AI-powered discovery is driving the evolution of search, changing how users interact with digital content. As search moves beyond traditional engines and into AI-driven platforms, topical maps remain a key strategy for structuring content in a way that aligns with how AI systems process and retrieve information. In this article, we’ll explore how AI Agents can be leveraged to build topical maps, ensuring content remains relevant, discoverable, and strategically structured for AI-driven search.
This article is a follow-up to a previous one published on my LinkedIn profile. You can read it here.
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SEO in the Age of AI: Adapting to AI Discovery and Conversational Queries
Modern SEO has moved beyond traditional search engine optimization, shifting towards a broader, AI-driven discovery model. Instead of simply refining keywords and meta descriptions, SEO now involves aligning content with Large Language Models and AI platforms, each with unique data processing methods. These systems analyze vast amounts of information, interpret user intent with increasing complexity, and personalize content dynamically across multiple platforms and not just Google.
A key change in this scenario is the rise of conversational queries, where users interact with AI in a natural, question-based format. These queries are longer, more specific, and often involve multi-turn interactions, requiring content that is contextually rich, adaptive, and capable of addressing nuanced user needs. I ran a test on a client’s website, first tracking organic traffic from Google Search Console for conversational queries with 8+ characters:
Then I compared the graph with the one of aggregated traffic from AI platforms (ChatGPT, Microsoft Copilot, Perplexity AI…), the report showed a steady increase starting in May and spiking from July onwards:
The two graphs are very similar, emphasizing the growing importance of structuring content to meet these evolving search behaviors.
To optimize for this shift, content must be scalable (easily produced and updated), multipurpose (adaptable for different versions of user queries), and validated (aligned with a brand’s core knowledge). This is where Semantic SEO and the Ontological Core become crucial. Semantic SEO prioritizes meaning, relationships, and structured knowledge, allowing AI systems to better understand and retrieve content.
The Ontological Core, a concept introduced by Tony Seale that I adapted for content strategy, provides a structured framework defining a business’s key entities, relationships, and industry concepts. By establishing this foundation, organizations can ensure their content is semantically rich, discoverable, and aligned with AI-driven search requirements. Without it, content risks being overlooked by AI systems that rely on structured data for relevance and accuracy.
In my talk at Search Seekers, I explored how the Ontological Core bridges internal knowledge with external AI demands, positioning businesses to thrive in an era where search is no longer just about visibility in traditional engines, but about meeting user needs across AI-powered discovery systems.
Why Topical Maps Matter for AI Discovery
We already said that with the rise of LLMs, Generative AI, and AI Agents, search has become more conversational and context-driven. AI platforms like ChatGPT, Microsoft Copilot, and Perplexity AI don’t just index web pages; they interpret relationships between topics, understand user intent, and provide synthesized answers. This shift makes structured, interconnected content essential—and topical maps help achieve that.
A topical map is a structured representation of key entities, concepts, and subtopics within a domain. It ensures content is logically interconnected, improving navigation for both users and search engines. When optimized correctly, topical maps:
Improve content discoverability across AI platforms.
Enhance semantic relevance, aligning content with AI-driven search behavior.
Strengthen internal linking, helping users and AI agents navigate content clusters.
Optimize content for conversational queries, making it more suitable for voice search and AI interactions.
To demonstrate this, I tested WordLif’s SEO Agent’s ability to build topical maps for one of our clients’ websites. The results of this experiment showcase how AI can transform content structuring and organization.
How to Build Topical Maps with AI Agents
I tested an automated approach to building topical maps for the site of one of our clients: Glasses.com – I followed the same structured methodology I typically use when creating topical maps with my human intelligence, manually.
WordLift AI Agent is an SEO agent that is grounded in the data from the knowledge graph of a specific website, its knowledge is also coded by the SEOntology that educates the model to perform SEO tasks, and has the intelligence of an LLM.
Below are the key steps to creating AI-optimized topical maps and how they contribute to a more intelligent, scalable, and SEO-friendly content strategy.
1. Identifying Key Entities
When doing semantic SEO, everything starts with entities; equally, the foundation of a strong topical map lies in defining the main entities that represent your brand, industry, or niche. AI Agents use entity recognition to connect concepts, products, and topics meaningfully, ensuring structured relationships between different content elements.
To achieve this the agent will:
Analyze existing content to determine core themes.
Look up the knowledge graph of glasses.com to map relationships between entities.
