Are Wire Weaving Loom Setters at Risk Due to AI?

All metal workers and plastic workers not listed separately.

The occupation "Metal Workers and Plastic Workers, All Other" displays an automation risk of 0.0%, according to our analysis. This base risk indicates that the tasks performed within this occupational category are, on balance, highly resistant to full automation by current and near-future technologies. This resistance is likely attributed to the unique, variable, and often custom nature of the tasks involved, which are not easily standardized for automation. While certain repetitive or straightforward tasks can be managed by machines, the collective breadth of duties in this category often requires nuanced judgment, adaptability, and hands-on problem-solving, preventing widespread automation. Despite the low overall automation risk, there are still specific tasks within this occupation that are more amenable to automation than others. The top three most automatable tasks include operating computer-controlled equipment for repetitive processes, conducting routine quality control inspections using measurement instruments, and sorting or processing standardized parts on assembly lines. These tasks tend to involve repetitive motions, standardized procedures, and minimal need for complex decision-making, making them prime candidates for robotic or computer-aided systems. However, within this occupational group, these repetitive tasks constitute only a portion of overall job responsibilities. On the other hand, the most automation-resistant tasks are those that require advanced manual dexterity, creative problem-solving, or highly adaptive skills in uncertain environments. The top three resistant tasks are: fabricating custom or prototype parts according to unique specifications, repairing or modifying specialized machinery and devices, and troubleshooting unusual or unforeseen production issues on-site. The primary bottleneck skills here involve complex manual manipulation (high level), creative and critical thinking (high level), and adaptive learning for new materials or tools (very high level). These skills are difficult to encode in current AI and robotics, requiring continuous human oversight and the integration of tacit knowledge accumulated through experience. This combination of bottleneck skills is the main reason why the occupation maintains a negligible risk of automation.