Search has changed more in the last two years than in the previous decade. Google AI Overviews now reach over 2 billion monthly users across 200+ countries. ChatGPT processes over 2.5 billion prompts daily, 65% of which function as search queries. So, where is the future of search heading?
AI SEO is the new baseline of how people browse the internet. If you’re a marketer, content creator, or business owner, these numbers mean one thing: the rules of visibility have changed, permanently.
This guide explains exactly what AI SEO is, why it matters, and how to build a strategy around it. Let’s jump into it.
Key Takeaways
- AI SEO means two things: using AI tools to do SEO work faster, and optimizing content so AI tools cite you in their answers.
- About 76% of URLs cited in AI Overviews also rank in Google’s top ten, so technical SEO and quality content still matter.
- Instead of chasing keyword rankings, you now want citation share, meaning how often AI tools pull from your content when they build an answer.
- Original content is what gets you cited. AI tools only reference material they cannot generate on their own, such as proprietary data, original research, and expert insight.
- Brand mentions now carry more weight than backlinks as a visibility signal. Mentions correlate with AI Overview appearances at 0.664, compared to just 0.218 for backlinks.
- Traffic from AI tools converts at 23 times the rate of regular organic traffic.
- Short, self-contained paragraphs, question-based headings, FAQ schema, and a direct answer within the first 150 words all improve your chances of being cited.
- Check your robots.txt file. Blocking AI crawlers like GPTBot or ClaudeBot cuts you off from those platforms entirely, often without the site owner realizing it.
- GEO, AEO, and LLMO are different names for largely the same approach. Structured content, clear answers, and strong authority signals work across all AI platforms at once.
- Referral visits from AI platforms were already up 357% year over year as of mid-2025, and research projects AI search will surpass traditional search by 2028.
What Is AI SEO?
AI SEO has two distinct but complementary meanings.
The first is using AI tools to do SEO work faster and at scale. This applies to tasks such as keyword research, content briefs, technical audits, optimization, and more.
The second, newer meaning is optimizing your content for AI-powered search engines. These answer engines can then select your content as a source in their generated responses. This means prioritizing semantic clarity, structured information, and direct answers to real questions. AI SEO relies on strong E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals over keyword density.
Both matter, but the SEO industry has adopted the latter meaning as an umbrella term for all strategies that have to do with optimizing content to show up in AI citations.
You’ll also see this practice under other acronyms. The most popular of which are Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or Large Language Model Optimization (LLMO). We’ll break down how those terms differ later in this article, so stick around.
AI has always been inside search engines
It’s important to highlight that AI in search is not new. While generative AI as we know it is a new technology, Google has relied on machine learning since the early 2000s.
RankBrain (2015) helped interpret never-before-seen queries. BERT (2019) enabled Google to understand nuance and context in language. Every Helpful Content Update since has used AI to evaluate quality at scale. What changed around 2023 was, as we said, the emergence of generative AI. These systems don’t rank pages but synthesize answers from multiple sources and present them directly to users.
The AI search platforms that matter
There are countless AI tools out there, and the number is growing each day. However, we can shortlist a few platforms that have solidified their presence on the market as the most prominent tools. Each one has different indexing logic and user intent:
| Platform | Monthly Reach | Retrieval Method | Top Ranking Signals | Optimization Priority |
| Google AI Overviews (powered by Gemini) | 2B users | Hybrid: Google index + RAG + Knowledge Graph | Traditional rankings, E-E-A-T, schema markup, brand mentions | FAQPage schema, question-based headings, featured snippet eligibility, strong topical authority |
| ChatGPT (OpenAI) | 3.8B+ visits/mo | Training data + RAG via Bing (when web search is enabled) | Training data authority, brand presence in reputable sources, and Bing indexation | Get featured in high-authority roundups; ensure GPTBot is not blocked; build branded mentions across the web |
| Perplexity | 100M+ queries/mo | Always RAG: real-time live retrieval, cites every source | Freshness, direct answers, structured content, and domain authority | Fastest to influence: publish comprehensive Q&A content, update regularly, ensure PerplexityBot has crawl access |
| Google AI Mode | 100M active users (US + India) | Multi-stage RAG: runs multiple sub-queries, synthesizes across sources | Topical depth, comprehensive coverage, internal linking, factual density | Long-form pillar content with deep sub-topic coverage; content that can serve as a definitive resource |
| Microsoft Copilot | Enterprise default | RAG via Bing index + training data | Bing rankings, authority signals, structured data | Bing Webmaster Tools indexation; same structured content and schema principles as Google |
AI SEO vs. traditional SEO: what changes, what stays the same
The good news is that SEO fundamentals have not been invalidated by these new developments. Google’s own Danny Sullivan stated publicly that “Good SEO is good GEO.” The same practices that earn traditional rankings also drive AI visibility. Technical health, authoritative content, quality backlinks, and clear site structure remain essential.
