A human reader can look at your page and infer that this is a company, that is a price, and those are your opening hours. A machine cannot, unless you tell it. Structured data is the plain, standardised way of labelling the facts on your pages so a search engine or an assistant knows exactly what each one means. With over a billion people now relying on tools that read the web this way, being explicit about your facts has moved from technical nicety to competitive basics.
What to label first
You do not need to mark up everything. Start with the facts an assistant most often needs to name or recommend you: who you are as an organisation, what your products are and what they cost, your frequently asked questions, and any step-by-step guidance you publish. These map onto the schema.org types for Organization, Product, FAQPage and HowTo, and together they cover most of what a buyer would ask an assistant about you.
A minimal Organization label looks like this, dropped into the page as a script the crawler reads and the visitor never sees.
{
"@context": "https://schema.org",
"@type": "Organization",
"name": "Your Brand",
"url": "https://yourbrand.com",
"description": "One clear sentence on what you do and who you serve.",
"sameAs": [
"https://www.linkedin.com/company/yourbrand",
"https://x.com/yourbrand"
]
}What this means for your business
Treat structured data as writing your facts down in the language machines read, then keeping them accurate. Label your organisation, your products, your FAQs and your guides, and make sure the values match what the page actually says, because a mismatch confuses more than it helps. Done well, it lets an assistant describe you correctly rather than guessing, which is the difference between a right answer and a wrong one about your brand. The question-and-answer side of this sits in FAQ and how-to structure for AI answers, and access basics in llms.txt, explained.