The Impact of Artificial Intelligence on Operational Risk Management at Taif International Airport

Abstract

This study investigates the impact of Artificial Intelligence (AI) on Operational Risk Management (ORM) at Taif International Airport, a rapidly developing hub within Saudi Arabia’s aviation sector. The research explores how AI technologies enhance airport environment identification, prediction, and mitigation of operational risks. Employing a mixed-methods approach, data were collected through semi-structured interviews with airport personnel and structured surveys targeting ORM officers, ICT specialists, and operational staff. Quantitative data were analyzed using SPSS for descriptive and inferential statistics, while qualitative responses were examined using thematic analysis. The findings reveal that AI tools, such as predictive analytics, machine learning algorithms, and AI-driven surveillance, significantly improve operational efficiency and reduce the frequency and severity of risk incidents. However, organizational and technical barriers impede AI integration, including staff training and infrastructural constraints. The study supports the hypothesis that AI positively impacts ORM performance and that institutional readiness influences implementation success. Recommendations include phased AI adoption in airport ORM practices and strategic policy support to foster aviation safety and digital transformation in the Middle East.