Adoption of artificial intelligence in tourism agencies: realities and challenges. A field study of tourism agencies in Bordj Bou Arréridj, Algeria.

This research investigates the extent to which tourism agencies in the province of Bordj Bou Arreridj (Algeria) integrate artificial intelligence (AI) applications into their operational and strategic activities. The study further explores the key challenges hindering the effective adoption of such technologies. Employing a descriptive method design, the data were analyzed using SPSS v27 to address the central research question and to measure the degree of AI adoption in several domains, including marketing strategy development, trip planning and decision-making, booking operations, and customer experience enhancement. The results demonstrate that the implementation of AI applications within the surveyed agencies is moderate across these domains, nevertheless, the agencies encounter significant barriers, most notably a shortage of qualified experts and persistent technical constraints.

1 Introduction

Travel is constantly changing and developing dramatically, especially with the introduction of new technologies (Melusova 2025) In recent years, organizations operating in the tourism sector have increasingly adopted artificial intelligence (AI) technologies, recognizing their wide-ranging benefits, these advantages span from enhancing personalized customer experiences to improving operational efficiency, tools such as chatbots, predictive analytics, and machine learning have significantly transformed the processes and mechanisms of tourism-related services. Tourism agencies, in particular, leverage these technologies to tailor their marketing strategies to the individual preferences of clients, thereby boosting customer satisfaction and enriching their overall travel experiences. Moreover, the integration of AI provides these agencies with a sustainable competitive edge in a sector marked by rapid growth and continuous development
The application of artificial intelligence (AI) in the tourism sector has proven to be highly valuable in enhancing both customer experience and organizational performance. By analyzing customer data, travel records, and individual preferences, AI enables personalized recommendations for destinations and accommodations (Doğan and Niyet 2024). Machine learning algorithms further contribute by interpreting customer sentiment and predicting future trends, thereby supporting the development of more effective marketing strategies (Akdoğan and Durmaz 2024). In addition, AI-powered chatbots provide real-time customer support manage bookings, and minimize waiting times, which in turn increases customer satisfaction and operational efficiency (Doğan and Niyet 2024). Automated systems also streamline processes, facilitate smarter decision-making, and optimize service management. Beyond operational improvements, AI contributes to economic growth by generating new employment opportunities, particularly in developing markets, thus offering both economic and competitive advantages (Lina and Talukder 2024). Moreover, the strategic adoption of AI strengthens customer loyalty and creates sustainable competitive advantages for organizations operating in the rapidly evolving tourism sector (Sharma 2024).
However, despite the substantial benefits of artificial intelligence (AI) in supporting, organizations operating in the tourism sector, its implementation presents several challenges including data privacy concerns, high initial investment costs, and the risk of job displacement (Sharma 2024). Moreover, the willingness and competencies of managers and employees to adopt AI technologies are critical factors for successful implementation (Akdoğan and Durmaz 2024). These challenges highlight the need for careful consideration, as privacy issues, workforce implications, and significant financial requirements may hinder the adoption of AI, particularly among smaller travel agencies.

2 Research problem

Artificial intelligence (AI) is experiencing an unprecedented boom, driven by the rapid pace of its development and the expansion of its applications. It has emerged as a trans formative force across diverse industries, serving as a primary driver of organizational change. Owing to its distinctive technological advantages and innovative capabilities, AI has demonstrated significant potential in sectors such as healthcare, education, and industry, where it has opened new avenues for addressing persistent practical challenges while simultaneously enhancing efficiency and quality (Wang 2024, p. 40). With recent advances in generative artificial intelligence (GenAI) and machine learning (ML), AI applications within the tourism sector have expanded considerably. These range from personalized trip planning to live simulation models and virtual reality (VR) training designed to improve frontline decision-making. Such technological innovations are reshaping the way organizations in the travel industry operate and interact with customers. According to Skift Research, the average amount of time individuals spend on digital devices has increased by 70% since 2013, a trend that further accelerated during the COVID-19 pandemic, when online interactions often substituted face-to-face contact and consumers became increasingly accustomed to digital tools (Almasi, Cosmas, Cowan and Ellencweig 2023, p. 4).
In Algeria, tourism and travel agencies represent one of the most active and influential, components of the tourism sector. By 2023, the total number of agencies reached 4,999 reflecting a growth rate of 6% with the establishment of 507 new agencies, including 404 main agencies and 103 sub-agencies (Ayrade 2025).
Given the pivotal role of these agencies in supporting and developing the tourism industry it becomes essential to examine the extent to which they are keeping pace with the technological transformations driven by artificial intelligence (AI), which has witnessed an expanded range of applications since November 2022.
For this purpose, the wilaya of Bordj Bou Arreridj was selected as the geographical focus of the study, as it encompasses 58 tourism agencies along with their respective branches.
Accordingly, the research problem is formulated in the following central question: To what extent do the tourism agencies under study adopt artificial intelligence programs in their activities, and what are the most common challenges hindering their effective use?
For more details, we raise the following sub-questions:
• Do the tourism agencies under study use artificial intelligence programs to develop their marketing strategies, from the perspective of the research sample?
• Do the tourism agencies under study use artificial intelligence programs in planning and decision-making related to trips, from the perspective of the research sample?
• Do the tourism agencies under study use artificial intelligence programs in booking activities, from the perspective of the research sample?
• Do the tourism agencies under study use artificial intelligence programs to enhance the customer experience, from the perspective of the research sample?
• Are there any obstacles preventing the tourism agencies under study from using artificial intelligence programs in their activities, from the perspective of the research sample?

