SYSTEMATIC REVIEW article
Front. Artif. Intell.
Sec. AI in Business
Volume 8 - 2025 | doi: 10.3389/frai.2025.1599391
Artificial Intelligence in the Tourism Business: A Systematic Review
Provisionally accepted
Alexandra Lorena López Naranjo1
Mariana Isabel Puente Riofrío1*Verónica Adriana Carrasco Salazar1Juan Diego Erazo Rodríguez2Pamela Alexandra Buñay-Guisñan1- 1National University of Chimborazo, Riobamba, Ecuador
- 2Escuela Superior Politécnica del Chimborazo, Riobamba, Chimborazo, Ecuador
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In the tourism sector, AI has been gradually integrated to optimize operations, personalize customer experiences, and improve resource management, thereby transforming the way companies operate and connect with travelers. The aim of this re-search is to explore the application of AI in the tourism industry, identifying the main AI technologies used in the business, the specific areas or processes, their benefits, and challenges. For this purpose, a systematic literature review methodology was used, following PRISMA guidelines, from which 112 primary studies were obtained that contributed to answering the research questions. The main findings indicate that, in the tourism industry, the most commonly used AI technologies include Natural Language Processing (NLP) and deep learning with Neural Networks, with chatbots and models such as CNNs and LSTMs being particularly prominent. These technologies facilitate everything from the automation of interactions (such as bookings and customer service) to advanced data analysis for the personalization of services and strategic decisions, demonstrating their broad applicability and benefit in the sector. However, multiple challenges are also identified, ranging from high costs and advanced technological infrastructure to ethical and privacy concerns. Therefore, for proper implementation of AI in the tourism sector, it is crucial to carefully manage both the benefits and challenges to ensure its success.
Keywords: AI, tourism, optimization, Technology, machine learning
Received: 24 Mar 2025; Accepted: 14 Jul 2025.
Copyright: © 2025 López Naranjo, Puente Riofrío, Carrasco Salazar, Erazo Rodríguez and Buñay-Guisñan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mariana Isabel Puente Riofrío, National University of Chimborazo, Riobamba, Ecuador
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