Front. Artif. Intell. Frontiers in Artificial Intelligence Front. Artif. Intell. 2624-8212 Frontiers Media S.A. 10.3389/frai.2025.1599391 Artificial Intelligence Systematic Review Artificial intelligence in the tourism business: a systematic review López-Naranjo Alexandra Lorena 1 Puente-Riofrio Mariana Isabel 1 * Carrasco-Salazar Verónica Adriana 1 Erazo-Rodríguez Juan Diego 2 Buñay-Guisñan Pamela Alexandra 3 1Facultad de Ciencias Políticas y Administrativas, Universidad Nacional de Chimborazo, Riobamba, Ecuador 2Facultad de Mecánica, Escuela Superior Politécnica de Chimborazo, Riobamba, Ecuador 3Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba, Ecuador

Edited by: Xiao-Guang Yue, European University Cyprus, Cyprus

Reviewed by: Aleksandr Raikov, National Supercomputer Center, China

Kai Wang, Xi’an Jiaotong University, China

 *Correspondence: Mariana Isabel Puente-Riofrio, mariana.puente@unach.edu.ec 25 07 2025 2025 8 1599391 24 03 2025 14 07 2025 Copyright © 2025 López-Naranjo, Puente-Riofrio, Carrasco-Salazar, Erazo-Rodríguez and Buñay-Guisñan. 2025 López-Naranjo, Puente-Riofrio, Carrasco-Salazar, Erazo-Rodríguez and Buñay-Guisñan

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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.

 AI tourism optimization technology machine learning section-at-acceptance AI in Business Introduction

Since the beginning of the 21st century, the tourism industry has experienced rapid growth, becoming a central axis of economic development for many cities. These cities seek to enhance their overall economy through this activity (Wang, 2024a). This industry is highly interconnected and involves multiple sectors. Functionally, it is structured according to the theory of the five elements (eat, live, travel, shop, entertainment) and the four-point method, which includes tourism origin subsystems, destination, travel, and support. This interconnection promotes the development of sectors such as retail, food service, accommodation, and entertainment and even medicine, enhancing the tourist offering through the synergy between information, technology, capital, and labor, and forming an industrial conglomerate (Wei et al., 2021; Ashrafuzzaman et al., 2024).

Additionally, tourism is a dynamic and complex sector, influenced by changes in demand and tourist behavior, the emergence of new markets and players, and socioeconomic factors such as various crises. This makes decision-making, investment planning, and strategy creation in this field significant challenges (Savvopoulos et al., 2019).

Like all economic sectors, the tourism industry has undergone significant transformation with the integration of digital technologies. Among them, the Internet of Things, artificial intelligence, and data analytics have revolutionized both management and the experience of tourist destinations, driving the need for advanced technological tools (Srinivasan et al., 2025). Although the concept of smart tourism—which integrates functions such as data collection, resource management, product design, sales, tourist services, order management, and statistical analysis—has emerged, the actual implementation of these technologies is still limited, with few tourism enterprises fully adopting them (Gao et al., 2022).

Artificial intelligence (AI) technologies are transforming the tourism and hospitality industry, enabling machines to perform tasks previously done by humans, encompassing technologies such as machine learning and natural language processing, which facilitate travel planning and booking through recommendation systems based on the analysis of large volumes of data (El Hajal and Yeoman, 2024). These technologies help tailor services to the preferences and economic capabilities of travelers, overcoming informational and linguistic barriers (Pasichnyk and Artemenko, 2015).

In relation to previous research and the state of the art in the field, several fundamental studies have set significant guidelines. These works have explored various facets that relate artificial intelligence to the tourism sector. These studies provide a deeper understanding of the current technological capabilities and outline the path for future innovations in the field.

González-Mendes et al. (2024) conducted a bibliometric analysis to explore the state of the art of AI and its application in the hospitality and tourism industry from 1996 to January 2023, with the purpose of identifying research trends, opportunities, and challenges in this field. The main results indicate a growing adoption of AI technologies, which enhance operational efficiency, customer experience, and enable innovations in tourism management, especially in the context of the COVID-19 pandemic, which accelerated the implementation of technological solutions.

Puerta et al. (2024) conducted a study aimed at analyzing the impact of applied technologies in tourist risk management. They performed a literature review and bibliometric analysis of 128 documents indexed in scientific databases such as Scopus and Web of Science. The main findings reveal a consensus among authors on the relevance of technologies like Machine Learning, Artificial Intelligence, and Genetic Algorithms to enhance operational efficiency and competitiveness in the tourism sector. Additionally, the study highlights the low research output at the intersection of technology, risk management, and tourism, suggesting a need for more studies in this area to foster sustainable development of the sector.

On the other hand, the study by Mariani and Wirtz (2023) focuses on whether analytics have been defined precisely and consistently in hospitality management research and if cognitive analytics have been explicitly analyzed. The methodology employed consists of a systematic literature review (SLR) of articles published up to July 2022. The results reveal an exponential growth in the publication of articles on analytics in these fields, but they show that definitions of analytics are rarely specified and that cognitive analytics have not been properly labeled or analyzed in the current literature, suggesting a mismatch between data analysis practices and the terminology used by researchers.

Huang et al. (2021) investigate the integration of artificial intelligence (AI) applications in the hospitality and tourism industry, aiming to assess the adoption of 25 AI applications from the perspective of field experts. The main results indicate that AI applications offering personalized search and recommendations, as well as immersive experiences, are more likely to be adopted by consumers and workers. The study emphasizes the importance of considering attributes like relative advantage, complexity, and perceived risks in implementing these technologies in the tourism sector.

Although there is a growing number of studies addressing the application of artificial intelligence in the tourism sector, there are still significant questions that have not been fully explored. Research, such as that conducted by González-Mendes et al. (2024) and Puerta et al. (2024), has begun to unravel the impact and opportunities that artificial intelligence offers to enhance operational efficiency and customer experience in the hospitality and tourism industry. However, there are still less explored areas and open questions, such as what specific AI technologies are predominant in tourism, in which business areas they are most intensively used, and what are their specific benefits and challenges.

These gaps in the research suggest a need for deeper analysis that could include assessing how different AI technologies, such as machine learning, natural language processing, or recommendation systems, are applied in various aspects of tourism, from reservation management to the personalization of travel experiences. Additionally, it is crucial to understand the tangible benefits these technologies bring to the sector, as well as the potential challenges or barriers to their adoption. For this reason, the research through systematic literature review seeks to identify the following research questions:

What are the artificial intelligence technologies present in the tourism industry?

What business areas or processes does artificial intelligence optimize in the tourism industry?

How does artificial intelligence optimize or benefit management in the tourism industry?

What challenges do tourism companies face when implementing artificial intelligence technologies?

 Theoretical framework

Figure 1 presents a diagram of the main variables of the study on the application of Artificial Intelligence (AI) in the tourism business, addressing three key aspects: first, the identification of AI, its types of technologies and tools; second, the application of these tools in various areas of the tourism business, such as marketing, customer management, and operational optimization; and third, the potential benefits and improvements in process optimization. This framework seeks to analyze how AI can transform the tourism industry by enhancing efficiency and strategic decision-making.

Map of the main variables of the study.

Diagram showing the relationship between artificial intelligence and tourism business. AI tools depicted like chatbots and data analysis, suggest various applications in tourism. Resulting in improvements in optimization and processes within the tourism industry. Artificial intelligence

Artificial Intelligence (AI) aims to enable computers to perform tasks similar to human thinking, such as reasoning, which involves fundamental psychological processes like perception, association, prediction, planning, and motor control. Intelligence is not a singular concept but a complex set of interconnected abilities; similarly, AI uses various techniques to solve a wide range of tasks (Boden, 2017). According to Rouhiainen (2018), AI enables machines to use algorithms, learn from data, and apply that knowledge in decision-making, mimicking human abilities. Unlike humans, AI systems can process large volumes of information tirelessly and with fewer errors. Their learning and decision-making capabilities have grown exponentially.

Prominent applications of AI include robots and autonomous vehicles, facial recognition, natural language processing, and virtual agents, all of which enhance efficiency and user interaction across industries (Berente et al., 2021). Other key applications involve image recognition, business strategy optimization, predictive maintenance, object detection in autonomous vehicles, and cybersecurity. AI also supports business decision-making and automates tedious or hazardous tasks.

 Tourism business

The tourism business focuses on the creation and management of tourist destinations, which are specific places that offer unique experiences to visitors by combining resources, culture, infrastructure, and services (Alcocer, 2013). Fernandez (2018) states that tourism is a growing global phenomenon that generates divisions of opinion regarding its effects, being viewed by some as a driver of economic and social development, and by others as a source of exploitation and environmental degradation.

The management of tourism involves various organizational and strategic actions focused on achieving objectives through planning, organizing, leadership, and control. Additionally, the importance of implementing social and knowledge management is emphasized, which promote interaction between local actors and the transfer of experiences and resources, vital for the sustainable development of tourism activities in communities (Matos et al., 2022).

 Materials and methods

The study is a systematic literature review that follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a structured approach that ensures transparency and rigor in the selection and analysis of scientific studies. PRISMA is based on four main phases: identification, screening, eligibility, and inclusion, which allows for the objective selection of the most relevant articles for the research topic (Page et al., 2021). This methodology contributes to the reproducibility and quality of the study, providing a comprehensive and well-founded overview of the impact of artificial intelligence on tourism.

Eligibility criteria and sources of information

The sources of information used in this research are SCOPUS and Web of Science, databases recognized for their wide interdisciplinary coverage and high scientific rigor, which ensures the quality and relevance of the selected studies. Regarding the inclusion criteria, an analysis period of the last 10 years was established, since artificial intelligence applied to tourism is a constantly evolving field, requiring the incorporation of updated literature. Publications in all languages were considered to provide a more global and comprehensive perspective. However, in the Web of Science database, emerging scientific journals were excluded, prioritizing those with greater impact and consolidation in the academic community. Additionally, errata and corrections were removed, ensuring the integrity and validity of the data analyzed in the systematic review.