3 Research hypotheses

Artificial intelligence is still new in Algeria, and the benefits of its use remain somewhat uncertain. In light of concerns about potential negative effects, as well as challenges that may hinder its adoption by tourism agencies such as material requirements, financial costs, and other constraints the following hypotheses is proposed main hypothesis: Tourism agencies under study do not adopt artificial intelligence programs in their activities due to several obstacles that hinder their effective use.

There are identified sub-hypotheses in the following fields:
• Marketing strategies
H₀: Tourism agencies under study do not use artificial intelligence programs in building their marketing strategies.
H₁: Tourism agencies under study use artificial intelligence programs in building their marketing strategies.
• Planning and decision-making
H₀: Tourism agencies under study do not use artificial intelligence programs in planning and decision-making related to trips.
H₁: Tourism agencies under study use artificial intelligence programs in planning and decision-making related to trips.
• Travel agencies’ booking activities
H₀: Tourism agencies under study do not use artificial intelligence programs in booking. Activities.
H₁: Tourism agencies under study use artificial intelligence programs in booking activities.
• Customer experience
H₀: Tourism agencies under study do not use artificial intelligence programs to enhance the customer experience.
H₁: Tourism agencies under study use artificial intelligence programs to enhance the customer experience.
• Obstacles
H₀: There are no significant obstacles preventing tourism agencies under study from using artificial intelligence programs in their activities.
H₁: There are significant obstacles preventing tourism agencies under study from using artificial intelligence programs in their activities.

4 Importance of the research

In the face of the enormous technological developments that the world is witnessing today, Algeria must keep pace with these changes, especially given the digital divide that artificial intelligence has created between developed and developing countries. With the rapid acceleration in the use of this technology across economic, political, and social sectors, and since the tourism sector plays a significant role in the economic development of any country, it is necessary to examine the use of artificial intelligence in the activities of tourism agencies in Algeria. There are identified main research objectives:
• To examine the current use of artificial intelligence technology in the activities of selected tourism agencies in Algeria.
• To identify the main challenges hindering the adoption of artificial intelligence in these activities.
• To propose practical solutions to address these challenges and to encourage further studies and research on this topic.

5 Overview of previous studies

Due to its highly responsive nature to customer feedback, the tourism sector has always been among the first to adopt new technologies to better serve customers and increase revenues Artificial intelligence (AI) represents a major technological innovation that has already transformed the ways in which tourism and hospitality companies conduct their business (Pandey, Bansal and Valuskar 2025). In their paper „Understanding the Adoption of AI in Tourism and Marketing Services“, the authors highlight several important conclusions regarding the role of AI in the tourism industry, including its potential to enhance operational .efficiency and customer engagement, as well as its impact on the labor market.
Furthermore, AI can facilitate the integrated development of tourism services. By leveraging AI technologies, companies can improve their marketing strategies, which may lead to growth and innovation in the sector (Singh and Ansari 2024). Another study found that the use of AI and machine learning in tourism marketing is more effective in recognizing customers emotions and perceptions, predicting future behaviors, and ultimately providing services that meet customer expectations – an outcome that modern marketing continuously strives to achieve (Akdoğan and Durmaz 2024, p. 45). In addition, AI can enhance safety and security in the tourism sector through predictive analytics and real-time monitoring, which improve resource allocation and reduce waste (Ma 2024, p. 89). Predictive maintenance systems powered by AI algorithms also contribute to safer operations by analyzing equipment data sensor readings, and historical performance to prevent potential failures and enhance overall reliability (Traversa 2024).

6 Theoretical background

Artificial intelligence is regarded as one of the most significant outcomes of the fourth industrial revolution. It has numerous applications across various domains, such as politics, the military, the economy, public services, and social life. AI represents an attempt to simulate human intelligence by developing computer programs capable of mimicking human behavior. Today, it is embedded in many aspects of daily life, including translation software, aviation systems, self-driving cars, robotics, smart homes, and cognitive simulations, among others.
A tourism agency is an institution primarily engaged in providing travel information and services, it functions as an intermediary in organizing tours and excursions, transportation, car rentals, and accommodation for travelers. According to Algerian legal texts, the main services offered by tourism agencies are as follows (Ayrade 2025):
• Organizing and marketing travel, tours, and individual or group stays.
• Arranging guided tours and visits to cities, sites, and monuments of tourist, cultural, or historical significance.
• Providing translators and tour guides for tourists.
• Reserving accommodation in hotel establishments and offering related service.
• Selling tickets for entertainment venues, concerts, and cultural, sporting, or other event.
• Ensuring customer insurance against risks arising from tourism activities.
• Representing other local or foreign agencies in providing various services on their behalf.
• Receiving and assisting tourists during their stay.

