Manual quoting is expensive and slow. A quote that should take minutes turns into an account lookup, a pricing check, an inventory check, a margin review, a document, and a follow-up email. In a 2025 survey of 200 decision-makers at manufacturers, wholesalers and distributors, 71% said a quote takes a day or more, and 88% said slow, manual quoting has cost them deals.
The goal is not to let your AI invent a quote. The goal is to let it prepare the quote the way your company already decided quotes get prepared, and to keep the human on the decisions that matter.
1. Get how the job is actually done out of people's heads
Most of what makes a quote correct is unwritten: which system wins when two disagree, what the document has to contain, who signs off on a discount and at what threshold. You do not write that document yourself. Your AI asks how your team does the job, reads whatever you have already written if you have any of it, and drafts a playbook for a leader to correct and publish.
2. Keep pricing out of the model
A model must never author a price. The playbook names the system the number comes from and how old a reading may be, and says what to do when that system is unreachable or two sources disagree: ask, stop, use once, or propose a correction. A refusal is a correct answer.
3. Put a human on the gate
Anything discretionary goes to the person with the context to decide: a discount beyond policy, a special term, a margin call, a relationship judgment. The playbook names which approval is needed and in which of your own systems it has to be raised, so the approval stays where your records are.
4. Start with one workflow
Pick the quote that repeats most often and turn that one into a playbook. Use it on a second request, find where it was wrong, and correct it. A change proposed from a real exception becomes company policy only when a leader publishes it.