Keyword Research for AI Search: Finding Topics That Get Cited

Keyword research for AI search differs fundamentally from traditional SEO. While search volume still matters, the key question changes from “how many people search this?” to “how likely is AI to cite content on this topic?” Some high-volume keywords rarely trigger AI citations; some niche queries have excellent citation rates. Understanding this dynamic is essential for AI-first keyword strategy.

According to Search Engine Journal, 73% of SEO professionals are adapting their keyword research for AI search. The shift requires new methods: analyzing AI response patterns, understanding which query types trigger citations, and prioritizing topics where AI systems seek authoritative sources.

AI vs Traditional Keyword Research #

Traditional keyword research optimizes for search engine rankings. AI-first keyword research optimizes for citation likelihood:

Table 1: Traditional vs AI-first keyword research comparison

Query Types and Citation Rates #

Not all queries trigger AI citations equally. Based on our analysis of 5,000 AI responses, here are citation rates by query type:

High-Citation Query Types #

Table 2: AI citation rates by query type (Seenos research, n=5,000 queries)

Key insight: Comparison and definitional queries have the highest citation rates because AI systems need authoritative sources to synthesize balanced information. Target these query types preferentially.

AI-First Keyword Research Methodology #

Step 1: Start with Topics, Not Keywords #

Unlike traditional SEO, start by identifying topics where you want to build authority:

• What topics align with your business expertise?
• What topics do your customers ask about?
• What topics have underserved AI citation opportunities?

Once you have 3-5 core topics, map the keyword landscape within each.

Step 2: Generate Question Variations #

AI users ask questions, not keyword fragments. For each topic, generate natural language questions:

// Question generation framework for topic: "Topic Clusters"

DEFINITIONAL:
- What is a topic cluster?
- What are topic clusters in SEO?
- Topic cluster definition

COMPARISON:
- Topic clusters vs keyword targeting
- Pillar pages vs cluster content
- Topic authority vs domain authority

HOW-TO:
- How to create a topic cluster
- How to structure pillar content
- How to build internal links for clusters

WHY:
- Why do topic clusters work?
- Why is topic authority important?
- Why do AI systems prefer clusters?

BEST/LIST:
- Best topic cluster examples
- Topic cluster templates
- Steps to build topic clusters

Step 3: Analyze AI Citation Patterns #

For each question variation, analyze current AI responses:

• 1Run the query through ChatGPT, Gemini, and Perplexity
• 2Record which sources are cited
• 3Note the content type of cited sources (guide, comparison, etc.)
• 4Assess citation quality (first-position vs. later citations)

This reveals which queries have strong citation activity (good targets) vs. weak citation activity (harder to break into).

Step 4: Map to Content Types #

Different query types require different content formats. Map your target queries to appropriate content:

• Definitional queries → Pillar pages, comprehensive guides
• Comparison queries → Structured comparison articles with tables
• How-to queries → Step-by-step guides with examples
• Best/List queries → Comprehensive listicles with criteria

Step 5: Prioritize by Impact Potential #

Score each keyword opportunity using this formula:

Impact Score = (Citation Rate × 0.3) + (Search Volume × 0.25) + 
               (Business Fit × 0.25) + (Competition Gap × 0.2)

Where:
- Citation Rate: How often AI cites sources for this query (1-5)
- Search Volume: Traditional search demand (1-5)
- Business Fit: Alignment with your products/services (1-5)
- Competition Gap: Room to outperform current citations (1-5)

Focus on keywords with Impact Scores above 3.5.

Tools for AI Keyword Research #

Combine traditional keyword tools with AI-specific analysis:

Traditional Tools (Still Useful) #

• Ahrefs/SEMrush — Search volume, competition, keyword gaps
• Google Search Console — Current ranking keywords and performance
• Answer The Public — Question-based keyword suggestions
• Google Keyword Planner — Volume and trend data

AI-Specific Analysis #

• Seenos AI Visibility Monitor — Track citation rates across AI platforms
• Manual AI queries — Test queries directly in ChatGPT/Gemini/Perplexity
• Citation tracking spreadsheets — Log which sources AI cites for target queries

Common Keyword Research Mistakes #

Mistake 1: Ignoring Question Format

Targeting “topic cluster SEO” when users ask “How do I create a topic cluster?” AI responds to natural language—match your content to question format.

Mistake 2: Volume-Only Prioritization

High-volume keywords with low AI citation rates waste resources. A 10,000 volume keyword with 15% citation rate delivers fewer AI citations than a 2,000 volume keyword with 70% citation rate.

Mistake 3: Skipping Comparison Keywords

“X vs Y” queries have the highest citation rates (72%). Many SEOs skip these because they seem “competitive.” In reality, comprehensive comparison content consistently gets cited.

Mistake 4: Single-Keyword Focus

AI systems evaluate topic authority, not individual keyword optimization. Build content clusters around topics; individual keyword rankings follow naturally.

Frequently Asked Questions #

Further Reading #

• Content Strategy Guide — Complete framework for AI visibility
• Topic Cluster Strategy — Building content that AI cites
• Pillar Content Guide — Creating hub pages
• GPT for Content Planning — AI-powered keyword ideation

About the Author

Yue Zhu@Seenos.ai

Product Manager at Seenos.ai. Pioneer in AEO research since 2024, exploring the convergence of SEO and GEO (Generative Engine Optimization). Led multiple AI-powered content optimization projects that achieved 300%+ citation increases in ChatGPT and Perplexity.