AI SEO & GEO: Optimize for Generative Search & LLMs

With the rise of artificial intelligence (AI) and generative tools, website optimization is shifting from keyword-stuffing and static strategies to dynamic, intent-driven experiences. This AI revolution presents significant opportunities, including faster content creation, enhanced personalization, and predictive insights into user behavior. However, it also brings challenges: maintaining content quality, standing out amidst AI-generated noise, and adapting to new search behaviors like AI summaries and Generative Engine Optimization (GEO). The new focus for digital presence is on structured data, brand authority, and delivering human-centered value.

SEO vs. GEO: Understanding the New Divide

While search engine optimization (SEO) and Generative Engine Optimization (GEO) share core optimization principles like content quality, structure, and relevance, they differ significantly in user behavior, interaction style, data sources, and how content is retrieved or presented. Understanding this distinction is crucial for adapting how users find information in the evolving digital landscape.

Here’s a comparison of the core differences:

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This distinction matters because it dictates how you adapt your content and technical SEO strategies to meet users where they are, whether that’s through classic SEO on traditional search results or through AI-powered search engines.

How Search Giants Advise Optimizing for AI

The SEO strategies are evolving to optimize for generative AI and all its tools, yet they share fundamental principles. Both Bing and Google offer insights into how to adapt.

The Importance of Bing

It’s important to consider Bing because AI platforms like Microsoft Copilot and ChatGPT rely on Bing for web browsing, making it a significant source for AI-generated summaries and responses.

Bing’s Strategic Advice

To succeed in the era of AI-powered search engines, marketers must plan strategically. Bing advises conducting regular content audits, maintaining freshness with AI-focused content calendars, and targeting high-intent search queries. Structuring content for AI with Schema markup, clear formatting, and natural language improves discoverability and semantic relevance. Prioritizing authority and accuracy using expert insights, structured formats like FAQs, and conversational elements builds brand trust and enhances brand visibility. Continuous content optimization through A/B testing, performance tracking, and behavioral insights ensures content remains effective, while tools like IndexNow enable quick indexing to keep AI models up to date. You can find more details on Bing’s advice here:Mastering AI-Powered Search.

Google’s Evolving Approach (AI Mode & AI Overviews)

Google has launched its AI Mode recently in the US, with plans for a global rollout soon. This differs from Google AI Overviews, which is currently available in some regions like Switzerland, showing AI-generated responses without direct user interaction.

AI Mode covers more complex search queries, offers deeper exploration via follow-up questions, and includes features for shopping. It provides more advanced reasoning and multimodality in real-time search (such as video search) and deep search capabilities. Soon, you’ll see a new tab for AI Mode appear in Search and in the search bar, with an option to opt out.

Google’s John Mueller, Search Advocate, says in a video: “make sure a search engine can find and understand your content”.

Google’s content advice for succeeding in AI search emphasizes creating unique, people-first content that delivers real value to users, especially for more specific and conversational queries. Ensure your site offers a great page experience across devices, with clear navigation and fast load times. Meet technical requirements so Google AI crawlers can access, crawl, and index your content, and use preview controls (e.g., nosnippet, noindex) to manage brand visibility in AI search results. Keep structured data accurate and consistent with visible content, and enrich your pages with quality images and videos for success in multimodal search.

Recognize that traffic from AI-enhanced results often leads to more engaged visitors. Therefore, measure value beyond just clicks—think conversions, time on site, or brand discovery. Finally, stay adaptable: as user behavior evolves, so does Search. Continuously align your content strategy with changing needs to remain visible and valuable in both traditional and AI-powered search systems.

Deep Dive: Google’s “Query Fan-Out” Technique

Google uses its Gemini 2.5 AI model, integrated into AI Mode and AI Overviews, for enhanced intelligence and capability. Gemini 2.5 models are “thinking models,” capable of reasoning through their thoughts before responding, resulting in enhanced performance and improved accuracy.

What is Query Fan-Out?

Google’s AI Mode utilizes a “query fan-out” technique. This technology breaks down complex search queries into subtopics and simultaneously issues multiple queries for deeper web exploration and hyper-relevant results. This is a drastic change to classic SEO.

AI Mode uses the query fan-out technique to break down your question into subtopics and issue a multitude of queries simultaneously on your behalf. This enables Search to dive deeper into the web than a traditional search on Google, helping you discover even more of what the web has to offer and find incredible, hyper-relevant content that matches your question.

