In the evolving world of search engine optimization, where artificial intelligence increasingly dictates how information is surfaced, a pressing question has emerged: Does implementing structured data, often through schema markup, truly enhance visibility in AI-powered search features like Google’s AI Overviews? Recent experiments and industry analyses suggest the answer might not be as straightforward as many marketers hoped.

A comprehensive test conducted by SEO experts, detailed in an article from Search Engine Land, pitted pages with and without schema markup against each other to gauge their performance in AI-generated responses. The results were telling—structured data did not provide a discernible edge in getting content featured in these overviews, challenging long-held assumptions about its role in modern SEO strategies.

As AI transforms search dynamics, professionals are reevaluating the tools that once promised richer visibility, only to find that schema’s impact on generative AI outputs remains limited based on empirical tests.

Delving deeper into the experiment, researchers created duplicate content sets, one enhanced with detailed schema for elements like FAQs and products, and monitored their inclusion in AI Overviews over several weeks. Despite meticulous implementation, the schema-enriched pages showed no consistent advantage, appearing in overviews at rates similar to their unmarked counterparts. This finding aligns with broader sentiments echoed in posts on X, where SEO insiders like Aleyda Solis have highlighted ongoing debates about structured data’s efficacy in AI contexts.

Further supporting this, a report from Search Engine Roundtable referenced multiple tests concluding that schema does not yet boost AI search visibility, urging caution against over-reliance on it. Industry observers note that while schema aids traditional search features like rich snippets, its influence on large language models powering AI searches appears negligible, as these systems prioritize content quality and relevance over structured annotations.

With generative AI relying more on natural language understanding than rigid data formats, the traditional benefits of schema markup are being overshadowed, prompting a shift toward content-centric optimization strategies.

However, not all voices dismiss schema entirely. An analysis in Search Engine Journal posits that structured data serves as a “strategic data layer” for enterprises, potentially aiding AI in contextualizing information even if direct visibility gains are elusive. For e-commerce, guides from sites like Get Passionfruit advocate schema for products to drive sales through AI recommendations, suggesting niche benefits in specific sectors.

Recent news from Jitendra.co questions whether structured data falls short in AI visibility for 2025, emphasizing that while it enhances machine readability, it may not translate to prominence in AI answers. This is echoed in X discussions, where figures like Matt Diggity share methods for dominating AI platforms through entity optimization rather than schema alone.

Amidst these mixed signals, forward-thinking SEO practitioners are integrating schema as part of a broader toolkit, combining it with AI-specific tactics like LLMs.txt files to ensure content is accessible and interpretable by emerging search technologies.

Looking ahead, experts from Geostar recommend mastering schema for AI optimization, arguing it helps systems cite and recommend content more accurately. Yet, the consensus from sources like Quoleady is that while schema impacts large language model visibility to some degree, it’s not a silver bullet—content authority and freshness play larger roles.

In practice, businesses are advised to audit their schema implementations not just for compliance but for alignment with AI’s semantic processing. As Sundar Pichai noted in an X post about Google’s expansions, 2025 promises significant search innovations, potentially elevating schema’s role if integrated with evolving AI capabilities. Ultimately, while experiments cast doubt on its immediate benefits, structured data remains a foundational element, poised for greater relevance as AI search matures.