TL;DR
Keyword research in 2026 requires a dual approach: traditional keyword metrics (volume, difficulty, intent) plus AI query analysis (what people ask AI search engines, how AI selects citations, and what content structures earn citations). The biggest shift is from keyword strings to question-based topics. AI search users ask complete, specific questions rather than typing fragmentary keywords. Your research process must now identify both the keywords that drive organic traffic and the questions that earn AI citations.
Why Has Keyword Research Changed in the AI Search Era?
Keyword research has been the foundation of SEO since search engines existed. You find what people search for, create content targeting those terms, and optimize to rank. That process still works for traditional organic search, but it’s now only half the picture.
AI search engines have introduced a fundamentally different search behavior. When users interact with ChatGPT, Perplexity, or Google AI Overviews, they don’t type two-word keyword phrases. They ask complete questions, provide context, and expect synthesized answers.
This changes keyword research in three critical ways:
The Query Length Shift
| Search Type | Average Query Length | Example |
|---|---|---|
| Traditional Google search | 2-4 words | ”best CRM software” |
| Voice search | 5-8 words | ”what’s the best CRM software for small business” |
| AI search query | 10-25 words | ”what CRM software would you recommend for a 10-person sales team that needs email integration and costs under $50 per user per month” |
AI search queries are 3-5x longer than traditional search queries. They include context, constraints, and specificity that traditional keyword tools don’t capture.
The Intent Complexity Shift
Traditional keyword research categorizes intent into four buckets: informational, navigational, transactional, and commercial. AI search queries often combine multiple intents in a single query.
A query like “Compare HubSpot and Salesforce for a B2B SaaS startup that just raised Series A and needs to scale from 5 to 50 sales reps” is simultaneously informational (compare features), commercial (evaluating options), and contextual (specific business situation). Traditional keyword tools would miss the nuance entirely.
The Answer Format Shift
In traditional SEO, you optimize a page to rank for a keyword. In AI search, you optimize paragraphs to be extracted and cited as part of a synthesized answer. This means your keyword research must also consider:
- What format does the AI’s answer typically take for this topic?
- What specific claims or data points does the AI extract?
- How many sources does the AI cite for this type of query?
- What content structure earns the citation?
How Do You Research Keywords for Traditional Search and AI Search Simultaneously?
The most effective approach in 2026 is a layered research process that captures both traditional keywords and AI query patterns. As we discuss in Micro-Niches Win in AI Search: Why Specificity Beats Scale, this is a critical factor.
Layer 1: Traditional Keyword Research (Still Essential)
Start with the fundamentals. Traditional keyword metrics still determine your organic rankings, and organic rankings are a prerequisite for AI Overview citations.
Step 1: Seed keyword generation Brainstorm core topics related to your business. Use your product categories, service offerings, industry terminology, and customer pain points as starting points.
Step 2: Keyword expansion Use tools like Ahrefs, SEMrush, or Google Keyword Planner to expand seed keywords into comprehensive keyword lists. Look for:
- Search volume (monthly searches)
- Keyword difficulty (competition level)
- Search intent (informational, commercial, transactional)
- SERP features (featured snippets, AI Overviews, PAA)
- Related keywords and variations
Step 3: Competitor keyword analysis Identify what your competitors rank for that you don’t. This reveals content gaps and opportunities.
Step 4: Prioritization Score keywords by a combination of volume, difficulty, business relevance, and conversion potential. If you want to go deeper, How to Write Atomic Paragraphs That AI Engines Love to Cite breaks this down step by step.
Layer 2: AI Query Research (The New Layer)
This is where most teams fall short. AI query research goes beyond traditional keyword data to understand how people interact with AI search engines.
Step 1: Direct AI platform research Manually query ChatGPT, Perplexity, and Google with questions related to your core topics. Document:
- What questions users ask (visible in Perplexity’s trending queries and related questions)
- What sources get cited in the AI answers
- What content format the AI uses (paragraph, list, comparison)
- What specific passages get extracted and quoted
- What follow-up questions users ask
Step 2: People Also Ask mining Google’s PAA boxes reveal question chains that users explore. For each of your core keywords:
- Search the keyword on Google
- Click through all PAA questions and note them
- Each click reveals additional PAA questions — follow the chain 3-4 levels deep
- Compile the full question tree
These PAA questions represent real questions that users ask, and many of them trigger AI Overviews and featured snippets.
