How Often to Update Content for AI Search

The short version: refresh anything with buyer intent at least quarterly, refresh anything AI is already citing every 30 to 60 days, and refresh evergreen pages twice a year. That is how often to update content for AI search if you want to stay in the pool of sources engines trust. The reason is simple. AI weights recency, and a citation you earned last quarter is already decaying.

Most freshness advice is still written for Google rankings, where a well-aged page can hold position for years. AI search does not work that way. The page an engine quoted in March can be gone by May, replaced by a competitor who updated more recently. Freshness stopped being a nice-to-have. It became a maintenance schedule.

This post gives you the evidence for why recency matters, the signs that a page has gone stale, and a refresh cadence you can actually run.

How often should you update content for AI search?

Update high-intent commercial pages at least once a month, pages AI already cites every 30 to 60 days, statistics and data pages quarterly or whenever the numbers change, product pages on every release, and evergreen guides twice a year. Set each cadence by how fast the page can go wrong, not by a fixed calendar slot. Recency is a trust signal, and stale pages get replaced.

That is the answer in one paragraph. The rest of this guide explains where those numbers come from and how to run the schedule without turning your team into a content treadmill.

AI search doesn't reward the page you published. It rewards the page you updated.

Why AI search rewards fresh content

Recency is not a soft preference in AI search. It is a measurable input that decides which pages enter the candidate pool and which get dropped. Three independent findings point the same direction.

83% of commercial AI citations come from pages updated in the last year

The clearest number comes from AirOps' 2026 State of AI Search report, published in December 2025. It found that more than 70% of all pages cited by AI had been updated within the past 12 months, and for commercial queries that figure climbs to about 83%. More than 60% of commercial citations came from pages refreshed within six months.

The sharper finding is what happens when you fall behind. AirOps reported that pages not updated at least quarterly are over three times more likely to lose AI visibility than recently refreshed ones. Quarterly is not the aspiration. It is the floor.

Cited pages have a half-life measured in weeks, not years

Freshness matters because citations decay fast. Scrunch and Stacker analyzed 3.5 million citation events and found the average AI citation loses half its visibility in about 4.5 weeks, and ChatGPT is faster at roughly 3.4 weeks. We broke that data down in why AI citations expire faster than you think.

A large-scale study of Chinese-language generative engines found the same pattern with a cleaner number. Across 214,119 records analyzed, the researchers fitted a cited-page half-life of roughly 39 days for time-sensitive queries and 68 days for slower-moving ones. Different market, same physics: the clock on a citation starts the day you earn it.

A citation is a lease, not a deed. Freshness is how you renew it.

Recency is one of the four signals AI uses to pick sources

When Notified and PRWeek studied how AI systems select content, they packaged the answer into a framework called SOAR: Structure, Originality, Authority, and Recency. Recency sits alongside authority as a first-class selection signal, not a tiebreaker. Their data showed syndicated releases getting cited within about eight hours of publishing, which tells you how quickly engines reward newly-dated content.

Put the three together and the conclusion is hard to argue with. Fresh pages get cited, cited pages decay in weeks, and engines actively score recency. That is why cadence beats one-time optimization.

Five signs your content is too stale to get cited

Before you set a schedule, you need to spot which pages have already aged out. These are the five staleness patterns we see most when auditing client libraries, in the order they cost you citations.

Sign 1: The most important number on the page is more than a year old

A statistic dated 2024 on a commercial page is a downgrade signal. Engines treat an old number as a reason to prefer a fresher source, even if your analysis is better. If the figure moved and your page did not, you handed the citation away.

Sign 2: The page still describes a product or price that changed

Nothing dates a page faster than a claim the buyer can disprove in one click. When your pricing page, feature list, or integration count no longer matches reality, an engine that catches the mismatch stops trusting the page, and so does the reader.

Sign 3: A competitor published something newer on the same question

Recency is relative. Your page can be objectively current and still lose if a rival updated last week and you updated last quarter. The engine picks the freshest credible source, so the benchmark is not your own history. It is whoever moved most recently.

Sign 4: The visible last-updated date is missing or old

Engines and readers both look for a date. A page with no visible update signal, or one stamped a year ago, reads as abandoned. The fix is not to fake a date. It is to make a real update and let the timestamp reflect it.

Sign 5: The page hasn't moved since the day it was published

Publish-and-forget is the default failure. A page that has not been touched since launch is the single most common thing we find when a formerly-cited URL disappears from AI answers. It did not do anything wrong. It just stood still while everything around it moved.

Stale content doesn't get corrected. It gets replaced.

Each of these is a maintenance gap, not a writing failure. That is the good news. Maintenance gaps are cheap to close once you have a schedule.

How to set a content refresh cadence for AI search

A cadence is not a spreadsheet of dates. It is a rule for which pages get touched, how often, and what a refresh actually changes. Run these four steps in order and the schedule builds itself.