 Search strategy

The search strategy was carried out through an iterative and evidence-based process to ensure the inclusion of relevant studies. Initially, a preliminary search was conducted in the SCOPUS database, using a combination of key terms aligned with the research topic: TITLE (management) AND TITLE (tourism) AND TITLE-ABS-KEY (“artificial intelligence”). From the studies obtained in this initial search, additional terms were identified through keyword analysis with R Studio software, which allowed for detecting recurring concepts in the literature and optimizing the search strategy. This procedure was repeated twice, progressively refining the key terms to construct a robust final search string that ensured the collection of relevant and high-quality studies for the present systematic review. Figure 2 presents the word cloud with the main terms used in the search string.

Keyword cloud.

Word cloud featuring terms related to tourism and technology, including "tourism," "tourism management," "information management," "decision making," "big data," "deep learning," and "neural networks," in various colors and sizes.

In Table 1, the search strings used for each database are presented, along with the total number of studies. It is important to note that each database has different parameters and configurations for conducting searches, which implies the need to adapt the strategies to the specifics of each platform. These differences can include variations in the syntax of Boolean operators and options for filtering by thematic areas. Therefore, the construction of the search strings was carried out rigorously, ensuring methodological coherence and the acquisition of studies relevant for analysis.

Search strings.

Database String Total
Scopus (TITLE (management OR “decision making” OR “planning” OR “business” OR “administration”) AND TITLE (tourism) AND TITLE-ABS-KEY (“artificial intelligence” OR “AI” OR “neural networks” OR “deep learning” OR “machine learning”)) AND PUBYEAR > 2014 AND PUBYEAR < 2026 AND (EXCLUDE (DOCTYPE, “er”) OR EXCLUDE (DOCTYPE, “tb”) OR EXCLUDE (DOCTYPE, “bk”) OR EXCLUDE (DOCTYPE, “cr”)) 150
Web of science ((TI = (management OR “decision making” OR “planning” OR “business” OR “administration”)) AND TI = (tourism)) AND ALL = (“artificial intelligence” OR “AI” OR “neural networks” OR “deep learning” OR “machine learning”) 69
219
Study selection process and data extraction

For the selection of studies, a systematic process was carried out. Initially, the results obtained from the two databases used, SCOPUS and Web of Science, were grouped together, resulting in a total of 219 studies (see Figure 3). Subsequently, duplicates were removed (41 studies), reducing the number of documents to 178. Next, the authors conducted a detailed review of the title, abstract, and keywords of each study to discard those that deviated from the research topic, which led to the exclusion of 22 studies, leaving a total of 156. Of these, 13 studies could not be downloaded in full text, reducing the corpus to 143 studies. Finally, the selected documents were subjected to a bias risk assessment process, aiming to exclude those that presented a high risk of methodological bias. As a result, 112 primary studies were used for data extraction, from which the answers to the research questions posed in this study were obtained.

PRISMA flowchart.

Flowchart titled "Identification of studies via databases and registers" depicting the study selection process. Initially, 219 records were identified from Scopus (150) and Web of Science (69). After removing 41 duplicates, 178 records were screened. From this, 22 records not related to the research topic were excluded (Scopus 9, Web of Science 13). Of 156 reports sought, 13 were not retrieved. During eligibility assessment, 31 records were excluded due to bias or irrelevance (Scopus 26, Web of Science 5). Finally, 112 studies were included in the review. Bias risk assessment

A methodology adapted from the Cochrane strategy was used to assess the risk of bias in the studies, considering six fundamental criteria: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, completeness of outcome data, and selective reporting. This evaluation took into account the type of study, recognizing that not all criteria are relevant for each research. This process allowed for the identification of studies that presented an insignificant or minimal risk of bias, ensuring the reliability of the collected data.

Figure 4 provides a graphical representation of the bias risk analysis, based on six specific criteria, applied to a total of 143 studies. This figure details the distribution of bias risk according to the evaluated criteria. Of these studies, 112 were classified as primary studies because they demonstrated a low risk of bias on average. For a more detailed understanding of the process and the results of the evaluation, Supplementary material can be consulted, where the full details of the analysis are provided.

Bias risk assessment.

Bar chart showing averages for risk of bias assessment across six categories. Each category has sections for high risk (red), moderate risk (yellow), and low risk (green). Categories include random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective outcome reporting. Most categories show significant low risk, some moderate risk, and fewer high risk instances. Synthesis methods

For the processing of information, responses to the research questions from each analyzed study were extracted. It was observed that some responses were not explicitly mentioned, and in several cases, the studies presented similar information but expressed in different ways. To simplify and clarify the information for a more accessible presentation to the reader, the responses were categorized into different categories. This classification process and the methodology used for the synthesis of the information are described in the Supplementary material, allowing interested parties to thoroughly review the procedure and criteria used.

 Results

Summary of the bibliometric data from the studies included in the systematic review, providing a statistical and descriptive basis about the consulted literature. Subsequently, the four research questions posed in the study are addressed in detail. The discussion begins with the artificial intelligence technologies used in the tourism sector, identifying specific areas of application within this sector. Next, the benefits that these technologies bring to tourism are explored, as well as the challenges they pose for their implementation and ongoing development.

Bibliometric data

Figure 5 presents a keyword co-occurrence graph, in which the nodes and connections visualize the relationship between terms related to artificial intelligence and tourism. Different color groups are observed, indicating thematic clusters that group similar concepts. At the center, key terms such as “artificial intelligence,” “tourism,” and “management” are highlighted, which are larger, suggesting that they are the most recurrent and connected concepts within the field of study. The connections between the nodes indicate the frequency with which these terms appear together in scientific publications, reflecting the main research lines on the topic.

SLR keyword co-occurrence.

A network map showing connections between concepts related to tourism and artificial intelligence. Central nodes include "artificial intelligence," "tourism," and "tourism management." Other connected terms are "neural networks," "decision making," "big data," "sustainable development," "social media," and "machine learning," all linked by lines demonstrating relationships. Various colors differentiate the clusters of concepts.

In the global landscape of research on technology and artificial intelligence, China stands out as a leader with a total of 43 studies, demonstrating its potential and strong interest in technological development (see Figure 6). This figure places China significantly ahead of other countries, with the United States and India each holding second place with 9 studies. They are followed by Spain and Italy with 6 studies each. Countries such as South Korea, Kyrgyzstan, Malaysia, Portugal, the United Kingdom, Thailand, and Taiwan have contributed with 3 studies each. Additionally, nations such as Germany, Australia, Bangladesh, Brazil, Colombia, Greece, Iran, Morocco, Russia, Sweden, Turkey, and Ukraine have contributed 2 studies each. Moreover, a diversity of countries including Azerbaijan, Belgium, Bulgaria, Cyprus, Slovakia, Hungary, Indonesia, Japan, Kazakhstan, Nepal, New Zealand, the Netherlands, Pakistan, Poland, the Czech Republic, Singapore, Sri Lanka, and South Africa have registered one study each, reflecting a broad geographical dispersion in interest and research in this field.

Geographic distribution of studies.

Map of the world showing a color gradient from light to dark blue, indicating a range from 1 to 43 in an unspecified metric. China is in dark blue, indicating the highest value. The United States and several other countries are shaded lighter blue.

Figure 7 displays a graph representing the number of publications on artificial intelligence (AI) and tourism over the last 10 years. A progressive increase in the number of publications is observed, with a notable rise starting in 2021. It is evident that AI has experienced a significant surge in recent years, with 2022 and 2024 standing out as the years with the highest number of publications. The year 2025 shows a decline, which is due to the data only covering the month of January. However, the fact that there are already publications in such a short time suggests that 2025 will see an increase in research on AI and tourism, continuing the upward trend observed in recent years.

Yearly distribution of studies.

Line graph showing the number of studies per year from 2015 to 2025. The number remains steady under five studies until 2020, rises sharply to over 30 by 2023, then drops to about five in 2025. AI technologies in the tourism industry

Artificial intelligence (AI) technologies have the potential to profoundly transform the tourism industry, optimizing operations and enhancing customer experience while opening new business opportunities. AI enables advanced personalization of services, adapting to the specific needs and preferences of each user. In a global context increasingly focused on digitalization, understanding how AI integrates into tourism is important for industry professionals, urban planners, technology developers, and policymakers. Table 2 presents a summary of the technologies mentioned in the studies.

AI technologies used in the tourism sector.

