What Generative Engine Optimization Means for the Restaurant That Isn’t Recommended

Last summer, while traveling in Japan, I relied on AI to find local restaurants. What impressed me wasn't the recommendations themselves, but how easily I could connect them to maps, customer reviews, and public transportation information. Within minutes, I had identified a restaurant, read its reviews, worked out how to get there, and was on my way, without moving between multiple websites.

As someone with a background in hospitality, I began to wonder what this meant for the restaurant that appeared on the list and, perhaps more importantly, for the one that did not.

The role of AI in travel planning is no longer merely theoretical. In Deloitte’s 2025 Holiday Travel Survey, 24% of respondents said they planned to use generative AI (GenAI) for travel planning this holiday season, up from 16% in 2024 and 8% in 2023 (Deloitte, 2025).

This trend raises an important question for hospitality operators: if travelers increasingly use AI to identify destinations and businesses, how should marketing strategies adapt so that those businesses remain visible and relevant?

Aggarwal et al. (2024) laid the academic foundation for Generative Engine Optimization (GEO) by testing techniques for improving content visibility in generative search responses. They reported gains of up to 40% in their benchmark experiments. These results should not be read as guaranteed improvements in commercial AI recommendations.

Connecting AI Discovery with Maps and Physical Visits

Google’s 2024 travel-planning features offer an early example of how generative AI, business information, and digital maps can work together. In March 2024, Google began testing AI-generated trip itineraries in Search through its Search Generative Experience, available in English to U.S. users who opted in through Search Labs. An itinerary could include attractions and restaurants alongside an overview of flight and hotel options, and could be exported to Gmail, Docs, or Maps. Google Maps also added curated recommendation lists drawn from publishers and the Maps community (Google, 2024).

These developments point to a broader opportunity for the hospitality industry: connecting AI-assisted discovery with location-based decision-making.

For restaurants, attractions, and other local businesses, the opportunity extends beyond appearing in AI answers and search results. It lies in helping travelers evaluate a recommendation, understand how to reach the business, and ultimately visit it.

I therefore propose the following framework for AI-driven local hospitality marketing:

This framework is a strategic proposition, not an empirically validated conversion model. Nevertheless, it offers a useful starting point for examining how AI-driven discovery may influence local hospitality demand.

Information Accuracy as a Marketing Responsibility

Google’s guidance on generative AI features in Search says that foundational SEO practices still apply, including unique, helpful content and a clear technical structure. It also notes that Google Business Profiles can help local businesses appear in AI responses (Google Search Central, 2026). Google’s Business Profile guidelines, in turn, ask businesses to keep details such as address and opening hours accurate (Google Business Profile Help, n.d.).

For hospitality businesses, maintaining this information should no longer be seen only as administrative upkeep. It is increasingly part of how travelers discover and evaluate businesses through digital intermediaries.

GEO remains an evolving field. A 2026 critical survey of the GEO literature found that already-retrieved content can causally influence an AI answer, but identified no technique with a stable, cross-platform causal effect on organic discoverability or downstream clicks and conversions (Martinez, 2026). Google’s own guidance tells site owners that many suggested AEO and GEO tactics are not effective or not supported by how Search works (Google Search Central, 2026).

Hospitality operators should therefore prioritize measurable information quality and customer accessibility, such as accurate hours, addresses, and contact details; consistent listings across platforms; and timely responses to reviews, rather than assume any single optimization technique guarantees AI recommendations.

The strategic objective is straightforward: provide accurate, relevant, and accessible information that helps both travelers and AI-supported discovery systems make better decisions.

References

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5–16). Association for Computing Machinery. https://arxiv.org/abs/2311.09735

Deloitte. (2025). 2025 Deloitte holiday travel survey. https://www.deloitte.com/content/dam/insights/articles/2025/us188641_cic_2025_holiday-travel-survey/pdf/DI_Holiday-travel-survey-2025.pdf

Google. (2024, March 27). 6 ways to travel smarter this summer using Google tools. The Keyword. https://blog.google/products/search/google-summer-travel-tips-2024/

Google Business Profile Help. (n.d.). Guidelines for representing your business on Google. https://support.google.com/business/answer/3038177

Google Search Central. (2026). Optimizing your website for generative AI features on Google Search. Google for Developers. https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://arxiv.org/abs/2607.14035