Your Next European Trip: Is AI Guiding You to Crowds or Hidden Gems?

Europe's most beloved cities are groaning under the weight of overtourism, yet the very technology many travelers rely on to plan their trips often makes the problem worse. While European authorities are actively seeking to disperse visitors beyond the usual suspects like Paris and Rome, artificial intelligence (AI) search algorithms and fragmented transport systems inadvertently funnel tourists into the same congested areas.

The AI Dilemma: Digital Overtourism

A university study conducted in June 2026, which analyzed 420 AI-generated travel recommendations from ten different systems, revealed a consistent bias. Regardless of the specific user query, these AI tools predominantly suggested 'highly visible' places. This phenomenon, dubbed 'digital overtourism' by some, means that as more travelers turn to AI for inspiration, the cycle of concentrating visitors in already popular destinations intensifies.

This isn't just an abstract problem; it has tangible consequences. Public transport systems in major European cities, for instance, are frequently strained by seasonal surges, leading to overcrowding and a diminished experience for both tourists and residents, particularly near major attractions.

AI as a Solution: Redirecting the Flow

Despite the current challenges, AI is also emerging as a key part of the solution. Forward-thinking cities and organizations are actively deploying AI to manage visitor flows and promote lesser-known locales. Amsterdam and Copenhagen, for example, have been using AI since August 2024 to guide tourists away from their most crowded areas.

In a significant move, OpenAI is partnering with Dublin to develop a GPT-4-based 'Itinerary Planner.' This tool aims to offer personalized recommendations that extend beyond generic lists, encouraging exploration of a wider array of attractions and neighborhoods. National Tourism Organizations (NTOs) across Europe are also experimenting with AI, with many marketing teams reporting notable productivity gains from these new tools.

The Rise of Eastern Europe's Hidden Gems

Perhaps the most compelling evidence of AI's potential to diversify tourism comes from Eastern Europe. Cities like Gdańsk, Bucharest, Kraków, and Riga are experiencing a tourism boom, partly attributed to AI travel tools recommending these destinations. Interest in Gdańsk, for instance, has surged by 97%, according to data from April 27, 2026. These cities offer rich cultural experiences without the intense crowds or premium prices often found in Western European capitals.

For travelers, this shift presents a clear financial advantage. While daily travel costs in Europe for 2026 can range from approximately €60 in Albania to a hefty €250-280 in Switzerland, many European cities can be visited for $135 or less per day, covering accommodation, sightseeing, and food. Prague, Czechia, stands out as a 'best-value vacation' for 2026. As of October 16, 2026, a three-star hotel in Prague costs around $140, a three-course meal for two is about $66, and public transport is a mere $2. This contrasts sharply with the roughly 25-35% increase in daily budgets seen across Europe due to post-COVID travel inflation. Exploring these emerging destinations can also align with more sustainable travel choices, demonstrating that sustainable travel doesn't always come with a higher price tag.

What to Watch Next

The long-term effectiveness of these new AI strategies and policy changes in combating overtourism remains a critical watch point. Travelers should monitor the rollout of new AI-powered itinerary planners and the marketing efforts of NTOs, particularly ahead of the 2027 summer travel season, to see if the promise of diversified, less crowded European experiences truly materializes.

Related reading

A useful background piece for this story is What is CPI.

Readers who want the wider market context can also use What is FOMC.

Sources

• Europe Wants Tourists Beyond Big Cities. AI Search Stands in the Way.
• CPI — Consumer Price Index (FRED official data)
• PCE — Personal Consumption Expenditures Price Index (FRED official data)
• UNRATE — Unemployment Rate (FRED official data)
• PAYEMS — Nonfarm Payrolls (FRED official data)
• FEDFUNDS — Effective Federal Funds Rate (FRED official data)
• DGS10 — 10-Year Treasury Yield (FRED official data)
• DGS2 — 2-Year Treasury Yield (FRED official data)