Housekeeper (hotels and other establishments)
AI Exposure Rank
35/100
Range 21–44/100 across source-weight sensitivity checks
Housekeeper (hotels and other establishments) has an AI Exposure Rank of 35/100, meaning its work is more exposed to current AI capabilities than approximately 35% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.
Service & Sales Workers·SGD 3,233/mo (2,312–4,606)·~3.3K workers in SG·Updated 2026-07-17
Relative AI exposure, not a prediction of job loss. Hiring, wages and role design depend on many forces this rank does not forecast.
How This Rank Is Built
The source percentiles are combined using the displayed reliability weights, then ranked against all 562 Singapore occupations. This is not a percentage of tasks and not a job-loss probability.
Likely job pathway
Limited direct change
Current demand context
Mixed current demand
Pathway and demand are reported beside the exposure rank. Current demand does not change the rank.
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 35% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and Eloundou GPT exposure), the Housekeeper (hotels and other establishments) tasks most exposed include: reservation management, menu recommendations, order processing, loyalty program tracking, and basic customer query handling via chatbots.
- • Make and confirm reservations.
- • Answer inquiries pertaining to hotel services, guest registration, and travel directions, or make recommendations regarding shopping, dining, or entertainment.
- • Greet, register, and assign rooms to guests of hotels or motels.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 78% human bottleneck protection, the tasks that remain hardest to automate for Housekeeper (hotels and other establishments) include: genuine hospitality and warmth, reading customer moods, handling complaints gracefully, creating memorable experiences, and adapting service to cultural expectations.
Main insulation channels: Accountability for others + Non-routine work — the work-context dimensions behind this occupation's human bottleneck.
Skills to focus on
Brynjolfsson et al. (2023) found customer service agents using AI saw +14% productivity, with the biggest gains among junior workers — AI compressed the experience gap.
Sources: Felten AIOE (2021), Anthropic Economic Index (2026), Eloundou GPT Exposure (Science, 2024), Pizzinelli et al. bottleneck model. Full methodology.
From exposure to employment
What could change the employment outcome?
AI capability is only the starting point. Adoption, task design, human oversight, demand, mobility and evidence quality determine how exposure may resolve in practice.
Workplace adoption
Leading sectors represented
Some listed industries have MOM sector adoption evidence; observed-sector average 57.5%.
Task structure
Task evidence available
100% weighted task match · 12% effective coverage
Human advantage
Coordination and judgment still matter
At 78% human bottleneck protection, the tasks that remain hardest to automate for Housekeeper (hotels and other establishments) include: genuine hospitality and warmth, reading customer moods, handling complaints gracefully, creating memorable experiences, and adapting service to cultural expectations.
Hiring and demand
3.2% vacancy rate · 2026 Q1
Clerical, sales and service vacancies recovered slightly from Q4 but remained well below a year earlier. Hiring turnover slowed, retrenchment incidence stayed low, and six-month re-entry improved. In 2025, this broad occupation group had 4.0% time-related underemployment and 16.0% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -1.9% to -0.1% from 2018 to 2023.
Career transitions
Housekeeping matron
easy modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
3 exposure sources · sensitivity 21–44/100 across source-weight sensitivity checks.
These lenses are reported separately and do not alter AI Exposure Rank. Labour-market figures describe a broad official occupation cluster, not this SSOC occupation alone.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies and re-entry are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
3.2%
↓ 20.0% YoY
Hiring
2.1%
vs 1.4% resign
Retrenchment
0.7
per 1,000 · low
Re-entry
68%
find work in 12mo· -10.5pp
Clerical, Sales & Service Workers · 2026 Q1
Top Industries
Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.
What You Can Do
Housekeeper (hotels and other establishments) 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 Service & Sales Workers
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 35% of occupations · V8 AI Exposure Rank· Polytechnic / ITE Diploma
Raw scores
AIOE -0.570 · θ 0.722 · C-AIOE -0.435
Stability
watch · Optimistic 4% · Pessimistic 8%
Score range (best/worst case)
Exposure sensitivity 21–48% · Rank sensitivity 21–44/100 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 2,312 · Median 3,233 · 75th 4,606
Evidence & sources
Data matching
direct · SSOC 51501
Real-world AI usage: +40% vs estimated
Data quality
medium evidence · 3 exposure sources · direct mapping
100% weighted task match · 12% effective coverage
AI overlap by data source
Weights: aioe 32% · anthropic 35% · eloundou 33%
Conflicting data signals
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
- A meaningful share of the work can likely be reorganized around AI rather than removed outright.
What could slow it down
- Current demand support is thin, so offsets may take longer to show up.
Worker profile & local context
- Vacancy rate is 3.2% and rose by 0.1 points from last quarter.
- Hiring read: recruitment is running above resignation (2.1% vs 1.4%).
- Retrenchment was low at 0.7 per 1,000 employees.
- 68% of retrenched workers re-entered employment within 12 months.
Worker profile
Gender mix
38% male / 62% femalePublished Singapore worker composition for the detailed occupation family 51 Personal Service Workers.
Employment structure
Employee-heavy91% employees, 9% employers or self-employed workers.
Work arrangement
Part-time meaningful24% part-time and 76% full-time in 2025.
Age profile
Older-skewing17% aged 15 to 29, 32% aged 30 to 49, and 51% aged 50 or older.
Qualification mix
Non-degree heavySecondary 26%; Post-secondary 25%.
Gross wage by sex
Female median 13% lowerPublished June 2024 gross wage medians: male $3,500, female $3,056.
Where this work is concentrated
Top planning areas
Woodlands, Tampines, Yishun22% 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 33.8 minutes. 28% 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.
General role profile on a 0–100 scale. These are modelling inputs, not measurements of an individual job or worker.
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 Housekeeper (hotels and other establishments)?
Housekeeper (hotels and other establishments) has an AI Exposure Rank of 35/100, meaning its work is more exposed to current AI capabilities than approximately 35% 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: 35/100 (Low). Median wage: SGD 3,233/month.
What is the AI exposure rank for Housekeeper (hotels and other establishments)?
Housekeeper (hotels and other establishments) has an AI Exposure Rank of 35/100, rated Low. It ranks higher than approximately 35% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Housekeeper (hotels and other establishments)?
Housekeeper (hotels and other establishments) has modeled transition pathways to related occupations. The strongest adjacent pathway is Housekeeping matron, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Housekeeper (hotels and other establishments) salary compare in the live market?
Housekeeper (hotels and other establishments) earns a median gross wage of SGD 3,233/month in the live market (25th-75th percentile: SGD 2,312-4,606). This is 28% below median across all 562 scored occupations, and 8% above group median within Service & Sales Workers occupations.