Artificial intelligence applications are increasingly being integrated into the activities of tourism organizations. These technologies enable tourism companies to better understand customer preferences, predict future trends, develop effective marketing strategies, and deliver personalized marketing messages. As a result, they enhance customer satisfaction, increase return on investment (ROI), and strengthen the company’s competitiveness through various technologies, including the following:
• Virtual reality and augmented reality: Virtual and augmented reality technologies are increasingly used to create immersive experiences, allowing potential tourists to explore destinations virtually and thereby enhancing their decision-making process (Ahmed 2024).
• Predictive analytics and recommendation systems: AI-powered predictive analytics help travel marketers anticipate customer needs and preferences, enabling more targeted marketing campaigns. In addition, recommendation systems use AI to suggest personalized travel experiences and destinations, improving customer engagement and satisfaction (Dewi, Putra, Widodo, Yudithia and Soares 2025).
• Automated customer service: Chatbots and virtual assistants provide 24/7 customer support by handling inquiries and bookings efficiently, which enhances the overall customer experience. These AI tools also improve operational efficiency by automating routine tasks thereby freeing up human resources for more complex interactions (Zhizhileva 2024).
• Personalized marketing: AI enables personalized marketing through the analysis of customer data to deliver customized content and offers, increasing the likelihood of conversion. Moreover, recommendation algorithms personalize the user experience by suggesting content and products that match individual preferences (Laki and Miklosik 2025).
• Dynamic pricing: AI-powered dynamic pricing models adjust prices in real time based on demand and customer behavior, thus increasing revenue for tourism companies (Lina and Talukder 2024).

While AI offers numerous benefits to the tourism sector, its adoption still faces significant and widespread barriers, including technological limitations, financial obstacles, and concerns about preserving cultural authenticity (Dewi, Putra, Widodo, Yudithia and Soares 2025). Overcoming these challenges requires collaborative efforts to strengthen digital infrastructure foster a culture of innovation and ensure equitable access to AI technologies within the tourism industry.
Technological challenges represent one of the main obstacles to the adoption of AI in the tourism sector. A key issue lies in the complexity of data processing, as tourism organizations often struggle to manage vast amounts of information, making the effective application of AI difficult (Teemu 2023). Furthermore, the technological readiness of many companies remains limited, with insufficient infrastructure and expertise hindering their ability to adopt advanced AI solutions (Ferhataj and Memaj 2024). Another pressing concern relates to job displacement since automating tasks with AI may reduce employment opportunities and raise uncertainties about workforce stability (Cristian and Cosmin 2024). In addition to technological barriers ethical concerns also pose significant challenges. Issues of data privacy are particularly important, as travelers increasingly worry about how their personal information is collected stored, and utilized, which underscores the necessity for transparent data practices, equally critical is the lack of transparency in AI decision-making, where the opaque nature of algorithms often creates ethical dilemmas and diminishes consumer trust in tourism services (Ferhataj and Memaj 2024).
Beyond technological and ethical issues, economic barriers play a decisive role in slowing the adoption of AI in tourism. One of the most pressing obstacles is the substantial financial investment required to implement AI technologies, which creates a particular burden for small and medium-sized enterprises (SMEs) that often operate with limited resources (Teemu 2023). This financial challenge is further compounded by the uncertainty surrounding the return on investment (ROI). Many companies remain hesitant to invest in AI due to the difficulty of quantifying its long-term benefits and the risks associated with technological adoption in a highly competitive and unpredictable market environment (Sharma 2024). Consequently, the combination of high initial costs and ambiguous financial outcomes discourages many tourism stakeholders from embracing AI solutions, even when they recognize the potential strategic advantages.

7 Methodological procedures of the applied study

This study adopted a descriptive research design to examine the variables theoretically through a literature review, followed by empirical data collection using a structured questionnaire. The data were analyzed, and the research hypotheses were tested using SPSS version 27 ensuring the validity and reliability of the findings.
The study population consisted of tourism agencies in the, Bordj Bou Arreridj governorate, Algeria. From a total of 58 agencies, 19 were selected including 36 employees and managers, all of whom participated in the survey. This sampling approach ensured comprehensive coverage of the target population while maintaining the feasibility of data collection.
The questionnaire was structured into three distinct sections. The first section gathered general information regarding the research sample, The second section examined the application areas of artificial intelligence in activities within tourism agencies, The third section focused on the challenges encountered by tourism agencies in implementing artificial intelligence programs.
Data analysis was conducted using SPSS Statistical Package, Version 27, employing the following tools:
• Reliability coefficient (Cronbach’s Alpha) to assess the consistency and credibility of the responses.
• Arithmetic mean and standard deviation to determine the attitudes of sample members toward the questionnaire items.
• One-sample T-test to examine the research hypotheses.

