Brand accuracy17 de septiembre de 20268 min de lecturaEquipo de estrategia de OwnlyReal

AI gets your company facts wrong. Here is the enterprise correction workflow

Where wrong answers come from, how long each engine takes to change its mind, and the ledger that keeps Legal calm.

INCIDENT#0142

SURFACEAI chat · UAE · EN

WRONG CLAIM"Handover delayed to 2028"

TRACED TO2023 portal listing

STATUSCorrected · 24 days

It usually surfaces sideways: a broker forwards a screenshot, an investor quotes an assistant during due diligence, or a sales call opens with "I read that your project is delayed." The wrong fact had been served for weeks before anyone noticed.

60%+

of responses contained factual errors when Columbia University's Tow Center tested 1,600 queries across eight AI search engines^[1].

Wrong answers have four common causes

You can't edit an AI answer directly. You change it by fixing what the engine reads. The first step is tracing the claim back to one of these causes^[1]:

01Stale training dataThe model remembers an old version of you. Only a model refresh changes it fully.

02A wrong source pageAn engine trusts a page that states the wrong fact, and cites it.

03Name confusionYou are merged with a similarly named company or project.

04Outdated listingsA directory or portal entry from years ago is still indexed.

How long each engine takes to change its mind

Engines that answer from live search can flip within weeks of the corrected page being recrawled. Errors from training data don't move until the model is updated. One published dataset of median correction times^[2]:

Copilot

19 days

AI chat with search

24 days

AI Overviews

31 days

Gemini

33 days

Training data

180+

Median days from corrected source to corrected answer. Medians, not promises. Low-authority domains ran slower.

The workaround for errors in training data is to make the correct fact dominant in the sources the engine retrieves, so answers stay right while the model's memory stays wrong^[2].

The six-step correction workflow

1. Audit

Run the 15–25 questions buyers actually ask, on every surface, in every language.

2. Trace

Map each wrong claim to the page, listing or article it most likely came from.

3. Fix the owned canon

Correct your site first: one plain facts page, consistent across every language version.

4. Corroborate

Get the correct fact onto sources that engines trust more than you, such as portals, press and reviews.

5. Use feedback channels

Send neutral, sourced reports through each engine's own feedback tool. Worth a minute, not a strategy.

6. Re-test weekly

Keep each error as its own line item until it flips on every surface.

The error ledger

Legal and Brand don't need a dashboard. They need a ledger: every wrong claim, where it came from, what was changed, and when it was confirmed fixed.

Surface	Question	Wrong fact	Source fixed	Status
AI chat	Is the project on schedule?	Handover 2028	Portal listing · 12 Aug	Corrected
AI overview	Developer escrow bank	Wrong bank named	Facts page · 14 Aug	Monitoring
Answer engine	Payment plan options	60/40 missing	Press release · 18 Aug	Open

Own-goals to avoid

Blocking AI crawlers in frustration. Your corrections become invisible too.
Publishing rebuttals that repeat the false claim word for word.
Rewriting pages the engines never read, instead of the pages they cite.
Fake reviews or personas. They rarely last, and they create legal risk.

SOURCES

[1] Subscribe PR — How to fix wrong AI information (2026)[2] MaxAEO — ChatGPT wrong information about my company