Motor vehicle cleaner/polisher
AI Exposure Rank
3/100
Range 0–5/100 across source-weight sensitivity checks
Motor vehicle cleaner/polisher has an AI Exposure Rank of 3/100, meaning its work is more exposed to current AI capabilities than approximately 3% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.
Cleaners, Labourers & Related Workers·SGD 1,700/mo (1,400–2,100)·~6.5K workers in SG·Updated 2026-06-11
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
Why This Score
7% of tasks overlap with current AI
11% human advantage from judgment & presence
70% demand buffer from the local labour market
AI usage 11pp above theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. Score stability: watch. How this works
Tasks AI can handle
With 7% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Motor vehicle cleaner/polisher tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Rinse objects and place them on drying racks or use cloth, squeegees, or air compressors to dry surfaces.
- • Apply paints, dyes, polishes, reconditioners, waxes, or masking materials to vehicles to preserve, protect, or restore color or condition.
- • Clean and polish vehicle windows.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 11% human bottleneck protection, the tasks that remain hardest to automate for Motor vehicle cleaner/polisher include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: Accountability for others + Non-routine work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), ILO GenAI (2025), Pizzinelli et al. bottleneck model. Full methodology.
Singapore Now
Current labour market conditions and how they affect this role.
Still healthy locally. Hiring remains positive and retrenchment stays low, even if demand is not accelerating.
Vacancy
2.8%
↑ 16.7% YoY
Hiring
2.4%
vs 1.5% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.1%
find work in 12mo· -4.5pp
Production & Transport Operators, Cleaners & Labourers · 2025 Q4
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Motor vehicle cleaner/polisher has some offset potential, but it depends on transition pathways holding up in practice and on workers clearing the main switching frictions.
Published transition support
Related roles you could transition to
Similarity-basedCompare within Cleaners, Labourers & Related Workers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 2% of occupations · V8 AI Exposure Rank· GCE O-Level / Secondary
Raw scores
AIOE -1.514 · θ 0.606 · C-AIOE -1.331
Stability
watch · Optimistic 0% · Pessimistic 9%
Score range (best/worst case)
Exposure sensitivity 4–11% · Rank sensitivity 0–5/100 across source-weight sensitivity checks
Scoring basis
V8 AI Exposure Rank. A relative Singapore occupation index. It ranks AI task exposure; it is not a probability of job loss or a percentage of tasks.
Wage range (SGD/mo)
25th 1,400 · Median 1,700 · 75th 2,100
Evidence & sources
Data matching
direct · SSOC 91220
Real-world AI usage: +11% vs estimated
Data quality
medium evidence · 4 exposure sources · direct mapping
100% weighted task match · 0% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Worker profile & local context
- Vacancy rate is 2.8% and rose by 0.8 points from last quarter.
- Hiring read: recruitment is running above resignation (2.4% vs 1.5%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 recent Singapore-relevant company signals.
Worker profile
Gender mix
44% male / 57% femalePublished Singapore worker composition for the detailed occupation family 91 Cleaners & Related Workers.
Employment structure
Employee-heavy96% employees, 4% employers or self-employed workers.
Work arrangement
Part-time meaningful33% part-time and 67% full-time in 2025.
Age profile
Older-skewing5% aged 15 to 29, 14% aged 30 to 49, and 81% aged 50 or older.
Qualification mix
Non-degree heavyBelow secondary 56%; Secondary 18%.
Where this work is concentrated
Top planning areas
Jurong West, Woodlands, Bedok22% of workers in this occupation group live in these three planning areas.
Residential concentration
Moderately clustered35% live across the top five planning areas in the 2020 Census.
Commute pattern
Mid-range commutesEstimated average commute 32.8 minutes. 27% take 46 minutes or more.
Role profile
How this role's work breaks down across key dimensions. This is a general profile, not an individual measurement.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Career stage can change the task mix and human context. These directional profiles are illustrative, not occupation-level forecasts of hiring or displacement.
Frequently asked questions
Will AI replace Motor vehicle cleaner/polisher?
Motor vehicle cleaner/polisher has an AI Exposure Rank of 3/100, meaning its work is more exposed to current AI capabilities than approximately 3% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss. AI Exposure Rank: 3/100 (Very Low). Median wage: SGD 1,700/month.
What is the AI exposure rank for Motor vehicle cleaner/polisher?
Motor vehicle cleaner/polisher has an AI Exposure Rank of 3/100, rated Very Low. It ranks higher than approximately 3% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Motor vehicle cleaner/polisher?
Motor vehicle cleaner/polisher has modeled transition pathways to related occupations. The strongest adjacent pathway is Hand launderer/presser (non-household), based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Motor vehicle cleaner/polisher salary compare in the live market?
Motor vehicle cleaner/polisher earns a median gross wage of SGD 1,700/month in the live market (25th-75th percentile: SGD 1,400-2,100). This is 62% below median across all 562 scored occupations, and 12% below group median within Cleaners, Labourers & Related Workers occupations.