AI is Probably Wrong About Your Business
Ask ChatGPT what time check-in is at your hotel. Or what your restaurant's signature dish is. Or whether your SaaS product offers a free trial.
There is a reasonable chance the answer is wrong. And it will be stated with complete confidence.
The Accuracy Problem Nobody Measures
Most of the conversation about AI visibility focuses on whether you appear in AI answers. That matters. But there is a second problem that gets less attention: when AI does mention your business, is it accurate?
AI platforms do not flag uncertainty. They do not say "I'm not sure about this, but..." They state facts. Check-in time is 2 PM. The pool is heated. Breakfast is included. Whether or not any of that is true.
The scale of this problem is not small. Generative AI hallucinates 3-10% of the time in general use, according to Kore.ai. BizTech Magazine estimated that AI errors cost businesses $67.4 billion in 2024. Google's own AI Overviews were found to be incorrect in 40-60% of previewed summaries when they first launched.
When we audit what AI platforms say about businesses, we find errors in basic operational details: wrong check-in times, outdated pricing, amenities that do not exist, descriptions that confuse one business with another. These are not edge cases. They are common.
Where the Errors Come From
AI gets business information from three sources. Each introduces different error types.
Training data. The model learned about your business from web content that existed before its training cutoff. If your website had different pricing two years ago, the model may still have that pricing in its weights. If a blog post from 2023 described your business inaccurately, that description may be baked into the model's knowledge.
Third-party listings. OTA listings, directory pages, and review aggregators often have outdated or conflicting information. When AI retrieves from these sources, it inherits their errors. If Booking.com says your check-in is 2 PM but Google Maps says 3 PM, the AI picks one. It does not ask you.
Inference from patterns. When AI lacks specific information, it fills gaps by pattern-matching from similar entities. A boutique hotel in Kerala gets described with attributes common to boutique hotels in Kerala, whether or not those attributes apply. This is where "the pool is heated" appears for a hotel with no pool.
Why This Matters More Than Visibility
Being invisible in AI search is a missed opportunity. Being misrepresented is worse. A customer who never sees your name moves on. A customer who sees wrong information forms a wrong impression, and you never get the chance to correct it.
Consider: AI tells a potential customer that your hotel does not have airport pickup. They book a competitor. You do have airport pickup. You lost the booking not because your service was worse, but because the AI was wrong.
Or: AI says your restaurant closes at 10 PM. It actually closes at 11 PM. A customer looking for late dinner goes elsewhere. You had capacity. The AI killed the lead.
The worst version: AI confuses you with a similarly-named business and attributes their negative reviews to you. This happens when entity disambiguation fails, which is more common than you would expect for businesses with generic names.
The Audit Process
Testing AI accuracy requires structured queries about specific, verifiable facts. Not "tell me about this hotel" but "what time is check-in?" and "does this hotel have a swimming pool?" and "what is the nightly rate for a standard room?"
For each question, you compare the AI's answer to the verified truth. Then you trace the error to its source: is it in the training data? A third-party listing? An inference?
The source matters because the fix is different for each:
- Training data errors require updating your online presence and waiting for the next model update to incorporate the correction. This is slow but eventual.
- Third-party listing errors require auditing and correcting your information on OTAs, directories, and review platforms. This is within your control.
- Inference errors require making the correct information explicitly available in structured format. Schema markup, FAQ pages, and direct factual statements reduce the need for AI to guess.
What You Can Do Today
- Run the audit. Ask ChatGPT, Gemini, and Perplexity 10 specific factual questions about your business. Record the answers. Check each against reality.
- Identify the error sources. For each wrong answer, trace it. Is this information wrong on your Google Business Profile? On an OTA listing? On an outdated blog post?
- Fix the sources you control. Update your Google Business Profile, your website's structured data, and your OTA listings. Make the correct information explicit, structured, and consistent everywhere.
- Add schema markup. JSON-LD on your website with explicit, machine-readable facts about your business. Check-in time, amenities, pricing, contact information. Do not make the AI guess what you could just tell it.
- Monitor over time. AI answers change as models update and web search pulls new results. What is wrong today may be correct next month. What is correct today may break next month. Check quarterly at minimum.
The Uncomfortable Truth
Your business now has a representation inside AI systems that you did not create, do not control, and probably have not seen. That representation is being shown to hundreds of millions of people every week. Some of it is wrong.
The businesses that will do best in AI search are not just the ones that appear. They are the ones that appear accurately. Fixing what AI says about you is as important as making sure it says anything at all.