[Japan’s Voices No.10] Japan’s Winning Strategy in the Era of Physical AI: Transforming Frontline Excellence into AI-Ready Data to Reshape Industrial Structures for Global Leadership

The next phase of AI transformation: the domain of Physical AI

The replacement of intellectual work in cyberspace by AI is advancing at an unprecedented pace, driven by the rapid evolution of generative AI. However, the primary arena of value creation is already shifting into its next phase: the domain of Physical AI — systems that integrate multimodal data such as images, audio, tactile information and various sensor inputs to understand the real world and execute physical tasks.

The global race in AI development, particularly around large language models, has been driven by massive web datasets, enormous computing resources, and global cloud infrastructure. In this arena, global technology giants have established a clear lead. By contrast, the social implementation of Physical AI will not be determined by AI model performance alone. What matters is the ability to integrate hardware, sensors, control systems, safety protocols and operational design — while continuously improving systems in real-world environments. The axis of competition is shifting from the scale of models to the power of integration and operation that creates value in the field.

Japan’s historic opportunity: Can frontline workplace excellence be transformed into AI-ready data?

This shift may present a historic opportunity for Japan, a country that has long cultivated precision manufacturing and high-quality services. Yet Japan’s strength lies not merely in the operational excellence that underpins these capabilities. The essential question is whether Japan can transform the tacit knowledge accumulated on frontline workplaces into data structures that AI can learn from, evaluate and continuously improve — and ultimately elevate that knowledge into reusable industrial infrastructure.

Across manufacturing, logistics, retail, healthcare, nursing care, infrastructure maintenance and primary industries, Japanese workplaces contain vast amounts of knowledge that have never been fully verbalized. This includes skilled workers’ judgments, subtle control of force, the ability to detect early signs of abnormalities and the procedures required to maintain quality. Traditionally, such capabilities have been regarded as human skills or experience. In the era of Physical AI, however, the ability to capture this on-site knowledge through multimodal operational data — including images, sounds, movements, environmental information and operational histories — and convert it into features and protocols that AI can process will become a core source of competitiveness.

In other words, Japan cannot win the next phase of the AI race simply by possessing high-performing frontline operations. It must transform high-quality operational data into an “AI-ready” state that AI systems can directly learn from and process, thereby creating a continuous cycle of learning, operation and improvement. Only through such a cycle can excellent operations generate excellent data; excellent data trains excellent AI; and excellent AI further enhances productivity and quality in the field. This could become the foundation of a new “Japan Standard” for the era of Physical AI.

Redesigning workplaces for an era in which AI and robotics become operational actors

At the same time, Japan must address structural challenges if it is to translate frontline excellence into sustainable competitive advantage. Many Japanese workplaces still suffer from dependence on individual expertise, local optimization, excessive customization and delayed standardization — all of which may become barriers to effective AI implementation. Furthermore, many companies have attempted to achieve incremental efficiency gains by partially introducing generic software or cloud services into existing business processes. Yet improving only isolated portions of operations has limited impact on overall productivity.

To achieve genuine transformation, it is not sufficient to retrofit AI onto processes originally designed around human labor. Companies must redesign business processes on the assumption that AI systems and robots will become primary actors. Work procedures, judgment criteria, data acquisition methods, exception handling and accountability boundaries must all be designed in advance. Workplaces themselves must be transformed into environments in which AI can learn. This is not merely digital transformation. It is business process reengineering for the era of Physical AI.

The need for “Zero PoC (Proof of Concept)” and Human-in-the-Loop Mechanisms

In this context, the concept of “Zero PoC” is important. It does not mean we should reject proof-of-concept activities. Rather, it means avoiding projects that end as isolated PoCs, and instead designing implementation from the beginning with real-world operation in mind. Safety, KPIs, human supervision phased expansion and feedback loops must be built into the design from the outset. In the early stages, humans should support areas where AI or robotics cannot yet function effectively. Through continuous feedback from the field, AI systems can gradually be trained and improved.

By embedding Human-in-the-Loop mechanisms, companies can evolve implementation through collaboration between humans and AI, rather than rushing prematurely toward full automation.

Data governance and data sovereignty

Data security infrastructure is also indispensable for the social implementation of Physical AI and industry-specific vertical AI systems. In sectors such as defense, healthcare, finance and critical infrastructure — all closely tied to the foundations of the nation and industry — excessive dependence on foreign hyperscalers and generic AI platforms could create serious economic security vulnerabilities. It is therefore essential to pay close attention to data governance and data sovereignty.

This does not mean Japan should reject global technology platforms. However, for critical data and operations, Japan must retain the capability to manage systems securely within the country and customize them locally at a high level when necessary.

To achieve this, the public and private sectors must work together to develop domestic data and AI infrastructure capable of supporting the full cycle: acquiring on-site data, standardizing it, ensuring security, using the data for AI training and reintegrating them into real-world operations. In particular, tackling the complex and mission-critical challenges faced by enterprise organizations — while supporting domestic companies in developing mission-critical systems — will be directly linked to Japan’s future industrial competitiveness.

Japan’s true winning strategy: redesigning industrial structures for the era of Physical AI

Japan’s winning strategy in the era of Physical AI is not to celebrate frontline excellence as a virtue in itself. It must convert that excellence into data, protocols and industrial infrastructure through which AI systems can learn and improve. If such a foundation can be established, Japan’s high-quality operations may contribute not only to solving domestic challenges such as labor shortages and productivity constraints, but also to strengthening international competitiveness — particularly among countries seeking alternatives to excessive dependence on specific global platforms — through a new form of trusted social infrastructure.

Physical AI is likely to become one of the core technologies shaping the next industrial structure. Whether Japan can lead this era will depend on its ability to transform the excellence cultivated on frontline workplaces into data and systems that AI can effectively utilize. What Japan needs now is not the adoption of individual software tools, but the redesign of its industrial structure itself. That is Japan’s true winning strategy in the era of Physical AI.

The views expressed in this article are the author's and do not reflect those of JIIA CGO.