Hitachi bets on industrial expertise to win the physical AI race

AI News

by Dashveenjit Kaur

February 23, 2026

AI-Generated Deep Dive Summary

Hitachi is betting on its decades of industrial expertise to carve out a niche in the physical AI race, challenging tech giants like OpenAI, Google, and Nvidia by emphasizing the importance of foundational knowledge in physics and engineering for successful real-world AI applications. Unlike others focused on scaling multimodal models or building platforms, Hitachi argues that true progress in physical AI requires deep understanding of the systems it aims to control, such as robots and industrial machinery. Hitachi’s approach centers around integrating specialized simulation tools and signal-processing technologies into its physical AI framework. For instance, its thermal fluid simulation technology models gas and liquid behavior, while advanced signal-processing tools monitor equipment conditions—foundational engineering capabilities that underpin its expertise in product design and control systems. While Hitachi’s broader Integrated World Infrastructure Model (IWIM) is still in the concept phase, early deployments with partners like Daikin Industries and JR East demonstrate tangible results. With Daikin, Hitachi developed an AI system that diagnoses malfunctions in commercial air-conditioner manufacturing equipment by analyzing maintenance records and design data. Similarly, for JR East, its AI identifies root causes of railway control device failures and assists operators in creating response plans—a critical capability given the massive impact of delays on Tokyo’s metro network. Hitachi is also tackling a key bottleneck in industrial AI: reducing the time and effort required to develop control software. By leveraging techniques like retrieval-augmented generation, Hitachi has automated the creation of integration test scripts for vehicle ECUs, cutting man-hours by 43% in one automotive project. In logistics, its variability management technology modularizes robot control software into reusable components, adapting to different warehouse environments. This focus on practical, industry-specific AI solutions highlights why physical AI matters—it bridges the gap between theoretical advancements and real-world implementation, offering tangible benefits for industries like manufacturing, transportation, and infrastructure. Hitachi’s approach underscores that success in physical AI often requires more than cutting-edge algorithms; it also demands deep domain knowledge to navigate the complexities of the physical world.

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Originally published on AI News on 2/23/2026