Somewhat Resilient
Last Update: 8/30/2026
AI Resilience Score for Textile Machine Operator:
47.9%
Median Score
Meaningful human contribution
Measures the parts of the occupation that still require a human touch. This score averages data from up to four AI exposure datasets, focusing on the role’s resilience against automation.
Med
Long-term employer demand
Predicts the health of the job market for this role through 2034. Using Bureau of Labor Statistics data, it balances projected annual job openings (60%) with overall employment growth (40%).
Low
Sustained economic opportunity
Measures future earning potential and career flexibility. This score is a blend of total projected labor income (67%) and the role’s inherent ability to adapt to economic and technological shifts (33%).
High
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
There are a reasonable number of sources for this result, but there is some disagreement between them.
AI Resilience Report forTextile Knitting and Weaving Machine Setters, Operators, and Tenders
$39,530 median salary•1,300 annual openings•SOC Code: 51-6063.00
Textile Knitting and Weaving Machine Setters, Operators, and Tenders are somewhat less resilient to AI impacts than most occupations, according to our analysis of 7 sources.
This career sits in the "Somewhat Resilient" category because AI and smarter machines are genuinely changing a big chunk of the day-to-day work, like catching fabric defects and adjusting yarn tension, but they are not replacing workers entirely. The hands-on skills that keep mills running, such as threading yarn, troubleshooting finicky machines, and spotting problems that cameras miss, still need a real human on the floor.
Learn more about how you can thrive in this position
This role is somewhat resilient
This career sits in the "Somewhat Resilient" category because AI and smarter machines are genuinely changing a big chunk of the day-to-day work, like catching fabric defects and adjusting yarn tension, but they are not replacing workers entirely. The hands-on skills that keep mills running, such as threading yarn, troubleshooting finicky machines, and spotting problems that cameras miss, still need a real human on the floor.
Read full analysisAnalysis of current AI resilience
Textile Machine Operator
Updated quarterly
How can Textile Machine Operator prepare for AI?
Latest AI news for Textile Machine Operator
These articles highlight the impact of AI on careers in textile knitting and weaving. With a 65/100 replacement risk, it's crucial for students to understand how automated systems are changing the industry. For example, AI-driven quality control in computerized knitting machines can increase efficiency but may reduce the need for manual oversight. However, operators can enhance their skills in machine maintenance and troubleshooting, positioning themselves as valuable assets in an evolving landscape. Embracing technology can lead to resilience in this career path.
Will AI Replace Textile Knitting and Weaving Machine Setters ...
www.replacedbai.com • 9/20/2026
Textile Knitting and Weaving Machine Setters, Operators, and Tenders have a high AI replacement risk (65/100). See what AI can automate, what still needs ...
Will AI Replace Textile Knitting and Weaving Machine ...
jobzonerisk.com • 9/20/2026
Computerised knitting machines and automated looms with AI-driven quality control are displacing operators in modern textile facilities. Read more
More Career Info
Career: Textile Knitting and Weaving Machine Setters, Operators, and Tenders
They operate machines to create fabrics by setting them up, monitoring their performance, and fixing any issues to ensure smooth weaving and knitting processes.
Parent Careers
Similar Careers
Employment & Wage Data
Median Wage
$39,530
Jobs (2025)
13,900
Growth (2025-35)
-13.7%
Annual Openings
1,300
Education
High school diploma or equivalent
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2025-2035
Task-Level AI Resilience Scores
AI-generated estimates of task resilience over the next 3 years
1
Repair or replace worn or defective needles and other components, using hand tools.
2
Clean, oil, and lubricate machines, using air hoses, cleaning solutions, rags, oil cans, or grease guns.
3
Install, level, and align machine components such as gears, chains, guides, dies, cutters, or needles to set up machinery for operation.
4
Remove defects in cloth by cutting and pulling out filling.
5
Set up, or set up and operate textile machines that perform textile processing and manufacturing operations such as winding, twisting, knitting, weaving, bonding, or stretching.
6
Thread yarn, thread, and fabric through guides, needles, and rollers of machines for weaving, knitting, or other processing.
7
Study guides, loom patterns, samples, charts, or specification sheets, or confer with supervisors or engineering staff to determine setup requirements.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.
