Checking What AI Says About Your Founders and Executives

AI answers about people draw on two different sources, and each fails in its own way.

The first is the model's training data. With no web search, a model answers from what it learned before its knowledge cutoff. As of October 2026, OpenAI documents a February 16, 2026 cutoff for its GPT-5.6 models and August 31, 2025 for the Instant path used in ChatGPT. A founder who stepped back in spring 2026 may still be named as CEO in an ungrounded answer. Nothing you publish this quarter changes that answer.

The second is retrieval. When an engine searches, it pulls pages into its context and writes from them. If a directory, an old press release or a conference speaker page lists the wrong title, the engine can repeat it with a citation attached.

This split decides your next move. An error with no citation is usually a training-data problem, and you wait it out while fixing the web. An error with a citation is a retrieval problem, and the cited page is the thing to fix. The difference between a hallucination and an outdated answer is worth knowing before you log anything.

Leadership facts are a common failure point because they change, and the old version stays online on dozens of sites the company does not control.

Build one prompt set and reuse it every time. Consistency matters more than volume.

1. List the facts you will check. For each executive: full name, current title, start date in role, prior roles you state publicly, founding year, co-founders, and any departure. Write the correct value next to each.
2. Write 8 to 12 prompts. Mix direct and indirect questions: "Who is the CEO of [company]?", "Who founded [company] and when?", "Who runs product at [company]?", "What did [founder name] do before [company]?", "Is [former executive] still at [company]?".
3. Add one comparison prompt. Buyers ask "Who leads [company] versus [competitor]?" and engines sometimes swap people between similar firms.
4. Run each prompt on every engine you care about. ChatGPT, Google AI Overviews, AI Mode, Gemini, Perplexity and Claude behave differently. Record each separately.
5. Run logged out, with memory off. OpenAI's help documentation says ChatGPT may use saved memories when rewriting a search query, so a logged-in test reflects your own history.
6. Record whether the answer searched. As of October 2026, ChatGPT searches at the model's discretion and Claude.ai search is a user toggle. Note whether sources were shown. No sources means you cannot tell where the claim came from.
7. Save the full answer and every cited URL. Screenshots fade. Paste text and links into your log with the date.

Say you run a 40-person software company whose co-founder moved from CEO to chair in March. Your test should include both "Who is the CEO?" and "What is [co-founder]'s role?", because engines often get one right and the other wrong.

A log turns a pile of answers into a fix list. Use one row per claim, not per answer.

Keep "unverifiable" separate from "wrong". If an engine says your CTO has a PhD and you have no record either way, that is a claim you cannot check, not a proven error. Not every flag is actually false, and treating them alike sends you chasing the wrong fixes.

Rate severity by business impact. Naming the wrong person as CEO, or saying a founder left when they did not, is critical. A start year that is off by one is minor. Fix critical rows first.

For every wrong or outdated claim that came with a citation, open the cited page and find the sentence.

• The page states the wrong fact. This is the clean case. You know exactly which site to contact.
• The page states the right fact. The engine misread or merged sources. Note it and look for a second page that carries the wrong version.
• The page is your own site. Check old blog posts, press pages and team pages. A 2023 announcement saying "our CEO" is still a page an engine can retrieve.
• No citation at all. Treat it as training data. Fix the public record and recheck after future model updates.

Remember that a citation is not proof of influence. OpenAI's API returns the sources a model consulted separately from the ones it cited, and the consulted list is often longer. Research on how models cite (arXiv 2606.28358) found citation behaviour can change without the sources changing. So treat a cited URL as the best lead, not a confirmed cause.

When two sources disagree, engines may pick either one. See what AI does when sources conflict before assuming the newest page wins.

Your own leadership page is the one source you fully control. Stating a specific fact on a page an engine can fetch gives it a sentence it can quote. It does not guarantee the engine uses that sentence.

Checklist

• One page lists every current executive with full name and exact title.
• Each entry states the start date in role ("CEO since March 2026").
• Founders and founding year appear in one plain sentence ("Founded in 2019 by A and B").
• Role changes are stated directly ("B served as CEO from 2019 to 2026 and is now chair").
• Former executives are not listed as current anywhere on the site.
• The text is in the HTML before JavaScript runs. Vercel's crawler study found ChatGPT and Claude crawlers fetch JavaScript files but do not execute them, so bios loaded by script may be invisible to them.
• The page is not marked noindex, nosnippet or max-snippet:0, and no bio block sits inside data-nosnippet.
• robots.txt lets the search crawlers in: OAI-SearchBot, PerplexityBot, Claude-SearchBot and Googlebot. OpenAI says blocking GPTBot affects training only.
• Your CDN or firewall does not return 403 to those crawlers. A robots.txt allow means nothing if the firewall blocks the request. The AI Crawler Checker requests a page as each AI crawler and shows which ones get turned away.
• Old press releases about past leadership carry a dated note pointing to the current page.

For Google's AI features, Google says a page must be indexed and eligible to show with a snippet, and that no special optimization is required beyond that.

When engines cite other sites instead of yours, being stated correctly on those sites is the route into the answer. The evidence shows a strong association here, not a promised effect.

1. Rank the cited pages by how often they appear in your log. A directory cited by three engines is worth more than a one-off blog.
2. Use each site's own correction route. Directories and data providers usually have an edit or claim-profile flow. Publishers have a corrections contact.
3. Send one short, specific request. State the wrong sentence, the correct fact, and a link to your leadership page as the reference.
4. Update profiles you control. Executive profiles on professional networks, speaker bios and investor pages often get cited.
5. Log the request date and outcome in the same sheet as the error.

You can also report the answer to the engine itself. Whether that changes anything varies by platform; see whether OpenAI or Google will correct company information and the steps for wrong facts in AI Overviews.

Never try to shortcut this with hidden text or instructions aimed at AI crawlers. Research on search agents (arXiv 2606.16821) shows manipulated content can change what agents endorse, which is exactly why it is a reputational risk when someone else does it to you.

Good results

• Every engine that searched names the current CEO and founders correctly.
• Citations point to your leadership page or to third-party pages you have corrected.
• Remaining errors are in answers with no sources, which you can explain as training-data lag.
• Your log shows fewer critical rows than last month, engine by engine.

Bad results

• A searched answer cites a page that is still wrong after your request.
• Your own site is the cited source of an outdated title.
• One engine is accurate and another consistently names a former executive. Do not average these together. Each engine draws on its own index.

How often to repeat

• Monthly, with the same prompt set, so changes are comparable.
• Within a week of any leadership change, then again a month later.
• After major model releases, since a new knowledge cutoff can fix or reintroduce old facts.

When you share results internally, report per-engine counts of wrong, outdated and unverifiable claims rather than one blended score. The guide to reporting AI visibility to your CEO covers framing. For a quick baseline, the free checker on this site runs up to three buyer questions and shows how engines answer them today.