AI Strategy Agent for Airline Logistics: A Multi-Layered, KPI-Governed Architecture for Real-Time Optimization and Ethical Orchestration

Abstract

This study presents the design and empirical validation of the AI Strategy Agent for Airline Logistics (AISAL) a multi-role, four-layer architecture (Perception, Cognition, Strategy, Action) that autonomously orchestrates 110 airline logistics Key Performance Indicators (KPIs) in real time. Using a mixed-methods approach combining expert elicitation and digital twin simulation within the aviation sector, the model bridges the persistent gap between descriptive dashboards and adaptive, KPI-governed execution. Results indicate a 22% improvement in forecast accuracy and over 11% reduction in CASK, alongside enhanced ESG alignment and operational resilience. The AISAL agent embeds ethical auditing, explainability, sustainability scoring, and disruption response transforming airline logistics from reactive inventory tracking to anticipatory, ethically governed orchestration. Theoretical contributions include the operationalization of agentic AI in aviation logistics governance; practical implications advocate for integrating AISAL-like agents into fleet support, AOG management, and ESG-sensitive inventory systems. This research offers a foundational template for airlines seeking digital transformation amid geopolitical and infrastructural constraints.