70
/100

SOC 51-9023

Mixing and Blending Machine Setters, Operators, and Tenders

High RiskFrey/Osborne: 83.0%

Risk Score

โš ๏ธ

70/100

High Risk

US Employment

๐Ÿ‘ฅ

100,840

Total workers

Median Wage

๐Ÿ’ฐ

$48K

$35K โ€“ $68K

Projected Growth

๐Ÿ“ˆ

-6.8%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

61/100

High exposure

How we calculate these numbers โ†’

๐Ÿ’ก Mixing and Blending Machine Setters, Operators, and Tenders face a risk score of 70/100 โ€” 26 points above the national average of 44. With 61/100 GenAI exposure, this occupation faces significant pressure from AI tools despite weak projected growth. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $48K ($1K 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 70/100, Mixing and Blending Machine Setters, Operators, and Tenders faces significant automation pressure. Key threats include large language model automation of analysis tasks and generative ai producing marketing and creative copy. The 100,840 Americans in this role should actively develop skills in setup and calibration of custom production runs and handling non-standard materials and configurations to remain competitive. Workers who proactively adapt will find new opportunities even as traditional tasks are automated.

Will AI Replace Mixing and Blending 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

Large language model automation of analysis tasks

2

Generative AI producing marketing and creative copy

3

Cobots handling repetitive material handling tasks

4

AI coding assistants reducing developer demand

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Setup and calibration of custom production runs

โœ“

Handling non-standard materials and configurations

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

Weigh or measure materials, ingredients, or products to ensure conformance to requirements.

โ€ข

Read work orders to determine production specifications or information.

โ€ข

Observe production or monitor equipment to ensure safe and efficient operation.

โ€ข

Mix or blend ingredients by starting machines and mixing for specified times.

โ€ข

Stop mixing or blending machines when specified product qualities are obtained and open valves and start pumps to transfer mixtures.

๐Ÿ’ป Technology Skills

โ€ข

Electronic mail software

โ€ข

Enterprise resource planning ERP software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Operating system software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Production and Processing

โ€ข

English Language

โ€ข

Mechanical

โ€ข

Public Safety and Security

โ€ข

Mathematics

๐Ÿ“Š vs National Average

Median Wage$48K
+$1K

National avg: $46K

Risk Score70/100
+26

National avg: 44/100

GenAI Exposure61/100
+23

National avg: 38/100

Projected Growth-6.8%
-10.5%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
Engineers20$106K65%
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K55%
Fabric and Apparel Patternmakers33$68K74%