Artificial intelligence as a driver of efficiency in air passenger transport: A systematic literature review and future research avenues

Abstract

Despite the claim that artificial intelligence (AI) has the potential to increase efficiency in and for airlines, current literature is limited concerning models and frameworks to assess AI applications and their implications for airline efficiency. In response, we a) conceptualize and propose an AI-Airline-Efficiency-Model (AAEM) that allows for a more structured management approach for a systematic review and analysis of existing literature, and b) present a framework explicating the identified areas of AI application for airline efficiency based on a the AAEM model. In particular, using the four AI elements Machine Learning, Deep Learning, Reinforcement Learning and Natural Language Processing and their applications within six identified airline departments, we systematically review and analyze key attributes and characteristics of both AI and airline efficiencies to critically assess current research efforts. We found that AI applications are built around four overarching improvement areas predictive analytics, resource optimization, safety & autonomous processes and passenger experience, but lack a cross-department and inter-organizational focus and are often theoretical in nature. This study provides insight into most prevalent AI applications and the less popular applications applied in and for passenger transport, thereby presenting the dominating AI techniques that are covered by existing literature as well as highlighting a wide range of emerging AI techniques with the potential to become more influential for future studies. We discuss theoretical and managerial implications and offer avenues for future research.