AI search dominates headlines, but does SEO still matter? Recent research of 25,000 user searches reveals that websites ranked #1 on Google show up in AI search answers just 25% of the time. Top-ranked content misses out on AI results 75% of the time.
Don’t rush to drop your SEO strategy yet. Google handled 14 billion searches daily in 2024. This number is 373 times higher than ChatGPT’s daily “search-like” prompts. Google’s search volume grew by 21% from 2023 to 2024. These numbers prove that SEO isn’t dead – it just keeps evolving. Research shows that AI Overviews cut top-ranking organic results’ traffic by up to 45%. Businesses need to understand SEO’s value as they navigate this new digital world.
This piece will show you how AI changes search behavior and why SEO basics still work. You’ll learn ways to stay visible in both traditional and AI-powered search results. SEO remains relevant – the key lies in using it right for this new era.
The rise of AI search and what it means for SEO
Search is going through a major transformation. Google has been our gateway to information for decades, but AI-powered search now reshapes how people find and consume content online. This transformation means big changes for SEO professionals and businesses that depend on organic traffic.
How AI Overviews and LLMs are changing search behavior
AI Overviews and large language models (LLMs) have turned search from a link-based experience into an answer-based one. About 50% of Google searches now feature AI summaries, and experts predict this number will exceed 75% by 2028. These AI-generated responses appear at the top of search results pages and give instant answers to user questions.
The numbers tell an interesting story. AI Overviews showed up in 13.14% of U.S. desktop searches in March 2025, jumping from 6.49% in January that year. These numbers show just how fast this technology is becoming part of our search experience.
Nielsen Norman Group’s research shows AI disrupts how people look for information. While AI offers quick shortcuts around time-consuming research, it hasn’t replaced traditional search. People often use both methods together, and sometimes they even use one to verify the other.
Why users are moving from links to answers
People now want instant answers instead of clicking through websites. Bain’s research shows about 80% of consumers use “zero-click” results at least 40% of the time. AI summaries give users what they need right on the search results page.
A Pew Research Center study reveals some eye-opening trends. When Google users see an AI summary, only 8% click on traditional search result links, compared to 15% for those who don’t see one. The numbers get even more interesting – users click links within AI summaries just 1% of the time.
Google users tend to end their search session after seeing a page with an AI summary (26%) more often than those who see traditional results (16%). This shows a basic change in search engine behavior.
Some types of searches are more likely to trigger AI summaries:
- Question-based searches trigger AI summaries 60% of the time
- Searches with 10+ words get AI summaries 53% of the time
- Full sentence searches bring up AI summaries 36% of the time
How this affects organic traffic and click-through rates
Websites that rely on organic traffic have felt the pain. Some businesses have seen traffic drop by 49% compared to last year. Publishers’ revenue has fallen by more than a third.
Click-through rates have dropped sharply across industries. Data shows organic CTRs for queries with AI Overviews have fallen by nearly two-thirds, reaching around 0.6% in summer 2025. Paid CTR has also taken a hit, dropping from 11% to 3% in just one month.
All the same, AI search makes up less than 1% of referral traffic, even though it grows by double digits each month. Organic search still drives most conversions across industries.
SEO isn’t going away – it’s becoming more vital. As AI search grows, strong SEO basics matter more than ever. Major AI engines build on traditional search indexes. This means good SEO optimization pays off twice: your content becomes visible in traditional search, AI overviews, ChatGPT, Perplexity, and new platforms too.
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Why SEO is not dead: Understanding AI’s dependence on SEO signals
The buzz around AI search is everywhere, but traditional SEO remains the life-blood of digital visibility. SEO professionals worried their skills would become obsolete as AI rose to prominence. The reality turned out quite different. AI doesn’t replace SEO—it makes it more valuable in unexpected ways.
AI still relies on structured, crawlable content
AI’s sophisticated capabilities can’t create answers from nothing. Large language models (LLMs) that drive AI search functions need content that search engines have already found and indexed. Your content must be well-laid-out and crawlable. Without these elements, it stays invisible to both traditional search engines and their AI-powered counterparts.
Here’s something to think over: Google’s AI Overview feature pulls information from websites that already rank well for specific queries. These websites earned their positions through solid SEO practices. They use proper HTML structure, descriptive metadata, clear heading hierarchies, and logical content organization.
