July 6-12, 2025, saw a series of groundbreaking developments in the field of AI in the manufacturing industry. The week was especially highlighted by major investments in industrial AI platforms, the rapid growth of the autonomous mobile robotics market, and the accelerated adoption of AI in the life sciences manufacturing industry. This article details the specific impact and potential applications of these latest developments on the manufacturing industry.
Rush of Large Investments in Industrial AI Field
Foundation EGI’s $23 Million Raising and “Engineered General Intelligence”
On July 10, Foundation EGI, a startup spun off from MIT, announced $23 million in funding to expand its industrial AI platform. The company aims to develop the world’s first “engineered general intelligence” (EGI) platform to turn manufacturing’s biggest bottlenecks into breakthroughs.
The Foundation EGI platform combines a purpose-built large-scale language model with physics-based context and engineering best practices to address the complex nature of engineering from design to manufacturing to documentation. The system has the ability to transform fragmented specifications, siloed “tribal knowledge,” and outdated instructions into concise, structured, auditable human and machine executable workflows.
Of particular note is the company’s philosophy that “we are building an AI that knows when to break the rules, not just automate tasks. The greatest leaps in engineering come not from following best practices, but from challenging assumptions, and Foundation EGI is designed to do just that.
Explosive Growth Projections for the U.S. AI Manufacturing Market
According to a market study released on July 9, the U.S. AI manufacturing market is projected to reach $6.08 billion by 2028, up from $920 million in 2023, representing an impressive CAGR of 46.0%.
A major factor in this growth is the rapid adoption of industrial robots in manufacturing plants. These robots generate large amounts of data, which is used to train machine learning algorithms to improve the robots’ capabilities. Another important factor is the U.S. leadership in the adoption of the Industrial IoT (IIoT).
By market segment, the hardware segment is expected to show significant growth during the forecast period. In particular, rising demand for high-performance processors that can efficiently process complex AI algorithms and rising demand for advanced hardware solutions such as GPUs, MPUs, and FPGAs are driving market expansion.
Dramatic growth of the autonomous mobile robot (AMR) market
To $9.56 billion market by 2030
The autonomous mobile robot (AMR) market is projected to grow from $4.07 billion in 2024 to $9.56 billion in 2030, at a CAGR of 15.10%, according to the latest market research released on July 2.
With Asia Pacific accounting for the largest share of market revenue in 2024, technological innovation is making automation and robotics solutions an attractive means for online grocery retailers to reduce delivery costs while delivering orders to customers more efficiently.
Examples of practical applications of AMR technology
ABB Robotics presented its new Flexley Mover P603 autonomous mobile robot at Automatica 2025 in June. This compact self-propelled cart uses Visual SLAM navigation to transport loads of up to 1,500 kg with an accuracy of ±5 mm for indoor logistics.
Comau has also released the MyMR AMR family of three models, offering a variety of payload capabilities to support a wide range of logistics tasks.
According to the market analysis, the following segments will lead the market in 2024
- Commodity-to-Person Picking Robot Has Largest Market Share
- 100kg-500kg payload capacity is the main market
- Manufacturing is the largest application area
Rapid Acceleration of AI Adoption in Life Science Manufacturing
Overcoming the learning curve to the practical stage
Rockwell Automation’s July 9 State of Smart Manufacturing Report: Life Sciences Edition reveals that 95% of life sciences manufacturers are using or evaluating smart technologies.
Notable findings:
- Quality improvement (53%),operational efficiency (50%), andcybersecurity enhancement (48%) are the primary applications of AI
- Availability of skilled labor (26%) is the biggest barrier to growth in 2025
- AI (48%) andautomation (46%) to support the workforce
- Planned investments in generative and causal AI (36%) anddigital twin simulation (35%)
Of particular importance is the reality that although many manufacturers are collecting large amounts of data, only 46% are using it effectively. As digital tools scale, the ability to translate insights into action is key to smarter and faster decision making.
Innovative Evolution of Predictive Maintenance Systems
Comprehensive Maintenance Strategies with AI
Industry data shows that AI-driven predictive maintenance can reduce traditional maintenance costs by 25 %削減し、計画外停止を約50% according to the latest data from July 2025.
In the Siemens case study, the company reported a $1.4 trillion equivalent reduction in unplanned downtime worldwide by using AI to predict mechanical wear and optimize machine uptime.
Technical breakthroughs in implementation
Modern predictive maintenance systems integrate the following technologies
- Anomaly detection algorithm: Analyzes sensor data such as vibration, temperature, pressure, etc.
- Real-time diagn ostics: Guides technicians through real-time diagnostics
- AI-powered inspection robot: scans for microdefects undetectable by humans
In particular, Gecko Robotics has deployed AI-powered inspection robots to detect micro-defects that exceed human detection capabilities.
Convergence of Digital Twin and Generative AI
Merging the boundaries between virtual reality and reality
Digital twin technology in manufacturing is entering a new phase when combined with AI technology. Real-time virtual replicas of physical assets, driven by IoT and AI data, are enabling engineers to predict performance and simulate changes.
Automotive companies such asBMW andVolvo have adopted AR goggles with GenAI-powered diagnostics to streamline technician onboarding and reduce human error.
