The integration of artificial intelligence (AI) in air traffic management (ATM) is expected to play a key role in improving efficiency, prediction, and safety. International programs such as SESAR and NextGen outline recurring domains where AI could add value, including trajectory prediction, conflict detection, speech recognition, anomaly detection, and human–machine teaming. For LVNL, however, it is not yet clear which of these applications are both relevant and feasible in daily operations.
My thesis focuses on identifying and evaluating AI applications that can realistically be adopted within the Dutch ATM context in the short term. The research begins by reviewing international frameworks and academic studies to determine which applications are sufficiently mature to be considered within the coming five years. These applications are then linked to LVNL’s organizational functions and assessed against three feasibility dimensions: operational fit, compliance with safety and cybersecurity requirements, and acceptance by air traffic controllers. The outcome of the study will be a prioritized set of AI opportunities that combine potential benefits with practical feasibility. In this way, the research provides LVNL with a concrete basis for deciding which AI applications to explore further, supporting both operational efficiency and the continued modernization of Dutch air traffic management.