The State of Generative Engine Optimization in 2025

Generative engine optimization (GEO) has emerged as a critical discipline for brands that want to remain discoverable in the age of generative search. Generative engines—large‑language‑model (LLM) systems such as ChatGPT, Gemini, Bing Chat and Perplexity—combine retrieval and generation to synthesize information directly in conversational answers. This shift is already reducing organic traffic from traditional search and moving power from search engines to AI platforms. In response, marketers are learning to optimize content not only for rankings but also for inclusion and citation in AI‑generated answers. This article synthesizes practitioner guidance, recent academic research, and market data to provide chief marketing officers (CMOs) and directors with a strategic overview of GEO. We examine the evolution of search, compare GEO with search engine optimization (SEO), outline core optimization tactics, summarize the first academic framework for GEO, and forecast future developments. Charts and a concise infographic highlight adoption trends and key differences between SEO and GEO.

1 Introduction

Search has always been the gateway to digital content. Traditional search engines such as Google rank pages based on backlinks, keywords and technical signals. Users click on blue links, visit websites and convert. This paradigm is changing quickly. Large language models can now answer complex queries conversationally and cite sources. Generative engines search, retrieve and summarize information from multiple documents, producing an answer that may satisfy the user without any clicks. Researchers formalize these systems as generative engines (GEs). GEs are rapidly replacing traditional search engines for many tasks; ChatGPT had more than 400 million weekly users by February 2025 and new research suggests LLM traffic will overtake Google search by the end of 2027.

For content creators and businesses, this shift poses challenges. GEs are opaque: they cite sources at will, often without sending traffic. Websites can lose visibility even when their information is used in an answer. To give creators tools to adapt, academics introduced Generative Engine Optimization (GEO), a framework for optimizing content to improve its visibility in generative responses. Practitioners have embraced GEO as the next evolution of SEO, and agencies now offer GEO audits and strategies. This paper reviews both the practitioner perspective and the underlying research.

2 Evolution of search and the rise of generative engines

2.1 Shifting search behaviour

Generative engines are already impacting search traffic. Walker Sands reports that ChatGPT alone surpassed Bing in daily visitors in 2024, handling more than 10 million queries per day. Backlinko notes that ChatGPT reached 100 million users faster than any app in history and had over 400 million weekly users by February 2025. Google’s AI Overviews now appear in billions of searches every month, showing up in at least 13 % of all search result pages. Gartner predicts that AI‑driven search could cause a 50 % drop in traditional organic traffic by 2028, and 30 % of browsing sessions may be screenless (voice‑based or AI‑driven) by 2026.

The chart below illustrates a conceptual projection of how generative‑AI search may overtake traditional search. While the numbers are illustrative, they reflect the trend identified by Semrush research that predicts LLM traffic will surpass Google search by 2027.

2.2 Impact on publishers and brands

The adoption of generative AI tools is widespread. EducationDynamics reports that roughly 70 % of modern learners use AI tools such as ChatGPT and 37 % use them specifically to research colleges or universities. An ePublishing analysis found that 85 % of news organizations were using or experimenting with generative AI in 2025 and that 63 % of users trust AI‑generated content when the source is credible. Adoption of AI‑powered search is therefore not a futuristic concept but a present reality. At the same time, GEs reduce direct clicks: zero‑click searches, where users find answers without visiting a website, are becoming more common. GEO is thus a response to both the opportunities and risks of generative search.

The following bar chart summarizes several adoption and trust metrics relevant to GEO.

3 Understanding generative engine optimization

3.1 Definition and rationale

Multiple sources define GEO in similar terms: it is the practice of optimizing content for AI‑driven generative models so that a brand’s message is accurately represented and cited in generative responses. Walker Sands describes GEO as optimizing content and website structure for AI‑driven models to ensure the brand’s message is represented. Backlinko defines GEO as creating and optimizing content so that it appears in AI‑generated answers, including publishing content where AI tools can find it, earning brand mentions and ensuring technical accessibility. Kontent.ai notes that GEO focuses on structuring content so that large language models can reuse it accurately and cites the practice as a logical extension of structured content strategies. EducationDynamics emphasises that GEO leverages machine‑learning algorithms to analyse user intent, generate personalized content and optimize websites for improved rankings.

At the academic level, researchers propose GEO as a formal optimization framework for generative engines. The KDD ’24 paper “GEO: Generative Engine Optimization” argues that generative engines pose a challenge for content creators because they synthesize information from multiple sources and deliver it without necessarily sending traffic. To give creators control, the authors introduce a flexible black‑box optimization framework that can boost visibility by up to 40 % in generative responses. Their approach defines new visibility metrics tailored to generative engines and introduces GEO‑bench, a benchmark of 10 000 queries across multiple domains . These contributions provide the first systematic evaluation of optimization strategies for generative search.