One of the most important coefficients used to test the stability of the study tool is Cronbach’s Alpha, which ranges from 0 to 1. The test results are presented in Table 1.

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Table 1: Results of the reliability test
Source: Authors according to SPSS output

By calculating the Cronbach’s Alpha reliability coefficient for each section, it was concluded that the questionnaire exhibits a high level of internal consistency regarding the items within its dimensions. Since the overall Cronbach’s Alpha is 0.782, which is greater than the threshold of 0,7. This confirms the reliability and credibility of the responses provided by the study sample.

8 Presentation and analysis of the study data

To analyze the responses of the study sample to the questionnaire items, a five-point Likert scale was employed, with the corresponding values presented in the Table 2.

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Table 2: Scores of the five-point Likert scale
Source: Authors

8.1 Data analysis of the first theme

The first theme examined the personal characteristics of the study sample. Tables 3 and 4 summarize key demographic variables, providing an overview of the sample profile for interpreting subsequent responses.

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Table 3: Distribution of the sample by age and job position variables
Source: Authors according to SPSS output

Table 3 shows that the research sample, based on the job position variable includes 15 managers and 21 employees from the tourism agencies involved in the field study. In terms of age, the majority of the sample consists of young individuals: 58.3% are under years old, 36.1% are between 31 and 40 years old, and the remaining 5.6% belong 30 to older age groups. This demographic distribution is significant for the research findings, as younger individuals are typically the most frequent users of artificial intelligence, owing to their familiarity with technology and their extensive interaction with digital tools.

As for the variables of educational level and years of experience, the results are presented in Table 4.

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Table 4: Distribution of the sample by educational level and years of experience Source: Authors according to SPSS output

Table 4, indicates that the majority of the sample participants possess a high educational level with 77.8% being Algerian university graduates, while the remaining 22.2% hold a secondary education level. This distribution is expected to strengthen the research findings, as university graduates are generally more skilled and digitally aware. University education typically equips, students with digital research and analytical skills to a greater extent than secondary education due to the broader academic fields it covers. Consequently, their use of artificial intelligence is more likely to be efficient and effective. Regarding years of experience, the sample, distribution shows that 52.8% fall under the category of less than 5 years of experience followed by 38.9% in the category of 5 to 10 years. This result is logically consistent with the fact that the majority of employees are recent graduates of Algerian universities.

8.2 Data analysis of the second theme

In theme applications of artificial intelligence in tourism agency activities was assessed through four main dimensions:
• Marketing strategy development
• Travel planning and decision-making
• Booking activities
• Enhancing customer experience

8.2.1 Marketing strategy development

Table 5 illustrates that the degree of approval concerning the agency’s adoption of artificial 5 intelligence (AI) technologies in formulating marketing strategies ranges from moderate to relatively weak. The mean scores of the examined items (3.2500, 3.1944, 2.5556, 3.3889). Suggest an overall tendency toward acknowledging the integration of such technologies. Notably, the highest mean was associated with the statement related to customer data analysis and the targeting of appropriate market segments (3.39), whereas the lowest mean was observed for the application of dynamic pricing models (2.55). This disparity underscores a differentiated reliance on AI applications across various marketing dimensions. In addition, the standard deviation values (0.99-1.22) indicate a moderate level of response dispersion reflecting relative variation in the participants’ perceptions regarding the extent of AI utilization. Collectively, these findings highlight that the tourism agencies under investigation employ AI technologies in a selective and uneven manner when shaping their marketing strategies, with a pronounced emphasis on customer data analysis and targeting, while demonstrating comparatively limited reliance on dynamic pricing practices.

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Table 5: Arithmetic average sand standard deviations of the sample’s answers
Source: Authors according to SPSS output

