The rapid advancements in artificial intelligence (AI) have revolutionized industries worldwide, with the air cargo sector poised to benefit significantly. However, the adoption of AI in this sector is hindered by complex interdependencies among various enablers. This study systematically identifies and analyzes the critical enablers influencing AI adoption in the air cargo industry using the Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) approach. An expert-driven evaluation shortlisted sixteen enablers, categorized into two groups: drivers (cause enablers) and outcomes (effect enablers). The findings indicate that Real-Time Analytics, Access to Real-Time Information, Automation, Transparency and Visibility, and Quick Adaptation to Changing Market Demand serve as the primary drivers facilitating AI integration. These enablers emphasize data-driven decision-making, predictive capabilities, and operational adaptability as critical success factors.

On the other hand, the study reveals that Cost Savings, Knowledge Sharing, Competitive Advantage, and Sustainability Initiatives emerge as key outcomes influenced by AI adoption. Strengthening these effect enablers ensures long-term collaborative and competitive benefits, including enhanced customer satisfaction, resource optimization, and regulatory compliance. The research highlights the need for strategic investments in AI infrastructure, leadership commitment, and cultural transformation to enable a seamless integration into the Industry 4.0 framework.