Structured Data in an AI-Driven Search Era – What You Should (and Shouldn’t) Do

Most folks who work in SEO have heard plenty of mixed advice about structured data. Some swear you need to mark up everything. Others say Google’s AI can figure out almost anything on its own. This post is for people caught in the middle — the ones trying to sort out what’s actually worth their time and what isn’t in an AI-heavy search environment. It’s a quick guide to knowing when schema markup helps, when it adds nothing, and how to make smarter calls.

The Schema Dilemma in Modern SEO

I recently found myself explaining the difference between structured data and schema markup to a colleague. He was surprised when I said you don’t always need to mark up every little thing on your site.

Here’s the distinction:

• Structured data is any structured description of information. It can take multiple forms (JSON-LD, Microdata, RDFa, feeds, APIs, etc.).
• Schema.org is the vocabulary — the “names and labels” for describing things like products, events, FAQs, and people.
• Schema markup is the actual code you put on the page (typically JSON-LD) using the Schema.org vocabulary.

A simple way to think about it:

Structured data is the concept. Schema.org is the dictionary. Schema markup is the sentence you write using that dictionary.

With that in mind: Use schema markup only when a search engine would struggle to understand your page without clearer structure.

An FAQ page or how-to guide? Usually easy for Google to understand from plain text alone. A complex product page with prices, sizes, colors, reviews, SKU, availability? That benefits from schema markup because it translates those details into structured data a machine won’t misread.

• Simple page: Schema markup is optional.
• Complex page: Schema markup prevents lost meaning and keeps Google from guessing.

Search engines are smarter than they used to be. With current AI systems, they can interpret much of a page from the natural structure of HTML. That doesn’t make structured data irrelevant — it just means you should be selective about where you add schema markup.

Price feeds, Schema.org markup, product data feeds

Product, Review, Event

<script type="application/ld+json">…</script>

Schema Markup as Training Wheels

Think of structured data like training wheels on a bicycle for search engines. In the early days, those training wheels were a lifesaver — they helped a “wobbly” younger Google keep its balance and understand what our pages were about. We added schema markup for everything under the sun because the search engine needed that extra guidance to avoid misunderstandings. We labeled questions and answers with FAQ schema, wrapped step-by-step instructions in How-To schema, marked up recipes, events — all to spell things out clearly. Back then, those training wheels often led to rich search results and snippets, rewarding the effort.

But what happens when the kid grows up and gets better at riding? At some point those training wheels start slowing them down. Today’s search engines have grown up, at least for a lot of basic content. With modern AI and natural language processing, Google can understand the intent and structure of your content without as much hand-holding.

In fact, Google itself has started to phase out or reduce certain rich results that depended on schema. In August 2023 Google announced it was downgrading the visibility of FAQ rich results for most sites and removing How-To results on mobile — telling publishers that marking up every Q&A or tutorial isn’t a guaranteed ticket to a special snippet anymore. For most websites, FAQ rich results “will no longer be shown regularly,” except for a few authoritative cases. This was a big sign of the times: The search engine has gotten better at finding the question-and-answer pairs or the how-to steps in your content on its own (and it also wanted to declutter the results).

The training wheels analogy is key: When the path is smooth and straight, you don’t need extra support. Likewise, when your content is simple or inherently structured by itself, adding schema markup on top can be overkill.

A straightforward FAQ section on a page – with clearly written questions and answers in HTML – is usually crystal clear to Google’s algorithms and AI without any special JSON-LD code. An article with a list of steps like “Step 1, Step 2, Step 3…” is a how-to guide, even if you didn’t explicitly wrap it in <script type=”application/ld+json”>. In other words, for many scenarios, Google has learned to ride without training wheels.

As one SEO expert, Carolyn Shelby at Search Engine Journal put it recently: “Structured data is optional. Structured writing and formatting are not.” The way you structure your content on the page (headings, lists, clear formatting) is now more important for AI-driven search results than a bunch of schema tags. If you present information in a logical, labeled way, an AI like Google’s can extract a FAQ or a how-to answer directly from your page — no schema required.

When Search Engines “Get It” (No Schema Required)

What can you safely skip marking up? Anything an AI can easily infer from context, including frequently asked questions (FAQs), how-to guides, and standard articles.