On top of that, it turns out AI search engines favor content with clear information architecture. This has sections for:
- Content with well-laid-out headings (H1, H2, H3, etc.)
- Pages with appropriate schema markup and structured data
- Sites with logical internal linking and clear navigation paths
- Content in scannable, digestible formats
AI systems ended up needing SEO-optimized content to work properly. They would have no reliable information to retrieve, analyze, and show users otherwise.
The role of E-E-A-T in AI-generated answers
Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) are the foundations of Google’s search quality evaluation. These principles have grown even more important in the AI search era.
AI systems favor credible sources when generating answers. They spot credibility through traditional SEO signals—backlink profiles, author expertise, content depth, and brand authority. AI search engines tend to cite content from sources that show strong E-E-A-T signals.
Companies should focus more on building real expertise and authority in their niche. Content that only targets keywords will fall short as AI search engines get better at finding truly authoritative resources. The goal should be becoming the go-to source in your industry that AI systems naturally reference.
How traditional SEO supports AI accuracy
Traditional SEO practices do more than help content get noticed—they boost the accuracy of AI-generated answers. Implementing proper keyword research, semantic SEO, and topical clustering helps AI systems grasp your content’s context and relevance better.
Technical SEO elements like structured data and schema markup tell AI exactly what your content means. To cite an instance, recipe schema helps AI understand cooking instructions, while FAQ schema identifies questions and answers clearly within your content.
Good on-page SEO practices associate with AI accuracy. Content that really covers a topic from multiple angles lets AI extract precise, nuanced information for generating answers.
SEO and AI work together rather than against each other. Strong SEO practices make AI search more accurate, and AI search encourages better SEO. This beneficial cycle means businesses that invest in quality SEO now set themselves up for success in both traditional and AI-powered search.
Local businesses should keep focusing on local SEO basics—consistent NAP information, Google Business Profile optimization, and location-specific content. These elements help AI systems understand and represent your business correctly in local search.
How AI search engines select and use content
AI’s content selection process reveals why traditional SEO remains a vital part of digital marketing. AI search doesn’t replace indexed content—it revolutionizes content delivery to users.
The process of retrieval-augmented generation (RAG)
RAG are the foundations of modern AI search engines. This process combines traditional information retrieval’s strengths with large language models’ (LLMs) natural language capabilities.
RAG works through a multi-step process:
- User input processing: The system captures questions and relevant conversation history when someone makes a query
- Information retrieval: The system searches indexed content to find relevant information
- Context augmentation: Retrieved content goes into the prompt sent to the LLM
- Response generation: The LLM creates an answer based on its training and retrieved content
This approach helps overcome LLMs’ limitations by adding fresh, authoritative external content. “For an enterprise solution, it’s possible to fully constrain generative AI to your enterprise content. The decision about which information retrieval system to use is critical because it determines the inputs to the LLM”.
RAG doesn’t train the model on your content—it provides context to generate better answers. This difference explains traditional SEO’s continued importance; your content needs proper indexing and retrievability to be part of this process.
What makes content extractable and reusable
Your content needs proper chunking and structure for AI search engines to use it. Content chunking—breaking larger documents into manageable segments—makes retrieval quick and efficient.
Content becomes more extractable with these factors:
- Clear structure: Content with logical heading hierarchies and distinct sections
- Semantic clarity: Information in straightforward, unambiguous language
- Layout awareness: Properly formatted tables, lists, and visual elements that keep their meaning during extraction
- Defined entities: Content that identifies people, places, concepts, or things clearly
“To generate answers using the source data, the ingested data is segmented into smaller segments or chunks to organize and efficiently retrieve the most relevant data set when required”. This chunking process changes based on content type, expected query patterns, and embedding models.
Layout-aware extraction plays a significant role too. This method “identifies objects in documents by combining OCR technology, layout detection models, and layout awareness rules”. AI systems can understand tables, graphs, and charts—not just plain text.
Why semantic clarity and formatting matter
Semantic search capabilities boost how AI selects and uses content. Semantic search understands intent and context, unlike traditional keyword matching, and delivers relevant results.
