The numbers
10–30%
of small businesses have a materially wrong AI answer about them at a given time
Triangulated from the three audits below; no one has published a direct SMB number yet
45%
of AI assistant answers audited carried at least one significant issue (81% had some problem)
EBU/BBC multi-assistant audit, October 2025
9–15%
flat factual error rate across 4,326 audited Google AI Overviews — with 37–56% of claims ungrounded in the cited sources
Oumi SimpleQA-based AI Overview study, 2026
34.8%
of answer variance comes from just re-asking the same question — one-off checks are statistical noise
arXiv 2607.13304, 12,933-response study on brand-answer reliability
No study has yet published a direct answer to "what percent of small businesses are misrepresented by AI right now" — the 10–30% range triangulates the audits above. PositionBird's monitoring fleet measures exactly this question, continuously (see methodology below).
When it goes wrong, it goes to court
In 2025 a Minnesota solar contractor, Wolf River Electric, sued Google after an AI Overview falsely told searchers the state attorney general had sued the company — the business claims $110–210M in lost contracts, in one of six AI defamation suits filed in two years. In July 2026 an independent bookshop discovered — from a customer — that AI engines were describing it as "permanently closed" at a wrong address; the answer stood for weeks. In June 2026 a Bavarian court ruled that AI Overview statements are legally attributable to Google (Google is appealing), and a US federal court has ordered AI chat logs preserved as evidence in litigation.
The pattern in every incident: the business found out late, from a customer, and had no record of what the AI had been saying or for how long. The first US AI defamation case (Walters v. OpenAI) was dismissed in 2025 — winning in court is hard, which makes early detection and documentation, not litigation, the practical defense.
What can actually be fixed
"Getting a wrong AI answer corrected" means changing what the engine reads, then waiting for it to re-read. How possible that is — and how fast — differs sharply by engine:
| ENGINE | FIXABLE? | MECHANISM AND TIMESCALE |
|---|---|---|
| Perplexity | Yes (partial) | Cites exact source pages; owned-page fixes re-crawled in 24–72h, third-party sources days–2 weeks |
| Google AI Overviews | Partial | Source and index dependent — weeks; the feedback button rarely resolves a claim on its own |
| ChatGPT (browsing) | Partial | Days–weeks when it retrieves your corrected sources |
| ChatGPT (no browsing) | No | Answers from training data; changes at the next retrain, months out |
| Gemini, Claude | No | Answers from training data; fix sources for the long term and document the wrong answers meanwhile |
Two things that don't work, despite common advice: llms.txt (Google's documentation confirms it has no effect on Search or AI Overviews, and major crawlers largely ignore it) and the answers' report/feedback buttons (feedback, not overrides — one documented case saw a formal cease-and-desist ignored until a court intervened).
How we measure it
Because a third of answer variance comes from resampling alone, a single check proves nothing. PositionBird's methodology: each business fact (status, hours, prices, policies) is checked with 3 differently-phrased questions × 3 samples per engine per weekly cycle; an LLM judge classifies each answer against the merchant's verified fact sheet; a fact is flagged only when the Wilson lower bound of its contradiction rate clears a threshold; and a claim is counted "misrepresented" only after two consecutive flagged cycles. Every sampled answer is stored with a SHA-256 hash and timestamp, so each data point is independently re-verifiable.
Our own fleet measurement — the direct "% of businesses misrepresented right now" number nobody has published — is being collected across monitored stores and will be published here once the sample is large enough to be defensible. Monitoring your store adds it to the measurement.
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