August 25, 2025

Answers Instead of Links: How SEO Is Changing and How to Prepare Right Now

Search is no longer a catalog of links. Today, more and more often, users receive a ready-made answer directly on the page — in Google’s AI Overviews, Microsoft Copilot Search, Perplexity, or even in Yandex experiments. This fundamentally changes the way businesses compete for attention: it is no longer just about getting into the top 10 results, but about being among the sources that artificial intelligence cites and reshapes.

Traditional SEO in its familiar form is fading away. In its place, a new discipline is emerging — optimization for generative engines (AEO/GEO). Companies that adapt to this shift first will receive leads not “through rankings,” but directly from AI-generated answers.

The Era of Search Answers: Why Links Are Moving to the Background

Search is no longer just a list of links. In 2024–2025, major platforms shifted their interfaces to an answers-first mode: the user receives a concise answer “on the spot,” supplemented with sources for deeper exploration. This changes both the click model (fewer transitions to results) and content requirements (more structured, easily quotable fragments).

AI Overviews are an AI-generated “snapshot” with key information and links to dig deeper.

What “Answers-First” Search Means

In an “answer-centric” search, the engine first constructs the output — a paragraph, a list of steps, or a mini-table — and only then offers links. Essentially, it is a “layer” on top of classic search results that:

collects facts from multiple documents and consolidates them into a single answer;
displays sources right next to the text;
often changes user behavior: part of the task is resolved without a click.

Google formally describes the mechanics: AI Overviews take “key information” and accompany it with links for further study — meaning that sources remain part of the interface but are no longer the first screen.

We see that people are asking longer and more complex questions and exploring a wider range of sources.

The Spread of AI Overviews and Other Systems (Google, Microsoft, Perplexity, Yandex)

Within a year, answers-first became the de facto standard among major players:

Google: AI Overviews moved out of experimentation and scaled globally; the company reports over 2 billion monthly users of these answers across more than 200 countries and 40 languages.
Microsoft Copilot (Bing): responds with paragraphs and footnote-style links to sources, emphasizing answer verifiability. (blogs.bing.com)
Perplexity: delivers “research-style” answers with numbered citations and fast access to the original.
Yandex: introduced its own YandexGPT models and announced the development of generative services and answers within its ecosystem products, including Search.

Mini-table of formats:

Platform	How the Answer Looks	Presentation of Sources	Note
Google AI Overviews	Summary + steps/lists	Links “for deeper dive” next to the answer	Actively expanded in 2025 (Search Engine Land)
Microsoft Copilot	Paragraph(s) with “footnotes”	Numbered links to websites	Focus on verifiability (blogs.bing.com)
Perplexity	Research-style summary with “References”	Explicit, numbered citations	Focus on exploratory queries
Yandex	Generative blocks	Links depend on scenario	Proprietary LLMs (YandexGPT)

⚠️ Important: the share of AI Overview impressions fluctuates by topic and time. For example, in March 2025 their presence noticeably increased in entertainment, restaurants, and travel.

Changing User Behavior: Longer Queries and Trust in AI

Answer-based interfaces encourage the use of natural language: queries are becoming longer and more specific, and the share of “multi-step” tasks (what → how → compare) is growing. This is not just anecdotal — Google itself confirms that users are formulating longer and more complex questions.

At the same time, the zero-click phenomenon is intensifying. Fresh summaries handle part of the informational task “on the spot,” which reduces transitions to organic results:

A SparkToro 2024 study showed a sustained share of zero-click in web search, reinforcing the trend of declining click-throughs.
Industry data from Amsive (Spring 2025) recorded a significant drop in CTR for queries that trigger AI Overviews, especially for non-branded keywords and for results ranked below the top.
Meanwhile, seoClarity monitoring showed that the share of cases where AI Overviews rank below position #1 on U.S. desktop grew to 12.4%, creating a “window” for competition over clicks.