Type of AI technology Algorithm/Method Studies
Generative and natural language processing technologies Chatbots Dey and Shukla (2020), Tsang and Benoit (2020), Huang et al. (2021), Ansari et al. (2022), Chen and Xue (2022), Ivanov et al. (2022), Pradhan et al. (2022), Yuensuk et al. (2022), Abraham and Thazhathethil (2023), Kuo (2023), Çalışkan et al. (2023), Buitrago-Esquinas et al. (2024), Chen and Wei (2024), González-Mendes et al. (2024), Ku (2024), Ku and Chen (2024), and Nag and Mishra (2024b)
Generative AI (ChatGPT) Mariani and Wirtz (2023), Schuhbert et al. (2023), Buitrago-Esquinas et al. (2024), Christensen et al. (2024), Zhu et al. (2024), and Kim et al. (2025)
Natural language processing (NLP) Abeysinghe et al. (2018), Bustamante et al. (2020), Paolanti et al. (2021), Huseynova (2022), Ivanov et al. (2022), Yuensuk et al. (2022), Yu et al. (2022), Kuo (2023), Schuhbert et al. (2023), Zuo et al. (2023a), Ashrafuzzaman et al. (2024), Chen and Wei (2024), El Hajal and Yeoman (2024), Hu et al. (2024), Nag and Mishra (2024a, 2024b), Souha et al. (2024), Sánchez-Franco and Rey-Tienda (2024), and Wang (2024a)
Attention-based models Souha et al. (2024)
Conditional random field (CRF) Abeysinghe et al. (2018)
Sentiment analysis and emotion detection Abeysinghe et al. (2018), Yu et al. (2022), Ranga and Nagpal (2023), Schuhbert et al. (2023), Ashrafuzzaman et al. (2024), Nag and Mishra (2024b), Razali et al. (2024), Souha et al. (2024), and Rong et al. (2025)
Labeled-LDA (Labeled Latent Dirichlet Allocation) Yu et al. (2022)
Clustering BIRCH for data clustering Tzitziou et al. (2024)
Fuzzy C-means clustering algorithm Gao and Yan (2023)
K-means algorithm Dey and Shukla (2020)
Classification Decision Trees Silva et al. (2023) and Xu (2024)
Support vector machines (SVM) Bo and Shi-Ting (2015), Dey and Shukla (2020), Obogo and Adedoyin (2021), Wang (2022a), Silva et al. (2023), and Wu et al. (2023)
Random forest Chen et al. (2017), Bellodi et al. (2022), Mishra et al. (2024), Razali et al. (2024), and Xu (2024)
Gradient boosting classifier (GBC) Razali et al. (2024)
Linear support vector machine (LSVM) Razali et al. (2024)
CART (classification and regression trees) Wu et al. (2023)
Naïve Bayes Dey and Shukla (2020) and Yuensuk et al. (2022)
Support vector regression (SVR) Borrero et al. (2022)
Fuzzy classification algorithms (not specified) Wei (2022)
Associative classification algorithms (not specified) Pasichnyk and Artemenko (2015)
Association Association rule algorithms (not specified) Savvopoulos et al. (2019)
Fuzzy association rule algorithms (not specified) Wei (2022)
Neural networks and deep learning Artificial neural networks (type not specified) Abeysinghe et al. (2018), Ghaderi et al. (2018), Savvopoulos et al. (2019), Shi et al. (2019), Bustamante et al. (2020), Dey and Shukla (2020), Klimova et al. (2020), Tsang and Benoit (2020), Obogo and Adedoyin (2021), Yuensuk et al. (2022), Gao and Yan (2023), Hao and Zheng (2023), Khan et al. (2023), Schuhbert et al. (2023), González-Mendes et al. (2024), Ku and Chen (2024), Meng (2024), Nag and Mishra (2024a, 2024b), and Puerta et al. (2024)
Back propagation neural networks (BPNN) Bo and Shi-Ting (2015), Zhang et al. (2015), Li et al. (2019), Qian and Ge (2021), Gao et al. (2022), Wang and Wu (2022), Cai and Gunaban (2023), and Meng (2024)
Convolutional neural networks (CNN) Wei et al. (2021), Hou (2022), Li et al. (2022a), Lyu and Han (2022), Yang and Huang (2022), Yu et al. (2022), Zuo et al. (2023a), Meng (2024), Wang (2024b), and Srinivasan et al. (2025)
Bidirectional long short-term memory networks (BiLSTM) Meng (2024)
Radial basis function networks (RBF network) Hou and Wang (2024) and Zhang and Li (2025)
Deep neural networks (DNN) Kim et al. (2017), Shi et al. (2019), Dey and Shukla (2020), Paolanti et al. (2021), Li (2023), and Razali et al. (2024)
Long short-term memory (LSTM) Bellodi et al. (2022), Li (2023), Neshat et al. (2024), Qi and Han (2024), Razali et al. (2024), Souha et al. (2024), and Jung et al. (2025)
Long-term recurrent neural networks (LSTM) Li et al. (2022a), Meng (2024), and Srinivasan et al. (2025)
Nonlinear autoregressive networks (NAR) Borrero et al. (2022)
Reinforcement learning algorithms (Deep Q-Learning) Wang (2022b) and Rong et al. (2025)
Multilayer perceptron (MLP) Ghaderi et al. (2018), Siroosi et al. (2020), Li (2023), Razali et al. (2024), and Yang et al. (2024)
Optimization Particle swarm optimization (PSO) Zhang et al. (2015), Gao et al. (2022), Song and Xu (2023), and Meng (2024)
Alternating direction method of multipliers (ADMM) Yu and Wang (2022)
Bee colony optimization algorithms Gao and Yan (2023)
Genetic algorithm Pasichnyk and Artemenko (2015), Zhang et al. (2015), Gao and Yan (2023), and Puerta et al. (2024)
Fastest virtual reality feature selection algorithm (FVR) Zhang and Li (2025)
Stochastic gradient descent (SGD) Zhou et al. (2023), Razali et al. (2024)

Among the most mentioned technologies are those related to Natural Language Processing (NLP), Neural Networks, and Deep Learning, demonstrating their relevance in recent research. Within Generative and Natural Language Processing Technologies, Chatbots are the most supported technology in the literature. Among these, ChatGPT stands out as the most prominent generative artificial intelligence model. Developed by OpenAI, ChatGPT has gained wide recognition for its versatility in understanding and generating human-like text, supporting tasks such as content creation, language translation, virtual assistance, and strategic analysis. Its adaptability across various domains, including education, tourism, and customer service, underscores its role as a leading example of how generative AI can enhance decision-making, user engagement, and information accessibility in real time.

This demonstrates their growing application in automating human interactions through natural language processing and advanced conversational models. Natural Language Processing (NLP) also has a significant number of mentions, highlighting its fundamental role in language analysis and understanding in various contexts. In contrast, more specific approaches such as Attention-based models and the Conditional Random Field (CRF) algorithm have less presence in the literature.

In the field of Neural Networks and Deep Learning, there is a strong presence of references across various architectures. Artificial Neural Networks stand out, evidencing their versatility in multiple applications. Additionally, models of Convolutional Neural Networks (CNN) and LSTM (Long Short-Term Memory) networks also receive extensive support in the literature, confirming their importance in the analysis of sequential data and computer vision. Other architectures like Deep Neural Networks (DNN) and Multilayer Perceptron (MLP) have a considerable presence in the literature, reinforcing their application in complex problems.

In the area of Classification, the most referenced algorithms are Support Vector Machines (SVM) and Random Forest. This indicates their popularity in classification tasks compared to other approaches such as Decision Trees and Naïve Bayes, which have less support in the literature. Regarding Optimization, the Particle Swarm Optimization (PSO) Algorithm and Genetic Algorithm stand out with multiple references, suggesting their utility in optimization problems in artificial intelligence.

 Areas or processes optimized by AI in the tourism industry

Artificial intelligence (AI) is revolutionizing the tourism industry by optimizing various processes and business areas. Figure 8 presents a classification of the areas or processes within the tourism sector where artificial intelligence (AI) is most utilized, ordered by the frequency with which the studies mention each area. This graph provides a clear view of the priorities and current trends in the application of AI in tourism, starting with the most frequently cited processes and descending towards those less mentioned.

Main areas or processes utilizing AI in the tourism sector.

Bar chart showcasing various tourism industry skills with percentages: Customer Service (68%), Marketing (32%), Strategic Planning (28%), Operational Management (23%), Resource Management (19%), Tourism Demand Forecasting (16%), Tourism Route Planning and Reservation Management (14% each), Human Resources and Risk Management (7% each).

One of the most frequently mentioned applications in the studies analyzed is customer service, where AI significantly enhances the tourist experience. This is achieved through chatbots that provide immediate responses to inquiries, facial recognition systems for faster and more efficient check-ins at hotels and airports, and virtual assistants that offer recommendations and personalized assistance during the trip.

The field of marketing is also frequently mentioned in the studies, where AI helps tourism businesses analyze large volumes of data to identify trends and consumer preferences, allowing for more precise segmentation and the development of personalized and effective marketing campaigns. Additionally, in strategic planning, AI contributes to resource optimization and decision-making improvement through predictive analysis and modeling of future scenarios, resulting in more efficient resource allocation and greater adaptability to market changes.

AI tools such as GPT and large language models (LLMs) can support the design and evaluation of tourism development plans by processing vast datasets, generating strategic insights, and facilitating scenario simulations. These technologies can be integrated with SWOT (Strengths, Weaknesses, Opportunities, and Threats) matrices to enrich strategic diagnosis and assist in identifying critical success factors. Moreover, when combined with methods such as the Analytic Hierarchy Process (AHP), AI can help build goal hierarchies, prioritize strategic objectives, and construct comprehensive decision trees that align with long-term development goals.

In operational and resource management, AI facilitates the automation of routine tasks and the optimization of resource allocation, such as staffing based on projected demand. Also in human resource management, AI can be used for staff selection, virtual reality-based training, and performance evaluation through algorithms that analyze employee effectiveness and efficiency.

AI also plays a crucial role in reservation management and predicting tourist demand, where predictive algorithms analyze booking patterns and market trends to adjust prices in real-time and maximize occupancy and revenue. Regarding the planning of tourist routes, trips, and activities, personalization is key; AI allows for the creation of customized itineraries that match the specific preferences and needs of each tourist, thus enhancing the overall travel experience.

Another highlighted process is also risk management, where AI helps identify and assess potential risks, from adverse weather conditions to socio-political changes in tourist destinations, allowing companies to take proactive measures to mitigate negative impacts on their operations and the safety of tourists.

Additionally, there are other processes mentioned to a lesser extent in the studies, such as environmental sustainability, where it helps monitor and manage the ecological impact of tourism activities. Financial management, where AI optimizes resource allocation and improves cash flow predictions, is essential for effective economic planning. It also intervenes in the evaluation and management of investments for tourist development, facilitating the identification of more profitable and sustainable investment opportunities. In terms of conservation, AI contributes to the planning and execution of strategies that balance tourist development with the conservation of natural and cultural resources. In energy management, it implements systems that reduce consumption and costs, optimizing energy use in tourism facilities. Moreover, AI is instrumental in the planning and management of events, from logistics to the personalization of experiences for participants. Lastly, in cybersecurity, AI strengthens defenses against digital attacks, protecting sensitive customer data and business operations in the tourism sector.

