Brief sections
The leak
A quote goes out Tuesday. The customer had two questions Thursday. Nobody answered until the following Wednesday, because the estimator was on site and the inbox doesn’t escalate. Multiply by every open quote and you get the pattern we find in most operating businesses: the pipeline isn’t short of demand, it’s short of attention. The cost shape is specific — revenue you already earned the hard way (site visit done, quote built, trust established) quietly expiring.
What AI realistically does here
Not “an AI salesperson.” A follow-up copilot: it watches the quote pipeline, knows which quotes are aging past your response norms, drafts the follow-up in your voice with the quote attached, and routes judgment calls (discount requests, scope changes) to the owner instead of answering them itself. A human approves sends for the first month; after adoption settles, routine nudges go out on schedule and exceptions still stop at a person.
What has to be true first
- Quotes live somewhere queryable — an estimating tool, a spreadsheet with discipline, even a folder with consistent naming. If quotes live in text-message photos, we fix that first (that’s a workflow redesign, not an AI problem).
- One named owner for the pipeline. The system reports to a person, not to “the team.”
- A response norm you actually believe: “every open quote gets touched every 5 business days” is enforceable; “we stay on top of things” is not.
What we'd install
This brief maps to the operating loop we install around it: the opportunity gets scored before anything is built (value, data readiness, adoption path), the workflow is redesigned before AI touches it, and the copilot ships with an owner, an SOP, and a line in the value ledger — hours returned and quote-response time, reviewed with leadership. The rest of the loop is on the AI operating partnership.
Worth it?
Count the quotes that go stale in a normal month and the ones you’d plausibly have closed with a same-week touch — even a rough delta shows the shape. The calculator below turns hours and rates into an illustrative annual range: illustrative math, not a promise. Whether the data underneath can support the build is what the scorecard checks.