Generative Engine Optimization (GEO): The Marketer’s Guide to AI-Driven Search

Generative Engine Optimization (GEO) is an emerging marketing strategy for ensuring that your content gets cited by AI-driven search tools. In the new era of AI search, SEO is changing, it’s no longer enough to rank on Google’s SERPs; brands must also be visible to generative models like ChatGPT, Google AI, Perplexity, and others.

In practice, GEO means optimizing your website and content so that when an AI “engine” answers a user’s query, it draws on your content as a source.

In other words, rather than competing only for clicks on search links, GEO targets the way AI models synthesize answers. One description explains that GEO involves making your content easy for large language models to find and incorporate into their responses.

This typically involves using natural, conversational language, authoritative information, and clear structure so that AI “reads” and cites your content when generating answers. As we enter the AI age of search, marketers need to adapt: GEO is about ensuring your brand still reaches users via AI answers, complementing traditional SEO for search listings

Generative Engine Optimization vs Traditional SEO

While GEO and SEO share the goal of increasing visibility, they differ in focus, metrics, and content approach. Traditional SEO is about ranking high in search engines like Google or Bing. It involves optimizing keywords, backlinks, and technical factors so your pages appear on page one. GEO, by contrast, is about being included in AI-generated responses. In GEO, the goal is not a page-rank position but being referenced by AI.

For example, one expert explains that SEO aims to influence page ranking, whereas GEO “allows brands to be cited within [AI] composed responses” (on ChatGPT, Bard, etc.). Similarly, SEO success is usually measured by traffic and conversions, but GEO success is measured by how often your content is used or referenced by the AI model’s answers.

Key differences between SEO and GEO include:

• Objective: SEO focuses on improving a page’s ranking in traditional search results; GEO focuses on making your content appear in AI-generated answers.
• Content Delivery: SEO delivers a ranked list of links; GEO delivers a single synthesized answer. Generative AI provides a direct answer (often with a list or summary of sources), whereas SEO returns multiple websites to click.
• User Experience: SEO relies on the user clicking through to a site; GEO presents information immediately. In a GEO context, users often get an answer with source links included, reducing the need for clicks.
• Measurement: SEO uses metrics like pageviews and conversions; GEO is measured by AI citation frequency. In other words, how often is your site used as an AI answer source?.
• Content Approach: SEO typically targets specific keywords and on-page tactics, while GEO emphasizes depth and authority. For GEO, you create comprehensive, well-structured content (with facts, quotes, and statistics) that an AI can easily parse and trust.

Importantly, GEO doesn’t replace SEO, it extends it. Brands are advised not to abandon SEO but to adapt it for the AI era. In practice, that means continuing to optimize for keywords and links and also structuring content so that AI engines will pick it up. For now, combining strong SEO fundamentals with GEO tactics is the best strategy, ensuring visibility on both human-driven and AI-driven search platforms.

Implementing Generative Engine Optimization (Step-by-Step)

To get started with GEO, marketers can follow these steps, blending traditional SEO skills with new AI-focused tactics:

1. Research AI-oriented queries. Identify the topics and questions your audience is asking AI tools. Use keyword research and user data to find conversational, long-tail queries (e.g. “best X for Y” or “how to solve Z”) that people might ask an AI assistant. Tools like SEMrush’s AI toolkit or Google Trends can surface AI-driven keyword suggestions. Foundation Inc. advises using search tools to see what target audiences ask, then verifying if those questions would be posed to an AI. HubSpot similarly suggests researching related keywords and question clusters before writing content. This ensures you create content that aligns with the queries generative engines are likely to answer.
   
2. Create in-depth, authoritative content. Write comprehensive, high-quality content that fully addresses the identified queries. Your content should demonstrate experience and expertise (E-E-A-T), include relevant facts, and cite authoritative sources. For example, add statistics, quotes from experts, case examples, or scientific findings. An academic GEO study found that including citations, quotations and statistics can boost your site’s visibility in AI answers by up to 40%. Cover topics exhaustively rather than just surface-level keyword matching: detailed blog posts, guides, or comparison articles tend to perform well. For instance, one report showed that comparative “best of” listicles were frequently cited by AI (about 32.5% of AI-cited sources), so well-researched comparison posts are a smart content format. Essentially, think of content that would satisfy a curious AI (and its user) completely – your readers will benefit too.
   
