The 2025 AI SEO E-commerce Report of 100 Websites

Who is winning the AI SEO race in e-commerce? The answer is not who you think. Our latest research, based on a deep analysis of 100 ecommerce websites using AI Page Ready, shows that Top e-commerce giants are being outmaneuvered by their smaller, more technically adept Mid-Level counterparts. This report provides an overview of the current landscape, detailing exactly where and why different segments of ecommerce stores are succeeding or failing in their journey towards AI readiness.

• Mid-level websites outperform e-commerce giants: The most surprising finding is that Mid-Level e-commerce sites are the clear leaders in AI SEO. They excel at the technical fundamentals where the largest Top-tier brands are significantly lagging.
• The Structured Data Gap: A massive, industry-wide opportunity exists to improve the use of JSON-LD structured data. Implementation is weak across all segments, especially on critical Product and Category pages, limiting the ability of AI to understand page content.
• Category Pages are a Universal Blind Spot: Nearly all websites neglect their Category pages, which consistently have the lowest scores in content, metadata, and structured data. Optimizing these pages is a major competitive advantage waiting to be claimed.
• Accessibility Remains a Critical Weakness: Outside of the leading Mid-Level segment, most websites scored poorly on accessibility. A poor Critical DOM Score, likely from overly complex HTML markup, hinders both users with disabilities and AI agents and represents a key area for technical improvement.

To provide a comprehensive and nuanced benchmark of AI SEO readiness across the e-commerce landscape, this study employed a systematic, multi-stage methodology for website selection, page and category identification, and data collection. The objective was to move beyond surface-level domain analysis and evaluate how different types of e-commerce businesses are optimizing their specific pages that matter most to users, search engines and AI chatbots.

1. Cohort Selection

A cohort of 100 e-commerce websites was selected for this analysis. To ensure the findings were representative of the broader market, the websites were divided into four distinct segments based on their scale, traffic, and market position — top ecommerce giants, mid level websites, lower mid level and indie stores.

The selection process for each segment was based on specific, publicly available data and community sources to ensure objectivity. The table below outlines the exact sources used to compile the list of websites for each cohort.

Note: No Chinese ecommerce websites are included

2. Product & Category Page Identification

For each of the 100 websites, we identified three distinct page types to get a holistic view of their on-page AI SEO strategy. This crucial step was performed manually by our team to replicate a real user journey and identify the most relevant pages.

1. Home Page
2. Category Page: Our team manually navigated each site to identify a primary, high-importance product category. The goal was to select a major category page that a typical user would click on from the home page.
3. Product Page: From the chosen category page, a representative product was selected for analysis. This was typically the first product listed, a "best seller," or a featured item, ensuring it was a page the business was actively promoting.

By following this structured protocol, we ensured that the analysis was based on each website's most critical pages.

3. AI SEO Score Analysis

Each of these URLs was then processed through the AI Page Ready product to generate the scores that form the basis of our findings.

AI Page Ready checks across 40+ parameters to determine the visibility of a webpage on LLMs like ChatGPT, Gemini and Claude.

These parameters are classified under the following categories and scores:

• Structured Data:
  Checks for structured data like JSON-LD Schema and meta tags that translates your content into a logical format for AI agents.
• LLM-Friendly Formatting:
  Checks use of headings, FAQs, lists, and tables that allow LLMs to easily parse and extract information.
• Accessibility:
  Checks that key content renders without JavaScript and that core web vitals are healthy.
• LLM as a Judge:
  Uses Gemini to assess how well the page answers high-intent real user questions.
• Discoverability:
  Assesses foundational signals (like robots.txt and sitemaps) that enable AI crawlers to find and index your pages.
• Content Readability:
  Measures how easily your content can be understood by both humans and AI models to avoid ambiguity.

Here is a sample of scores for select websites across these segments, with links to their complete AI SEO reports.

