Traditional search optimization was built for ranking pages. Modern AI systems are built to synthesize answers. As users increasingly rely on large language models such as ChatGPT, Gemini, Claude, and Copilot for discovery and decision-making, brand visibility has quietly shifted from links and clicks to recall and recommendation. Many brands are not losing visibility because they rank poorly, but because they are never remembered or cited by AI systems at all.

The AEO Playbook introduces Answer Engine Optimization (AEO) as a strategic discipline focused on how AI systems learn, store, and reproduce information. Rather than treating AI search as a technical problem or a continuation of SEO, the book frames it as a systems-level challenge involving authority formation, consistency, and narrative coherence. The book explains how LLMs decide what to say, why repetition and alignment matter more than volume, and how fragmented brand signals lead to invisibility. It introduces the F.A.C.T. framework (Findability, Authority, Consistency, Traceability) as a practical model for diagnosing and improving AI visibility across owned, earned, and third-party assets. Structured as a combination of conceptual explanation, audits, and execution guidance, the book walks readers through how to assess their current AI presence, design content and messaging systems that AI models can reliably summarize, and build long-term recall rather than short-term traffic spikes.

Unlike existing SEO or AI marketing books, this work does not focus on tools, prompts, or tactics that quickly become obsolete. Instead, it focuses on durable principles that reflect how AI systems ingest information and how trust compounds over time. The perspective is practitioner-led, grounded in real-world operating constraints, and written for decision-makers responsible for long-term visibility and growth.

What You Will Learn

How AI systems form memory and decide which brands to cite or recommend

Why traditional SEO metrics fail to capture AI-driven discovery

How to audit brand visibility across AI answer engines

How to design consistent, quotable assets that compound recall over time

How to measure progress without relying on clicks or rankings

Who This Book is For

This book is written for founders, CMOs, growth leaders, marketers, consultants, and product leaders responsible for brand visibility and demand generation in AI-driven environments. Readers should have a basic understanding of digital marketing or strategy but do not need technical or engineering backgrounds.

Table of Contents

Chapter 1: From Ranking to Recall

Chapter 2: How LLMs Learn

Chapter 3: Why Most Brands Are Invisible

Chapter 4: The F.A.C.T. Framework

Chapter 5: Auditing Al Visibility

Chapter 6: Building for Recall

Chapter 7: Cadence, Compounding, and Time

Chapter 8: AEO As Infrastructure

About the Authors

Matt Hussey is a UK-based journalist and marketer who has worked on global campaigns for companies including Google, Samsung, Adidas, and Uber. A former staff writer at Wired Magazine, he brings a deep understanding of how emerging technologies shape attention, credibility, and narrative framing.

Ravi Kiran is a growth strategist from India with over ten years of experience building and scaling growth engines across e-commerce, ed-tech, fintech, and technology startups in the US, UK, Europe, and emerging markets.

Jacopo Buriollo is an Switzerland-based go-to-market leader and co-founder with over fifteen years of experience spanning finance, product, marketing, and growth, with a strong focus on systems, incentives, and long-term strategic clarity.

Together, they bring a practitioner-led perspective on how brands earn visibility and trust in AI-driven discovery systems.