A post on LinkedIn raised doubts about the idea that Schema.org structured data can influence ranking in AI search engines. The post questioned the belief held by some SEOs that structured data could be used to improve rankings in AI search engines like ChatGPT.

In his LinkedIn post, Patrick Stox commented, “Did I miss something? Why do SEOs think schema markup will impact LLM output?” The term “LLM output” is likely referring to large language model outputs in the context of AI-driven search engines. This raises the question: do AI search engines rely on structured data to gather their information?

Large language models (LLMs) like ChatGPT are trained on a vast range of data sources, including web text, books, government records, and legal documents. They process this data to produce summaries and answers without directly copying or plagiarising the original sources. As such, the idea that optimising web content for Schema.org structured data would influence how an LLM responds seems misguided.

AI search engines operate based on search indexes and knowledge graphs, with Retrieval Augmented Generation (RAG) playing a central role. Search indexes themselves are built from crawled data, not from Schema structured data. For example, Perplexity AI ranks content from web crawls using a modified version of PageRank. Major search engines like Google and Bing follow a similar process, crawling and manipulating text data extracted from HTML, without relying heavily on structured data.

Google only utilises a small portion of Schema.org structured data for certain types of search experiences and rich results, which limits its applicability for publishers. This is an important point because it underscores that not all structured data is relevant for improving search rankings or visibility.

Moreover, both Bing and Google’s crawlers render HTML, identifying key elements like headers, footers, and main content. They extract text from these sections for ranking purposes, raising the question: if they are capable of doing this, why would they need to rely on Schema structured data for ranking?

The idea that using Schema.org structured data will help in ranking better with AI search engines is based more on speculation than fact. It could be a case of a miscommunication, where an original idea is misinterpreted and exaggerated over time—what one person proposes gets distorted through repeated sharing. For instance, Jono Alderson suggested that structured data could serve as a standard to help AI search engines understand the web better. However, he wasn’t claiming that AI search engines currently use it, just that it could be a helpful consideration for future adoption. This idea seems to have evolved into a broader theory, with the message becoming twisted by the time it reached several others in the SEO community.

Unfortunately, there are many such unfounded claims circulating within SEO circles. A recent example involved an SEO posting on social media that Google Local Search doesn’t use IP addresses to tailor “near me” search results. Testing this could easily debunk the claim—by using a VPN and selecting a different geographic location, one can see that the IP address affects the results for “near me” searches. This clearly shows the importance of verifying claims before accepting them as truth.

 

Schema.Org Structured Data And AI Search Results

Google only utilises a small portion of Schema.org structured data for specific types of search experiences and rich results. This means that publishers are limited in how they can use this structured data to affect their search rankings. Since structured data isn’t as broadly applied as some may believe, it suggests that optimising your content specifically for Schema markup won’t necessarily lead to better results, especially when it comes to AI search engines like ChatGPT Search and others.

Both Bing and Google’s crawlers render HTML, identifying the key components of a web page such as headers, footers, and the main content. These crawlers then extract text from these elements for ranking purposes. If they’re already capable of doing this, one might question why these search engines would rely on Schema structured data to improve rankings. Given this functionality, it seems clear that the need for structured data isn’t as critical to ranking as some might suggest.

The notion that Schema.org structured data can boost rankings in AI search engines isn’t backed by solid evidence; it seems to be little more than speculation. This could be the result of misinformation or a simple “game of telephone,” where an idea gets passed along and distorted through multiple interpretations. For instance, SEO expert Jono Alderson proposed that structured data could potentially become a useful standard for AI search engines to better understand the web. However, Alderson didn’t claim that AI search engines currently use structured data; he merely suggested that they could in the future. Unfortunately, this subtle proposal appears to have been misunderstood and exaggerated into a broader theory, leading many SEOs to believe that Schema markup is already essential for ranking in AI-driven search engines.

This misinterpretation is part of a larger trend of unverified claims floating around in the SEO community. A recent example involved an SEO on social media who suggested that Google Local Search does not use IP addresses to influence “near me” search results. Testing this claim is straightforward—by using a VPN and selecting a different location for the IP address, users can see first-hand that their search results are indeed affected by the geographical location associated with the IP. This simple test reveals the importance of verifying claims and debunking myths before they gain traction in the community.

It’s essential for SEO professionals and marketers to rely on facts rather than speculative ideas, especially when it comes to emerging technologies like AI search engines. As AI continues to evolve and shape search behaviours, it’s important to stay grounded in practical, evidence-based approaches rather than jumping on bandwagons that may not have a solid foundation. While it’s exciting to think about new opportunities with AI-driven search, the most effective strategies are those rooted in proven, current methods that align with the way search engines are functioning today.

 

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