- How to find the specific queries that AI engines answer about your industry
- AI keyword research is fundamentally different from Google keyword research
- Includes 5 research methods, a scoring system for query prioritization, and real examples
- Focus on questions, comparisons, and “explain” queries because those are what AI engines answer most
Google Keywords and AI Queries Are Different Things
Here’s something most people get wrong about AI search. They take their existing Google keyword list and try to apply it to AI engines. That doesn’t work.
Google keywords are often short, commercial, and navigational. “CRM software.” “Best CRM.” “CRM pricing.” AI queries are longer, more conversational, and often framed as questions. “What CRM should a 10-person startup use?” “How does HubSpot compare to Pipedrive for small sales teams?” “Explain the difference between CRM and marketing automation.”
The overlap between Google keywords and AI queries is about 30-40% based on what I’ve seen. The other 60-70% are queries people would never type into Google but regularly ask AI engines. That’s a whole world of traffic you’re missing if you only do traditional keyword research.
Sources: DemandSage ChatGPT statistics, SparkToro zero-click data.
The 5 Types of AI Queries
Before you start researching, understand the query types. AI engines get asked these five categories most often:
| Query Type | Example | Citation Likelihood | Why It Matters |
|---|---|---|---|
| Definition/Explain | “What is answer engine improvement?” | High | AI engines cite sources for factual definitions |
| Comparison | “Compare Semrush vs Ahrefs for AI tracking” | Very High | Comparisons almost always cite multiple sources |
| How-to | “How do I add schema markup to WordPress?” | High | Step-by-step content gets cited when AI provides instructions |
| List/Best | “Best AI search tools for agencies” | Medium-High | AI often cites review sites and comparison articles |
| Opinion/Advice | “Should I invest in AI search improvement?” | Medium | AI cites thought leadership and data-backed opinions |
Comparison and definition queries have the highest citation rates. If you’re starting from scratch, target those first.
Method 1: Mine AI Engines Directly
The most obvious method, but most people skip it. Ask AI engines what people ask them.
Ask ChatGPT for Common Questions
Prompt: “What are the 20 most common questions people ask about [your industry/topic]? Include specific, detailed questions, not generic ones.” The results aren’t based on search volume data, but they reflect what ChatGPT actually gets asked.
Check Perplexity’s Related Questions
Search a topic on Perplexity. It often shows “Related questions” at the bottom. These are queries other users have asked. Gold mine for AI query research.
Use Google’s “People Also Ask”
Google’s PAA boxes increasingly overlap with AI queries. They show the question-format queries that trigger AI Overviews. Cross-reference these with what ChatGPT suggests.
I typically get 40-60 unique queries from this method alone. Many of them won’t show up in traditional keyword research tools because they’re conversational and long-tail.
Method 2: Analyze Your Support and Sales Conversations
Your customers are already asking AI engines the same questions they ask your support team.
Sources to Mine
Pull the last 100 support tickets. What are people asking? The exact phrasing they use is how they’d ask AI engines too.
What questions do prospects ask during sales calls? “How does this compare to [competitor]?” is a comparison query waiting to happen.
If you have a website chatbot, the questions people type in are essentially AI queries. Same format, same intent.
Reddit, industry Slack groups, Discord servers. The questions people ask in communities are the same questions they’ll ask AI engines.
This method produces the highest-quality queries because they come from real customers with real problems. Not estimated search volumes from a tool.
Method 3: Reverse-Engineer Competitor Citations
Find out what queries your competitors get cited for. Then target those same queries.
Identify Competitor Content
Look at your top competitors’ blogs and resource sections. What topics do they cover? What’s their most-linked content?
Test Their Content as Queries
Turn their blog post titles into questions. “Ultimate Guide to CRM” becomes “What is CRM and how does it work?” Search that on Perplexity. Do they get cited?
Document Citation-Earning Queries
Build a list of every query where a competitor gets cited. These are proven citation-earning queries. They’re your target list. Use our citation checker to test systematically.
Method 4: Use Traditional Tools Differently
SEO tools like Semrush, Ahrefs, and AnswerThePublic can still be useful. You just need to filter differently.
