Why AI Answers About Your Company Can Be Wrong
Many people researching a company, a product or a service now put the question to an AI assistant, sometimes before they open a search results page, and the answer they receive is assembled from whatever sources the system found and judged credible. When those sources are thin, outdated or inconsistent, the answer can be as well, and nothing in the format of the response distinguishes a confident correct statement from a confident incorrect one.
Independent testing shows the scale of the underlying problem for at least one task. In a study released on March 6, 2025, researchers at Columbia University’s Tow Center for Digital Journalism gave eight AI search tools 1,600 queries, each supplying a quote from a news article and asking the tool to identify the article’s title, publication date, publisher and URL, and the tools failed to return the correct information more than 60 percent of the time, with failure rates that ranged from 37 percent for Perplexity to 94 percent for Grok-3 Search, as reported by Nieman Lab. The study covered news citations only, so it does not measure how often these tools misstate facts about a company, and the results should not be applied to that question directly. It does show that attribution, the link between an answer and its source, is an area where current systems are unreliable.
For a business the practical consequence is that its own footprint, meaning the pages and publications that describe it, has a direct effect on what an assistant says, and that the effect is worth checking rather than assuming. The sections below describe how to check, how to find where a wrong answer originates, and what to do about it.
How to Run a Baseline Check on Your Core Queries
A baseline check records what the major assistants currently say about a company, so that later changes can be measured against something fixed. It needs no special software to begin, only a consistent method.
- List the queries that matter. Write down the questions a buyer, investor or journalist would put about the company: what it does, who runs it, what it costs, how it compares with named competitors, and whether it is trustworthy. Include a handful of category queries that do not name the company, such as “best provider of [service]”, because those show whether it appears at all.
- Run each query in each assistant you care about. Use the same wording every time, and record the date, the assistant and the full answer. Run searches in a logged-out or fresh session where possible so that personalization does not color the result.
- Score the answers against a fact sheet. Keep a short, verified list of the company’s facts: legal name, founding date, leadership, products, prices, locations and key claims. Mark each statement in each answer as correct, outdated, wrong or missing.
- Record the sources the assistant cites. Where an assistant shows its sources, note which pages and publications it relies on, since those are the pages that shape the answer.
- Save everything in one place. A simple spreadsheet with one row per query and assistant is enough, and it becomes the baseline for the later re-test.
Results from these assistants vary from one run to the next, so a single answer is an observation, not a finding. Repeating each query a few times and noting how often a given error appears gives a more reliable picture than any one response.
How to Trace a Wrong Answer Back to Its Source
An incorrect statement almost always has an origin, and finding it is the step that makes a correction possible. Begin with the sources the assistant cited, and where it gave none, search the exact wording of the wrong claim in a traditional search engine, because the phrase often appears on the page the assistant drew from.
Three patterns account for most of what turns up. The first is a page the company controls that is out of date, inconsistent with other pages or too vague to quote, such as an old pricing page, a leadership page that was never updated or an About page that describes the business differently from its profiles elsewhere. The second is a third-party page that is wrong: a directory listing, a data aggregator’s profile, an old press release or an article that contained an error. The third is an absence, where there is no clear, authoritative page stating the fact and the assistant fills the gap with something plausible or with a competitor’s description.
Each pattern points to a different fix, and it helps to record, for every error in the baseline, which of the three it appears to be. Where no source can be found, note that as well. An error with no traceable origin is most likely to be a generation error, and the corrective step there is to strengthen the clear statements available so that the assistant has something authoritative to use.
How to Fix It
The table matches each type of problem to its likely cause and the most direct corrective action. None of these actions can force an assistant to change its answer, and results arrive over weeks as sources are updated and systems refresh what they retrieve.
| Problem | Likely cause | Corrective action |
|---|---|---|
| Outdated fact (price, leadership, product) | Old page on your site or a third-party profile | Update the owned page first, then request corrections on third-party listings |
| Wrong fact | Error on a third-party page, or conflicting descriptions across your own pages | Contact the publisher for a correction; align naming and descriptions across your site and profiles |
| Company missing from category answers | Little independent coverage or unclear positioning | Build coverage on relevant publications and state what you do plainly on key pages |
| Competitor described in your place | Your facts are not stated clearly or authoritatively anywhere | Publish a canonical page stating the key facts, and reinforce it with consistent coverage |
| Fabricated claim with no source | Generation error | Strengthen clear, authoritative statements; report persistent errors through the assistant’s feedback channel |
Two habits improve every row. The first is a single canonical page that states the company’s key facts in plain language, kept current and linked from the places that describe the business. The second is consistency, because assistants reconcile the descriptions they find, and a business described three different ways across its site, its profiles and its press coverage gives them room to choose the wrong one. Independent coverage on credible publications adds corroboration, which makes a single stray error less likely to dominate the answer.
How to Re-Test and Keep Monitoring
A correction is not finished until the baseline has been repeated and the result compared. Allow several weeks after changing a page or securing a correction, since assistants draw on sources that update at different speeds, then rerun the same queries in the same assistants and score them against the fact sheet. Track three measures from one check to the next: how many statements are correct, which errors persist, and whether the company appears in the category queries it did not appear in before.
For most companies a monthly check on the core queries is a reasonable cadence, with an additional check after any significant change such as a rebrand, a funding announcement, a pricing change or a leadership change, because those are the moments when old information is most likely to conflict with new. Keep the same query wording each time, otherwise a change in the answers may reflect a change in the question.
Monitoring also serves a defensive purpose. Anyone able to publish on a domain that an assistant trusts can influence what it says, so a periodic check for new or unfamiliar claims about the company is a basic form of reputation management for the AI era.
Where an Authority Audit Fits
A manual baseline answers what the assistants say today. The harder question is why, and what the company’s footprint looks like next to its competitors’, since an assistant that describes a competitor in detail and a company vaguely is usually reflecting a difference in coverage. The AI Authority Audit measures a site against named competitors, reports the AI visibility and citation patterns it can observe, and identifies the publications and keywords the company is missing. Its findings give the fixes in the table above a priority order.
Where the gap is independent coverage, the next step is placements on relevant publications, which can be reviewed in the PR Marketplace. The publisher mentions article reviews how independent coverage relates to the sources assistants cite, and the schema markup article covers the structured data that helps systems read a company’s own pages. No step in this process can guarantee a particular answer from an assistant, and the useful standard is whether each repeat check shows fewer errors and more consistent coverage than the last.
Sources
- AI search engines fail to produce accurate citations in over 60% of tests, according to new Tow Center study, Nieman Lab, opened 2026-10-23, published 2025-03-10.
- AI Authority Audit and PR Marketplace, Visionary Financial.
- Publisher mentions and AI search citations and Schema markup and AI search citations, Visionary Financial.
