You’ve found an error in how an AI system describes your brand. Now what? The honest message upfront: direct correction of AI answers is not possible in most cases. AI systems don’t have a “correction panel”. But there are indirect strategies that can work.
The indirect correction strategy
AI systems draw on sources. If you improve the sources a system draws on, the answer can follow — but the pace depends on the layer the error sits in. If it comes from retrieval, an improved source can carry through relatively quickly once it is fetched again. If it sits in training knowledge, something changes only at a model update — or not at all. So first check whether a visible source accompanies the wrong answer: with a source, the route is clear; without one, it is presumably memorised model knowledge, and patience is part of the strategy.
Step 1: Identify the source of the error
Find out where the incorrect information comes from. Is it an outdated web page on your own site? An incorrect news article from a third party? A wrong listing on a comparison site? The source determines the correction strategy.
Step 2: Correct the source
Update your own content directly if it concerns one of your pages. Contact external publishers for corrections in their content. Report incorrect information to comparison sites and directories.
Step 3: Reinforce the correct information
Publish new content that explicitly and prominently states the correct information. Ensure that content is technically accessible and quickly indexed. Repeat the correct information consistently across multiple channels.
Step 4: Build counterweight
If the incorrect information is widely spread, one correction in an obscure place is not enough. But what works is not numerical outshouting — it is authority and consistency: one canonical, authoritative correction on your own domain, confirmed in the relevant reliable sources. Artificial repetition starts to look like spam, and that is a risk, not a strategy.
Platform-specific correction options
Wikipedia
Wikipedia has a correction process for factual errors. If your brand appears on Wikipedia with outdated or incorrect information, you can submit corrections through the official edit procedures — always supported by verifiable sources.
Wikidata
Wikidata is the structured data source behind Wikipedia and is used as a reference by many systems. A correction there is time well spent — though a direct effect on AI answers is not documented.
Google Knowledge Panel
Google offers a formal process to claim your Knowledge Panel and suggest changes — suggest, not control. A correct panel at least prevents Google’s own entity layer from repeating the error; a documented direct effect on AI Overviews there is not.
Direct feedback to AI providers
OpenAI, Google, Anthropic and Microsoft all have feedback mechanisms for incorrect AI answers. Nothing is published about effectiveness; what structured, documented reports do in any case is make the complaint assessable.