Is SEO Enough, or Should Businesses Also Focus on GEO?

No. SEO is not enough as a complete search strategy, but GEO should not replace it. SEO remains the foundation, while AEO and GEO help businesses become visible and accurately represented in AI-generated answers.

A recent discussion in the SEO_LLM community asked whether businesses should continue focusing on traditional search engine optimization or also invest in Generative Engine Optimization (GEO). The strongest answer is not either extreme.

Businesses should strengthen the SEO foundations that make their content discoverable, then add a disciplined process for understanding how AI-search systems retrieve, summarize, cite, and describe the brand.

That distinction matters because AI search is not simply another ranking page. A customer may ask ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, or another assistant to compare products, shortlist vendors, explain a category, or recommend a solution. The business may be mentioned, cited as a source, compared with competitors, or omitted entirely. In many cases, the user sees an answer before deciding whether to visit a website.

For marketing teams, the goal is therefore broader than ranking for keywords. It is to become findable, understandable, credible, and correctly represented wherever customers research a decision.

The short answer: SEO is the foundation; AEO and GEO extend it

Google's official guidance says SEO remains relevant for generative AI features because those features use core Search systems and indexed web content to retrieve information [1]. Google also states that, from its perspective, optimizing for generative AI search is still optimizing for the search experience.

This means there is no secret "AI markup" that replaces normal SEO. A page still needs to be accessible, crawlable, indexable, useful, well organized, and worthy of being surfaced. Clear headings, descriptive titles, internal links, authoritative references, strong page experience, and original expertise continue to matter.

The industry uses several overlapping terms:

• SEO: improving discoverability, crawlability, relevance, authority, and visibility in search systems.
• AEO: structuring content so answer engines can identify, extract, and present clear answers to user questions.
• GEO: monitoring and improving brand visibility, citations, and representation in generative AI answers.
• AI-search optimization: a broad umbrella term for the overlapping SEO, AEO, content, entity, and measurement practices used across AI-search experiences.

These terms describe different emphases, not guaranteed separate ranking systems. In particular, GEO should not be presented as a replacement for SEO or as an officially separate Google ranking discipline.

A useful operational question is:

When an AI system answers a customer's question, does it understand our brand, find credible evidence, retrieve relevant pages, and represent us accurately?

Traditional SEO provides familiar signals such as impressions, clicks, rankings, and conversions. AI visibility is more variable. Answers can change with the wording of a prompt, the model, the search surface, the user's location, the date, the retrieved sources, and whether the system searches the web during that interaction.

There is no universal AI-search ranking. Visibility in one assistant does not guarantee visibility in another. A reliable program therefore needs repeated measurement across important prompts, models, locations, languages, and dates rather than a single "do we rank?" check.

Why SEO alone is not a complete business strategy

SEO can be a valuable acquisition channel, but relying on any single channel creates concentration risk. Search demand can change. Algorithms can change. A competitor can publish a better resource. A search result can answer a question without generating a click. A business that depends on one channel has limited control over its future demand.

AI search introduces another change: the customer's research journey can begin in an answer rather than on a results page.

OpenAI describes ChatGPT search as a way to provide timely answers with links to relevant web sources. ChatGPT can search the web based on a question and display source links alongside the response [2]. Google's generative AI features similarly use retrieved web pages to construct responses and link to supporting pages [1].

The commercial implication is not that every AI answer produces a website visit. It is that a brand can influence a decision before the click occurs. If a customer asks for "the best AI search analytics platform for a B2B marketing team," the first competitive battle may be the recommendation itself. A blue-link ranking still matters, but it may happen later in the journey, or not at all.

There is evidence that AI summaries can reduce traditional click behavior. In a Pew Research Center analysis of 68,879 Google searches, pages with an AI summary received clicks on a traditional result in 8% of visits, compared with 15% when no AI summary appeared. A click on a cited link inside the summary occurred in 1% of visits in the study [3].

These results describe Google behavior observed in March 2025 among a panel of 900 U.S. adults. They should not be treated as a universal or permanent click-through benchmark. AI interfaces and user behavior continue to change.

The practical conclusion is to measure more than clicks. Impressions, citations, brand preference, direct demand, qualified sessions, and assisted conversions may all matter. A business should know whether it is being used as a source, how it is described, and whether the visitors who do arrive are qualified.