• Frequently Asked Questions: If your FAQ section has a clear question followed by an answer, Google can identify that Q&A pair on its own. Google’s generative AI can answer user queries by pulling from your FAQ content even without markup. If your page says “Q: How long does shipping take? A: Shipping takes 5-7 business days”, you don’t need JSON-LD for an AI to understand it.
• How-To Guides: If your post clearly outlines numbered steps, AI-based search can follow that. LLM-powered answers can pull steps from your content without needing formal markup. Write a well-structured how-to with concise steps, and you’re feeding Google’s AI the answer in plain text. I’ve seen cleanly written tutorials summarized by Google’s AI answer box with zero schema.
• Standard Articles: Normal articles with headlines and subheadings might not need special schema beyond basic Article type (which many CMSes add by default). Google can parse title, author, publish date from HTML. According to Google’s documentation, unused structured data isn’t harmful, but it has “no visible effects in Google Search.” If your writing is clear, AI can ingest your page and figure out key points.

Don’t use schema just for the sake of using schema. If you’re adding FAQ markup to a page that isn’t really an FAQ, you won’t fool Google into a rich snippet. Google’s AI is looking for real, valuable content presented clearly.

There’s a practical cost to unnecessary schema: maintenance overhead. While modern schema implementation is often automated and data-driven, an astonishingly high number of people who misunderstand schema generate static payloads without realizing the ongoing need to maintain them. Every time you add structured data, you’re creating a second representation that must stay in sync with the page. Change a price? Update both HTML and schema. This schema-content drift creates error opportunities. For simple content that search engines already understand, you’re adding technical debt with little benefit.

If your content is simple enough that a human reader instantly gets the layout and purpose, an AI likely will too.

Where Schema Still Shines: Complex Data and Rich Results

Before you rip all JSON-LD out, know where structured data is still worth the effort: Some content is inherently complex or ambiguous unless you provide explicit structure. Schema markup can make the difference between search engines understanding your page versus guessing, especially with e-commerce product pages, complex entities like events and recipes, job postings, and more.

Product Pages (E-commerce)

Product pages are the poster child for useful structured data. Think about everything on a typical product page: product name, images, price, currency, availability (in stock or not), SKU, reviews and ratings, maybe multiple color or size options, etc. That’s a ton of information. Yes, Google can crawl the text on the page, but it might not confidently connect all the dots. Is that string of numbers the price or the model number? Is “S” a size or part of the title?

Structured data helps by explicitly telling the search engine “This is the price, this is the size, this is the color, this is the average rating, and here’s a structured review.” It removes ambiguity. Google’s systems still love well-structured product data because it enables rich results like product carousels, star ratings, and pricing info right on the SERP. If you sell products, implementing Product schema (and related types like Offer for price/stock, Review for ratings) is recommended.

In fact, Google continues to expand support for product-related schema. A recent addition is the ProductGroup schema, which lets you group all the variants of a product (say, a shirt that comes in different colors and sizes) under one umbrella. This is useful for e-commerce sites with variant products. By using ProductGroup with properties like variesBy, hasVariant, and productGroupID, you give Google a clear map of how your products are related and what the options are.

The payoff? Your product might become eligible for special displays that show variant information — for example, showing multiple colors or prices in one result. Without that structured data, Google might only treat each variant as a separate product or miss the connections between them. Complex commerce data is exactly where schema earns its keep.

Other Complex Entities

In general, any content that has a lot of attributes or needs disambiguation is a good candidate for schema. Here are a few examples:

• Events have dates, locations, performers, and ticket prices, so marking those up with Event schema can help Google present your event in a nice calendar snippet or list. 
• Recipes benefit from schema (Recipe type) to highlight cook time, ingredients, calories, etc., in rich recipe results (though Google has gotten good at parsing some recipe info from text, the schema here still boosts consistency and eligibility for those coveted recipe cards with star ratings and photos).
• Job Postings (JobPosting schema) benefit from structured markup if you’re listing jobs on your own website, so you can appear in Google for Jobs results. 
• Local Business Info (LocalBusiness schema with your NAP — name, address, phone — plus hours, etc.) feeds Google’s knowledge panels and map results with accurate info. 
• Books, movies, and creative works connect to Knowledge Graph entries, and schema helps ensure your content links to the right entity (like which book or movie your page is about).