As we mentioned at the start, the landscape of AI SEO readiness in e-commerce reveals a surprising hierarchy. It is not the largest players who lead, but the agile Mid-Level websites. The Mid-Level and Lower Mid-Level sites demonstrate a stronger overall AI readiness than both their smaller Indie counterparts and, most notably, the Top-tier e-commerce giants.

As the chart clearly shows, the Top segment has the lowest average 'Overall Score', a finding that underscores a significant gap between brand recognition and AI-centric technical optimization.

Here is a breakdown of the patterns observed within each of the four segments.

1. Top Websites

• Overall: This segment remains at the bottom of the rankings with the lowest average 'Overall Score'. Their performance is held back by significant foundational issues.
• Page-Type Performance: The scores are poor and relatively flat across all page types. Home Page scores marginally better than Category and Product pages.
• Strengths and Weaknesses:
  • Weakness - Structured Data: They score poorly on 'Structured Data', missing a key opportunity to clearly define their content for AI bots. They have the lowest score for JSON-LD Schema among all the segments. This is a surprising finding, as one would expect these larger players to be more advanced in this area.
  • Weakness - Accessibility: This is another failure point. A big part of the reason here is low critical DOM score, which means a lot of the crucial content is being rendered by JavaScript which LLMs can't access.
  • Area of Improvement - LLM Friendly Formatting: They do well on this front compared to other categories, but still lag behind other segments. Their low Q&A Score and Steps & Tables Score shows their content lacks the structured formats that LLMs rely on for direct answers. Their Headings Structure Score is also consistently mediocre.
  • Strength - Content Readability on Product Pages: Their one relative bright spot is the score for 'Content Readability' on Product pages, which is the highest of any segment. However, this is not enough to lift their overall score.

2. Mid-Level Websites

• Overall: This segment leads all others, earning the highest average 'Overall Score' and setting the standard for AI readiness.
• Page-Type Performance: They show strong performance across all page types, with a familiar pattern of Home Page first, followed by Product Pages, and then Category Pages.
• Strengths and Weaknesses:
  • Strength - Technical Excellence: They have perfect Robots.txt Scores and the highest average Sitemap Scores, indicating a mastery of basic discoverability. They have good Open Graph and Twitter Cards scores for Product Pages but much lower scores for category pages. This indicates that while their product page templates are well-optimized for social sharing, their category pages are overlooked.
  • Strength - Strong Formatting: Their pages are well-structured for LLMs, with high Steps & Tables Scores and the best Headings Structure Score on Product pages. They are also one of the few segments to make use of Q&A sections on product pages, reflected in their Q&A Score.
  • Strength - Accessibility: This is their defining advantage. They have, by a wide margin, the highest 'Accessibility Scores', demonstrating a commitment that pays dividends for AI SEO.
  • Area of Improvement - LLM as a Judge: LLM as a Judge is asking an AI model to generate real world prompts for a page and then testing how well the page content answers them. While their product pages have high scores here, home and category pages can use improvement.
  • Weakness - JSON-LD Implementation: The JSON-LD scores start at a mediocre ~50 on the Home Page, fall to a low ~31 on Product pages and are extremely poor on Category pages, at only ~18. While this is common across segments, this hurts their Structured Data score and what is otherwise a solid performance.

3. Lower Mid-Level Websites

• Overall: This segment closely rivals the Mid-Level sites, proving to be highly adept at creating structured, AI-friendly content. They excel at providing clear, parsable information within their pages.
• Page-Type Performance: Home Pages are exceptionally well-optimized. Product Pages are also strong, while Category Pages lag behind.
• Strengths and Weaknesses:
  • Strength - LLM as a Judge: This is their standout feature. They have the highest scores for 'LLM as a Judge' across all segments, particularly on Home Pages and Product Pages, meaning their content is highly suitable for answering real user prompts in LLMs.
  • Best in Class for Structured Content: They have nearly perfect Steps & Tables Scores on Home and Product pages, are a leader in using Q&A sections on their Product pages, and their Home Pages have the best Headings Structure Score of any segment.
  • Strength - Balanced Performance: They have solid scores across most categories, without the glaring weaknesses seen in other segments.
  • Weakness - Accessibility: While better than Indie and Top sites, accessibility is still an area for improvement compared to the Mid-Level sites.

