{"id":28808,"date":"2025-02-05T16:22:46","date_gmt":"2025-02-05T15:22:46","guid":{"rendered":"https:\/\/wordlift.io\/blog\/en\/?p=28808"},"modified":"2025-02-19T11:12:29","modified_gmt":"2025-02-19T10:12:29","slug":"how-to-build-a-topical-map-with-ai-agents","status":"publish","type":"post","link":"https:\/\/wordlift.io\/blog\/en\/how-to-build-a-topical-map-with-ai-agents\/","title":{"rendered":"How to Optimize content for AI Discovery: Building Topical Maps with AI Agents"},"content":{"rendered":"\n

AI-powered discovery<\/strong> is driving the evolution of search, changing how users interact with digital content<\/a>.<\/strong> As search moves beyond traditional engines and into AI-driven platforms, topical maps remain a key strategy for structuring content <\/strong>in a way that aligns with how AI systems process and retrieve information. In this article, we\u2019ll explore how AI Agents can be leveraged to build topical maps<\/strong>, ensuring content remains relevant, discoverable, and strategically structured for AI-driven search.<\/p>\n\n\n\n

This article is a follow-up to a previous one published on my LinkedIn profile. You can read it here<\/a>.
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Fill out the form to download Beatrice Gamba’s presentation at Search Seekers.<\/p>\n

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SEO in the Age of AI: Adapting to AI Discovery and Conversational Queries<\/h2>\n\n\n\n

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<\/strong> 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.<\/p>\n\n\n\n

A key change in this scenario is the rise of conversational queries<\/strong>, 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<\/strong>. I ran a test on a client\u2019s website, first tracking organic traffic from Google Search Console for conversational queries with 8+ characters:<\/p>\n\n\n\n

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Then I compared the graph with the one of aggregated traffic from AI platforms (ChatGPT, Microsoft Copilot, Perplexity AI\u2026), the report showed a steady increase starting in May and spiking from July onwards:
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The two graphs are very similar, emphasizing the growing importance of structuring content to meet these evolving search behaviors.<\/p>\n\n\n\n

To optimize for this shift, content must be scalable<\/strong> (easily produced and updated), multipurpose<\/strong> (adaptable for different versions of user queries), and validated<\/strong> (aligned with a brand\u2019s core knowledge). This is where Semantic SEO<\/a><\/strong> and the Ontological Core<\/strong> become crucial. Semantic SEO<\/strong> prioritizes meaning, relationships, and structured knowledge, allowing AI systems to better understand and retrieve content.<\/p>\n\n\n\n

The Ontological Core<\/strong><\/a>, a concept introduced by Tony Seale that I adapted for content strategy, provides a structured framework defining a business\u2019s 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<\/strong>. Without it, content risks being overlooked by AI systems that rely on structured data for relevance and accuracy.<\/p>\n\n\n\n

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In my talk at Search Seekers, I explored how the Ontological Core bridges internal knowledge with external AI demands<\/strong>, 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<\/strong><\/a>.
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Why Topical Maps Matter for AI Discovery<\/h2>\n\n\n\n

We already said that with the rise of LLMs, Generative AI<\/a>, and AI Agents, search has become more conversational and context-driven.<\/strong> AI platforms like ChatGPT, Microsoft Copilot, and Perplexity AI don\u2019t just index web pages; they interpret relationships between topics, understand user intent, and provide synthesized answers. This shift makes structured, interconnected content essential\u2014and topical maps help achieve that.<\/p>\n\n\n\n

A topical map<\/strong> 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:<\/p>\n\n\n\n