3. Structure for AI readability. Format your content so an AI model can easily parse it. Use clear headings (H1, H2, H3…), bullet or numbered lists, and short paragraphs. Break complex information into digestible chunks. HubSpot advises that AI scanning works best on content using headers, lists, quotes, and simple, scannable language. For example, use bullet points to summarize key data, and include descriptive section headings like chapters in a book. This helps both human readers and AI “readers” to find and extract relevant information. Avoid long, unbroken blocks of text – instead, aim for clarity. Tools like Hemingway App can help you write concise, clear sentences (AI models prefer easy-to-understand text). In short, make your page “AI-friendly” by organizing it logically and keeping sentences straightforward.
   
4. Use schema and structured data. Add structured data (schema markup) to your pages to give context to AI and search engines. Even if users won’t see it, schema tags (like FAQ, HowTo, Product, Article, etc.) help machines understand the purpose of each piece of content. Google’s AI is believed to utilize schema in generating answers. For example, marking up FAQs or product info can make it easier for an AI to identify those answers. HubSpot notes that Google already uses schema for rich results, and generative AI likely does too. Don’t limit yourself – use relevant schema types for your content (schema.org has many). Proper markup ensures key details (like definitions, steps, or specs) are clearly communicated to the AI engine.
   
5. Build authority (E-E-A-T) with PR and linking. Strengthen the trust signals around your content. That means including expert quotes, citing credible sources, and earning media mentions or backlinks. Generative models favor content they recognize as authoritative. For instance, being featured on industry blogs, podcasts, or news articles (digital PR) can increase your brand’s authority cues online. Ensure any data or claims in your content are backed by reliable sources or expert interviews. The GEO study found that adding external references and statistics significantly boosted AI citations. In practice, get industry experts to contribute quotes or write guest posts. Provide original research or data when possible. All of these efforts signal to AI (and users) that your content is trustworthy and should be included in answers.
   
6. Optimize for user intent across query types. Tailor your content to different search intents that AI queries may have. Cover a mix of informational, navigational, and transactional content. For example, create detailed guides and FAQs for informational queries (“What is X?”, “How to do Y?”), comparative or review pages for commercial queries (“Best tool for X?”, “X vs Y”), and clear landing pages for navigational/transactional queries (branded searches, pricing, “Buy X online”). A balanced content strategy ensures that when an AI-driven assistant handles any of these intents, it has relevant pages to pull from. For instance, if your site provides a “Top 10 ” list, it might get cited in a ChatGPT answer about recommendations. Similarly, maintaining up-to-date product pages and support docs helps with brand-specific searches in AI. The key is covering the entire customer journey with well-structured content.
   
7. Monitor performance and iterate. As GEO is new, use whatever tools and analytics you have to track progress. For example, HubSpot offers an AI Search Grader that analyzes how your content might perform in generative answers. Regularly review which pages appear in AI overviews or chat responses. Check your site analytics for any emerging patterns (e.g. increase in referral traffic from “AI”-type queries, if available). Stay informed on evolving best practices: Google’s priorities may shift (currently focusing on E-E-A-T), and new AI search features will emerge. In practice, this means continuing to update and optimize content as AI search develops. Experiment with small changes (like adding a quote or schema) and see if it improves your visibility. By iterating based on feedback and trends, you’ll refine your GEO strategy over time.

Generative Engine Optimization in Business: Examples & Use Cases

To illustrate GEO, consider these scenarios in real business contexts:

• Local & Travel example: Researchers demonstrated GEO with a travel query. Initially, an AI asked “Things to do in NY?” provided a generic answer. After the website about NYC attractions was optimized (adding relevant details and citations), the AI’s response shifted – it started prioritizing the optimized content (e.g. highlighting a New York–style pizza recommendation first). This is shown in the figure below. In other words, the same question led to different AI answers before and after GEO, favoring the improved content.
  