What changes is the success metric. Traditional SEO measures position on a SERP, while AI SEO measures citation share — how often AI systems select your content when constructing answers. Since there is no universal homepage to rank for, the main goal of AI optimizations is citations and mentions.
| Dimension | Traditional SEO | Answer Engine Optimization (AEO) | Generative Engine Optimization (GEO) |
| Goal | Rank on page one of Google SERPs for target keywords | Own the direct answer to a specific single query | Be cited and recommended by AI across multi-step conversations |
| Target platform | Google, Bing search results pages (blue links) | Google AI Overviews, featured snippets, voice assistants | ChatGPT, Perplexity, Gemini, Claude, Copilot |
| Success metric | Keyword rankings, organic traffic, CTR | Featured snippet ownership, AI Overview inclusion rate | Citation frequency, brand mention share, AI share of voice |
| Content focus | Keyword-optimized pages targeting specific search intent | Concise, direct answers to specific questions; FAQ structure | Comprehensive topical coverage; original data; entity-rich content |
| Key signals | Backlinks, technical health, on-page optimization, Core Web Vitals | Schema markup (FAQPage, Article), E-E-A-T, structured formatting | Brand mentions, topical authority, Information Gain, RAG extractability |
| Query type | Navigational, informational, transactional keyword queries | Question-based, voice-style (“What is…”, “How to…”) | Conversational, multi-step, research and comparison queries |
| Relationship to SEO | The foundation | Built on top of SEO fundamentals | Built on top of SEO fundamentals |
Popular AI Search Terms Explained: GEO, AEO, LLM SEO, and LLMO
As we established, the AI search space has generated a proliferation of acronyms. Here’s what the most popular ones mean:
What is GEO (Generative Engine Optimization)?
GEO is the practice of structuring digital content and managing online presence to improve visibility in responses generated by AI systems. According to Wikipedia’s definition, it influences how LLMs such as ChatGPT, Google Gemini, Claude, and Perplexity retrieve, summarize, and present information. Crucially, GEO is focused on multi-step conversational queries, as in how AI models talk about your brand across extended conversations, not single-query answers.
What is AEO (Answer Engine Optimization)?
Answer Engine Optimization focuses on owning direct, single-query answers — earning the featured snippet, the Google AI Overview inclusion, or the voice assistant response. AEO is built around the assumption that users want immediate, concise answers rather than a list of websites. It requires clear formatting, scannable structure, and strong authority signals, especially for YMYL (Your Money, Your Life) topics.
What is LLM SEO or LLMO?
LLM SEO, also called LLMO, is the broadest umbrella term in the stack and, in ways, a synonym of AI SEO. These include the practice of optimizing your website to be cited, referenced, and recommended by AI tools like ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot. The keyword “LLM SEO” grew +23% month over month in early 2026 as businesses began gaining interest in such services that allow them to track their presence inside AI-generated answers rather than just blue links.
GEO vs. AEO vs. LLM SEO: Are they any different?
