Despite the transformative potential of Artificial Intelligence (AI) in logistics, its adoption in the air cargo industry remains limited and fragmented due to several unresolved challenges. This study aims to develop and validate a hybrid decision-support framework that diagnoses barriers, prioritizes them, and evaluates mitigation strategies. Drawing on expert insights, critical barriers were identified and prioritized using a hybrid multi-criteria decision-making approach. The study applies Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) to explore causal interrelationships, Analytic Hierarchy Process (AHP) to rank barrier significance, and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to evaluate strategies for overcoming them. Key challenges include inadequate top management commitment, poor Information Technology (IT) infrastructure, data quality concerns, and cybersecurity risks. Among these, top management commitment emerged as the most influential factor affecting organizational readiness and strategic alignment. Technology and infrastructure was the highest-ranked barrier category, with data quality and maintenance costs as major sub-factors. To address these, the study recommends enhancing leadership involvement, upgrading IT systems, and improving cybersecurity. Sensitivity analysis confirms the robustness of these strategies under varying conditions. This research offers a structured framework for AI adoption, providing practical guidance for industry stakeholders and contributing to the academic discourse on digital transformation in logistics.