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Continue reading our latest insights
Continue reading our latest insights
Across our analysis, AI assistants stated something inaccurate or outdated about a brand in 1 in 5 answers (19%) — wrong pricing, discontinued products, outdated leadership, or features that don't exist.
34% of brand mentions carried neutral or negative sentiment — even for brands with strong reputations elsewhere.
The same brand was described positively on one engine and with caveats on another in 23% of cases — your reputation isn't consistent across AI.
Brands that were frequently cited from their own up-to-date pages had a 62% lower error rate — accuracy correlates with controllable, machine-readable content.
Almost none of the brands we looked at were monitoring how AI describes them — they simply didn't know.
Here's a scenario playing out millions of times a day: a potential customer asks ChatGPT or Gemini about your brand. The AI answers confidently, in a friendly, authoritative tone. The customer believes it.
The problem? That AI might be telling them your old pricing, a product you discontinued two years ago, a founder who left, or a "limitation" it absorbed from a stale review — all stated with total confidence.
AI has quietly become a spokesperson for your brand. And unlike a PR team, it never checked its facts with you.
So we measured how often it gets things wrong.
Using Sourceable, we asked ChatGPT, Gemini, Claude, and Perplexity factual and reputational questions about a set of real brands — across 1,800 prompts — and scored each answer for accuracy (does it match reality?) and sentiment (positive, neutral, negative).
(Full methodology at the end.)
In 19% of answers, the AI stated something factually inaccurate or outdated about the brand. The most common errors:
Outdated facts — old pricing, former executives, past funding rounds.
Discontinued or imaginary products — describing offerings that no longer exist, or hallucinating ones that never did.
Wrong positioning — placing the brand in the wrong category or comparing it to the wrong competitors.
None of these are malicious. They're the natural result of models learning from a web snapshot that's always slightly out of date — and then speaking with complete confidence anyway.
Being mentioned isn't the same as being mentioned well. 34% of brand mentions carried neutral or negative framing — a caveat, a hedge, or a repeated criticism — even for brands with glowing reputations on their own channels.
A negative or hedged mention in an AI answer can do more damage than silence, because it reaches the customer at the exact moment they're forming an opinion.
The same brand, asked about on different engines, often got different verdicts. In 23% of cases, one engine described a brand positively while another added caveats or outdated information.
So checking ChatGPT and seeing a flattering answer tells you nothing about what Gemini or Perplexity is telling the next customer.
Here's the encouraging part. Brands whose own, up-to-date pages were frequently cited by the AI had a 62% lower error rate than brands the model described from memory alone.
In other words: when you give the model clean, current, machine-readable facts to pull from — and earn citations to them — it gets you right far more often. Accuracy isn't luck; it's a function of how well you feed the machine.
Unlike a bad review you can find and respond to, an inaccurate AI answer leaves no trace in your analytics. There's no notification, no comment, no flagged mention. The misinformation just gets delivered, privately, to one customer at a time — and you never see it.
That's what makes it dangerous: it's reputation damage with no paper trail, scaling silently across every buyer who asks.
Audit what AI says about you — across all four engines. Ask them factual and reputational questions and read the answers critically.
Fix the source, not the symptom. Update your site, structured data, and key facts so the model has accurate material to learn from and cite.
Earn citations to your own current pages. The more the AI pulls from you, the lower your error rate.
Track sentiment, not just presence. A confident-but-wrong or hedged mention is a problem you can only fix once you see it.
Monitor continuously. Models update; one accurate answer today doesn't guarantee an accurate one next month.
Sourceable's AI visibility platform tracks accuracy and sentiment across ChatGPT, Gemini, Claude, and Perplexity, so you find out before your customers do.
We ran 1,800 factual and reputational prompts across 240 brands from March to May 2026, querying ChatGPT, Gemini, Claude, and Perplexity on a recurring schedule via Sourceable. Each answer was scored for factual accuracy (against verified brand facts) and sentiment. Limitation: AI answers are probabilistic; we sampled across multiple runs and treated a claim as an "error" only when it contradicted current, verifiable facts.
AI has become a brand spokesperson that talks to your customers all day — confidently, at scale, and without ever fact-checking with you. Our data shows it gets things wrong about 1 in 5 times, frames a third of mentions as less than positive, and contradicts itself across engines.
The brands that win the AI era won't just be visible — they'll be accurately and positively represented, because they monitored what the machine was saying and fixed the sources feeding it. The ones who don't will keep losing customers to a spokesperson they never hired and never heard.
Want to know what AI is actually telling your customers? Run a free AI visibility check with Sourceable.
Research and analysis by the Sourceable team. Sourceable tracks how AI assistants describe and recommend your brand across ChatGPT, Gemini, Claude, and Perplexity.
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