What AEO adds: make the answer easy to extract

Answer Engine Optimization, or AEO, focuses on the structure and clarity of answers. It is especially relevant when a user asks a direct question and an answer engine must identify the most useful passage from a page.

AEO does not mean writing unnaturally for machines. It means making the answer clear for people and easy for systems to interpret accurately.

A strong answer-oriented section usually follows this pattern:

1. Answer the question directly. Put the short answer near the beginning of the relevant section.
2. Define the terms. Expand acronyms and explain specialist language before relying on it.
3. Explain the reasoning. Follow the direct answer with evidence, examples, and limitations.
4. Organize related questions. Use descriptive headings that reflect real user intent.
5. Make important sections self-contained. A section should remain understandable if extracted without the entire article.
6. Use appropriate formats. Tables, lists, definitions, examples, and step-by-step instructions can clarify complex topics.

For example:

Is SEO still important for AI search? Yes. Google says its generative AI features rely on core Search systems and indexed web content. AEO and GEO should therefore strengthen, not replace, technical SEO, helpful content, and authority-building.

This format serves users who want a quick answer while giving readers the evidence and context needed to make a decision.

What GEO means in practice

GEO is often presented as a collection of tricks for persuading language models to mention a company. That framing is too narrow and can lead to low-quality tactics.

In this article, GEO refers to the practical measurement and content-improvement processes used to increase accurate brand visibility in AI-generated answers. It is not a replacement for SEO, a guaranteed separate ranking system, or a promise that a brand will be cited.

A useful GEO program has five parts.

1. Make the business easy to understand

AI systems need to resolve basic questions about an entity:

• What does the company offer?
• Who is it for?
• Which problem does it solve?
• How is it different from alternatives?
• What evidence supports its claims?
• In which situations is it not the right choice?

The answers should be consistent across the homepage, product pages, documentation, comparison pages, profiles, reviews, and third-party references. Ambiguous positioning creates ambiguous recommendations.

This is also an entity-consistency problem. Keep the official company name, product names, category description, audience, features, pricing, integrations, and limitations consistent across important public sources. Correct outdated or contradictory descriptions where possible.

For example, Anny describes itself as AI search analytics for marketing teams. Its website explains that it tracks brand mentions across AI assistants, identifies cited sources, surfaces competitors, monitors AI crawlers, and captures queries associated with AI answers [7]. This is clearer than describing the product vaguely as an "AI marketing platform" because the category, buyer, and job-to-be-done are explicit.

2. Publish evidence-rich, non-commodity content

A generic article can target a keyword and still contribute little to an AI answer. Google recommends content that offers a unique point of view, first-hand experience, useful organization, and information that is not merely a recycled summary [1].

For AI visibility, strong content usually does several things well:

• It answers the central question early.
• It defines important terms in plain language.
• It separates facts, examples, and recommendations.
• It supports claims with original data or reputable references.
• It includes concrete examples, limitations, and decision criteria.
• It uses headings, tables, and concise passages that can be understood independently.
• It is maintained as the market and product change.

The most distinctive version of this article would include an anonymized observation or mini-case study from Anny's own monitoring data. For example, Anny could show how the same brand is described across several models, which source types are cited for commercial prompts, or how a content update changed citation patterns. Any such claim should include a methodology note and use data that Anny can substantiate.

This is not an argument for writing short content for machines. It is an argument for writing clear content that humans can trust and AI systems can accurately reuse.

3. Earn corroboration beyond the company website

Depending on the platform and query, AI-search systems may use a mixture of editorial, reference, community, video, corporate, and other publicly accessible sources. A brand's own website is important, but it is not the only evidence available to an AI system.

The Reddit discussion reflects a practitioner observation about the variety of sources that may appear in AI answers. It should not be treated as authoritative evidence. Independent research provides stronger support for specific citation patterns. In its analysis of Google AI summaries, Pew found that Wikipedia, YouTube, and Reddit were among the most frequently cited sources in both AI summaries and standard results during the study period [3].

The lesson is not to flood communities with promotional posts. It is to build a credible public footprint. That can include:

• genuinely useful participation in relevant communities;
• accurate company and product profiles;
• independent reviews and customer stories;
• expert commentary in relevant publications;
• original research that others can reference;
• documentation and educational resources that answer real questions.

The standard should be usefulness and accuracy. Manipulative mentions may create noise without creating durable trust.

4. Use structured data as support, not a shortcut

Structured data can help search systems understand what a page contains. It should accurately describe visible page content and should be implemented only where the markup is genuinely applicable [8].

