Cutters and Trimmers, Hand

Use hand tools or hand-held power tools to cut and trim a variety of manufactured items, such as carpet, fabric, stone, glass, or rubber.

The occupation "Cutters and Trimmers, Hand" faces a moderate automation risk of 56.4%, closely aligning with a base risk of 56.9%. This risk level reflects the significant but not complete potential for machines or computer-controlled processes to replicate many core tasks associated with this role. The primary duties—such as physically cutting, shaping, and trimming materials with hand or power tools—are repetitive and depend more on dexterity and uniformity than on creative judgment. As technology continues to advance, automated machinery can increasingly detect imperfections and perform complex cuts with precision. Thus, the structural characteristics of the role inherently expose it to a medium-high likelihood of automation. The top three most automatable tasks reveal why this occupation's risk rating is elevated. Identifying and discarding defective items leverages machine vision and quality control systems, which are increasingly accurate and cost-efficient. Trimming excess material or loose threads is a repetitive and standardized process, well-suited to robotic arms or automated trimming equipment. Similarly, the core responsibility of cutting, shaping, and trimming diverse materials using various hand and power tools can be performed by programmable robotic machinery, which minimizes variability and increases output. Each of these core tasks is grounded in physical action and visual inspection, areas where automation technologies are rapidly progressing. Despite these vulnerabilities, some work activities remain difficult to automate, contributing to the 43.6% resistant portion of the job. Tasks such as transporting items with carts, routing items for cutouts with handheld tools, and cleaning or polishing finished products require physical mobility, adaptive handling, and situational awareness. Such duties often involve unstructured environments or non-routine actions, which are still challenging for current automation technologies to handle cost-effectively. Furthermore, bottleneck skills like originality—measured at just 2.0% and 1.8% for relevant tasks—are minimally required. The low demand for creativity or novel problem-solving in these roles makes them even more susceptible to automation, as high originality requirements often act as a significant barrier to full mechanization.