AI search changes not only clickability, but the very form of the query: people formulate tasks in full sentences and expect an expert summary on the first screen. – Amsive

SEO Expert Forecasts

The industry does not dispute the trend — the debate is about the scale of impact and metrics. At the end of 2024 and beginning of 2025:

Lily Ray notes that the industry “desperately” wants visibility and click data for AI Overviews, but Google does not provide full transparency — meaning brands will have to build their own dashboards and proxy metrics.
Search Engine Journal and others agree: classic SEO will need to be complemented with a GEO strategy — optimization for generative engines (structured snippets, clear definitions, tables, FAQs).
An SEJ expert roundup (22 experts) emphasizes that the rise of “answers” requires investment in entity authority, data, and verifiability — otherwise content will not make it into answer blocks.

SEO is changing forever: the priority is brand trust, content structure, and the ability to appear in the answer, not just in the “blue links.” – Search Engine Journal

What This Means for Strategy

Answers-first is not a “temporary layer,” but a new norm for the search interface. It rewards sources that provide clear definitions, steps, comparisons, citations, and proper markup — as well as brands whose entities are well “connected” (author, organization, product).

Already now, content should be designed as a set of verifiable fragments that can be easily surfaced in answers — while in parallel, analytics should account for the share of answer-triggering queries, visibility within them, and downstream signals (scrolling to the cited fragment, clicks on local CTAs, micro-conversions).

How AI Is Changing the Search Ecosystem

The transformation of search is shifting the balance between links and ready-made answers. If previously the SEO market lived by the logic: position → CTR → traffic, now the key metric is increasingly visibility in the AI answer. This changes the distribution of clicks, the value of content, and the very economics of attention.

Fewer Clicks on Links: “Zero-Click Search” in a New Form

Zero-click has long been a familiar phenomenon (snippets, knowledge panels, “people also ask” blocks). But generative answers have amplified it dramatically: now an entire paragraph or instruction resolves the user’s task without the need to click.

According to SparkToro, as early as 2022–2023 more than 60% of searches ended without a click. With the arrival of AI Overviews, this share stabilized but changed form: now the lack of a click is explained not by a snippet, but by a full AI-generated summary.
An Amsive study (2025) recorded a drop in CTR specifically for queries where Google displayed an AI Overview. Non-branded keywords and sites ranked below the top were hit the hardest.
At the same time, seoClarity data showed that in the U.S. on desktop, AI Overviews appeared below the first result in 12.4% of cases — opening a window of opportunity for the top position: the user can see both the answer and the “classic link” side by side.

In other words: zero-click has become not the exception, but the norm. Content is still needed — but now clicks go to a smaller portion of players, while the value concentrates in the answer block.

AI search changes the very form of consumption: users expect an expert summary “here and now” and only click in cases of deeper interest. – Search Engine Journal Analytics, 2025

Who Wins: Sources in AI Answers

The logic is simple: if AI constructs an answer, it must cite sources. Being included in this list is the new “Top 3.” Winners are sites that have:

Authority — a strong brand or author expertise (E-E-A-T);
Quality structure — text fragments that are easy to cite: definitions, mini-procedures, tables, FAQ lists;
Relevance — data that can be pulled in real time (news, research, updated databases).

Example: Perplexity always accompanies its answers with a “References” block containing links. In such conditions, the winner is not the one “higher in position,” but the one whose text best fits the quote.

Google also emphasizes: links in AI Overviews are presented “in different formats so that people can more easily click and go to the web.” This means that sources become part of the answer — but the role is distributed “within the text,” not “by position.”

Mini-Scheme:

Content → Fragment → Citation in AI → Click to Source

Who Loses: Classic SEO and Traffic-Only Content

The biggest hit is felt by monetization models that depend specifically on click flow:

Classic SEO for mid-frequency and long-tail queries — many of these queries are now resolved directly in an AI answer without a transition.
Content farms and traffic-only websites — generative models rarely cite low-quality or rewritten texts.
Platforms without uniqueness — if there is no expertise or proprietary data layer, the chances of being included in the answer are minimal.

