Impact of Generative AI Models on Labor Utilization and TFP Growth in the U.S. Airline Industry: An Exploratory Analysis

The airline industry may stand at a transformative juncture as generative AI models reshape labor utilization and operational efficiency. While AI technologies promise productivity gains through automation, optimization, and enhanced decision-making, their impacts may vary across airline business models due to differences in operating strategies, staffing structures, and service complexity. This study provides a quantitative assessment of AI’s influence on Total Factor Productivity (TFP) growth in the airline industry by incorporating AI exposure into workforce dynamics. Using data from U.S. airlines between 2000 and 2019, we simulate the impact of AI adoption on labor utilization. Our results suggest that AI could improve labor utilization by 30.2%, contributing to an average industry-wide TFP increase of 0.1%. Ultra-Low-Cost Carriers (ULCCs) are projected to experience the largest gains, averaging 0.3%, driven by their streamlined workforce structures, simplified operations, homogenous passenger bases, and reliance on labor efficiency to sustain low-cost models. Low-Cost Carriers (LCCs) experience modest improvements of 0.1%, reflecting their balance between cost-efficiency practices and operational complexity. Full-Service Airlines (FSAs) show intermediate growth of 0.2%, constrained by their diverse task requirements and complex hub-and-spoke operations. These findings are consistent with the broader literature, which finds that AI’s productivity gains are most pronounced in labor-intensive sectors, while complexity tempers efficiency gains.