55
/100

SOC 51-6011

Laundry and Dry-Cleaning Workers

ElevatedFrey/Osborne: 71.0%

Risk Score

โš ๏ธ

55/100

Elevated

US Employment

๐Ÿ‘ฅ

195,360

Total workers

Median Wage

๐Ÿ’ฐ

$34K

$26K โ€“ $42K

Projected Growth

๐Ÿ“ˆ

+5.4%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

31/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Laundry and Dry-Cleaning Workers face a risk score of 55/100 โ€” 11 points above the national average of 44. With only 31/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $34K. 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 55/100, Laundry and Dry-Cleaning Workers 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 195,360 workers in this occupation should focus on strengthening skills in coordinating workflow across diverse production teams and handling non-standard materials and configurations to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Laundry and Dry-Cleaning Workers?

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“

Coordinating workflow across diverse production teams

โœ“

Handling non-standard materials and configurations

โœ“

Setup and calibration of custom production runs

โœ“

Troubleshooting complex equipment malfunctions

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

Load articles into washers or dry-cleaning machines, or direct other workers to perform loading.

โ€ข

Start washers, dry cleaners, driers, or extractors, and turn valves or levers to regulate machine processes and the volume of soap, detergent, water, bleach, starch, and other additives.

โ€ข

Operate extractors and driers, or direct their operation.

โ€ข

Remove items from washers or dry-cleaning machines, or direct other workers to do so.

โ€ข

Sort and count articles removed from dryers, and fold, wrap, or hang them.

๐Ÿ’ป Technology Skills

โ€ข

Point of sale POS software

โ€ข

Electronic mail software

โ€ข

Spreadsheet software

โ€ข

Office suite software

โ€ข

Operating system software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Customer and Personal Service

โ€ข

Production and Processing

โ€ข

English Language

โ€ข

Public Safety and Security

โ€ข

Mathematics

๐Ÿ“Š vs National Average

Median Wage$34K
$-13K

National avg: $46K

Risk Score55/100
+11

National avg: 44/100

GenAI Exposure31/100
-7

National avg: 38/100

Projected Growth5.4%
+1.7%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K65%
Fabric and Apparel Patternmakers33$68K74%
Supervisors of Production Workers34$71K72%