 Benefits of AI in tourism industry management

Table 3 presents the main benefits of using artificial intelligence (AI) technologies, classified by areas and processes optimized in the tourism sector. This organization specifically details how various AI applications, such as reservation automation, natural language processing, facial recognition, among others, are implemented to improve operational efficiency, personalize the customer experience, make predictive decisions, and much more. Specific references are included that demonstrate the frequency and impact of each technology in the sector, providing a clear view of the current state and trends in the integration of AI in tourism.

Benefits of AI in the tourism business.

Areas Optimized processes AI used
Reservation and operations management: Automation of reservations and check-ins. Natural Language Processing, facial recognition, neural network chatbots, virtual assistants with generative AI (Dey and Shukla, 2020; Tsang and Benoit, 2020; Huang et al., 2021; Zhang et al., 2021; Borrero et al., 2022; Abraham and Thazhathethil, 2023; Ku, 2024; Sánchez-Franco and Rey-Tienda, 2024; Xu, 2024).
Optimization of tourist flow management. Random Forest, LSTM, clustering techniques (Bellodi et al., 2022; Tzitziou et al., 2024).
Efficient management of demand and resources. Machine Learning, predictive analysis, Deep Learning, neural networks, natural language processing, optimization algorithms (Li et al., 2019; Huang et al., 2021; Wei et al., 2021; Bellodi et al., 2022; Wang, 2022a, 2024a; Li, 2023; Schuhbert et al., 2023; Thumrongvut et al., 2023; Wu et al., 2023; Nag and Mishra, 2024a, 2024b; Jung et al., 2025).
Predictive maintenance of facilities. Machine learning, deep learning, neural networks (Nag and Mishra, 2024b; Wang, 2024a).
Personalization and customer experience: Personalized recommendations for itineraries and services. Chatbots, generative AI, machine learning, natural language processing, deep learning, neural networks, classification and clustering algorithms (Huang et al., 2021; Gao et al., 2022; Kuo, 2023; Zuo et al., 2023a; Ashrafuzzaman et al., 2024; Chen and Wei, 2024; Christensen et al., 2024; Ku, 2024; Nag and Mishra, 2024a; Zhu et al., 2024; Kim et al., 2025; Rong et al., 2025).
Customization of experiences based on analysis of consumer behavior and preferences. Generative AI ChatGPT, natural language processing, deep neural networks, decision trees, random forest, Deep Learning (Lisi and Esposito, 2015; Paolanti et al., 2021; Borrero et al., 2022; Schuhbert et al., 2023; Wu et al., 2023; Zhou et al., 2023; Chen and Wei, 2024; Christensen et al., 2024; Mishra et al., 2024; Wang, 2024a; Zhu et al., 2024).
Chatbots for real-time support and overcoming language barriers. Generative AI ChatGPT, chatbots (Tsang and Benoit, 2020; Huang et al., 2021; Ansari et al., 2022; Chen and Xue, 2022; Ivanov et al., 2022; Pradhan et al., 2022; Yuensuk et al., 2022; Abraham and Thazhathethil, 2023; Kuo, 2023; Çalışkan et al., 2023; Buitrago-Esquinas et al., 2024; Chen and Wei, 2024; González-Mendes et al., 2024; Ku, 2024; Ku and Chen, 2024; Nag and Mishra, 2024b).
Predictive analysis and decision making: Prediction of tourist demand to improve planning and operational efficiency. Natural language processing, Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), particle swarm optimization (PSO), deep learning, decision trees, random forest (Bo and Shi-Ting, 2015; Pasichnyk and Artemenko, 2015; Yamaka et al., 2015; Li et al., 2019; Tsang and Benoit, 2020; Wei et al., 2021; Bellodi et al., 2022; Borrero et al., 2022; Gao et al., 2022; Wang, 2022a; Lan, 2023; Li, 2023; Wu et al., 2023; Nag and Mishra, 2024b; Skare et al., 2025).
Evaluation of historical data to adjust pricing and understand consumer behaviors. LSTM (Long Short-Term Memory), recurrent neural network, Decision Trees, Random Forests (Tsang and Benoit, 2020; Huseynova, 2022; Christensen et al., 2024; Sánchez-Franco and Rey-Tienda, 2024; Xu, 2024; Jung et al., 2025).
Informed decisions in the management of cultural heritage and adaptations to climate change. Machine Learning (Li et al., 2022b; Xiao et al., 2024).
Marketing and customer relationship management (CRM): Market segmentation and targeted marketing based on detailed data analysis. Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), deep learning, Natural Language Processing (Stylos and Zwiegelaar, 2019; Bustamante et al., 2020; Paolanti et al., 2021; Zhang et al., 2021; Lan, 2023; Schuhbert et al., 2023; Razali et al., 2024; Wang, 2024a; Jung et al., 2025).
Management and analysis of customer opinions and sentiments. Neural networks, Natural Language Processing, deep learning, sentiment analysis (Kim et al., 2017; Abeysinghe et al., 2018; Paolanti et al., 2021; Pradhan et al., 2022; Meng, 2024; Razali et al., 2024; Sánchez-Franco and Rey-Tienda, 2024; Srinivasan et al., 2025).
Sustainability and environmental responsibility: Mitigation of negative environmental impacts in tourist areas. Decision trees and Support Vector Machines (SVM), neural networks, genetic algorithms (Khan et al., 2023; Silva et al., 2023; Zuo et al., 2023b).
More sustainable and resilient tourism management. BP neural network, genetic algorithms, and particle swarm optimization (Zhang et al., 2015; Tzitziou et al., 2024).
Innovation and product development: Development of new tourist products and services based on trends and predictive data. Particle swarm optimization algorithm (PSO), Deep Learning, convolutional neural networks (Yamaka et al., 2015; Paolanti et al., 2021; Gao et al., 2022; Yang and Huang, 2022; Zuo et al., 2023a).
Dynamic evaluation of tourist attractions and satisfaction levels. Virtual agents and chatbots, convolutional neural networks, natural language processing, Deep Learning (Ghaderi et al., 2018; Stylos and Zwiegelaar, 2019; Huang et al., 2021; Wei et al., 2021; Ansari et al., 2022; Li et al., 2022a; Yang and Huang, 2022; Yuensuk et al., 2022; Ranga and Nagpal, 2023; Zuo et al., 2023a; Souha et al., 2024).
Security and risk management: Optimization of security assessments and management of risks associated with tourism. Back Propagation Neural Network (BP), Particle Swarm Optimization algorithm (PSO), Deep Learning (Qian and Ge, 2021; Abraham and Thazhathethil, 2023; Song and Xu, 2023, 2023; Zuo et al., 2023b; Neshat et al., 2024; Qi and Han, 2024).
Use of biometric data to enhance security and travel experience. FVR algorithm (Fastest Virtual Reality), RBF Network (Zhang and Li, 2025).
Operational efficiency and cost reduction: Reduction of operational costs through process automation. Deep Q-Learning, facial recognition systems, Natural Language Processing algorithms, Machine Learning, neural networks, generative AI (Chen and Xue, 2022; Ivanov et al., 2022; Suanpang et al., 2022; Yang and Huang, 2022; Dalkıran, 2023; Ashrafuzzaman et al., 2024; González-Mendes et al., 2024; Zhang, 2024; Zhu et al., 2024).
Improvements in staff selection and reduction of repetitive tasks. Natural language processing, Q-Learning, fuzzy association rule algorithms, fuzzy classification algorithms, chatbots (Wang, 2022b; Wei, 2022; El Hajal and Yeoman, 2024; Hu et al., 2024; Ku and Chen, 2024).
Challenges of using AI in the tourism industry

The implementation of artificial intelligence (AI) in the tourism industry presents significant challenges ranging from technical and operational issues to ethical and cultural concerns. Each of these challenge categories requires detailed consideration to ensure successful integration of AI in this sector:

Costs and infrastructure

Adopting AI in tourism involves high implementation and maintenance costs (Yamaka et al., 2015; Siroosi et al., 2020; Razali et al., 2021; Wei et al., 2021; Ku, 2024; Puerta et al., 2024; Zhang, 2024), as the technology involved is complex and advanced. Companies must make substantial investments in appropriate technological infrastructure, which is not always possible due to the lack of existing infrastructure that can support new technologies (Klimova et al., 2020; Siroosi et al., 2020; Suanpang et al., 2022; Wang, 2022b; Lan, 2023; Thumrongvut et al., 2023; Çalışkan et al., 2023). This translates into the need to create or significantly enhance technological infrastructure, which can be prohibitive for many organizations, especially small and medium-sized enterprises.

 Ethical and privacy aspects

The implementation of AI in tourism also raises significant ethical concerns, particularly related to the privacy and security of the collected data (Bustamante et al., 2020; Dey and Shukla, 2020; Tsang and Benoit, 2020; Zuo et al., 2023a, 2023b; Ashrafuzzaman et al., 2024; Chen and Wei, 2024; Zhang, 2024; Nag and Mishra, 2024a, 2024b; Wang, 2024a; Zhang and Li, 2025). Ethical dilemmas arise from the use and implications of AI technologies, including concerns about unemployment due to automation and the impact on traditional (Song and Xu, 2023; Christensen et al., 2024; El Hajal and Yeoman, 2024; Ku, 2024; Nag and Mishra, 2024a; Puerta et al., 2024). Protecting personal data is critical for maintaining consumer trust.

 Integration and operability

Integrating AI with existing processes represents a significant challenge due to the complexity of the technologies and the variability in legacy systems (Dey and Shukla, 2020; Obogo and Adedoyin, 2021; Ansari et al., 2022; Borrero et al., 2022; Gao et al., 2022; Meng, 2024; Qi and Han, 2024; Zhang, 2024). Difficulties in data collection and standardization (Schuhbert et al., 2023) directly affect the AI’s ability to function efficiently, requiring continuous technological updates (Abraham and Thazhathethil, 2023) and adaptations in the daily operations of tourism companies to keep up with technological advances.