4. Indie Websites

• Overall: Indie sites hold their own with a respectable average 'Overall Score', performing significantly better than the Top sites.
• Page-Type Performance: There is a clear hierarchy in their optimization efforts. Home Pages are their best-performers, followed by Product Pages. Category Pages are a distant third and represent a major area for improvement.
• Strengths and Weaknesses:
  • Strength - Content Readability on Home Pages: Indie sites have the best score for 'Content Readability' on Home Pages, suggesting a focus on creating strong initial impressions.
  • Strength - Structured Data and 'LLM as a Judge' (on Home Pages/Product Pages): They perform very well on these metrics for their Home Pages and Product Pages, indicating good technical optimization where they focus their efforts.
  • Weakness - Accessibility: This is a critical blind spot. Along with the Top sites, Indie sites have very low 'Accessibility Scores'.

Key Patterns: Structured Data

These are the Structured Data checks AI Page Ready looks for:

• JSON-LD Schema
• Meta Title and Description Tags
• Entity Linking (sameAs, Wikidata IDs)
• Date markup in articles
• Media semantics

• Top Sites are Weakest in Advanced Schema: The Top segment has the poorest implementation of structured data, particularly in the more advanced areas. Their scores for JSON-LD are the lowest across the board, indicating a significant gap in communicating detailed context to AI.
• JSON-LD is a Universal Challenge: While Indie and Lower Mid-Level sites show surprising strength on their Home Pages, every single segment sees a dramatic drop in their JSON-LD Score on Product and Category pages. This is a massive, industry-wide blind spot.
• Basics are Covered, Social is Not (Especially for Top Sites): Most segments handle the basic Meta Tags Score reasonably well, with scores mostly in the 70-90 range. However, there's a huge performance gap when it comes to social metadata. The Top e-commerce sites, in particular, fall dramatically behind with very low Open Graph Score and Twitter Cards Score. Their average scores in these areas are often less than half of what the other segments achieve.
• The "Shopify Factor" is Highly Evident: The strong performance of Mid-Level, Lower Mid-Level, and Indie sites on Open Graph and Twitter Cards strongly suggests their e-commerce platforms are automatically generating this metadata from product information. The failure of the Top sites here points to a systemic issue where their custom-built platforms are not configured to do this.

Key Patterns: LLM Friendly Formatting

These are the LLM Friendly Formatting checks AI Page Ready looks for:

• Details / Q&A Blocks
• Ordered Steps and Tables
• Heading pattern analysis

• Top Sites have a zero Q&A Score: This indicates a significant missed opportunity for them. Indie and Lower Mid-Level sites, on the other hand, are strong adopters of it.
• Top Sites Lack Tabular and Stepped Content: Top sites have the lowest Steps & Tables Score, suggesting their pages are less likely to contain structured formats like "how-to" guides or detailed specification tables that are very useful for LLMs. Other segments, particularly Lower Mid-Level and Indie, excel here.
• Headings are Handled Best by Mid-Tier and Indie Sites: The Headings Structure Score is highest on the Home Pages of Lower Mid-Level and Indie sites. Mid-Level sites have the best-structured headings on their Product pages. Again, Top sites show the weakest performance in this foundational area of content structure.

Key Patterns: Accessibility

These are the Accessibility checks AI Page Ready looks for:

• Critical DOM before JS
• Core Web Vitals

• Code Structure (DOM) is a Key Differentiator: The Critical DOM Score reveals how much content is rendered before JavaScript. If this is low, then it makes it difficult for AI to parse. Mid-Level sites have by far the best scores whereas Top and Indie sites have the worst scores.
• Core Web Vitals Suffers on Product Pages: Nearly every segment sees a drop in their Mobile Performance Score on Product pages. This is a common e-commerce issue, likely caused by large, unoptimized images and third-party scripts that are prevalent on product detail pages.

