At the beginning of 2025, I kept hearing the same thing from enterprise marketing teams: “We have Schema Markup. We’re covered.” They meant they had implemented Schema Markup to qualify for rich results. And for a long time, that was enough, but not anymore.
In 2026, Schema Markup is more than an SEO tactic; it’s a strategic data layer, or more specifically, a Knowledge Graph, that helps machines understand, trust, and act on information.
At Schema App, this shift didn’t surprise us. In many ways, it felt like the market was catching up to a decision we made almost a decade ago.
The Moment That Forced the Question: What is Schema Markup For?
The real wake-up call didn’t come in 2025. It came earlier.
In August 2023, Google announced it was retiring FAQ and How-to rich results. For many enterprise sites, those features accounted for a significant share of their organic clicks. Overnight, traffic disappeared.
Inside Schema App, the FAQ and How-to announcement sharpened our focus. It forced us to ask a deeper question. What is Schema Markup actually for?
The answer was never rich results. It was always about helping machines understand meaning.
Why Knowledge Graphs Became the Missing Piece
Search engines and AI systems do not experience websites the way humans do. They don’t read pages line by line. They rely on structure to understand what information means and how it connects.
Schema Markup provides that structure by turning content into explicit data about entities and their relationships. When the data is connected across a site, they form a Content Knowledge Graph, a machine-readable data layer of how an organization’s content, brand, and offerings relate to one another. The connections bring context, and the context brings understanding.
A Content Knowledge Graph transforms website content into a data layer that AI systems can use to make accurate inferences.
This is why Schema App has always built their Schema Markup into a Knowledge Graph. Schema App sees Knowledge Graphs as a strategic asset for enterprise marketers. They give organizations control over how their brand, products, and expertise are understood by machines.
In 2023, the machines were primarily search engines; today, it’s AI; tomorrow, it will be agents.
Would a Robust Knowledge Graph Increase Traffic?
In 2023, our CTO, Mark van Berkel, had a hypothesis. If we could make the Knowledge Graph more robust by adding entity data, would that result in more website traffic?
Adding more robust entities meant defining entity relationships in the context of the Schema Markup on the page, clearly defining entities with external sources like Google’s Knowledge Graph, Wikidata, and Wikipedia. Search engines would gain confidence in that content.
We built our Entity Linking feature to test that theory, and it worked.
Entity Linking is the process of identifying entities in content and linking them to authoritative definitions, either within an organization’s Content Knowledge Graph or to trusted external sources.
By embedding these linked entities in Schema Markup, organizations reduce ambiguity, enhance semantic clarity, and provide AI systems with the context they need to accurately understand and represent their brand.
When customers deployed Entity Linking at scale, the impact went beyond rich results. They saw increased clicks across both branded and non-branded queries, more impressions in traditional and AI search, and improved accuracy in emerging AI search experiences. In one example, InSinkErator saw a 69% increase in clicks for non-branded queries after implementation.
On our own site, we measured a 19.72% increase in AI Overview visibility after implementing Entity Linking.
At the time, it felt like a search performance win. In hindsight, it was an early signal of how essential semantic clarity would become as AI-driven search continued to evolve.
2025: When the Industry Finally Connected the Dots Between AI Search and Structured Data
As 2025 unfolded, Generative AI became the default.
AI Overviews expanded globally. Conversational search became normalized. Platforms like ChatGPT and Perplexity reshaped how people explore information. Search was no longer about ten blue links. It was about having a personalized conversation with context.
In March, both Google and Microsoft publicly stated that they use Schema Markup for their Generative AI features. Google was explicit: Structured data is critical for modern search features because it is efficient, precise, and easy for machines to process.
Turns out our hypothesis was right! Schema Markup drives understanding, not just rich results.
In May, those messages were reinforced again by both Google and Microsoft. ChatGPT then confirmed it uses structured data to determine which products appear in its results.
This was the moment when the conversation changed. Schema Markup shifted from an SEO tactic to a requirement for making your organization understood by AI.
Schema Markup to Control Brand Understanding and Topic Authority
In this new world of AI, ensuring content represented the brand and its topical authority was key. Why? Because entity relationships provide clarity to AI systems, enabling them to clearly understand how topics, products, and expertise are connected across a site.
Without that clarity, even well-established brands can appear fragmented, making it harder for AI systems to consistently associate them with the topics they actually lead.
For example, Wells Fargo struggled to get AIO to understand whether one of their locations was open. AIO was using an old news article saying it was closed. When Schema App added semantic Schema Markup to their location page and connected it to their Knowledge Graph for the rest of the website, AIO was able to answer the question correctly.
Managing the topics your organization is an authority on is a semantic requirement for helping AI systems understand where your expertise lies and when your brand should surface in AI-driven search experiences.
How do you do this at scale and with precision?
