What site structure do AI systems prefer? Trying to figure out if we need to reorganize our entire site

Site structure matters, but not the way you might think.

What AI systems “see”:

AI doesn’t crawl your site like Google does. It’s more about:

1. How Google indexes your content (which AI then references)
2. How well your pages stand alone as authoritative resources
3. Topic clustering and internal signals

What actually matters:

My honest assessment:

You probably don’t need a massive restructure. What you likely need:

1. Consolidate fragmented topic coverage
2. Build clear pillar + cluster relationships
3. Improve internal linking for topic authority

The ROI question:

Major restructures are expensive and risky. Focus on high-impact improvements to key content clusters, not a complete overhaul.

Content architecture perspective.

The ideal structure for AI:

Think of it less as “site structure” and more as “knowledge structure.”

What AI systems learn from:

1. Pillar content - Comprehensive pages that own a topic
2. Cluster content - Supporting pages that go deeper on subtopics
3. Internal links - Signals showing topic relationships
4. Entity connections - How your topics relate to known entities

The practical structure:

Topic Pillar (comprehensive guide)
├── Subtopic A (detailed deep-dive)
├── Subtopic B (detailed deep-dive)
├── Subtopic C (detailed deep-dive)
└── Related FAQ (common questions)

How to evolve organically:

1. Identify your core topics (10-20 main pillars)
2. Map existing content to these pillars
3. Identify gaps and consolidation opportunities
4. Build internal linking structure
5. Create comprehensive pillar pages where missing

You don’t need to restructure everything - focus on your most important topic areas first.

Migration/restructure perspective - done this for 50+ sites.

The restructure decision framework:

Major restructure worth it when:

• Severe content fragmentation across all topics
• URL structure actively harming SEO
• Site architecture preventing content discovery
• Technical debt making updates impossible

Targeted improvements better when:

• Fragmentation in some topics but not all
• URLs work but aren’t ideal
• Core structure is sound
• Resources are limited

Your situation (based on description):

3,000 pages, 5 levels deep, some fragmentation = targeted improvements, not major restructure.

What I’d prioritize:

1. Audit top 10 topic areas for fragmentation
2. Consolidate where needed (don’t touch what works)
3. Build pillar content for priority topics
4. Improve internal linking throughout
5. Flatten deep hierarchy only for important content

The risk of major restructures:

• URL changes = potential traffic loss
• Redirect complexity
• Resource intensive
• Disrupts what’s working

My rule: Minimum viable restructure. Fix what’s broken, enhance what’s good, leave alone what works.

Enterprise perspective with similar scale (5,000+ pages).

What we learned about AI and structure:

1. Flat vs deep doesn’t matter as much as claimed
2. Topic clustering matters a lot
3. Individual page quality matters more than site structure
4. Internal linking is the key structural signal

Our approach:

We didn’t reorganize our entire site. Instead:

1. Created topic hubs - New pillar pages linking to existing content
2. Improved internal linking - Added contextual links between related content
3. Consolidated thin content - Merged fragmented topics
4. Added schema - Better entity recognition

Results:

• AI citation rate: +40% over 6 months
• Didn’t change URLs for existing content
• Minimal SEO risk

What we track:

Am I Cited broken down by content cluster. Shows which topic areas have good AI visibility and which need work.

My advice:

Focus on making your content structure (topics, clustering) clear, not on URL or folder structure.

Small site perspective (200 pages).

Our situation:

Also had organic growth, inconsistent structure, topic fragmentation.

What we did:

1. Topic audit - Listed all topics we cover
2. Content mapping - Assigned each page to a topic
3. Gap analysis - Found topics with thin coverage
4. Consolidation - Merged thin related pages
5. Pillar creation - Built comprehensive guides for core topics

The structure we ended up with:

/topic-a/
  - comprehensive-guide (pillar)
  - subtopic-1
  - subtopic-2
/topic-b/
  - comprehensive-guide (pillar)
  - subtopic-1
  ...

AI visibility impact:

• Before: 18% citation rate
• After: 41% citation rate (6 months later)

The key changes:

Content consolidation and pillar creation had the biggest impact. URL changes were less important.

Timeline:

This took us 3 months of gradual work, not a big-bang restructure.

Technical documentation perspective.

What works for technical content:

The same principles apply, but with some specifics.

Our structure:

/docs/
  /getting-started/
    - quick-start (entry point)
    - installation
    - configuration
  /features/
    - feature-a (comprehensive)
    - feature-b (comprehensive)
  /guides/
    - use-case-1
    - use-case-2
  /reference/
    - api-reference

What AI cites from us:

• Most cited: Comprehensive feature pages
• Second: Getting started guides
• Rarely cited: Deep reference pages

The insight:

AI cites content that answers “what” and “how” questions. Pure reference material (API docs, etc.) is less frequently cited.

Structure recommendation:

Ensure every topic has a user-friendly comprehensive overview, not just reference material.

AI-specific perspective on structure.

What I’ve observed:

AI systems don’t “see” your site structure the way you think. They see:

• Individual pages as standalone documents
• Entity relationships between your content and topics
• Authority signals from internal/external links

Structure matters for:

1. Google indexing (which AI references)
2. Topic authority signals (clustering)
3. Content discoverability (for crawling)

Structure doesn’t matter for:

1. Direct AI processing (AI reads content, not structure)
2. URL format specifically (content matters more)
3. Hierarchy depth (flatten if you want, but it’s not critical)

What to focus on:

1. Make individual pages comprehensive - Each page should stand alone
2. Build topic clusters - Show AI you’re an authority
3. Internal link strategically - Connect related content
4. Consolidate fragmentation - One strong page > five weak pages

The monitoring approach:

Use Am I Cited to see which pages get cited. Often reveals structure issues - if only certain sections get visibility, that tells you something.

Great insights from everyone. Here’s my plan:

What I learned:

1. Don’t need major restructure - targeted improvements are better
2. Topic clustering matters more than URL/folder structure
3. Consolidation has high ROI - merge fragmented content
4. Internal linking is key structural signal
5. Individual page quality trumps site structure

My action plan:

Phase 1 (Month 1):

• Audit top 10 topic areas
• Identify fragmentation
• Map content to topic clusters

Phase 2 (Month 2-3):

• Consolidate fragmented topics
• Build pillar pages for gaps
• Improve internal linking

Phase 3 (Ongoing):

• Track AI visibility by topic cluster
• Iterate based on data
• Apply learnings to other topics

What I’m NOT doing:

• Major URL restructure
• Complete site reorganization
• Deep hierarchy flattening (unless specific issue)

Thanks everyone - saved us from an unnecessary major project.