Query fan-out is an information retrieval technique that expands a single user query into multiple sub-queries to capture different possible user intents, retrieving more diverse, broader results from different sources—including the live web, Knowledge Graph, and specialized data like Google Shopping. This allows for deconstructing complexity, especially beneficial for comparative analyses, multi-criteria decision-making, and questions requiring synthesis from different sources, to provide a comprehensive response.

When a user submits a query in AI Mode, Google’s systems analyze the query using advanced natural language processing to establish user intent, complexity level, and the type of response needed, and if fan-out is necessary. Simple factual queries like “capital of Spain” might not trigger extensive fan-out, while complex queries like “how to optimize website performance” would activate the fan-out process extensively.

By “fanning out” the original query, the system can explore various facets and subtopics simultaneously based on semantic understanding, user behavior patterns, and logical information architecture around the topic, leading to a more complete and contextually rich understanding of the user’s need.

Unlike traditional search where one query returns one set of search results, AI Mode simultaneously retrieves information for all fan-out queries. This happens in parallel, expanding the information pool available for answer synthesis, evaluating the content using Google’s ranking and quality signals, combining information from multiple sources and fan-out queries to create a coherent, comprehensive response that addresses the original query while incorporating relevant supporting information.

How does it Work

Consider a user searching: “Could you suggest Bluetooth headphones with a comfortable over-ear design and long-lasting battery?”

The fan-out technique covers many facets of the original query, anticipating users’ follow-up questions or underlying needs they might have, such as design (over-ear, comfortable), technology (Bluetooth), and performance (long-lasting battery), and even warranty. These are core facets.

The system would also check for related intents, like “charging speed” or “portability”—which a user might implicitly care about. It may draw on synonyms (like “long battery life” and “long-lasting battery”). It doesn’t assume the user is asking just for one product list. Sub-queries might target:

• Product listings (Shopping graph)
• Expert reviews and comparisons (Review pages, editorial content)
• User reviews and experiences (Forums, product reviews)
• Technical specs and features (Official product pages, specification sheets)

All this would end up resulting in sub-queries like:

• A list of recommended products, with reasons why they’re chosen.
• Product packs including specifications with reviews, that when clicked display product knowledge panels showcasing options from vendors to directly buy it, along with related products.
• Summaries of comfort features and battery performance.
• A sidebar with links to the 20 pages from where the information was sourced, also linked from icons showcased at the end of paragraphs.

Adapting Your SEO Strategy for AI Search

Key Implications of Query Fan-Out

For SEO specialists, the query fan-out technique has some key implications for SEO strategies:

• Deeper Intent Understanding: The AI deconstructs the query into its core intents and related sub-questions. SEOs need to move beyond simply optimizing for single keywords and instead focus on understanding the entire user journey and the many questions someone might ask around a topic. This shifts the focus from keyword research to comprehensive user intent.
• Comprehensive Topical Authority: Instead of just ranking for individual pages, content strategies must aim for topical authority. This means covering a subject exhaustively, addressing all relevant sub-queries and facets, and linking them semantically.
• Anticipating Follow-Up Questions: The AI’s ability to engage in conversational search means content should naturally lead users to their next questions and provide those answers, even if they’re not explicitly asked in the initial query.

Embracing an “Answer a Facet” Mentality

Because of the above, you’ll need to embrace an “answer a facet” mentality in your content. For any broad topic you target, you’ll need to brainstorm the sub-questions or angles a user might explore and provide in-depth, focused answers for each subtopic to increase the chances that your content will be chosen to answer one of the AI’s fan-out queries.

Organizing your site content into clear topic clusters becomes even more important since each cluster centers on a broad theme and includes multiple pages or sections addressing specific subtopics—or facets—of that theme. This doesn’t only help users to browse your content but allows you to show comprehensive coverage, reinforcing your topical authority.

Prioritizing Content Criteria for AI Answers

The query fan-out technique used by AI Mode is changing search from a query-focused process to a context-focused one, and the way we optimize (as well as measure) our content must evolve accordingly. A few content-related criteria should be prioritized further when optimizing for AI Answers:

• Increased EEAT Importance: Since the AI is synthesizing information, it relies heavily on trustworthy sources. Content from authors and websites demonstrating strong EEAT (Experience, Expertise, Authoritativeness, Trustworthiness) signals, like credentials, original research, positive user reviews, strong brand recognition, and mentions from other authoritative sites, will be prioritized.
• Unique Insights: Unique, proprietary data, original research, and first-hand experiences that AI cannot easily replicate or find elsewhere become really valuable.
• Scannable and Digestible Formats: Content needs to be structured in a way that AI can easily parse and summarize. This means clear headings, subheadings, bullet points, numbered lists, and concise answers to common user questions. This also helps with Featured snippets and Zero-click results.
• Bigger Role of Brand Mentions: Unlinked brand mentions and positive sentiment across the web are signals to assess trustworthiness and recommend your brand.

