AI Visibility in 2026: What 50 websites reveal

Overview of Results

Across the 50 websites evaluated:

• Average AI visibility score: 64/100
• Median score: 63
• Lowest score: 42
• Highest score: 82

Most sites demonstrated partial readiness. Very few could be considered structurally optimised for AI interpretation.

The distribution suggests that while foundational web standards are generally in place, machine-level clarity remains inconsistent.

Evaluation Framework

Each website was scored across seven weighted categories:

1. Entity Clarity
2. Structured Data Quality
3. Content Structure & Extractability
4. Topical Authority & Depth
5. Technical AI Readiness
6. Internal Linking Architecture
7. Conversion & Intent Alignment

The framework does not measure keyword rankings or traffic performance. It assesses whether a site communicates its identity, authority and content structure in a way that AI systems can reliably interpret.

Structured Data: The Most Consistent Weakness

Structured data quality recorded the lowest average performance across the dataset.

Common gaps included:

• Missing Article or BlogPosting schema
• Incomplete Organization schema properties
• Limited or inconsistent JSON-LD implementation

In several cases, structured data was present but minimal, lacking optional properties that improve entity completeness.

Given that AI systems rely on explicit, machine-readable signals, incomplete schema reduces structural clarity even when the visible content is strong.

Entity Clarity: Authority Often Implied, Not Defined

A significant number of sites lacked structured author signals.

Frequent observations included:

• No Person schema on content pages
• Missing author credentials
• Weak linkage between content and organisational entities

Human readers can infer authority from context. AI systems require explicit markers. When expertise is not formally defined, attribution confidence decreases.

Content Structure: Generally Strong

Content hygiene was one of the stronger-performing categories.

Most websites demonstrated:

• A single H1 per page
• Logical heading hierarchy
• Consistent use of lists
• Readable sentence length

This indicates that many teams are building for clarity and usability.

However, structural readability alone does not guarantee extractability at scale.

Topical Authority: Breadth Without Depth

While most websites had blogs or resource sections, deeper topic clustering was less common.

Patterns observed included:

• Standalone articles without clear pillar relationships
• Limited interlinking between related topics
• Shallow coverage across core subject areas

AI systems increasingly evaluate contextual depth when determining authority. Fragmented content structures may dilute perceived expertise.

Technical AI Readiness: Mixed Implementation

Baseline technical standards were generally met:

• HTTPS was consistently implemented
• Sitemap.xml files were present
• Canonical tags were widely used

However, some sites:

• Blocked AI-specific crawlers
• Lacked emerging machine-facing configuration standards

This suggests that AI crawler access is not yet fully integrated into standard technical workflows.

Internal Linking: Strong Discoverability

Internal linking architecture performed well across the dataset.

Most websites:

• Avoided orphan pages
• Maintained shallow navigation depth
• Linked effectively between key sections

Discoverability does not appear to be the primary limitation. Interpretability does.

Interpretation of the Data

An average score of 64/100 indicates that most established websites are structurally competent but not AI-optimised.

The gap is not in content volume or design quality.

It is in explicit structural signalling:

• Clear entity definition
• Complete structured data
• Deep topical organisation
• Deliberate machine readability

Websites have evolved significantly for users over the past decade. The structural layer for AI interpretation has not evolved at the same pace.

Implications

As generative interfaces become more integrated into search environments, visibility may increasingly depend on structured clarity rather than traditional ranking signals alone.

AI systems do not rely on inference in the same way users do. They rely on:

• Schema completeness
• Entity relationships
• Structural hierarchy
• Technical accessibility

Where these signals are incomplete, visibility becomes inconsistent.

Testing Structural Readiness

To conduct this evaluation consistently, we developed an AI Visibility Scanner based on the same framework used in this study.

It assesses:

• Structured data implementation
• Entity clarity
• Topic clustering
• AI crawler access
• Content extractability

The free version provides a high-level structural score and category breakdown.
A full report expands on findings and prioritised recommendations.

https://tools.codeandwander.com/visibility

Closing Observation

If 50 established websites average 64/100 in structural AI readiness, the ecosystem remains early.

The opportunity is not necessarily more content.

It is clearer structure.

Clearer entities.
Deeper topical organisation.
More complete machine-readable signals.

AI systems interpret structure.
And in 2026, structure is becoming a visibility factor in its own right.

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