August 2, 2025 – August 9, 2025｜A comprehensive summary of AI news that works for manufacturing

2025-08-09

The first week of August was a week of simultaneous progress in AI implementation, regulation, and infrastructure development directly related to the manufacturing industry: the critical phase of the EU AI Act came into effect, and “transparency” and “accountability” in the use of generative AI began to be incorporated into business assumptions at once; the shift to smart factories in the workplace (a large-scale project in India); and a multi-agent infrastructure from Japan. In addition, topics directly related to solving specific productivity, quality, and human resource difficulties were lined up, such as the smart factory in the field (a large-scale project in India) and a multi-agent infrastructure originating in Japan. In this blog, we organize the major topics in Japan and abroad from the four perspectives of “implementation,” “regulation,” “technology infrastructure,” and “management,” and propose points of application and next steps in the manufacturing industry. group+4

TOC

1. advance in the implementation phase: accelerate large-scale smart factories and on-site DX

Dixon Technologies, a leading EMS company in India, has appointed Tech Mahindra as its “prime” partner to embark on an AI x Industry 4.0 transformation across 24 plants and 6 research centers in the country. The initial phase is consultation and design, focusing on data silo elimination, legacy renewal, and end-to-end integration, with an eye toward future private 5G and edge computing integration. This approach is an implementation sequence that fits the practical issues common to multi-site manufacturing in Japan, such as “IT/OT linkage barriers,” “data specification differences between plants,” and “mixed equipment generation. rcrwireless

NEC is presenting the forefront of industrial application to labor shortages under the slogan “Thinking with Agentic AI, changing the future. NEC is presenting the forefront of industrial applications to address labor shortages. Siemens also presented a three-pronged strategy to address increasing complexity, including Teamcenter AI integration, and emphasized the direction of embedding AI across PLM/ALM/manufacturing execution. prtimes+3

Practical Implications:.

It is standard practice to stage reforms across multiple plants in the order of “data design (common vocabulary and tags), connection and integration, and AI application.rcrwireless
On-site utilization of No-Code x AI to digitize the area of “paper and oral traditions” in a short period of time and create a data base for quality, safety, and maintenance.prtimes
Establish a “common data model” that overlooks data from PLM/MES/SCADA and maintenance departments, and develop learning materials for AI from an early stage.monoist.itmedia

2. regulation and governance: EU AI Act’s GPAI obligation enters into force, changing the premise of supplier selection

On August 2, the EU AI Act’s obligations regarding General Purpose AI (GPAI) became applicable: according to the EU’s official announcement, GPAI model providers must immediately address “transparency and copyright” related obligations, and in the case of cutting-edge and high impact models (>10^25 FLOPs) additional obligations (notification, safety, Models that have been on the market for two years have until August 2, 2027 to comply, but future adoption of generative AI in the EU will be subject to a “purchasing requirement” to confirm training data summary, rights handling, and security measures. digital-strategy.europa+2

The commentary of legal experts also reaffirms the prohibited acts already in force on February 2 (in principle prohibiting manipulative methods, vulnerable layer exploitation, real-time biometrics in public spaces, etc.), the GPAI mandate on August 2, and the roadmap for full-scale application of high-risk AI over 26 to 27 years. In manufacturing practice, it is inevitable to identify and develop operational governance for “high-risk” applications that could fall under Annex III, such as quality inspection, recruitment and staffing, and supplier evaluation. velaw+1

Practical Implications:.

Standardized items such as “AI Act alignment,” “provision of training data summary,” “copyright considerations,” and “EU Code of Practice compliance (when applicable)” in vendor selection RFPs.latticeflow+1
High-risk applications (quality assessment, personnel assessment, critical infrastructure maintenance, etc.) are assumed to implement risk management, data governance, and model auditing.technologyslegaledge+1
Developed a “Global AI Policy” with EU requirements as a minimum standard for consistent operation at global locations.digital-strategy.europa+1

3. technological infrastructure: multi-agent, industrial network, and security trinity

On August 8, NTT announced the basic technology for autonomous cooperative multi-agent AI capable of executing complex projects. The framework, which enables human-like role-sharing and collaborative decision-making and execution, will directly lead to the automation of “setup knowledge” in manufacturing, such as automatic planning of maintenance plans, optimization of line change setups, and automation of cross-functional meetings between purchasing, quality, and production. The key to implementation is “agent operation with governance” that incorporates business authority, audit trails, and safety constraints .

