If you've heard the terms "structured data" or "schema markup" and quietly moved on because they sound technical, this article is for you. Understanding what structured data is — and why it's now fundamental to being found in AI search — doesn't require a developer background. It requires about ten minutes and this article.
Here's the short version: 68% of Australian small business websites have no schema markup at all. The businesses that do have it are 2.5x more likely to appear in AI-generated answers. That gap is your opportunity.
What Is Structured Data?
Structured data is machine-readable code added to a web page that tells search engines — and AI systems — exactly what the page is about. It doesn't change how the page looks to human visitors. It's an invisible layer of metadata that communicates meaning to machines.
Think of it this way. When a human reads your "About" page, they understand from context that you're a Melbourne-based web design agency, that you've been operating since 2020, and that you're located in Mulgrave. They pick up these facts naturally from the text.
A search engine or AI crawler reads raw HTML. Without structured data, it has to guess what's a business name versus a heading, what's an address versus a description, what's a service versus a product. It gets most of it right most of the time — but uncertainty reduces confidence, and reduced confidence means reduced citation likelihood.
With structured data, you explicitly state: "This is my business name. This is my address. These are my services. These are my reviews. Here is how to contact me." No guessing required.
The most common format for structured data is JSON-LD (JavaScript Object Notation for Linked Data). It's Google's preferred format because it sits in the <head> of your HTML as a separate script block, cleanly separated from your visual content.
Why AI Loves Structured Data
Before March 2026, schema markup was primarily about rich results — the star ratings, FAQ dropdowns, and review snippets you occasionally see in Google search results. Valuable, but optional.
Google's March 2026 structured data update changed this. Schema markup now influences both traditional rich result eligibility and AI Mode source selection. In plain English: it's now a factor in whether you get cited in AI-generated answers, not just whether you get a rich snippet.
The research backs this up:
- Pages with proper schema markup have a 2.5x higher chance of appearing in AI-generated answers
- Schema can increase overall AI visibility by up to 30% (Averi.ai, 2026)
- Only 3% of Australian SMB websites currently have the complete AI-optimised schema stack (Article + FAQ + HowTo + Author)
When AI models retrieve content to synthesise an answer, they have to decide which sources to trust. Schema provides explicit, structured evidence of what a page is and who it's from. That explicit evidence is far more persuasive to an algorithm than implicit inference.
The Most Important Schema Types for Australian Small Businesses
LocalBusiness Schema
This is the foundation. LocalBusiness schema identifies your business to every AI system and search engine, telling them your exact name, address, phone number, operating hours, service area, and links to your social profiles.
Here's a working example you can adapt:
{
"@context": "https://schema.org",
"@type": "LocalBusiness",
"name": "Your Business Name",
"image": "https://yourdomain.com.au/images/your-business.jpg",
"url": "https://yourdomain.com.au",
"telephone": "+61-3-XXXX-XXXX",
"address": {
"@type": "PostalAddress",
"streetAddress": "123 Example Street",
"addressLocality": "Melbourne",
"addressRegion": "VIC",
"postalCode": "3170",
"addressCountry": "AU"
},
"geo": {
"@type": "GeoCoordinates",
"latitude": -37.8136,
"longitude": 144.9631
},
"openingHoursSpecification": [
{
"@type": "OpeningHoursSpecification",
"dayOfWeek": ["Monday","Tuesday","Wednesday","Thursday","Friday"],
"opens": "09:00",
"closes": "17:00"
}
],
"priceRange": "$$",
"sameAs": [
"https://www.facebook.com/yourbusiness",
"https://www.linkedin.com/company/yourbusiness"
]
}Key detail for Australian businesses: use "addressCountry": "AU" and include your full postcode. AI models use this to confidently identify you as a Melbourne or Australian entity when answering geo-specific queries.
FAQPage Schema
FAQPage schema is arguably the highest short-term ROI schema type for AI citation. AI models extract FAQ answers almost verbatim — meaning your FAQ answers can appear word-for-word in AI Overviews, ChatGPT responses, and Perplexity citations.
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [
{
"@type": "Question",
"name": "How much does a website cost in Australia?",
"acceptedAnswer": {
"@type": "Answer",
"text": "A professional website for an Australian small business typically costs between $1,500 and $8,000 depending on scope. Simple brochure sites with 5–8 pages start around $1,500–$2,500. E-commerce sites with product catalogues and payment processing range from $3,000 to $8,000+. Ongoing hosting and maintenance adds $50–$200 per month."