Sophisticated ranking methods power this process. Azure AI Search shows that “semantic ranker is a collection of query-side capabilities that improve the quality of an initial BM25-ranked or RRF-ranked search result for text-based queries”. These systems use language understanding models to identify relevant matches.
Your content’s technical formatting affects how AI systems extract meaning. “AI systems like Google Overviews and Perplexity prefer structured, high-authority sources. Blog posts that demonstrate E-E-A-T (Experience, Expertise, Authoritativeness, Trust) and provide concise, structured answers are far more likely to be cited”.
Proper heading structure shows content hierarchy to AI systems. “Use a logical heading hierarchy. Structure your pages with a single clear H1 that sets the context, followed by H2s and H3s that nest logically beneath it”. This organization helps AI comprehension and increases your content’s chances of selection as a source.
The selection process rewards content creators who focus on clarity, structure, and expertise. AI search has raised the stakes instead of making SEO irrelevant. Modern search engines’ sophisticated filtering process selects only the most well-laid-out, semantically clear content.
Adapting your SEO strategy for AI search
Your SEO strategies need to adapt as search continues to evolve. You need to optimize for both traditional rankings and AI-powered results to succeed.
Break content into question-based sections
AI models process information differently from humans. Content that mirrors natural questions and answers works best for them. Your sections should use explicit questions to help AI systems grasp intent and extract responses more easily.
You need to teach your content to speak “AI.” Models can instantly map user needs when headings appear as real questions. This creates a direct connection between user intent and how information flows.
Start by checking if your headings sound like actual questions from your audience. Next, determine if an LLM would spot the underlying question. Your content becomes more extractable when both conditions are met.
The structure should follow this pattern:
- Direct answers to predicted questions come first
- Supporting details and context follow
- Related questions provide complete coverage
- Actionable next steps wrap it up
This method works better not just for AI but also makes reading easier for humans. Content organized around real user questions naturally matches how people look for information.
Use schema markup and structured data
Schema markup stands out as one of the most effective tools to optimize for AI search. Search engines and AI systems understand your content’s context better through these explicit clues about your pages.
FAQPage schema implementation boosts your chances of showing up in featured snippets and AI-generated answers. AI platforms get clear signals: “Here’s a question. Here’s the authoritative answer. These elements connect”.
Studies show AI-generated answers include list formats 78% of the time. FAQ schema naturally presents content as question-answer pairs—exactly how AI platforms show information to users.
Best results come from:
- FAQ Page schema for each Q&A pair
- Schema confirmation through Google’s Rich Results Test
- Unique and well-laid-out question-answer pairs
- Internal links that build topical authority
Experts suggest building your “content knowledge graph” by adding other relevant schema types like HowTo, Article, and Organization. This structured data layer helps AI grasp your brand’s entities and relationships.
Focus on long-tail and conversational queries
People interact with AI search quite differently from traditional keyword searches. Complete questions now replace simple keyword phrases. Research points out that long-tail queries make up half or more of all searches.
Voice search drives an increase in these conversational queries. Examples include:
- Specific questions (“Where can I find a pet-friendly hotel with free parking near downtown?”)
- Feature-related searches (“men’s brown loafers with arch support”)
- Compatibility searches (“appetizers for a gluten-free dinner”)
- Concept searches (“something to be visible while running at night”)
This change means moving away from keyword-focused thinking toward question-focused content strategy. Simple two-word phrases no longer cut it—success depends on addressing complex, context-rich questions that AI users naturally ask.
Optimize for both humans and machines
Humans remain your primary audience even as AI optimization becomes vital. The best strategy balances technical optimization with real value creation.
Clean text structure and readability matter most. Short paragraphs under 120 words work best, along with bullet points, numbered lists, and mini-tables. Both AI crawlers and human readers prefer content that’s clean, consistent, and follows logical order.
Back up your claims with solid sources and evidence. AI models favor content based on facts you can verify. They tend to extract and cite information from primary sources—industry reports, official documentation, or authoritative research.
Your brand voice should stay unique. AI systems can tell generic, templated content from valuable, distinctive views. A professional yet conversational tone with natural language and personality appeals to both humans and machines.
New SEO metrics in the AI era
SEO success measurement in 2025 needs a fundamental change in metrics. AI has revolutionized search visibility. Traditional analytics reveal only partial insights. Marketers must now adopt new performance indicators.
