Exploring artificial intelligence for third-party logistics service providers: a dynamic capabilities perspective

Purpose – Artificial Intelligence (AI) is increasingly considered a transformative force within the logistics industry, improving efficiency and enhancing effectiveness for many organisations including third-party logistics service providers (3PLs). However, the academic literature reveals a limited understanding of how 3PLs can approach the opportunities offered by AI. To fill this gap, we leverage the dynamic capabilities theory to explore AI adoption within the 3PL industry. Design/methodology/approach – We developed a single case study focusing on a leading British 3PL that introduced AI to improve warehousing operations and planning activities. We collected data through two rounds of qualitative interviews with managerial-level stakeholders from different departments and two on-site visits. Through abductive reasoning, we iteratively compared empirics with the available theoretical knowledge to illuminate how 3PLs can approach AI opportunities through their sensing, seizing, and reconfiguring dynamic capabilities. Findings – Findings illustrate several micro-foundations underpinning higher-order dynamic capabilities. Sensing AI opportunities critically depends on building internal AI awareness as well as involving customers to embed their perspectives, leading to prioritising AI use cases. Seizing starts with aligning use cases with the business strategy, then procuring AI solutions and assessing their security and ethical implications before embedding different AI tools. These initiatives foster resource reconfiguration by embracing a cultural shift involving 3PLs and their customers and developing a robust data infrastructure to support AI efforts. Building on these findings, we suggest evolutionary patterns for dynamic capabilities through AI adoption. Originality/value – Existing research has yet to fully explore how 3PLs can approach AI adoption. The study contextualises the dynamic capabilities theory for AI-driven opportunities, elaborating on earlier studies to identify micro-foundations for 3PLs’ higher-order dynamic capabilities. It proposes a set of research propositions and offers a research agenda to foster future exploration about embedding AI into logistics operations. By focusing on 3PLs in the context of rising digitalisation, the study highlights how firms can navigate the complexities of AI adoption, offering original insights to leverage the synergies among human workforce, technological tools, and physical assets.