Exploring Barriers to the Adoption of Artificial Intelligence in the Transportation Sector in the Context of Industry 5.0: A Qualitative Expert-Based Study

Abstract

This study investigates barriers to the adoption of Artificial Intelligence (AI) in the transportation and logistics sector within the context of Industry 5.0, emphasizing human centricity, sustainability, and system resilience. A qualitative expert-based research approach was applied using asynchronous email interviews with 54 experts representing transport operators, logistics service providers, SMEs, public sector institutions, technology providers, and academia from multiple countries. The analysis identified eight interrelated categories of barriers affecting AI implementation in transport systems, including data quality and integration issues, infrastructure limitations, financial constraints, lack of human competencies, managerial challenges, psychological resistance, ethical and safety concerns, and regulatory uncertainty. SME specific challenges were further examined using Latent Dirichlet Allocation, confirming the central role of data, competences and resource constraints. The findings indicate only partial alignment of current AI initiatives with Industry 5.0 values and provide implications for intelligent transport systems, transport safety, and policy development.