AI search is killing our keyword strategy - anyone else struggling with conversational query optimization?

You don’t need to restructure all 500+ pages at once. Here’s a prioritized approach:

Phase 1: High-value pages (Week 1-2)

• Identify top 50 pages by traffic/conversions
• Add conversational headers and Q&A sections
• Add FAQ schema markup

Phase 2: Category expansion (Month 1-2)

• Create new conversational hub pages for each category
• Link to existing keyword pages as supporting content
• The hub answers questions, the spokes provide depth

Phase 3: Gradual conversion (Ongoing)

• Update 10-20 pages per week during regular content updates
• Prioritize based on AI citation tracking data

The framework:

For each page, ask:

1. What question is someone trying to answer?
2. What follow-up questions would they have?
3. How would I answer this if someone asked me directly?

Then restructure accordingly.

E-commerce SEO here. Same challenge, different scale (10,000+ product pages).

What worked for us:

1. Category pages became Q&A hubs

   • Old: “Women’s Running Shoes” (product grid)
   • New: “How to Choose Women’s Running Shoes” (guide + product grid)

2. Added FAQ blocks to product pages

   • “Is this shoe good for flat feet?”
   • “What’s the difference between this and [competitor]?”
   • “How do I know my size?”

3. Created comparison content

   • “[Product A] vs [Product B]” pages
   • “Best [Category] for [Use Case]” guides

Results:

• AI citations up 280% in 4 months
• Keyword rankings: unchanged or improved
• Conversion rate from AI traffic: 2.3x higher than average

No trade-off between conversational and keyword optimization.

Here’s the quick conversion template I use:

Before (keyword-focused):

H1: Best Trail Running Shoes 2026
H2: Top Trail Running Shoes Features
H2: Trail Running Shoes Comparison
H2: Trail Running Shoes Buying Guide

After (conversational):

H1: What Are the Best Trail Running Shoes in 2026?
H2: What Features Should I Look for in Trail Running Shoes?
H2: How Do Popular Trail Running Shoes Compare?
H2: How Do I Choose the Right Trail Running Shoes?

The changes:

• Headers are now questions
• Each section opens with a direct answer
• Natural language throughout
• FAQ section added at bottom

Same content, better structure for AI.

From a technical standpoint, there are some quick wins:

Schema markup:

Add FAQPage schema to existing pages. This doesn’t change content but tells AI systems there are Q&A elements.

Structured data additions:

• HowTo schema for instructional content
• Product schema with questions
• Review schema with FAQ elements

Internal linking:

Link from conversational hub pages to keyword pages using question-based anchor text:

• “If you’re wondering which shoes are best for flat feet, see our detailed analysis”

These technical changes:

• Don’t require content rewrites
• Improve AI understanding
• Can be implemented at scale with templates

Voice search optimized content naturally works for AI search.

The connection:

Voice search trained users to ask conversational questions. AI search follows the same pattern.

What voice search taught us:

1. Long-tail questions are the norm
2. Intent is explicit, not implied
3. Local and contextual modifiers are common
4. Follow-up questions happen naturally

Apply to your content:

Structure content to answer questions the way you’d answer someone asking out loud. Because that’s how people query AI too.

Practical tip:

Record yourself answering customer questions. Transcribe it. That natural Q&A flow is what you want in your content.

Track the transition with data.

Metrics to monitor:

1. AI citation rate - Use Am I Cited to track conversational query performance
2. Keyword rankings - Ensure traditional SEO isn’t suffering
3. Conversion rate by source - AI traffic often converts better
4. Time on page - Conversational content usually has higher engagement

What we found:

Pages we converted to conversational format:

• AI citations: +340%
• Keyword rankings: +/- 5% (neutral)
• Conversions: +28%
• Time on page: +45%

There’s genuinely no trade-off. Conversational optimization improves everything.

We’ve productized this as a service. Here’s our methodology:

Step 1: Question mapping

• Identify all questions users ask about the topic
• Use: Reddit, Quora, forums, customer support data, People Also Ask

Step 2: Content audit

• Map existing content to questions
• Identify gaps (questions without content)
• Identify optimization opportunities

Step 3: Conversion prioritization

• Score pages by: traffic + conversion value + AI citation potential
• Start with high-score pages

Step 4: Systematic conversion

• Update 10 pages per week
• Track results for each batch
• Refine approach based on data

Typical timeline:

• Quick wins (schema, FAQ blocks): Week 1
• High-value pages: Weeks 2-4
• Category hubs: Month 2
• Full catalog: Months 3-6

You don’t need to do everything at once.

This thread has given me a manageable plan. Key takeaways:

Strategic approach:

1. Don’t try to restructure everything at once
2. Create conversational hub pages that link to keyword pages
3. Add FAQ blocks and schema to existing pages
4. Prioritize by traffic/conversion value

Tactical wins:

1. Convert headers to questions
2. Answer questions in the first sentence
3. Add FAQ schema for quick improvements
4. Use customer support questions as guides

Tracking:

• Monitor both AI citations and keyword rankings
• Track conversion rate from AI traffic specifically
• Use Am I Cited to measure progress

Our 90-day plan:

• Week 1-2: Quick wins (schema, FAQ blocks on top 50 pages)
• Week 3-4: Create 5 conversational hub pages
• Month 2: Convert top 50 individual pages
• Month 3: Expand to next 100 pages

The hub-and-spoke model is the key insight. I don’t need to touch all 500 pages immediately - I need strategic hubs that capture conversational traffic.

Thank you all for the practical guidance.