Step 3: Community and forum research AI search queries often originate from questions people ask in communities. Mine these sources:
- Reddit: Search your topic on Reddit and note the questions in post titles and top comments
- Quora: Review questions and upvoted answers in your topic area
- Industry forums: Check specialized forums for your niche
- Stack Exchange/Stack Overflow: For technical topics
- Social media comments: Questions people ask under industry content
Step 4: Customer question analysis Your own customers are a goldmine of AI-era keyword data:
- Support ticket questions
- Sales call FAQs
- Chat bot logs
- Customer survey responses
- Review and feedback questions
These represent the exact questions your target audience has — and likely asks AI search engines. (We explore this further in Core Web Vitals Explained: LCP, INP, and CLS for SEO in 2026.)
Layer 3: Citation Opportunity Analysis
The third layer identifies where AI citations are available and attainable.
Step 1: AI citation audit For your prioritized topics, check which queries trigger AI answers and what gets cited:
| Query | AI Overview? | Sources Cited | Your Content Present? | Gap |
|---|---|---|---|---|
| ”what is [topic]“ | Yes | 3 sources | No | Need definition content |
| ”how to [task]“ | Yes | 4 sources | Yes (position 3) | Optimize for position 1 |
| ”[product] vs [product]“ | Yes | 5 sources | No | Need comparison page |
Step 2: Citation format analysis Note the format AI engines use when answering different query types:
- Definition queries → paragraph citations
- Process queries → step-by-step citations
- Comparison queries → table or multi-source synthesis
- Data queries → statistic-heavy citations
- Opinion queries → expert quote citations
This tells you exactly what content format to create for each topic.
What Is Topic-Based Research and How Does It Replace Pure Keyword Targeting?
The most significant shift in AI-era keyword research is the move from keyword targeting to topic targeting.
Keywords vs. Topics
| Approach | Focus | Output | AI Readiness |
|---|---|---|---|
| Keyword targeting | Individual search terms | Pages optimized for specific keywords | Low — fragmented |
| Topic targeting | Comprehensive subject coverage | Content clusters covering all aspects | High — comprehensive |
AI search engines don’t match keywords — they understand topics. When a user asks Perplexity “how should I structure my website for AI search,” the AI doesn’t look for pages targeting that exact keyword. It looks for content that comprehensively covers website structure, AI search optimization, and related subtopics.
How to Build Topic-Based Research
Step 1: Define topic clusters Group your keywords into topic clusters based on semantic relationships. A topic cluster includes:
- Pillar topic: The broad subject (e.g., “Generative Engine Optimization”)
- Subtopics: Specific aspects (e.g., “content structure for GEO,” “technical GEO,” “GEO tools”)
- Supporting questions: Individual questions within each subtopic
Step 2: Map the topic comprehensively For each pillar topic, create a topic map that covers every angle:
- Definition and fundamentals
- How-to guides and processes
- Tools and resources
- Common mistakes and troubleshooting
- Case studies and examples
- Comparisons with alternatives
- Advanced techniques
- Future trends and predictions
Step 3: Identify coverage gaps Compare your topic map against your existing content. Where are the gaps? These gaps represent opportunities for both organic rankings and AI citations.
Step 4: Prioritize by AI citation potential Not all subtopics have equal AI citation potential. Prioritize subtopics that: This relates closely to what we cover in Featured Snippet Types: Complete Guide.
- Trigger AI Overviews or AI answers
- Have clear, extractable answer formats
- Align with questions your audience actually asks
- Have weak current competition in AI citations
- Connect to your business goals
How Do You Analyze Search Intent for AI Optimization?
Search intent analysis has always been important for SEO. For AI optimization, it requires deeper nuance.
The Expanded Intent Framework
Traditional SEO uses four intent categories. AI-era research requires a more granular framework:
| Intent Type | Traditional Query | AI Query | Content Format Needed |
|---|---|---|---|
| Definitional | ”GEO meaning" | "What exactly is GEO and how does it differ from SEO?” | Concise definition paragraph + comparison |
| Procedural | ”optimize for AI" | "Walk me through the steps to optimize my blog for ChatGPT citations” | Numbered steps with details |
| Comparative | ”GEO vs SEO" | "Compare GEO and SEO approaches, including which one I should prioritize first” | Comparison table + recommendation |
| Evaluative | ”best GEO tools" | "What are the most reliable GEO tools for a small marketing team with limited budget?” | Ranked list with context-specific recommendations |
| Diagnostic | ”why no traffic" | "My content ranks #3 but I’m not getting cited by Perplexity — what could be wrong?” | Troubleshooting guide with specific checks |
| Predictive | ”future of SEO" | "How will SEO change over the next 2 years with the rise of AI search?” | Expert analysis with specific predictions |
| Contextual | N/A (too specific for traditional search) | “I run a B2B SaaS blog with 200 articles. Which ones should I optimize for GEO first?” | Framework + prioritization criteria |
Intent Signals to Watch For
When analyzing queries for your research, look for these intent signals:
Specificity level: More specific queries (mentioning budget, team size, industry, timeline) indicate users seeking personalized recommendations. Create content with decision frameworks and conditional recommendations.