In practice, the execution is largely the same. All three approaches share the same core tactics: structured content, clear answers to specific questions, strong authority signals, and schema markup. The differences are primarily in scope: AEO targets single answers, GEO targets brand-level conversational presence, and LLM SEO covers all AI surfaces. For most businesses, treat them as one integrated strategy rather than three separate initiatives.
The 7 Core Principles of AI-Driven Search Engine Optimization
Seven principles define what it takes to earn visibility across both traditional and AI-powered search in 2026.
Principle 1: Topical authority over keyword targeting
AI systems assess whether a source deeply understands an entire subject. Building topical authority means creating one comprehensive pillar page per topic, supported by 5–10 cluster articles that cover sub-topics in depth, all interlinked. This hub-and-spoke architecture signals to both Google and LLMs that you are the source on a subject. The clearer and more interconnected your topic universe, the more likely you are to be recognized as an authority.
Principle 2: Entity-first content architecture
AI models understand the world through entities — people, places, brands, concepts, products — and the relationships between them. Google’s Knowledge Graph evaluates entity associations and semantic relationships. This means your content should explicitly identify and connect the entities relevant to your topic using structured data (schema markup), consistent naming, and comprehensive coverage of related sub-entities.
Principle 3: E-E-A-T as the trust foundation
Experience, Expertise, Authoritativeness, and Trustworthiness remain the quality framework Google uses to evaluate content, and what works for Google works for AI search engines. Since the 2025 Quality Rater Guidelines update, Google emphasizes the “Experience” component even more strongly, whether the author truly has first-hand experience. In practice: list qualified authors with verified credentials, add personal insights and original examples, and use Schema.org Person markup on author profiles.
Principle 4: Semantic SEO and natural language content
Search engines no longer match keyword strings — they evaluate meaning, context, and intent. Semantic SEO means organizing content around complete topic clusters with rich entity coverage, targeting intent clusters rather than individual keywords, and writing naturally so that the full context of a subject is covered. Content should address the full query spectrum: informational, navigational, and transactional variants of the same topic.
Principle 5: Information Gain: say something AI can’t generate itself
This is the most important principle for citation eligibility. AI systems are trained on vast amounts of existing content. They don’t need to cite sources for information they already know. AI only cites content that provides some form of information gain: proprietary data from original surveys, unique frameworks with named methodologies, expert quotes that can’t be reproduced, and first-party benchmarks. Generic AI-regurgitated content is invisible to AI citation systems for exactly this reason.
Principle 6: Structured extractability (the Island Test)
Retrieval-Augmented Generation (RAG) systems break your content into chunks of roughly 200–500 words, convert each into a mathematical vector, and match those vectors against user queries. Self-contained paragraphs produce cleaner embeddings and higher citation rates.
The Island Test asks: “If this paragraph were shown alone, would a reader understand it completely?” If not, the paragraph is too context-dependent. Additionally, research shows that pages using 120–180 words between headings receive 70% more ChatGPT citations than pages with sections under 50 words.
Principle 7: Brand signals as a ranking factor
One of the most counterintuitive findings from 2025 research on AI SEO shows that branded web mentions have a 0.664 correlation with AI Overview appearances. This is far higher than backlinks at 0.218.
AI systems learn about your brand from the aggregated signal of how third-party sources talk about you online. This makes off-site brand building through PR, thought leadership, community presence, and review generation a direct input into AI search visibility.
How AI Search Works: RAG, Chunking, and Why It Changes Everything
What is Retrieval-Augmented Generation (RAG)?
Most modern AI search tools use a three-stage pipeline called Retrieval-Augmented Generation. RAG enhances LLMs by retrieving relevant documents from the web before generating a response, rather than relying solely on its pre-existing training data.
Here’s how it works in simple terms:
- Stage one is Retrieval: the AI queries a search index for relevant pages.
- The second step is Extraction: retrieved pages are chunked and vectorized.
- The last phase is Synthesis: the LLM generates a response from the highest-matching chunks and attributes sources.
