The Future of Japanese Industry: How Generative AI will Transform Manufacturing -2/2-

Knowledge Worker Productivity

Opportunity: Generative AI offers substantial advantages for increasing the productivity of knowledge workers. AI could draft emails, reports, meeting notes, or standard documents, freeing up time for higher-value activities. AI could also condense lengthy documents, research papers, or meeting transcripts into key insights, reducing information overload.

Case Study: Mitsui & Co. has implemented generative AI to automate and expedite the review of complex documents. This adoption has led to a reduction in review time by up to 80%, allowing employees to allocate more time to value-added tasks and decision-making processes source: https://aws.amazon.com/solutions/case-studies/bedrock-mitsui)

Concerns: As with all advanced technologies, AI has the potential to do harm. While generative AI tools have improved greatly over the last year, the technology is still far from perfect. For example, AI-generated articles might contain incorrect or incomplete data, obsolete information, or biases due to the choice of content used to train models. Therefore, any use of AI in corporate knowledge practice needs to be carefully supervised.

Innovation in Advanced Materials

Opportunity: Japan’s traditional strengths in materials science—from semiconductors to advanced polymers—can be further enhanced by generative AI. AI models could accelerate the discovery of new materials with desired properties, reducing reliance on costly and time-intensive lab experiments.

Case Study: NEC Corporation, in collaboration with Tohoku University, developed AI technologies to predict the characteristics of unknown materials. By applying these technologies, they achieved a thermoelectric conversion efficiency 100 times greater than previous methods within approximately one year. This breakthrough was accomplished by combining NEC's proprietary machine learning techniques with extensive material data, enabling rapid identification of promising thermoelectric materials (source: https://www.nec.com/en/press/201802/global_20180209_04.html)

Concerns: The development of AI models for scientific discovery requires robust scientific databases for training. Some high quality data sets are available from universities and other research institutions. Building internal data ecosystems and partnering with trusted collaborators will be key to success in this area.

Sustainability

Opportunity: Japan’s commitment to carbon neutrality by 2050 positions sustainability as a strategic priority. Generative AI can simulate energy-efficient manufacturing processes, optimize supply chain emissions, and suggest product redesigns to reduce environmental footprints.

Case Study: Hitachi applies generative AI for energy optimization in smart factories. Their Lumada platform uses AI to analyze and generate optimal operational scenarios, reducing energy consumption and supporting ESG commitments (source: https://www.smart-energy.com/industry-sectors/digitalisation/hitachi-to-incorporate-ms-generative-ai-in-lumada/ )

Concerns: Culturally, Japanese companies’ cautious approach to adopting new technologies may slow sustainability innovations. Executives must foster a culture of experimentation aligned with long-term environmental and sustainability goals.

Leadership in Robotics and Automation

Opportunity: Japan’s leadership in robotics can be strengthened through generative AI capabilities in scene understanding, contextual learning, motion planning, and control code generation. Generative AI technologies could enhance robot efficiency, flexibility, and operational safety.

Case Study: At Toyota Research Institute, researchers are developing robots that learn household tasks by observing human actions. Using a machine-learning system called diffusion policy, these robots can determine appropriate actions rapidly. The goal is to enable robots to learn tasks by watching instructional videos, potentially transforming platforms like YouTube into valuable training resources (source: https://www.wired.com/story/fast-forward-toyota-robots-learning-housework ).

Concerns: Enhanced flexibility and freedom in robot operation gives rise to concerns about safety. Efforts to augment robotic and automation capabilities with AI need to be combined with efforts to prevent inadvertent damage to property or harm to human operators and users.

Overcoming Cultural and Language Barriers

For generative AI to realize its transformative potential in Japan, executives must address two intertwined challenges:

• Cultural Resistance: Japan’s deep-rooted emphasis on perfection, hierarchy, and risk aversion can slow AI adoption. Executive leadership must champion agile decision-making, cross-functional collaboration, and controlled experimentation to embed AI into traditional workflows.

• Language Limitations: Most generative AI models are English-centric, limiting their effectiveness for Japanese-language documentation, technical instructions, or customer interactions. Language barriers can limit AI’s operational effectiveness unless companies invest in Japan-specific AI models and hybrid human-AI workflows.

Conclusions

Generative AI offers Japanese industrial companies a once-in-a-generation opportunity to enhance competitiveness, foster innovation, and drive sustainability. However, capturing this value requires deliberate cultural adaptation, strategic data management, and investment in language-specific AI capabilities.

The leaders who navigate these complexities with foresight and agility will define the next era of Japan’s industrial legacy.