Conducting an economic analysis of an AI solution for truck ETA prediction using a developed theoretical cost–benefit framework

Purpose Logistics companies, particularly trucking companies, operate in a highly competitive market. This competition arises from both stakeholders within the ground logistics sector (other trucking companies) and players from other modes of transport, such as inland and rail transportation. As a result, trucking companies must prioritize efficiency and sustainability to remain competitive. In recent years, artificial intelligence (AI) solutions have become a major trend across various industries, including logistics. Many companies are exploring ways to leverage this technology to their advantage, and trucking companies are no exception. However, industry stakeholders often require solid evidence before investing in such innovative technologies. This study assists trucking companies in decision-making about an AI solution for truck estimated time of arrival (ETA) prediction. Design/methodology/approach This study develops a cost–benefit framework based on a comprehensive literature review. The framework is then used to conduct an economic analysis of an AI-powered solution designed to predict truck ETA. The analysis considers multiple scenarios, reflecting different levels of digital maturity and competitive positioning among trucking companies. Findings The findings indicate that trucking companies relying on legacy transportation management systems and expecting revenue growth post-implementation face significant cost-efficiency risks. Therefore, it is crucial for these trucking companies to carefully assess their cost estimates before adopting this truck ETA prediction solution. Originality/value This study explores the economic aspects of AI implementation in logistics by developing a cost–benefit framework for truck ETA prediction.