A thought-provoking conversation sparked on Twitter after Jono Alderson published an article exploring Schema.org structured data as a tool for helping emerging AI technologies better understand and process online content.
Understanding Schema.org Structured Data
Web content is typically unstructured, meaning it lacks an organized framework that makes each element readable to machines. In contrast, structured data labels elements like images, authors, and text, making it immediately interpretable by algorithms. In SEO, Schema.org structured data markup has traditionally been viewed as a means to qualify a webpage for Google’s rich results. This perspective is evident in most SEO and Schema.org plugins, which focus on structured data solely for potential rich results.
However, new AI technologies now offer more extensive uses for structured data, prompting search marketers to rethink how they implement it. Jono’s article encourages a forward-looking approach: treating structured data as a “data-first foundation” for future AI integrations. He suggests using Schema.org markup not just for rich results but as a way to signal the content’s broader context and relevance across a site. As Jono puts it:
“Don’t shy away from building a connected graph of broader, ‘descriptive‘ schema just because Google’s not showing an immediate return. These ‘descriptive‘ types and relationships might end up being the lifeline between your content and the AI models of the future.”
The article gained traction on Twitter (now X), with Martha van Berkel, founder of SchemaApp, echoing Jono’s ideas. She tweeted:
“I agree with you that the role of schema markup is changing. Building a knowledge graph to manage how your website/content is understood with schema, and then asking it questions will be more important than optimizing for Rich Results or for Google.”
SEO expert Ammon Johns offered a counterpoint, cautioning that structured data, much like meta tags, is self-declared and thus not inherently trustworthy:
“The biggest issue with Schema is that it is largely just self-declaration, no different in essence to META content, and we know how reliable Google decided that stuff was. So Google will use it, but they are unlikely to fully trust it.”
Ammon’s skepticism highlights a known issue with structured data: trustworthiness. Some AI models, like Perplexity AI, address this by relying on a curated index of high-quality sources.
Gagan Ghotra also chimed in, expressing frustration over limited options for structured data in current SEO and structured data tools, which often constrain broader implementation.
The discussion underscores a paradigm shift in how search marketers and webmasters view structured data—not just as a pathway to rich results but as a foundational element for AI-driven understanding in the digital landscape of tomorrow.
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