Your fix reaches AI answers on Google's clock

Correction latency is the time between fixing a fact on your page and the moment Google's AI answers stop repeating the old version, and Google has now put rough numbers on the steps that make it up. At the Search Central Live Deep Dive in Barcelona, Gary Illyes presented typical and slowest timelines for crawling, indexing and serving, and said AI features use "exactly the same processes as traditional results". That turns a vague question ("when will the AI Overview update?") into a budget you can plan against.

#The pipeline is the one you already know

Google's documentation is plain about where AI Overviews and AI Mode get their supporting pages. A page must be indexed and eligible to be shown in Google Search with a snippet, and there are no additional technical requirements. There is no separate AI index to submit to and no faster lane for corrections.

So when a pricing page, a spec sheet or an about page carries a wrong fact and an AI answer repeats it, the repair path runs through the same stages as any other change: Google has to recrawl the URL, process the new content, and then serve something built from the updated index.

#What the timelines say

Barry Schwartz published the full set of figures from the Barcelona session, with a second write-up from We Are Roast. The rows that matter most for a correction:

• Refresh of a known URL: about 30 days typical, "weeks to never" at the slow end.
• Discovery of a new URL: about 20 hours typical.
• Sitemap processing: about 24 hours typical, up to 14 days or never.
• Indexing end to end: about 1.5 hours typical, months or never at the slow end.
• Structured data updates: hours to 1 to 2 weeks typical, weeks or never at the slow end.
• Snippet update: 1 to 2 days typical, several weeks to months at the slow end.
• Canonicalisation change: 1 to 3 weeks typical.
• Removal from the index: 1 to 3 weeks typical.

Illyes added a caveat that matters more than any single row: the steps are linked, so delays stack up. A page cannot be indexed before it is crawled, and a snippet cannot change before the page is reprocessed.

Read together, the numbers say something uncomfortable. Processing is fast once Google fetches the page. Waiting for the fetch is the slow part. A known URL that Google has no reason to revisit sits on a typical refresh cycle of about a month, and that refresh, not the indexing step, dominates how long a wrong AI answer lives.

#Why AI answers can feel slower than blue links

Two points are inference from the published material, not stated by Google, and are worth keeping separate from the figures above.

First, a correction often touches more than one URL. If the wrong fact sits on your product page, your FAQ and a comparison page, an AI answer built from several retrieved pages can keep pulling the stale version from whichever URL has not been recrawled yet. Each page has its own refresh clock.

Second, the serving layer has its own changes that do not depend on your page. In September, Google's Gemini 3.8 Flash model in AI Mode briefly returned answers without links or citations until Google shipped a fix the next day. A change like that can move what AI Mode shows overnight while your content stays untouched. When you measure correction latency, separate "our page changed" from "the system changed" or you will credit the wrong event.

#Shortening the clock you control

You cannot make Google crawl on demand, but you can stop adding delay on your side.

Request recrawls for the pages that carry the fact. Use URL Inspection for a short list of important URLs. Google's own guidance is that crawling can take anywhere from a few days to a few weeks, that there is a quota for individual submissions, and that repeating a request for the same URL does not speed it up. Submit once per URL and move on.

Keep lastmod honest so it keeps working. Google says it uses the sitemap lastmod value if it's consistently and verifiably accurate, and that a change to main content, structured data or links counts as significant while a copyright date change does not. A sitemap that bumps every date on every deploy teaches Google to ignore the field, which removes one of the few signals that can pull a corrected page forward in the queue.

Fix every copy at once. Before editing, search your own site for the wrong value: product pages, help articles, PDFs, comparison tables, structured data. Correct all of them in one release so the recrawls happen in parallel instead of leaving one stale page to feed the answer for another month.

Change the markup and the page together. Structured data updates have their own timeline, from hours to a couple of weeks typical, and can lag the text. If the visible page says one price and the markup still says the old one, you have given Google two versions of the truth during the window when it is deciding which to show.

Prefer editing the existing URL over publishing a new one for routine fixes. A new URL is discovered faster on paper, but it still has to earn its way into results, and canonicalisation changes run 1 to 3 weeks typical. For a corrected number, updating the page that is already indexed and cited is usually the shorter path.

#Measure it like a service level

Treat correction latency as a metric with a start and an end.

1. Log the date and time each correction ships, with the URLs it touched.
2. Check the last crawl date for each URL in Search Console's URL Inspection tool until it moves past the ship date.
3. Run the affected prompts in AI Overviews and AI Mode on a schedule and record the answer text and cited URLs, not only whether you appear.
4. Record the date the AI answer first reflects the corrected value and the date it stops showing the old one. Those can differ.
5. Annotate system events, such as model changes and core or spam updates, on the same timeline.

After a few corrections you will have your own distribution, which is more useful than any general figure. A site with frequently refreshed pages may see answers update within days. A deep page that Google revisits monthly will not, no matter how good the edit is.

#What to tell stakeholders

When a wrong fact shows up in an AI answer, the useful answer to "when will it be fixed?" is not "soon". It is a range tied to the pipeline: typical refresh for a known URL is about a month, reprocessing after a fetch is fast, and the slow end of every stage is open-ended. Ship the fix everywhere, request recrawls for the pages that matter, keep sitemap dates truthful, and track the answer until it changes. Correction speed is mostly a crawl problem, and crawl priority is earned page by page.