79
/100

SOC 51-4032

Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic

High RiskFrey/Osborne: 94.0%

Risk Score

โš ๏ธ

79/100

High Risk

US Employment

๐Ÿ‘ฅ

5,310

Total workers

Median Wage

๐Ÿ’ฐ

$47K

$35K โ€“ $65K

Projected Growth

๐Ÿ“ˆ

-19.6%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

24/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic face a risk score of 79/100 โ€” 35 points above the national average of 44. With only 24/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $47K ($320 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 79/100, Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic faces significant automation pressure. Key threats include automated cnc programming and machine operation and cobots handling repetitive material handling tasks. The 5,310 Americans in this role should actively develop skills in quality judgment requiring tactile and visual inspection and troubleshooting complex equipment malfunctions to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Drilling and Boring 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

Automated CNC programming and machine operation

2

Cobots handling repetitive material handling tasks

3

AI quality inspection via computer vision systems

4

Predictive maintenance reducing manual inspection roles

5

Industrial robotics replacing manual assembly tasks

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Troubleshooting complex equipment malfunctions

โœ“

Coordinating workflow across diverse production teams

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

Verify conformance of machined work to specifications, using measuring instruments, such as calipers, micrometers, or fixed or telescoping gauges.

โ€ข

Study machining instructions, job orders, or blueprints to determine dimensional or finish specifications, sequences of operations, setups, or tooling requirements.

โ€ข

Move machine controls to lower tools to workpieces and to engage automatic feeds.

โ€ข

Verify that workpiece reference lines are parallel to the axis of table rotation, using dial indicators mounted in spindles.

โ€ข

Establish zero reference points on workpieces, such as at the intersections of two edges or over hole locations.

๐Ÿ’ป Technology Skills

โ€ข

Inventory management software

โ€ข

Industrial control software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Word processing software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mathematics

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

English Language

โ€ข

Engineering and Technology

๐Ÿ“Š vs National Average

Median Wage$47K
+$320

National avg: $46K

Risk Score79/100
+35

National avg: 44/100

GenAI Exposure24/100
-14

National avg: 38/100

Projected Growth-19.6%
-23.3%

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$68K77%