Google appears to be testing AI-generated summary snippets inside AI Overview citation cards. According to Search Engine Roundtable, the cards that appear to the right or bottom of an AI Overview - the ones that link out to source content - can now carry their own AI-written descriptions. That is a meaningful shift in how citation visibility actually works.
Until now, the citation card was essentially a link with a title and perhaps a favicon. The user clicked through or they did not. Now there is an intermediate layer: an AI-generated summary of what your page says, written by Google, sitting between your content and the user's decision to visit. That changes several things at once.
What the Citation Card Now Does
A citation in an AI Overview has always been a partial win. Your content informed the answer, but the user rarely had a strong reason to click the source unless they wanted more depth. The main AI Overview text did the explaining. The citations sat below or to the side as references.
With AI-generated summaries appearing inside those cards, each citation becomes a mini-answer in its own right. Google is now telling the user not just that your page exists, but what it contains - in Google's own words. That is both an opportunity and a risk. If Google summarises your content accurately and compellingly, the card itself could drive clicks. If the summary misrepresents or flattens your content, you may be cited and still lose the visit.
The practical implication is that content which is structured clearly and written with a specific point of view is more likely to survive summarisation intact. Vague or hedged content is harder for an AI to summarise usefully, and a weak summary in the citation card reduces your chance of earning the click even when you have earned the citation.
AI Extraction Has Always Happened - Now You Can See It
This is not the first time Google's AI has interpreted content rather than quoted it directly. The main AI Overview text has always been a synthesis, not a copy. What is new here is that the extraction is now visible to users at the citation level - not just inside the generated answer.
That visibility matters because it changes what citation quality means. Previously you could roughly assess your citation value by checking whether your domain appeared in AI Overviews for relevant queries. Now there is an additional dimension: whether the AI-generated summary of your content is accurate, distinctive, and persuasive enough to earn engagement.
For teams running AEO or GEO programmes, this is a signal that content quality standards need to account for summarisability. A page that reads well to a human but buries its core argument in the third section will produce a weaker AI summary than a page that states its position clearly at the top and supports it precisely in the body.
What This Means for Content Structure
The pages most likely to produce good AI summaries share a common structure: they have a clear, declarative opening that states what the page is about and what conclusion it reaches. They use headings that describe content rather than tease it. They do not rely on narrative flow to carry meaning - meaning is present at the paragraph level.
This is sometimes described as writing for AI extraction, but it is really just disciplined writing. Each section should be able to stand alone. If someone - or something - reads only one paragraph, they should still understand what that paragraph is arguing. AI summary generation works by identifying the most informative units of text. Give it clear units.
Schema markup remains relevant here too. If your content has structured data that correctly identifies the subject, author, and type of content, you are providing additional signals that help Google understand what the page is about before it generates a summary. That context can influence which sentences get surfaced. It is one more reason to treat schema as part of AEO infrastructure rather than an optional SEO tactic.
The Attribution Question This Creates
If AI-generated citation card summaries increase click-through rates from AI Overviews, that should appear in referral data - eventually. Traffic arriving via AI Overview citations tends to come through with google.com referrers or direct depending on how the click is handled, and GA4 does not reliably separate AI Overview clicks from standard organic visits without additional configuration.
The measurement challenge is that better citation cards might produce more engaged visitors - people who clicked because the summary matched their need - rather than simply more visitors. That distinction matters for revenue attribution. A smaller number of high-intent visits driven by an accurate AI summary of a commercial page is more valuable than a larger number of curious visits driven by a generic reference.
Teams tracking AI search attribution through CRM should be alert to changes in visit quality from AI-referred sessions, not just volume. If Google rolls this feature out broadly, it could shift the profile of who arrives via AI citations - and that shift will be visible in conversion rates and lead quality before it becomes obvious in traffic numbers.
What to Do Now
This is still a test, and it is not confirmed that AI-generated citation card summaries are live universally. But the direction is clear. Google is adding more AI interpretation at every stage of the search result - the overview itself, the citation cards, and likely more surfaces over time. Waiting for full rollout to respond is not a useful posture.
Audit your most-cited pages against a simple test: if someone read only the opening paragraph and the first sentence of each subsequent section, would they understand what the page argues and why it is credible? If not, restructure. This is the format most likely to produce accurate, compelling AI summaries - whether in citation cards, AI Overviews, or any other AI-mediated surface.
Also check your schema coverage on those pages. Article, FAQPage, and HowTo schema give Google structured signals about content purpose and structure. They are not a guarantee of better AI summaries, but they reduce ambiguity in how your content is classified - and less ambiguity generally produces more accurate extraction. The goal is not to trick the summary into saying what you want. It is to make your content clear enough that an accurate summary is also a persuasive one.