AI Integration in SME Logistics : Challenges, Opportunities, and Practical Solutions

Abstract

Artificial intelligence (AI) is bringing about a major shift in the logistics sector by facilitating advances in real-time decision-making, automation, and predictive analytics. Small and medium-sized businesses (SMEs) in the logistics industry have had difficulty implementing AI due to resource limitations, a lack of technological competence, and scalability challenges, while major firms have benefited greatly from AI's potential. The integration of AI in SME logistics is investigated in this research, which looks at the prospects and adoption constraints. The research identifies key areas where artificial intelligence (AI) may improve operations, such as demand forecasting, route optimization, inventory management, and customer service automation. It does this through a thorough examination of the literature and qualitative interviews with SME logistics operators. The study also examines the particular difficulties that SMEs encounter, like expensive implementation fees, data management limitations, and the complexity of AI tools. To address these challenges, the study proposes a scalable framework for AI adoption in SMEs, emphasizing incremental investment, modular AI solutions, and access to cloud-based AI platforms. Moreover, it discusses the role of government policies, public-private partnerships, and training programs in fostering AI adoption within SMEs. This research contributes to a better understanding of how AI can drive operational efficiency and competitiveness for SMEs in logistics.