Human-Centric AI Integration in Airline Operations: Enhancing Safety, Efficiency, and Workforce Resilience

This study investigates the transformative impact of generative AI on expertise hierarchies within airline operations and explores strategies for seamless human-AI collaboration in safety-critical workflows. The primary objective is to examine how AI-driven analytics reconfigures decision-making roles empowering junior staff with advanced insights while allowing senior experts to assume mentoring and oversight responsibilities. Employing a mixed-methods approach that combines qualitative case studies with quantitative KPI analysis, our research demonstrates significant operational benefits, including reduced turnaround times, enhanced predictive maintenance, and improved resource utilization. However, the study also reveals risks associated with workforce skill atrophy when AI support dominates routine tasks. The findings underscore the necessity of robust training programs, clear communication protocols, and the implementation of dual-axis KPI frameworks (e.g., Efficiency-Skill Ratio, Risk-Adjusted ROI) to balance technological gains with human expertise retention. These insights have significant implications for theory and practice in digital transformation and airline management, offering actionable recommendations for airline managers and policymakers to foster sustainable operational excellence.