Why AI Gives Out Wrong Information About Your Business (And How to Fix It)

What an AI hallucination actually is for your business

In plain terms, a hallucination is when a model states something as fact that is not true. For a consumer brand that might be a made up statistic. For a local or service business it is usually more mundane and more damaging: the wrong opening hours, an address you left two years ago, a phone number that rings a stranger, or a service you no longer offer being pitched to someone who wants it today.

The models are not lying on purpose. A large language model predicts the most likely next words based on patterns in its training data and whatever it can retrieve at answer time. When the trustworthy signal about your business is weak, the model fills the gap with the most statistically plausible answer it can assemble, and plausible is not the same as correct. OpenAI is candid about this in its own help documentation, noting that ChatGPT can produce confident sounding output that is simply wrong.

That confidence is the trap. A hallucinated fact does not arrive with a warning label. It is delivered in the same calm, authoritative tone as everything else, so the person reading it has no reason to doubt it. If you want the plain definition, Wikipedia's entry on hallucination in artificial intelligence lays out how and why these systems generate content that has no basis in their source material.

Why AI gets your business facts wrong

Almost every wrong answer we traced back during our audits came down to one of four causes. Usually more than one was in play at once.

Your data is thin or unstructured

If the only place your opening hours live is a line of text buried on a contact page, you are asking the model to guess. Machines read structured data far more reliably than prose. When your site does not spell out the key facts in a format built for machines, the model has to infer them, and inference is where errors creep in. This is the single most common gap we see, and it is also the most fixable.

The web disagrees with itself

Your website says one thing, an old directory listing says another, a review site has a third version, and a data aggregator quietly syndicates the oldest one to a dozen more places. When a model retrieves several sources that contradict each other, it has to pick, and it does not always pick yours. Conflicting information is corrosive because even a perfect website can be outvoted by a chorus of stale listings.

The snapshot is out of date

Models are trained on data captured at a point in time, and retrieval layers cache what they crawl. If you moved, rebranded or changed your services after that snapshot, the model may still be working from the old picture. This is why a business can update its own site and still see the previous version quoted back weeks later. The old data has not been overwritten yet.

Your entity is weak

AI systems understand the world as entities, the people, places and organisations, and the relationships between them. If your business is not clearly established as a distinct entity, with consistent signals tying your name to your location, your category and your real attributes, the model can confuse you with a similarly named business or simply guess at the blanks. Strengthening those associations is the heart of entity optimisation for AI search visibility, and it is what makes every other fix stick.

There is also a structural reason these errors are hard to stamp out entirely. As researchers writing in The Conversation have argued, the very design that lets these models be fluent and creative is what makes some level of fabrication difficult to remove without breaking the thing people find useful. You are not going to get the error rate to zero. What you can do is make the correct version of your facts so strong that the model reaches for it first.

How to find out what AI is already saying about you

You cannot fix what you have not seen, so start by auditing the output. Open each major assistant and ask it plain, factual questions the way a customer would. What are the opening hours for your business. Where is it located. What services does it offer. Who runs it. Does it serve a particular suburb or area.

Ask the same questions across ChatGPT and Gemini, because they draw on different data and cache on different schedules. One may be perfectly accurate while another is quoting a fact you retired years ago.

Write down every wrong or outdated detail, and where you can, note whether the tool cites a source. Perplexity in particular tends to show its references, which hands you a direct lead on where the bad data lives.

Run the same prompt more than once, ideally on different days. Because these systems are non deterministic, a single correct answer does not prove the problem is gone, and a single wrong one does not prove it is systemic. What you are looking for is the pattern: the facts that come back wrong consistently are the ones worth fixing first. If you want a structured way to think about testing visibility across assistants, our guide on local business AI visibility walks through the same approach applied to local search.

How to fix wrong business information in AI answers

Once you know what is wrong and roughly where it comes from, you fix it in a deliberate order. Arguing with the output directly almost never works. You change the inputs, and the output follows.