If your page isn’t in the index or doesn’t produce clean, query-matching chunks, you’re invisible regardless of your traditional ranking.
Training data vs. real-time retrieval: what this means for your strategy
Different platforms operate differently. Perplexity is primarily RAG-based — it searches live sources in real time and cites specific pages. ChatGPT uses RAG when web search is enabled, but also draws heavily on training data for common knowledge. Google AI Overviews use a hybrid system. This matters because influencing training data is a long game (it requires sustained authority signals over months), while earning RAG citations from systems like Perplexity can happen within days of publishing new, relevant content.
Query fan-out: how AI expands one question into multiple searches
When a user submits a prompt to ChatGPT, the system often runs 1–3 sub-queries behind the scenes — this is called query fan-out. A user asking a question, such as “What’s the best CRM for a 50-person B2B team?” will trigger additional background searches for “best CRM software SMB,” “CRM comparison mid-market,” and “top-rated CRM reviews.” Identifying and directly targeting these sub-queries with dedicated pages, well-structured H2 sections, and social content is one of the most actionable tactics in LLM SEO.
Why blocking AI crawlers is silently killing your visibility
Previous online data mentions that 35.7% of the top 1,000 websites block GPTBot, often as an unintentional consequence of legacy robots.txt configurations or overly aggressive WAF rules. These figures are hard to verify and seem highly inflated. There are tools that can show you this rate more accurately, such as the Originality.ai bot blocking dashboard. But you can also look into your robots.txt and hosting settings to check that your site allows: GPTBot (OpenAI), ClaudeBot (Anthropic), PerplexityBot, Google-Extended, and OAI-SearchBot. If you’re blocking any of these, you’re invisible to the AI systems they power. This may be a desired effect for you if you wish to keep your site off of AI quotations, but you’d be losing out on exposure.
How to Build an AI SEO Strategy: Step-by-Step Implementation
As an AI SEO agency, we’ve created a baseline LLM visibility strategy that we apply to all of our clients. Here is a step-by-step outline of our general process that you can use to inspire your own AI optimization workflow:
Step 1: Audit your AI visibility baseline
Before optimizing, measure where you stand. Manually run your 10–20 most important target queries across ChatGPT, Perplexity, Google AI Overviews, and Gemini. Record which sources are cited and whether your brand appears. Simultaneously, check your robots.txt for AI crawler restrictions and run a technical crawl to identify indexation gaps. Tools like Semrush AI Visibility Toolkit and Ahrefs Brand Radar can partially automate this, but manual prompt testing remains the most reliable method.
Step 2: Conduct AI-era keyword and intent research
Shift your keyword research from volume-based targeting to intent-cluster mapping. For this, you must look into query fan-out in more detail to fully understand how AI tools “think”.
The basics of this approach are that instead of targeting a single keyword phrase, such as “project management software,” for instance, you must map the full constellation of questions a buyer asks: “How do I manage a distributed team?”, “What’s the difference between Asana and Monday?”, “Best project management tools for agencies under 50 people?” Use AI tools (ChatGPT, Claude) to identify the underlying intent layers and conversational sub-queries around your core topics.
Step 3: Build topic clusters and pillar content
The topical authority content strategy is a must for any website trying to establish a niche presence on the web. Create one authoritative pillar page per core topic, a comprehensive resource that covers the subject from all angles. Then build 5–10 supporting cluster articles that go deep on each sub-topic. Every cluster article should link back to the pillar page with relevant anchor text, and the pillar page should link out to each cluster. This creates a semantic web that both search engines and AI systems can parse to confirm your topical authority.
Step 4: Optimize content structure for AI extractability
Structure each article with the following in mind: open with a direct, 2–3 sentence answer to the core question (within the first 150 words) to maximize featured snippet and AI Overview eligibility. Use question-based H2 and H3 headings. Write in short, self-contained paragraphs of 120–180 words each. Include a dedicated FAQ section at the end. Use tables for comparisons and bullet lists for enumerable items because AI systems extract these formats cleanly.
