Japanese companies' AI applications: Have they also fallen into the trap of "thinking too small"?

From "Replacement" to "Reconstruction": The Blind Spot in Japanese Corporate AI Strategies

While global companies race to integrate AI into supply chains and financial processes, Japanese firms remain accustomed to asking a familiar question: "How many employees can AI replace?"

This query is not unique to Japan. Blue Yonder CEO Duncan Angove stated in an interview with Newsweek that companies are focusing on the wrong "unit of change" — the focus should not be on individual users or roles, but on the entire system and the results it delivers. He noted: "When factories first started adopting electricity, many simply replaced steam engines with electric motors, while retaining the original belts, pulleys, and process layouts. The result was almost no productivity improvement." Angove warned that the danger of AI in supply chains lies in "attaching intelligence to yesterday's processes, rather than reimagining how the supply chain should operate."

This remark carries particular significance for Japanese manufacturing. Japan was once synonymous with lean production and continuous improvement, yet in the wave of digitalization, many companies still rely on highly fragmented information systems built in the 1990s. Angove's research shows that supply chain leaders prioritize "improving efficiency" and "faster, better decision-making," while "building an AI-driven supply chain" ranks only as a secondary goal — precisely exposing a split in thinking: companies want AI to improve old metrics but are unwilling to redesign the processes themselves.

The "Smart Silos" Trap in Japanese Supply Chains

Japanese companies' supply chain management has long been known for its meticulousness, but meticulous does not equal intelligent. In his interview, Angove described a typical dilemma: "Historically, supply chains were fragmented, with each node isolated from others. This design cannot support an AI-driven supply chain, because AI requires systems to communicate with each other and cascade data across the entire chain."

This is precisely the reality for many Japanese manufacturers. While giants like Toyota have partially introduced AI for inventory prediction optimization, data barriers between suppliers, factories, and logistics remain rigid. AI is stuffed into the existing system, resulting in "smarter silos, not smarter supply chains."

Japan's Ministry of Economy, Trade and Industry's 2025 "Industry Digital Transformation Report" points out that local companies generally exhibit a phenomenon of "digital tool accumulation": over 60% of manufacturing companies have introduced at least one AI tool, but only 12% have achieved cross-departmental data collaboration. This mirrors the global trend observed by Angove: companies treat AI as just another tool, rather than a strategic lever to change the logic of collaboration.

The Financial Industry: Questions After Productivity Liberation

AI's application in the financial sector shows a similar narrative. Kevin Buehler, Chief Innovation Officer at Rogo and former senior partner at McKinsey, believes that AI is pushing financial professionals beyond the superficial story of "productivity improvement" into a more fundamental question: "What is the new capacity used for?"Buehler divides AI adoption in financial institutions into three stages: junior employees using AI to accelerate daily tasks → middle managers learning to collaborate with AI teams → full-process business reengineering. He admits that most companies have not yet reached the third stage. "They need data, and that is often the Achilles' heel of these projects; they need talent, and talent must be obtained through retraining and upskilling."

The situation in Japan's financial industry is particularly typical. Banks like Mitsubishi UFJ and Mizuho have deployed AI customer service and anti-fraud systems, but core processes like credit approval and M&A transactions still rely mainly on manual work. A 2025 survey by Japan's Financial Services Agency showed that only 7% of financial institutions have established an AI-driven end-to-end process transformation roadmap. Efficiency and cost reduction are the main goals, rather than expanding service scope or achieving a leap in decision quality.

Unique Obstacles and Breakthrough Points in Japan

There are deep-rooted reasons why Japanese companies tend to fall into the "thinking too small" trap.

• Lifetime employment and seniority-based wages: When measuring AI returns, companies focus more on reducing man-hours than creating new value, because labor costs are explicit while system value-added is difficult to quantify.
• Vertical integration culture: Japanese companies are accustomed to internal closed optimization and are reserved about external data sharing and ecosystem collaboration, which contradicts the data fluidity required by AI.
• Obsession with the "workplace": Tacit knowledge on the manufacturing floor is regarded as a bible, and AI is seen as a threat to experienced workers' skills rather than an enhancement.

But breakthroughs also exist. Japan has deep accumulation in robotics, sensors, and industrial automation, which are fundamental building blocks for building intelligent supply chains. Angove emphasizes: "Supply chains are not a general reasoning problem but a deep operational environment requiring decisions with physical consequences under real-time constraints." The engineering mindset of Japanese companies is well-suited for developing such specialized models—they do not need larger general models, but a hybrid architecture that combines cutting-edge models with domain-specific models.

Conclusion: A Systemic Reconceptualization is Brewing

Japan is not without pioneers. Hitachi's Lumada platform is attempting to connect manufacturing and logistics data flows; Rakuten is restructuring its omnichannel supply chain through AI. But these cases remain isolated.

Angove's warning is worth heeding for Japanese companies: "Those left behind will be the ones using AI to do the same old things faster." With AI now a national strategy, Japan needs to shift from "how much labor can be replaced" to "what new possibilities can be realized"—this is not only a technological transformation but also a reconstruction of industrial thinking.

---*This article partially references information from Newsweek's report "AI Impact: Are Companies Thinking Too Small About AI?" (June 12, 2026), as well as the "Supply Chain Compass" study by Llamasoft (surveying 678 supply chain executives from companies with over $500 million in revenue in North America and Europe). Japanese industry data is cited from the Ministry of Economy, Trade and Industry's "Industry Digital Transformation Report" (2025) and internal research by the Japanese Financial Services Agency (2025).*