8.2.2 Travel planning and decision-making

Table 6 demonstrates that most of the arithmetic means of the respondent’s answers fall within a moderate level on the five-point Likert scale. The corresponding standard deviations indicate an acceptable degree of dispersion around the central values. This suggests that the respondents hold a moderate inclination toward believing that tourism agencies employ artificial intelligence (AI) in planning and decision-making related to trips, without showing either strong adoption or complete rejection of such practices.
Regarding the use of AI in predicting flights and potential delays, this item achieved an average of (2.97) with a standard deviation of (1.23), ranking second in the order of responses. This reflects a moderate level of awareness among respondents that the tourism agencies they are affiliated with utilize AI systems to monitor possible delays or disruptions in flight schedules. A plausible explanation for this finding is that flight forecasting applications are becoming increasingly common within the global travel industry. As for flight schedule planning and crew allocation, this item ranked third, with an average of (2.72) and a standard deviation of (1.08). Although this value is slightly lower than the preceding item, it still falls within the moderate level, this implies that there are some initiatives to integrate AI technologies into human resource management for flights; however, their adoption remains limited.
When examining the reliance on predictive analytics to accelerate tasks and reduce waiting times, this item recorded the highest mean value (3.08) with a standard deviation of (1.18). This result indicates a relatively strong understanding of the significance of artificial intelligence (AI) in enhancing the efficiency of operational procedures. However, the fact that the mean value still falls within the moderate range suggests that such practices have not yet attained the level of organizational maturity. Finally, regarding the item related to making trip-related decisions based on artificial intelligence insights, the results show that it obtained the lowest mean value with a standard deviation of (1.20). This finding reflects a weak reliance on AI (2.55) outputs in making final decisions within tourism agencies, it also indicates that AI is still perceived and utilized more as a supportive tool rather than a strategic partner in decision-making processes. Such limited adoption may be attributed to factors associated with trust, organizational risk, or other contextual challenges that remain unclear.

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Table 6: Arithmetic average sand standard deviations of the sample’s answers
Source: Authors according to SPSS output

8.2.3 Booking activities

Table 7 illustrates that the arithmetic means of the respondents’ perceptions regarding the dimension of booking activities ranged between (2.86-3.77), with acceptable levels of variation as indicated by the standard deviation values (0.93-1.33). The overall mean was (3.41) with a standard deviation of (0.84), reflecting a relatively high level of agreement among the sample members on the statements pertaining to this dimension.
The first item ranked third, with a mean of (2.86) and a standard deviation of (1.33), indicating a moderate level of automation, this may be attributed to the limited utilization of artificial intelligence in certain operational procedures of the tourism agencies under study compared with other services. The second item recorded a mean of (3.61) with a standard deviation of (0.93), which suggests that the agencies are actively investing in building smart customer databases, thereby enhancing the personalization of services. Regarding the third statement, it ranked first with the highest mean (3.77) and a standard deviation of (1.26), reflecting a very high level of adoption and confirming that the use of smart chat systems or virtual assistants has become a central element in improving customer service and streamlining the booking process.
Accordingly, it can be concluded that the surveyed tourism agencies place considerable emphasis on the use of AI applications in booking activities, particularly in creating customer profiles and providing intelligent platforms for direct interaction. However, the aspect of full automation of repetitive processes still requires further development.

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Table 7: Arithmetic average sand standard deviations of the sample’s answers
Source: Authors according to SPSS output

8.2.4 Enhancing customer experience

The results of the responses to the items related to the dimension of enhancing the customer experience indicated that the overall mean score reached 3.05, with a standard deviation of 0.76, reflecting a moderate level of the agency’s adoption of digital technologies and artificial intelligence to improve the customer experience. The highest indicator was associated with the use of an AI-powered chatbot to identify appropriate tourist destinations, which achieved a mean of 3.55 and a standard deviation of 1.08 representing a high level. This result highlights the agencies’ awareness of the importance of this interactive tool in facilitating the selection process and the customization of services.
As for the remaining indicators, they were assessed at moderate levels. Specifically, augmented reality technology used to introduce customers to tourist destinations ranked second, with a mean of 3.13 and a standard deviation of 1.09. This was followed by virtual reality technology, which provides an immersive sensory experience, with a mean of 2.83 and a standard deviation of 1.36. Lastly, reliance on the analysis of previous data (demographic, behavioral, and booking records) using artificial intelligence ranked lowest, with a mean of 2.69 and a standard deviation of this finding reflects that the agencies under study are focusing more on live 1.26 interactive tools such as chatbots, while the potential of predictive analytics and virtual/augmented reality remains underutilized, thereby limiting their ability to provide a more personalized and innovative experience.
Moreover, the relatively high standard deviations observed across the indicators suggest variations in respondents’ experiences and perceptions toward these applications, which may be attributed to differences in their level of technological awareness or the availability of these services. Accordingly, the findings reflect a partial adoption of digital technologies in enhancing the customer experience emphasizing the need for greater investment in data analytics and immersive technologies to strengthen the competitiveness of tourism agencies.