 Data and analysis

The quality and accuracy of data are fundamental for the effective operation of machine learning models (Abeysinghe et al., 2018; Savvopoulos et al., 2019; Stylos and Zwiegelaar, 2019; Paolanti et al., 2021; Hu, 2022; Zyma et al., 2022; Silva et al., 2023; Wang, 2024a; Xu, 2024; Skare et al., 2025). Issues related to incomplete, inaccurate, or biased data can lead to erroneous conclusions or ineffective predictions. Moreover, AI depends on large volumes of data for its learning and optimization, which poses challenges in managing and analyzing these massive data sets (Razali et al., 2021; Bellodi et al., 2022; Yu, 2022; Song and Xu, 2023).

 Training and resistance to change

There is a natural resistance to change within organizations that can hinder the adoption of new technologies like AI (Stylos and Zwiegelaar, 2019; Gao et al., 2022; Meng, 2024). This resistance often stems from a lack of understanding of the technology and fear of the unknown. Intensive training is crucial to ensure that staff understand and embrace new technologies, but finding and developing the technical skills necessary to manage these advanced technologies is another significant challenge (Razali et al., 2021; Sifolo and Henama, 2021; Wei et al., 2021; Ansari et al., 2022; Wang, 2022a, 2022b; Meng, 2024; Puerta et al., 2024; Qi and Han, 2024).

 Reliability and accuracy of the technology

Limitations of AI in terms of contextual accuracy and depth of analysis are concerning (Kim et al., 2025). Variability and noise in data can affect the accuracy of predictions (Li, 2023), which in turn can lead to the generation of incorrect information, damaging consumer trust and the company’s reputation (Christensen et al., 2024; Zhu et al., 2024).

 Cultural and social

It is crucial to ensure that the implementation of AI does not alter the cultural authenticity of tourist destinations and the real interaction between humans (Boukherouk et al., 2020, 2020; Schuhbert et al., 2023; Nag and Mishra, 2024b). Additionally, the cultural and organizational adaptation to new technologies is a process that requires time and effort, and the acceptance of these advanced technologies by customers and employees is essential for their success.

Each of these challenges requires a well-thought-out and executed strategy to overcome them, ensuring that the technology is integrated in a way that benefits both businesses and consumers in the tourism sector.

 Discussion

The integration of emerging technologies, particularly artificial intelligence (AI) applications, has been gaining traction in the tourism sector, demonstrating significant transformative potential. According to Ivanov et al. (2022), technologies such as automated check-ins at hotels and airports, as well as customer-centric solutions like chatbots and voice-activated systems, have been implemented to enhance both operational efficiency and customer satisfaction. These technologies enable smoother management and more personalized service, critical factors in the digitalization era. This information aligns with the technologies identified in the literature review.

In the realm of artificial intelligence, ChatGPT and other generative intelligence applications stand out as a relevant tool for the tourism industry, specifically in customizing customer service and enhancing user experience. According to Kim et al. (2025), tools like ChatGPT utilize natural language processing (NLP) to interact more fluidly and effectively with users (Kuo, 2023). ChatGPT has the potential to revolutionize the service model in the hospitality and tourism industry (Tuo et al., 2025). It enables faster and more autonomous travel decision-making for tourists, while enhancing service quality through user feedback mechanisms and emotional intelligence programming.

One of the most notable applications of neural networks is in predicting tourist demand. As highlighted by Tsang and Benoit (2020) and Hou and Wang (2024), these networks are capable of analyzing historical data and behavioral patterns to forecast tourist flows, enabling companies to adjust their operational and pricing strategies accordingly. Furthermore, neural networks also play a crucial role in customizing the customer experience. Through classification and clustering algorithms, these networks can analyze travelers’ preferences and offer personalized recommendations for itineraries, accommodations, and activities (Bustamante et al., 2020). This adaptability results in an experience more aligned with consumer desires, which in turn can increase customer satisfaction and loyalty.

Regarding benefits, one of the most notable contributions of AI is process optimization. For example, automating reservations and using customer relationship management (CRM) systems allows businesses to handle large volumes of transactions more efficiently (Stylos and Zwiegelaar, 2019). This not only saves time and resources but also reduces the likelihood of human errors in managing bookings and orders.

AI can also be integrated into strategic business planning and operations by enabling advanced forecasting models for customer demand and more effective revenue management strategies (Gursoy and Cai, 2025). Additionally, it can automate repetitive administrative tasks, improving operational efficiency (Wang et al., 2025). However, despite growing interest in AI applications, few studies have explored its potential to drive business development or streamline organizational management.

The customization of the customer experience is a major contribution of AI technologies, enabling businesses to analyze consumer behavior and offer personalized services, such as tailored itineraries and travel recommendations, which enhance satisfaction and brand loyalty. However, as noted by Christensen et al. (2024), yet, this innovation comes with practical challenges, including the risk of “AI hallucinations,” or the generation of inaccurate or misleading information. Such misinformation can undermine trust and pose significant risks to the tourism industry.

AI also enables the analysis of large data sets that help predict market trends and behaviors. Analytical models like deep neural networks can forecast tourist demand, helping businesses adjust their prices and offers based on more accurate predictions (Xu, 2024). This anticipatory capacity is crucial for maximizing profitability and operational efficiency.

In most studies analyzed, the incorporation of AI in the tourism industry brings various operational or financial benefits and also enhances the customer experience by offering more efficient personalization and care. However, to maximize these benefits, companies must address challenges related to implementation, such as data management and consumer acceptance.

Limitations and future work

While artificial intelligence (AI) and emerging technologies have the potential to revolutionize the way tourism operations are managed and services are delivered, their effective adoption depends on a deep understanding of the dynamics of the sector, consumer expectations, and the ethical context in which these technologies are implemented. An integrative approach that addresses these aspects will be crucial to maximize the effectiveness of the proposed solutions and ensure alignment with market demands.

The adoption of such technologies also raises concerns about job loss and the dehumanization of service, requiring companies to perform a detailed cost–benefit analysis before implementation (Ivanov et al., 2022). An important aspect that companies must consider is the ethical and social implications of automation in tourism, offering technological solutions that not only focus on efficiency but also align with customer expectations, needs, and trust.

It is also to acknowledge that most current AI models rely heavily on statistical patterns derived from large datasets, which, while effective for identifying trends and optimizing existing processes, may inadvertently overlook original or context-specific solutions. This limitation is particularly relevant in the field of tourism development, where innovative strategies such as collective strategic planning and the construction of multi-level objective trees play a crucial role. These approaches emphasize participatory decision-making and nuanced goal-setting that adapt to the unique social, cultural, and environmental contexts of cities, megacities, and countries. Therefore, while AI can enhance analytical capacity, its integration with human-centered and collaborative planning methodologies remains essential for generating holistic and sustainable development strategies.

Despite its benefits, the use of AI also presents environmental challenges, particularly due to the high energy consumption involved in training large language models. This process contributes to a significant technological carbon footprint, raising concerns about sustainability. As AI becomes more integrated into tourism planning, it is essential to balance innovation with environmental responsibility by promoting energy-efficient practices and aligning digital tools with sustainable development goals.

Therefore, for AI to be effectively implemented in the tourism sector, an approach that considers not just technological capabilities but also user experiences and concerns is essential. For the future, it is suggested that studies delve deeper into evaluating the effectiveness of artificial intelligence technologies in different segments of the tourism sector, such as hospitality, travel planning, and destination management. It is crucial to explore how AI integration can enhance sustainability in tourism, especially in a context of growing concern over environmental impact. Additionally, future research should compare the performance and applicability of traditional machine learning approaches versus large-scale models like LLMs, in order to better understand their respective advantages, limitations, and the contexts in which each offers the greatest value.

 Conclusion

The study addresses the growing integration of artificial intelligence (AI) in the tourism industry and how this phenomenon has transformed both management and the customer experience. The results indicate an increasing adoption of AI technologies, which are enhancing operational efficiency and the customer experience, especially from the context of the COVID-19 pandemic, which accelerated the implementation of technological solutions.

Regarding AI technologies, it was identified that it encompasses a wide range of algorithms and specific applications according to the studies reviewed. In the realm of generative technologies and language processing, chatbots and generative models like ChatGPT stand out, which use natural language processing (NLP) and attention-based models to analyze sentiments and detect emotions, as well as for more specialized tasks such as clustering and classification. These studies employ everything from traditional algorithms like decision trees and support vector machines to more advanced techniques like random forest and gradient boosting classifier. On the other hand, neural networks and deep learning remain a crucial pillar in AI development. These technologies are applied in varied contexts that include data analysis, pattern recognition, and optimization, using methods like the particle swarm algorithm and genetic algorithms, demonstrating their versatility and capacity to adapt to different problems and sectors.

The implementation of artificial intelligence (AI) technologies has provided several benefits in the tourism sector, such as the management of reservations and operations which has significantly improved processes through the automation of bookings and check-ins using natural language processing, facial recognition, and virtual assistants. In terms of personalization and customer experience, personalized recommendations through chatbots and classification algorithms stand out, enhancing customization based on the analysis of consumer behaviors and preferences.

However, despite its potential to foster innovation and improve operational efficiency, the sector faces significant challenges, including resistance to adopting new technologies and an infrastructure often insufficient to support such changes. It was also evident that applications seeking to automate operations face greater resistance, highlighting the importance of considering factors such as relative advantage and perceived risks in the development and implementation of these technologies. These issues underline the need for an integrative approach to understanding and promoting the effective application of AI in this field.