Step 1: Sort your pages by how fast they can go wrong

Group your library by decay speed, not by traffic. Pricing, comparison, and product pages go wrong fastest because the underlying facts change constantly. Evergreen definitions move slowly. This sort tells you where monthly attention pays off and where twice a year is enough.

Step 2: Assign a cadence to each group, not each page

Give every group one interval: commercial pages monthly, pages you are already cited for every 30 to 60 days, data pages quarterly, product pages on release, evergreen twice a year. Managing five cadences is realistic. Managing 200 individual schedules is not, and the ungoverned pages are the ones that go stale.

Step 3: Refresh the answer and the proof, not the word count

A refresh that adds 300 words of filler does nothing. Update the number, the date, the pricing, the example, and the leading answer. The goal is a page that is genuinely more current than the version an engine last read, so the change is real enough to re-earn trust. This is the opposite of the bland, padded content AI ignores.

Step 4: Re-test the prompts the page should win

After a refresh, run the buyer prompts that page is meant to answer and check whether you are cited. A refresh you cannot verify is a guess. If the page still loses, the problem is authority or structure, not recency, and you can stop refreshing and fix the real gap. The full loop lives in how to build a GEO content refresh queue.

Publishing is the start of the clock, not the finish line.

What to refresh on a page, and what to leave alone

Not every part of a page carries recency weight. Refreshing the wrong elements wastes the update and risks breaking a page that works. Here is where the freshness signal actually lives.

Element	Refresh priority	Why it matters for AI citation
Key statistics and dates	High	The first thing an engine downgrades when it looks outdated, and the easiest to verify against a rival.
Pricing, plans, and product claims	High	A mismatch with reality kills trust in the whole page, not just the wrong line.
The leading answer under each heading	Medium	Keeps the passage an engine extracts current and quotable.
Examples and screenshots	Medium	Dated examples signal an unmaintained page even when the argument holds.
URL and page structure	Low	Changing these breaks existing signals. Leave a working URL alone.

The pattern is clear. Refresh the facts that can go wrong and the answer an engine quotes. Leave the plumbing that already works. A good refresh changes what is true on the page, not what is stable about it.

This is also why cadence has to be paired with measurement. AI visibility is volatile on its own: in our own CITE Index tracking of 34,000+ AI answers, the top-cited brand in a category changes in roughly a quarter of daily editions. If leadership flips that often without you touching anything, a set-and-forget content library will drift out of the answers within weeks. A managed GEO program runs the tracking and the refresh loop together so the two stay in sync.

You don't need to write more. You need to let the pages you have go stale less often.

FAQ

How often should you update content for AI search?

Set the interval by page type. Refresh commercial and pricing pages monthly, pages AI already cites every 30 to 60 days, statistics pages quarterly or when the data changes, product pages on every release, and evergreen guides twice a year. The AirOps 2026 data shows pages not refreshed at least quarterly are over three times more likely to lose AI visibility.

Does updating content actually help AI citations?

Yes, when the update is real. AirOps found 83% of commercial AI citations come from pages updated within the last 12 months. A genuine refresh of the facts, dates, and leading answer re-earns the recency signal engines score. Adding filler word count without changing what is true on the page does not.

How fresh does content need to be to get cited by AI?

Fresher than the best competing source, which usually means within the last six to twelve months for commercial queries. Cited pages decay fast: the average AI citation loses half its visibility in about 4.5 weeks, and Chinese-language engines show a cited-page half-life of 39 to 68 days. Recency is relative, so the real benchmark is whoever updated most recently on the same question.

Should I update old pages or publish new ones for AI search?

Update the pages that already have authority before you publish new ones. A page an engine once cited carries trust that a brand-new URL has to earn from scratch. Refreshing a proven page is usually faster to re-cite than building a new one, which is why a refresh cadence often beats a publishing calendar for AI visibility.

Does changing the last-updated date help if I don't change the content?

No, and it can hurt. Engines and readers both compare the visible date against the actual content. A fresh timestamp on unchanged content is a mismatch that erodes trust once caught. Make a real update to the numbers, answer, or proof, then let the date reflect it.

Where to start this week

Pick the ten pages you most want AI to cite, the ones tied to your highest-intent queries. Check each against the five staleness signs: old numbers, changed products, a fresher competitor, a missing date, and no edits since launch.

Fix the two or three that are clearly stale first. Update the key statistic, correct anything that no longer matches your product, refresh the leading answer, and re-test the prompts the page should win. That is an afternoon of work, not a content sprint.

Then set the cadence. Give each page group one interval and put the refresh dates on a calendar you will actually keep. The teams that win AI citations are not the ones publishing the most. They are the ones whose best pages never get old enough to replace. If you would rather have that run for you, a GEO agency can own the tracking and the refresh loop end to end.