54
/100

SOC 51-9196

Paper Goods Machine Setters, Operators, and Tenders

ElevatedFrey/Osborne: 67.0%

Risk Score

โš ๏ธ

54/100

Elevated

US Employment

๐Ÿ‘ฅ

96,950

Total workers

Median Wage

๐Ÿ’ฐ

$49K

$37K โ€“ $69K

Projected Growth

๐Ÿ“ˆ

-6.3%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

10/100

Low exposure

How we calculate these numbers โ†’

๐Ÿ’ก Paper Goods Machine Setters, Operators, and Tenders face a risk score of 54/100 โ€” 10 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 $49K ($3K 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 54/100, Paper Goods Machine Setters, Operators, and Tenders 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 96,950 workers in this occupation should focus on strengthening skills in coordinating workflow across diverse production teams and quality judgment requiring tactile and visual inspection to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Paper Goods Machine Setters, Operators, and Tenders?

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

Cobots handling repetitive material handling tasks

3

AI quality inspection via computer vision systems

4

Predictive maintenance reducing manual inspection roles

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Coordinating workflow across diverse production teams

โœ“

Quality judgment requiring tactile and visual inspection

โœ“

Setup and calibration of custom production runs

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

Examine completed work to detect defects and verify conformance to work orders, and adjust machinery as necessary to correct production problems.

โ€ข

Observe operation of various machines to detect and correct machine malfunctions such as improper forming, glue flow, or pasteboard tension.

โ€ข

Start machines and move controls to regulate tension on pressure rolls, to synchronize speed of machine components, and to adjust temperatures of glue or paraffin.

โ€ข

Disassemble machines to maintain, repair, or replace broken or worn parts, using hand or power tools.

โ€ข

Install attachments to machines for gluing, folding, printing, or cutting.

๐Ÿ’ป Technology Skills

โ€ข

Document management software

โ€ข

Graphics or photo imaging software

โ€ข

Desktop publishing software

โ€ข

Spreadsheet software

โ€ข

Office suite software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

Mathematics

โ€ข

Customer and Personal Service

โ€ข

English Language

๐Ÿ“Š vs National Average

Median Wage$49K
+$3K

National avg: $46K

Risk Score54/100
+10

National avg: 44/100

GenAI Exposure10/100
-28

National avg: 38/100

Projected Growth-6.3%
-10.0%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

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