Study limitations

Some inherent limitations of the study, which must be considered, include the dependence on bibliometric analysis which, while providing an overview of the evolution of research in artificial intelligence in the tourism sector, may not fully capture the diversity of approaches and specific contexts addressed in individual studies. Another limitation refers to the risk of methodological bias in the reviewed studies. These considerations should be taken into account for future complementary studies that include a wider variety of sources and approaches to obtain a more holistic understanding of the phenomenon in question.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

 Author contributions

AL-N: Writing – original draft, Resources, Investigation, Validation, Supervision, Conceptualization, Writing – review & editing, Funding acquisition. MP-R: Project administration, Funding acquisition, Formal analysis, Data curation, Writing – review & editing, Investigation, Software, Conceptualization. VC-S: Validation, Investigation, Methodology, Funding acquisition, Visualization, Writing – original draft. JE-R: Methodology, Investigation, Writing – review & editing, Validation, Funding acquisition. PB-G: Validation, Software, Investigation, Writing – review & editing, Funding acquisition.

 Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

 Generative AI statement

The author(s) declare that Gen AI was used in the creation of this manuscript. All content has been generated by the authors without the use of Generative AI; however, ChatGPT was used for correction and translation.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frai.2025.1599391/full#supplementary-material

 References Abeysinghe S. Manchanayake I. Samarajeewa C. Rathnayaka P. Walpola M. J. Nawaratne R. (2018). “Enhancing decision making capacity in tourism domain using social media analytics,” in 2018 18th International Conference on Advances in ICT for Emerging Regions (Institute of Electrical and Electronics Engineers Inc.), 369–375. Abraham S. T. Thazhathethil B. V. (2023). Tourism disaster management through chatbots as an alternative tool of communication. J. Tour. Dev. 41, 349–367. doi: 10.34624/rtd.v41i0.30195 Alcocer J. A. A. (2013). Competitividad en el sector turístico: una revisión de la literatura. Rev. Lebret, 271–291. doi: 10.15332/rl.v0i5.833 Ansari N. Tyagi S. Mehrwal N. Rastogi N. Chibber S. Memoria M. (2022). “A critical insight into the impact of Technology in Transformation of tourist business into smart tourism,” in 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Institute of Electrical and Electronics Engineers Inc.), 832–837. Ashrafuzzaman M. Era R. R. Awal S. B. Sultana I. Thimmaiah A. Sharma A. K. . (2024). “Transforming medical tourism through an integrated business intelligence platform: data-driven insights and healthcare industry impact,” in Data-Driven Business Intelligence Systems for Socio-Technical Organizations (IGI Global), 233–266. Bellodi E. Zese R. Bertasi F. (2022). “Machine learning in a policy support system for smart tourism management,” in International Conference on Machine Learning, Optimization, and Data Science. eds. Nicosia G. Ojha V. La Malfa E. La Malfa G. Jansen G. Pardalos P. M. . (Cham: Springer Science and Business Media Deutschland GmbH), 457–472. Berente N. Gu B. Recker J. Santhanam R. (2021). Managing artificial intelligence. MIS Q. 45, 1433–1450. doi: 10.25300/MISQ/2021/16274 Bo X. Shi-Ting L. (2015). “Management of tourism resources and demand based on neural networks,” in 2014 7th International Conference on Intelligent Computation Technology and Automation (348–351). IEEE. Boden M. A. (2017). Inteligencia Artificial. Nashville, TN: Turner. Borrero J. D. Mariscal J. Vargas-Sánchez A. (2022). A new predictive algorithm for time series forecasting based on machine learning techniques: evidence for decision making in agriculture and tourism sectors. Stats 5, 1145–1158. doi: 10.3390/stats5040068 Boukherouk M. Ed-dali R. Dbibirha Y. (2020). “Sharing tourism and its impact on hospitality Management in Essaouira: analysis of the evolution of booking.Com and Airbnb” in Cultural and Tourism Innovation in the Digital Era: Sixth International IACuDiT Conference, Athens 2019. eds. Katsoni V. Spyriadis T. (Cham: Springer Science and Business Media B.V), 145–159. Buitrago-Esquinas E. M. Yñiguez-Ovando R. Puig-Cabrera M. Santos M. C. Santos J. A. C. (2024). Artificial intelligence and sustainable tourism planning: a hetero-intelligence methodology proposal. Tour. Manag. Stud. 20, 45–59. doi: 10.18089/tms.2024SI04 Bustamante A. Sebastia L. Onaindia E. (2020). BITOUR: a business intelligence platform for tourism analysis. ISPRS Int. J. Geo Inf. 9:671. doi: 10.3390/ijgi9110671 Cai M. Gunaban M. G. B. (2023). “Construction of Instructional Quality Assessment Model of Tourism Management Specialty Based on BPNN Algorithm,” in 2023 5th International Conference on Applied Machine Learning (ICAML) (260–266). IEEE. Çalışkan G. Yayla İ. Pamukçu H. (2023). The use of augmented reality technologies in tourism businesses from the perspective of UTAUT2. Eur. J. Innov. Manage. 28, 1498–1526. doi: 10.1108/EJIM-04-2023-0271 Chen C. Wei Z. (2024). Role of artificial intelligence in travel decision making and tourism product selling. Asia Pac. J. Tour. Res. 29, 239–253. doi: 10.1080/10941665.2024.2317390 Chen N. C. Xie J. Tinn P. Alonso L. Nagakura T. Larson K. (2017). “Data mining tourism patterns-call detail records as complementary tools for urban decision making,” in Protocols, flows, and glitches: Proceedings of the22nd international conference on computer-Aided Architectural Design Research in Asia (CAADRIA 2017), 685–694. Chen C. Xue Q. (2022). “Using big data and empirical analysis to optimize the decision-making of Smart City tourism logistics service system,” in 2022 Asia conference on algorithms, computing and machine learning (CACML), 817–822. Christensen J. Hansen J. M. Wilson P. (2024). Understanding the role and impact of generative artificial intelligence (AI) hallucination within consumers’ tourism decision-making processes. Curr. Issues Tour. 28, 545–560. doi: 10.1080/13683500.2023.2300032 Dalkıran G. B. (2023). “Health tourism components and intermediary institutions as supplier businesses in the context of Health 4.0,” in Health 4.0 and Medical Supply Chain (147–162). Singapore: Springer Nature Singapore. Dey S. Shukla D. (2020). “Analytical study on use of AI techniques in tourism sector for smarter customer experience management,” in 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) (1–5). IEEE. El Hajal G. Yeoman I. (2024). AI and the future of talent management in tourism and hospitality. Curr. Issue Tour. 22, 1–18. doi: 10.1080/13683500.2024.2439395, PMID: 40640861 Fernandez A. M. (2018). Turismo, negocio o desarrollo: el caso de Huasca, México. PASOS Rev. Tur. Patrim. Cult. 16, 233–251. doi: 10.25145/j.pasos.2018.16.015 Gao X. Qi Y. Chai Y. Lei C. Wang J. (2022). Tourism information management system using neural networks driven by particle swarm model. Comput. Intell. Neurosci. 2022:6386360. doi: 10.1155/2022/6386360, PMID: 35733567 Gao Y. Yan X. (2023). Data analysis framework of tourism enterprise human resource management system based on MySQL and fuzzy clustering. Intl. J. Syst. Assur. Eng. Manage. 14, 1647–1659. doi: 10.1007/s13198-023-01969-2 Ghaderi Z. Hatamifar P. Khalilzadeh J. (2018). Analysis of tourist satisfaction in tourism supply chain management. Anatolia 29, 1–12. doi: 10.1080/13032917.2018.1439074, PMID: 40640861 González-Mendes S. González-Sanchez R. Alonso-Muñoz S. (2024). “Exploring the influence of artificial intelligence on the management of hospitality and tourism sectors: a bibliometric overview” in Artificial intelligence enabled management: An emerging economy perspective. eds. Singh R. Khan S. Kumar A. Kumar V. (Berlin: De Gruyter), 215–231. Gursoy D. Cai R. (2025). Artificial intelligence: an overview of research trends and future directions. Int. J. Contemp. Hospit. Manag. 37, 1–17. doi: 10.1108/IJCHM-03-2024-0322 Hao Y. Zheng L. (2023). Application of slam method in big data rural tourism management in dynamic scene. Soft. Comput. 1–12. doi: 10.1007/s00500-023-08521-1 Hou T. (2022). Research on management efficiency and dynamic relationship in intelligent Management of Tourism Engineering Based on industry 4.0. Comput. Intell. Neurosci. 2022:1062. doi: 10.1155/2022/5831062 Hou W. Wang K. (2024). Research on modeling of influencing factors of new eco-tourism management based on economic fluctuation prediction. Procedia Comput. Sci 243, 1179–1188. doi: 10.1016/j.procs.2024.09.139 Hu Y.