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Table 8: Arithmetic average sand standard deviations of the sample’s answers
Source: Authors according to SPSS output

8.3 Data analysis of the third theme

Table 9 shows that the obstacles to the use of artificial intelligence in tourism agencies were, on average, at a moderate level, with a general mean of 3.09 and a standard deviation of 0.52. The most significant obstacle identified was the difficulty of finding experts in the field of artificial intelligence to develop and implement smart solutions (mean = 3.63), followed by technical barriers, such as language issues and application malfunctions (mean = 3.44), and the high cost of AI implementation (mean = 3.38). Cultural and social concerns, such as the potential decline in the cultural heritage of tourist destinations due to reliance on technology, were also noted (mean = 3.16). In contrast, obstacles related to weak infrastructure (mean = 2.75), privacy and data security concerns (mean = 2.72), and the loss of the human touch (mean = 2.55) were considered less significant by the study sample. Overall, the findings indicate that, while the level of obstacles is moderate, the lack of experts, technical challenges, and high costs represent the most prominent barriers to adopting AI in tourism agencies.

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Table 9: Arithmetic averages and standard deviations of the sample’s answers
Source: Authors according to SPSS output

9 Results of hypothesis testing

The study hypotheses will be tested for acceptance or rejection using the One-Sample T-Test, by defining the null and alternative hypotheses.

9.1 Marketing strategies development

H₀: Tourism agencies under study do not use artificial intelligence programs in building their marketing strategies.
H₁: Tourism agencies under study use artificial intelligence programs in building their marketing strategies.
To test the first hypothesis, we use the One-Sample T-test, the results are shown in the Table 10.

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Table 10: One-Sample T-Test results for the marketing strategies development dimension
Source: Authors according to SPSS output

The results of the table above show that the arithmetic average of the answers to the statements that make up respondents’ attitudes toward building a marketing strategy reached 3.0972. With a standard deviation of 0.73988 which is morally significant, because the significance level is less than (0.000), which is also less than (0.05). This confirms the validity of the alternative hypothesis. So, we reject H0 and accept H1.

9.2 Planning and decision-making

H₀: Tourism agencies under study do not use artificial intelligence programs in planning and decision-making related to trips.
H₁: Tourism agencies under study use artificial intelligence programs in planning and decision-making related to trips.
To test the second hypothesis, we use the One-Sample T-test, the results are shown in the Table 11.

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Table 11: One-Sample T-Test results for the planning and decision-making dimension
Source: Authors according to SPSS output

The results of the table above show that the arithmetic average of the answers to the statements that make up respondents’ attitudes toward the planning and making decisions about trips reached 2.8333. With a standard deviation of 0.67876 which is morally significant, because the significance level is less than (0.000), which is also less than (0.05). This confirms the validity of the alternative hypothesis. So, we reject H0 and accept H1.

9.3 Travel agencies’ booking activities

H₀: Tourism agencies under study do not use artificial intelligence programs in booking activities.
H₁: Tourism agencies under study use artificial intelligence programs in booking activities.
To test the third hypothesis, we use the One-Sample T-test, the results are shown in the Table 12.

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Table 12: One-Sample T-Test results for the Travel agencies’ booking activities dimension
Source: Authors according to SPSS output

The results of the table above show that the arithmetic average of the answers to the statements that make up respondents’ attitudes toward the Travel agencies’ booking activities reached 3.4169. With a standard deviation of 0.84092 which is morally significant, because the significance level is less than (0.000), which is also less than (0.05). This confirms the validity of the alternative hypothesis. So, we reject H0 and accept H1.

9.4 Customer experience

H₀: Tourism agencies under study do not use artificial intelligence programs to enhance the customer experience.
H₁: Tourism agencies under study use artificial intelligence programs to enhance the customer experience.
To test the fourth hypothesis, we use the One-Sample T-test, the results are shown in the Table 13.

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Table 13: One-Sample T-Test Results for enhancing customer experience dimension
Source: Authors according to SPSS output

The results of the table above show that the arithmetic average of the answers to the statements that make up respondents’ attitudes toward the Enhance customer experience reached 3.0556. With a standard deviation of 0.76324 which is morally significant, because the significance level is less than (0.000), which is also less than (0.05). This confirms the validity of the alternative hypothesis. So, we reject H0 and accept H1.

9.5 Obstacles

H₀: There are no significant obstacles preventing tourism agencies under study from using artificial intelligence programs in their activities.
H₁: There are significant obstacles preventing tourism agencies under study from using artificial intelligence programs in their activities.
To test the last hypothesis, we use the One Sample T test, the results are shown in the Table 14.

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Table 14: One-Sample T-Test Results for the AI obstacles dimension
Source: Authors according to SPSS output

The results of the table above show that the arithmetic average of the answers to the statements that make up respondents’ attitudes toward the Barriers to AI adoption reached 3.0952. With a standard deviation of 0.52015 which is morally significant, because the significance level is less than (0.000), which is also less than (0.05). This confirms the validity of the alternative hypothesis. So, we reject H0 and accept H1.

