Schema is how you control what it says.
In 2024, schema markup was good to have. In 2026 it is documented infrastructure — confirmed by Google, Microsoft, and independent research as one of the primary signals AI systems use to decide whether to cite your business accurately, guess about it, or ignore it entirely.
The numbers below are not projections. They are documented findings from 2025 and 2026 studies, confirmed platform statements, and third-party measurement panels. The Treasure Valley has approximately 28,000 businesses. Most have a schema score under 30%. This page exists to explain what that means and what to do about it.
Every website serves two audiences simultaneously. The human who reads it. The machine that processes it. Schema is the bridge between the two — a standardized vocabulary that lets a website declare what it is in a format any machine can read, parse, and reason from.
The technical format is JSON-LD — JavaScript Object Notation for Linked Data. It lives in the <head> of an HTML document as a script block. Invisible to human visitors. Structural to every machine that processes the page — search engines, AI systems, voice assistants, knowledge graph crawlers, and every automated system that reads the web.
The vocabulary is schema.org — 827 types, 1,528 properties, covering every category of entity that exists on the web. Business name. Location. Hours. Services. Relationships to industry, geography, and adjacent entities. All of it typed, labeled, and machine-readable in a format that search engines and AI systems are built to read.
When your website has no schema markup, AI systems must infer everything about you from unstructured text — and inference produces hallucination. When your website has full schema coverage, AI systems read confirmed facts. The difference between a business AI hallucinates about and a business AI cites accurately is, in large part, the difference between no schema and verified schema.
@context declares which vocabulary you're using. Always https://schema.org.
@type declares what kind of entity this is. Restaurant. Plumber. Dentist. School. Church. One of 827 defined types.
@id is the canonical URL for this entity — the address of its identity on the web. Used by knowledge graphs to resolve the same entity across multiple sources.
sameAs is one of the most powerful properties. It declares that this entity is the same as the entity at another URL — your Google Business Profile, your Yelp listing, your Wikipedia page. Every sameAs link is a confirmed graph edge connecting your entity to an authoritative external record.
knowsAbout declares what topics, regulations, industries, and concepts your entity is associated with. This is how AI systems build context around what you do and where you operate.
In 2011, four competing companies — Google, Microsoft, Yahoo, and Yandex — agreed on a shared vocabulary for structured data on the web. They launched schema.org with 297 types and 187 properties. One standard. Every major search engine would accept it. This was an extraordinary act of cross-competitor cooperation, driven by a shared recognition that the web's data layer was too fragmented to be useful to anyone.
The three engineers who drove schema.org into existence were R.V. Guha at Google — who had previously created RSS and co-led the Cyc project — Dan Brickley, who had contributed to the Semantic Web project at W3C, and Steve Macbeth at Microsoft. Their work is documented in a 2016 paper published in ACM Communications: cacm.acm.org/practice/schema-org/
Within four years of launch, 31.3% of pages in the Google index carried schema.org markup. The vocabulary has grown continuously — version 30.0 released March 25, 2026 contains 827 types and 1,528 properties. The W3C issued JSON-LD 1.1 as a full Recommendation on 16 July 2020 — the highest level of endorsement the standards body issues. As of March 2026, 53.2% of all websites use JSON-LD — up from 18.1% in January 2018.
| Format | 2018 | 2020 | 2022 | 2024 | Jan 2026 | Mar 2026 |
|---|---|---|---|---|---|---|
| Open Graph | 36.0% | 46.1% | 60.1% | 65.5% | 69.8% | 70.3% |
| Twitter / X Cards | 19.3% | 29.8% | 46.7% | 50.1% | 55.3% | 56.0% |
| JSON-LD ◈ | 18.1% | 28.2% | 41.3% | 46.5% | 52.5% | 53.2% |
| Generic RDFa | 13.1% | 13.5% | 32.4% | 38.9% | 39.4% | 39.0% |
| Microdata | 13.1% | 15.3% | 21.5% | 24.7% | 23.1% | 22.7% |
| No structured data | 55.1% | 44.3% | 30.3% | 25.1% | 21.7% | 21.4% |
These statistics measure binary presence — JSON-LD exists on a page or it does not. They do not measure coverage depth against the schema.org vocabulary. Boise Standard measures schema coverage as a percentage of the available vocabulary for a given entity type, scored against all 827 types and 1,528 properties. Most Treasure Valley businesses score under 30% coverage — meaning AI systems are still guessing about 70%+ of their most important properties.
Schema.org launched in 2011 supporting three implementation formats. All three declare the same information. They differ in how they integrate with your HTML — and that difference determines how reliably machines can read them.
