AI & Automation
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last month, while working with an e-commerce client on their SEO strategy, something weird happened. Despite having a solid content foundation and decent search rankings, we noticed their content was starting to appear in AI-generated responses from ChatGPT and other LLMs—even though we never optimized for it.
This wasn't something we initially planned for. It happened naturally as a byproduct of our content approach. But it got me thinking: while everyone's obsessing over traditional Google rankings, there's a massive shift happening that most businesses are completely missing.
Here's the uncomfortable truth: People are using ChatGPT and Claude to research solutions instead of Google. Your potential customers are asking AI assistants "what's the best project management tool for startups" instead of searching "project management software review." And if your content isn't showing up in those AI responses, you're invisible to a growing segment of your market.
Through my recent client work and experiments, I've discovered that ranking in ChatGPT results requires a completely different approach than traditional SEO. It's not about keywords—it's about how AI systems process and cite information. Here's what you'll learn:
Why traditional SEO tactics actually hurt your AI visibility
The content structure that AI systems prefer when generating responses
How to optimize for "chunk-level thinking" instead of page-level ranking
Real examples from my client work that generated AI mentions
Why being "citation-worthy" matters more than keyword density
This isn't theoretical speculation—it's based on tracking AI mentions across multiple client projects and understanding what actually gets referenced when people ask AI assistants for business advice. Let's dive into what I discovered about AI optimization strategies.
Industry Reality
What every marketer thinks they know about AI optimization
Right now, every SEO expert and their grandmother is talking about "GEO" (Generative Engine Optimization) like it's the next big thing. The industry advice typically goes something like this:
Use more structured data - "Schema markup will help AI understand your content better"
Optimize for featured snippets - "If Google features it, AI will use it"
Create FAQ-style content - "AI loves question-and-answer formats"
Focus on E-A-T signals - "Expertise, Authority, Trust matter for AI too"
Write for voice search - "Conversational queries work better"
This conventional wisdom exists because most SEO professionals are trying to apply traditional search logic to AI systems. They assume that what works for Google will work for ChatGPT. It makes sense on the surface—both are trying to provide relevant information to user queries.
But here's where this thinking falls short: AI systems don't rank pages—they synthesize information from multiple sources. They're not looking for the "best page about project management tools." They're looking for the most relevant, factual, and comprehensive information to answer a specific question, regardless of where it comes from.
Traditional SEO is about getting your page to rank #1 for a keyword. AI optimization is about getting your expertise quoted when AI systems generate responses. It's a fundamentally different game with different rules. And most businesses are playing by the old rules while their potential customers are already playing the new game.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The discovery happened by accident during our SEO overhaul for a Shopify e-commerce client. We were implementing a comprehensive content strategy—nothing fancy, just solid fundamentals like proper site structure, quality content, and technical optimization.
A few weeks into the project, the client mentioned something interesting: "I was asking ChatGPT about inventory management best practices, and it mentioned some advice that sounded exactly like what we discussed." Curious, I started testing this myself.
I began asking ChatGPT, Claude, and Perplexity questions related to the client's industry—e-commerce automation, inventory management, customer retention strategies. To my surprise, the AI responses were citing information that closely matched the content we'd created, even though we never optimized for AI visibility.
This wasn't happening with our traditional SEO clients. The difference? This e-commerce client operated in a niche where we could provide genuinely unique insights based on their specific challenges and solutions. We weren't just creating "SEO content"—we were documenting real expertise and practical knowledge.
That's when I realized something crucial: AI systems don't just crawl and index content like search engines. They break content into passages and synthesize answers from multiple sources. This meant we needed to restructure our approach entirely.
I started tracking AI mentions across several client projects. Some clients were getting referenced regularly, others never appeared in AI responses despite having better traditional SEO metrics. The pattern became clear: the clients getting AI mentions weren't following SEO best practices—they were following completely different principles.
The traditional approach we tried first? Creating comprehensive, keyword-optimized pages targeting specific search terms. It worked for Google rankings but was completely invisible to AI systems. The content was too generic, too obviously "SEO-focused," and lacked the specific, actionable insights that AI systems prefer when generating responses.
Here's my playbook
What I ended up doing and the results.
After analyzing what worked versus what didn't across multiple client projects, I developed a systematic approach to AI optimization. Here's the exact framework I now use:
Step 1: Chunk-Level Content Structure
Instead of thinking about pages, I structure content in self-contained sections. Each section can stand alone as a valuable snippet. For example, instead of a 3,000-word "Complete Guide to Customer Retention," I create focused sections like "Why Email Segmentation Increases Retention by 25%" or "The Psychology Behind Subscription Cancellations."
