Executive Summary
Traditional SEO is evolving into Generative Engine Optimization (GEO) as AI systems reshape search behavior. Businesses must optimize for AI-driven platforms like ChatGPT, Perplexity, and Google’s AI Overviews while maintaining traditional search visibility. Success requires structured content, conversational language, proprietary data, and technical optimization that helps AI systems understand and cite your content. The goal is to be referenced in AI responses, not just clicked in search results.
Act II of Search
The digital marketing landscape is undergoing its most significant transformation since the advent of search engines themselves. With Apple’s announcement that AI-native search engines like Perplexity and Claude will be built into Safari, Google’s distribution chokehold is in question. The foundation of the $80 billion+ SEO market just cracked. We’re entering what industry experts call “Act II of search,” where Large Language Models (LLMs) and generative AI platforms are fundamentally reshaping how businesses connect with their audiences.
At Zilker Partners, we’ve been monitoring these shifts closely and helping our clients navigate this new terrain. While traditional SEO remains crucial, a new discipline has emerged: Generative Engine Optimization (GEO). This comprehensive guide represents our thinking on how businesses can maintain and expand their visibility in an AI-driven world.
Key Takeaway: The same things that are important for traditional SERP optimization (quality content, technically sound site and structure, third-party mentions/backlinks) are still relevant. They’re effectively table stakes and still must be done well. Now, on top of that foundation, there are expanded elements to focus on.
How Search Behavior Is Changing in 2025
Search is becoming conversational and AI-driven. Perplexity’s CEO notes queries on AI platforms now average 10-11 words, up from 2-3 on Google. This move to conversational searches means content must adapt, with less keyword stuffing and more natural answers. Users are increasingly turning to AI systems that provide direct answers rather than lists of blue links.
The numbers reveal a dramatic transformation happening right now. According to recent research, 58% of users have already replaced traditional search engines with AI-driven tools for product and service discovery, while 63% of websites report traffic coming from AI search. Most importantly, 64% of customers express readiness to purchase products suggested by AI.
What’s the difference between traditional search and generative search? Intent fulfillment. Traditional SEO is laser-focused on clicks and rankings on search engine results pages (SERPs). GEO optimizes for being cited, referenced, and included in AI-generated responses that directly answer user queries.
Do Traditional SEO Fundamentals Still Matter?
Yes, absolutely. Before diving into new optimization strategies, it’s crucial to understand that traditional SEO fundamentals aren’t going anywhere, at least not yet. According to Google’s guidance, the best practices for SEO remain relevant for AI features in Google Search (such as AI Overviews and AI Mode). There are no additional requirements to appear in AI Overviews or AI Mode, nor are other special optimizations necessary.
The core elements that made content discoverable and valuable for traditional search engines continue to matter in the AI era. Quality content creation remains paramount because AI systems, like traditional search engines, prioritize content that provides real value and answers user questions thoroughly. A technically sound website with proper indexing, clean URLs, fast loading times, and mobile optimization creates the foundation for both traditional and AI visibility.
Authority signals from other websites continue to matter. In the case of AI search, backlinks actually do not play that large of a role, and a brand mention without a backlink will have just as strong an effect. User experience optimization also remains critical. Fast, accessible, well-designed sites perform better across all platforms, including AI systems that increasingly factor user experience into their recommendations.
These elements form the foundation, but they’re no longer sufficient on their own. Success in the AI era requires building upon this foundation with new optimization strategies specifically designed for how LLMs process and cite information.
How Is Zilker Partners Structuring Content for AI Comprehension?
AI systems excel at parsing well-structured content. According to Andreessen Horowitz, traditional SEO rewards precision and repetition; generative engines prioritize content that is well-organized, easy to parse, and dense with meaning (not just keywords). Phrases like “in summary” or bullet-point formatting help LLMs extract and reproduce content effectively.
We agree with Hubspot’s key insight: Stop writing blog posts and start writing answers. AI models love summaries, bullets, numbers, and tables, so it’s impossible to miss the point. AI models don’t read—they extract. Structure content like the answer sheet it needs to be.
Among our many optimization strategies are:
- Summary sections: Leading with clear, concise answers to primary questions
- Hierarchical structure: Using descriptive headers and subheadings that break content into logical sections
- Scannable formatting: Implementing bullet points, numbered lists, and clear paragraph breaks
- Question-and-answer formats: Structuring content to directly address common queries
Why Does Conversational Language Matter More Now?
