Ranking in AI Search: The Hidden Keywords You’re Missing

The Secret Behind AI Search: Understanding Query Fanout

Imagine you ask an AI chatbot, like ChatGPT, “What’s the best payroll software for consultants?” If you’re a payroll software vendor, you want your brand to be in that answer. But how does the AI decide which brands to mention?

It’s not as simple as ranking for the exact phrase you typed. Behind the scenes, the AI generates a series of related, hidden search queries to gather comprehensive information. For that one question about payroll software, the AI might actually search for:

• “gusto contractor only plan pricing 2025”
• “square payroll contractor only pricing 2025”
• “quickbooks payroll pricing 2025 features”

These hidden searches are called query fanout. The AI’s final answer is built from the combined search results of all these queries, not just your original question. This means you could be ranking #1 for “best payroll software for consultants,” but if you’re invisible for these hidden fanout queries, you may never get cited by the AI. Many marketers have no idea this is even happening.

Why Your Current Keyword Strategy is Failing in the AI Era

If you’re still focusing on ranking for individual keywords, you’re playing a game that’s already changed. Groundbreaking research into over 1,600 AI search queries reveals a new reality for SEO, and it’s governed by two critical principles.

The 2-5 Fanout Rule: AI’s Strategic Expansion

First, the good news: AI isn’t going down infinite rabbit trails. Most of the time, an AI model generates between two and five additional keywords for a user’s query. It strategically expands the search just enough to provide a well-rounded answer without creating information overload. This is a targeted, efficient process designed to find the best possible information quickly.

The 66% Chaos Factor: The Unpredictability of AI Queries

Here’s where it gets complicated. The research found that a staggering 66% of these AI-generated fanout keywords are completely unpredictable. When the same user query was run multiple times, two-thirds of the fanout keywords appeared only once. Less than 1% appeared consistently across all tests.

What does this volatility mean for your strategy? If you try to optimize for a specific AI-generated keyword you discovered yesterday, you’re essentially chasing a ghost. It might never appear again.

This is where many marketers throw their hands up, declaring AI search impossible to optimize for. But they’re missing the pattern hidden within the chaos.

The Antidote to Chaos: Winning with Semantic Clustering

Even though individual fanout keywords are volatile, they aren’t random. The research uncovered that 84% of these queries are semantic neighbors. This means they share common URLs in their top Google search results.

Think back to the payroll software example. Even though the AI generated 17 different fanout queries, searching for them individually on Google would reveal that many of the same websites appear in the top results for each. This is the key.

Instead of chasing unpredictable individual keywords, the winning strategy is to focus on ranking for keyword clusters—groups of related terms that topically and semantically cover a subject. The goal is to create a comprehensive piece of content, or a series of interconnected pieces, that addresses a topic from every conceivable angle. This approach is a cornerstone of modern content strategy and is essential for success in both traditional and AI search, fitting perfectly within a broader strategy of marketing automation examples that drive consistent growth.

By building this topical authority, you position your brand to be relevant no matter which specific fanout queries the AI generates. You’re no longer worried about the chaos because your content ecosystem is prepared for any variation.

Why Traditional SEO is More Important Than Ever

The irony of all this AI complexity is that it makes SEO fundamentals more critical than ever. Research shows that 70% of the sources cited in Google’s AI Overviews come from the top 10 organic search results. This isn’t just for the original query but for all those hidden fanout queries, too.

AI models are, at their core, sophisticated data processors. They rely on web search to gather the raw material for their answers, which involves a process similar to how one might scrape Google Search results to find relevant information. If you’re ranking well organically for a whole cluster of related terms, you’re signaling to search engines and AI models that you are an authority on that topic.

The future of search isn’t about learning a completely new discipline with a trendy acronym. It’s about executing user-focused SEO with a deeper understanding of semantic relationships.

3 Practical Steps to Dominate AI Search Results

Enough theory. Here’s how you can adapt your strategy today to capture this invisible search traffic and secure your visibility in the age of AI.

1. Think in Clusters, Not Keywords

Shift your mindset from optimizing single pages to building a content ecosystem. Your goal is to become the ultimate authority on your core topics.

Instead of just targeting “remote work trends,” expand your content to cover related clusters like “remote work challenges,” “remote workplace culture,” and “remote employee engagement.” Create comprehensive content that doesn’t just answer the main question but also anticipates and addresses the related questions an AI might generate.

This is how you win in the new era of search. By creating in-depth, authoritative content, you naturally cover multiple angles of your topic. Learning how to write better with AI can help you scale this process, ensuring your content is both comprehensive and high-quality. This approach doesn’t just optimize for AI; it builds the kind of content that performs well everywhere, earning brand mentions and backlinks naturally.

2. Reverse-Engineer AI Queries

This is a manual but highly effective tactic. Start asking AI tools like ChatGPT and Perplexity the same questions your target audience would ask. Pay close attention to the sources they cite and, if possible, the underlying queries they use to find those sources.

Google those hidden terms and analyze the content that ranks. What topics do they cover? What is their structure? How can you create something similar, but better? While you shouldn’t focus on ranking for a single branded competitor query, almost all non-branded queries are fair game. Use this research as a signpost to understand the types of queries AI generates, not as a rigid list of keywords to target.

3. Track and Measure Your AI Visibility

You can’t improve what you don’t measure. Start tracking mentions of your brand across popular AI platforms. Monitor how often your content is cited in Google AI Overviews, ChatGPT, Perplexity, and other answer engines.

This allows you to see when and how your brand gets cited, giving you direct feedback on your content strategy. It helps you identify which content clusters are performing well and where your topical gaps are, so you can continuously refine your approach for maximum impact.

The Future is Semantic: Your AI Search Playbook

AI search isn’t a mysterious black box that’s impossible to understand. While individual fanout keywords are unpredictable, the underlying patterns are clear. The path to visibility is paved with topical authority. No more data headaches from chasing ghost keywords.

Focus on building deep, comprehensive content around semantic clusters. Rank well organically for groups of related keywords. Create content that answers user questions from multiple angles. In other words, execute a brilliant, user-focused SEO strategy with the understanding that AI evaluates your content through a semantic lens, not just simple keyword matching. Scale your content, not your headcount, and you’ll be positioned to win in the new era of search.