How will the relationship between structured data and generative AI evolve over the next 18 months? - Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast

Summary

Voices of Search Podcast: How Will the Relationship Between Structured Data and Generative AI Evolve Over the Next 18 Months?

Release Date: June 18, 2025

In this insightful episode of the Voices of Search podcast, host Jordan Cooney engages in a deep conversation with Sam and guest expert Martha Van Berkel from Schema App. The discussion centers on the evolving interplay between structured data and generative AI, projecting developments over the next 18 months. This summary captures the key points, discussions, insights, and conclusions drawn during the episode, enriched with notable quotes and organized into clear sections for comprehensive understanding.

1. Introduction to the Future of Structured Data and Generative AI

The episode kicks off with Sam posing a forward-looking question to Martha:

Sam [00:42]: "Crystal Ball we're looking into the future now, Martha. Tell me how the relationship between structured data and generative AI will evolve over the next 18 months."

This sets the stage for a discussion on the symbiotic relationship between structured data and generative AI technologies.

2. Structured Data as the Foundation for AI Models

Martha Van Berkel emphasizes the foundational role of structured data in feeding AI models:

Martha [00:55]: "I think we're going to basically use structured data as the feed into the models and it’s going to become like, this is like your Data strategy for LLMs."

She highlights that structured data will transcend traditional SEO applications to become integral to data strategies for Large Language Models (LLMs).

3. The Evolution of Data Strategies for LLMs

Building on Martha's point, Sam discusses the necessity of frameworks and protocols:

Sam [01:21]: "Frameworks and protocols are going to be a way for us to organize and structure better... these frameworks and models are going to be critical over the next 18 months."

This underscores the importance of developing robust structures to manage and optimize data for AI applications.

4. The Critical Role of Context-Rich Data

Martha delves deeper into the type of data needed for effective AI performance:

Martha [01:47]: "It's data with context... knowledge graphs work well because it's data with context. So it's structured data... defining relationships between things."

She explains that context-rich structured data enables better inferencing, which is essential for reducing inaccuracies in AI outputs.

5. Knowledge Graphs and Their Significance

The conversation shifts to the importance of knowledge graphs:

Martha [02:40]: "The most important, the reason why knowledge graphs are important because it's data with context."

Martha reiterates that knowledge graphs are pivotal in providing the contextual backbone necessary for advanced AI functionalities.

6. Challenges and Early Stages of Integration

Sam and Martha acknowledge that the integration of structured data and generative AI is still in its nascent stages:

Sam [01:21]: "...we don't know if these models will verticalize themselves or they'll become super critical for certain types of consumers."

This highlights the uncertainties and the need for careful planning and strategic data management as the technologies evolve.

7. Future Outlook and Excitement for Progress

Concluding their discussion, Martha expresses optimism about the future advancements:

Martha [00:55]: "I'm excited because we're going to just make that happen."

Her enthusiasm reflects confidence in the ongoing developments and the potential breakthroughs in the integration of structured data with generative AI.

Conclusion

This episode of Voices of Search provides a forward-thinking exploration of how structured data and generative AI are set to co-evolve. Martha Van Berkel offers valuable insights into the necessity of context-rich data, the pivotal role of knowledge graphs, and the emerging frameworks that will shape data strategies for LLMs. Sam complements these points by highlighting the critical need for structured approaches to manage this integration effectively.

For professionals in SEO and content marketing, the discussion underscores the importance of adopting robust data strategies and staying abreast of evolving AI technologies to maintain a competitive edge. As Martha aptly puts it, the journey is just beginning, and the next 18 months promise significant advancements in how we leverage structured data within the realm of generative AI.

Notable Quotes:

Martha Van Berkel [00:55]: "This is like your Data strategy for LLMs."
Sam [01:21]: "Frameworks and protocols are going to be a way for us to organize and structure better."
Martha Van Berkel [01:47]: "It's data with context... defining relationships between things."
Martha Van Berkel [02:40]: "The most important, the reason why knowledge graphs are important because it's data with context."
Martha Van Berkel [00:55]: "I'm excited because we're going to just make that happen."

For more insights and discussions on SEO and content marketing, consider subscribing to the Voices of Search podcast through your preferred podcast app or visit voicesofsearch.com.

Transcript

Jordan Cooney (0:00)

The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Jordan Cooney.

Sam (0:42)

Crystal Ball we're looking into the future now, Martha. Tell me how the relationship between structured data and generative AI will evolve over the next 18 months.

Martha Van Berkel (0:55)

I think this is why MCP was such an interesting topic. I think we're going to basically use structured data as the feed into the models and is going to become like, even less of an, like, just an SEO thing. And it's going to become like, this is like your Data strategy for LLMs, but we're going to have to just like, journey on that. Like, I would say, like, we're still pretty early days in that. I'm excited because we're going to, we're going to just like make that happen.

Sam (1:21)

Absolutely. I agree. Frameworks and protocols are going to be a way for us to organize and structure better because none of these, none of these models, and we don't know if these models will verticalize themselves or they'll become super critical for certain types of consumers. As that happens, we have to be very prudent about what we supply. And I think these frameworks and models are going to be critical over the next 18 months.

Martha Van Berkel (1:47)

I think it's going to be data feeds with context. And I say that very specifically because I don't know, I read like four articles this week where I was like, you LLM Txt, that's not going to be good enough. Right. The reason why all the research says knowledge graphs and learning language models work well is because it's data with context. So it's structured data. Think that broad concept, but it has context, which means you're defining relationships between things because it's when those relationships are defined that you can do inferencing. And the problem we're solving for large language models is the inferencing part. That's the expensive part. Right. It's the part that hallucinates. And so I think, like, historically, if you'd asked me a year ago, I'd be like, enterprise are going to be investing in knowledge graphs. I think like as we have new standards like that's hard to do and we're seeing people still do that work. But I think the most important, the reason why knowledge graphs are important because it's data with context.