64
/100

SOC 51-9111

Packaging and Filling Machine Operators and Tenders

High RiskFrey/Osborne: 98.0%

Risk Score

โš ๏ธ

64/100

High Risk

US Employment

๐Ÿ‘ฅ

383,860

Total workers

Median Wage

๐Ÿ’ฐ

$41K

$32K โ€“ $59K

Projected Growth

๐Ÿ“ˆ

+4.5%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

38/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Packaging and Filling Machine Operators and Tenders face a risk score of 64/100 โ€” 20 points above the national average of 44. With only 38/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $41K. 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 64/100, Packaging and Filling Machine Operators and Tenders 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 383,860 workers in this occupation should focus on strengthening skills in troubleshooting complex equipment malfunctions and quality judgment requiring tactile and visual inspection to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Packaging and Filling Machine Operators and Tenders?

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

Automated CNC programming and machine operation

3

Cobots handling repetitive material handling tasks

4

AI quality inspection via computer vision systems

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Troubleshooting complex equipment malfunctions

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

Attach identification labels to finished packaged items, or cut stencils and stencil information on containers, such as lot numbers or shipping destinations.

โ€ข

Sort, grade, weigh, and inspect products, verifying and adjusting product weight or measurement to meet specifications.

โ€ข

Stop or reset machines when malfunctions occur, clear machine jams, and report malfunctions to a supervisor.

โ€ข

Observe machine operations to ensure quality and conformity of filled or packaged products to standards.

โ€ข

Remove finished packaged items from machine and separate rejected items.

๐Ÿ’ป Technology Skills

โ€ข

Electronic mail software

โ€ข

Enterprise resource planning ERP software

โ€ข

Label making software

โ€ข

Spreadsheet software

โ€ข

Office suite software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

Mechanical

โ€ข

Public Safety and Security

โ€ข

Education and Training

โ€ข

English Language

๐Ÿ“Š vs National Average

Median Wage$41K
$-5K

National avg: $46K

Risk Score64/100
+20

National avg: 44/100

GenAI Exposure38/100
0

National avg: 38/100

Projected Growth4.5%
+0.8%

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$68K77%