Filter Settings for AI Queries
Use the “Questions” filter in Semrush or Ahrefs. AI engines primarily answer questions, so question-format queries are your priority.
AI queries tend to be longer than Google searches. Filter for queries with 5 or more words to find the conversational queries AI users type.
Queries that trigger PAA boxes on Google are more likely to trigger AI Overviews. Semrush can filter for these.
AI citations are easier to win than Google #1 rankings. Queries with moderate search volume but high specificity are your sweet spot.
Method 5: Test and Validate
Once you have your query list, validate it. Not every query actually gets answered by AI engines in a citable way.
Test Each Query on 3 Platforms
Search on Perplexity, ChatGPT (with browsing), and Google. Does the query trigger an AI response with citations? Some queries get deflected (“I can’t advise on that”) or answered without citations.
Score Each Query
Rate each query on three dimensions: citation likelihood (does AI cite sources?), business value (would this traffic help?), and competitive difficulty (how strong is existing cited content?).
Prioritize Your Final List
Target 20-30 queries for your first 90 days of AI content work. Focus on high citation likelihood + high business value + low-to-medium competition.
The Query Scoring System
I use a simple 30-point scoring system to prioritize AI queries:
| Factor | Low (1-3) | Medium (4-7) | High (8-10) |
|---|---|---|---|
| Citation Likelihood | AI rarely cites sources for this query | AI sometimes cites, depends on content quality | AI almost always cites sources (comparisons, definitions) |
| Business Value | Informational only, no buying intent | Some buying intent, awareness stage | High intent, close to purchase decision |
| Competitive Gap | Strong competitor content already cited | Moderate competition, room to compete | No strong content cited, open opportunity |
Queries scoring 21+ are your top priority. 15-20 are worth pursuing. Below 15, skip them for now.
Real Examples: AI Queries vs Google Keywords
To make this concrete, here’s how the same topic looks different in Google keyword research vs. AI query research:
| Topic | Google Keyword | AI Query |
|---|---|---|
| CRM selection | “best crm software” | “What CRM should a 10-person B2B startup use if we’re currently tracking leads in spreadsheets?” |
| Schema markup | “schema markup seo” | “How does schema markup help my content appear in ChatGPT and Perplexity answers?” |
| Email marketing | “email marketing tools” | “Compare Mailchimp vs ConvertKit for a creator with a 5,000 subscriber newsletter” |
| Content strategy | “content strategy template” | “How do I create content that AI search engines will cite as a source?” |
See the difference? AI queries include context, constraints, and specific use cases. Your content needs to match that specificity to get cited. For more on this, read how ChatGPT selects sources and how Perplexity ranks sources.
Building Your Final Query List
After running all five methods, you’ll have 100+ potential queries. Here’s how to narrow it down:
- Remove duplicates and near-duplicates
- Score each remaining query using the 30-point system
- Select your top 20-30 queries
- Group them by topic cluster (usually 4-6 clusters)
- Map each cluster to existing or planned content
This final list becomes your AI content strategy for the next quarter. Each query either maps to an existing page that needs improvement or a new page that needs to be created.
For the broader context on building an AI search strategy, read our AI search improvement pillar guide. And for measuring results, check our AI search improvement checklist.
Want AI Keyword Research Done for Your Industry?
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Frequently Asked Questions
Yes, significantly. SEO keyword research focuses on search volume, keyword difficulty, and click-through rates for Google’s link results. AI query research focuses on question-format queries that AI engines answer with citations. The overlap is only about 30-40%. You need both for a complete search strategy in 2026.
You can do basic AI query research for free using ChatGPT, Perplexity, and Google’s People Also Ask. For more advanced research, traditional SEO tools like Semrush and Ahrefs help with filtering question queries and identifying competitors. Our free citation checker helps validate which queries actually earn citations.
Start with 20-30 high-priority queries for your first quarter. That’s enough to create meaningful content without spreading too thin. Expand to 50-75 queries once you have results from the first batch and understand which query types work best for your brand.
Test it on Perplexity and ChatGPT. If the AI engine answers with citations, it’s worth targeting. If it answers without citations or deflects the question, the citation opportunity is lower. Also check if any competitor already dominates the citation for that query. Open opportunities with no strong cited content are your best bets.