Laundry and Dry-Cleaning Workers

Operate or tend washing or dry-cleaning machines to wash or dry-clean industrial or household articles, such as cloth garments, suede, leather, furs, blankets, draperies, linens, rugs, and carpets. Includes spotters and dyers of these articles.

The occupation “Laundry and Dry-Cleaning Workers” is projected to have an automation risk of 31.2%, which is close to its base risk estimate of 31.5%. This moderate risk suggests that although some functions in this field are susceptible to automation, a significant portion of the work still demands human intervention. Automation in laundry and dry-cleaning operations is driven largely by advancements in machinery and robotics that can perform repetitive and physically demanding tasks efficiently. However, the presence of complex and variable tasks that require manual dexterity, problem-solving, and sensory observation continues to provide a buffer against full automation. The industry, therefore, finds itself in a transitional phase where technology can complement but not wholly replace the workforce. The top three most automatable tasks for laundry and dry-cleaning workers include: loading articles into washers or machines, starting machines and regulating their processes through valves and levers, and operating or overseeing extractors and driers. These activities are well-suited for automation because they involve consistent, routine actions that can be standardized, requiring minimal adaptive decision-making. Machines can be programmed to handle the loading, sorting, and processing of laundry at scale, and automated controls can maintain the appropriate chemical and thermal conditions for effective cleaning. As a result, businesses in this sector are increasingly able to deploy automated solutions to reduce labor costs and increase throughput in these specific operational areas. Conversely, the most automation-resistant tasks demonstrate why complete replacement of laundry and dry-cleaning workers is unlikely. Workers often need to spread soiled articles carefully, position stained areas accurately over vacuum heads, and immerse items in bleaching baths at precise times—tasks that benefit from nuanced observation and decision-making. Identifying fabric types and original dyes relies heavily on human senses and can involve subjective judgments or testing samples with fire or chemicals, requiring experience and intuition. The bottleneck skills for the occupation, notably originality (scoring 2.0%), further illustrate that creative problem-solving and adaptability—such as noticing unique stains or damage—are critical components of the job. These skills, with currently low automation potential, act as significant barriers to fully automating this occupation, securing a continuing role for human labor in the near future.