In essence, projects built “for ad impressions” without real value are becoming obsolete. Where previously an optimized text for a keyword was enough, today what is required is substance, data, and expertise.

Old SEO worked on reach. New SEO works on trust and suitability for citation. – Gartner Report on Search and AI, 2025

The Attention Economy: Traffic Moves Into AI Answers

SEO has always been a battle for user attention. With the rise of AI blocks, attention is concentrated at the very top of the screen, where the user receives almost everything:

Total time on the search results page is increasing: users are reading AI answers rather than scanning a list of links.
The number of clicks on classic results is falling, especially on desktop, where “above the fold” may be occupied entirely by the AI Overview.
The growing role of branded queries: companies are working more actively to ensure that AI answers cite their resources — so that users see the brand even without clicking.

Example: According to BrightEdge, in 2025 AI Overviews most frequently appear in travel, restaurants, and entertainment queries — precisely where users are “satisfied” with a quick summary and less likely to click through to websites.

SEO is turning into a competitive struggle not only “for clicks,” but also “for visibility inside the answer.” The winner will be the one who embeds their content into the generation chain — from data and structure to brand trust.

New SEO Optimization: AEO/GEO Instead of Links

As search engines move from a “list of links” to a “ready-made answer,” the rules of the SEO game change. If previously the main task was to rank high and earn a click, now the key challenge is to appear directly inside the AI answer. Classic optimization is being replaced by AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization).

What Are AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization)?

AEO is the optimization of content for “answer engines” (engines that deliver answers instead of a list of links). The goal: to make algorithms pull your text as part of the answer.
GEO goes further: it is the adjustment of site structure, content, and signals so that generative models (Google AI Overviews, Microsoft Copilot, Perplexity, Yandex “Sense”) cite your materials specifically.

The fundamental difference:

SEO works for positions in the list of links.
AEO/GEO works for fragments in the answer.

Answer engine optimization is not the future of SEO, but its present. In 2025, optimizing for citations in AI answers brings more impact than the fight for position #1. – Moz

How to Prepare Site Structure and Content for Generative Answers

The main rule: content must be easy to lift into an AI answer. Models do not rewrite websites; they extract ready-made chunks — a definition, a table, or a step-by-step instruction.

Practical approaches:

Fragmentation of content: instead of long walls of text — use blocks (FAQ, lists, definitions, diagrams).
One-paragraph answers: concise formulations that can become a “ready-to-quote” excerpt.
Tables and comparisons: AI engines often lift structured data.
Short summaries at the end of sections: this increases the chance of appearing in AI Overviews.

Example: an article about “optimizing job application responses.”

In the SEO version: a long text + keywords.
In the GEO version: a dedicated paragraph “What are job application responses?”, a table “Top reasons for irrelevant applications,” and a list “3 ways to fix it.”

The Role of Markup (schema.org, JSON-LD) and Microformats

AI is learning to “understand” data structure. If a website is equipped with proper markup, its content is easier to use as a source.

Schema.org — for describing entities (products, companies, events, articles).
JSON-LD — a machine-readable form of structured data.
Microformats — highlighting recipes, job postings, reviews, etc.

Research from Searchmetrics shows: websites with proper markup are 2.5 times more likely to appear in AI Overview blocks.

Type of Markup	What It Provides	Where It Is Critical
Article / FAQ schema	Elevation of definitions & answers	Blog, help center
Product schema	Prices, features, reviews	E-commerce
Organization schema	Authority, brand data	Homepage, About page
Event schema	Dates, locations, schedules	Events, calendars

Quality Signals: E-E-A-T in the AI Era

As early as 2019, Google established the principle of E-A-T (Expertise, Authoritativeness, Trustworthiness). In 2023, Experience was added, turning it into E-E-A-T. For AI-first search, this becomes critical:

Expertise — the text must demonstrate expertise, not just paraphrase Wikipedia.
Experience — references to personal practice, case studies, real-world applications.
Authoritativeness — citations in media, publications on reputable platforms, brand signals.
Trustworthiness — transparent authorship, cited sources, accurate data.

