What are the different intent categories for AI search? Trying to understand user behavior

Great question. Let me share the framework we use:

AI Search Intent Categories:

Key differences in AI:

1. Queries are longer - Average 25+ words vs 6 in traditional
2. Intents are often combined - “Tell me about X AND do Y”
3. Expectations are higher - Users want answers, not links
4. Funnel is compressed - Awareness → Decision in one session

What this means for content: You need to satisfy the immediate intent AND anticipate follow-ups.

The funnel compression insight is huge. Let me expand:

Traditional search funnel:

• Awareness: “What is project management software?”
• Consideration: “Best project management tools”
• Comparison: “Monday vs Asana”
• Decision: “Asana pricing”
• Action: Navigate to Asana.com

Each step is a separate search. Different content for each.

AI search funnel:

• Single query: “What’s the best project management tool for a 20-person marketing team, comparing features and pricing?”

AI provides:

• Explanation (awareness)
• Recommendations (consideration)
• Comparison (evaluation)
• Pricing (decision support)

All in ONE response.

What this means for content:

Old approach: One page per funnel stage New approach: One page that addresses ALL stages

The comprehensive content advantage: Content that satisfies the full journey (from “what is X” to “where do I buy X”) has more citation opportunities.

Let me detail the AI-native intent types:

1. Generative Intent “Create an image of…” “Write a policy for…” “Draft an email about…”

These don’t exist in traditional search. Users want AI to CREATE, not just find.

Content implication: Templates, examples, and frameworks get cited as starting points.

2. Conversational/Follow-up Intent “Now tell me more about point 2” “What about for small businesses?” “Can you explain that differently?”

Context-dependent queries that reference earlier conversation.

Content implication: Comprehensive content that addresses multiple angles wins because AI can pull different parts for follow-ups.

3. Agentic Intent (emerging) “Book me a table at…” “Schedule a meeting with…” “Order this for me”

User wants AI to take action, not just provide information.

Content implication: Make your business “callable” - APIs, integrations, structured data that AI can act on.

4. No-Intent Interactions “Thanks” “Okay” “I see”

Nearly 50% of AI interactions are conversational without explicit intent.

Content implication: Less relevant for optimization, but shows AI is becoming a dialogue partner, not just a search tool.

Here’s how to optimize content for each AI intent:

Informational Intent:

• Lead with direct answer
• Provide comprehensive depth
• Include expert credentials
• Add supporting data/statistics

Comparison Intent:

• Use comparison tables
• Include pros/cons for each option
• Be balanced (AI values objectivity)
• Cover pricing, features, use cases

Problem-Solving Intent:

• Step-by-step format
• Number your steps
• Include troubleshooting tips
• Anticipate common mistakes

Transactional Intent:

• Clear pricing information
• Next steps/CTAs
• Make action pathway clear
• Include trust signals

Generative Support Intent:

• Provide templates
• Include examples
• Show before/after
• Make content adaptable

Content structure for multi-intent:

1. Direct answer (immediate intent)
2. Context (background)
3. Comparison/options (if relevant)
4. Next steps (action intent)
5. Related considerations (follow-up prep)

This structure covers the funnel in one piece.

Some data on AI query patterns:

Query length distribution:

Intent breakdown in AI queries:

Insight: Generative intent is the second-largest category - unique to AI.

Multi-intent queries: ~30% of AI queries contain multiple intents in one query.

What this means: Your content needs to handle complexity. Single-intent pages miss opportunities.

Here’s a practical framework for content planning:

Step 1: Map your topic to likely intents

Example: “Project management software”

Step 2: Create comprehensive content

One long-form piece that addresses ALL intents:

• Definition section (informational)
• Comparison table (commercial)
• How-to section (problem-solving)
• Pricing section (transactional)

Step 3: Structure for extraction

Each section should:

• Start with clear heading (question format)
• Lead with direct answer
• Include supporting details
• Be standalone-worthy

Step 4: Track by intent

Use Am I Cited to see which types of queries cite you. This shows which intents you’re winning.

This is exactly what I needed. My new framework:

Intent categories I’ll use:

1. Informational - “What is X?”
2. Comparison - “X vs Y” or “Best X for…”
3. Problem-solving - “How to X” or “Fix X”
4. Transactional - “Buy/Get/Sign up for X”
5. Generative - “Create/Write/Draft X”
6. Follow-up - Context-dependent queries

Content strategy changes:

Before:

• One page per narrow intent
• Short, keyword-focused pieces
• Separate content for each funnel stage

After:

• Comprehensive pages covering multiple intents
• Longer, structured content
• Each section is extractable/standalone
• Tables for comparison intents
• Step-by-step for how-to intents

Tracking plan:

• Use Am I Cited to see which intents cite our content
• Identify gaps (intents we’re NOT appearing for)
• Create content to fill gaps

The key insight: Traditional intent categories apply but are often combined in AI queries. Comprehensive, well-structured content wins because it can satisfy multiple intents in one response.

Thanks everyone!