AI + Digital Twins Reshape Industrial Development: Native Systems Transform Business Personnel into Application Developers

September 28,2025

As industrial digital transformation accelerates, AI (artificial intelligence) and digital twins are becoming the twin engines driving innovation in manufacturing. The integration of these two technologies not only improves efficiency and intelligence across design, production, and operations, but is also reshaping the industrial software development model. With the rise of native low-code and visual development platforms, more and more business personnel are transitioning from "system users" to "application developers," propelling industrial intelligence toward a higher level of independent innovation.

I. The Integration of AI and Digital Twins: The Core Driving Force of Industrial Intelligence
The combination of AI and digital twins provides industrial enterprises with unprecedented insights and predictive capabilities. By building virtual models that mirror real-world equipment, production lines, and even entire factories, digital twins can reflect the physical operating status in real time. AI algorithms analyze historical and real-time data to identify potential issues and provide optimization solutions.
In this process, AI not only serves as an "analyst" but also as a supporting tool for decision-makers. For example, in complex manufacturing scenarios, AI can automatically adjust production parameters based on simulation data from digital twin models to maximize yield and minimize energy consumption. Combined with machine learning and deep learning technologies, the system can continuously optimize itself, enabling adaptive and self-learning production systems.

II. The Rise of Domestic Industrial Software: Enabling More Flexible and Contextualized Development
In the past, industrial software development relied heavily on professional programmers and long-term custom services, resulting in long project cycles, high costs, and difficulty meeting rapidly changing business needs. However, with the rapid development of domestic low-code platforms and visual modeling tools, in-house process engineers, production managers, and even equipment maintenance personnel can independently build industrial applications tailored to their business needs through drag-and-drop interfaces, logical configuration, and AI-assisted modeling.
This "everyone can develop" model empowers enterprises with greater flexibility and innovation. For example, production personnel can quickly create equipment monitoring dashboards, energy consumption analysis reports, or production forecasting models based on digital twin platforms without in-depth programming knowledge. The embedded AI algorithm modules enable the system to automatically generate application logic based on user input, significantly lowering the development barrier.

3. AI Empowerment: From Data Insights to Decision-Driven Operations
AI not only streamlines the development process but also transforms business personnel's accumulated experience into actionable intelligent decision-making logic. In a digital twin system, AI models can predict equipment failures, optimize process paths, and assess production risks by analyzing equipment status, process parameters, and environmental data.
For example, at a large equipment manufacturing company, the operations and maintenance team implemented predictive maintenance for critical equipment through AI model training and digital twin simulation. When AI detects abnormal trends in vibration frequency or temperature, the system automatically issues maintenance recommendations or generates work orders, avoiding unplanned downtime. Furthermore, AI can assist process personnel in simulating production plans and determine optimal operating strategies under different operating conditions, achieving data-driven decision-making.

4. Digital Twins Promote Industrial Innovation Through "Virtual-Real Integration"
Digital twins are not only data visualization tools but also a "testing ground" for industrial system innovation. By creating a virtual simulation environment, companies can test new processes, verify control strategies, and simulate emergency scenarios without disrupting real production. This integrated virtual-real development approach significantly improves the safety and efficiency of industrial innovation.
Powered by AI, digital twin models possess the ability to intelligently iterate. The system can automatically collect field data and update the model status in real time, achieving "learning-as-it-runs." This means that digital twins are no longer static simulation tools, but rather "intelligent hubs" for companies to continuously optimize production and decision-making.

V. Localized Innovation Empowers Industrial Autonomy and Control
Notably, with the continuous maturity of domestic industrial software and AI platforms, Chinese manufacturing companies are rapidly moving away from reliance on external systems and building autonomous, controllable intelligent ecosystems. From PLC control systems to cloud-based analytics platforms, from device management to industrial app development, domestic solutions are gaining market share with superior cost-effectiveness and localized support.
These platforms not only support multiple industrial communication protocols and mainstream device interfaces, but also offer high scalability and modular design, enabling companies to quickly customize them to meet their business needs. The combination of AI and digital twins enables these systems to not only "see" the field but also "understand" and "optimize" it.

VI. Conclusion: Everyone is an "Industrial Application Developer"
The integration of AI and digital twin technology is breaking down traditional barriers to industrial development. Through low-code, visualization, and intelligent recommendation mechanisms, business personnel are no longer just "users" of the system; they become "creators" of innovation. This model not only accelerates the iteration of industrial software but also promotes the intelligent and autonomous manufacturing industry.
In the future, as AI model training costs decrease and edge computing and cloud-native technologies become more widespread, "business experts" will be able to quickly build "industry-savvy systems." The deep integration of AI and digital twins is opening up a new path of intelligent innovation for China's manufacturing industry.

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AI + Digital Twins Reshape Industrial Development: Native Systems Transform Business Personnel into Application Developers

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