Also, rich media and app content: If you have a mobile app, using App Indexing and structured data (MobileApplication schema) can help it appear in search. If you have video content, using VideoObject schema gives Google details like the embed URL, description, thumbnail, etc., which can enhance video search results or make your video eligible for the video carousel. Yes, Google can “watch” or listen to some video/audio content with AI, but providing the metadata via schema is still hugely helpful for accurate indexing.

Use schema where it genuinely clarifies your content’s meaning or important details. If it’s complex, granular, or structured data by nature (product catalog, event schedule), break out the schema toolbox.

Beware the Shiny New Schema

We’re in exciting times in SEO. Every few months there’s a buzz about a new Google feature or a schema.org update. It’s easy to get shiny object syndrome and want to implement the latest schema type or SEO trick as soon as you hear about it. But here’s my caution: Don’t adopt new or experimental standards too early as a broad practice. By all means, test and tinker (if you have the bandwidth and a proper testing environment), but don’t make something a standard operating procedure until it’s officially supported or proven.

Schema.org might roll out a new vocabulary (like that ProductGroup we discussed). Maybe you read an article or tweet from an SEO influencer that says “Google is going to love this, it’s the next big thing!” It might indeed be awesome — eventually. But if Google’s own documentation or rich results program hasn’t fully caught up to it, implementing it site-wide might do absolutely nothing for now. Or worse, if you implement it incorrectly, it could introduce errors. In the case of ProductGroup, Google did update their documentation to include it (so that one’s a fairly safe bet to use, with careful testing). But we’ve seen other schemas come and go or never really take off in terms of SEO impact.

The lesson is: Stay patient and pragmatic. If a standard isn’t officially adopted or recommended by the major search engines, don’t rush to bake it into your entire site’s template. When a new schema is in testing, maybe try it on a handful of pages and monitor results. But don’t make it your new religion overnight. For example, if tomorrow a Schema.org v15.0 introduces SuperFAQ schema (totally hypothetical), you might experiment with it, but you’d wait to see Google actually recognizing and rewarding it before telling your team “we must add this everywhere.” Otherwise, you’re just adding code that at best does nothing and at worst could break something.

Caution > craze. In short, prioritize your efforts. Focus on the foundational stuff (clear content structure, proven schema for complex info, etc.), and layer on new tactics carefully. We live in exciting times, but you also want to make sure you’re building on solid ground.

The Bottom Line: Clarity First, Schema Second

Key takeaways for SEO practitioners navigating schema markup in the AI era:

• Don’t overdo schema for easily understood content. If your page is straightforward (simple Q&As, step-by-step guides, basic articles), trust that search engines can interpret it. Spend time making the FAQ clear and comprehensive, not marking it up. A well-structured page can shine with zero JSON-LD.
• Use schema where it counts. For pages with complex data (products with variants, events with details, recipes), proper schema is your friend. It improves how your content appears in search and ensures AI doesn’t miss important info. Use schema as a precision tool for the really important or hard-to-parse stuff (prices, dates, relationships).
• Stay updated, but be skeptical. Follow Google’s announcements and credible SEO news. When you hear “New schema X is the next big thing!,“ look for confirmation from official sources. Don’t implement new schemas just because they exist — implement them because they make sense and search engines will use them.
• Focus on content structure and user experience. Ensure HTML structure is logical (headings in order, sections labeled clearly) and information is clean and accessible. LLMs care about clarity and hierarchy in your content more than invisible markup tags. Good writing and formatting is the new SEO superpower, with schema as a supporting player.
• Don’t panic-remove existing schema. If you have schema not currently doing anything, you don’t necessarily need to rip it out. Google has said unused structured data doesn’t harm your site. It’s just not active.

Use schema where it makes sense, and don’t use it where it doesn’t. As search gets smarter, we can be more discerning.Don’t waste time on SEO busywork that doesn’t move the needle. Use that time to create better content and better experiences. Schema is a tool in your toolbox — use the right tool for the right job.