Increasingly more searches are voice or image-based, driving the need for more conversational content. AI systems favor content written in natural language that mirrors how people actually speak and ask questions.
This shift requires a fundamental change in content strategy, where hyper-targeted content rules and real-world scenarios offer better context than hypothetical ones. Our goal is to always match content with intent.
The transformation goes beyond keywords to authentic, helpful explanations that feel conversational while maintaining authority and accuracy. Our writers must think like conversationalists first, optimizers second.
Semantic Richness and Entity Relationships
LLMs understand context and relationships between concepts, people, places, and things. Content that clearly establishes these entity relationships and provides comprehensive coverage of topics performs better in AI systems.
Best practices we implement include:
- Entity optimization: Clearly identifying and describing relevant entities (people, companies, products, concepts)
- Contextual connections: Explaining relationships between different entities and concepts
- Comprehensive topic coverage: Addressing multiple aspects and perspectives of the subject matter
- Cross-referencing: Linking related concepts and providing additional context
3 Critical Technical Considerations for AI Visibility
- Indexability: The Foundation of AI Discovery
One of the most fundamental principles for AI visibility is that LLMs can only surface content they can access and crawl. Just as with traditional search engines, if your pages aren’t indexed, they can’t appear in AI-generated responses.
Key indexability factors:
- Robots.txt configuration: Ensuring AI crawlers can access your important content
- Clean URL structure: Using descriptive, crawlable URLs
- Internal linking: Creating clear pathways for crawlers to discover your content
- Sitemap optimization: Keeping sitemaps updated and comprehensive
- Performance and User Experience
LLMs increasingly favor sources that provide excellent user experiences, particularly for voice and mobile queries. Page speed, mobile optimization, and overall site performance directly impact your chances of being cited by AI systems.
Performance optimizations we implement:
- Core Web Vitals: Optimizing loading speed, interactivity, and visual stability
- Mobile-first design: Ensuring seamless experiences across all devices
- Accessibility: Implementing proper semantic markup and accessibility features
- Clean code: Maintaining well-structured, valid HTML and CSS
- Metadata and Schema: The Language of AI Understanding
Metadata and structured data serve as critical communication tools between your content and AI systems. LLMs love structured data because they can find, understand and cite your content with precision, providing concise outputs that quickly answer the query. LLMs like third-party validation from community forums because they discuss what people are talking about—and that’s what LLMs are trying to serve up in outputs. AI pays attention to community conversations, and metadata now must be written for machines.
Where we focus:
- Title tags: Clear, descriptive titles that accurately represent content
- Meta descriptions: Concise summaries that provide context
- Schema markup: Structured data that helps AI understand content types, relationships, and context
- Open Graph and Twitter Cards: Social metadata that provides additional context
Schema implementation priorities:
- Article markup: For blog posts and informational content
- FAQ markup: For question-and-answer content
- How-to markup: For instructional content
- Organization markup: For business information and credentials
- Review markup: For testimonials and evaluations
The Strategic Imperative: Beyond Traditional SEO
The marketing industry faces a critical inflection point. Hubspot says, “Traditional SEO is dead. Long live AI optimization. AI models prefer factual content backed up with data. Bottom line? If you’re still optimizing for 2019 Google, you’re building content for a search engine that’s about to be irrelevant.”
This transition requires businesses to fundamentally rethink their content and optimization strategies. Success in the AI era demands a new approach, one that we are taking that combines the proven fundamentals of digital marketing with emerging technologies and platforms.
How Do You Optimize for Google’s AI Overviews?
Depending on the source, AI Overviews now appear in 13%-52% of searches as of early 2025, representing one of the most visible manifestations of AI in search. Understanding how to optimize for these features provides crucial insight into broader AI optimization strategies.
What Content Structure Works Best for AI Overviews?
Generative AI is trained to detect and pull clear answers to specific questions. It’s now common to see articles (like this one) with a summary box or TL;DR that answers the primary query in 50–70 words using simple, accessible language that aligns with user intent.
