How do you handle AI search results that cite outdated information about your company?

Outdated AI responses trace to outdated sources. An engine reflects whatever the surrounding source ecosystem says, and recently-updated, fact-dense content is what it most reliably pulls from – so a brand still being described according to its 2022 profile is being held there by sources that no one has refreshed. The work runs on three parallel tracks, each updating a different part of that picture, followed by monitoring for the fresh information to land.

Three freshness tracks, run in parallel

1. Update your own pages. The About page, leadership bios, key product pages, and FAQ blocks – all carrying current dates and current facts. Structured data and schema markup help here, because they attach each fact to the right company or person so engines surface the current version.
2. Update Wikipedia and Wikidata. These are lasting references that tend to stay stale until someone corrects them, and Wikidata feeds the Google Knowledge Graph, so a Wikidata correction spreads outward. Do the Wikipedia work through the proper edit-request process; correct Knowledge Graph values through Google’s feedback channels and Wikidata.
3. Generate recent authoritative third-party content. New press coverage, updated registry entries, and fresh structured-data signals give the engines current, independent confirmation of the present picture. Properly sourced coverage enters the pool of sources the engines draw on; reputable outlets will also correct documented factual errors when they are properly sourced.

How fast the freshness lands

The two engine types pick up the refreshed sources on different clocks:

The point of running all three tracks at once is corroboration: when your own pages, the reference sources (Wikipedia and Wikidata), and independent third-party coverage all show the current picture, the engines see fresh agreement rather than a single updated page contradicting a stale set of sources.