How Structured Data Boosts Job Board SEO & AI Search

Nelson Ryan

September 4, 2025

Structured data has long been a cornerstone of optimizing online content for search engines, but its evolution continues to shape the future of SEO and AI-driven search. In a recent discussion between industry leader Yano Vandreel and Jack Chambers Ward, they explored the profound shifts happening in digital marketing, the role of structured data, and the rise of AI technologies. This article unpacks key insights from their conversation, offering actionable guidance for entrepreneurs, small business owners, and creators aspiring to optimize niche platforms like job boards.

Understanding Structured Data: Debunking Common Myths

Many assume structured data is about helping machines "understand" information. Vandreel clarifies that this isn’t entirely accurate. Instead, structured data acts as a labeling system, enabling machines to recognize and categorize pieces of data effectively. There’s no "understanding" or consciousness at play - structured data simplifies data processing by providing a clear structure that’s easy for machines to parse.

This functionality is critical because machines rely on structured data to fuel knowledge graphs, support AI search capabilities, and enhance feature-rich results like reviews, product attributes, and job listings.

Why Structured Data Still Matters Despite Advancements in AI

One pressing question is whether advancements in AI and natural language processing (NLP) will eventually make structured data obsolete. Vandreel argues that structured data will remain essential, primarily because:

Cost Efficiency: Structured data significantly reduces the computational resources needed to process unstructured content.
Accuracy: It provides a reliable dataset for comparison, validation, and application, minimizing errors and inconsistencies.
Scalability: Structured data supports the creation of applications and features, such as Google's AI-generated overviews, that depend heavily on labeled datasets.

As Vandreel notes, even with AI’s capabilities, structured data offers a financially and operationally viable way to ensure seamless functionality across search engines and platforms.

The Role of Structured Data in AI-Driven Search

AI is poised to transform how we interact with the web, with tools like Google’s "AI mode" and large language models (LLMs) such as ChatGPT opening new possibilities. However, these systems rely heavily on structured data to provide accurate, actionable insights.

E-Commerce as a Key Driver

The e-commerce industry serves as a prime example of structured data’s importance. Google’s product knowledge graph - containing billions of products - is fueled by structured data supplied by merchants. This data powers features like virtual try-ons, price tracking, and multimodal search capabilities, offering users a frictionless shopping experience.

However, structured data’s role in AI extends beyond retail. It lays the foundation for knowledge graphs and helps machines synthesize vast amounts of data into meaningful insights. For instance, star ratings, reviews, and pricing data enhanced by structured data already appear in AI overviews, showcasing the powerful intersection of SEO and AI.

Beyond SEO: Structured Data’s Broader Business Applications

Structured data isn’t just an SEO tool - it’s a means to streamline business operations and create unified data systems. Vandreel shares examples of enterprises using structured data to:

Standardize Internal Communication: Large organizations with multiple brands often struggle with inconsistent vocabularies across marketing, PPC, and affiliate teams. Structured data provides a unified framework, enabling these teams to collaborate effectively.
Enhance Business Intelligence: Labeling and organizing data with schema.org or similar vocabularies allows businesses to generate deeper insights into performance metrics, customer behavior, and marketing ROI.
Fill Data Gaps: Structured data projects can expose gaps in information critical to marketing campaigns and product taxonomy, leading to improved decision-making and resource allocation.

For instance, Vandreel recounted a project where implementing structured data revealed systematic issues across multiple marketing departments, enabling the client to address inefficiencies and boost performance across channels.

Challenges and Misconceptions

While structured data offers immense potential, it’s not without its challenges. One common misconception is that implementing schema.org’s entire vocabulary will yield better results. In reality:

Focus Matters: Only use schema that aligns with your business goals or serves a specific use case. For SEO, stick to Google’s documented features, which ensure measurable impact.
Applications are Limited: Much of schema.org is aspirational, designed for future applications rather than current functionality. Use cases like "speakable schema", which once aimed to power voice search devices, never gained significant traction due to limited adoption.

Additionally, the fragmented nature of structured data adoption means businesses must stay informed about which formats (e.g., JSON-LD) and features are actively supported by platforms like Google and Bing.

Preparing for the Future: Structured Data and Generative AI

AI’s rapid evolution, particularly in the context of generative models and multimodal search, will undoubtedly drive changes in how structured data is used. Key trends to watch include:

Bridging Gaps Between AI and Knowledge Graphs: Many AI systems currently operate independently of structured databases like Google’s knowledge graph. Efforts are underway to integrate these systems, paving the way for more advanced applications.
Emerging Use Cases: The integration of structured data into AI-driven shopping experiences, multimodal search, and conversational interfaces will create new opportunities for businesses to stand out.
Regulatory Oversight: As AI’s influence grows, governments and organizations will likely implement regulations to ensure ethical, transparent use of data.

Key Takeaways

Structured data simplifies and enhances SEO and AI-driven search. It’s a cost-effective way to provide clear, labeled datasets for machine interpretation.
Focus on Google-supported features for measurable SEO benefits, such as product markup, star ratings, and local business schema.
Structured data is essential for cost efficiency, enabling search engines to process information with lower computational overhead.
It goes beyond SEO, offering internal business benefits such as standardizing communication, improving business intelligence, and identifying data gaps.
Don’t use schema indiscriminately. Focus on what serves specific business goals, and avoid overloading your site with unused or unsupported vocabularies.
Stay informed about AI developments, as the integration of structured data with generative AI will open new opportunities for innovation and optimization.

Conclusion

Structured data remains a critical tool for businesses navigating the complexities of SEO and AI search. As AI technologies mature, structured data’s role in supporting cost-efficient, scalable, and accurate information processing will only grow. For entrepreneurs and small business owners, investing in structured data isn’t just about search rankings - it’s about future-proofing your digital presence.

By understanding the nuances of schema.org, aligning your efforts with Google’s guidelines, and exploring applications beyond SEO, you can unlock the full potential of structured data to drive growth, efficiency, and innovation.

Source: "How Structured Data & Schema Work for AI Search with Jarno van Driel" - Candour Agency, YouTube, Aug 4, 2025 - https://www.youtube.com/watch?v=x3oIZ_hiaGA

Use: Embedded for reference. Brief quotes used for commentary/review.