Artificial Intelligence Microfoundations and Capabilities as Value Drivers in Transport and Logistics Organisations

Transport and Logistics (T&L) organisations have traditionally struggled with operating costs, efficiency, productivity, and sustainability challenges. Artificial intelligence (AI), robots, and advanced analytics can ease these challenges and drive business value if T&L organisations develop effective strategies for integrating and harnessing AI. Nevertheless, research on AI in the T&L industry has primarily focused on technical experiments to show how AI can resolve a specific problem instead of how T&L organisations use AI in their business operations to deliver value. To address this problem, this study builds on the microfoundations and dynamic capabilities theories and the AI use experiences of five dominant T&L companies in Australia. The findings show that T&L organisations are implementing both generic (chatbots, virtual assistants, machine learning) and industry specific (self-driving trucks, camera-fitted long-haul tracks, autonomous guided vehicles in warehouses, robotic forklifts, collaborative robots, digital twins to replicate ground operations) AI technologies and systems. The companies are using these technologies for sensing, responding, and transforming transport, warehouse, and delivery operations, which in turn is enabling them to enhance environmental and workers’ safety, operational efficiency and customer experience. To do so, they are developing AI skills and AI enabling organisational routines and governance processes. Based on the findings, we contribute a process model to explain how T&L organisations can effectively use AI.