Step 5: Implement schema markup and structured data
Schema markup acts as a metaphoric nutrition label for AI systems, explicitly defining what your content means and connecting it to known entities in the Knowledge Graph. Priority schema types for AI SEO: FAQPage (especially effective for long-form content), Article with author attribution, Organization with consistent NAP data, and Person markup on author profiles. FAQPage schema, in particular, is heavily weighted in the AI Overviews selection.
Step 6: Strengthen E-E-A-T and brand authority signals
Every article should have a named author with a structured bio that includes credentials, professional background, and links to verifiable external profiles. Add first-person experience signals with real-world examples, original observations, and case-specific insights that a generalist writer couldn’t fabricate. Maintain consistent brand facts across your website, Wikipedia page, industry directories, and social profiles to build what practitioners call your entity home — the canonical record AI systems draw on to understand who you are.
Step 7: Build off-site citations and brand presence
Search Everywhere Optimization is the 2026 framework for off-site visibility. YouTube is the single most cited domain in Google AI Overviews, accounting for nearly 30% of all citations. Every piece of content you publish should also become a video. Beyond YouTube: get your brand featured in authoritative industry roundups (TechCrunch, relevant trade publications), build a presence in Reddit communities your audience uses, pursue PR placements that generate branded mentions, and distribute press releases to build the external signal layer that AI systems use to validate your authority.
Step 8: Add an llms.txt file to your site
The llms.txt file is an emerging standard, essentially a handshake with AI crawlers. It tells AI systems who you are, what topics you cover, and where your most important content lives. Think of it as robots.txt for the AI web. Every credible website in 2026 should have one at yourdomain.com/llms.txt. It won’t guarantee citation, but it removes friction for AI systems trying to understand your site’s structure and expertise.
Step 9: Monitor, measure, and iterate
Establish a monthly cadence of citation audits: run 20–30 target queries across ChatGPT, Perplexity, Gemini, and Google AI Overviews. Track which sources are cited, how often your brand appears, and where competitors outflank you.
In Google Analytics 4, segment referral traffic to identify AI-sourced visits. Track branded search volume as a proxy for AI-driven brand awareness. Improvement in citation frequency over 8–12 weeks indicates your strategy is compounding.
Using AI Tools to Power Your SEO Workflow
AI for keyword research and topic discovery
AI-assisted keyword tools now go far beyond search volume. They identify intent clusters, surface semantic gaps in your existing content, and map topic relationships at scale.
Platforms like Semrush, Ahrefs, Surfer, and MarketMuse can map how topics relate to each other and flag semantic gaps in your existing content. More importantly, they can identify topics that are gaining search momentum before they become competitive. The most valuable use is predictive: AI analyzes historical demand patterns and suggests topics likely to gain traction before they peak, letting you build topical authority in advance of competitive interest.
AI for technical SEO
Large sites accumulate technical problems faster than manual audits can catch them. AI-powered crawling surfaces issues like keyword cannibalization, crawl budget waste, broken internal link chains, and unrendered JavaScript across thousands of pages in one pass. Tools like Alli AI can apply schema markup rules and internal linking improvements across thousands of pages automatically, something that’s impractical to do manually at scale.
AI for backlink analysis and link building
AI tools accelerate both sides of link building: they analyze competitor backlink profiles to surface patterns in anchor text and content formats that attract links, and they predict which sites are most likely to respond positively to outreach by training on historical link-acquisition data. This turns what was a largely intuitive process into a data-driven one.
How AI Is Changing Search Behavior and What It Means for Site Traffic
The zero-click reality
The data is unambiguous: organic CTR drops 61% when AI Overviews appear for a query (from 1.76% to 0.61%). Individual websites experience an average 34.5% CTR reduction when AI Overviews appear for their target keywords.
Search behavior is fracturing as users increasingly turn to AI platforms for quick informational answers and to traditional search for deeper research and commercial evaluation. This means purely informational content is losing traffic, while transactional and research-intensive content holds its value.
