As AI assistants like ChatGPT, Gemini, Perplexity, and AI Overviews become primary discovery interfaces, a new layer is emerging between users and the web: the AI visibility layer.

This session explores how large language models and answer engines decide which sources to surface, cite, or ignore when generating responses—and why some companies, products, and content consistently appear in AI answers while others remain invisible.

We will break down the mechanics of AI-driven discovery in practical terms, focusing on how information is retrieved, ranked, and synthesized across LLM-based systems. Instead of traditional SEO thinking, we will examine how visibility is shaped by AI interpretation, source selection, and citation behavior.

Key areas covered include:

1️⃣ How AI systems “see” the web – understanding retrieval, ranking, and grounding in LLM-based answers
2️⃣ Why certain sources dominate AI citations – patterns in authority, structure, and trust signals
3️⃣ The rise of AI-mediated discovery – how AI Overviews and chat-based search replace traditional SERPs
4️⃣ What influences AI visibility today – from structured data to external mentions and content consistency
5️⃣ A practical mental model for AI visibility – how to think about being discoverable in an AI-first information ecosystem

By the end of this session, attendees will understand the new visibility layer created by LLMs and gain a clear framework for how AI systems decide what gets shown, cited, or ignored in generated answers.