Generative engine optimization (GEO) and brand visibility in AI-generated tourism recommendations: an exploratory analysis

Abstract

The emergence of generative artificial intelligence systems has transformed information search and recommendation processes, giving rise to new intermediaries capable of influencing brand visibility. In this context, the concept of Generative Engine Optimization (GEO) has emerged as an analytical framework for understanding how brands are represented in AI-generated responses. Using an exploratory, observational, black-box methodology, this study examines the visibility of hotel brands and the informational sources cited in tourism recommendations produced by different AI systems in response to the same generic query, based on outputs generated at three different points in time. The results reveal a high degree of volatility in the presence of hotel brands, as well as notable variability in the sources cited, highlighting non-stable dynamics of algorithmic mediation over time. From a communication perspective, these findings suggest that brand visibility in generative environments is configured in a contingent manner and depends on the context of generation. In conclusion, the study emphasizes the need to conceptualize generative AI as an emerging informational intermediary, capable of shaping the selection and presentation of brands and sources in tourism recommendations.