Why AI-referred traffic is more valuable than it looks
Despite the volume compression, AI-referred traffic converts at 23x the rate of traditional organic. Only 1% of users click on AI Overview citations, but those who do are deeply qualified leads.
A Growth Memo study from April 2026 found that 88% of AI Mode users accepted the AI’s shortlist without external verification, and the AI’s top pick became the user’s top pick 74% of the time. Being in that answer is high-intent brand exposure at the moment of decision.
What content types do AI systems cite most?
The listicle strategy has so far been one of the most impactful content types that AI answers reference.
Research from Ahrefs found that “ best of” listicles account for 43.8% of all content types cited in ChatGPT responses. Wikipedia is cited 29.7% of the time, homepages and landing pages 23.8%, and educational pages 19.4%.
Listicles are far from the only format worth your attention, though. A BuzzStream study analysing over 4 million AI citations found that blog and editorial content as a whole accounts for more than half of all citations, with several content types pulling significant weight:
- Comparison and alternative content is the most cited blog subtype overall, particularly for bottom-of-funnel queries where someone is weighing options.
- Data-driven articles, market analysis, and pieces with new insights and statistics give AI something substantive to synthesise and reference as a source.
- Definition and explainer content are exactly what AI reaches for when answering informational, top-of-funnel questions.
- News and editorial coverage, earned media from third-party publications, account for roughly 14% of citations across the board.
- Product and About pages are used as a reference for brand-specific queries. AI pulls directly from your own site around 35% of the time, making owned pages more important than most realise.
- Social content from platforms such as Reddit, YouTube, and LinkedIn all surface in citations, with LinkedIn being the standout for brand awareness queries.
As for content length, articles over 2,900 words average 5.1 citations from ChatGPT, while those under 800 words average only 3.2. Pages with factual density also win: the average AI Overview-cited article covers 62% more facts than the typical non-cited one.
The new AI SEO KPIs to keep track of
Traditional ranking reports are becoming insufficient on their own. The forward-looking measurement stack includes:
Ranking reports don’t capture what AI systems are doing with your content. Add these to your measurement stack:
- LLM citation frequency, which you can manually audit monthly across ChatGPT, Gemini, Perplexity, and others. There are also new AI mentions tracking tools that allow you to monitor whether you’re cited, how, and against which competitors.
- AI share of voice. Are you the brand AI recommends, or just one it mentions?
- AI-referred traffic in GA4, which can be segmented with custom explorations. Segmenting out the referring sources is your clearest signal that citations are actually converting to visits.
- Branded search volume gives a downstream proxy for AI-driven awareness. If AI keeps naming you, people start searching for you directly.
- Featured snippet ownership is still a strong predictor of AI Overview eligibility.
- AI Overview impressions and clicks are now reported separately in Google Search Console for users to track.
8 Common Questions About AI SEO Answered
Is traditional SEO dead?
No. SEO is the foundation on which AI visibility is built. 76.1% of URLs cited in AI Overviews also rank in Google’s top ten. Technical SEO, quality backlinks, and authoritative content remain prerequisites for overall ranking success.
The difference is that page-one rankings are no longer sufficient on their own. You also need to be structured and authoritative enough for AI systems to extract and synthesize your content into their answers.
Does AI-generated content rank well?
Not consistently at the top. Semrush’s analysis of 42,000 blog posts found purely AI-generated content at position 1 only 9% of the time vs. 80% for human-written. The issue isn’t that AI was involved, but that raw AI output lacks the originality, first-hand experience, and proprietary insight that both Google and LLMs reward. Human-led AI-assisted content, where AI handles structure and drafting but a human expert adds a unique perspective, performs significantly better.
How long does it take to see results from AI SEO?
It depends on the AI platform. RAG-based systems like Perplexity can start citing newly published content within days of indexing, if the content is high quality and directly answers a query. AI Overviews should be able to fetch any source that is indexed and doesn’t block Google’s crawlers. Building consistent citation authority across multiple AI platforms takes 3–6 months of sustained effort. Think of it as a compounding investment: each citation earned increases the likelihood of the next.
