AI search is different from the traditional page of blue links in many ways, and one of them is that the people who click through tend to be more intentional. Put another way, AI might be nuking your referral traffic, but at least it’s sending you people more likely to engage, and potentially become loyal readers.
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The numbers back that up. Still, it’s a flattening of a more interesting picture. It turns out that the audience in AI search isn’t just a blob of traffic that you need to work extra hard to get the attention of.
People who turn to AI portals for information arrive with wildly different intentions, and those intentions can change as they do more research, often in the same AI conversation. The click and the engagement remain the goal, but how to get there requires understanding the journey before that happens.
What the funnel looks like now
Some of the sharpest work I’ve seen on segmenting that “blob” comes from a study from Scrunch, an AI search analytics company. The report is aimed at brands selling products rather than publishers—it focuses specifically on AI search behavior around GLP-1 medications like Ozempic—but the framework around how different information seekers behave inside AI search transfers well to a publishing context.
Scrunch sorts AI users into buckets defined by intent. There are knowledge seekers, who are curious but not yet committed; evaluators, who are comparing options; access seekers, who are ready to buy; and a few additional categories such as side effect navigators and regimen planners (again, the report is centered on a medication, so these are specific to that vertical).
Importantly, each category has a different likelihood to convert and a different tendency to move into other categories. As you might expect, those deliberately seeking to buy or transact are very valuable for conversion, but it’s a relatively small group (just 9% of the overall “conversation”). Because evaluators are a much larger group (20%), they may offer more aggregate opportunity than the smaller, higher-intent access seekers.
Peeling back the audience layers
The study looks at one category, so the exact buckets won’t apply universally. The taxonomy itself, though, maps neatly onto publisher audiences.
- Orientation readers: These are readers who may be new to a topic or are just seeing the latest information. They want to understand the basics about a story or topic, or simply get caught up on the latest news about it.
- Evaluation readers: These readers want to go beyond the surface. They’re deliberately asking for more analysis and different perspectives on a particular topic. Remember, they’re still doing all this in an AI service.
- Action readers: These readers have a clear picture of something they want to do and are seeking guidance on taking that action, or a place to do so.
- Support readers: This group has already taken action and wants some kind of ongoing support with their area of interest.
For a publisher, conversion isn’t necessarily just readers buying third-party products (which would be relevant only to sites that do affiliate marketing) but engaging in a deeper, deliberate way: subscribing to premium content, signing up for a newsletter, downloading an app, buying an event ticket, and more.
Each of these groups is seeking a different kind of information, and publishers need to respond with different kinds of content to reach them. The idea isn’t new—it echoes a content model the BBC and others adopted more than a decade ago—but in the AI era there’s a new wrinkle: whether large language models (LLMs) can cite the content with confidence.
Take an EV reader, for example. An orientation-stage one may be wondering whether the electric-car market is shrinking or growing, while an evaluator may be comparing the coverage in The Wall Street Journal with competitors or publications deep in the niche, like Electrek.
An action reader might go straight to “Which EV newsletter should I subscribe to?” or “What’s the best site to follow for EV policy and pricing?” That’s a big reason why niche and B2B publications often punch above their weight in AI search.
Why citations matter
Here’s the key idea: concentrating only on the people who click through from AI search is too narrow a focus. Readers go from one state to another within AI search. Even when you’re not capturing a click on every query you’re cited in, the fact that it’s your narrative guiding them, with attribution, can begin their orientation toward your publication.
Publishers that grasp this will need to offer content across different types of readers to stay present across their journey. Showing up in more answers obviously increases the chance of scoring a visitor, but it will also keep your brand visible as the reader continues exploring, even if that journey is largely taking place in an AI platform.
The Scrunch report maps the path inside AI. A separate example illustrates what publishers should be thinking about once a reader actually lands.
On3 is a college sports outlet and network of more than 70 team fan sites. Like a lot of publishers, it has watched declines in traffic from search and social, so it chose to focus on revenue per session as a key metric. It uses AI recommendations and first-party data to keep readers moving among articles, forums, video, email, and commerce.
Few publishers can match that full stack of resources, but the underlying lesson holds: in an AI world, the click matters less as a finish line than as the start of a carefully managed next step.
All of this presumes the content is built to be AI-friendly in the first place. Writing informational passages in a straightforward way, repeating common questions about the topic and answering them directly (there’s a reason you’re seeing FAQ sections everywhere), and practicing good technical maintenance are all important to get right.
Where visibility turns into value
Yes, AI is gutting traffic, but it also functions as an audience qualifier, with the people who click through being the most likely to engage with your publication.
The less obvious piece is that the publisher has an active role in that qualification. You can influence what audiences see in those summaries, but it depends on being present, understanding the different types of readers, and offering them the right mix of valuable content around your core topics, structured so the bots can parse it easily.
In AI search, the publishers that win will be the ones shaping reader intent before they ever win the click.
A version of this column appears in Fast Company.