Successful AI Overview optimization requires direct answer formats that lead with clear, concise answers to primary questions, followed by supporting details with comprehensive explanations. Content should address multiple perspectives and viewpoints of topics while creating citation-worthy content that serves as a definitive source on the subject matter.
How Does E-E-A-T Apply to AI Systems?
Content optimized for E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) is more likely to be included in AI Overviews and other AI-generated responses.
Building E-E-A-T for AI requires clearly displaying author expertise and qualifications while referencing authoritative sources and studies. Content must stay current with recent data and insights, and transparent methodology should explain how conclusions were reached. This establishes the credibility that AI systems increasingly prioritize when selecting sources to reference.
What Content Strategy Works in the AI Era?
Optimizing for Question Intent
Click-through rates are important for SEO, but reference rates are what matter with AI. Reference rates are how often your brand or content is cited or used as a source in model-generated answers. Companies and marketing agencies have to rethink content strategy around question intent rather than just keyword targeting.
Question-intent strategies focus on identifying common questions in your industry and domain, then providing complete, authoritative responses. Content should address informational, navigational, and transactional intents while optimizing for long-tail conversational queries that mirror natural language questions.
Why Is Content Freshness More Critical Than Ever?
AI systems prioritize current, relevant information above outdated content. Marketers know that you must keep content fresh and updated because although some models might not have the latest information, others may use real-time data. If your content uses old events or outdated numbers, it might lose out against competitors using more current information.
Freshness optimization requires regular content audits to review and update existing content quarterly. For example, we include recent statistics and examples while addressing current industry developments, and clearly display when content was last updated to signal relevance to both AI systems and users.
How Does Multimodal Content Impact AI Visibility?
Users now expect richer, more interactive responses. A blog post with images and videos is more likely to be featured in AI-generated answers than a plain-text article.
Zilker Partner leans heavily into multimodal optimization, which includes relevant images, diagrams, and infographics alongside instructional or explanatory videos. Content plans should consider podcasts or audio explanations and add calculators, tools, or interactive guides that provide additional value beyond text-based content.
Building Authority in the AI Era
What Makes Content Authoritative to AI Systems?
While traditional backlinks remain important, AI systems evaluate authority differently. Research indicates that in GEO, there is no “link juice” but rather “trust embedding,” which means how credible and useful your answer is from the AI’s perspective.
The challenge for businesses is standing out in an increasingly crowded content landscape. It’s not about regurgitating what’s already available online in a dozen results. Today, you have to take a position, offer unique insight, and share real customer use cases.
AI authority building requires demonstrating consistent expertise in your subject area through original research and proprietary studies. Industry recognition through mentions and citations from authoritative sources strengthens your position, while contributing unique perspectives and analyses establishes thought leadership that AI systems recognize and value.
How Should You Approach Multi-Platform Presence?
Authority signals now include GitHub mentions, Reddit threads, internal linking, schema markup, and clean HTML. This expanded definition of authority signals requires a broader approach to building credibility across multiple platforms and communities.
Multi-platform authority strategies include maintaining active LinkedIn and industry-specific profiles while engaging in relevant Reddit, Stack Overflow, and industry forums. Writing for authoritative industry publications and participating in conferences and podcasts extends your authority beyond your own content properties.
Measuring Success in AI Optimization
What Metrics Matter for GEO?
Traditional metrics like rankings and click-through rates remain important, but new metrics are emerging for AI visibility. Industry tracking shows that the number of sessions from AI search tools like ChatGPT and Perplexity, CTR from AI answers, impact on revenue/conversions, and brand visibility as a cited source now determine success.
Key GEO metrics include citation frequency (how often your content is referenced in AI responses), source attribution (whether AI systems link back to your content), AI-driven traffic (visitors coming from AI platforms), conversion quality (engagement levels of AI-referred visitors), and brand mention volume (frequency of brand mentions in AI-generated content).
What Tools Help Track AI Performance?
Platforms like Profound, Goodie, Gumshoe, and Daydream enable brands to analyze how they appear in AI-generated responses, track sentiment across model outputs, and understand which publishers are shaping model behavior.
Monitoring strategies include regularly querying AI systems with relevant keywords, conducting competitive analysis to monitor how competitors appear in AI responses, using analytics to identify AI-driven traffic patterns, and performing content gap analysis to identify topics where competitors dominate AI responses.
How Does Zilker Partners Approach AI Optimization?
