Using AI to improve tourism decisions

Tourism is influenced by many factors: major market players like international tour operators and platforms control supply and demand, technological developments change booking and travel behavior, strategic decisions by destinations set priorities, while research, expert knowledge, and public opinion amplify or dampen trends. Additionally, social, economic, and ecological changes affect travel in the long term.

To systematically understand these dynamics and use them proactively, futures research works with a three-step model: scanning, foresight, and transfer.

This transforms complex influences into actionable foresight knowledge.

SFT model extended with an AI solution

Extension of SFT with AI

In the scanning phase, relevant signals and trends are identified – such as technological innovations, new travel needs, or regulatory changes.

The foresight phase analyzes these insights, links them to possible future scenarios, and highlights opportunities and risks for tourism.

In the transfer phase, the gained insights are finally translated into concrete strategies, decisions, and actions, helping tourism stakeholders actively and sustainably prepare for future developments.


Scan section with eight data silos from diverse future sources. Additional AI analysis decouples trends from bias and opens new strategic options.

Risks of Distortion and Bias

Distortions can occur in tourism as well as in futures research when information, assessments, or data sources are not balanced. Causes include the strong influence of individual market players, limited or selective data, subjective evaluations, and short-term trend dynamics.

Such distortions carry the risk of misjudging developments or overlooking relevant changes. Therefore, it is essential to consciously reflect on these influencing factors to develop viable and differentiated future perspectives within scanning, foresight, and transfer.

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