10 Results discussion

The results of the first axis indicate that the level of reliance on artificial intelligence (AI) techniques in developing marketing strategies was generally at an average level. The highest score was observed for the item related to the use of AI programs to analyze customer data and target appropriate customer segments, whereas the lowest score was associated with the application of dynamic pricing models based on machine learning algorithms. This finding aligns with Chatterjee, Tamilmani, and Sharma (2021), who reported that organizations tend to rely more on AI applications for customer analysis and personalization in marketing campaigns than on dynamic pricing models, which still face technical and ethical challenges (Chatterjee, Tamilmani and Sharma 2021).
In contrast, the results show that reliance on AI-powered recommendation engines to optimize the timing of announcements and messages was moderate. This is consistent with the findings of Jarek and Mazurek (2019), who indicated that organizations adopt recommendation engines to a moderate degree due to the need for advanced technological investments to ensure predictive accuracy and consumer satisfaction. (Jarek and Mazurek 2019). Similarly, the use of data analysis to forecast market trends and reduce risk also appeared at a moderate level, which corresponds with Davenport, Guha, Grewal, and Bressgott (2019), who found that AI contributes to improving market forecasting decisions, but the degree of actual adoption remains limited due to the complexity of AI models and challenges in integrating them with traditional decision support systems (Davenpor, Guha, Grewa and Bressgot 2019).
Based on these findings, it can be concluded that the current study is consistent with existing literature, emphasizing that organizations tend to invest more in AI for customer analysis and targeting than for dynamic pricing or advanced strategic forecasting. This reflects a practical trend toward applications that deliver tangible results more rapidly.
Regarding the second theme, the results indicate that the use of AI applications by tourism agencies in trip planning and decision-making was generally at an average level, reflecting a transitional phase in technology adoption. The use of AI was more concentrated in flight forecasting and delay prediction, as well as in leveraging predictive analytics to expedite tasks and reduce waiting times. In contrast, AI was less utilized in planning flight schedules, distributing human crews, or making decisions directly based on AI insights. These findings suggest that the reliance of tourism agencies on AI remains primarily focused on operational efficiency rather than strategic decision-making. This is supported by Milton (year), who emphasized that AI adoption in operational contexts is more prevalent than in strategic planning (Milton 2023), The overall average level of AI usage indicates significant development opportunities. Tourism agencies can enhance their technical strategies by training personnel and increasing integration between intelligent systems and decision-making platforms. Chatterjee, Tamilmani and Sharma (2021) demonstrated that systematic integration of AI into decision-making processes improves forecasting accuracy and reduces operational costs, highlighting the importance of a gradual shift from limited use to widespread institutional adoption.
Regarding the third theme, the study’s findings indicate that the tourism agencies under investigation exhibit a notable interest in adopting artificial intelligence (AI) technologies in booking activities, particularly through interactive smart platforms that enable customers to inquire and complete reservations efficiently. This aligns with recent literature highlighting that smart chats and chatbots have become among the most prominent AI applications in the tourism sector, due to their capacity to enhance customer experience and accelerate responses to inquiries (Wüst and Bremser 2025).
The results also reflect a trend among agencies to invest in building detailed customer profiles through AI, supporting previous studies that emphasize the role of personalized services in improving traveller satisfaction and loyalty. Conversely, the findings suggest that the digital maturity of the tourism agencies studied remains partial, as efforts are concentrated on the customer-facing service interface rather than on backend processes. This observation is consistent with systematic reviews indicating that most tourism establishments initiate their digital transformation by optimizing customer touch points, gradually extending automation to internal operational systems.
The fourth pillar’s results indicated a moderate adoption of digital technologies and AI by tourism agencies for enhancing customer experience, reflecting a transition toward full digitization without reaching maturity. AI-powered smart chat programs scored highest, showing awareness of the importance of instant interaction and service personalization (Nguyen, Simkin and Canhoto 2015). In contrast, augmented and virtual reality technologies were adopted at medium to low levels, highlighting underutilization of their immersive potential due to cost and limited user awareness (Dieck and Jung 2015). Predictive analyses of behavioral and demographic data ranked lowest, signalling limited investment in extracting customer insights, despite their key role in personalization and competitiveness. High variation in participants’ experiences further reflects differences in digital awareness and resource availability, consistent with Buhalis and Sinarta (2019). Overall, agencies tend to focus on basic interactive tools rather than advanced analytics or immersive experiences, emphasizing the need for strategic investment, training, and enhanced digital awareness to sustainably improve customer experience (Buhalis and Sinarta 2019). The results of the third section of the questionnaire indicate that the most significant obstacles facing the studied tourism agencies in adopting artificial intelligence technologies are the scarcity of specialized experts. This reflects a gap in human capital capable of developing and implementing smart solutions. This finding aligns with Seyfi, Kim, Nazif, Murdy and Thanh (2025), who confirmed that the lack of human competencies is one of the most prominent challenges hindering tourism institutions from fully leveraging artificial intelligence. The results also showed that technical barriers, such as language limitations and technical failures, ranked second, with a relatively high average (Seyfi, Kim, Nazif, Murdy and Thanh 2025). The high cost of AI applications ranked third, at an average level, suggesting that investment in these technologies is still perceived as a financial burden for SMEs. This finding aligns with Gretzel, Sigala, Xiang, and Koo (2015), who indicated that the high cost of developing and maintaining intelligent systems poses an obstacle to their widespread adoption. In fourth place, cultural concerns regarding the impact of artificial intelligence on tourist identity emerged at a moderate level (Gretzel, Sigala, Xiang and Koo 2015). Some stakeholders fear that simulated experiences may replace authentic cultural performances, as noted by Tussyadiah who warned that overreliance on AI could strip the tourist experience of its cultural and human dimensions (Tussyadiah 2020, p. 344).
The results also indicated that inadequate infrastructure, privacy and security concerns, and the loss of human touch are medium-impact barriers. This is consistent with Webster and Ivanov (2020), who noted that the absence of digital infrastructure, along with ethical and humanitarian concerns, represents moderate but addressable obstacles through supportive policies and increased awareness among tourism organizations (Webster and Ivanov 2020).
Overall, the arithmetic mean indicates that the tourism agencies under study face a set of medium- to high-impact obstacles, most notably the lack of specialized experts and technical challenges. This underscores the need to invest in training, develop digital infrastructure, and adopt balanced strategies that integrate technology while preserving the human and cultural aspects of tourism. While AI can improve operational efficiency and accelerate booking processes, it cannot fully replace human expertise, especially in high-end tourism or complex trips. Therefore, the most effective strategy is to combine human capabilities with smart technologies, enhancing service quality and ensuring sustainable competitive advantage.