Tools to Aid AI Content Optimization

A few SEO tools can help you put these strategies into practice:

• AlsoAsked: Allows you to understand your users with live “People Also Asked” data and intent clustering by Google.
• Keyword Insights: Includes a keyword discovery feature that sources live data from Google Autocomplete, Reddit, and People Also Ask, besides search intent classification and content clustering features.
• Waikay: Lets you identify topic gaps in your content versus competitors, aiding in content optimization.
• InLinks: Provides entity-based analysis of your site, structured data implementation, and internal linking optimization.

Frequently Asked Questions (FAQs)

What’s the difference between SEO and GEO?

SEO (Search Engine Optimization) refers to optimizing content for traditional search engines like Google or Bing. It focuses on improving visibility in ranked search results by targeting keywords, building backlinks, improving technical performance, and ensuring structured content.

GEO (Generative Engine Optimization) is a newer discipline focused on optimizing content for AI search systems powered by large language models (LLMs), like ChatGPT or Google’s Search Generative Experience (SGE). GEO aims to ensure that your content is cited, summarized, or recommended by generative AI tools. It emphasizes prompt/topic coverage rather than keyword matching, conversational context, content structure and clarity for AI synthesis, and authority through mentions/citations rather than backlinks alone.

What are Google AI Mode features?

Google AI Mode offers a range of advanced features:

• Introduction of AI Mode in Search: Rolled out in the U.S., no sign-up needed. Offers advanced reasoning, multimodal understanding, and deeper exploration via follow-up questions. Features a dedicated AI Mode tab in Search and the Google app.
• Upcoming “Deep Search” in AI Mode: Enables expert-level, fully-cited research reports in minutes. Uses hundreds of simultaneous queries and advanced reasoning.
• Live capabilities with Project Astra in Search: Real-time interaction via camera input (multimodal). Users can ask questions about what they see, receiving explanations, suggestions, and relevant links.
• Agentic capabilities from Project Mariner introduced in AI Mode: Automates tasks like purchasing event tickets or making reservations. Analyzes real-time pricing and inventory, handles form filling, and presents personalized options.
• New AI shopping experience: Combines Google Gemini AI and Shopping Graph for product browsing and inspiration. Supports virtual try-ons with user-uploaded images. Enables agentic checkout via Google Pay, with user control. AI-generated shopping results are a key feature.
• Personal context integration in AI Mode: Offers tailored suggestions based on past searches and optionally connected Google apps (starting with Gmail). Examples: customized restaurant and event recommendations based on personal data. User controls data sharing and can opt in or out anytime.
• Custom charts and graphs generation: AI Mode can analyze complex datasets and create interactive visualizations for sports, finance, and other queries.

How to optimize for semantics? How to build brand trust?

Optimizing for semantics and building brand trust in the age of AI-driven search engines involves a multi-faceted approach. As detailed by iLoveSEO, here are tactical steps to develop your SEO strategy for semantic relevance and brand recognition:A Guide to Semantics

1. Defining the ontology domain: Clearly define what your business is about (e.g., industrial cables).
2. Defining the entities: Identify entities related to your ontology domain, such as industries served (mining, telecom, energy providers) and specific products/components
3. Retrieving queries from Google features: Use Google as a tool to retrieve all queries that Google associates with your entity seeds by scraping features like topic filters, People Also Ask, People Also Search for, Image Search tags, and Google Suggests.
4. Performing Named Entity Recognition (NER) analysis: Analyze all queries retrieved from Google’s Search Engine Results Pages (SERPs) to further improve the entity search for the website.
5. Determining a taxonomy based on entity search.
6. Analyzing the targeted audience search journeys: Understand discovery, evaluation, and decision-making phases.
7. Clustering the phases of the search journeys: Implement a carefully crafted internal linking strategy to strengthen topical authority for all ontology domains.
8. Doing embeddings and cosine vicinity analyses: For existing content (and competitors) to identify content gaps.
9. Updating existing content and creating new ones: Use insights from previous steps, considering entity salience and clarity of language (monosemanticity) as key elements, along with expert matter knowledge.
10. Content formats: Consider various content formats to best convey information.