At Zilker Partners, we believe the future belongs to businesses that master both traditional optimization and emerging AI technologies. Our integrated approach ensures clients succeed across all discovery channels.
- Foundation Building ensures strong technical SEO and content quality as the baseline for all visibility efforts.
- AI-First Content Strategy creates content specifically designed for AI comprehension while maintaining human value and readability.
- Multi-Platform Optimization addresses traditional search engines, AI Overviews, conversational AI platforms, and emerging technologies.
- Continuous Adaptation keeps us ahead of algorithm changes and new platform developments through ongoing testing and optimization.
- Measurable Results implements tracking and analysis systems that measure success across both traditional and AI-driven channels.
In Summary: Embracing the AI-Driven Future
The evolution of AI-driven search represents both a challenge and an opportunity. While the landscape is evolving rapidly, businesses that embrace both traditional optimization fundamentals and new AI-specific strategies will be best positioned for success.
As industry leaders note, GEO is the competition to get into the model’s mind. In a world where AI is the front door to commerce and discovery, the question for marketers is: Will the model remember you?
The answer depends on how well you adapt your digital marketing strategy to this new reality. Traditional SEO provided a roadmap for visibility in the first era of search. Now, Generative Engine Optimization offers the roadmap for the AI era.
At Zilker Partners, we’re committed to helping our clients navigate this transition successfully. By combining proven digital marketing expertise with cutting-edge AI optimization strategies, we ensure our clients remain visible, relevant, and competitive in an AI-driven world.
The future of search is here, and it’s generative. The question isn’t whether to adapt—it’s how quickly you can implement these strategies to secure your place in the AI-driven discovery ecosystem. Contact Zilker Partners today to discuss how we can help you optimize for the next generation of search and discovery.
Frequently Asked Questions About Generative Engine Optimization
What exactly is Generative Engine Optimization (GEO)?
GEO is the practice of optimizing content and websites to appear in AI-generated responses from platforms like ChatGPT, Perplexity, Google’s AI Overviews, and other large language models. Unlike traditional SEO, which focuses on ranking in search results, GEO optimizes for being cited, referenced, or included in AI-generated answers.
Is traditional SEO dead now that AI search is growing?
AI tools are definitely reshaping the foundation of how companies get found, and content has to follow LLM best practices. However, traditional SEO fundamentals like quality content, technical optimization, and authority building remain important as the foundation for AI visibility.
What’s the most important change I should make first?
You can always partner with Zilker Partners to do this for you, but if you decide to go at it alone, begin by restructuring your content to lead with clear, direct answers to common questions in your industry. Add summary sections at the top of your articles, use question-based headers, and implement comprehensive schema markup. Focus on creating content that serves as the definitive answer to specific questions.
How long does it take to see results from GEO efforts?
AI systems can incorporate new content relatively quickly, often within weeks of publication. However, building authority and consistent citations in AI responses typically takes 3-6 months of consistent optimization efforts. The key is creating a steady stream of high-quality, AI-optimized content.
Can small businesses compete with large companies in AI search?
Yes, and often more effectively than in traditional search. AI systems prioritize relevance, specificity, and authoritative answers over domain size. Small businesses can succeed by focusing on niche expertise, local knowledge, and providing detailed answers to specific customer questions that larger competitors might overlook.
Should I stop doing traditional SEO and focus on GEO?
No. We believe the most successful approach combines both traditional SEO and GEO strategies. Many AI systems still reference content that ranks well in traditional search results. Maintain your SEO fundamentals while adding AI-specific optimizations on top of that foundation.
How do I measure ROI from AI optimization efforts?
Track new metrics including citation frequency in AI responses, referral traffic from AI platforms, brand mentions in AI-generated content, and conversion rates from AI-driven visitors. Use traditional analytics tools to identify traffic patterns from AI sources and measure their impact on business goals.
What industries benefit most from GEO right now?
Currently, industries with informational content see the most AI visibility, including healthcare, technology, finance, education, and professional services. However, AI systems are expanding into commercial and local search, making GEO relevant for virtually all industries.
This guide represents Zilker Partners’ current thinking on generative engine optimization and AI visibility strategies. As the landscape continues to evolve, we’ll continue updating our approaches and sharing insights with our clients and the broader business community.
