New hotel book maps the AI discovery gap

Amber S. Hoffman’s new book argues that AI-driven travel planning is now shaping which hotels get booked and which revenue streams get found. The release targets hotel leaders who want to understand why strong properties can still be invisible to travelers using tools like ChatGPT, Gemini, Perplexity, and Claude. Why it matters: - Hotel bookings are increasingly influenced by AI tools that decide which properties travelers see first. - The book argues that invisibility in AI can cost hotels not just room revenue, but dining, spa, event, and experience revenue too. - The gap matters because hotels can lose demand without seeing it in occupancy, RevPAR, or other standard dashboards. What happened: - Amber S. Hoffman released Before the Booking: Closing the Hotel AI Discovery Gap to Drive Total Revenue on Amazon in Kindle and paperback. - The book is aimed at hotel owners, asset managers, general managers, and directors of sales and marketing. - Hoffman frames the book around how AI-driven travel planning affects hotel discovery and booking decisions. The details: - The book explains how travelers use AI tools like ChatGPT, Gemini, Perplexity, and Claude to plan trips and choose hotels. - Hoffman introduces the term “Hotel AI Discovery Gap” to describe the distance between a hotel’s actual quality and how clearly that quality appears in the sources AI reads. - The core argument is that AI rewards legibility, not quality. - A hotel with strong rooms, dining, and experiences can still be overlooked if those offerings are not described clearly, accurately, and consistently. - A lesser property can be recommended instead if its descriptions are easier for AI systems to find and summarize. - Hoffman says the central commercial challenge is making a hotel readable to AI systems that shape booking decisions. - The book broadens the revenue lens beyond rooms to include restaurant reservations, spa visits, event inquiries, bars, and experiences. - Hoffman says AI now surfaces or hides those revenue streams across the property. - The book argues that RevPAR captures only the room impact of this visibility problem and misses broader revenue loss. - One example in the book follows a fictional food editor who finds a hotel restaurant through an AI dining query. - That guest spends about £220 on dinner and a nightcap, mentions the restaurant in print, and later recommends it to a colleague. - The colleague books six rooms and a private dining experience. - Hoffman uses that chain to show how one AI query can generate more than £3,000 in total revenue from someone who never stayed at the hotel. - The book uses fictional properties in Shoreditch, Marrakech, Bali, and Mexico City to illustrate different commercial scenarios. - Hoffman writes that the book is not a technology manual and does not explain how large language models work under the hood. - She argues hotel leaders need to understand how travelers ask questions and how AI decides whether a hotel appears in the answers. - The book is the first in a planned series, with a companion title, Before the Itinerary , planned for tourism boards and destination marketing organizations. - Hoffman offers a free Hotel AI Discovery Gap self-assessment through The FS Agency. - The Amazon link for the book is the book listing . Between the lines: - Hoffman’s pitch is a response to a larger industry shift: AI is becoming the first filter for travel discovery. - The book suggests many hotels may be losing revenue because their offerings are not described in the exact language AI systems can reliably interpret. - Hoffman’s own career shift from food and travel publishing to AI consulting reinforces the book’s warning about how quickly search and discovery have changed. - The message is as much about commercial visibility as it is about technology adoption. What’s next: - Hospitality leaders can use the self-assessment to gauge how visible their property is to AI systems. - Hoffman’s next book in the series will extend the framework to destinations and tourism boards. - The broader test will be whether hotels update their descriptions and source material fast enough to stay visible as AI-driven trip planning grows. The bottom line: - The book argues that the next hotel revenue battle is not just winning the booking, but becoming legible to AI before the traveler ever chooses where to stay.