For years, the search strategy was simple enough. Rank well, earn the click, keep the user. The page was the destination, and almost every metric in the report revolved around that click.

AI Overviews change that. In 2026, a growing share of search journeys is shaped by answers users read without ever leaving the results page. The way people interact with those summaries is now feeding back into how often brands are surfaced, cited and trusted.

If you treat AI Overviews as a design change, you will miss what they really are. They are feedback loops. The way users engage with them trains the model, and that training quietly decides which brands stay visible.

How AI Overviews learn which brands to trust

AI Overviews are not static descriptions. Under the surface, they are retrieval systems that pull in pieces of content from sources the model is confident in. Over time, user behaviour nudges that confidence up or down.

If users regularly expand an AI Overview that cites your content and follow through to your page, that is a strong reinforcement signal. If they consistently click away to a different brand when you are listed alongside competitors, that sends a very different message.

Google’s move toward engagement-based AI is the ultimate enforcement of their people-first content standards. If the AI cannot find real experience in your text, it will not retrieve it, no matter how carefully you have placed keywords or tuned meta tags.

You can think of it this way. Rankings tell you who got invited to the stage. Engagement tells the model who deserves to stay on the microphone.

The risks of chasing “engagement” the wrong way

Any time engagement becomes a ranking signal, marketing has a habit of trying to game it.

In the context of AI Overviews, that can look like:

Writing over-promising hooks that the content does not actually back up

Forcing needless “curiosity gaps” into answers that should be clear

Overloading pages with jumpy design elements that win clicks but frustrate readers

In the short term, you might see a bump. Long term, those same patterns create signals of disappointment. People bounce quickly, refine their query or scroll past your brand in favour of another one that gives a cleaner, more direct answer.

The model is not measuring who shouts the loudest. It measures who consistently resolves the question.

What does this change for content and UX?

AI Overviews reward content that is simple to reuse.

In practice, that means:

Clear headings that match how people actually phrase questions

Short, self-contained explanations that can be lifted into a summary

Examples that show real experience rather than generic theory

Layouts that make the answer easy to scan on mobile

If your content is written in long, meandering blocks, or if the main answer is buried halfway down the page, you are harder to quote and harder to trust. If you write in a way that allows a paragraph to stand on its own, it is easier to reuse in an overview.

UX plays into this as well. Pages that load quickly, read clearly and avoid distraction keep people engaged long enough to send positive signals back to the system. If your site feels like hard work, people will back out, and the model will learn that too.

How to measure AI Overview impact without new tools

Most teams do not have direct API access to the frequency with which they are cited in AI Overviews. You can still build a useful picture with a structured manual process.

Pick a fixed set of high-intent questions your audience actually asks. Things like:

“SEO agency in Perth”

“best solar installers in WA”

“best [service] in [city]”

Check those queries regularly across devices and in private browsing and note:

Whether an AI Overview appears at all

Whether your brand is cited, and in what context

Whether competitors are cited instead

How often does the overview seem sufficient to end the search?

Log these observations alongside rankings, impressions and branded search trends. Over a few months, patterns emerge. You start to see the difference between “we rank” and “we are used”.

Practical steps marketers can take now

You do not need to rewrite your whole site for AI Overviews. You do need to make it easier for the model to trust and reuse you. (see Google’s guide to helpful, reliable content)

A simple checklist:

Rewrite key pages so that the primary question is answered clearly near the top.

Add specific, real examples that show you have done the work you describe

Reduce visual clutter that distracts from the main answer.

Make sure your brand name and service description are consistent across the site, profiles and media

Watch how high-intent queries surface you across both traditional results and AI Overviews.

This approach aligns neatly with Google’s own guidance on helpful, reliable and people-first content. It just recognises that the “reader” now includes a model that needs clear, grounded material to work with.

The calm conclusion

Engagement-based AI Overviews are not the end of SEO. They are a new layer between your content and your audience, one that learns quickly from how people behave.

If you focus only on blue link rankings, you will miss a growing part of the story. Brands that adapt their content, UX and measurement to this new reality will quietly gain an advantage. They will be the ones the model retrieves, cites and leans on when it has to answer questions for real people.

The question for marketers is simple. Is your brand being retrieved, or just ranked?

If you are unsure, this is the moment to find out. At PunkFox, we are running AI visibility audits for Perth businesses who want to understand where their influence sits in this new search landscape and how to lift it in a controlled, evidence-led way.

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