11 Limitations of the study

Despite the findings of this study, it has several limitations that should be considered. The most significant limitation is the restricted geographical scope, as the study was confined to tourism agencies in the Wilaya of Bordj Bou Arreridj, which limits the generalizability of the results to other provinces in Algeria or beyond. This affects the comprehensiveness and accuracy of the findings. Additionally, the reliance on questionnaires as the primary data collection tool may not fully capture all dimensions of the topic, particularly regarding actual practices within tourism agencies. Considering the temporal aspect, the study was conducted over a limited period, while the adoption of AI technologies is a dynamic process that evolves over time. Consequently, AI capabilities are still developing, and some programs implemented in tourism agencies are in their early stages, which restricts the possibility of comprehensively assessing their long-term effectiveness, especially given that Algeria is relatively new to employing this technology.

12 Prospects of the study

Based on the limitations discussed above, several directions for future research can be proposed, including:
• Expanding the geographical scope of the study to include other regions or conducting comparisons between different areas in Algeria, allowing for a deeper understanding of regional differences in AI adoption.
• Increasing the sample size to encompass a larger and more diverse number of tourism agencies, thereby enhancing the accuracy and generalization of the findings.
• Employing various research tools, such as field interviews, case studies, and direct observation, to better capture the actual impact of AI on tourism activities.
• Conducting longitudinal studies to monitor the evolution of AI adoption over several years, in order to assess changes in organizational behavior and outcomes achieved.
• Comparing the tourism sector with other service sectors (e.g., transportation or hospitality) to understand how AI adoption differs across industries.

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Kľúčové slová/Key words

artificial intelligence, tourism agency, marketing strategy, customer experience, privacy, tourist destination, Algeria
umelá inteligencia, cestovná kancelária, marketingová stratégia, zákaznícka skúsenosť, ochrana súkromia, turistická destinácia, Alžírsko

JEL klasifikácia/JEL Classification

O33, L83, M31, D83, O55

Résumé

Využitie umelej inteligencie v cestovných kanceláriách: realita a výzvy. Terénny prieskum cestovných kancelárií v Bordj Bou Arréridj v Alžírsku.
Táto štúdia skúma, do akej miery cestovné kancelárie v provincii Bordj Bou Arreridj v Alžírsku integrujú aplikácie umelej inteligencie do svojich prevádzkových a strategických činností. Štúdia sa ďalej zaoberá kľúčovými výzvami, ktoré bránia efektívnemu zavádzaniu týchto technológií. S využitím deskriptívnej metodiky boli údaje analyzované pomocou softvéru SPSS v27 s cieľom zodpovedať hlavnú výskumnú otázku a zmerať mieru zavádzania AI v niekoľkých oblastiach, vrátane vývoja marketingovej stratégie, plánovania ciest a rozhodovania, rezervačných operácií a zlepšovania zákazníckej skúsenosti. Výsledky ukazujú, že implementácia aplikácií umelej inteligencie v rámci skúmaných agentúr je v týchto oblastiach mierna, agentúry však narážajú na významné prekážky, najmä na nedostatok kvalifikovaných odborníkov a pretrvávajúce technické obmedzenia.

Recenzované/Reviewed

23. January 2026 / 3. April 2026