote and agree a feasible date. After correction, the approved quote shows the reserved date, so packing staff receive a commitment they can schedule. Time preparation and checking separately, counting approved quotes. An accepted quote is still not a completed, paid job.
When AI fits the allowance
Now change the hypothetical setup: the assistant reads confirmed reservations, and checking takes 6 minutes. Then 40 × (3 + 6) ÷ 60 = 6 staff hours. AI becomes the better time choice. Require matching reservations and checking below 15 minutes to beat today’s workflow; 16.5 minutes is the maximum within the 13-hour allowance.