These are the very attributes that determine whether content will be selected by the model for citation.

AI does not look for the longest text, but for the most reliable and well-structured fragment. In the end, it is not the quantity of content that wins, but its quality. – Search Engine Journal

AEO/GEO is the new reality of SEO. Getting into AI answers requires not only technical optimization, but also a rethinking of the content approach: it must be fragmented, structured, marked up, and reflective of real expertise.

Practical Implementation: How to Get Into AI Answer Blocks

Getting into AI answer blocks means learning to write in a way that makes text convenient not only for humans but also for models. Today, this is the main challenge for SEO specialists and marketers: if before the task was to secure a position in the top 10, now it is to become a source cited by neural networks.

This is the real shift: search engines stop “sending” users to dozens of sites and instead deliver a ready-made answer. But you must be included in that answer — and this depends not on luck, but on systematic work with content.

What Formats Does AI Prefer?

Analysis shows that models most readily pull compact and structured snippets from texts: clear definitions, mini-tables, and lists. However, this does not mean turning your entire site into a giant FAQ. It is much more important to embed such elements within longer materials.

AI does not replace long-form articles, but it prefers to extract compact blocks from them. The winner is the one who combines depth with brevity. – Search Engine Land

Example: HubSpot achieved high visibility precisely because their pillar content is broken into fragments: first an in-depth explanation, then a clear one-paragraph or tabular takeaway.

Short Fragment Within a Long Article

It is important to remember: AI works with balance. Long-reads build authority and E-E-A-T (expertise, experience, authoritativeness, trustworthiness), but what gets inserted into answers are short, self-contained segments.

That is why Google and Perplexity most often cite blocks like “What is it?”, “Key steps,” or “Two-sentence summary.”

BrightEdge research confirms this: structured blocks (FAQs, lists, summaries) are 40% more likely to appear in AI Overviews than texts without internal logic.

External Signals and Trust

Even a perfect text will not be selected if the site appears “invisible” to the ecosystem. Answer engines analyze external signals: links from authoritative resources, brand mentions on social networks, freshness of updates. These are the factors that build trust in the source.

Signals Table

Signal	Why It Matters	Example
Citability	Demonstrates authority	Link from Forbes or HBR
Freshness	Ensures relevance for models	Updating an article quarterly
Mentions	Strengthens trust	Discussion on VC.ru or LinkedIn

Practical Examples

Gartner: their reports are cited by Microsoft Copilot thanks to clear structure (summaries, charts, lists).
Yandex.Practicum: in the Russian segment, their guides appear in Yandex “Sense” AI blocks because they perfectly fit the FAQ format.

📊 According to Search Engine Journal, websites that implemented AEO practices gained +18–25% additional traffic from AI answers within a year.

Appearing in AI answer blocks is not luck, but a predictable result. Winners are those who combine depth of analysis with model-friendly formatting, work with external signals, and regularly update their materials.

In essence, this is the new form of “search trust”: AI chooses sources that manage to be both expert and easy to cite.

The New SEO Metrics: How to Measure Success Without CTR

In the classic SEO model, everything was simple: rankings and CTR were considered the main indicators. If you ranked first in Google — you captured 30–35% of clicks; second place — around 15%; beyond that, the share dropped sharply. For decades, these metrics were the foundation of strategy.

But by 2025, the rules of the game have changed. With the rise of AI Overviews, Copilot Search, and alternative engines like Perplexity, traditional “organic traffic” is dissolving. Users are increasingly receiving a ready-made answer directly in search. They read summaries, quotes, and AI-generated tables — and often do not click on any website at all.

Thus, a new dilemma was born: if the old metrics are outdated, how do we now measure SEO effectiveness?

Why Rankings and CTR Are Losing Value

Zero-click search had already been noticeable years ago when Google actively started inserting featured snippets, maps, calculators, and direct answers into results. But now the situation has taken on a new dimension: artificial intelligence generates a complete answer and fully satisfies the user’s need.