3.2 How generative engines work

Generative engines combine retrieval with LLM‑based summarization. A generative engine retrieves documents via a search engine, uses a query‑reformulation model to generate search queries and a summarization model to condense each retrieved document; a response‑generation model then synthesizes the summaries and embeds citations. Citations and attribution are crucial, as they help reduce hallucinations and allow users to verify information. Unlike traditional search results, generative responses are structured narratives with inline citations, making visibility metrics more nuanced. The authors propose metrics such as normalized word count per citation to measure how prominently a source is cited.

4 GEO vs. SEO: similarities and differences

SEO and GEO share a common goal—making useful information discoverable—but they differ in audience, discovery mechanics, consumption patterns and optimization tactics. In traditional SEO, the audience is human searchers; discovery relies on rankings, backlinks and keyword signals; content is consumed by reading or skimming; optimization focuses on metadata, keywords and technical factors; and metrics include traffic, click‑through rate (CTR) and rankings . GEO, by contrast, targets AI models; discovery is driven by semantic structure, contextual relevance and modular content; content is reused or synthesized by AI; optimization involves prompt testing, schema markup and readability; and metrics include mentions in AI responses, accurate citations and inclusion in generative answers . ePublishing summarizes this distinction succinctly: “SEO is about getting found; GEO is about getting featured” and GEO “helps your content get cited in AI‑generated answers”.

The table below visualizes these differences.

4.1 Overlap and complementarity

GEO does not replace SEO; it builds on it. Walker Sands notes that GEO complements SEO by ensuring visibility across both traditional search engines and AI platforms. Backlinko likewise emphasises that good GEO is generally good SEO: high‑quality content, crawlable websites, authority and trust signals remain critical. EducationDynamics stresses that SEO remains essential for discoverability, but GEO adds personalization and automation to deepen engagemen. Together, SEO and GEO provide a holistic framework to “own the full user journey—from query to answer”.

5 Core principles and strategies for effective GEO

5.1 Research & analysis

Walker Sands lists research and analysis as the first key ingredient of GEO. Marketers should identify long‑tail keywords and representative prompts, analyse AI‑generated responses to understand key terms, assess competitor strategies and evaluate a brand’s share of voice in generative engines. Backlinko’s step‑1—“nail the basics of SEO”—echoes this guidance, highlighting crawlability, mobile‑friendliness and server‑side rendering.

5.2 Content strategy and structure

Content needs to be concise, authoritative and structured. Walker Sands recommends concise introductions, authoritative links, schema markup, author pages and updated content to maintain freshness. Kontent.ai emphasises modular, structured content—FAQs, how‑to steps, tables, summaries and pros/cons lists—that can be reused by AI models. EducationDynamics notes that GEO tools generate original content tailored to user intent, refine readability and integrate keywords, while also personalizing content based on demographics and past interactions. Clear, specific language and self‑contained sections make content easier for AI systems to lift and cite.

5.3 Content distribution & brand authority

Generating high‑quality content isn’t enough; it must also appear where AI models look. Backlinko advises building mentions and co‑citations—participating in industry round‑ups, surveys and community discussions to ensure the brand is mentioned alongside relevant competitors and topics. Going multi‑platform is essential: AI tools draw from forums, social media, YouTube and podcasts, so brands should maintain a presence on Reddit, LinkedIn, YouTube and other channels. Walker Sands similarly highlights content distribution in online communities and social platforms, along with building brand authority through backlinks, public relations and influencer mentions ePublishing recommends citing sources, including expert quotes and up‑to‑date statistics, summarizing complex stories with TL;DR boxes, mentioning trending entities and repurposing user‑generated content to increase inclusion in AI results.

5.4 Technical and measurement considerations

Technical hygiene remains a foundation. Walker Sands points to optimizing page tags, improving page speed and addressing crawl issues to ensure content is accessible to generative engines. Backlinko highlights the importance of HTTPS and server‑side rendering, and suggests demonstrating Experience, Expertise, Authority and Trustworthiness (E‑E‑A‑T) through real results, expert authorship and citations. Schema markup helps search engines and AI tools understand page structure, and ePublishing recommends using article, FAQ and How‑to schema to enhance visibility . Because AI traffic is harder to measure, brands should track inclusion in generative responses, AI‑driven referrals and brand mentions in LLM summaries.