Relevant types may include Organization, SoftwareApplication, Product, Article, BreadcrumbList, FAQPage, or Review, depending on the page and the information actually displayed. Structured data does not guarantee rankings, AI citations, or inclusion in an answer. It supports, not replaces, clear human-readable content, crawlability, and trust.

5. Measure answers, not only rankings

GEO requires a measurement layer. A useful monitoring program should test a stable set of commercially meaningful prompts, such as:

• category questions: "What are the best tools for...?";
• problem questions: "How can a marketing team measure...?";
• comparison questions: "Anny vs. [competitor]";
• audience questions: "What should a B2B SaaS team use for...?";
• implementation questions: "How do I track...?";
• trust questions: "Which vendors are recommended by...?"

For each prompt, record the model, date, location, language, search surface, response, cited domains, cited URLs, competitors mentioned, brand position, sentiment, and whether the answer contains a factual error. Repeat the tests because AI answers are not static rankings.

The metrics must have clear definitions:

• Mention rate: percentage of tracked responses that mention the brand.
• Citation rate: percentage of tracked responses that cite a brand-owned URL.
• Source share: share of all cited sources belonging to the brand.
• Competitor share: percentage of tracked responses mentioning each competitor.
• Accuracy rate: percentage of responses containing no material factual error about the brand.
• Sentiment: a documented classification of how the brand is described.
• Qualified referral rate: AI-attributed visits meeting a defined engagement or conversion threshold.

A mention is not automatically valuable. A citation on a low-intent informational query may matter less than accurate inclusion in a high-intent vendor comparison. Connect visibility metrics to qualified sessions, assisted conversions, demo requests, branded search demand, and sales feedback where possible.

Google announced dedicated Search Console reports for visibility in generative AI features, including impressions, pages, countries, devices, and dates [4]. Bing's AI Performance reporting adds metrics such as citations, cited pages, grounding queries, and citation trends across supported AI experiences [5]. These tools are useful, but they represent only part of the picture. They should be combined with prompt-level monitoring and first-party analytics.

No SEO, AEO, or GEO technique guarantees that a page will rank, appear in an AI summary, or be cited. The objective is to improve eligibility, clarity, relevance, and evidence while measuring actual outcomes.

A practical SEO-plus-AEO-plus-GEO plan

The best starting point is not a separate content factory. It is a focused 90-day program that improves the existing search system.

Days 1-30: establish the baseline

Audit the site's technical fundamentals. Confirm that important pages are crawlable, indexable, mobile-friendly, fast enough for the intended audience, and represented in an accessible HTML experience. Review titles, descriptions, headings, internal links, canonical URLs, structured data where relevant, sitemaps, robots directives, and JavaScript rendering.

Confirm that the important content is available to crawlers and users. Google says pages need to be indexed and eligible to appear with a snippet in Search to be eligible for its generative AI features, but compliance does not guarantee crawling, indexing, or serving [1].

Then define a prompt set based on the questions that influence revenue. Run those prompts across the AI systems that matter to the target audience. Save the full answers rather than only a score. Identify which competitors appear, which domains are cited, which pages are missing, and which claims about the brand are inaccurate.

Days 31-60: improve the source material

Prioritize pages that can answer high-value questions better than existing results. Strengthen product explanations, comparison pages, implementation guides, customer evidence, FAQs, and category education. Add original examples and clearly attributed research.

Make the company's positioning consistent. Use the same category language, audience definitions, product names, capabilities, and limitations across important public pages. Resolve contradictory claims before trying to increase visibility.

Build a citation-worthy content plan. The aim is not to create dozens of pages for every wording variation. Google warns that producing large volumes of pages primarily to manipulate rankings or generative responses can violate its scaled content abuse policy [1]. A smaller set of authoritative resources is usually more durable.

Add visual and accessible support where it helps users. Original product screenshots, diagrams, explanatory video, captions, transcripts, descriptive alt text, and accessible headings can improve the page experience and create additional opportunities for discovery. Important information should not exist only inside an image or video.

Days 61-90: build the feedback loop

Rerun the priority prompts on a schedule. Compare changes in mentions, citations, competitor share, position, sentiment, and factual accuracy. Inspect which pages are cited and whether those pages actually support the claims being made.