AI Agent’s Output & Optimization
The AI Agent successfully identified core entities but missed some key topics relevant to the website, such as eye health, tips, and glasses fitting. By manually reviewing the AI-generated map, I was able to fill in content gaps, ensuring comprehensive coverage of all essential topics.
2. Analyzing User Queries
Understanding how users search is crucial for structuring content that aligns with AI-driven discovery. AI Agents can analyze different query types, including:
Conversational Queries: Natural language questions that mimic human speech.
Long-Tail Queries: Specific, intent-driven searches that AI models prioritize.
Question-Based Queries: Commonly asked questions related to your domain.
By targeting entity-related queries, we can enhance site authority, align content with search behavior, and improve semantic relevance for AI-driven search engines.
They also reveal content gaps and weak areas while enhancing semantic relevance for search engines. By targeting entity-related queries, we can improve the site’s authority, aligning it with user search behavior.
The three primary topics for the blog were identified as:
Eye Wellness
Eyewear Trends & Materials
Care Tips for Glasses & Lenses
These categories represent user intent and are essential for organizing content effectively. The AI Agent helped cluster queries into these main themes, guiding the content structuring process.
3. Identifying The Top Ranking Content On The Site
Once key entities and user queries were mapped, the next step was analyzing top-ranking content to:
Identify high-performing pages.
Uncover optimization opportunities.
Detect missing content gaps.
Connected to the knowledge graph, the AI Agent retrieved and clustered existing high-ranking content for each group of queries. This made it easier to monitor content performance and adjust strategies accordingly, ensuring a comprehensive coverage of all important user queries.
To ensure content fully addresses user intent, I conducted additional research to find missing conversational queries and integrate them into existing articles.
For each piece of content identified by the AI Agent, I manually checked if it included key user questions and optimized where necessary.
✅ Already addressed:
How do I find glasses that match my face shape?
What hairstyles work best with eyeglasses?
How do I accessorize to make my glasses stand out?
❌ Missing and added:
What makeup tips can help me look good with glasses?
How can I choose stylish glasses for everyday wear?
By expanding long-tail queries for each content cluster, I ensured that each article was optimized for maximum engagement across AI-driven search platforms.
I also built a specific set of long-tail queries for each entity cluster, expanding the content optimization to all sections of the blog.
4. Leveraging Internal Linking for AI Optimization
Internal links play a crucial role in helping search engines understand a site’s structure and content hierarchy while also guiding visitors to relevant content.
Using embeddings, AI Agents can generate internal link suggestions that match each page with the most relevant interlinked content, ensuring that link equity is distributed to high-priority pages and improving overall site navigation.
By reducing content silos and reinforcing topical authority, an AI-driven internal linking strategy enhances content discoverability, making it easier for AI-powered platforms to retrieve and rank information effectively.
This step aims to ensure that link equity is passed to the most important pages.
We added the agent’s suggestions to the category pages of the site. The intervention resulted in an increase in clicks on PLPs of 30%.
5. Generating Scalable Content To Match Selected Queries
The content that performs well on AI-powered search today has to be scalable, multi purpose and validated.
The agent can help generate content at scale that is grounded in our knowledge graph’s data and near to our brand’s tone of voice.
🚨 Human Review Is Essential AI-generated content should never be published without editorial oversight.
During this experiment on Glasses.com, we observed a 27% increase in clicks on pages featuring personalized, AI-assisted content compared to static pages.
7. Building The Maps
The final step was assembling the AI-generated insights into a complete topical map, structuring the entire content ecosystem around key entities, queries, and content clusters.
The AI agent generated two maps: one that structures the existing content based on its insights and another that identifies and connects content gaps. The second map highlights missing categories and queries, revealing areas that are not yet covered on the site, providing a clear roadmap for content expansion and optimization.
Conclusions: The Future of AI Discovery
The future of search lies at the intersection of knowledge graphs, AI Agents, and user-focused content. The defining factor is no longer the debate between human-generated and AI-generated content—it’s about creating high-quality, structured, and semantically rich content that aligns with how AI systems process and retrieve information.
By leveraging topical maps, businesses can:
Enhance content discoverability across AI-driven platforms.
Improve semantic SEO by structuring information meaningfully.
Future-proof their content strategy for AI-powered search experiences.
For those ready to embrace this shift, the opportunities are immense. Now is the time to build smarter, AI-optimized ecosystems, ensuring content remains intuitive, accessible, and highly relevant in the evolving landscape of AI discovery.
Let’s start shaping the future of search today.
You can find a first part of this article on LinkedIn at this link.