Tracking AI citations and brand mentions
AI citations show how often your content appears in AI-generated responses. Studies reveal brands on Google’s first page have a strong correlation (~0.65) with LLM mentions. Backlinks show less effect than expected.
Citation analysis now tracks:
- Brand mentions across AI platforms
- Citation frequency and context
- The accuracy of AI summaries that reference your content
LLM Pulse and similar tools detect how brands appear in AI-generated answers across ChatGPT, Perplexity, or Google AI Mode. You get a complete picture of AI visibility by tracking both mentions and citations.
Measuring semantic authority and answer ownership
Semantic authority now depends on how well AI understands your expertise in the digital world. We built it through backlinks before. AI now calculates authority based on semantic integrity, not just link numbers.
Modern SEO teams track:
- Inclusion Rate (IR): The percentage of prompts where your brand appears
- Citation Coverage (CC): How often your mentions include clickable attribution
- AI Share of Voice (SOV): Your mentions compared to competitors
Why traditional KPIs like traffic are no longer enough
Of course, traffic has value—but click tracking alone creates massive blind spots. You might miss 60-80% of your brand’s appearances if you only track clicks. Content creates value without driving website visits.
Zero-click interactions happen more often than clicks throughout the customer trip. Your content gets surfaced, summarized, or cited within AI tools. Businesses must adapt their measurement approach.
The future demands tracking trip-based metrics. These include awareness metrics (search feature impressions, brand mention volume), consideration metrics (return visit rate, newsletter signups), and conversion metrics (assisted conversions from organic search).
Organic growth today isn’t about getting clicks. It’s about becoming an authoritative source that AI systems reference and promote.
Why SEO is more important than ever for businesses
Modern search visibility facts might surprise you. 86% of AI citations come from sources brands already control, such as websites, listings, and reviews. This insight shows why strong SEO foundations matter more than ever.
AI magnifies top sources, not all sources
AI search doesn’t democratize visibility—it magnifies existing authorities. Studies show websites ranking #1 on Google appear in AI search answers 25% of the time. First-party websites generate 44% of all AI citations, and business listings account for 42%. Brands with strong traditional SEO performance sometimes find they’re virtually invisible in AI responses. This gap exists because AI platforms prioritize identifiable experts backed by real data and human insight.
SEO builds long-term visibility and trust
AI systems prioritize content that shows Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T). Google gives extra weight to content with strong E-E-A-T signals for topics affecting health, financial stability, or safety. Without doubt, SEO must focus on building genuine expertise across controlled touchpoints. Brand mentions work as modern backlinks that signal authority to both users and algorithms.
Being cited in AI answers drives brand influence
AI-generated answers create powerful first impressions that build trust before site visits. Marketers should track their brand’s frequency in AI-generated summaries. The benefits are substantial—brands that match AI citation patterns see improved credibility within responses, even with weaker traditional link signals. Note that visibility today isn’t just about clicks—it’s about becoming an authoritative source that AI systems reference and magnify.
Conclusion
SEO remains crucial even as AI search continues to grow. Google still rules the digital world and processes billions of searches each day. The numbers keep going up year after year. AI overviews now affect visibility and traffic patterns, but this represents a development in SEO rather than its end.
Your SEO basics now work twice as hard. They boost your rankings in regular search results and make your content more visible in AI-generated answers. Content that’s well-laid-out with clear headings, proper schema markup, and strong E-E-A-T signals gives you better chances to stay visible in both spaces. On top of that, businesses should organize their content around questions. They need to focus on conversational queries and make sure AI systems can easily extract their information.
This new era needs different ways to measure success beyond counting traffic. AI citations, brand mentions, and semantic authority help you learn about your digital presence. Showing up in AI-generated answers creates strong first impressions before users visit your website.
SEO and AI search work together naturally. AI search engines rely heavily on content that shows expertise, follows best practices, and delivers clear value. Your investment in quality SEO sets your business up for success whatever direction search technology takes.
SEO was never about quick tricks or gaming algorithms. It has always rewarded businesses that deliver valuable, trustworthy information consistently. This principle stays true even as AI alters the map of how people find and use content. The businesses that will thrive are those that welcome both traditional SEO excellence and AI visibility requirements. They’ll stay discoverable no matter how their customers search.