Building brand trust also involves transparency, a human touch in your content, ethical AI practices, community feedback, and team training. Brand mentions play an increasingly important role in AI-based search systems.

Is SEO dead? Will we shift from SEO to GEA? How soon?

The shift from classic SEO to Generative Engine Optimization (GEO) or Generative Engine Advancement (GEA) is already happening. However, SEO is not dead; it remains essential as Generative AI (GAI) tools rely heavily on search engines in their processes. Therefore, top-ranking pages on search engines usually get cited by generative AI tools. For Google AI response, if your website is not accessible to Google AI crawlers, it will not be covered there. The evolution is continuous, and SEO professionals are now focusing on both traditional SEO and GEO principles.

Traffic from AI-enhanced results often leads to more engaged visitors, why?

Traffic from AI-enhanced results often leads to more engaged visitors due to higher intent and relevance:

• Pre-qualified context: AI platforms like Google SGE or Microsoft Copilot summarize content and present your brand as a trusted source within a broader answer. Visitors clicking through are already partially informed and more aligned with what you offer.
• More specific queries → more relevant answers: AI-enhanced search tends to handle long-tail or complex questions better. If your content is featured, it’s usually because it matches nuanced user intent—meaning the user is more likely to find it useful and stay longer.
• Deeper trust upfront: Being selected by AI as part of a summarized or curated response builds immediate credibility. Visitors arrive with more trust and curiosity compared to generic SERP clicks.
• Contextual exposure: AI doesn’t just list links—it explains, compares, and frames your content. That framing often positions your site as a solution, not just an option—resulting in lower bounce rates and higher engagement. This also helps with brand discovery.

What do you mean by half the internet is bots?

When people say “half the internet is bots,” they mean that a large portion of online traffic—often estimated around 40-50%—is generated not by real humans but by automated programs called bots. These bots can be harmless (like search engine crawlers indexing websites) or malicious (like spam bots, scrapers, or fake accounts). This statistic highlights the significant presence of non-human activity across the web.

What metrics do you find most valuable when evaluating digital marketing success?

The AI revolution is underway, and search engine optimization is no exception. Digital agencies need to help you sort through the noise and weak signals to adapt your SEO strategies to the real changes underway.

• Fewer keywords, more intent: With the arrival of Google’s Search Generative Experience (SGE) and AI assistants in search results, Google increasingly values content that clearly addresses user intent. The days of “keyword stuffing” are over; make way for conversational relevance.
• AI, the new arbiter of perceived quality: Automatically generated content is everywhere, but Google only ranks it if it provides real added value. What matters: content structure, readability, completeness, and originality. The algorithm is more sensitive than ever to signs of trust and expertise (EEAT).
• The role of netlinking is evolving: Backlinks aren’t disappearing, but their impact is becoming more nuanced. Google is increasingly relying on semantic network signals, combining themes, reputation, and interaction. Create citable, useful organic content that’s referenced elsewhere than in directories.
• New metrics, new SEO: Click-through rate (CTR) is no longer enough. You need to consider post-click engagement, response clarity, time spent, and implicit satisfaction. AI models analyze how a user interacts with your content to draw conclusions about its true quality. AI Traffic Analytics will become crucial.

The Evolving SEO Landscape

The digital landscape is undergoing a profound transformation, shifting from query-focused to context-focused optimization. This means that while traditional SEO principles remain foundational, the emphasis is now on understanding the deeper user intent behind search queries and providing comprehensive, authoritative answers that AI-powered search engines can easily synthesize. The rise of Generative Engines and AI Summaries necessitates a focus on structured data, content quality, and brand authority, moving beyond simple keyword matching to semantic relevance and comprehensive topical coverage. Zero-click results and AI-generated summaries are changing how users interact with search results, demanding a new approach to content performance.

The path forward for digital presence demands more editorial intelligence, strategic direction, and a long-term vision. SEO professionals must become adept at anticipating user needs, creating content that addresses multiple facets of a topic, and ensuring their brand is recognized as a trustworthy source by AI platforms. Tools like AI Search Grader and AI Traffic Analytics will become increasingly important for understanding content performance in this new environment.

To remain visible and valuable in this evolving SEO landscape, continuous adaptation is key. Embrace the changes, understand the nuances of AI-driven search, and prioritize creating high-quality, user-centric content that stands out in the age of artificial intelligence. Your digital presence depends on your ability to evolve with the Generative Engines.