Transformative Technologies and the Future of AI in Trucking

Abstract

The long-haul trucking industry is facing unprecedented economic challenges, including inflation-driven operational costs, persistent driver shortages, fuel price volatility, and evolving supply chain demands. This article investigates how transformative artificial intelligence (AI) technologies can address these issues and shape the future of trucking. While AI applications in freight logistics are growing, a clear gap exists in understanding the practical implementation pathways and measured impacts on economic resilience and operational efficiency. This study uses a qualitative, case-based methodology, analyzing industry reports and real-world deployments by major logistics firms such as UPS, Convoy, and C.H. Robinson. Key findings highlight that AI-powered freight matching reduces empty miles by up to 33%, dynamic pricing engines improve rate responsiveness, and intelligent dispatch systems enhance route efficiency and driver utilization. These AI tools are shown to mitigate the effects of fuel volatility, labor constraints, and cost inflation. Results suggest that integrating AI leads to measurable economic benefits, including lower per-mile costs and improved asset utilization, especially when phased deployment and workforce training are prioritized. The implications are twofold: first, that AI offers a viable strategy for increasing trucking sector resilience in volatile conditions; and second, that stakeholder collaboration between tech providers, carriers, and policymakers is essential for scalable, responsible adoption. This article contributes to the field by bridging the gap between AI potential and implementation reality in long-haul trucking, offering evidence-based insights for researchers, industry leaders, and regulators.