Until recently, internet search seemed straightforward and predictable. A query, a results page, ten blue links, and a battle for the top spot. For years, SEO strategies, marketers’ KPIs, and business budgets were built around this logic. But this model is rapidly losing its relevance.
Today, search results are less and less like a list of websites. Google, Bing, and other platforms are embedding AI answers directly into the interface. The user asks a question and receives a ready-made result compiled by an algorithm right on the screen. No clicks. No transition to the website. No classic “compare several sources.”
This is convenient for the user. For marketers and SEO specialists, this is a signal that is difficult to ignore. Traffic is no longer guaranteed, even with good rankings. The mere fact of being at the top no longer means that a person will reach your content.
In this new reality, the very basic question that SEO works with is changing. It is no longer about how to get a page to the top of the search results. It’s about something else: how to make a brand, product, or expertise visible in AI responses. Because that’s where first impressions are now formed and decisions are made about who to trust.
What is AI search and how does it differ from classic SEO?
In classic SEO, search worked simply. The algorithm evaluated pages, compared signals, ranked results, and showed the user a list of links. Then everything depended on the title, snippet, and position in the search results. The user chose where to click.
AI search changes the very logic of this process. As described by Search Engine Land and Wikipedia, the algorithm is no longer limited to ranking pages. It collects the answer. The system analyzes several sources, compares information, takes into account the context of the query, the user’s intent, and the connections between topics, and then forms a single, generalized answer.
For the user, it looks simple. They ask a question and immediately get a result. In many cases, there is no need to go to the website. That is why more and more queries end on the search page rather than on the brand’s page.
This change is giving rise to a new paradigm, which is referred to in Western materials as Answer Engine Optimization, or AEO. While classic SEO was focused on positions and clicks, AEO focuses on a different task. Content must be such that the AI system can use it as a source for answers.
In fact, marketers and content teams are facing a new reality. It is important not only to get into the search results, but to become part of the answer that the user sees. It is from this moment that SEO ceases to be just a game of keywords and begins to work with meaning, context, and a clear knowledge structure.
Why are traditional SEO metrics no longer sufficient?
Until recently, SEO success was measured quite directly. There is a position in SERP, there is traffic, there are conversions. If the page is at the top, then the strategy is working. But materials from Clearscope and Prismic show that this logic is gradually losing its reliability.
Positions in search results no longer guarantee clicks. Even if a site ranks high, the user may not reach it. The reason is simple. AI responses are increasingly meeting user needs right on the search page. People get a concise explanation, a list of recommendations, or a ready-made solution and see no point in clicking further.
This leads to an increase in the number of so-called zero-click queries. Formally, the query took place, but the site did not receive any traffic. For marketers, this looks like a paradox. The content seems to be working, but the metrics are not growing.
Against this backdrop, a new focus is emerging, which Western materials refer to as AI visibility. The question is no longer just what position the page ranks in. Something else is much more important:
In fact, we are talking about presence in a new search ecosystem, where users see not websites, but summarized answers. In this system, you can lose some traffic, but at the same time maintain or even strengthen your brand’s influence.
That is why marketers are advised to look broader. Clicks remain important, but they are no longer the only indicator of success. Presence in AI search, mentions, citations, and brand recognition are becoming new signals by which the effectiveness of SEO strategies will have to be evaluated in 2026.
How is user behavior changing in AI search?
Changes in search algorithms go hand in hand with changes in user behavior. As Semrush and Prismic materials show, people are gradually moving away from “searching for pages” and starting to search for answers.
The first noticeable shift is in the queries themselves. They are becoming longer and more complex. Users are no longer limited to two or three keywords. Instead, they formulate complete questions, add context, and specify conditions. Search is starting to look like a dialogue rather than a set of tags.
The second point is the language of queries. It is becoming more natural. People write and speak the way they are used to communicating with each other. The query “best CRM for small business 2026” is giving way to phrases such as “which CRM is best for small businesses in 2026.” This is normal for AI search, but until recently, this logic was atypical for classic SEO.
The third important change is multimodality. Search is no longer limited to text. Users:
- enter queries manually;
- ask voice questions;
- search by images or combine several formats at once.
AI systems have learned to work with all these signals, combining them into a single answer. This further distances search from the classic results page.
As a result, user expectations are changing. Previously, users were willing to browse several sites, compare information, and draw conclusions on their own. Now they expect search to do it for them. The algorithm’s task is to provide a clear, concise answer here and now.
For marketers, this means something simple but not always pleasant. Content no longer competes only with other pages. It competes for a place in the answer generated by AI. And it is precisely this logic that will require a rethinking of the approach to SEO and content strategy in 2026.
AEO as a logical continuation of SEO
When search ceases to be a list of links and starts to provide ready-made answers, the logic of optimization also changes. It is at this intersection that Answer Engine Optimization, or AEO, appears. As explained in Wikipedia and Search Engine Journal, AEO does not replace SEO, but continues it under new conditions.
While classic SEO focused on the visibility of a page in search results, AEO works with a different task. Content should help the AI system form a response. Not hint, not push to click, but directly answer the user’s question.
Hence the key content requirements that are repeated in the sources:
In this model, content ceases to be simply informational material. It becomes a building block for AI responses. The algorithm selects not the text that is best optimized for keywords, but the one that is easier and more accurate to integrate into the consolidated result.
For marketers, this means a shift in thinking. The question is no longer how to lure users to a page. The question is how to formulate knowledge so that AI recognizes it as a relevant and reliable source. In this sense, AEO is not an alternative to SEO, but its logical next step in the world of AI search.
What marketers should do in 2026: practical directions for adaptation
Prismic, Clearscope, and Search Engine Land all agree on one thing. In 2026, marketers will have to change not just individual tactics, but their entire way of thinking about SEO and content. It’s not about “new tricks,” but about rethinking why and for whom content is created.
The first shift from keywords to search intent
Previously, strategy often began with a list of queries. Today, this is not enough. AI search analyzes not only the wording, but also the context of the question, the expected depth of the answer, and related topics. Therefore, the marketer’s task is to understand what exactly the user wants to get as a result. An explanation, comparison, recommendation, or clear algorithm of actions. Content that responds to this intent is much more likely to be included in the AI response.
The second direction is depth instead of mass
Sources directly indicate that superficial SEO articles are losing their effectiveness. Materials created solely to cover queries are increasingly less likely to be used by AI systems. Instead, the value of deep, contextual texts that fully explore a topic, explain cause-and-effect relationships, and demonstrate expertise is growing. Such content is easier to use as a reliable source rather than as a fragment of general information.
Step three: structure as a separate strategy
AI does not read text the way humans do. It looks for logic, blocks, and clear answers to questions. That is why structuring becomes critically important. Material in which it is easy to find definitions, explanations, or conclusions is easier to quote and embed in a response. Headings, subheadings, lists, and logical hierarchy are no longer just a convenience for the reader but become a tool for visibility in AI search.
The fourth focus is brand visibility instead of chasing clicks
Clearscope and Prismic emphasize that in a zero-click search environment, traffic is no longer the only goal. Even if a user does not visit the site, they can see the brand, expertise, or quote in the AI response. For marketers, this means a new approach to evaluating effectiveness. It is not only important how many people came, but whether the brand was present at the moment of decision-making.
As a result, adapting to AI search in 2026 looks not like a set of technical fixes, but like a strategic change. Content must be meaningful, structured, and useful enough that AI wants to use it as an answer. Only those teams that start working with this logic now will be able to maintain visibility and influence in the new search ecosystem.