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Will AI replace First-Line Supervisors of Housekeeping and Janitorial Workers?

Directly supervise and coordinate work activities of cleaning personnel in hotels, hospitals, offices, and other establishments. The percentage shown is a task-exposure model, not a prediction of certain job loss.

O*NET 30.3 · 37-1011.00 · modeled exposure, not guaranteed job loss
LOW TASK EXPOSURE
23%
Estimated share of representative tasks exposed to current AI capabilities.
Last updatedAugust 20, 2026
Evidence confidenceMedium
Browse this risk tierLower exposure jobs →
Task automation shareⓘ
23%Automatable
Automatable23%
Augmentable42%
Hard to automate35%
Based on analysis of representative responsibilities and current AI capability maturity.
Category#
Trades & Physical
Field & Physical
Modeled pay midpoint$
$83,300/yr
Directional estimate — verify locally
Time until impact⌁
6–10+ years
Earliest 2032 · widespread 2036+
Task-level replaceability ⓘ
Representative tasks ranked from highest to lowest AI exposure.
◇Supervise in-house services, such as laundries, maintenance and repair, dry cleaning, or valet services.33%
⚙Select the most suitable cleaning materials for different types of linens, furniture, flooring, and surfaces.26%
✦Physical dexterity in unpredictable settings26%
✦Safety responsibility and on-site judgment22%
▥Advise managers, desk clerks, or admitting personnel of rooms ready for occupancy.18%
Impact timeline ⓘ
How AI assistance may move from early support to wider adoption.
2032Next2036+
Light supportAI provides limited help with supervise in-house services, such as laundries, maintenance and repair, dry cleaning, or valet services..
AugmentationTools improve productivity without owning outcomes.
Human-ledCore work stays anchored in physical dexterity in unpredictable settings.
6–10+ yearsdirectional confidence window
HOW AI CHANGES THIS JOBtyping the logic in plain English
01>Start with the real work done by First-Line Supervisors of Housekeeping and Janitorial Workers.
02>AI can help with: Supervise in-house services, such as laundries, maintenance and repair, dry cleaning, or valet services.
03>If a task is repetitive and easy to check, AI pressure goes up.
04>But a person is still needed for: Physical dexterity in unpredictable settings.
05>Likely result: AI mostly acts as a helper while the core job stays human-led.
06>Important: 23% is estimated task exposure—not a 23% chance of losing the job.
What AI can do
Supervise in-house services, such as laundries, maintenance and repair, dry cleaning, or valet services.LOW MATCH
Select the most suitable cleaning materials for different types of linens, furniture, flooring, and surfaces.LOW MATCH
Advise managers, desk clerks, or admitting personnel of rooms ready for occupancy.LOW MATCH
How estimates work →
Your human moat
◆

Physical dexterity in unpredictable settings

HIGH MOAT

Combined with safety responsibility and on-site judgment, this keeps a human in the loop.

Strength9.1
Similar-role comparison
RoleRisk
First-Line Supervisors of Housekeeping and Janitorial Workers23%
Recreational Vehicle Service Technicians23%
Agricultural Inspectors24%
First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers24%
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Why this score?

✓The model evaluates 3 representative core tasks.
✓The core work depends on presence, dexterity, or accountability.
✓Occupation and task language comes from O*NET 30.3.
✓The strongest human advantage is physical dexterity in unpredictable settings.
Research basis

O*NET 30.3 occupation tasks plus the ILO 2025 exposure framework. Scores are directional estimates.

Read ILO research →
Data: O*NET 30.3 · Model framework: ILO 2025O*NET 37-1011.00Share report
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Sources for this report

Official occupation tasks; timeline remains uncertain Scores are directional estimates and sources do not endorse this site’s exact percentage.

O*NET 37-1011.00 occupation profile ↗ILO Generative AI and Jobs 2025 ↗Scoring methodology →

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