Comparison language: Queries containing “vs,” “compared to,” “better than,” or “difference between” signal evaluation intent. Create comparison tables and side-by-side analyses.
Problem language: Queries containing “problem,” “issue,” “not working,” “why can’t I” signal diagnostic intent. Create troubleshooting guides and common mistake lists.
Action language: Queries containing “how to,” “steps to,” “guide to,” “tutorial” signal procedural intent. Create step-by-step guides with numbered lists.
How Do You Find Question-Based Keywords That AI Engines Love?
Question-based keywords are the currency of AI search. Here are the most effective methods for finding them.
Method 1: The Answer Pyramid
Start with a broad topic and build a pyramid of questions from general to specific:
Level 1 — Foundational questions:
- What is [topic]?
- Why does [topic] matter?
- How does [topic] work?
Level 2 — Practical questions:
- How do you implement [topic]?
- What tools are needed for [topic]?
- How long does [topic] take?
Level 3 — Specific questions:
- How do you [topic] for [specific use case]?
- What are the common mistakes in [topic]?
- How do you measure [topic] success?
Level 4 — Advanced questions:
- How do you scale [topic] across [context]?
- What’s the future of [topic]?
- How does [topic] compare to [alternative] for [specific scenario]?
Each level represents increasingly specific queries that your content should address.
Method 2: AI Auto-Suggest Mining
AI search engines have their own suggestion mechanisms:
- Perplexity: Shows “Related” questions after answering
- ChatGPT: Suggests follow-up questions in conversation
- Google AI Overviews: Links to related searches and PAA questions
Use these suggestions to build comprehensive question lists for your topics. For more on this, see our guide to On-Page SEO Checklist 2026: 25 Essential Optimizations.
Method 3: The “Also Asks” Chain
Start with one question and follow the chain of related questions across platforms:
- Ask the question on Google → note PAA questions
- Ask the same question on Perplexity → note related questions
- Ask on ChatGPT → note suggested follow-ups
- Search the question on Reddit → note comment questions
- Compile all unique questions into a master list
This process typically yields 30-50 unique questions from a single starting question — each one a potential content opportunity.
Method 4: Customer Language Mining
Your customers use language that keyword tools don’t capture. Mine their actual words:
- Support tickets: What exact phrases do customers use when asking questions?
- Sales calls: What questions do prospects ask during the buying process?
- Reviews: What terminology do customers use in product reviews?
- Social media: How do followers phrase questions in comments?
This customer language often matches AI search query patterns more closely than traditional keyword data. Our Question-Style Headings That AI Engines Pull guide covers this in detail.
How Do You Build a Keyword Strategy That Serves Both SEO and GEO?
The goal is a unified strategy that drives organic rankings and AI citations simultaneously.
The Unified Keyword Matrix
Create a matrix that combines traditional keyword metrics with AI citation potential:
| Topic/Keyword | Monthly Volume | Difficulty | Current Rank | AI Overview? | Citation Status | Priority |
|---|---|---|---|---|---|---|
| what is geo | 4,400 | Medium | #5 | Yes | Not cited | High |
| geo vs seo | 2,900 | Medium | #3 | Yes | Cited (pos 2) | Medium (defend) |
| geo tools | 1,200 | Low | Not ranked | Yes | Not cited | High (new content) |
| geo audit | 800 | Low | #8 | No | N/A | Low |
Content Planning from the Matrix
Use the matrix to plan content types:
High volume + AI Overview present + not cited: Create new citation-ready content or restructure existing content. This is your highest priority — there’s organic traffic and AI citation opportunity.
High volume + AI Overview present + already cited: Defend your position. Update content, strengthen structure, maintain freshness.
High volume + no AI Overview: Focus on traditional SEO optimization. The AI citation opportunity may emerge later as AI Overviews expand.
Low volume + AI Overview present + not cited: Often worth targeting — low competition, clear citation opportunity, and AI search drives visibility beyond search volume.
Content Format Mapping
Map each topic cluster to the optimal content formats for both SEO and GEO:
| Topic Cluster | SEO Format | GEO Format | Combined Approach |
|---|---|---|---|
| Definitions | Long-form guide | Atomic definition paragraphs | Comprehensive guide with extractable definitions |
| How-to | Step-by-step tutorial | Numbered steps with action verbs | Tutorial with clean step structure + depth |
| Comparisons | Detailed comparison page | Comparison tables | In-depth analysis with summary tables |
| Tools/reviews | Review roundup | Ranked list with one-line verdicts | Detailed reviews with extractable summaries |
| Case studies | Narrative case study | Data tables and key findings | Story format with extractable statistics |
How Do You Track and Update Your Keyword Strategy for AI?
AI search is evolving rapidly. Your keyword strategy needs regular maintenance and updates.
Monthly Review Process
Each month, review:
- New AI citations earned: Which topics are gaining AI visibility?
- Lost citations: Where have you lost AI citations?
- New AI Overview queries: What queries now trigger AI Overviews that didn’t before?
- Query pattern changes: Are user questions shifting in phrasing or specificity?
- Competitor movements: Who’s earning citations in your topic areas?
Quarterly Strategy Update
Each quarter, conduct a deeper review:
- Full citation audit across all major AI platforms
- Topic cluster expansion based on new opportunities
- Content gap analysis comparing your coverage to competitors
- Priority re-ranking based on performance data
- Format optimization based on what’s earning citations
Signals That Your Strategy Needs Adjustment
Watch for these indicators:
- Declining citation rate despite maintaining content quality → competitors have improved, or AI algorithm has shifted
- New query types appearing in your industry → expand your topic coverage
- Content format changes in AI answers → update your content structure
- New AI search platforms gaining market share → add them to your research process
Common Mistakes in AI-Era Keyword Research
Mistake 1: Ignoring Long-Tail and Question Queries
Many teams still focus exclusively on high-volume head terms. In AI search, long-tail question queries are where citations happen. A query with 100 monthly searches that triggers an AI Overview may be more valuable than a 10,000-volume keyword that doesn’t.
Mistake 2: Using Only Traditional Keyword Tools
Ahrefs and SEMrush are essential but insufficient. They don’t capture AI-specific query patterns, citation data, or the conversational nature of AI search queries. Supplement with manual AI platform research and community mining.
Mistake 3: Targeting Keywords Without Checking AI Behavior
Before investing in content creation, check whether your target keywords trigger AI Overviews or AI answers. If they don’t, you’re optimizing for traditional search only (which may be fine, but know that going in).
Mistake 4: Not Updating Research Regularly
AI search is evolving monthly. Query patterns shift, new AI Overviews appear, citation preferences change. A keyword strategy created 6 months ago may be significantly outdated. Review and update quarterly at minimum.
Mistake 5: Treating All AI Platforms the Same
ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot all have different query patterns, citation preferences, and user behaviors. Research each platform independently rather than assuming uniform behavior.
Mistake 6: Forgetting About User Intent Complexity
AI search queries carry more complex intent than traditional searches. Don’t force AI-era queries into the simple four-category intent framework. Develop a more nuanced understanding of what users need when they ask specific questions.
Mistake 7: Over-Relying on Search Volume
Search volume measures traditional search behavior. It doesn’t measure how many people ask AI search engines about a topic. A topic with low Google search volume may have high AI query volume. Use multiple data sources to estimate total demand.
Action Items: Your AI-Era Keyword Research Checklist
Getting Started (This Week):
- Export your current keyword list from existing SEO tools
- Query your top 20 topics across ChatGPT, Perplexity, and Google AI Overviews
- Document which queries trigger AI answers and what gets cited
- Mine PAA questions for your core topics (3 levels deep)
Building the Foundation (This Month):
- Create topic clusters for your 5-10 core subjects
- Build the unified keyword matrix (volume + difficulty + AI citation status)
- Conduct customer language mining (support tickets, sales calls, reviews)
- Map content formats to each topic cluster
Ongoing Maintenance:
- Monthly: Review AI citations, update keyword matrix, check for new AI Overviews
- Quarterly: Full strategy review, competitive analysis, topic cluster expansion
- Continuously: Mine communities, customer questions, and AI platform suggestions for new opportunities
The websites that win in AI search are the ones that understand the complete picture — what people search on Google and what they ask AI. Your keyword research must cover both worlds. Start layering AI query research onto your existing SEO process today, and you’ll be positioned to capture visibility as AI search continues its rapid growth. As we discuss in Technical SEO Audit Checklist: 50+ Points for 2026, this is a critical factor.