Physical AI Powering the New Age of Industrial Operations 2025

The process characteristics determine which robotic system to use: –Rule-based robotics continues to deliver unmatched precision and cycle-time performance in structured environments with repetitive tasks and predictable processes. These systems, ubiquitous in automotive body shops and similar settings, remain indispensable for operations where consistency and low variability are paramount. Ongoing advances in programming interfaces and AI-supported coding (such as Siemens Industrial Copilot for generative AI-assisted programmable logic controller [PLC] programming)9 are extending their applicability and easing deployment challenges. –Training-based robotics is rising to prominence in more variable environments. Enabled by advanced reinforcement-learning algorithms and simulations, these robots learn through virtual and real-world experiences. The virtualization of training significantly reduces deployment effort, as robots can be trained and validated in simulated environments before real-world rollout, thereby expanding the scope of economically viable automation. They demonstrate resilience in tasks involving controlled variation – such as flexible parts kitting or adaptive logistics – and are increasingly viable for mid-volume or non- repetitive production where rule-based robotics lacks flexibility. –Context-based robotics, the newest frontier, makes use of robotics foundation models and zero-shot learning to autonomously perceive, reason and act in unfamiliar scenarios. These systems interpret high-level instructions and respond to real-world complexity without prior task-specific training, making them particularly valuable in unpredictable environments with unknown parts or new environments. Robotics foundation models form the cognitive core that enables context-based general-purpose robots – such as humanoids – to flexibly execute diverse tasks across different environments without reprogramming. While the three system types – rule-based, training-based and context-based – form a layered automation strategy, their boundaries often overlap, and a single robot can use a hybrid approach that combines all three. For example, in a collaborative assembly cell, a robot might follow rule-based logic to perform tasks with high precision. Simultaneously, it monitors its environment using perception systems. When deviations from the expected workflow occur – such as a missing part or human intervention – the robot switches to context-based reasoning to interpret the situation and resolve it autonomously, before returning to its rule-based execution. Comparison of traditional and physical AI-enabled robotics FIGURE 2 Capable of handling unpredictable scenarios and unknown parts (e.g. random bin picking, flexible material handling)Requires relatively less engineering effort through training and self-learning (up to 70% less effort)Accelerated deployment via few-shot/zero-shot or imitation learning (up to 50% faster time-to-value)Scales flexibly across diverse tasks, environments and robot typesEnables intuitive control via natural language, gestures or voice commandsFutureVision of the differences today vs. the future Effective in predictable tasks or in controlled scenarios with known partsHigh and complex manual effort for coding and trainingMid/long industrialization time (several months/ weeks for coding and implementation)Field of automation Limited scalability across similar set-ups or use casesHuman can adapt robot through interfaces or by guiding robotImplementation processTime to industrialization ScalabilityHuman-machine interaction Today Economically viableTechnologically feasible Source: BCG, World Economic Forum. Physical AI: Powering the New Age of Industrial Operations 9