Navigating Barriers: AI Adoption in Air Cargo Industry

This study investigates the barriers inhibiting the adoption of Artificial Intelligence (AI) in the air cargo industry. An extensive literature review initially identified 48 barriers, which 12 industry experts validated and refined to produce 16 critical barriers for additional analysis. The Fuzzy DEMATEL (FDEMATEL) approach was applied which quantified the interrelationships among these barriers, highlighting their prominence and cause/effect dynamics. Key findings reveal that high investment costs, lack of top management commitment, data security issues, and data quality management challenges are significant barriers. The study categorizes these barriers into cause-and-effect groups, with emphasis on improving data quality and security to mitigate their impact on investment and management commitment issues. The research concludes with managerial implications, suggests strategies to enhance AI adoption in air cargo, and identifies areas for future research. This comprehensive analysis provides insights into overcoming the obstacles to AI integration, aiming to facilitate the advancement of digital technologies in the air cargo sector.