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

Abstract

This study examines the quantitative and qualitative impact of Artificial Intelligence (AI) adoption on Operational Risk Management (ORM) at Taif International Airport, Saudi Arabia. Using a mixed-methods design, data were collected from 32 ORM officers, ICT specialists, and operational staff via structured surveys (response rate: 84%) and 12 semi-structured interviews. Quantitative analysis using SPSS revealed that AI integration led to a statistically significant reduction in both the frequency and severity of operational risk incidents: AI-enabled predictive maintenance tools reduced unplanned downtime by 28.4% (p < 0.01), and AI-driven surveillance improved security incident detection rates by 24.6% (Cohen’s d = 0.61). Regression analysis showed that AI implementation explained 42% of the variance in operational risk reduction (R² = 0.42, p < 0.001), while institutional readiness accounted for a further 19% of the success rate in AI adoption (R² change = 0.19). Thematic analysis of qualitative interviews confirmed that while AI-enhanced systems improved risk identification and mitigation, significant challenges persist, including inadequate staff training (mentioned by 83% of interviewees) and infrastructural constraints (noted by 67%). Evidence supports the hypothesis that AI not only positively impacts ORM performance but also that organizational and technical preparedness are critical for successful AI deployment. The study recommends a phased AI implementation strategy, targeted competency development, and policy alignment for sustainable digital transformation and aviation safety in Middle Eastern airports.