-C. (2022). Combination forecasting using multiple attribute decision making in tourism demand. Tour. Rev. 77, 731–750. doi: 10.1108/TR-09-2021-0451 Hu J. Huang G. I. Wong I. A. Wan L. C. (2024). AI trust divide: how recruiter-candidate roles shape tourism personnel decision-making. Ann. Tour. Res. 109:103860. doi: 10.1016/j.annals.2024.103860 Huang A. Chao Y. Velasco E. d. l. M. Bilgihan A. Wei W. (2021). When artificial intelligence meets the hospitality and tourism industry: an assessment framework to inform theory and management. J. Hosp. Tour. Insights 5, 1080–1100. doi: 10.1108/JHTI-01-2021-0021 Huseynova N. F. (2022). “Decision making on tourism by using natural language processing” in Lect. Notes networks Syst. eds. Aliev R. A. Kacprzyk J. Pedrycz W. (Cham: Springer Science and Business Media Deutschland GmbH741747). Ivanov S. H. Webster C. Stoilova E. Slobodskoy D. (2022). Biosecurity, crisis management, automation technologies and economic performance of travel, tourism and hospitality companies – a conceptual framework. Tour. Econ. 28, 3–26. doi: 10.1177/1354816620946541 Jung S. Zhang R. Y. Chen Y. Joe S. (2025). Optimizing demand forecasting for business events tourism: a comparative analysis of cutting-edge models. J. Hosp. Tour. Insight. 8, 370–390. doi: 10.1108/JHTI-12-2023-0960 Khan S. Gulistan M. Kausar N. Kadry S. Kim J. (2023). A novel method for determining tourism carrying capacity in a decision-making context using q−rung orthopair fuzzy hypersoft environment. CMES 138, 1951–1979. doi: 10.32604/cmes.2023.030896 Kim J. H. Kim J. Park J. Kim C. Jhang J. King B. (2025). When ChatGPT gives incorrect answers: the impact of inaccurate information by generative AI on tourism decision-making. J. Travel Res. 64, 51–73. doi: 10.1177/00472875231212996 Kim K. Park O.-J. Yun S. Yun H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technol. Forecast. Soc. Change 123, 362–369. doi: 10.1016/j.techfore.2017.01.001 Klimova T. B. Bogomazova I. V. Anoprieva E. V. Semchenko I. V. Plokhikh R. V. (2020). Digital supply chain management in the tourism and hospitality industry: trends and prospects. Int. J. Supply Chain Manag. 9, 843–849. Ku E. C. S. (2024). Embracing the future of interactive marketing with contactless technology: evidence from tourism businesses. J. Res. Interact. Mark. 19, 630–650. doi: 10.1108/JRIM-04-2024-0183 Ku E. C. S. Chen C.-D. (2024). Artificial intelligence innovation of tourism businesses: from satisfied tourists to continued service usage intention. Int. J. Inf. Manag. 76:102757. doi: 10.1016/j.ijinfomgt.2024.102757, PMID: 40653438 Kuo C.-H. (2023). “Enhancing inbound tourism through the Taiwan outdoor project and AI-enabled Chatbot management” in Proc. Int. Conf. Electron. Bus. (ICEB). eds. Li E. Y. Chang S.-I. Yen B. (Cham: International Consortium for Electronic Business), 216–223. Lan M. (2023). Big data and cloud computing- integrated tourism decision-making in smart logistics technologies. Int. J. e-Collab. 19, 1–20. doi: 10.4018/ijec.316880 Li Y. (2023). Research on the development of digital business forecasting in the “commerce, culture, and tourism plus” model based on big data and predictive models. ACM Int. Conf. Proc. Ser. 1, 1055–1059. doi: 10.1145/3656766.3656940 Li M. Li R. Wu Y. (2022a). Dynamic monitoring method of rural tourism popularity based on big data management technology. Math. Probl. Eng. 2022, 1–8. doi: 10.1155/2022/9086946 Li K. Lu W. Liang C. Wang B. (2019). Intelligence in tourism management: a hybrid FOA-BP method on daily tourism demand forecasting with web search data. Mathematics 7:531. doi: 10.3390/MATH7060531 Li P. Xiao X. Seekamp E. (2022b). Climate adaptation planning for cultural heritages in coastal tourism destinations: a multi-objective optimization approach. Tour. Manag. 88:104380. doi: 10.1016/j.tourman.2021.104380 Lisi F. A. Esposito F. (2015). “An AI application to integrated tourism planning” in Lect. Notes Comput. Sci. eds. Riguzzi F. Gavanelli M. Lamma E. (Berlin: Springer Verlag), 246–259. Lyu H. Han Z. (2022). Algorithm of scenic spot tourism route planning based on convolution neural network. Wirel. Commun. Mob. Comput. 2022, 1–9. doi: 10.1155/2022/5330611 Mariani M. Wirtz J. (2023). A critical reflection on analytics and artificial intelligence based analytics in hospitality and tourism management research. Int. J. Contemp. Hospit. Manag. 35, 2929–2943. doi: 10.1108/IJCHM-08-2022-1006 Matos J. J. Mullo E. d. C. Juanes B. Y. Álvarez I. Matos J. J. Mullo Romero E. d. C. . (2022). La gestión en el turismo y sus experiencias en Latinoamérica. Rev. Univ. Soc. 14, 649–658. Meng L. (2024). The convolutional neural network text classification algorithm in the information management of smart tourism based on internet of things. IEEE Access 12, 3570–3580. doi: 10.1109/ACCESS.2024.3349386 Mishra M. Kala S. M. Amano T. Yamaguchi H. (2024). “Balancing privacy and planning: using counterfactuals to predict and optimize tourism in Wakayama City,” in Proceedings of the 2nd ACM SIGSPATIAL international workshop on geo-privacy and data utility for smart societies (New York, NY: Association for Computing Machinery), 25–30. Nag A. Mishra S. (2024a). Revitalizing mining heritage tourism: a machine learning approach to tourism management. J. Min. Environ. 15, 1193–1225. doi: 10.22044/jme.2024.13770.2554 Nag A. Mishra S. (2024b). Tourism management with AI integration for mining heritage: a literature review approach. J. Min. Environ. 15, 115–149. doi: 10.22044/jme.2023.13473.2490 Neshat N. Moayedfar S. Rezaee K. Amrollahi Biuki N. (2024). Sustainable planning of developing tourism destinations after COVID-19 outbreak: a deep learning approach. J. Policy Res. Tour. Leisure Events 16, 1–21. doi: 10.1080/19407963.2021.1970578 Obogo J. U. Adedoyin F. F. (2021). “Data-driven business analytics for the tourism industry in the UK: a machine learning experiment post-COVID,” in 2021 IEEE 23rd Conference on Business Informatics (CBI). eds. Almeida J. P. A. Bork D. Guizzardi G. (Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc). 2, 78–86. Page M. J. Moher D. Bossuyt P. M. Boutron I. Hoffmann T. C. Mulrow C. D. . (2021). PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews. BMJ 372:n160. doi: 10.1136/bmj.n160, PMID: 33781993 Paolanti M. Mancini A. Frontoni E. Felicetti A. Marinelli L. Marcheggiani E. . (2021). Tourism destination management using sentiment analysis and geo-location information: a deep learning approach. Inf. Technol. Tour. 23, 241–264. doi: 10.1007/s40558-021-00196-4 Pasichnyk V. Artemenko O. (2015). “Intelligent advisory systems and information technology support for decision making in tourism,” in 2015 Xth international scientific and technical conference “computer sciences and information technologies” (CSIT), 16–21. Pradhan S. Lama S. Bunker D. (2022). “ICT adoption for tourism disaster management: a systematic review” in Proc. Int. ISCRAM Conf. eds. Huggins T. J. Lemiale V. (Kristiansand: Information Systems for Crisis Response and Management, ISCRAM), 215–227. Puerta A. Cantillo S. Sanabria E. Fajardo-Moreno W. Ricardo K. Devoz Z. (2024). “Digital entrepreneurship and technological transformation within the reach of risk management in tourism,” in Proc. LACCEI int. multi-conf. Eng. Educ. Technol. Latin American and Caribbean Consortium of Engineering Institutions. Qi H. Han Z. (2024). Emotion recognition and management in the tourism industry during emergency events using improved convolutional neural network. IEEE Access 12, 32660–32667. doi: 10.1109/ACCESS.2024.3370431 Qian W. Ge Y. (2021). The implementation of leisure tourism enterprise management system based on deep learning. Intl. J. Syst. Assur. Eng. Manage. 12, 801–812. doi: 10.1007/s13198-021-01103-0 Ranga K. K. Nagpal C. K. (2023). Trip planner: a big data analytics based recommendation system for tourism planning. Int. J. Recent Innov. Trends Comput. Commun. 11, 159–174. doi: 10.17762/ijritcc.v11i3s.6176 Razali M. N. Manaf S. A. Hanapi R. B. Salji M. R. Chiat L. W. Nisar K. (2024). Enhancing minority sentiment classification in gastronomy tourism: a hybrid sentiment analysis framework with data augmentation, feature engineering and business intelligence. IEEE Access 12, 49387–49407. doi: 10.1109/ACCESS.2024.3362730 Razali M. N. Moung E. G. Yahya F. Hou C. J. Hanapi R. Mohamed R. . (2021). Indigenous food recognition model based on various convolutional neural network architectures for gastronomic tourism business analytics. Information 12:322. doi: 10.3390/info12080322 Rong A. Jianwei S. Xiaowei X. (2025). Intelligent entertainment robots based on path navigation planning in tourism intelligent services and user entertainment experience analysis. Entertain. Comput. 52:100829. doi: 10.1016/j.entcom.2024.100829 Rouhiainen L. (2018). Inteligencia artificial: 101 cosas que debes saber hoy sobre nuestro futuro. Madrid: Alienta Editorial. Sánchez-Franco M. J. Rey-Tienda S. (2024). The role of user-generated content in tourism decision-making: an exemplary study of Andalusia. Spain. Manage. Decis. 62, 2292–2328. doi: 10.1108/MD-06-2023-0966 Savvopoulos A. Kalogeras G. Mariotti A. Bernini C. Alexakos C. Kalogeras A. (2019). “Data analytics supporting decision making in the tourism sector,” in 2019 First International Conference on Societal Automation (SA), 1–6. Schuhbert A. Thees H. Pechlaner H. (2023). Deep and organizational learning as innovation catalyzer in digital business ecosystems – a scenario analysis on the tourism destination Berlin. Eur. J. Innov. Manag. 