Optimize your website for AI with Schema Markup

The evolution of SEO with the introduction of AI is transforming how content creators structure information online.

ChatGPT, Perplexity, and other AI-based tools provide immediate and contextual responses, often without requiring users to visit the original websites. Information is delivered directly, eliminating intermediary steps. This fundamentally changes the way web content is written: the main goal is no longer just to drive traffic to a site but to ensure that AI can understand it. It is essential to optimize content for AI algorithms, making it clear, structured, and authoritative. Only then can a site become a reliable reference source, chosen by AI to provide users with precise and relevant answers.

Let’s explore how websites can optimize their content to be recognized as trustworthy sources and cited by AI, using a specific tool like schema markup.

Schema Markup: Optimization for AI

One of the most effective tools for making content easily readable by artificial intelligence is schema markup. Created in 2011 through a joint initiative by Google, Bing, Yahoo, and Yandex, schema markup provides AI algorithms with structured and specific details about various content elements. This system consists of metadata embedded within HTML pages, invisible to the user but crucial for search engines and AI.

Schema markup enables the identification of elements such as the type of business described on the website, operating hours, location, reviews, and even current offers. This improves the accessibility of key information and clarifies the relationships between informational elements. It helps AI assess the site as clear and complete. A well-structured site becomes a preferred source, chosen by AI to provide reliable answers to users.

This represents a shift in perspective: whereas the primary goal of SEO used to be attracting audiences with persuasive and well-structured content, the focus now shifts to the clarity and readability of data for algorithms.

An Example of Schema Markup for a Restaurant

To better understand how schema markup works and why it is advantageous for SEO in the age of AI, let’s consider a practical example of a restaurant. Suppose a restaurant called "Trattoria Bella Napoli" wants to improve its visibility for local searches. Here’s how it might structure its content using schema markup:

{
  "@context": "https://schema.org",
  "@type": "Restaurant",
  "name": "Trattoria Bella Napoli",
  "address": {
    "@type": "PostalAddress",
    "streetAddress": "Via Roma 123",
    "addressLocality": "Naples",
    "postalCode": "80100",
    "addressCountry": "IT"
  },
  "telephone": "+39 081 1234567",
  "openingHoursSpecification": [
    {
      "@type": "OpeningHoursSpecification",
      "dayOfWeek": ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday"],
      "opens": "12:00",
      "closes": "23:00"
    }
  ],
  "servesCuisine": "Italian",
  "menu": "https://www.trattoriabellanapoli.it/menu",
  "priceRange": "$$",
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.5",
    "reviewCount": "240"
  },
  "acceptsReservations": "True",
  "reservationUrl": "https://www.trattoriabellanapoli.it/reservations"
}

How It Works and Why It’s Effective

In this example, the restaurant provides detailed information about its opening hours, cuisine type, address, phone number, menu, and reviews. Each detail is structured in a format that artificial intelligence can easily read and understand. These data, embedded through schema markup, communicate to ChatGPT exactly what to expect from "Trattoria Bella Napoli," making it a potential source for answering local questions like "Where to eat Italian cuisine in Naples?" or "Restaurants open in Naples until 11 PM."