Japanese Daiwa Steel Tube drives AI in Fourth Industrial Revolution
Industrial AI surge widens gap between large firms and SMEs as China’s rollout accelerates across factories and supply chains
The race to embed artificial intelligence (AI) into manufacturing is accelerating, but the benefits remain unevenly distributed. As governments and large corporations push ahead with industrial AI, smaller factories risk being left behind due to structural constraints in talent, capital, and production models.
China has moved aggressively to formalize this transition. On January 7, 2026, the Ministry of Industry and Information Technology released a sweeping “AI + Manufacturing” action plan, marking a shift from policy guidance to execution. The initiative aims to upgrade more than 50,000 enterprises by 2028, embedding AI across production, logistics, design, maintenance, and management.
The plan targets the development of 3–5 general-purpose industrial models, 100 high-quality datasets, and more than 500 application scenarios, while promoting embodied AI, industrial intelligent agents, and humanoid robotics.
Shinichiro Nakamura, president of Daiwa Steel Tube Industries and one to ONE Holdings Pte. Ltd., said China’s rapid push to integrate AI into manufacturing is forcing Japan and other Asian producers to accelerate their strategies, with pressure building across the region.
“We recognize that this pressure is higher for large corporations and tends to be relatively lower for smaller-scale entities,” Nakamura said. “Furthermore, against the backdrop of recent geopolitical factors, the impact of tariffs and regulations is also perceived to be growing significantly.”
He said the fundamental reasons for the large AI gap between factories of large corporations and SMEs lie in the quality of existing resources and the required performance levels, adding that there is a clear difference in the quality of talent that large corporations and SMEs can attract.
“Regarding required performance, large corporations typically engage in mass production of a few product types, while SMEs handle small-batch production of many product types,” he said. “The former environment facilitates automation with robots, requires less human involvement, and allows for easier investment, whereas the latter is the complete opposite. This gap is expected to be more severe during the initial AI implementation phase, easing somewhat afterward. However, fundamentally closing this gap is not easy and will require time.”
The divergence reflects deeper operational realities: large manufacturers benefit from standardized processes and scale that make automation more viable, while smaller factories face variable production environments that complicate AI deployment.
Geopolitical pressures, including tariffs and regulatory shifts, are accelerating the need for industrial transformation, particularly among globally exposed manufacturers.
AI adoption gap
Nakamura shared his views in an email interview with TechJournal.uk, focusing on China’s industrial AI strategy and the direction of manufacturing in Asia.
He said that, despite the scale of China’s ambitions, the reality of AI adoption remains uneven, particularly among small- and mid-sized manufacturers.
“For SMEs in manufacturing, AI adoption is progressing in terms of general-purpose applications through tools like Gemini and NotebookLM included in Google Workspace, or Microsoft’s Copilot and ChatGPT,” he said.
“However, attempts to transform the entire workflow of their manufacturing processes through AI adoption are still rare and are considered to be in the developmental stage.”
While awareness of AI tools is growing, deeper integration into production systems—such as predictive maintenance, automated quality control, and end-to-end optimization—remains limited outside large enterprises.
Government policies are likely to reinforce this divide, favoring companies with scale and resources.
“Large corporations, with their significant social impact, abundant resources, and ability to deliver results and outcomes, are likely to lead the way,” Nakamura said. “Conversely, micro-enterprises, with their limited social impact and scarce resources, face a high risk of being left behind.”
Policy and paths
Japan is also accelerating its Fourth Industrial Revolution agenda, aiming to quadruple investment in AI and semiconductors to boost productivity and offset a shrinking workforce. The push aligns industrial policy, capital deployment, and skills development to modernize manufacturing at scale.
Central to this effort is the 2025 AI Promotion Act, a pro-innovation framework that prioritizes AI development and adoption across industry and the public sector. Overseen by the prime minister–led AI Strategic Headquarters, it emphasizes research funding, talent cultivation, and shared computing infrastructure.
The strategy builds on “Society 5.0,” integrating data-driven AI across manufacturing, healthcare, and mobility to address labor shortages. It favors agile, “soft law” governance with voluntary risk management and international coordination under G7 Hiroshima AI Process guidelines.
Against this policy backdrop, manufacturers face two distinct strategic paths: either accelerating to capture first-mover advantage or adopting a more incremental approach.
“We recognize two realistic paths: either taking risks to proactively pursue first-mover advantages, or undertaking the bare minimum based on government support and partnerships with large companies,” he said. “This decision depends on each company’s strategic intent.”
Stronger ecosystem support will be essential to enable more companies to pursue the first path.
“At the same time, we recognize that establishing mechanisms not only by government and public administration but also by financial institutions to support the former through subsidies and other means is extremely useful,” he said.
Such mechanisms could include targeted financing, shared industrial data platforms, and standardized AI tools tailored to specific manufacturing sectors.
Leadership profile
Nakamura is a third-generation industrialist and the grandson of Tomeichi Nakamura, who founded Nakamura Pipe Manufacturing Factory (now Daiwa Steel Tube Industries) in 1932. The company has grown into a global manufacturer of galvanized steel tubes, supported by proprietary technologies such as the in-line galvanizing “Daiwa Z Process.”
Under Shin Nakamura’s leadership, the business has expanded beyond traditional manufacturing. Having joined the company in the 1990s and becoming president in 2003, he led a restructuring of operations by adopting advanced ICT systems, modern management practices, and global expansion initiatives. Through Singapore-based o2Oh, he now oversees a network of companies spanning Japan, Vietnam, the United States, and India, combining industrial operations with digital innovation.
The group includes:
Daiwa Steel Tube Industries — a manufacturer of galvanized steel tubes and developer of in-line galvanizing processes
IndustrialML — develops real-time data platforms for smart factories
Superior Technologies (California) — focuses on advanced galvanizing systems and monitoring equipment
Daiwa Lance International (Vietnam) — manufactures lance pipes and supplies customers in more than 50 countries
Together, these companies integrate AI, machine learning, and industrial Internet of Things (IoT) technologies into production environments.
His strategy focuses on bridging the gap between legacy manufacturing and digital transformation, using real factory environments to test and scale new technologies.
Execution challenge
Looking ahead, the fourth Industrial Revolution will be defined not only by technological breakthroughs but also by how effectively they are implemented across diverse industrial environments.
His group’s approach combines traditional manufacturing expertise with investments in AI-driven systems and digital infrastructure. By developing and testing solutions within its own factories, and extending them across its global network, the company aims to create scalable models for industrial transformation.
The challenge lies in integration rather than invention—aligning advanced technologies with the realities of factory operations, workforce capabilities, and economic constraints.
In that context, the success of industrial AI may depend less on headline innovation and more on disciplined execution across thousands of factories worldwide, particularly among those currently at risk of being left behind.