How to Respond to “What If the AI Gets It Wrong?”: 3 Honest Answers
“What if the AI gets it wrong?”
The buyer wants to know what happens when the artificial intelligence makes a mistake. Three honest answers about limits, checks, and who's accountable.
Practice this objectionWhy buyers say it
This is different from “I don't trust it.” Here the buyer is willing to use the tool, but wants to know who pays when it goes sideways. They're picturing a wrong number on an invoice, a missed deadline, a customer getting false information. They want to know whether they'll catch the mistake before it does damage, who fixes it, and whether you take some responsibility or dump it all on them. Denying that AI makes mistakes makes you less credible, because they already know it does. Explaining how errors get caught and handled calms them far more than any promise of perfection.
What it often means
- Fear of an expensive mistake nobody catches in time
- Wanting to know who's accountable when it goes wrong
- A past tool that made silent errors
Three answers that work
It does get things wrong, yes. The question is what happens when it does. In our tool every extracted value stays flagged as “needs review” until you confirm it, and the uncertain ones are highlighted. You see the mistake before it hits your books, not after.
Why it works: You admit the limit right away, then show the concrete mechanism that contains it. The buyer doesn't want an infallible tool; they want one where mistakes are visible and stop at the right point.
Let me ask: today, does whoever does this by hand ever make mistakes? And how do they catch them? The tool doesn't have to be perfect, it has to get it wrong less often than now and make errors easier to spot. Let's check whether that's true on your documents.
Why it works: You compare the AI to the current method, not to perfection. The fair benchmark is the buyer's real situation, and checking on their documents turns a doubt into a test with an answer.
When you find a mistake, you flag it with one click and we fix it within [time]. If the error comes from a flaw in the tool, not from an unreadable document, we're accountable under [what the contract says]. You're not left alone with the problem.
Why it works: You answer the unspoken question, which is “who takes responsibility.” A written, realistic commitment weighs more than any reassurance, and the distinction between your error and an unreadable document is honest and defensible.
Avoid this one
It almost never gets it wrong, the accuracy is extremely high, you can relax.
Why not: The first mistake proves you wrong in front of the buyer, and they'll find it quickly because they're looking for it. Without verifiable numbers it's an empty line; with made-up numbers it's worse. Explain how mistakes are handled instead of denying them.
Example dialogue
BuyerThe software reads vendor invoices and books them. Nice, but what if it misreads an amount?
YouIt happens, especially with bad scans. That's why any amount the tool isn't confident about shows up in yellow, with the invoice right next to it, and you confirm or correct it.
BuyerAnd if it's confident but still wrong?
YouBefore anything is booked, you get a summary with totals, so an amount that's way off jumps out. And if you find an error that's on us, you flag it with one click and we fix it. Want to try it on ten real invoices from this month and count how many it gets wrong?
BuyerI've got ten invoices. Let's try it and see.
Practice this objection
A simulated buyer says it to you out loud (or in writing), you answer, and you get a score plus the model answer. Free, no account, 2 minutes.
FAQ
Should I admit that my AI tool can make mistakes?
Yes, always. The buyer already knows, and denying it costs you credibility. The right answer isn't “it doesn't make mistakes” but “here's how you see and fix them before they do damage.” A seller who talks calmly about limits sounds like someone who knows the product, not someone hiding something.
What can I build into the product to answer this objection better?
Three things: visibly flag uncertain results, ask for confirmation before irreversible actions, and give an easy way to report a mistake. If those exist, your answer to the objection becomes a demonstration instead of a speech. If they don't, that's a good reason to add them.
How do I handle the question of legal liability for AI mistakes?
With a clear contract that says what you cover and what you don't, written with someone who knows the law. In conversation, explain the distinction simply: if the error comes from a flaw in the tool, you handle it; if it comes from an unreadable document or a value the customer confirmed, you don't. Never promise coverage you don't have.