Housekeeper (hotels and other establishments)
AI displacement risk
5%
Range 2.74–7.75%
Housekeeper (hotels and other establishments) has 35% AI task overlap but 78% human bottleneck protection — lower risk than 75% of occupations in the live market. AI is more likely to enhance this role than replace it.
Service & Sales Workers·SGD 3,233/mo (2,312–4,606)·~3.3K workers in SG·Updated 2026-06-11
Structural pressure, not a prediction of job loss. Displacement tends to arrive through slower hiring, wage compression and role redesign before layoffs.
Why This Score
35% of tasks overlap with current AI
78% human advantage from judgment & presence
61% demand buffer from the local labour market
AI usage 40pp above theoretical exposure
These factors interact with each other — the final score is not a simple sum of these bars.
How much AI overlaps with this job's tasks, offset by human advantages and local demand. 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 AI 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.
Singapore Now
Current labour market conditions and how they affect this role.
Cooling, but not collapsing. Vacancies are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
3.1%
↓ 11.4% YoY
Hiring
2.6%
vs 1.6% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.5%
find work in 12mo· -1.6pp
Clerical, Sales & Service Workers · 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
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
Higher risk than 25% of occupations · V7 structural score
Raw scores
AIOE -0.570 · θ 0.722 · C-AIOE -0.435
Stability
watch · Optimistic 4% · Pessimistic 8%
Score range (best/worst case)
Exposure 21–48% · Net risk 2.74–7.75%
Scoring basis
V7 structural score. Uses task-concentration-weighted exposure, human bottleneck, and demand resilience. V6 baseline fields are retained for release-to-release comparison.-0.4pp versus retained V6 baseline.
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
69% · Matching 0.88 · Market data 0.70 · Freshness 0.80
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 3.1% and fell by 0.2 points from last quarter.
- Hiring read: recruitment is running above resignation (2.6% vs 1.6%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.5% 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.
Workflow dimensions (0 = low, 1 = high)
How this changes by career stage
Senior workers benefit from institutional knowledge and judgment that AI cannot replicate. Entry-level roles have higher task overlap with AI.
Frequently asked questions
Will AI replace Housekeeper (hotels and other establishments)?
Housekeeper (hotels and other establishments) has 35% AI task overlap but 78% human bottleneck protection — lower risk than 75% of occupations in the live market. AI is more likely to enhance this role than replace it. Net displacement risk: 5% (Low). Median wage: SGD 3,233/month.
What is the AI risk score for Housekeeper (hotels and other establishments)?
Housekeeper (hotels and other establishments) has an AI displacement risk of 5%, rated Low. AI task overlap: 35%. Human advantage: 78%. Local demand buffer: 61%.
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.