AI is deeply penetrating into the core scenarios of industrial automation from “technical concepts”, promoting the industry’s transition from traditional logical control to intelligent autonomous decision-making.
This change is not a simple technological superposition, but a systematic reshaping of the automation architecture, industrial ecology and talent system.
Industry giants represented by Siemens, Schneider, and Zhejiang University Central Control are building new competition barriers through AI strategic positions; while automation engineers are facing the dual challenge of “basic work being replaced and core capabilities need to be reconstructed” – AI will not eliminate engineers, but it will eliminate engineers who cannot use AI.
The core of traditional automation is deterministic logic execution, which relies on preset programs and fixed processes and can only cope with standardized scenarios; while the core of AI-driven automation is uncertain autonomous decision-making, which adapts to complex and ever-changing industrial scenarios through data learning, model reasoning and dynamic optimization, and achieves closed-loop autonomous operation of “perception-analysis-decision-execution”.
In terms of technical architecture, there has been a huge change from “hard wiring + PLC” to “AI + digital twin + cloud-edge collaboration”.
In the transformation of the control layer, PLC has been upgraded from a “logical executor” to an “edge intelligent node”, equipped with a lightweight AI model to achieve millisecond-level real-time parameter adjustment and abnormal warning.
The reconstruction of the platform layer makes the industrial cloud platform the core carrier of AI training and deployment, accumulates industry-specific model libraries, and supports cross-scenario reuse.
Opening up the data layer enables deep integration of OT and IT data, and unified management of time series data, equipment data, and quality data, providing high-quality training materials for AI.
In terms of industrial value, AI has brought efficiency improvements, cost reductions, and model innovations.
In terms of efficiency improvement, AI-driven predictive maintenance can reduce unplanned downtime by 30%-50%; intelligent optimization algorithms can improve overall equipment efficiency (OEE) by 10%-20%.
In terms of cost optimization, generative AI shortens the automated engineering design cycle by 40%-60%, increases code generation efficiency by three times, and significantly reduces labor costs.
Model innovation is reflected in the transformation from “selling hardware” to “selling AI solutions + subscription services”, with industrial software subscription becoming a new growth engine.
In terms of giant AI strategic positions, Siemens uses digital twins + industrial intelligence to build a full value chain AI ecosystem.
Its core product, Industrial Copilot (Industrial AI Assistant), supports natural language generation of PLC code, HMI interface design and fault diagnosis; the AI agent can autonomously perform full-process tasks without manual intervention.
In terms of technical barriers, the world has 1,500+ AI experts, 3,700+ AI patents, 300+ industrial AI models, and deep integration of digital twin technology to achieve virtual debugging and predictive optimization.
In the implementation case, the OEE of the stamping line of BMW Shenyang factory increased to 91.2%; the smelting process optimization system achieved high-quality and low-energy independent production.
Schneider Electric uses software definition + energy efficiency AI to focus on green intelligence and lightweight implementation.
The core product EcoStruxure Automation Expert Copilot (in cooperation with Microsoft) supports natural language generation of industrial code, function block design and intelligent debugging; Modicon M262 PLC integrates energy consumption prediction models to achieve real-time energy efficiency optimization.
The technical feature is deep cultivation of energy efficiency management AI, achieving catalyst life prediction error of less than 3% in refineries; lightweight AI model adapts to small and medium-sized scenarios, lowering the threshold for implementation.
In the implementation case, the Shanghai Putuo factory has full AI coverage, increasing production efficiency by 25% and reducing energy consumption by 18%.
Zhejiang University SUPCON has built a solid foundation for process industry AI with independent controllable + large industrial models.
Its “1+2+N” industrial AI architecture includes a factory operating system (unified data integration), two core large models (time series large model TPT + multi-modal industrial large model), and N industry application scenarios.
The core breakthrough is the self-developed TPT time series large model, which adapts to the characteristics of industrial time series data and has been implemented in more than 100 large industrial installations. DCS has a domestic market share of 40.4% (the first for 14 consecutive years), providing massive industrial data support for AI.
The strategic goal is to become a global leader in industrial AI and use AI to drive the four values of safety, quality, low carbon and efficiency.
AI also has a dual impact on automation engineers.
At the replacement layer, basic, repetitive and regular work is efficiently replaced by AI, such as basic programming, routine debugging, data entry and reporting, etc. Junior engineers, on-site operation and maintenance personnel, and basic programmers face the risk of job reduction.
But at the core level, high-value capabilities that cannot be replaced by AI, such as complex system architecture design, deep integration and innovation of processes, AI model training and tuning, project management and cross-domain collaboration, have become a new moat for engineers.
In the AI era, the core competency formula of automation engineers has been updated to: 40% production line experience + 30% algorithmic thinking + 20% communication technology + 10% design aesthetics.
In terms of engineer survival and advancement path, in the short term (1-2 years), they should embrace AI tools, improve basic efficiency, be proficient in using generative AI tools, and learn basic concepts of time series data analysis and industrial AI.
In the mid-term (3-5 years), it is necessary to deeply cultivate industry processes, master AI core skills, take root in subdivided industries, accumulate in-depth process experience, and systematically learn machine learning and other skills.
In the long term (5 years +), we must transform into a comprehensive expert, lead industry innovation, grow into an “industrial architect” and “AI solution expert”, and promote the deep integration of AI technology and industrial scenarios.
In short, AI’s changes to the automation industry are disruptive and irreversible. The industry is moving from “automation” to “intelligence”, and giants are reshaping the competitive landscape through AI; engineers are turning from “technical executors” to “value creators”, and composite capabilities have become the key to survival.
For the industry, AI does not replace humans, but releases human creativity, liberating engineers from repetitive work and focusing on innovation and solving complex problems. For engineers, anxiety is useless. Only by actively embracing AI, reconstructing the capability system, and deeply cultivating industry value can we gain a firm foothold in the AI era and achieve career advancement.