Which AI search platform should I prioritize?
For most businesses, the general recommendation is to start with Google’s AI Overviews, which has the broadest reach. Then target Perplexity, which is the easiest to influence quickly via RAG and is strong with research-intent users. Alongside that, you must keep ChatGPT in mind, which is the largest overall AI brand for discovery.
The good news is that the core tactics (using structured content, entity clarity, and strong authority signals) work across all platforms simultaneously. You don’t need a different strategy per engine, and a lot of these practices already overlap with what good SEO is all about.
What schema markup matters most for AI SEO?
FAQPage schema is the highest priority for most content sites. It explicitly signals the Q&A structure that AI Overviews are designed to surface. Article schema with author attribution strengthens E-E-A-T signals. Organization and Person schema connects your brand and team to the Knowledge Graph.
For e-commerce, Product schema with reviews and pricing is essential. A practical approach is to audit your competitors’ schema implementation in your niche and prioritize the types they use most.
Can a small site compete against large domains in AI search?
Yes, and this is one of AI search’s genuine opportunities for smaller publishers. AI citation systems favor depth and specificity over domain size for niche queries. Large generalist sites dominate broad topics, but for highly specific questions, a focused niche site with genuinely comprehensive, expert content can outperform a domain 100x its size. The long tail of AI search belongs to specialists.
How do I know if AI is citing my content right now?
Manual testing is the most reliable method. Run your 20 most important queries in ChatGPT, Perplexity, Gemini, and Google AI Overviews. Document which sources appear, for which prompts, and at what frequency.
For ongoing monitoring, tools like Semrush AI Visibility Toolkit, Ahrefs Brand Radar, Wellows, and Profound track citation frequency and brand mentions inside AI-generated answers. Google Search Console also separates AI Overview impressions from traditional organic results, which is a useful free baseline.
Will AI Overviews decrease my website’s traffic?
For purely informational queries, yes, some traffic decline is expected. However, 40% of AI Overview citations come from pages ranking below position 10. This means AI can surface your content to audiences who never would have found you through traditional rankings.
For commercial, branded, complex, or research-intensive queries, AI continues to direct users to websites for deeper information. The goal is to be cited in the answer and serve as the source users click through to when they want more.
The Future of AI SEO: What’s Coming Next
Agentic AI search and multi-step query behavior
Google AI Mode is rapidly evolving from an opt-in experiment toward a default search experience, adding deeper research capabilities, agentic actions, and personalization powered by Gemini. This signals a future of longer, more complex search interactions where users ask multi-step questions expecting comprehensive, synthesized answers. Content depth and completeness become even more critical as AI agents need to pull comprehensive knowledge from a single trusted source to complete complex research tasks.
Multimodal search is already here
As we already mentioned, YouTube is the single most cited domain in Google’s AI Overviews, which isn’t coincidental — it reflects Google’s ability to extract structured information from video transcripts and VideoObject schema. Every piece of expert content you publish as text should also be produced as a video for maximum reach. Future-proof optimization means treating text, video, audio, and structured data as parts of a single, interconnected content system.
AI search traffic is projected to surpass traditional search by 2028
Semrush’s AI Search Study projects that AI search visitors will surpass traditional search visitors by 2028. With referral visits from AI platforms increasing 357% year-over-year as of mid-2025, the compounding effect is already visible. Businesses building their AI citation strategy today are establishing authority in a channel that will become the dominant discovery layer within two years.
Final Word
Make no mistake: AI SEO is here to stay, but it’s not a replacement for traditional SEO. The path forward is a unified strategy: technical hygiene, real expertise, genuine authority: these were always the price of admission. Now they’re also what gets you cited by systems that answer questions before anyone ever clicks a link.
Search was never really one thing; it was always wherever people went to ask. That place keeps changing. Show up there.
Need help with AI search optimization? Follow these AEO experts.
