“This time, we are not competing in skills, but in ideas.” — Movie “The Grandmaster”
Siemens’ Industrial Copilot is quietly changing factories with code generated on-site at the Industrial Expo, and more shocking than the technology is the complete reshaping of the rules of the industrial world.Here, PLC engineers are no longer competing in programming skills, but are undergoing a complete upgrade in their thinking patterns.
At the Shanghai National Exhibition and Convention Center, the most attention-grabbing feature at the Siemens booth was not the new PLC hardware, but the generative industrial AI assistant: Industrial Copilot, which automatically generates PLC code for an engineer through natural language interaction. This scene attracted a large number of automation engineers to stop and watch. The Triple Challenges of Industrial AI Deployment Under the wave of industrial AI, Siemens, in collaboration with the Zhitong Think Tank, released the “2025 Industrial Intelligence Trend Outlook Report,” revealing a harsh reality: although over 80% of enterprises recognize the value of AI in improving production efficiency and reducing operational costs, actual deployment still faces significant challenges. 43% of enterprises have not yet deployed industrial intelligence, and only 8% have achieved multi-scenario applications. This data sparked deep reflection among the engineers present. The three major factors restricting the deployment of industrial intelligence are clear: high deployment costs, lack of professional talent, and insufficient technological maturity. The message conveyed by manufacturers emphasizes that when considering whether to apply industrial intelligence, enterprises generally regard stability and reliability as core considerations. Industrial AI must be able to operate stably in harsh industrial environments, which places extremely high demands on technological maturity. Breaking the Deadlock: Quantifiable Value in China’s First Pilot At the expo, Siemens and Zhongke Motong jointly showcased the new generation of electric vehicle EMB intelligent assembly equipment, which became the focus. This is not only the first pilot application of Siemens’ generative industrial AI assistant Industrial Copilot in the Chinese market but also a model of industrial AI that can prove its value with data. On this intelligent assembly line, AI demonstrated an astonishing improvement in efficiency: programming development time was reduced by 30%, on-site debugging cycles were shortened by 30%, and labor and material waste decreased by 10%. For the many manufacturing representatives present who were observing, these numbers were far more persuasive than any technical jargon. Data provided by the manufacturer shows that with AI-assisted development, the time from design to manufacturing completion was only two months, more than a third shorter than the industry standard development time. This efficiency improvement directly translates into cost advantages, providing a new breakthrough for China’s manufacturing industry facing fierce competition. Transformation of Engineers: From Code Craftsmen to System Architects During the debugging process of the EMB intelligent assembly equipment, Industrial Copilot assisted engineers in automated program development, significantly reducing repetitive work. This indicates a fundamental shift in the role of PLC engineers. Technical materials presented by the manufacturer indicate that PLC code, which previously needed to be written line by line, can now be directly generated by AI for PID control programs, and the accuracy of the programs can be automatically verified. This technological breakthrough is positioned by the manufacturer as “breaking the limitations of traditional algorithms and solving tasks that were previously inefficiently completed manually, such as code generation and process optimization in industrial scenarios.” The future career path of PLC engineers clearly presents four evolutionary directions: AI Collaborators: Engineers who master the use of industrial AI tools and can accurately guide AI to generate high-quality code through natural language. These individuals need to deeply understand the principles and limitations of AI, becoming a bridge for human-machine collaboration. System Architects: Freed from specific programming tasks, focusing on the planning and design of the entire automation system. These engineers need to have a global perspective, integrating various intelligent devices, control systems, and software platforms into an organic whole. Data Value Extractors: Utilizing data analysis tools to extract value from equipment operation data, optimizing production processes, and achieving predictive maintenance. This role requires engineers to master data analysis skills while deeply understanding production processes. Process Experts: Deeply cultivating process knowledge in specific industries, combining industry experience with AI capabilities to solve complex process problems. This is the direction that AI finds most difficult to replace, relying on engineers’ long-term accumulation in specific fields. Technical Foundation: Full-Stack Product Support for AI Deployment The deployment of industrial AI relies on a solid technical foundation. The products showcased by the manufacturer at the expo, under the “Diamond Heart 2.0” product matrix, concentrated on over a hundred core products. These products span the five layers of industrial architecture: field layer, control layer, operation layer, management layer, and cloud layer, forming full-stack solutions that meet manufacturing needs of varying scales and complexities. The information released indicates that the newly launched basic distributed I/O systems and the new series of exciting panels are not isolated products but part of a unified product family, reflecting systemic and interoperability. These new products not only continue the traditional advantages in reliability but have also been locally optimized for the cost sensitivity and usage scenarios of Chinese customers. Ecological Strategy: Localization and Open Platforms At this year’s Industrial Expo, Siemens signed agreements with nearly forty companies on-site, further expanding its cooperative ecosystem and fully realizing localization. The experience conveyed by the manufacturer is “progressing from low to high, promoting interdisciplinary knowledge and awareness of ‘Industry + Data + AI’ among all employees in the company.” Through an open digital business platform, the manufacturer has linked hundreds of thousands of users in China, gathering hundreds of ecological partners, over 60% of which are related to AI, building an open and win-win innovation ecosystem. The core of this ecological strategy is to create localized solutions that meet the needs of China’s manufacturing industry while maintaining technological advancement and reliability. The Future is Here: From “One-Time Correct” to Career Renewal Facing the pressures of manufacturing industry competition and transformation, Siemens proposed the “One-Time Correct” concept: through a combination of hardware and software products covering the entire lifecycle of industrial manufacturing, assisting customers in achieving “one-time correctness” in design, selection, debugging, manufacturing, operation, and low carbon across six dimensions. The essence of this concept is to “transfer the majority of debugging work to a digital twin environment, ensuring a high success rate upon physical delivery.” In the practice of this concept, virtual simulation technology shortens the design cycle by 30%, improves change efficiency by 50%; selection tools save 80% of selection time, reducing procurement costs by 10%; predictive maintenance can provide a 72-hour early warning of faults, reducing unexpected downtime by 40%. The message conveyed by Siemens is: “The true deployment of industrial AI begins with accurately capturing demand scenarios and is achieved by deeply integrating technology, data, and industry mechanisms. This is precisely the advantage of industrial AI: awakening dormant data with deep industry know-how, collaborating with ecological innovation partners, and helping thousands of Chinese industrial enterprises reap the true value and benefits of industrial AI.” At the expo, a young engineer interacted with Industrial Copilot in the Siemens booth, inputting a Chinese command: “Achieve sequential startup of three motors with a 5-second interval, and stop immediately in case of a fault.” Seconds later, a well-structured, clearly annotated SCL code appeared on the screen. He carefully examined it, his expression shifting from skepticism to conviction. This simple interaction scene reflects the fundamental reconstruction of the career path of PLC engineers. The future engineer is no longer one who compares programming skills, but one who competes in the depth of understanding of processes, system architecture capabilities, and mastery of human-machine collaboration. The technical evolution path presented by the manufacturer clearly points in this direction: those engineers who can quickly adapt to changes and actively embrace new roles will gain broader development space in the AI era. As AI takes over repetitive coding tasks, true engineering innovation is just beginning. This is not only a technological advancement but also a redefinition of talent value. The intelligent transformation of China’s manufacturing industry provides PLC engineers with a historic opportunity to transition from “technical craftsmen” to “industrial intelligence experts,” with the key being whether they can timely shift roles and find their new position in the industrial AI ecosystem.