Artificial intelligence is already transforming how travel experiences are curated, delivered, and optimized worldwide. But new research reveals that this transformation is anything but seamless. A cross-national qualitative study published in Tourism and Hospitality titled “Application of Artificial Intelligence in the Tourism Sector: Benefits and Challenges of AI-Based Digital Tools in Tourism Organizations of Lithuania, Latvia, and Sweden”, examines how tourism businesses in the Baltic and Nordic regions are navigating the digital transition.
The study is based on 17 semi-structured interviews with tourism professionals and highlights both the transformative power and the operational friction of AI-based digital tools. The findings offer insights into how AI enhances personalization, automation, and service development in tourism, while also identifying persistent roadblocks including cybersecurity concerns, high implementation costs, skill shortages, and customer skepticism. Drawing from tourism enterprises in Lithuania, Latvia, and Sweden, the research contextualizes these dynamics through the lens of the Technology Acceptance Model, focusing on how perceived usefulness and ease of integration shape adoption behavior.
How Are AI Tools Transforming Tourism Organizations?
Tourism organizations across the three countries are increasingly leveraging AI to personalize services, streamline internal processes, and boost operational efficiency. Interviewees consistently cited personalization as a top benefit. AI-enabled systems analyze customer behavior and preferences to tailor travel suggestions, marketing messages, and even service interfaces in real time. In Lithuania and Sweden, personalization was seen not just as a customer convenience, but a competitive differentiator in attracting and retaining clients.
Automation emerged as another major benefit. Businesses in Lithuania, Latvia, and Sweden are using AI to automate repetitive functions, including customer support via chatbots, data analysis, booking management, and even itinerary design. Respondents in Latvia and Lithuania highlighted how automation reduces staff workload, accelerates service delivery, and minimizes human error, while Swedish tourism operators emphasized the added value of continuous service availability.
Operational efficiency also ranked high among the reported advantages. AI tools were credited with expediting data processing, improving customer query resolution, and optimizing service workflows. In particular, Lithuanian and Latvian respondents noted the ability to achieve performance targets faster, with greater precision and fewer resource demands. Swedish stakeholders, while echoing efficiency gains, emphasized time savings over productivity metrics.
Beyond efficiency and automation, AI was linked to better decision-making. Lithuanian and Swedish tourism professionals reported using AI insights to adjust service offerings, optimize pricing, and improve marketing strategies. AI tools’ ability to analyze real-time customer data enabled quicker, evidence-based decisions – an advantage not yet fully recognized in the Latvian responses, suggesting a potential gap in either tool maturity or adoption depth.
The use of AI was also driving innovation. Respondents across all three countries described AI as instrumental in developing new tourism services and applications. These ranged from mobile platforms for travel planning to AI-assisted content creation and customer engagement strategies. Lithuanian and Swedish interviewees emphasized the creativity unlocked by AI tools, while Latvian organizations focused on leveraging AI to expand service portfolios and forecast demand.
Resource optimization was the final major benefit highlighted. AI allows tourism operators to achieve more with fewer inputs—particularly in terms of time and labor. Automating processes and gaining access to real-time data reduced reliance on human resources and made it easier to meet operational goals within tighter budgets and timelines. This benefit was universally acknowledged, though Lithuanian and Swedish professionals placed slightly greater emphasis on efficiency and speed compared to their Latvian counterparts.
What Barriers Are Slowing AI Adoption in Tourism?
Despite its benefits, AI deployment in tourism is far from frictionless. A primary concern across all three countries was data privacy and security. As AI systems depend on large datasets to function effectively, ensuring secure and compliant data handling is critical. Respondents expressed apprehension over GDPR compliance, risks of data leaks, and the ethical implications of profiling users based on behavioral data.
Customer skepticism was another widespread issue. Interviewees reported that many travelers remain wary of AI-mediated interactions, preferring human support for complex or sensitive service needs. This reluctance was particularly pronounced in Sweden, where tourism professionals observed a general fear of new technologies, and in Latvia, where customers were perceived as less familiar with AI capabilities.
Cost barriers loomed large. Tourism businesses, especially small and medium-sized enterprises, cited high upfront costs of acquiring AI tools, licensing software, and training staff as a deterrent. Even among early adopters, concerns persisted about ongoing maintenance expenses and the need for frequent updates to keep pace with rapidly evolving AI technologies.
Technical complexity further compounded these challenges. The integration of AI tools into existing IT ecosystems was often difficult, with respondents noting compatibility issues, system instability, and vulnerability to software bugs or cyber threats. The speed of AI development outpaced many organizations’ ability to adapt, particularly in Latvia and Lithuania, where technical resources and readiness levels were uneven.
A skills gap also hindered adoption. Tourism employees frequently lack the training needed to operate and interpret AI systems effectively. Respondents across all three countries reported a lack of digital literacy and confidence in using AI, with calls for continuous professional development and stronger collaboration with academic institutions to bridge this divide.
Finally, concerns about the reliability of AI outputs added to the list of obstacles. Informants warned that AI-generated content and recommendations are not always accurate or contextually appropriate, particularly when language limitations or niche market needs are involved. In Latvia, for example, underrepresentation in training data led to lower accuracy in outputs for Latvian users, reinforcing skepticism and mistrust.
What Strategies Can Support Responsible AI Integration in Tourism?
The study concludes with recommendations for tourism stakeholders seeking to implement AI tools responsibly and effectively. First, addressing data privacy concerns requires robust encryption, transparent data governance, and clear compliance with regulations like the GDPR. By protecting consumer information, organizations can build trust and mitigate reputational risk.
Second, overcoming customer skepticism calls for education campaigns that demystify AI and highlight its user benefits. Demonstrating that AI enhances, rather than replaces, human service can help bridge the trust gap. Piloting hybrid solutions, where human agents work alongside AI tools, may ease the transition.
Third, financial barriers can be mitigated through scalable deployment strategies. Starting with small-scale pilots and cloud-based platforms allows organizations to experiment without massive capital investment. Partnerships with AI vendors, startups, and public sector institutions can further reduce costs and accelerate adoption.
Fourth, solving technical and skill-based challenges means investing in digital infrastructure and training programs. Integrating AI into tourism education, facilitating knowledge exchange between sectors, and developing user-friendly interfaces can empower tourism professionals to harness AI more confidently and effectively.
Furthermore, to ensure output reliability, the study recommends refining AI models with localized data, continuous algorithmic testing, and human-in-the-loop verification. This approach not only boosts system performance but also reassures users about the validity and safety of AI recommendations.