Microdata embeds structured markup directly inside HTML tags using special attributes. The data layer and presentation layer share the same code. When your front-end code changes, your schema can break silently. Maintenance burden is high.
RDFa — Resource Description Framework in Attributes — is the more expressive academic format, implemented through HTML attributes. More flexible than Microdata. More complex. Adoption has been declining since 2022.
JSON-LD separates the structured data entirely from the visible HTML. Schema lives in a <script type="application/ld+json"> block in the document head — a self-contained JSON object that declares the entity without touching front-end code. Google began recommending JSON-LD in 2014. W3C issued it as a full Recommendation in 2020. In October 2025, SearchVIU confirmed that ChatGPT, Claude, Perplexity, and Gemini all process JSON-LD preferentially when accessing pages directly. It is the unambiguous standard format across all major search and AI systems. Use JSON-LD. The other formats are legacy.
Search engines historically used schema as a ranking signal — structured data provided explicit signals that improved result accuracy and enabled rich results like star ratings, opening hours, and price displays. Large language models introduced an entirely different and more consequential relationship with structured data.
AI systems construct entity models. When a system processes a query about a business, a location, or a concept, it draws from an entity model built during training and retrieval. Schema.org markup is one of the primary structural inputs into that model. Every declared property is a confirmed fact the system can reason from without inference. Every declared relationship is a graph edge — a confirmed connection between entities the system uses to build context.
The critical distinction: without schema, AI infers. With schema, AI reads. Inference produces hallucination — wrong hours, wrong services, wrong ownership, fabricated details. Reading confirmed data produces accurate citation. For a business in Nampa or Meridian, the difference between those two states is the difference between AI telling customers accurate information and AI sending customers to a competitor.
Schema coverage measures how many of the available properties for a given entity type are declared. Every entity type in the schema.org vocabulary has a defined set of applicable properties — every field that could meaningfully be declared for that type. Coverage is the ratio of declared properties to available properties, expressed as a percentage.
A plumbing company in Nampa with only a name and phone number declared has roughly 6% schema coverage. The AI system reading that profile can confirm two facts. Everything else — services, hours, service area, ownership, licensing, certifications, relationships to other entities — must be inferred. Inference means guessing. Guessing means hallucination.
A plumbing company with full schema coverage has 30+ properties declared. The AI system reading that profile can confirm everything. It cites accurately. It routes customers correctly. It represents the business as the business actually is. Coverage depth is the difference between a profile AI trusts and a profile AI guesses about.
You do not need to be a developer to implement schema on your website. You need to understand what it is, where it goes, and how to validate it. The six steps below take a typical business from zero schema to a validated, AI-readable entity profile. Total time for someone with basic website access: under an hour.
<head> section of your homepage HTML — before the closing </head> tag. If you use WordPress, the Yoast SEO or Rank Math plugins handle this without touching code. If you use Squarespace, Wix, or Webflow, each platform has a custom code or header injection section where it goes. It is invisible to website visitors. Only machines read it.Production-ready JSON-LD for six common Treasure Valley business types. Each block targets maximum field coverage for the entity class. Copy directly into the <head> of your HTML inside a <script type="application/ld+json"> tag. Replace placeholder values. Validate at search.google.com/test/rich-results.
The Treasure Valley has approximately 28,000 registered businesses. Every major AI system — ChatGPT, Google Gemini, Perplexity, Microsoft Copilot, Apple Intelligence — is answering questions about these businesses right now. When a customer in Eagle asks ChatGPT "who is the best HVAC contractor in Meridian?" or "what are the hours for [your business]?" — the AI is constructing its answer from whatever data it can find.
For most Treasure Valley businesses, that data is unstructured, unverified, and schema-poor. The AI guesses. Wrong hours reach customers. Wrong services get cited. Competitors with better structured data get recommended instead.
The schema standard exists to fix this — and it is free to implement. The six templates above are production-ready. The validation tool is free. The step-by-step guide in Section 6 takes under an hour. You can do this today.
What Boise Standard adds beyond self-implementation is dual provenance — a second, independent, graph-connected record of your verified business on the most machine-readable regional directory built specifically for this moment. When your sameAs array includes your Boise Standard entity URL alongside your Google Business Profile, AI systems have cross-referenced, independently verified sources confirming your information. That cross-referencing is what builds citation confidence at the graph level.
Every claim on this page traces to a primary document. Every statistic has a source. Every platform confirmation is cited. This is not a marketing page — it is a reference document. The sources below are the evidence.