Artificial intelligence has completely reshaped the digital landscape. In previous blogs we’ve written about AI’s effect on content strategies to get organic search results and specifically, the effect of Google’s “AI Overviews” on written content marketing. But we digital marketers are a tough bunch and know we must adapt our strategies and look for new opportunities to grow our client’s visibility and competitiveness. Enter Generative Engine Optimization (GEO), a new frontier that parallels but also diverges sharply from Search Engine Optimization (SEO).
While both GEO and SEO aim to increase visibility and engagement, the rise of AI-powered tools like ChatGPT, Google’s Search Generative Experience (SGE), and Perplexity have fundamentally changed how information is found and delivered. But before we get ahead of ourselves, let’s get back to the basics:
What is SEO?
Search Engine Optimization (SEO) is the practice of improving a website’s content ranking in traditional search engine results pages (SERPs), like Google, Bing, or DuckDuckGo. SEO focuses on organic search visibility through tactics such as:
- Keyword optimization
- Backlink building
- Technical improvements (e.g., site speed, mobile responsiveness)
- Content creation based on search intent
- Metadata and structured data (schema markup)
SEO aims to align content with how search engines crawl, index, and rank pages based on user queries.
What is GEO?
Generative Engine Optimization (GEO) is the practice of optimizing content for AI-driven generative search engines and assistants. Instead of ranking on a list of links, GEO targets visibility within the AI-generated answers from platforms like:
- Google’s Search Generative Experience (SGE)
- ChatGPT with browsing or plugins
- Perplexity AI
- You.com
These systems synthesize multiple sources into a cohesive response — meaning the traditional concept of a “ranking” is replaced by presence and citation in the AI’s generated output.
Key Differences Between SEO and GEO
Search Environment
- SEO targets Traditional Search Engine Result Pages (SERPs) with links to content on webpages.
- GEO targets AI-generated summaries and citations.
Ranking Focus
- SEO focuses on the top 10 organic positions on SERPs.
- GEO focuses on inclusion or citation in AI generated responses.
Optimization Method
- SEO utilizes Keywords, backlinks, and technical SEO.
- GEO utilizes clear, authoritative content structured for NLP parsing.
User Intent
- SEO focuses on specific queries, often transactional or navigational.
- GEO focuses on conversational, complex, or multi-intent queries.
Discovery Model
- SEO targets crawl-and-rank algorithms.
- GEO targets LLM (Large Language Model) content synthesis and summarization.
Measurement
- SEO focuses on click-through rate, bounce rate, and page ranking.
- GEO focuses on citation frequency, AI source mentions, brand visibility in answers.
Similarities Between SEO and GEO
Despite these differences, SEO and GEO share some foundational goals and overlapping strategies:
- Content Quality Matters: Whether it’s Google’s crawler or ChatGPT’s training data, content that is clear, well-structured, and useful tends to perform better.
- E-A-T (Expertise, Authoritativeness, Trustworthiness): Both models reward content that demonstrates expertise, backed by reputable sources and accurate data.
- Structured Data: Schema markup and well-organized headings help both search engines and AI models understand and extract relevant content.
- User-Centric Focus: Understanding and matching user intent remains a core goal in both approaches.
GEO Marketing Strategies
As generative AI continues to shape the way users interact with information, marketers need to consider how to optimize for AI-generated search. Here are several strategies to effectively implement Generative Engine Optimization (GEO):
Publish Authoritative, Fact-Based Content
AI tools tend to favor factual, structured, and well-sourced content. GEO-friendly content should include statistics, dates, references, and citations with links to original research, white papers, or studies. The formatting matters a lot here – clear definitions, bulleted lists, and concise how-to steps which Large Language Models (LLMs) can easily summarize. Of course being hosted on a high-authority domain can help immensely here.
Optimize for Citability, Not Just Ranking
Your content may not need to rank highly in traditional search, but it needs to be citable by the AI engine. This means using clear and consistent language, avoiding clickbait or vague headlines, and including named entities and topical relevance (e.g., “2025 housing trends in Florida”).
Use Structured Content and Clear Hierarchies
AI models rely on patterns and clarity. Help them by using a strong H1/H2 hierarchy. FAQ blocks and TL;DR summaries can often help here as they are often quoted directly in generative outputs. Lastly, make sure you are using Schema.org markup and clean HTML code.
Update Content Frequently and Monitor Mentions
Generative engines reward fresh, current content, especially for fast-changing topics like technology, finance, and health.
Leverage Platforms That Feed AI Models
AI engines often pull data from specific sources including Reddit, Wikipedia, Substack posts, Medium, and high-ranking blogs. Contributing to or syndicating content on these platforms increases your chance of being cited.
Conclusion
The rise of Generative Engine Optimization (GEO) doesn’t replace traditional SEO, it complements and expands it into a new arena. While SEO helps you rank in Google’s SERPs, GEO helps you appear in AI-generated answers where users are increasingly seeking direct, synthesized information.
Marketers who understand both will have the best shot at maintaining visibility in the hybrid search landscape of the future. GEO demands clarity, authority, and structure and by integrating these principles with traditional SEO best practices, brands can thrive across both search ecosystems.
If you need help improving your visibility on the web, don’t hesitate to reach out to our team. We are your tech partner!
