In the ever-changing world of SEO, there’s been growing chatter about the diminishing value of Schema Markup. At Schema App, we challenge that perspective. There is still value in Schema Markup —especially for enterprises navigating today’s complex search and AI landscape.
As search engines become more sophisticated at interpreting content and user intent to deliver precise search results, Schema Markup remains a cornerstone for making your content visible and easily understood.
The data we’ve gathered and the trends we’re observing point to one clear conclusion: Schema Markup isn’t just alive—it’s thriving.
In this article, we’ll explore three compelling reasons why Schema Markup is more strategic than ever, and why enterprises should be leveraging it as a key component of their SEO and AI strategy in 2025.
The Value of Schema Markup
Rich Results Drive Higher Click-Through Rates
There is still undeniable value in implementing Schema Markup, especially as rich results continue to play a critical role in driving click-through rates (CTR).
In a time when AI Overviews (AIO) dominate informational queries and zero-click searches are a growing concern, rich results remain a reliable way for businesses to capture clicks and direct traffic to their websites. Many technical SEOs try to find ways to drive results, and Google continues to recommend and award rich results.
In January 2025’s quarterly business reviews for Schema App’s customers, we’ve observed significant increases in CTR when rich results are awarded. Features like review snippets and product rich results consistently drive more clicks.
While Google has deprecated some popular rich results like FAQ and How-to, many valuable options remain—such as review snippets, product, events, and video carousels—that can help businesses with eligible content to stand out in search.
AIO may provide visibility and impressions by consolidating content from across the web, but rich results offer something unique: the opportunity for your business to shine independently on the SERP and drive higher click-through rates. This is especially important for high-converting pages, where clicks directly impact your bottom line.
The best part is that achieving these results is straightforward. Google’s robust documentation provides clear guidance, and with eligible content paired with accurate Schema Markup, businesses can unlock these opportunities to enhance their visibility and drive meaningful traffic.
Google is Still Using Structured Data
While incumbent LLMs (i.e. ChatGPT, Perplexity, etc.) have yet to make any official statement about their use of structured data, evidence shows that Google is still actively utilizing structured data for their advanced search features, reinforcing its value for enterprises today.
Google has long emphasized the importance of structured data in helping its systems understand web content. As stated in Google Search Central’s documentation, “Google Search works hard to understand the content of a page. You can help us by providing explicit clues about the meaning of a page to Google by including structured data on the page” (Google Search Central). In 2024, Google released several product markup-related updates, suggesting their increased interest in accurate ingestion and understanding of eCommerce-related data.
Google’s Gemini Uses Structured Data
Google’s traditional model uses structured data, and so does its new generative AI initiative, Gemini. Gemini uses multiple data sources, including Google’s Knowledge Graph, to develop its answers. Google crawls the web, including Schema Markup, to enrich that graph. As of December 2024, the market is showing that Google is winning the generative AI war with Gemini.
Since Google continues to own about 89% of the search traffic and Gemini is leading the LLM/Generative AI race, Enterprises should continue to invest in Schema Markup to take control of how Google understands and contextualizes their content.
While other AI-driven search engines (i.e perplexity, chatGPT, etc.) have not yet stated structured data as one of their sources, crawling is expensive, and structured data could be a cost-efficient way for these machines to improve their understanding of your content. But most structured data on the internet is implemented mainly for Google’s rich results instead of semantic understanding, which could explain why LLMs are not currently utilizing structured data.
Even though LLMs are getting better at understanding the content on a page by crawling the HTML, they are still prone to hallucinations. Implementing semantic structured data at this juncture could potentially futureproof your website for the day when these AI search engines do start utilizing this semantic data layer.
Semantic Data Enables the Semantic Web and AI
While optimizing your website with Schema Markup makes you eligible for rich results, and helps Google understand your content, this tactical optimization also plays a more important role in future-proofing your website.
By optimizing pages and using Schema Markup to define relationships between entities on your site, you’re creating a reusable semantic data layer. This data layer supports traditional search but also prepares your data to be re-used to get insights into your content structure and strategy.
Building a Reusable Semantic Data Layer
Schema Markup, when done right, transforms unstructured web content into structured, semantic data that can be leveraged across multiple applications, from search engines to enterprise AI tools. By using Schema.org properties to define relationships between entities on a website, businesses can build a robust Content Knowledge Graph for their organization.
Unlike relational databases that merely link data points, Knowledge Graphs define the meaning behind relationships, enabling more nuanced and accurate insights.
For enterprises, this means greater control over how their data is interpreted, reducing risks like hallucinations in large language models (LLMs) and ensuring their AI tools—like internal chatbots grounded in factual organizational knowledge—operate effectively.
Learn the fundamentals of Content Knowledge Graphs and actionable steps to develop your own using Schema Markup.
Driving Accuracy in AI
The importance of high-quality structured data for AI cannot be overstated. A benchmark study by Data World found that LLMs grounded in knowledge graphs achieve 300% higher accuracy compared to those relying solely on unstructured data.
This can have profound implications for enterprises preparing their data for AI. SEOs and marketers are uniquely positioned to prepare their web data to meet these demands, accelerating both search and AI-driven initiatives.
Ensuring Data Continuity Amid Rapid Change
The digital landscape is changing rapidly and there is a proliferation of ways that consumers and businesses can “search”. At the center of all the new experiences is the Enterprise’s web data. Enterprises can future-proof for these changes by preparing their data in a Knowledge Graph. By investing in Schema Markup and building a dynamic Content Knowledge Graph, organizations ensure their data is portable, reusable, and adaptable to future technologies.
SEOs Have the Opportunity to be the Heroes of Data Architecture
Throughout the evolution of search, we have seen a shift from strings to things. But that evolution has continued onto something more than “things”. Now “things” aren’t sufficient, we need to know what the “things” are contextually, and how they relate to other things. Relationships are the new “things”. SEOs must adapt by thinking beyond traditional optimization and embracing roles as data architects, helping build the semantic web that powers both search and AI.
Schema Markup gives SEOs the tools to articulate relationships, create meaningful connections between entities, and future-proof their organizations’ data.
This is more than optimization—it’s building a data-driven foundation for the future of search and AI.
Martha van Berkel is the co-founder and CEO of Schema App, an end-to-end Semantic Schema Markup solution provider based in Ontario, Canada. She focuses on helping SEO teams globally understand the value of Schema Markup and how they can leverage Schema Markup to grow search performance and develop a reusable content knowledge graph that drives innovation. Before starting Schema App, Martha was a Senior Manager responsible for online support tools at Cisco. She is a Mom of two energetic kids, loves to row, and drinks bulletproof coffee.