  Figure: Generative AI answers to “Things to do in NY?” before (left) and after (right) optimizing the content. The optimized content (right) caused the AI to prioritize that site’s recommendation (New York pizza) in its answer.
  
• E-commerce product search: In online retail, generative AI can surface products directly. For example, an AI answering “best running shoes for flat feet” might list specific products with links and brief rationales. In a practical test, SEO consultants found that every product listed by a generative search had page content explicitly tied to the query. The AI’s answer included those products along with a short explanation drawn from each product page. This suggests that product descriptions and category pages should be written conversationally, clearly matching the kinds of questions shoppers ask, so the AI will cite them when recommending products.
  
• Content strategy example: Certain content formats naturally perform well in AI answers. In one analysis, comparison listicles (like “Top 10 X” or “X vs Y”) accounted for about 32.5% of all sources cited by a generative AI. In other words, AI models frequently draw on well-structured comparison articles. For marketers, this implies that creating thorough “best-of” guides, reviews, or comparison pages can significantly boost your visibility in AI-driven search results. If you run a SaaS business, for instance, a detailed “Best CRM for [industry]” post is likely to be picked up by AI assistants searching for that topic.
  
• Brand and PR example: GEO also applies to brand awareness. Companies should check how often their brand is mentioned in AI chats. Foundation Inc. found that Lululemon was referenced in only about 24% of AI dialogues relevant to the brand. This low share suggests an opportunity: Lululemon (or any brand) could create more branded content (blog posts, guides, user reviews, etc.) to improve that visibility. The analysis also noted that generative engines respond well to user-generated content – for example, Reddit sentiment about the brand often correlates with how an AI perceives it. In practice, this means companies can use PR, influencer content, or testimonials as part of GEO. Getting quoted in industry articles or participating in interviews can add authoritative content to the web about your brand, making AI engines more likely to cite you

These examples show GEO in action: optimizing a site can literally change what an AI tells users. Whether it’s a local business improving its travel guide, an e-commerce site optimizing product descriptions, or a brand building its online presence, GEO tactics can help content get noticed by AI tools.

The Future of Generative Engine Optimization

The landscape of search is rapidly evolving. AI-driven search features are rolling out now and will only grow. For example, Google’s Search Generative Experience (SGE) – aka “AI Summaries” – launched in 2024. This new feature uses generative AI to answer queries directly on the search page, above the usual links. Early observations show it can dominate the screen: one report noted that in 84% of cases, the generative answer covered more than half the page. Moreover, an SGE study found that nearly 94% of the sources cited in those AI answers were not from the traditional top-10 search results. In practical terms, that means content from beyond the first page can still gain visibility via AI answers. For marketers, this underscores that GEO is not tied to classic rankings – you can be included in AI responses even if you’re not #1 on Google.

Other AI search tools are advancing too. Bing Chat, ChatGPT’s web browsing, Perplexity AI, and voice assistants are increasingly used for answers. As generative models become the new “gatekeepers” of information, content discoverability depends on these platforms. Marketers should view AI as an additional channel: optimizing for GEO makes your content available to voice assistants and chatbots, not just to search engines.

Looking ahead, marketing teams will need to measure and adapt. New tools are emerging to evaluate GEO performance (for instance, HubSpot’s AI Search Grader can assess your content’s AI-readiness). Over time, we expect metrics for GEO will improve. For now, keep monitoring trends: Google still prioritizes helpfulness and expertise, so creating useful, trustworthy content remains key.

Importantly, experts emphasize a balanced approach. SEO isn’t going away; rather, it’s evolving. Marketers are urged to maintain strong SEO fundamentals while learning GEO techniques. In fact, diversifying your strategy is recommended. As one SEO advisor notes, don’t abandon SEO – instead “optimize it for the new way customers are searching” (i.e. include generative queries in your strategy). Embracing GEO now means staying ahead of the curve. The future of search will be AI-driven, and brands that adapt their content for this future will retain discoverability in an AI-centric world.