69
/100

SOC 51-4033

Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic

High RiskFrey/Osborne: 95.0%

Risk Score

โš ๏ธ

69/100

High Risk

US Employment

๐Ÿ‘ฅ

70,110

Total workers

Median Wage

๐Ÿ’ฐ

$45K

$35K โ€“ $62K

Projected Growth

๐Ÿ“ˆ

-12%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

10/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic face a risk score of 69/100 โ€” 25 points above the national average of 44. With only 10/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $45K. The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 69/100, Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic faces moderate automation pressure. While tasks like industrial robotics replacing manual assembly tasks are increasingly handled by AI, the role retains significant human elements. The 70,110 workers in this occupation should focus on strengthening skills in coordinating workflow across diverse production teams and troubleshooting complex equipment malfunctions to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Industrial robotics replacing manual assembly tasks

2

AI quality inspection via computer vision systems

3

Cobots handling repetitive material handling tasks

4

Smart factory scheduling and production optimization

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Coordinating workflow across diverse production teams

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Quality judgment requiring tactile and visual inspection

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Inspect or measure finished workpieces to determine conformance to specifications, using measuring instruments, such as gauges or micrometers.

โ€ข

Measure workpieces and lay out work, using precision measuring devices.

โ€ข

Observe machine operations to detect any problems, making necessary adjustments to correct problems.

โ€ข

Move machine controls to index workpieces, and to adjust machines for pre-selected operational settings.

โ€ข

Study blueprints, work orders, or machining instructions to determine product specifications, tool requirements, and operational sequences.

๐Ÿ’ป Technology Skills

โ€ข

Computer aided design CAD software

โ€ข

Inventory management software

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mathematics

โ€ข

Administration and Management

โ€ข

Mechanical

โ€ข

English Language

๐Ÿ“Š vs National Average

Median Wage$45K
$-1K

National avg: $46K

Risk Score69/100
+25

National avg: 44/100

GenAI Exposure10/100
-28

National avg: 38/100

Projected Growth-12.0%
-15.7%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K63%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K53%
Fabric and Apparel Patternmakers33$68K72%