Fix your source of truth first

Everything downstream depends on a single, unambiguous version of the truth, and for most businesses that means your own website and your Google Business Profile agreeing perfectly. Update your site so the critical facts, name, address, phone, hours and current services, are correct and easy to find. Then bring your Google Business Profile in line with it. Google's own help documentation walks through how to edit your business profile so the information customers see stays accurate and current.

This matters beyond Google's own surfaces because so many other services and models read from that profile, directly or through the data supply chain. Keeping your listings tidy is unglamorous work, but it is foundational. Our local business listings guide for Australia covers the wider set of directories worth keeping consistent so nothing outvotes your source of truth.

Add structured data so machines read you correctly

Once the facts are right, mark them up so machines cannot misread them. Schema.org provides a shared vocabulary that describes your business in a way search engines and AI systems parse directly, and the LocalBusiness type exists precisely for this, with fields for opening hours, address, price range and the rest. When your hours are declared in structured data rather than left to be inferred from a sentence, you remove the guesswork that produces so many hallucinations.

Structured data is one of the highest leverage moves available, because it speaks the machines' native language rather than hoping they interpret yours correctly. If you want the practical how, our explainer on structured data for answer engine optimisation shows how to implement it without needing a developer for every change. Getting this right underpins the broader work of generative engine optimisation, which is about making your content legible to the systems that now summarise it.

Resolve the conflicting citations

Now go after the contradictions. Using the sources you noted during the audit, track down the stale listings, the old directory entries and the outdated aggregator data feeding the wrong version, and correct them at the origin. This is tedious, because bad data propagates, and you often have to fix the same fact in several places before the chorus starts singing the right tune.

Prioritise by authority. A wrong fact on a high trust source does more damage than the same error on an obscure page, so fix the sources the models actually lean on first. Understanding how ChatGPT selects its sources helps you spend this effort where it counts rather than chasing every low value listing.

Strengthen your entity so the fix holds

Correcting individual facts stops the immediate bleeding, but it does not prevent the next hallucination. For that you need the model to understand your business as a clearly defined entity whose attributes are consistent everywhere it looks. That means the same name, the same location signals and the same category across your site, your profile and the wider web, reinforced with structured data and credible third party mentions that all agree.

This is the durable layer of the work, and it is what separates a business that has to keep firefighting from one whose facts simply stay right. It sits at the centre of both answer engine optimisation and a serious AI visibility audit, which is where we start with most clients, because you cannot fix what you have not first mapped.

Search Engine Land's guide to identifying and fixing brand hallucinations lays out a similar sequence, from testing the assistants through to reinforcing your data and rebuilding trust.

How long corrections take, and how to keep them fixed

Set your expectations honestly. Once you have fixed the source, added structured data and corrected the conflicting listings, the wrong version does not vanish overnight. Models re-crawl and refresh caches on their own schedules, and the more entrenched the bad data was, the longer it takes to fade. In our audits, cleaner cases improved within weeks, while stubborn ones with widely syndicated errors took considerably longer.

Because the systems are non deterministic and the web keeps changing, accuracy is not a one time project. Build a light monitoring habit: re-run your key factual prompts across the assistants on a regular cadence, watch for regressions, and correct new stale listings as they appear. If you want to tie this to outcomes rather than vanity checks, our piece on measuring AEO return on investment helps you connect accurate AI answers to actual leads.

The businesses that stay accurate are not the ones that fixed everything once. They are the ones that made accuracy a routine, the same way you would keep any customer facing detail current. Our work with a previously invisible buyers agent followed exactly this arc: fix the foundation, structure the data, then keep it reinforced until the assistants stopped getting it wrong.

When to bring in help

If your business has a common name, operates across several locations, or has years of accumulated listings scattered across the web, the conflicting data problem can be genuinely hard to untangle on your own. That is the point at which a structured audit earns its keep, because it maps every source feeding the error before you spend hours fixing the wrong ones. Whether you do it yourself or bring in a partner, the sequence is the same. See the truth, fix the source, structure the facts, resolve the conflicts, and keep watching.