What to Do When AI Gets Your Business Wrong: Correcting AI Misinformation

When the AI is confidently wrong

AI engines are trained to produce fluent, confident responses. This is what makes them useful — and what makes their errors particularly damaging. When a traditional search result is wrong, a user can see the source and evaluate credibility. When an AI engine states something incorrectly about your business, it does so in the same clear, authoritative voice it uses for facts.

A business owner in Austin discovered that ChatGPT was describing her interior design firm as a real estate company — a plausible confusion given the company name. A law firm found that Perplexity was citing an outdated firm profile that included a partner who had left two years earlier and a practice area the firm had discontinued. A restaurant found that Google AI Overviews was stating incorrect hours because a third-party site had scraped and preserved outdated data.

These are not edge cases. AI misinformation about businesses happens regularly, it often goes undetected because business owners are not monitoring their AI representation, and when it does occur it influences buyer decisions in the moments before contact. A buyer who encounters an AI stating incorrect services, an outdated location, or a wrong specialty may never reach the business at all.

Understanding why AI misinformation occurs and how to address it systematically is a practical necessity for any business serious about AI visibility.

Why AI engines get businesses wrong

AI misinformation about specific businesses typically has one of three causes:

Outdated source data. AI engines retrieve from the indexed web, and the web contains outdated content. An old press release that mentions a former office location. An archived directory listing with an outdated phone number. A business profile on a platform that has not been updated in three years. When an AI retrieval engine pulls this content, it may reproduce the outdated information as fact. This is the most common cause of AI business misinformation and the most addressable.

Entity confusion. AI engines sometimes confuse one entity with a similar one — especially when business names are not unique, when businesses are in the same category and geography, or when a business has gone through a name change and both the old and new name appear in web content. The engine merges or confuses the entities, attributing information from one to the other.

Hallucination in training-based models. Model-based AI engines like ChatGPT generate responses from their training data. When the training data is thin or ambiguous about a specific business, the model sometimes fills in gaps with plausible-sounding but inaccurate information — a process called hallucination. A business that has limited structured online presence is particularly vulnerable to this; the model has little authoritative data to draw from and more room to generate inaccurately.

Aggregator distortion. Some third-party sites aggregate business information automatically, sometimes incorrectly. If an AI engine retrieves from these aggregators, it may reproduce their errors. The original source of the error may be an aggregator you have never heard of and would not naturally think to check.

How to discover AI misinformation about your business

Monitoring is the prerequisite. Business owners who discover AI misinformation about themselves typically find it by accident — a client mentions that the AI said something strange, or a Google alert surfaces it, or a competitor screenshots it. Systematic monitoring is more reliable than accident.

The manual query protocol. Regularly query major AI engines — ChatGPT, Perplexity, Google AI Overviews, Gemini — with your business name, your category plus location, and variations of "tell me about [business name]." Read the responses carefully for factual accuracy: Do they name the right services? The right location? The right founding context? The right team? Any inaccuracy, however small, should be documented and corrected.

Perplexity source inspection. Because Perplexity shows its sources, querying for your business name in Perplexity reveals which specific URLs and domains are being used to construct information about you. If you see an outdated source being cited, you can go to that specific source and address the inaccuracy.

Google alerts. Set up alerts for your exact business name, your founder's name, and any distinctive phrases associated with your business. Some AI-generated content about businesses gets published to the web and shows up in alert notifications.

Periodic "what does AI say about me" audits. Include a monthly AI representation audit as part of your monitoring practice. Ask several AI engines open questions about your business and read the responses critically, not looking for praise but looking for accuracy.

The correction playbook

When you find AI misinformation, the correction path has priority layers:

Layer 1: Update your own authoritative sources immediately.

Your Google Business Profile is the highest-priority authoritative source for most businesses. If the misinformation relates to services, location, hours, or description, update your GBP to reflect the correct information accurately and specifically. AI engines that draw from GBP will eventually reflect the update.

Your website's structured data is the second priority. Organization or LocalBusiness schema with accurate, current information — name, address, phone, services, founding date, official website — is a machine-readable authoritative declaration. Update your schema to explicitly correct any field that is being misrepresented.

Your website's About page, Contact page, and Services pages are the human-readable layer that retrieval engines read. If these pages contain or could be interpreted to contain the inaccurate information, update them with clear, specific language. If the inaccuracy is an omission (the engine doesn't know what you do), add explicit, specific content.

Layer 2: Address the specific erroneous sources.

If Perplexity is citing a specific outdated source, go to that source and update it. For directory listings, claim the listing and correct the data. For press coverage with outdated information, contact the publication and request a correction or addition. For aggregator sites you cannot directly edit, many have owner claim processes — use them.

For sources you cannot update (archived content, defunct sites, third-party content that predates your update), creating current authoritative content that explicitly states the correct information is your path. A current press release, a recently updated About page, a fresh blog post that states accurate details — these become the more current retrievable sources that engines will favor over older content.

Layer 3: Use platform feedback mechanisms.

Each major AI platform has a mechanism for reporting inaccurate information:

• ChatGPT: Use the thumbs-down feedback on the response, then the "Share Feedback" option to describe the inaccuracy specifically. OpenAI has processes for reviewing feedback about specific entities.
• Google AI Overviews: Use the "More about this result" feedback option to report inaccurate citations.
• Perplexity: Use the feedback option to flag specific inaccurate responses.
• Gemini: Report via the thumbs-down and "Report" options on specific responses.

These mechanisms do not guarantee immediate correction, but they do create a record that platform teams use in their ongoing improvement processes. For significant factual errors, report them.

Building the prevention layer

Correction is necessary when misinformation occurs, but prevention is more efficient. The same signals that drive AI visibility also reduce AI misinformation risk: a rich, consistent, authoritative source layer leaves less room for errors to fill.

Entity richness. The more authoritative information that exists about your business from credible sources, the less the AI engine needs to infer. Businesses with thin online presence are most vulnerable to hallucination — engines fill the gaps with plausible inferences. Businesses with comprehensive entity presence give engines less opportunity to generate incorrectly.

Source dominance for your business name. If you search your own business name and the top results are all authoritative, accurate representations of your business, an AI engine pulling those results will likely produce accurate descriptions. If your search results include outdated content, aggregator errors, or irrelevant entities with similar names, the retrieval risk is higher. Work to ensure that the most authoritative content about your business is also the most prominently indexed.

Regular content updates. Fresh content dates signal currency to retrieval engines. A website that was last updated three years ago may be superseded by a more recent aggregator page or directory listing. Regular publishing of accurate content keeps your owned sources current and maintains their authority in retrieval.

Proactive accuracy in schema and GBP. Updating your structured data and GBP proactively — when services change, when the team changes, when locations change — prevents the accumulation of outdated data that AI engines will eventually retrieve incorrectly.

What to do when you cannot fully correct the misinformation

Some AI misinformation cannot be corrected through the steps above, at least not immediately. Training-layer misinformation in large language models may persist until the next training cycle, which can be months away. Content on sites you cannot claim or edit may remain indexed for extended periods.

In these cases, the most effective strategy is authoritative content dominance: create enough current, accurate, well-indexed content about your business that the authoritative signal overwhelms the inaccurate one in retrieval probability. A Google Business Profile updated last week, an About page refreshed last month, a press mention from this quarter — these outcompete a directory listing from three years ago in a retrieval engine's source selection.

Monitor the issue over time. As authoritative sources accumulate and inaccurate sources age, the misinformation typically fades from AI outputs. The timeline is frustrating — weeks to months in retrieval-based engines, potentially longer in training-based ones — but the direction is reliably toward correction when authoritative sources are built consistently.