27, 2419–2456. doi: 10.1108/EJIM-08-2022-0448 Shi Y. Liu Z. Chen L. Li W. (2019). Research on intelligent tourism management based on wireless network development. J. Intelligent Fuzzy Syst. 37, 6039–6046. doi: 10.3233/JIFS-179186, PMID: 39743787 Sifolo P. P. S. Henama U. S. (2021). “Sustainable supply chain management practices in the hospitality and tourism sector in Africa” in New Frontiers in Hospitality and Tourism Management in Africa. eds. Ngoasong M. Z. Adeola O. Kimbu A. N. Hinson R. E. (Cham: Springer Nature), 125–142. Silva F. R. D. Câmara S. F. Pinto F. R. Costa F. J. D. Freitas L. M. D. Oliveira Júnior J. G. C. D. . (2023). Machine learning application to assess deforestation and wildfire levels in protected areas with tourism management. J. Nat. Conserv. 74:126435. doi: 10.1016/j.jnc.2023.126435 Siroosi H. Heshmati G. Salmanmahiny A. (2020). Can empirically based model results be fed into mathematical models? MCE for neural network and logistic regression in tourism landscape planning. Environ. Dev. Sustain. 22, 3701–3722. doi: 10.1007/s10668-019-00363-y Skare M. Gavurova B. Polishchuk V. (2025). A large-scale decision-making model for the expediency of funding the development of tourism infrastructure in regions. Expert. Syst. 42:13443. doi: 10.1111/exsy.13443 Song J. Xu H. (2023). Safety risk evaluation of tourism management system based on PSO-BP neural network. Wirel. Commun. Mob. Comput. 2023, 1–8. doi: 10.1155/2023/2968129 Souha A. Benaddi L. Ouaddi C. Bouziane E. M. Jakimi A. (2024). A new decision support system for enhancing tourism destination management and competitiveness., in Proc. Int. Conf. Wirel. Networks Mob. Commun., WINCOM, eds. Zaidi S.A.R. Ibrahimi K. Kamili M.El Kobbane A. Aslam N. (Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc.). Srinivasan J. Niranjanee M. Nandhana C. Azhagiri M. (2025). “A text classification in tourism reviews with the hybrid transformer-attention model in the information management of smart tourism” in Proceedings of fifth international conference on computing, communications, and cyber-security. eds. Gonçalves P. J. S. Singh P. K. Tanwar S. Epiphaniou G. (Singapore: Springer Nature), 479–488. Stylos N. Zwiegelaar J. (2019). “Big data as a game changer: how does it shape business intelligence within a tourism and hospitality industry context?” in Big data and Innov. In tour., travel, and Hosp.: Manag. Approaches, tech., and Appl (Singapore: Springer Singapore), 163–181. Suanpang P. Jamjuntr P. Jermsittiparsert K. Kaewyong P. (2022). Autonomous energy management by applying deep Q-learning to enhance sustainability in smart tourism cities. Energies 15:906. doi: 10.3390/en15051906 Thumrongvut P. Sethanan K. Pitakaso R. Jamrus T. Golinska-Dawson P. (2023). Application of industry 3.5 approach for planning of more sustainable supply chain operations for tourism service providers. Int. J. Logist. Res. Applic. 26, 1578–1601. doi: 10.1080/13675567.2022.2090529 Tsang W. K. Benoit D. F. (2020). Gaussian processes for daily demand prediction in tourism planning. J. Forecast. 39, 551–568. doi: 10.1002/for.2644 Tuo Y. Wu J. Zhao J. Si X. (2025). Artificial intelligence in tourism: insights and future research agenda. Tour. Rev. 80, 793–812. doi: 10.1108/TR-03-2024-0180 Tzitziou G. Dimara A. Stefanopoulou A. Papaioannou A. Michailidis I. Krinidis S. . (2024). “Tourist flow projection in response to weather variability for sustainable tourism and management” in IFIP advances in information and communication technology. eds. Maglogiannis I. Iliadis L. Karydis I. Papaleonidas A. Chochliouros I. (Cham: Springer Science and Business Media Deutschland GmbH), 476–489. Wang L. (2022a). Tourism demand forecast based on adaptive neural network Technology in Business Intelligence. Comput. Intell. Neurosci. 2022:3376214. doi: 10.1155/2022/3376296, PMID: 35087579 Wang N. (2022b). Application of DASH client optimization and artificial intelligence in the management and operation of big data tourism hotels. Alex. Eng. J. 61, 81–90. doi: 10.1016/j.aej.2021.04.080 Wang L. (2024a). Enhancing tourism management through big data: design and implementation of an integrated information system. Heliyon 10:e38256. doi: 10.1016/j.heliyon.2024.e38256, PMID: 39502210 Wang N. (2024b). Using the fuzzy method and multi-criteria decision making to analyze the impact of digital economy on urban tourism. Int. J. Comput. Intell. Syst. 17:517. doi: 10.1007/s44196-024-00517-5 Wang W. Iahad N. A. Al-Sharafi M. A. (2025). “Artificial intelligence implementation in the tourism industry: a literature review” in Current and future trends on AI applications: Volume 1. eds. Al-Sharafi M. A. Al-Emran M. Mahmoud M. A. Arpaci I. (Cham: Springer Nature Switzerland), 149–166. Wang W. Wu D. (2022). Construction of rural tourism brand value management model from the perspective of big data. Comput. Intell. Neurosci. 2022, 1–13. doi: 10.1155/2022/5623782, PMID: 35909864 Wei F. (2022). Performance evaluation of tourism human resource management based on fuzzy data mining. J. Mathematics 2022:3745377. doi: 10.1155/2022/3745377 Wei C. Wang Q. Liu C. (2021). Application of an artificial neural network optimization model in e-commerce platform based on tourism management. EURASIP J. Wirel. Commun. Netw. 2021:93. doi: 10.1186/s13638-021-01947-x Wu Y. Zuo L. Kalmanbetova G. (2023). “Research on carrying capacity and resource Management of Tourism Destinations Combined with machine learning,” in ACM Int. Conf. Proc. Ser. (New York, NY: Association for Computing Machinery). Xiao X. Li P. Seekamp E. (2024). Sustainable adaptation planning for cultural heritage in coastal tourism destinations under climate change: a mixed-paradigm of preservation and conservation optimization. J. Travel Res. 63, 215–233. doi: 10.1177/00472875221143479 Xu L. (2024). A multi-level DEEP neural network-based tourism supply chain risk management study. Scalable Comput. Pract. Exp. 25, 3100–3113. doi: 10.12694/scpe.v25i4.2894 Yamaka W. Pastpipatkul P. Sriboonchitta S. (2015). “Business cycle of international tourism demand in Thailand: a Markov-switching Bayesian vector error correction model” in Integrated uncertainty in knowledge modelling and decision making. eds. Huynh V.-N. Inuiguchi M. Demoeux T. (Cham: Springer International Publishing), 415–427. Yang L. Huang X. (2022). “Research on tourism resources management method based on deep learning and knowledge graph,” in Proc. - Int. Symp. Comput. Sci. Intell. Control, ISCSIC (Institute of Electrical and Electronics Engineers Inc.), 127–131. Yang S. Li Q. Jang D. Kim J. (2024). Deep learning mechanism and big data in hospitality and tourism: developing personalized restaurant recommendation model to customer decision-making. Int. J. Hosp. Manage. 121:103803. doi: 10.1016/j.ijhm.2024.103803 Yu X. (2022). Global multi-source information fusion management and deep learning optimization for tourism: personalized location-based service. JOEUC 34, 1–21. doi: 10.4018/JOEUC.294902 Yu T. Rita P. Moro S. Oliveira C. (2022). Insights from sentiment analysis to leverage local tourism business in restaurants. Int. J. Cult. Tour. Hosp. Res. 16, 321–336. doi: 10.1108/IJCTHR-02-2021-0037 Yu Z. Wang S. (2022). “Smart tourism route planning system based on machine learning algorithm,” in Int. Conf. Comput. Artif. Intell. Technol. CAIT (Institute of Electrical and Electronics Engineers Inc.), 16–20. Yuensuk T. Limpinan P. Nuankaew W. S. Nuankaew P. (2022). Information systems for cultural tourism management using text analytics and data mining techniques. Int. J. Interact. Mob. Technol. 16, 146–163. doi: 10.3991/ijim.v16i09.30439 Zhang Z. (2024). “How artificial intelligence optimizes customer relationship management in the tourism industry,” in 2024 3rd International Conference on Artificial Intelligence and Computer Information Technology (AICIT), 1–4. Zhang L. Hang L. Jin W. Kim D. (2021). Interoperable multi-blockchain platform based on integrated REST APIs for reliable tourism management. Electronics 10:2990. doi: 10.3390/electronics10232990 Zhang J. Ji M. Zhang Y. (2015). Tourism sustainability in Tibet - forward planning using a systems approach. Ecol. Indic. 56, 218–228. doi: 10.1016/j.ecolind.2015.04.006 Zhang Q. Li Y. (2025). Innovative design of digital neural network-based biodata integration technology in cultural tourism management: insights from cellular molecular biomechanics perspective. MCB Mol. Cell. Biomech. 22:981. doi: 10.62617/mcb981 Zhou Z. Li T. Liu C. Zhou Y. Li P. Wen S. (2023). Why do social media users follow tourism-related posts? Roles of bloggers and posts in trip planning. Ind Manage Data Sys 123, 3080–3108. doi: 10.1108/IMDS-11-2022-0692 Zhu S. Song M. Duan Y. (2024). Emotional arousal: how artificial intelligence-generated content influences tourism decision-making. Tour. Recreat. Res. 1–12. doi: 10.1080/02508281.2024.2439321 Zuo L. Savin V. Wu Y. (2023a). Insight analysis of tourists’ behavior and preference by deep learning in tourism destination management, in Proc. - Int. Conf. Intell. Comput., Commun. Converg Patnaik S. (Piscataway, NJ: Institute of Electrical and Electronics Engineers Inc.), 99–103. Zuo L. Wu Y. Kalmanbetova G. (2023b). “Design of Risk Management Decision-making Framework for tourism enterprises based on artificial intelligence,” in ACM Int. Conf. Proc. Ser., (New York, NY: Association for Computing Machinery). Zyma O. Guryanova L. Gavkalova N. Chernova N. Nekrasova O. (2022). The application of machine learning methods in determining attractive development directions for tourism businesses. Intellect. Econ. 16, 151–165. doi: 10.13165/IE-22-16-1-09