2025 Agenda

Air traffic controllers have long operated at the limits of human cognitive performance through simultaneously managing separation assurance, trajectory anticipation, traffic sequencing, inter-sector coordination, and time-critical decision-making under uncertainty. While artificial intelligence has transformed many areas of aviation, its benefits have yet to be consistently realized in everyday air traffic management operations.
This session will demonstrate how that gap can be closed by positioning AI as practical decision support rather than as a replacement for human expertise. The focus is on AI capabilities designed to reduce cognitive load, enhance situational awareness, and support controllers incrementally within existing workflows. Emphasis is placed on solutions that are operationally validated, transparent, and scalable beyond a limited number of high-investment initiatives.
The session will highlight concrete operational applications, including earlier conflict prediction with actionable resolution options, improved demand–capacity balancing and sector load forecasting, smarter AMAN/DMAN sequencing to reduce delays, and trajectory and route optimization to lower fuel burn and CO₂ emissions. Additional use cases include anomaly detection to strengthen safety nets and digital-twin simulations to support controller training and procedural validation. The lessons learned while coding and developing AI and LLM–based capabilities for air traffic management automation systems will also be shared in this session.
The central message is clear: when AI delivers measurable, usable, and trustworthy capability directly within the controller’s workflow, it becomes a genuine operational enabler for the ATC community.