57
/100

SOC 51-4192

Layout Workers, Metal and Plastic

ElevatedFrey/Osborne: 84.0%

Risk Score

โš ๏ธ

57/100

Elevated

US Employment

๐Ÿ‘ฅ

5,610

Total workers

Median Wage

๐Ÿ’ฐ

$62K

$40K โ€“ $93K

Projected Growth

๐Ÿ“ˆ

-5.4%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

22/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Layout Workers, Metal and Plastic face a risk score of 57/100 โ€” 13 points above the national average of 44. With only 22/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $62K ($16K above the national median). 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 57/100, Layout Workers, Metal and Plastic faces moderate automation pressure. While tasks like predictive maintenance reducing manual inspection roles are increasingly handled by AI, the role retains significant human elements. The 5,610 workers in this occupation should focus on strengthening skills in troubleshooting complex equipment malfunctions and coordinating workflow across diverse production teams to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Layout Workers, Metal and Plastic?

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

โš ๏ธ Top Risk Factors

1

Predictive maintenance reducing manual inspection roles

2

Industrial robotics replacing manual assembly tasks

3

AI quality inspection via computer vision systems

4

Automated CNC programming and machine operation

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Coordinating workflow across diverse production teams

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Handling non-standard materials and configurations

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

Mark curves, lines, holes, dimensions, and welding symbols onto workpieces, using scribes, soapstones, punches, and hand drills.

โ€ข

Plan locations and sequences of cutting, drilling, bending, rolling, punching, and welding operations, using compasses, protractors, dividers, and rules.

โ€ข

Fit and align fabricated parts to be welded or assembled.

โ€ข

Locate center lines and verify template positions, using measuring instruments such as gauge blocks, height gauges, and dial indicators.

โ€ข

Plan and develop layouts from blueprints and templates, applying knowledge of trigonometry, design, effects of heat, and properties of metals.

๐Ÿ’ป Technology Skills

โ€ข

Computer aided design CAD software

โ€ข

Procedure management software

โ€ข

Inventory management software

โ€ข

Spreadsheet software

โ€ข

Office suite software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mathematics

โ€ข

Design

โ€ข

Mechanical

โ€ข

Production and Processing

โ€ข

Engineering and Technology

๐Ÿ“Š vs National Average

Median Wage$62K
+$16K

National avg: $46K

Risk Score57/100
+13

National avg: 44/100

GenAI Exposure22/100
-16

National avg: 38/100

Projected Growth-5.4%
-9.1%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K59%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K69%
Fabric and Apparel Patternmakers33$68K78%