Code optimization and inventory management with generative AI
Generative AI is widely employed in manufacturing environments for the following tasks
- Debugging Machine Code: Programmable Logic Controller (PLC) Code Generation and Improvement
- Inventory optimization: processes supply chain data and sales patterns to forecast demand and calculate ideal inventory levels
According to a 2024 TechTarget study, companies using GenAI for inventory planning reduce out-of-stocks by 18 %削減し、余剰在庫を21%
New Developments in Collaborative Robotics and Autonomous Systems
Realization of true collaboration between humans and robots
Advances in AI are enabling collaborative robots (cobots) and autonomous mobile robots (AMRs) to handle more sophisticated tasks.
In a real-world success story, a Queensland bakery implemented AI-powered collaborative robots in packaging and logistics, resulting in a doubling of production capacity as human workers were shifted to more complex tasks.
The Rise of Adaptive and Agentic AI
Adaptive or agentic AI systems modify their response based on real-world inputs such as material changes, temperature fluctuations, and updated production targets.
By integrating agentic AI into its systems, Siemens has been able to reduce downtime by more than 25% and significantly improve energy efficiency. Adaptive AI tools also enable mass customization, which allows the company to manufacture a wide variety of products without stopping production or compromising quality.
Challenges and Solutions: Keys to Successful Implementation
Investment and Infrastructure Challenges
The biggest barrier to AI implementation is cost. From hardware and software to hiring personnel and maintaining the system, it requires a large investment, especially for smaller manufacturers.
Integration issues with legacy systems are also severe, with many manufacturing facilities lacking the proper data infrastructure to support real-time data collection and analysis.
Measures to Address Human Resource Shortages
More than 60% of industrial companies cite the AI talent gap as their biggest concern. As a solution to this problem:
- Upgrading the skills of existing teams
- Use of outside consultants
- Partnerships with specialized providers
In the Mobilunity case study, Mobilunity provided a team of Ukrainian AI/ML developers and data engineers to a European manufacturing company to build a predictive maintenance and inventory optimization system in nine months.
Outlook: Expectations for the second half of 2025
Accelerating Technology Integration
Toward the second half of 2025, the integration of the following technologies is expected to further accelerate
- Low latency real-time processing by fusion of edge AI and cloud AI
- High-speed data transmission through integration with 5G technology
- Solving complex optimization problems in combination with quantum computing
Developments in Emerging Markets
Support for AI adoption in emerging markets is also gaining momentum, including the establishment of a $2 million AI manufacturing award for African manufacturers. This is an effort aimed at democratizing AI technology and improving manufacturing competitiveness worldwide.
Integration with sustainability
AI technology is also making a significant contribution to improving the sustainability of the manufacturing industry. Real-time analysis of energy consumption patterns, minimization of material waste, and precise monitoring of carbon footprints are all possible to simultaneously reduce environmental impact and increase efficiency.
summary
The second week of July 2025 was a memorable period in which multiple significant milestones were achieved in the manufacturing AI sector: the innovative EGI platform of Foundation EGI, the dramatic growth of the AMR market, the rapid acceleration of AI adoption in life science manufacturing, and the evolution of predictive maintenance systems, all represent a fundamental transformation of the manufacturing industry.
Of particular note is the clear transition of AI technology from the pilot phase to the practical phase, with cost savings of 95 %の生命科学製造業者がスマート技術を導入し、予知保全で25%and the reality that the AMR market is growing at 15% per year, showing that the use of AI has become an essential component of maintaining competitiveness.
The key for manufacturers will be how to effectively integrate these technologies into their production processes. The key to success lies in the right investment strategy, human resource development, and a phased approach to implementation, which will undoubtedly intensify the competitive environment across the manufacturing industry as AI technologies further evolve and become more prevalent in the second half of 2025 and beyond.
Source List
- TS2 Space – “Latest Developments in AI (June-July 2025)”
https://ts2.tech/en/latest-developments-in-ai-june-july-2025/ - Automation.com – “AI Adoption Surges in Life Sciences Manufacturing”
https://www.automation.com/en-us/articles/july-2025/ai-adoption-surges -life-sciences-manufacturing - Yahoo Finance – “AI-Powered Manufacturing in the U.S. Set for $6.08 Billion Market by 2028”
https://finance.yahoo.com/news/ai-powered- manufacturing-u-set-123000389.html - SiliconANGLE – “Foundation EGI raises $23M funding round to expand industrial AI platform”
https://siliconangle.com/2025/07/10/foundation-egi- raises-23m-series-expand-industrial-ai-platform/ - Globe Newswire – “Autonomous Mobile Robots (AMRs) Market Outlook: Trends and Forecasts to 2030”
https://www.globenewswire.com/news-release/2025/ 07/02/3108940/0/en/Autonomous-Mobile-Robots-AMRs-Market-Outlook-Trends-and-Forecasts-to-2030 - Mobilunity – “AI Use Cases in Manufacturing: Benefits, Challenges, and Proven Results”
https://mobilunity.com/blog/ai-use-cases-in- manufacturing/ - TS2 Space – “Industrial Robotics & Automation Breakthroughs – June-July 2025”
https://ts2.tech/en/industrial- robotics-automation-breakthroughs-june-july-2025/ - The Robot Report – “Top 10 robotics developments of June 2025”
https://www.therobotreport.com/top-10-robotics-developments-june-2025/ - IIoT World – “The Rise of Industrial AI: Automation Trends to Watch in 2025”
https://www.iiot-world.com/artificial-intelligence-ml/artificial- intelligence/industrial-ai-trends-2025/ - Business Research Insights – “Autonomous Mobile Robots (AMR) Market Size | Growth Forecast”
https://www.businessresearchinsights.com/market- reports/autonomous-mobile-robots-amr-market-125042
