Artificial intelligence is already opening up new opportunities in the tourism industry, and major players are eager to experiment with ChatGPT and other forms of generative AI to offer tailored services to their customers and make the travel experience easier and more exciting.
Artificial Intelligence in the EU accomodation market
According to the European Accomodation Barometer Fall 2023 published by Statista in collaboration with Booking, Austria was the European country with the highest share of accommodation businesses (44%) that considered AI as a key opportunity over the next six months. Just 13% of the sample in Greece and 16% in France believed the same. The EU average stood at 23%.
Accommodations that considered AI as a key opportunity in Europe, by country (% of respondents, 2023)
In the EU, Germany held the highest share of accommodation businesses that already used AI. Overall, 2 out of the 10 German surveyed companies reported using AI, whereas in Italy and Spain only 8% and 4%, respectively.
Customer chatbots (58%) and dynamic pricing (52%) are the most popular applications of accommodation businesses in the EU, followed by customer review management (47%) and content marketing (45%).
Use of AI by accommodation businesses in Europe, by country (% of respondents, Aug 2023)
Use cases and opportunities of generative AI in the tourism sector
Therefore, artificial intelligence, and especially generative AI, is emerging as a revolutionary technology that is reshaping the travel and tourism landscape in Europe and in the rest of the world.
Tourism companies are now making a lot of investments in these technologies to gain a competitive edge and to create new opportunities in terms of enhancing customer experiences, efficient content generation, streamlining customer support, and optimizing marketing strategies.
For example, airlines and travel agencies are leveraging generative AI to create virtual travel assistants that can assist customers with booking flights, hotels and activities, making the booking process more convenient and user-friendly, and enhancing customer satisfaction. Moreover, online travel platforms are using Generative AI to offer personalized travel recommendations. By analyzing user preferences and historical data, these platforms suggest destinations, accommodations, and activities tailored to individual traveller tastes, enhancing the overall travel experience.
Finally, hotels and airlines are utilizing generative AI to optimize pricing strategies. These models analyze demand patterns, competitor pricing, and other factors in real-time to adjust prices dynamically, maximizing revenue while ensuring competitiveness.
Generative AI in the tourism market
Given the growing interest in generative artificial intelligence, tourism has been witnessing a significant market growth in recent years, and lucrative growth prospects are expected throughout the forecast period. Specifically, the size of generative AI in the tourism market was globally valued at $3647.43 million in 2023, and the total generative AI in tourism revenue is expected to grow at a CAGR of 17.5% from 2024 to 2030, reaching by the end of the current decade an estimated value of $11278.53 million.
Challenges in implementing AI in the tourism sector
However, the integration of AI in the tourism sector is not without challenges. Concerns about data privacy, security and ethical use of AI are critical issues. Compliance with data protection regulation is essential. Organizations must implement stringent security measures to safeguard sensitive data from unauthorized access, breaches, or cyber threats.
Training and upskilling professionals in the sector to effectively use and manage AI tools is another critical challenge. Ongoing training programs are essential to keep tourism professionals abreast of the evolving capabilities of AI and to ensure they can leverage these technologies to enhance their roles.
Finally, the availability and quality of data is a key factor in enabling such technologies. Inaccurate or incomplete data can compromise the effectiveness of AI algorithms, leading to erroneous conclusions and decisions. Addressing these issues requires a robust data governance framework, data validation processes, and continuous monitoring to ensure the accuracy and completeness of the data used by AI systems.