According to SparkToro (2024), already 65% of search sessions end without a click.
In niches with high informational demand (medicine, finance, e-commerce), the share of zero-click reaches up to 75%.

Even top-3 websites are losing traffic. Users see the answer in the AI block and do not go further.

This is not an algorithm error, this is the new reality. – Rand Fishkin, Founder of SparkToro

For businesses, this means that even excellent visibility in search no longer guarantees traffic growth. CTR ceases to be a reliable indicator of success.

New KPIs for the AI-First Search Era

To stay in control, companies are building a new set of metrics.

What Matters to Track Today:

Mentions in AI answers. How often the brand or article appears in AI Overviews, Copilot, or Perplexity Rank blocks.
Clicks from AI blocks. Not all answers are “closed”: links still exist. Their CTR is lower, but the value is higher — the users who click have stronger intent.
Brand visibility score. An aggregate indicator of brand mentions across different generative engines — even without clicks.
Leads and conversions. The key indicator: how many real inquiries, meetings, or purchases came from traffic generated by AI blocks.

Unlike old SEO, where you could demonstrate “traffic growth,” now you have to prove business value directly through P&L.

Table: From Old Metrics to New Ones

Metric (Before)	Why It Loses Value	New Alternative
Rankings in Top 10	Users no longer see “blue links,” AI assembles content from multiple sources	Frequency of mentions in AI blocks
CTR	Zero-click search reduces clicks even from the top positions	Clicks specifically from AI blocks
Traffic	Visits alone do not equal business value	Leads and SQLs (sales qualified leads)
Amount of Content	Mass publication for traffic no longer makes sense	E-E-A-T signals: quality and trust

Tools for Analysis

The problem is that Google Search Console still does not provide statistics for AI Overviews. Therefore, companies use a mix of tools:

Ahrefs, SEMrush. Still useful for backlink and ranking analysis, but do not reflect AI mentions.
BrightEdge, seoClarity. Already launched features for tracking visibility in AI Overviews.
AI-trackers (Perplexity Rank, SGE Monitor). Monitor how often websites appear in generative blocks.
Custom dashboards. Many companies manually collect queries and analyze mentions, cross-referencing them with CRM to track lead generation.

In 2025, a new class of SEO tools will emerge — “AI visibility platforms.” They will become as standard as Google Analytics was ten years ago. – Gartner

Economic Impact: Leads Instead of “Traffic for Traffic’s Sake”

The main shift is that SEO is no longer about traffic for the sake of traffic.

E-commerce case: after launching an AEO strategy, traffic from Google dropped by 18%, but leads increased by 22%. The reason: users clicking from AI Overviews arrived with ready-to-act intent.
B2B startup case: brand mentions in Copilot Search tripled within four months. As a result, SQLs (qualified demos) grew by 30%, even though organic traffic remained stable.
Media case: the site lost part of its traffic but increased brand reach — mentions in AI blocks became a source of collaborations and external citations.

In the end, businesses are beginning to think not in terms of “traffic volume” but of ecosystem presence.

SEO Metrics Are Transforming

CTR and rankings are fading into the past, replaced by new benchmarks:

Mentions in AI blocks,
Clicks and leads from generative answers,
Contribution to real revenue.

The main KPI for SEO today is not search ranking, but contribution to P&L. – Forrester

Thus, SEO returns to its roots: it is not about “ranking,” but about business value.

Strategy for the Future: Preparing for SEO 2.0

As search engines evolve into answer engines, the traditional principles of SEO stop working. Instead of competing for top-3 positions, companies must learn to be “embedded” into generative answers. This is the transition to SEO 2.0 — a new ecosystem where user attention is distributed differently, and value is measured not by clicks but by trust and presence.

How to Reshape Content Strategy for Answers, Not Rankings

In classic SEO, strategy revolved around keywords, density, headings, and backlink profiles. In the AI-first era of search, what matters is different: how structured the content is and how suitable it is for generative extraction.

