Integrating AI support into a framework for collaborative decision-making (CDM) for airline disruption management

Airlines are regularly confronted with disruptions that interfere with their flight operations, resulting in financial losses and lower operational performance. While collaborative decision-making (CDM) is a commonly used approach to mitigate these airline disruptions, it is unclear how artificial intelligence (AI) can support CDM to manage airline disruption. This study's purpose is to identify how the adoption of AI can support CDM to mitigate operational flight disruptions. Using a theory building approach, this conceptual paper advances the literature in aviation management by delineating the relationship between AI and CDM in the context of airline disruption management. First, we propose an AI–CDM framework illustrating the factors that influence disruption management in airlines. Second, we highlight the implications of AI-supported CDM for disruption management in and for airlines. We found that to effectively use AI-supported CDM for disruption management, airlines need to a) introduce data-driven CDM, b) enable AI management of complex systems, and c) transform disruption management into AI-supported performance management. As one of the first studies linking AI and CDM, the framework provides a structured recognition of the role of AI in CDM and its implications for airline disruption management.