Research report

How often is AI wrong about small businesses?

Shoppers now ask AI engines whether a store is open, legitimate, and worth buying from — and the engines answer with confidence whether or not they're right. Here is what the published evidence actually shows, what happened to the businesses that found out the hard way, and what can (and can't) be corrected, engine by engine.

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:

ENGINEFIXABLE?MECHANISM AND TIMESCALE
PerplexityYes (partial)Cites exact source pages; owned-page fixes re-crawled in 24–72h, third-party sources days–2 weeks
Google AI OverviewsPartialSource and index dependent — weeks; the feedback button rarely resolves a claim on its own
ChatGPT (browsing)PartialDays–weeks when it retrieves your corrected sources
ChatGPT (no browsing)NoAnswers from training data; changes at the next retrain, months out
Gemini, ClaudeNoAnswers 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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Dealing with an incident right now? Guides: "ChatGPT says my business is closed" · "AI says my store is a scam" · "Correct a Google AI Overview"