On the other hand, IT/OT convergence and data integration platforms such as Belden were highlighted as the foundation for the industrial side, emphasizing the importance of converting siloed data (estimated 70%) into a “sea of AI usable data.” Since AI effectiveness depends on the quality and consistency of input data, switch/ industrial network/sensor hierarchy to data model, data quality, and security must be designed in an integrated manner. In addition, new solutions are emerging in terms of data protection and confidentiality management, such as AI-based “deep data risk” visualization. solutionsreview+1

Practical Implications:.

Multi-agent application is from PoC to production from operations where coordination is the essence, such as “process change/setup,” “maintenance planning,” and “unofficial notice – supply and demand adjustment.group
Standardization of data models, tag naming conventions, and master management (equipment/parts/processes) were institutionalized at the same time the network was updated.ainvest
In the area of AI utilization of generation, data risk management that detects “unintentional leakage of confidential data” and “deviation from the authority of long-term storage media” is also being run.solutionsreview

4. management impact: lights and shadows of the data center boom, review of supply chain strategy

In the macro environment, data center construction driven by AI demand boosted sales and stock prices in manufacturing-related sectors such as construction machinery and air conditioning, but the possibility of a rebound from overheating was also pointed out. In medium- to long-term planning, it is wise to avoid excessive reliance on data center-biased demand assumptions, and to develop a multilayered scenario for capital investment payback. On the other hand, South Korea has launched a state-led “self-sustaining AI industry” concept and is aiming for a third pole that can compete with the U.S. and China. Vertical integration of semiconductors, models, cloud, and learning data may lead to supply chain reorganization, requiring a renewed geopolitical risk assessment of procurement and sales channel strategies. bloomberg+1

In addition, the news of Softbank’s acquisition of the Foxconn plant in Ohio, U.S., attracted attention as a way to secure a manufacturing base for the large-scale data center (Stargate) concept. The dynamic management of capacity and lead time is crucial in the value chain of servers, optical components, cooling, power distribution, etc., as it increases the risk of supply constraints and delivery conflicts as well as opportunities for orders from domestic and overseas manufacturers. reuters

Practical Implications:.

Demand scenarios are analyzed with sensitivity on the three axes of “sustained AI special demand,” “rebound,” and “regional dispersion” to ensure elasticity of components and production capacity.bloomberg
Monitored AI infrastructure policies in Korea, the U.S., and the EU, and developed a supplier portfolio that evaluated substitutability of components, IP, and human resources.cnbc
For DC-related (optical/cooling/power distribution/racks/sheet metal) manufacturing, quality and throughput will be raised to the level of quality and throughput through measures to address the shortage of skilled workers (work support AI/cobot/inspection AI).monoist.itmedia+1

AI in quality and maintenance: “Linkage” of inspection, predictive maintenance, and inventory optimization will become mainstream.

The most recent summary review organized that AI visual inspection has realized “accuracy that surpasses that of humans,” “throughput in milliseconds,” and “elimination of bottlenecks,” causing simultaneous improvements in quality and production speed. Predictive maintenance was also expanded from mere failure prediction to automatic optimization of MRO inventory linked to demand forecast (Just-in-Time for MRO), and examples were presented of both utilization rate and inventory cost through digitalization of parts sharing and horizontal stocking. In addition, a wave of “prototype-less” manufacturing is spreading, where a virtual factory is constructed using digital twin x AI to verify performance, failure, and optimization prior to start-up. amiko+1

Practical Implications:.

Appearance inspection is the key to overcoming the “low volume, high variety” barrier. Transferring current rule-based threshold and lighting conditions to AI learning and automatic adjustment. amiko
Predictive maintenance aims to minimize downtime and inventory retention simultaneously by designing KPIs as a set with “purchasing, inventory, and process planning.amiko
The digital twin will start with the initial application of the “new product/new line” and create a V&V mechanism to transfer the verified recipe to the actual facility.amiko

6. human resources and organization: field skills and AI literacy in the age of agents

With the spread of AI, signs point to a shift in role design beyond white- and blue-collar workers to “AI-amplified job functions. In practice, agents and generative AI are assisting with field standard procedures (SOPs) and troubleshooting, and workers with two years of experience are becoming more competitive in the form of veteran-level decision support. At the same time, the EU AI Act is increasingly referring to organizational responsibility for “AI literacy,” and the development of an education system that includes both field and management personnel will become part of governance. axios+4

Practical Implications:.

