Artificial Intelligence in the Management of Transportation Companies

Purpose: This article examines the transformative impact of artificial intelligence (AI) on the management of transportation enterprises, with particular emphasis on enhancements in planning accuracy, operational efficiency, environmental sustainability, and strategic decision-making.As transport systems grow increasingly interconnected, dynamic, and datarich, AI provides new capabilities that fundamentally reshape managerial practices. Design/Methodology/Approach: The study employs an integrative synthesis of contemporary scholarly literature to identify and organize the mechanisms through which AI influences managerial processes in the transport sector. Drawing from state-of-the-art research, a fourlayer conceptual framework comprising data acquisition, AI-driven analytics, decisionsupport functions, and operational execution is developed to systematize the multifaceted effects of AI on organizational management.Findings: The analysis demonstrates that AI significantly enhances and strengthens predictive planning, route optimization, fleet maintenance scheduling, warehouse coordination, and risk mitigation.Beyond automating routine tasks, AI enhances managerial cognition by enabling rapid adaptation to uncertainty, supporting evidence-based decision making, and facilitating more environmentally responsible transport operations.These effects collectively contribute to the modernization and increased resilience of transportation companies.Practical Implications: The proposed conceptual model offers managers, practitioners, and policy makers a structured tool for understanding and implementing AI solutions within transportation organizations.The findings highlight practical pathways for leveraging AI to improve operational performance, strengthen strategic planning, and advance sustainability objectives across the transport sector.Originality/Value: By integrating and synthesizing current research, this article provides a comprehensive and analytically grounded framework that elucidates the complex, multidimensional role of AI in transport management.The study contributes original value by offering a coherent conceptual model that can guide future empirical research and inform policy and managerial practices in the rapidly evolving domain of intelligent transportation systems.