Siemens is accelerating the integration of industrial artificial intelligence, digital twins, automation, and advanced manufacturing software as manufacturers look for new ways to improve production efficiency and flexibility. At IMTS 2026, Siemens demonstrated technologies designed to connect engineering, machine programming, production, and operational data within a more integrated digital manufacturing environment.

Industrial AI Moves Into the Manufacturing Workflow

Artificial intelligence is increasingly moving beyond experimental applications and into practical manufacturing operations.

For manufacturers, AI can support a wide range of tasks, including production optimization, machine monitoring, quality inspection, predictive maintenance, and engineering assistance.

Siemens is focusing on integrating AI directly into existing industrial workflows rather than treating AI as a separate software layer.

This approach allows engineers and production teams to combine AI capabilities with established automation technologies such as PLCs, CNC systems, industrial PCs, robotics, and machine tools.

Connecting Design and Production

One of the important concepts demonstrated by Siemens is the digital thread connecting product design with manufacturing.

In a traditional manufacturing environment, engineering information, CNC programming, production data, and machine information can exist in separate systems.

A connected digital manufacturing environment can link these stages together.

For example, engineering data can be transferred into manufacturing software, machine programs can be generated more efficiently, and production information can be returned to engineering teams for further optimization.

This creates a continuous information flow from design → engineering → programming → production → quality → optimization.

Digital Twins for Machine and Process Optimization

Digital twin technology is another important component of Siemens’ manufacturing strategy.

A digital twin creates a virtual representation of a machine, production process, or manufacturing environment. Engineers can use this virtual environment to evaluate production strategies before making changes to physical equipment.

For machine builders, digital twins can help with:

• Virtual commissioning
• Machine design
• PLC program testing
• CNC programming
• Production simulation
• Process optimization
• Operator training

Virtual testing can help identify potential problems earlier in the engineering process and reduce the amount of physical testing required during commissioning.

AI-Based Quality Inspection

Siemens is also expanding industrial AI applications for quality control.

Recent deployments with Procter & Gamble demonstrate how AI-based inspection technology can analyze products during production and provide real-time quality information.

The solution has reportedly helped reduce scrap rates by 10% to 20%, depending on the product, while new inspection deployments can be commissioned significantly faster than traditional customized vision systems.

AI-based inspection can be particularly valuable for high-speed manufacturing lines where conventional inspection methods may struggle to examine every product at full production speed.

AI and PLC-Based Automation

Although AI is receiving increasing attention, PLC technology remains fundamental to industrial manufacturing.

PLCs provide deterministic control for machines, conveyors, motors, valves, safety systems, and production equipment.

AI technologies can operate alongside PLC systems by providing higher-level analysis and decision support.

A typical architecture could include:

Sensors → PLC → Industrial Network → Edge Computer → AI Analytics → Production System

In this architecture, the PLC continues to perform real-time control while AI systems analyze operational information and provide additional intelligence.

This combination can allow manufacturers to introduce AI without replacing the core automation infrastructure.

CNC and Advanced Machine Manufacturing

Siemens is also connecting industrial AI with CNC manufacturing and machine-tool applications.

Modern CNC systems increasingly require integration with engineering software, simulation platforms, machine data, and production-management systems.

AI can potentially assist with machining parameter optimization, process monitoring, quality analysis, and engineering workflows.

For machine builders, this creates opportunities to develop more intelligent equipment capable of collecting operational information and adapting to changing production requirements.

Faster Engineering and Commissioning

Another important benefit of digital manufacturing is reducing the time required to develop and commission new production systems.

Traditional commissioning often requires engineers to test machines after physical installation.

Digital engineering allows many parts of the system to be tested before the equipment reaches the factory floor.

PLC programs, machine sequences, robot movements, production processes, and operator interfaces can be evaluated in virtual environments.

This can help reduce commissioning risks and accelerate production startup.

Toward More Flexible Smart Factories

Manufacturing requirements are becoming increasingly dynamic. Companies need to produce more product variants while maintaining quality and controlling production costs.

Highly connected automation systems can provide the flexibility required for this environment.

Combining PLC automation, industrial AI, digital twins, CNC technology, robotics, and manufacturing software can help manufacturers create production systems that are easier to modify and optimize.

Conclusion

Siemens’ latest industrial AI and digital manufacturing initiatives demonstrate how automation is evolving from isolated control systems toward integrated intelligent production environments.

The combination of industrial AI, digital twins, PLCs, CNC systems, edge computing, and connected manufacturing software provides manufacturers with new ways to improve engineering, quality, commissioning, and production efficiency.

As AI becomes increasingly integrated into industrial workflows, the future of smart manufacturing will depend not only on advanced algorithms but also on reliable automation infrastructure capable of providing accurate real-time data and deterministic machine control.