Answer Engine Optimisation: Structured Data

As search evolves continuously, answer engine optimisation strategies become essential for helping AI tools understand your content. Artificial intelligence now reshapes how audiences find information. People no longer just scan search results. Instead, they ask complex questions. They expect instant, contextual answers. Consequently, marketers must adapt their strategies. Structured data offers a powerful solution. It helps AI tools understand and trust your content.

The Rise of Answer Engines

Increasingly, traditional search engines now function as answer engines. For example, Caerley McShane from SAP highlights this crucial shift. Specifically, she notes a significant change in user behaviour. Consequently, B2B brands face a unique paradox here. On one hand, search traffic may decline. Conversely, brand influence often grows.

Citations now act as the new first page. Being a trusted source builds awareness. This happens even without clicks to your website. Therefore, visibility means more than just ranking. AI engines must understand your content ecosystem.

SAP’s Success Story

To validate this, SAP measured the impact directly. Specifically, they tracked traffic from large language models (LLMs). Remarkably, between 2024 and 2025, this traffic grew by 168%. Admittedly, these visitors comprise a small portion of total traffic. However, they bring significantly high value. In fact, they convert twice as often as other visitors. Therefore, AI understanding drives real business results.

Why Answer Engine Optimisation Improves Understanding

Clear writing helps readers. However, machines need well-organised information. Unstructured pages confuse AI systems. Machines struggle to grasp nuances without help. They infer meaning only from connections.

Consequently, Google and Microsoft support structured data. It boosts visibility in AI-driven experiences. Early research confirms this benefit. Aiso conducted an experiment on this exact topic. They tested identical content on structured and unstructured pages. Then, they asked ChatGPT questions about each version.

The results showed a clear winner. ChatGPT provided better answers using structured pages. These responses scored 30% higher for accuracy. They also showed better completeness.

Moving From Keywords to Entities

Schema markup acts as scaffolding for your content, effectively providing the necessary structure for answer engine optimisation success. It tells machines exactly what the content represents. Martha van Berkel calls it the language of AI. Previously, schema markup powered rich results like star ratings. Now, it serves a bigger purpose.

Schema turns simple text strings into entities. Machines then see specific things. They identify a person, a product, or an organisation. You define each entity with properties.

Creating Connections

Consider an unstructured site. Machines see separate, unrelated pages. A software page sits apart from an organisation page. Structured content changes this view. It connects these pages explicitly.

Schema language links a product to its provider. It connects an offer to a brand. This creates a robust web of meaning. Entities describe how things relate to one another. For instance, “shoe” becomes a specific entity. You define it as leather, white, and comfortable. Schema.org provides the shared vocabulary for this.

Building Your Knowledge Graph for Answer Engine Optimisation

Consistent entities form a knowledge graph. This system maps relationships across your website. It explains what your brand knows. You do not draw this graph manually. Instead, machines build it from your structured data.

This structure reduces AI errors. It minimises “hallucinations” by LLMs. The models query a trusted network of information. Furthermore, it helps marketers spot content gaps. You move beyond simple page optimisation. You manage a connected system of expertise.

Strategic Implementation

Building these graphs sounds technical. However, the steps remain largely strategic. You need not reinvent your entire strategy. Simply shift your focus.

First, you must focus each page on a single topic. Otherwise, multiple ideas on one page often confuse AI systems. Consequently, they struggle to identify the primary entity.

Second, use schema markup effectively. Define what the page represents. Signal if it shows a concept or a product. This helps machines interpret meaning consistently.

Third, link related entities internally. Connect authoritative content across your site. This reinforces your ownership of the subject.

Measuring Impact Without Clicks

While brand visibility remains the goal, mastering answer engine optimisation ensures your content succeeds in the AI era. However, traditional metrics struggle here. Attribution becomes harder in a zero-click world. The “top of the funnel” gets foggier.

Therefore, marketers must watch for directional signals. Look at the quality of AI-referral traffic. Monitor engagement and conversion rates. Check for citations in AI answers.

Fundamentally, success depends on clear presentation. Furthermore, you must present knowledge consistently. Fortunately, structured content makes this entirely possible. Specifically, it provides the context that machines need. As a result, this builds trust with AI agents.