Key Patterns: LLM as a Judge

These are the LLM as a Judge checks AI Page Ready looks for:

• Embedding Overlap
• LLM Content Clarity Analysis

• Top and Mid-Level Sites have low Semantic Richness: This is determined using the Embedding Overlap Score which uses an AI model to generate real world prompts for a page and then testing how well the page content answers them. But overall, no group has established a clear lead, indicating a universal opportunity to improve the semantic depth of their content.
• Product Pages are a Universal Strength: All segments receive their highest LLM Content Clarity Score on their Product pages. The focused, descriptive, and unambiguous nature of a product page makes it easy for the AI to determine its value and purpose.
• Top Site Content is Judged as Least Clear: The LLM Content Clarity Score is consistently the lowest for the Top segment. This suggests that despite their brand authority, their actual page content is seen as less coherent and purposeful by an AI judge compared to their smaller competitors.

Key Patterns: Discoverability

These are the Discoverability checks AI Page Ready looks for:

• robots.txt rules
• Presence and syntax of sitemap.xml
• HTTP Status Hygiene
• OpenGraph Tags
• Twitter Cards Tags
• llms.txt
• MCP endpoint

• Top Sites Falter on Basics: The most significant finding is that Top sites score surprisingly poorly on foundational discoverability signals. Their Sitemap Score is near zero across all page types, and their Robots Score and HTTP Hygiene Score are the lowest of all segments. This indicates potential gaps in basic technical SEO that other segments have mastered.
• Social Meta Tags are a Weak Point for Top Sites: Top sites have dramatically lower scores for Open Graph (for Facebook) and Twitter Cards compared to all other segments. Mid-Level and Lower Mid-Level sites appear to be the best at implementing these tags, especially on their Product pages.
• Universal Non-Adoption of Newer Protocols: The LLMs.txt Score and MCP Score are zero across all websites and page types. This shows a universal lack of adoption for these more niche protocols.

Key Patterns: Content Readability

These are the Content Readability checks AI Page Ready looks for:

• Readability Score (Flesch-Kincaid and SMOG scores)
• Content Density (real content vs noise)

• Inverse Relationship between Readability and Density: There appears to be an inverse relationship for some segments. Top sites have low Readability Scores but high Content Density Scores, suggesting their content is dense but may be complex or difficult to read.
• Indie Sites Prioritize Readability: Indie sites consistently have the highest Readability Scores across all page types, suggesting a focus on clear, simple language.
• Category Pages Have the Lowest Density: Across all four segments, Category pages consistently receive the lowest Content Density Score. This is expected, as these pages are often visually driven with less text, but it confirms a universal pattern.

Top-tier e-commerce websites often underperform in AI SEO not from a lack of resources, but from the immense weight of their own scale and history. Their reliance on complex, legacy platforms makes it difficult and risky to implement modern updates like structured data, while organizational silos can slow down even minor technical fixes. Historically, these giants have depended on sheer brand authority to rank, but this advantage is diminishing as AI-powered search begins to favor well-structured, directly answerable content over just name recognition, leaving them surprisingly vulnerable.

In contrast, Mid-Level stores, frequently built on modern platforms like Shopify, gain a significant technical head start. These platforms often automate or simplify crucial AI SEO elements like clean sitemaps, social meta tags, and secure infrastructure right out of the box. An extensive app ecosystem further allows these more agile businesses to easily add advanced structured data and accessibility features. This enables them to effectively compete and, in many technical aspects, surpass their larger rivals by being better prepared for the future of search, provided they also invest in good clear content that LLMs love.

The conclusion is clear: brand authority alone is no longer a sufficient moat. E-commerce businesses of all sizes must now prioritize technical AI SEO readiness to ensure they are not just seen, but understood, in a search landscape that values clarity above all else.