Several European countries are implementing strategies to address overtourism by encouraging visitors to explore regions outside prominent urban centers. France has set an ambitious target to increase annual international tourism revenue to €100 billion by 2030, representing a 29% increase from the previous year, while maintaining stable visitor numbers in Paris and other major attractions. The country plans to achieve this through extended stays, higher per-night expenditures, and increased travel during off-peak seasons.
Other nations have adopted similar approaches. Ireland’s tourism strategy, unveiled in December, aims for a 15% boost in international visitor revenue by 2031. The Netherlands, Spain, and Croatia have likewise incorporated visitor distribution objectives into their national tourism frameworks, recognizing the economic and infrastructure benefits of spreading tourist activity more evenly across their territories.
However, two substantial challenges threaten the success of these initiatives. First, travelers increasingly rely on artificial intelligence chatbots to determine their destinations, yet tourism boards have struggled to ensure their lesser-known attractions appear prominently in these AI-generated recommendations. Search algorithms tend to prioritize well-established destinations, making it difficult for emerging or secondary tourism sites to gain visibility.
Second, transportation infrastructure between major hubs and alternative destinations remains fragmented and cumbersome. Moving between hub airports and secondary locations via train, connecting flights, or rental car services involves complicated booking processes, high costs, and limited consumer protections in cases of disruption or cancellation.
While artificial intelligence holds potential for resolving both obstacles, the travel industry has not yet developed comprehensive solutions to leverage this technology effectively for dispersing tourist flows to underutilized regions.
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