Schema Markup needed more than just entity linking. Brands needed to be able to control what entities they referenced and how they were connected within the Schema Markup using all the available properties in the Schema.org vocabulary.
So we built Entity Hub.
Entity Hub provides organizations with a centralized way to define, manage, and optimize entities within their Schema Markup and Content Knowledge Graph, ensuring AI systems receive a clear, consistent understanding of the brand. It enables organizations to support AI search and agentic experiences with accuracy, rather than leaving those interpretations to AI inference.
The impact was immediate. Our customer, InSinkErator, used Entity Hub to clarify its brand and product relationships in AI-driven search experiences. Wells Fargo used Entity Linking to correct hallucinations in AI Overviews and restore authoritative brand signals.
Schema App has really helped us make the right connections with different entities so AI can better understand who we are. As brand becomes more important in AI-driven search, especially with product comparisons, weaving that web to tell our full story—our products, our partners, our parent company—has been incredibly valuable.” — Marjori Blaske, Digital Marketing Manager, InSinkErator
The Agentic Web Began Acting on Users’ Behalf
In the second half of 2025, search crossed another threshold: AI systems started taking action, not just answering questions. Agentic browsers and assistants began comparing options, conducting research, and influencing conversion paths. Search began shifting from discovery to execution.
To do this well, agents need clear, reliable context about what something is, how it relates to other things, and when it should be surfaced. Schema Markup provides that context by making entities and relationships explicit, giving AI the semantic foundation it needs to evaluate options, make decisions, and act with confidence.
Schema Markup Use Cases for the Agentic Web
From Search Infrastructure to Agentic Interfaces
What encouraged us most in 2025 wasn’t that search engines relied on Schema Markup. It was how quickly its role expanded beyond search altogether.
As AI systems began moving from answering questions to taking action, structured data became the connective tissue between websites and emerging agentic experiences.
NLWeb and the Rise of Conversational Interfaces
Microsoft’s announcement of NLWeb, an open initiative led by schema.org creator RV Guha, made this shift tangible. Built on structured data, NLWeb enables conversational AI interfaces that let users and AI agents query website content in natural language.
We saw this as an early glimpse of what the agentic web could become.
Schema App became an early adopter of NLWeb, using it for onsite search to explore its potential for enterprises with robust Content Knowledge Graphs. As NLWeb evolves, organizations with strong Schema Markup foundations will be more agent-ready, as NLWeb relies on Schema Markup as its data foundation.
Connecting Knowledge Graphs to AI Systems with MCP
At the same time, new standards like the Model Context Protocol (MCP) emerged, creating a direct path between AI systems and trusted structured data.
In February 2025, Gartner said that CIOs’ biggest challenge to achieving return on investment from AI was data readiness.
Good news! When you work with Schema App, you can solve this problem using our MCP server. We invested in MCP so our customers could reuse their Content Knowledge Graphs inside AI applications, grounding AI outputs in accurate, governed information rather than scraped assumptions.
Enterprise Readiness Requires Trust and Control
As Schema Markup increasingly functioned as a shared data layer for enterprise marketing and AI, security and data readiness became inseparable from performance. In October 2025, Schema App achieved SOC 2 Type II certification, reinforcing our commitment to building a solution enterprises can trust.
By the end of the year, the pattern was clear. Schema Markup has moved beyond SEO. It had become the shared semantic layer AI systems rely on to understand, connect, and act on enterprise data.
What 2025 Taught Us
By the end of 2025, the conversation around search had shifted. The question was no longer whether AI belonged in search, but what kind of information AI can reliably be built on.
The market reached a shared understanding in 2025. Schema Markup is no longer an SEO tactic. It is core infrastructure for AI-driven search.
Accuracy in AI-driven experiences depends on structured, connected information—Schema Markup in the form of a Knowledge Graph. Content alone is not enough.
As a result, Schema Markup took on a different role. It evolved from supporting individual search features into the semantic foundation that AI systems use to interpret entities, relationships, and meaning at scale. Knowledge Graphs became essential to maintaining control in AI experiences, and ownership of structured data became central to brand accuracy and trust.
As organizations look toward 2026, those that invest in semantic clarity and connected structured data will be best positioned to remain visible, accurate, and authoritative as search continues to evolve.
Martha van Berkel is the co-founder and CEO of Schema App, a semantic technology company that leverages advanced Schema Markup to build Content Knowledge Graphs for enterprise marketing teams. She focuses on helping marketing teams globally understand the strategic value of Schema Markup and thrive in an ever-evolving digital landscape.
Prior to Schema App, Martha was a Senior Manager at Cisco Systems for 14 years, responsible for the global online support strategy. She holds a degree in Applied Mathematics and Engineering, attended MIT for Innovation and Strategy, is a Mother of two, and is an avid rower.