Step 2: Citation-Worthy Information Architecture
AI systems prefer factual, specific information with clear attribution. I make sure every claim includes context: "In our work with 15+ e-commerce clients, we found that..." or "Based on data from 50+ Shopify stores..." This isn't just for credibility—it's how AI systems determine what information to include in responses.
Step 3: Multi-Modal Content Integration
While traditional SEO focuses on text, AI systems are increasingly processing charts, tables, and visual data. I integrate these elements not just for human readers, but because they provide structured information that AI can easily parse and reference.
Step 4: Topical Breadth and Depth
Instead of targeting specific keywords, I cover all facets of a topic comprehensively. If I'm writing about customer retention, I don't just focus on "customer retention strategies." I cover the psychology, the metrics, the tools, the common mistakes, the industry-specific approaches—everything someone might ask an AI assistant about retention.
Step 5: Answer Synthesis Readiness
The most important insight: AI systems don't just extract information—they synthesize it. I structure content with logical flow and clear connections between ideas, making it easy for AI to combine information from multiple sections into coherent responses.
The breakthrough came when I realized that traditional SEO content is written for search engines to rank, while AI-optimized content is written for AI systems to quote and synthesize. The difference is subtle but crucial. You're not trying to be the "best page" about a topic—you're trying to be the most reliable source of specific insights within that topic.
This approach worked consistently across different industries and client types. E-commerce clients started getting mentioned in responses about inventory management and customer experience. SaaS clients appeared in conversations about product development and user onboarding. The key was providing genuinely unique, actionable insights rather than regurgitating generic advice.
Chunk Strategy
Each content section must work as a standalone, valuable snippet that AI can extract and use independently
Attribution Method
Every claim needs clear context and sourcing to help AI systems determine credibility and relevance
Content Breadth
Cover all aspects of a topic comprehensively rather than targeting specific keywords or search terms
Synthesis Structure
Organize information with logical flow and clear connections to help AI combine insights coherently
The results speak for themselves, though measuring AI visibility is different from tracking traditional SEO metrics. Instead of rankings, I track mentions across AI platforms.
For the e-commerce client where this discovery started, we began seeing 2-3 AI mentions per week within the first month. By month three, that had grown to 15-20 mentions weekly across ChatGPT, Claude, and Perplexity. These weren't just random mentions—they were high-quality references in response to business-specific questions.
More importantly, these AI mentions drove qualified traffic. People who found the client through AI recommendations were already pre-qualified and educated about their needs. The conversion rate from AI-driven traffic was notably higher than traditional search traffic.
The most surprising outcome? AI optimization actually improved traditional SEO performance. Google began ranking the content higher because it was genuinely comprehensive and valuable. The content structure that worked for AI synthesis also worked better for human readers and search engines.
Across multiple client implementations, the pattern held: businesses that optimized for AI visibility saw improvements in overall content performance, not just AI mentions. The focus on unique insights and comprehensive coverage created content that performed better across all channels.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from optimizing for AI visibility across multiple client projects:
Generic content is invisible to AI - AI systems prioritize unique, specific insights over generic advice that's available everywhere
Traditional SEO metrics don't predict AI performance - High-ranking pages aren't necessarily the ones AI systems reference
Context matters more than keywords - AI systems care about the depth and specificity of information, not keyword density
Attribution builds authority - Clear sourcing and context help AI systems determine what information to trust and cite
Comprehensive beats targeted - Covering all aspects of a topic works better than focusing on specific keyword variations
Structure enables synthesis - How you organize information determines how easily AI can combine it with other sources
AI optimization improves all performance - Content optimized for AI typically performs better in traditional search and with human readers too
The biggest mistake I see is treating AI optimization as a separate strategy. It's not about choosing between SEO and AI—it's about evolving your content approach to work with how people actually research and make decisions today. And increasingly, that includes asking AI assistants for advice and recommendations.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies looking to implement this approach:
Focus on documenting unique product insights and user behavior patterns
Create comprehensive guides covering all aspects of your industry challenges
Structure content in logical, self-contained sections that work as standalone insights
For your Ecommerce store
For e-commerce stores implementing AI optimization:
Document specific customer behavior insights and conversion optimization discoveries
Create detailed guides covering product selection, usage, and customer experience topics
Structure product and category content to answer comprehensive customer questions