Business Journal spotlights physical AI trends in construction and logistics

On Nov. 7, 2026, Business Journal published an article on physical AI in construction and logistics, featuring companies including ARAV, T2 and Mujin.

Automation rates at logistics hubs, the number of driverless vehicles manufactured and validation of operations inside facilities each indicate a different stage of implementation. Distinguishing between them is important for companies adopting these technologies in construction and logistics as they assess their scope of application and operating requirements.

When assessing physical AI in construction and logistics, it is necessary to avoid treating deployment as a single stage and instead check separately for operational results in the field, the supply capacity for equipment and vehicles, and the facilities and operating conditions on the receiving end. These factors are interconnected, but progress in any one of them does not mean the other stages have also been achieved. For example, AEON: Automation rate at logistics hub reaches 50% reported that the automation rate for work at a logistics hub had reached 50%, with a target of 80% by 2030. This is a measure of how much work at the hub has been automated. By contrast, Aurora manufactures more than 20 driverless trucks, outlines production ramp-up at third-quarter earnings briefing focused on the number of driverless vehicles manufactured and their addition to the operating fleet. Work automation rates and vehicle production numbers are different measures, and implementation benefits and scale of operations cannot be compared using the same figures. In addition, [Three companies — Dynamic Map Platform, Daiwa House Industry and Hacobu — selected for MLIT project to test autonomous trucks inside logistics facilities](/en/news/dynamic-map-platform-daiwa-house-industry-and-hacobu-selected-for-mlit-9db193) focuses on testing vehicle operations inside logistics facilities and the operation of mobility hubs. The case shows that implementation depends not only on the performance of autonomous vehicles but also on the driving environment inside facilities and the operations for receiving them. Therefore, coverage comparing the current standing of companies or technologies should make clear which stage of progress its claims are based on. The next points to check are whether operational results in the field are leading to sustained use, the scale at which equipment and vehicles can be supplied, and how fully requirements on the facility side have been defined. Refer to the original sources for details of the individual cases and assessments in the article.

At Japanese logistics hubs, implementation depends not only on automation rates for work but also on the conditions for accommodating autonomous vehicles operating inside facilities. Reports on AEON’s hub automation and an MLIT project testing vehicle operations inside logistics facilities show why assessments of domestic adoption need to distinguish between work processes and facility operations.