Why schema markup is foundational infrastructure for AI brand management, not a growth hack.
For years, the industry has treated schema markup like an SEO tactic, a way to earn rich results, improve click-through rates, or gain incremental search visibility. Now, in the age of AI, schema markup is becoming something much more foundational.
As AI systems increasingly generate answers, recommendations, and decisions on behalf of users, brands need a reliable way to communicate meaning, context, and relationships at scale. This is no longer just about search optimization. It’s about machine understanding.
Last week Google published new guidance on Optimizing your website for generative AI features on Google Search, including a section called “Mythbusting generative AI search: what you don’t need to do”. In it, Google specifically called out practices like implementing llms.txt, chunking content, and also overfocusing on structured data.
It says,
Overfocusing on structured data: Structured data isn’t required for generative AI search, and there’s no special schema.org markup you need to add. However, it’s a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.”
We agree with the core point, structured data isn’t a shortcut or a hack for AI visibility: there is no special “AI schema” required to appear in generative AI search experiences.
But beneath that message is a much larger shift reshaping digital visibility itself.
Search engines and AI systems are no longer just retrieving webpages. They are interpreting entities, relationships, and context to generate answers, recommendations, and decisions on behalf of users. That changes the competitive landscape entirely. The brands that win will not be the ones with the most content, but the ones machines can understand with the greatest clarity and confidence.
This is why the market is moving beyond traditional SEO toward semantic infrastructure: governed, machine-readable data that gives AI systems the context needed to accurately understand, represent, and trust a brand.The future of visibility is not page optimization. It is machine understanding.
This need for grounded semantic understanding is becoming increasingly recognized across the industry. As AI systems move from retrieving information to generating answers and taking actions on behalf of users, leading technology companies are investing heavily in the infrastructure required to support trusted, contextual AI experiences.
Marc Benioff recently explained why Salesforce acquired Informatica in one of its largest deals ever:
AI is very probabilistic — it can kind of figure things out — but it needs to be grounded. It has to have real data and a semantic layer connected to a single source of truth, or it simply won’t work well.”
That statement reflects a broader market reality: AI systems are only as reliable as the semantic and data foundations beneath them. Without structured context, connected entities, and trusted sources of truth, AI-generated answers become inconsistent, inaccurate, and difficult to govern at scale.
This changes the role of structured data entirely.
We’re not talking about basic schema markup implementations or disconnected tags added to individual pages. AI systems require structured, connected, and consistently maintained data that reflects the relationships between a brand, its content, products, services, and expertise across the digital ecosystem. That’s where knowledge graphs become foundational.
At Schema App, we believe this points to a broader market shift: from optimizing content for visibility to building machine understanding.
Structured data and knowledge graphs create a connected semantic data layer that informs machines (AI and search) explicit context about who a brand is, what it offers and how its information relates across the digital ecosystem. It defines relationships and context with accuracy and consistency so that when machines answer questions about a brand, they get it right.
This is more than search optimization; this is AI-era brand management.
Search engines may not require schema markup on everything, but Brands should prioritize it now. Why? Because it is their control point for how their brand will be understood by AI and Search.
As AI systems increasingly shape how brands are discovered, interpreted, and recommended, semantic understanding becomes a competitive advantage. We believe knowledge graphs are becoming the foundation for that control, giving brands the ability to shape how machines understand their meaning across search engines, AI assistants, and autonomous agents.
That’s the future we’re building for.
Martha van Berkel is the co-founder and CEO of Schema App, a semantic technology company that helps enterprise brands control how they are represented in AI and search. By building a semantic data layer, Schema App enables organizations to improve visibility, protect their brand, and drive more efficient organic growth. She focuses on helping marketing teams navigate the shift from traditional SEO to AI-driven discovery by establishing a structured source of truth for their content.