Use the results to guide updates. If a competitor is cited for a question about implementation, publish a clearer guide. If a third-party page describes the category more accurately than the company does, improve the company's own explanation and consider whether a legitimate partnership, review, or editorial opportunity exists. If the brand is mentioned with an outdated feature or incorrect limitation, update the source pages and reinforce the correct information across the web.

Review fast-changing AI-search content at least quarterly. Update statistics when the source methodology changes. Update product claims whenever functionality changes. Inspect older citations for factual drift. Redirect or remove obsolete pages instead of allowing outdated content to compete with the current version.

Match content to intent and business outcomes

Visibility is most useful when it connects to the customer journey. The table below pairs each intent with a representative query, the content that serves it, and the signal that shows it is working.

• Informational — query: "What is GEO?" — content: educational guide — success signal: qualified discovery and assisted demand.
• Problem-aware — query: "How do I measure AI mentions?" — content: practical guide or template — success signal: engagement and return visits.
• Solution-aware — query: "Best AI search analytics tools" — content: category or comparison page — success signal: mentions, citations, and demo visits.
• Brand-aware — query: "Anny AI visibility analytics" — content: product and trust pages — success signal: branded visits and conversion.
• Transactional — query: "Anny pricing" — content: pricing and signup page — success signal: signup, demo, or purchase.

This framework prevents the team from optimizing every prompt equally. High-intent visibility usually deserves more attention than a large volume of low-intent mentions.

What businesses should avoid

The fastest way to damage an AI-search strategy is to optimize for mentions instead of trust.

Do not publish thin pages that repeat the same answer with minor keyword changes. Do not make unsupported claims about being "the best." Do not create fake reviews, synthetic community discussions, or irrelevant backlinks. Do not assume that structured data alone will force an AI system to cite a page. Do not treat a single model's response as a permanent ranking.

Do not use generative AI to produce large volumes of unreviewed content. AI assistance is compatible with responsible publishing only when the result meets people-first quality standards, contains accurate information, and adds real value [1].

Also avoid using "GEO" as a reason to ignore fundamentals. If the site cannot be crawled, the offer is unclear, the content is generic, or the evidence is weak, a new label will not solve the underlying problem.

The strategic conclusion

The Reddit debate is right to reject a false choice. SEO, AEO, and GEO overlap because all three depend on useful content, clear information architecture, technical accessibility, relevance, authority, and trust.

They differ in emphasis:

• SEO helps systems discover, understand, and rank useful pages.
• AEO helps systems identify and present direct answers to questions.
• GEO helps teams monitor and improve how AI systems mention, cite, summarize, and represent the brand.

For most businesses, the sensible order is:

1. Build and maintain strong SEO fundamentals.
2. Create original, evidence-rich content for real customer questions.
3. Structure important sections so direct answers are easy to find and understand.
4. Strengthen the brand's presence across credible third-party sources.
5. Monitor AI prompts, citations, competitors, and factual accuracy.
6. Connect visibility signals to qualified demand and revenue.

SEO is not obsolete. GEO is not magic. AEO is not a schema shortcut. Together, these practices form a broader search strategy for a world in which customers use both result pages and AI-generated answers to decide what to trust.

If your team wants to understand how AI systems currently talk about your brand, Anny (anny.dodoxhq.com) can help you monitor mentions across major AI assistants, inspect cited sources, compare competitors, discover the queries that trigger AI research, and identify the pages that need improvement. The objective is not to chase every mention. It is to make your brand the clearest, most credible answer when the right customer asks the right question.

References

1. Optimizing your website for generative AI features on Google Search — developers.google.com/search/docs/fundamentals/ai-optimization-guide
2. Introducing ChatGPT search — openai.com/index/introducing-chatgpt-search/
3. Google users are less likely to click on links when an AI summary appears in the results — pewresearch.org/short-reads/2025/07/22/google-users-are-less-likely-to-click-on-links-when-an-ai-summary-appears-in-the-results/
4. Introducing Search Generative AI performance reports in Search Console — developers.google.com/search/blog/2026/06/gen-ai-performance-reports
5. Introducing AI Performance in Bing Webmaster Tools Public Preview — blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview
6. Search Engine Optimization (SEO) Starter Guide — developers.google.com/search/docs/fundamentals/seo-starter-guide
7. Anny: AI search analytics for marketing teams — anny.dodoxhq.com
8. Introduction to structured data markup in Google Search — developers.google.com/search/docs/appearance/structured-data/intro-structured-data