UNLOCKING THE POTENTIAL: ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN SUSTAINABLE TOURISM

Authors

  • Mr. Nangyalay Khan, Dr. Ayesha Gul, Dr. Faisal Khan, Mr. Waleed Khan, Prof. Dr. Arab Naz

Keywords:

Tourism, Sustainable Development, Artificial Intelligence, Conservation, Destination.

Abstract

The current study is a review-based analysis combined with some case studies focusing on establishing a link between Artificial Intelligence (AI) and the emerging trends of regenerative tourism and green destinations. Regenerative tourism and green destinations are the new hallmarks, promoting sustainability in the travel industry by restoring ecosystems and encouraging friendly practices. Incorporating AI into sustainable tourism can revolutionize how one can approach tourism by providing customer experiences and contributing towards a sustainable future. AI has naturally found its place in industries due to the advancements in data analysis and computing power. In the context of tourism, AI's data-driven capabilities are discussed in the current review to showcase how they enable recommendations for intelligent automation and efficient resource management. With AI-powered technologies, tourism operations become more efficient, providing opportunities for sustainable development and conservation in green destinations. Integrating AI in destinations encompasses applications such as energy management, waste reduction, transportation optimization and sustainable resource management. These AI-driven solutions play an important role in minimizing the impact of tourism activities while conserving natural resources.

Additionally, AI facilitates delivering experiences that align with eco values through recommendation systems and virtual assistants. The chapter tackles AI-related issues such as protecting data privacy, addressing biases, dealing with job displacement and ensuring cultural relevance. It emphasizes the significance of inclusive implementation of AI and explores the challenges faced when implementing AI solutions in developing regions with limited resources.

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Published

2024-01-21

Issue

Section

Articles