Introduced “Outcome KPI based on AI support” in job definition to evaluate worker x agent collaborative outcomes.emag.directindustry+1
The educational system includes compulsory training in “On-site AI Literacy,” “Data Protection and Copyright,” and “Limitations and Monitoring of Models.digital-strategy.europa+1
A “decision log” is kept to centrally manage on-site proposals (Kaizen) and AI proposals, ensuring reproducibility and auditability.solutionsreview+1

7. “Next Steps” Based on Recent Trends (for Japanese Manufacturing Industry)

Developed EU-compliant AI procurement criteria: data summary of models, copyright compliance, availability of Code of Practice, and criteria for re-evaluation when updating models as standard in RFP.latticeflow+1
Defined design principles for multi-plant smartization: data model unification, equipment connection standards, edge processing and cloud role sharing, and OT security with a zero-trust philosophy are specified in the basic design.ainvest+1
Three key PoC pillars of multi-agent: (1) automatic planning of setup and changeover, (2) construction planning and personnel allocation for preventive maintenance, and (3) production planning replanning to respond to supply-demand fluctuations.group
Integrated KPI operation of Quality x Maintenance x Inventory: Cross optimization of false positives/false negatives in visual inspection, MTBF/MTTR, and MRO inventory turnover.amiko+1
Enhanced visibility of data risk: continuous monitoring of sensitive data exposure, policy violations, and deviations from long-term storage privileges in the area of generative AI and co-pilot implementation.solutionsreview
Focusing on both overheated demand and reactionary demand: Flexible production capacity of products for data centers through “modularization + external setup” to reduce the risk of idle capacity in the event of reactionary demand.reuters+1

8. postscript: AI from a “point” to a “surface” and then to an “institution

The past week has seen a series of events that symbolize the evolution of AI from “automation of individual processes” to “cross-plant optimization” and further to “corporate systems based on legal regulations. For the Japanese manufacturing industry, the winning strategy is clear. The “simultaneous” development of on-site data, the bridging of IT/OT, and governance and education in tandem. Agents and digital twins can only function optimally on top of this. The first step is to establish data standards and AI procurement criteria across sites. velaw+6

Source List

Artificial Intelligence News for the Week of August 8 – Solutions Reviewsolutionsreview
Bloomberg Opinion: AI Building Boom Is Bound to Bust for Manufacturersbloomberg
CNBC: South Korea charts one-of-a-kind course in AI race with U.S. and Chinacnbc
AInvest: Belden-Industrial AI and Smart Manufacturing analysisainvest
European Commission: EU rules on general-purpose AI models start to applydigital-strategy .europa
RCR Wireless: Dixon Tech picks Tech Mahindra for AI Industry 4.0 at 24 factoriesrcrwireless
Reuters: SoftBank buys Foxconn’s Ohio plant (Stargate data center )reuters
PRTIMES: Visualizing the workplace (AI x No Code, Aug 19) prtimes
NTT News: Multi-Agent AI Technology for complex projects (8/8/2025 )group
ITmedia MONOist: Siemens Collaborates on AI with Three Pillars of Complexity Responsemonoist.itmedia
Amiko Consulting: The Latest Trends in Manufacturing AI (7/27-8/2, Japanese/English) amiko+1
Legal commentary by DLA Piper and others: key points of the next phase and mandates of the EU AI Act technologyslegaledge+1
LatticeFlow: EU Code of Practice (GPAI) Commentary latticeflow

よろしければシェアをお願いします

Copied the URL !

Copied the URL !

August 2, 2025 – August 9, 2025｜A comprehensive summary of AI news that works for manufacturing

1. advance in the implementation phase: accelerate large-scale smart factories and on-site DX

2. regulation and governance: EU AI Act’s GPAI obligation enters into force, changing the premise of supplier selection

3. technological infrastructure: multi-agent, industrial network, and security trinity

4. management impact: lights and shadows of the data center boom, review of supply chain strategy

AI in quality and maintenance: “Linkage” of inspection, predictive maintenance, and inventory optimization will become mainstream.

6. human resources and organization: field skills and AI literacy in the age of agents

7. “Next Steps” Based on Recent Trends (for Japanese Manufacturing Industry)

8. postscript: AI from a “point” to a “surface” and then to an “institution

Source List

お問い合わせ

関連記事