What an AI mistake with a customer actually costs (and what making it right costs): a calculator

An AI mistake costs a small business in two ways. The first is the customers it traps, who leave and do not say why. The second is the cost of making a wrong promise right. Five9's survey of 1,006 consumers found 40% stop doing business with a company after one bad experience, and 95% tell others about it.¹ The public cases also show that fighting a bot's promise has cost more than honouring it.

This piece gives you a worksheet for both costs, the cases behind it, and the protocol we would follow on the day it happens to you: honour it, log it, fix the facts sheet.

The decision every owner using AI has to make in advance#

The decision is simple to state. When your assistant gets a price, a refund or a policy wrong, do you stand behind it? Most owners answer that question for the first time on the day it happens, with an angry customer waiting.

We made that call early at Valet, and it shaped the product. Money questions (discounts, refunds, haggling) go to the owner by default. The assistant answers only from the owner's own price list and policies, and anything not on the list waits for the owner. That choice has a real cost, which we come back to below. First, the case for letting the bot answer everything.

Why "let the bot handle it" looks right on a spreadsheet#

The spreadsheet case for full autopilot is tidy. Every money question the bot answers is one the owner does not. Discount requests, refund requests and "can you do better on price" arrive at all hours. Answering them takes judgement, and judgement takes the owner's time.

On paper, an occasional wrong answer is a rounding error. If the bot handles a few hundred conversations and gets a handful wrong, the error rate looks small. The opportunity cost of answering everything yourself looks large.

The flaw is that the errors are not evenly priced. A wrong opening-hours answer costs nothing. A wrong price on a $12,900 booking, or a refund the business will not honour, costs the sale and, often, the customer.

What the public cases cost: honour, fight, or apologise#

The documented cases give three ways to respond, and each has a known price.

Air Canada's case is the clearest lesson. The tribunal rejected the "separate legal entity" argument.² The award was small. The precedent was not. One Hacker News commenter, TomK32, wrote in February 2025 that the fight "cost the airline more money than they saved."³ That is a commenter's view, not an audited figure, but nobody has argued the reverse.

BMW Toronto shows the cost of hesitating. The bot, called Quinn, misread a figure a human employee gave it. The customer did not know he was talking to a bot until a salesperson called. He told The Drive: "I feel embarrassed, and I feel angry that I've been negotiating with this bot."⁴ The dealer honoured the offer in the end. It paid the same money either way, and got the story as well.

Owners are asking the same question at smaller scale. A thread in r/webdev titled "If your AI support system promised user refund, should you?" drew more than 60 comments.⁸ A founder's thread in r/micro_saas, "We lost 56 customers after I tried to save money with AI," drew more than 240.⁹

The operating principle: price the error before it happens#

The principle we took from these cases has two parts. The cheapest mistake is the one the assistant is never allowed to make. The next cheapest is one you honour quickly, record, and fix at the source.

That means two decisions made in advance, in writing. Which topics the assistant may never answer on its own. And the dollar threshold below which you honour an error without discussion. Above the threshold, the owner calls the customer. Nobody argues with a customer by email about what the bot "really" meant.

The AI mistake calculator: fill in your own numbers#

The worksheet below prices both costs: customers lost to a trap, and promises you have to honour. The model is ours and simple; the defaults come from published sources. Replace every default with your own number where you have one.

Line B drives everything, and nobody has published it for small businesses. Here is how the example moves with it, holding the other venue inputs fixed.

Compare that with line M. In the example, honouring a wrong promise costs about a twentieth of what the traps cost. The worksheet points the same way as the public cases: the expensive failure is the customer who cannot reach a person, not the occasional honoured error.

How to find your own trap rate#

Your trap rate can be measured in an afternoon. Take the last 100 conversations your assistant handled. Count the ones where the customer asked the same thing twice, asked for a person and did not get one, or received a price or policy that is not on your written list. That count is your trap rate as a percentage. If you run AI support on a helpdesk, the handoff gap runbook measures the related numbers in more detail.

Honour it, log it, fix the facts sheet: the protocol#

The protocol below is what we would do on the day a mistake surfaces. It is short on purpose. Paste it next to your threshold.

SET IN ADVANCE
Honour threshold: $____ (errors below this are honoured without discussion)
Never-on-autopilot topics: discounts, refunds, haggling, complaints,
  contracts, deposits, health/legal/safety, anything not on the price list

ON THE DAY
1. Pause the topic. Stop the assistant answering that question until fixed.
2. Decide. Below threshold: honour it. Above: the owner calls the customer
   the same day. No arguing by email about what the bot "meant".
3. Tell the customer plainly. "Our assistant gave you the wrong [price/policy].
   We will honour [X]." or "[Name] will call you by [time] to sort it out."
4. Log it. Date, what the customer asked, what the assistant said,
   which source or rule it used, what you honoured, what it cost.
5. Fix the source. Correct or add the line in your facts sheet
   (price list, policy, package) that caused the answer.
6. Re-test. Ask the same question five ways. Resume the topic
   only when every answer matches the sheet or hands off.

REVIEW MONTHLY
Total honoured: $____   Topics paused: ____   Trap count: ____

Step 4 is the one most owners skip, and it is the one that makes step 5 possible. Without a record of what the assistant said and why, you are fixing a guess. The record also shows whether the fault sat in the assistant or in the figure it was given: BMW Toronto's bot misread a number a human employee had passed it.⁴

The trade-off we accepted, and the question nobody has answered#

Routing money to the owner has a cost. A customer who asks for a discount at 22:00 waits for a person instead of getting an instant yes or no. Some will not wait. We accepted that, because a slower true answer is cheaper than a fast wrong one, and the customer is told when the owner will reply.

The unanswered question is line B. No public study yet measures how often small-business assistants trap customers, by channel and by setup. Until someone does, every calculator on this topic, including this one, runs on an assumption at its most important line.

Where Valet fits#

Valet answers written inquiries (email, website forms, listing leads) only from the owner's own facts, and never invents prices, discounts or policies. Money questions, complaints, contracts and deposits go to the owner with one-tap replies, and the customer is told when the owner will reply. Every reply carries a Valet receipt with what was asked, the rule used and the sources, with "Correct this" and "Pause topic" on it. During early access, Valet is priced as a small monthly base plus a fee only for bookings or quotes it wins, so its variable fee depends on bookings won, not on conversations closed. The free lead check reviews 10 to 20 recent inquiries and shows what went unanswered or stalled. For the handoff side, see what "never stuck with a bot" has to mean.