6 Academic research: the GEO framework and benchmark

The KDD ’24 paper formalizes GEO as a black‑box optimization problem. The authors argue that generative engines disadvantage website owners because they synthesize information and deliver it without clicks. GEO provides content creators with a framework to maximise visibility by tailoring presentation, text style and content. Key contributions include:

• Definition of generative engines – Generative engines use search to retrieve documents and multiple LLMs to reformulate queries, summarize sources and generate responses with citations
• Novel visibility metrics – The authors propose impression metrics such as normalized word count of sentences related to a citation to measure how prominently a source appears in a generative response.
• GEO‑bench – A benchmark of 10 000 queries across domains designed to evaluate GEO methods.
• Empirical results – Applying GEO can improve website visibility by up to 40 % across diverse queries and generative engines, with strategies such as adding citations and quotations boosting visibility by over 40 %

This research underscores that GEO is more than a marketing buzzword; it is a formal optimization problem with measurable benefits.

7 Adoption and market data

Market adoption of generative AI and GEO is accelerating. The chart in section 2 summarised key adoption and trust metrics, and the following points elaborate on the underlying data:

• News organizations – 85 % were using or experimenting with generative AI tools in 2025.
• User trust – 63 % of users trust AI‑generated content when the source is credible.
• Education – 70 % of modern learners use AI tools, and 37 % use them for researching colleges.
• Traffic outlook – Gartner forecasts a 50 % reduction in traditional organic traffic by 2028 due to AI‑generated search.
• Screenless browsing – By 2026, 30 % of browsing sessions may be screenless.

These statistics emphasize that GEO is not optional; it is essential for brands seeking to stay visible.

8 Future trends and goals

8.1 Short‑term developments (2025‑2027)

• LLM traffic overtakes search – Semrush projects that traffic from large language models will overtake traditional search by the end of 2027. Brands should expect generative engines to become a primary discovery channel.
• Rise of multi‑modal answers – Google’s AI Overviews and Gemini already include images and charts. Future generative engines will incorporate video, interactive maps and data visualizations. Content must be multi‑modal and structured for easy extraction.
• Improved citations and transparency – Generative engines will refine how they credit sources. Tools like Perplexity already provide clear citations, and future models may offer more granular attribution. Clear author bios and authoritative bylines will matter even more.

8.2 Long‑term trajectory (2028 and beyond)

• Screenless experiences and voice search – With 30 % of sessions expected to be screenless by 2026 voice‑driven AI assistants will handle complex interactions. GEO must adapt to audio‑first presentations, ensuring that content is concise and scannable.
• Personalized generative results – As AI models incorporate user profiles and context, generative answers will become highly personalized. EducationDynamics notes that GEO leverages demographics and past interactions to deliver tailored content Brands will need to manage data privacy while delivering personalized experiences.
• Regulation and ethics – Increased reliance on AI for information raises concerns about fairness, bias and misinformation. Brands should adhere to transparent sourcing, avoid manipulating content and ensure alignment with ethical guidelines. Academic frameworks like GEO‑bench may inform regulators about best practices.

8.3 Strategic goals for CMOs and directors

1. Invest in structured content and schema – Adopt headless CMS platforms and apply schema markup to articles, FAQs and how‑tos. Structured data helps AI models parse and reuse content.
2. Build authority through expertise – Demonstrate E‑E‑A‑T by publishing expert‑authored content, citing credible sources and earning mentions on reputable platforms. Use public relations and influencer engagement to generate co‑citations.
3. Diversify distribution channels – Engage in communities where AI models gather information—Reddit, LinkedIn, YouTube and podcasts. Encourage employees to participate in forums and professional networks.
4. Monitor AI visibility – Implement analytics to track inclusion in AI answers, referrals from AI tools and sentiment of AI‑generated mentions. Tools such as Semrush’s AI SEO toolkit can benchmark LLM visibility.
5. Iterate and test – Use prompt testing to see how AI engines cite your content, adjust structure and language accordingly, and iterate based on feedback.

9 Conclusion

Generative engine optimization is not a buzzword; it is the next frontier of digital discoverability. Generative engines synthesize information from multiple sources and deliver answers directly, reducing clicks and forcing brands to rethink how they deliver value. GEO builds on SEO but shifts the focus from rankings and clicks to visibility and authority in AI‑generated answers. Practitioners emphasise research, structured content, co‑citations, technical hygiene and measurement. Academic research provides formal frameworks and metrics that can increase visibility by up to 40%. Market data shows rapid adoption of AI tools and declining organic traffic, underscoring the urgency of GEO. CMOs and directors should start experimenting now—structure your content, build authority, diversify distribution and monitor AI visibility—to ensure your brand remains part of the conversation in a world where answers are generated.

References Further Reading:

arxiv.org educationdynamics.com. epublishing.com, backlinko.com walkersands.com