Key principles of the new content strategy:

Focus on semantic blocks. Content must be broken down into AI-friendly fragments: definitions, steps, lists, examples.
FAQ and micro-content. The more ready-made answers a site contains, the higher the chance of being cited in AI blocks.
Length ≠ value. AI “does not like fluff.” Even large articles must be built from self-contained chunks.
Freshness. Old texts lose priority — engines prefer updated information.

AI algorithms don’t read texts like humans. They search for atomic fragments of meaning. – Search Engine Journal

Integration With Marketing: “AI Landing Pages” as a New Entry Point

Even though users are clicking less often on links, clicks from AI blocks are much hotter. To convert them into business results, companies are creating AI landing pages — dedicated entry points for traffic from generative answers.

Features of AI landing pages:

Quick response to the query (within the first 5–7 seconds of scrolling).
Depth of meaning (more detailed than the AI summary, but without fluff).
Built-in CTA (inquiry, demo, subscription).
Personalization by segment (e.g., different landing pages for C-level vs. managers).

📊 Case (B2B SaaS): implementing AI landing pages increased conversion from AI traffic by 42% compared to regular blog pages.

Channel Balance: SEO, AI Answers, Social Media, Partner Content

In SEO 2.0, companies should not rely solely on search answers. To minimize risks, they combine multiple sources of attention:

Channel	Role in SEO 2.0	Strengths	Limitations
SEO (classic)	Foundation for long-term presence	Controlled channel	Declining CTR
AI Answers	Trust and qualified leads	High-quality clicks	Harder to forecast
Social Media	Freshness signals	Fast distribution	Short content lifespan
Partner Content	Authority amplification	E-E-A-T signals	Requires relationships and time

Practical 12-Month Plan: What Businesses Should Do Now

To stay relevant, companies must act systematically. Below is a sample 12-month roadmap adaptable to any industry.

Months 1–3:

Audit current content for AEO/GEO readiness.
Implement schema.org and JSON-LD on key pages.
Test 2–3 AI landing pages for priority queries.

Months 4–6:

Develop a strategy for FAQ and micro-content.
Create dashboards for tracking AI block mentions.
Set up lead analytics specifically from AI sources.

Months 7–9:

Integrate content with social signals (shares, quotes, expert mentions).
Strengthen E-E-A-T: case studies, interviews, expert articles.
Launch partner content (collaborations, guest publications).

Months 10–12:

Scale successful AI landing pages.
Test different formats (video, tables, infographics) for generative blocks.
Update the content model and integrate it into the overall marketing strategy.

Within a year, companies will split into those embedded in the AI ecosystem — and those that will have lost up to 70% of organic traffic. – Gartner

SEO 2.0 Is Not the End of Search, but a New Chapter

Winners will be those who:

Restructure content for answers, not just keywords.
Use AI landing pages to monetize hot clicks.
Balance between SEO, AI, and social media.
Act according to a plan, not react after the fact.

This is how the strategy of the future takes shape: not traffic for the sake of traffic, but a managed system of presence within the AI ecosystem.

SEO 2.0 — The Era of Answers, Not Rankings

The search ecosystem has entered a phase of radical transformation. Links and rankings no longer guarantee traffic flow: everything is now decided by generative answers, where users find value directly within the search interface.

Key Takeaways:

AI reshapes the attention model. The user receives an immediate answer, not just a list of links.
AEO/GEO are becoming the new SEO. The winners are websites prepared for generative extraction: structured texts, proper markup, and strong E-E-A-T signals.
Traffic is becoming “smarter.” Clicks from AI blocks are fewer in number but much higher in quality and intent.
A new strategy is required. AI landing pages, channel balancing, and partner content are becoming the foundation of digital marketing.
Metrics are shifting. CTR and positions are being replaced by indicators such as AI mentions, qualified leads, and incremental business value.

SEO as a battle for the top 3 positions is fading into the past. The present is a battle for visibility within AI answers. – Search Engine Land

For businesses, this all leads to a single conclusion: those who adapt to answer-based search today will secure a competitive advantage tomorrow.

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