Short answer: Schema.org markup helps, but AI models also lean heavily on clean HTML structure — real headings, real lists, real tables — and on consistent facts repeated across a site, not just formal markup.
Schema gets most of the attention in structured-data conversations, but it’s one signal among several. A page can be highly “readable” to a model without a single line of JSON-LD, and a page stuffed with schema can still confuse a model if the surrounding HTML is a mess.
Real headings and lists do real work
Using actual <h2>/<h3> tags and <ul>/<ol> lists (instead of bold text pretending to be a heading) gives a model a clean map of your content’s structure to parse.
Tables are underused for comparison content
A genuine HTML table for pricing, comparisons, or specs is easier for a model to extract accurately than the same information written out in prose.
Repetition of key facts across the site reinforces confidence
A fact stated once is a claim. A fact stated consistently across your homepage, footer, and relevant pages reads as more reliable, whether or not it’s also in schema.
Frequently Asked Questions
Is schema markup still worth adding?
Yes, it’s still a clear, explicit signal and worth implementing — it’s just not the only thing that matters.
Do heading tags need to match visual size exactly?
No, but they should follow a logical hierarchy (one H1, then H2s, then H3s) rather than being styled text with no real tag.
What’s the easiest structural fix to start with?
Auditing your pages for fake headings (bold text used instead of real heading tags) is usually the fastest win.
Want a read on how your site’s structure looks to AI models? Run a free AI visibility scan.
