Share and intensity of work current AI systems can materially affect.
SOC 51-6011
Laundry and Dry-Cleaning Workers AI displacement risk
Laundry and dry-cleaning workers sort, wash, dry, and fold articles in commercial and industrial laundries. Tunnel washers and automated folding systems now move most of the volume in large plants, with workers feeding machines, catching misfolds, and handling the delicate items automation cannot.
Likely potential for exposed tasks to move to software after workflow integration.
This is honest moderate-to-high pressure: industrial plants automate the throughput, concentrating human work at the feed and finish ends. Healthcare and hospitality linen demand keeps the sector large even as headcount per plant falls.
Distribution
Where Laundry and Dry-Cleaning Workers sits across 620 tracked roles
Displacement pressure 44 — higher than 73% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-15. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 51-6011. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $34,890 (May 2025, US national). The latest BLS row matched SOC 51-6011.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Laundry and Dry-Cleaning Workers
SOC 51-6011 places this role in the paper's all-other occupation group. These group-level outcomes do not change the 44/100 role score and are not an occupation forecast.
+1.1% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +1.6% GDP and 3.9% unemployment.
+5.9% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +8.3% GDP and 4.6% unemployment.
+33.6% group wage
Employment rises and unemployment falls for the all-other group, but the paper does not publish a separate group rate.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Laundry and Dry-Cleaning Workers
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC51-6011
- Core task / ID 12125
Operate extractors and driers, or direct their operation.
- Core task / ID 12124
Remove items from washers or dry-cleaning machines, or direct other workers to do so.
- Core task / ID 12121
Load articles into washers or dry-cleaning machines, or direct other workers to perform loading.
- Core task / ID 12119
Sort and count articles removed from dryers, and fold, wrap, or hang them.
- Core task / ID 12118
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.
- Core task / ID 12120
Examine and sort into lots articles to be cleaned, according to color, fabric, dirt content, and cleaning technique required.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Load articles into washers and dry-cleaning machines
Exposure 34, automation 19%, augmentation 30%.
O*NET evidence: Load articles into washers or dry-cleaning machines, or direct other workers to perform... (ID 12121)
Operate washers, driers, and extractors
Exposure 40, automation 22%, augmentation 34%.
O*NET evidence: Start washers, dry cleaners, driers, or extractors, and turn valves or levers to regula... (ID 12118)
Sort and count articles, folding and wrapping them
Exposure 42, automation 24%, augmentation 32%.
O*NET evidence: Sort and count articles removed from dryers, and fold, wrap, or hang them. (ID 12119)
Examine and sort articles by fabric and technique
Exposure 36, automation 19%, augmentation 38%.
O*NET evidence: Examine and sort into lots articles to be cleaned, according to color, fabric, dirt con... (ID 12120)
TaskExposureAutomationAugmentation
Load articles into washers and dry-cleaning machines3419%30%
Operate washers, driers, and extractors4022%34%
Sort and count articles, folding and wrapping them4224%32%
Examine and sort articles by fabric and technique3619%38%
Transition pathways
Adjacent moves that preserve existing skills
role redesign
ModerateLaundry Plant Supervisor
Training horizon: 6-12 months. Skill overlap 64. Wage preservation signal 122.
- Lead production crews
- Own machine uptime
- Manage linen inventory
role redesign
ModerateDry-Cleaning Specialist
Training horizon: 3-6 months. Skill overlap 58. Wage preservation signal 108.
- Master delicate-fabric care
- Learn spotting chemistry
- Serve retail customers
Comparison guides
Compare the next move before you commit
6-12 months
Laundry and Dry-Cleaning Workers to Laundry Plant Supervisor
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Laundry and Dry-Cleaning Workers into Laundry Plant Supervisor.
3-6 months
Laundry and Dry-Cleaning Workers to Dry-Cleaning Specialist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Laundry and Dry-Cleaning Workers into Dry-Cleaning Specialist.
What the AI risk score means for Laundry and Dry-Cleaning Workers
The displacement pressure score for Laundry and Dry-Cleaning Workers is 44. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Sort and count articles, folding and wrapping them carries 24% automation pressure, while Examine and sort articles by fabric and technique carries 38% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $34,890 (May 2025, US national). Employment context: Industrial laundry facing tunnel-washer automation. Typical education: No formal credential required; on-the-job training.
Wage vulnerability is 72, while transition feasibility is 56. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Tunnel systems automate throughput
- Feed and finish ends stay human
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Laundry and Dry-Cleaning Workers, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Machine operation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Textile handling
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Stain treatment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Production speed
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Laundry Plant Supervisor, such as lead production crews.
- By 90 days, compare internal openings and external postings for Laundry Plant Supervisor or Dry-Cleaning Specialist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Laundry and Dry-Cleaning Workers
Will AI replace Laundry and Dry-Cleaning Workers?
Laundry and dry-cleaning workers sort, wash, dry, and fold articles in commercial and industrial laundries. Tunnel washers and automated folding systems now move most of the volume in large plants, with workers feeding machines, catching misfolds, and handling the delicate items automation cannot. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Laundry and Dry-Cleaning Workers work are most exposed to AI?
Sort and count articles, folding and wrapping them and Operate washers, driers, and extractors show the strongest automation pressure in this model. Examine and sort articles by fabric and technique and Operate washers, driers, and extractors are better treated as AI-augmented work.
What should Laundry and Dry-Cleaning Workers learn next?
Start with Machine operation, Textile handling, Stain treatment. The most practical adjacent paths in this model are Laundry Plant Supervisor and Dry-Cleaning Specialist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
OECD / 11 July 2023OECD Employment Outlook 2023: Artificial Intelligence and the Labour MarketMcKinsey Global Institute / 26 July 2023Generative AI and the Future of Work in AmericaO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics