2025 Agenda

How can ANSPs benefit from the opportunities offered by Artificial Intelligence (AI) and Machine Learning (ML) solutions today to optimize decision-making and operational efficiency in the context of performance and flow management? This question will be illustrated in the presentation with specific examples focusing on two technological clusters.

The first cluster utilizes ML regression models for precise traffic and delay forecasting across airspaces and airports. These forecasts are integrated into an AI agent-driven framework that evaluates historical data and operational constraints to recommend optimal airspace sector configurations.

The second cluster uses Large Language Models (LLMs) to bridge the gap between complex data and actionable insights: using Natural Language to SQL (NL2SQL), users can intuitively query structured performance databases, while Retrieval-Augmented Generation (RAG) processes unstructured operational data, such as reports on specific events.

Collectively, these tools transform raw data into proactive intelligence, enabling more accurate evaluations and robust capacity planning in increasingly dynamic aviation environments.