Automakers to Deploy Humanoid Robots Across Entire Operations by 2026, Deepening AI Integration

Since the widespread adoption of industrial robots in the 1960s, the automotive manufacturing industry has continuously undergone technological transformation. By 2026, humanoid robots have moved from laboratories into mass production applications, with multiple automakers deploying them on production lines: BMW uses the Figure 03 for component assembly at its Spartanburg plant and is expanding this system to its European facilities; Tesla plans to deploy Optimus for battery transportation; Ford employs humanoid robots for parts sorting; Hyundai has introduced robots to perform inspection tasks on its production lines; Mercedes-Benz’s Berlin plant utilizes Aptronic’s Apollo robot; and Toyota, in collaboration with Agility Robotics, has deployed Digit robots in commercial settings. Automakers’ focus has shifted from assessing the feasibility of humanoid robots to their concrete implementation on production lines and the pace of large-scale deployment. Between 2023 and 2024, the manufacturing cost of humanoid robots dropped by 40%, and it is projected that by 2030, the unit cost will fall below $17,000. Currently, most robots are still limited to operating in isolated areas, and full human-robot collaborative production has yet to be realized. Bridging the gap between “technically achievable” and “scalable for enterprise operations” remains a critical challenge. Artificial intelligence (AI) not only powers robot operations but also penetrates deeper into process management, supply chain optimization, and quality control. AI-driven predictive maintenance, digital twins, and machine vision inspection have significantly enhanced production line efficiency and precision—particularly in quality inspection, where machine vision outperforms human inspectors. In supply chain management, AI helps stabilize inventory levels and provides early warnings of potential supply disruptions. In the short term, AI’s cost-reduction and efficiency-enhancing impact is more pronounced in vehicle after-sales service/maintenance and auto finance segments: maintenance models are shifting from “repair after failure” to “predictive fault detection and proactive intervention,” while auto finance is evolving from one-time sales toward long-term customer relationship management. Leveraging their massive manufacturing scale, mature supply chains, and core technologies accumulated in autonomous driving, automakers are accelerating the integration of humanoid robots and AI. Given the shared technological foundations between autonomous driving and robotics, experience gained in one domain can be readily applied to the other, extending intelligent transformation from production floors across the entire automotive value chain.