}
}
]
}Critical warning: post-March 2026, Google penalises FAQPage schema added to pages that don't genuinely contain FAQ content. Only implement this schema on pages that actually have questions and answers. Schema padding — adding FAQ schema to a landing page to game the system — now triggers eligibility restrictions rather than rewards.
Review / AggregateRating Schema
Review schema communicates your star rating and review count to AI systems. When an AI Overview answers "who is the best [service provider] in [suburb]?", review signals play a significant role in which businesses get cited.
{
"@context": "https://schema.org",
"@type": "LocalBusiness",
"name": "Your Business Name",
"aggregateRating": {
"@type": "AggregateRating",
"ratingValue": "4.9",
"reviewCount": "47",
"bestRating": "5",
"worstRating": "1"
}
}Only implement this if you actually have reviews. And keep it updated — stale review counts can undermine trust signals.
Service Schema
Service schema tells AI models exactly what you offer. This is particularly valuable for service businesses where the services aren't self-evident from the business name.
{
"@context": "https://schema.org",
"@type": "Service",
"serviceType": "Web Design",
"provider": {
"@type": "LocalBusiness",
"name": "Your Business Name"
},
"areaServed": {
"@type": "State",
"name": "Victoria"
},
"description": "Custom website design and development for small businesses across Melbourne and Victoria. Includes mobile-responsive design, SEO setup, and ongoing maintenance options.",
"offers": {
"@type": "Offer",
"priceSpecification": {
"@type": "PriceSpecification",
"priceCurrency": "AUD",
"price": "1500",
"minPrice": "1500"
}
}
}BreadcrumbList Schema
BreadcrumbList schema helps AI systems understand your site structure — which matters for multi-page sites where the relationship between pages carries meaning (e.g., Services > Web Design > E-commerce Web Design).
{
"@context": "https://schema.org",
"@type": "BreadcrumbList",
"itemListElement": [
{
"@type": "ListItem",
"position": 1,
"name": "Home",
"item": "https://yourdomain.com.au"
},
{
"@type": "ListItem",
"position": 2,
"name": "Services",
"item": "https://yourdomain.com.au/services"
},
{
"@type": "ListItem",
"position": 3,
"name": "Web Design",
"item": "https://yourdomain.com.au/services/web-design"
}
]
}What 68% of Australian SMBs Are Missing
The adoption gap in Australia is both a problem (for the 68% who have no schema) and an opportunity (for the 32% who do, and especially the 3% who have it fully implemented).
Here's what's typically missing:
The businesses that implement schema now — while most competitors haven't — will establish citation authority that compounds over time. AI models are more likely to cite sources they've learned to trust over repeated encounters. Getting into that loop early matters.
How to Test Your Schema
Before schema can help you, it needs to be valid. Use these free tools to test:
Google Rich Results Test (search.google.com/test/rich-results): paste your URL or code and see which rich results your schema qualifies for, plus any errors.
Schema.org Validator (validator.schema.org): checks your JSON-LD against the Schema.org specification for structural correctness.
Google Search Console: after deploying schema, check the "Enhancements" section. It shows which schema types Google has detected on your site and any validation errors.
Common errors to watch for: missing required fields, incorrect @type values, mismatched quotes in JSON, and schema that describes content that isn't actually on the page.
How to Add Structured Data
Manually (with a developer): The most reliable approach. A developer adds JSON-LD script blocks to the <head> of your pages. For static pages like your homepage and service pages, this is a one-time task.
CMS plugins: WordPress users can use Rank Math or Yoast SEO to add basic LocalBusiness and FAQ schema through a visual interface without coding. These tools handle the JSON-LD output automatically based on your inputs.
Via CodeQy: Every website we build includes LocalBusiness, FAQPage, Service, and Organisation schema as standard. We don't charge extra for it — it's part of how we build every site, because AI-readiness is now a baseline, not an upgrade.
For the full context on why structured data is part of a broader AI SEO strategy, read our complete AI SEO guide for Australian businesses.
Want us to implement schema markup on your existing website?
CodeQy can audit your current structured data, identify gaps, and implement the full AI-optimised schema stack — typically within 2–3 business days.