Digital Overtourism: When AI Concentrates Visibility Before Tourists Arrive

The claim

Generative AI and tourism recommendation systems may amplify existing digital visibility by repeatedly recommending destinations, attractions and businesses that are already strongly represented online.

If this happens systematically, AI could concentrate attention before a physical journey even begins.

This does not mean that AI has been demonstrated to cause physical overtourism. The claim requires a longer evidence chain:

digital representation → algorithmic selection → recommendation → exposure → intention → choice → travel → territorial pressure

Each stage must be measured separately.

What is digital overtourism?

Digital overtourism can be defined operationally as the systematic concentration of destination visibility inside digital recommendation environments, narrowing the range of places that travellers encounter before making a decision.

Traditional overtourism is observed on the territory through congestion, visitor pressure, infrastructure stress and effects on residents.

Digital overtourism happens earlier. It concerns the distribution of attention in the pre-travel phase.

Evidence of concentration

Research published in 2026 in Current Issues in Tourism explicitly examined the concept of digital overtourism by analysing hundreds of destination recommendations generated by multiple AI systems.

The study found that apparently diverse recommendation outputs could still converge repeatedly on a relatively narrow group of destinations.

This distinction is important: the number of different places mentioned at least once is not the same as the effective diversity of exposure.

Source: Current Issues in Tourism, 2026 .

Popularity bias did not begin with generative AI

Tourism recommender-system research has documented popularity bias for years. Better-known destinations and points of interest tend to have stronger visibility signals and therefore a greater probability of being recommended.

Less represented places can consequently remain underexposed even when they may be relevant to the traveller.

A review of fairness in tourism recommendation systems describes popularity and exposure as material factors affecting which destinations and providers enter recommendation results.

Source: Frontiers in Big Data, 2023 .

Generative AI changes the interface

A traditional search engine ranks documents.

Generative AI constructs an answer.

It selects which destinations to name, which ones to omit and which places to associate with concepts such as authenticity, sustainability, culture, affordability or tranquillity.

This makes the distribution of representation itself a relevant object of measurement.

Apparent diversity can hide systemic concentration

A single AI response may list ten different destinations and appear highly diverse.

Across hundreds or thousands of prompts, however, the same destinations may recur disproportionately.

The correct unit of analysis is therefore not only the individual answer, but the distribution produced by the system across repeated queries.

A possible visibility feedback loop

The central governance question concerns a potential recursive cycle:

visibility → recommendation → attention → experience → new content → greater visibility

A destination that already has more reviews, guides, indexed pages, structured information and editorial coverage may become easier for recommendation systems to recognise and reproduce.

No intentional bias is required for this effect to exist.

Unequal information environments may be sufficient.

AI can also support dispersion

The effect is not necessarily one-directional.

Tourism recommendation systems can also be designed to suggest less congested areas, alternative itineraries, different time windows or underrepresented destinations.

AI may therefore amplify concentration or help redistribute demand.

The outcome depends on data, ranking objectives, recommendation design and whether territorial indicators are incorporated into the system.

The question for destinations

For a destination management organisation, the useful question is no longer simply:

Are we visible in AI systems?

A more informative question is:

How is our territory represented compared with other destinations, and which parts of it remain systematically invisible?

What should be measured?

1. Recommendation frequency

How often does each destination, municipality, attraction or operator appear?

2. Effective diversity

How many places receive a meaningful share of total exposure?

3. Geographic concentration

How are recommendations distributed spatially across the territory?

4. Prompt sensitivity

Does the geography change when users ask about culture, nature, gastronomy, sustainability, authenticity, affordability or less crowded locations?

5. Evidence representation

Which institutional pages, datasets, structured content and authoritative sources make parts of the territory easier for AI systems to interpret?

From digital presence to territorial representation

A destination can have strong overall digital visibility while still having highly concentrated AI representation.

A region may be reduced to three famous towns. An island may be represented mainly through one coastal area. A city may be compressed into its historic centre.

The problem is therefore different from traditional SEO.

It concerns the distribution of machine-mediated territorial representation.

Evidence governance

Digital overtourism can be examined through four governance dimensions:

Representation. Which places enter the information environment?

Evidence. Which verifiable sources support their representation?

Distribution. How is attention distributed among territories and operators?

Accountability. Can the resulting concentration be measured and audited?

Epistemic assessment

The original claim is best classified as partially verified.

Current evidence supports the existence of popularity bias, recurrent destination concentration and unequal representation in tourism recommendation environments.

Recent research also explicitly identifies concentrated pre-travel AI recommendation patterns as digital overtourism.

What has not been established in general is a direct causal relationship:

AI recommendation → increase in physical overtourism

Demonstrating that relationship would require longitudinal evidence connecting algorithmic exposure to travel intentions, bookings, visitor movements and measurable territorial pressure.

The question before the journey

Overtourism is normally measured after visitors arrive.

AI makes it necessary to observe an earlier stage.

Before the flight. Before the booking. Before the itinerary.

At the moment when a traveller asks:

Where should I go?

If recommendation systems repeatedly converge on the same places, tourism concentration may have an informational component that precedes its physical manifestation.

Measuring that layer means moving beyond visitor counts and asking a new question:

Which territories do algorithms make imaginable before the journey begins?