Delivery man using motorised personal mobility aids/devices
AI Exposure Rank
22/100
Range 20–27/100 across source-weight sensitivity checks
Delivery man using motorised personal mobility aids/devices has an AI Exposure Rank of 22/100, meaning its work is more exposed to current AI capabilities than approximately 22% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.
Plant & Machine Operators & Assemblers·SGD 1,985/mo (1,700–2,000)·~5.0K 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 25% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Delivery man using motorised personal mobility aids/devices tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Respond to customer inquiries or complaints.
- • Use computer skills and software to manage Web sites or databases, publish newsletters, or provide webinars.
- • Provide individual support or counseling in general wellness or nutrition.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 39% human bottleneck protection, the tasks that remain hardest to automate for Delivery man using motorised personal mobility aids/devices include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: Non-routine work + High-stakes decisions — 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.
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
Occupation-level adoption unknown
MOM does not publish a directly mappable adoption rate for the listed industries.
Task structure
Task evidence available
100% weighted task match · 14% effective coverage
Human advantage
Coordination and judgment still matter
At 39% human bottleneck protection, the tasks that remain hardest to automate for Delivery man using motorised personal mobility aids/devices include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Hiring and demand
2.3% vacancy rate · 2026 Q1
Production and transport vacancies fell back from the Q4 spike and were slightly below a year earlier. Hiring remained net positive, retrenchments declined, and six-month re-entry improved. In 2025, this broad occupation group had 1.7% time-related underemployment and 10.3% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -4.1% to +5.0% from 2018 to 2023.
Career transitions
Motorcycle delivery man
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
medium confidence
4 exposure sources · sensitivity 20–27/100 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 are softer, yet retrenchment remains low and hiring still exceeds resignations.
Vacancy
2.3%
↓ 4.2% YoY
Hiring
1.7%
vs 1.2% resign
Retrenchment
0.3
per 1,000 · low
Re-entry
76.9%
find work in 12mo· -1.2pp
Production & Transport Operators, Cleaners & Labourers · 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
Delivery man using motorised personal mobility aids/devices still has credible offset paths. Demand persists, adjacent moves look viable, and enough of the work appears reorganizable around AI.
Published transition support
Related roles you could transition to
Similarity-basedCompare within Plant & Machine Operators & Assemblers
Modern roles using this occupation
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 22% of occupations · V8 AI Exposure Rank· GCE O-Level / Secondary
Raw scores
AIOE -0.939 · θ 0.666 · C-AIOE -0.770
Stability
watch · Optimistic 8% · Pessimistic 16%
Score range (best/worst case)
Exposure sensitivity 19–31% · Rank sensitivity 20–27/100 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 1,700 · Median 1,985 · 75th 2,000
Evidence & sources
Data matching
direct · SSOC 83212
Jobs in Demand: prefix match
Real-world AI usage: +24% vs estimated
Data quality
medium evidence · 4 exposure sources · direct mapping
Capped at high · Final rating: medium · capped for conflicting signals
100% weighted task match · 14% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Conflicting data signals
Tools & offset factors
What helps
- Demand still persists through current labour or hiring signals.
- Nearby moves and published transition support look reasonably strong.
Worker profile & local context
- Vacancy rate is 2.3% and fell by 0.5 points from last quarter.
- Hiring read: recruitment is running above resignation (1.7% vs 1.2%).
- Retrenchment was low at 0.3 per 1,000 employees.
- 76.9% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 captured Singapore-relevant company signals through 2025-10-06.
Worker profile
Gender mix
95% male / 5% femalePublished Singapore worker composition for the detailed occupation family 83 Drivers & Mobile Machinery Operators.
Employment structure
More self-employed49% employees, 51% employers or self-employed workers.
Work arrangement
Mostly full-time11% part-time and 89% full-time in 2025.
Age profile
Older-skewing4% aged 15 to 29, 27% aged 30 to 49, and 70% aged 50 or older.
Qualification mix
Non-degree heavyBelow secondary 35%; Secondary 28%.
Where this work is concentrated
Top planning areas
Jurong West, Woodlands, Tampines26% of workers in this occupation group live in these three planning areas.
Residential concentration
More concentrated39% live across the top five planning areas in the 2020 Census.
Commute pattern
Shorter commutesEstimated average commute 21.5 minutes. 14% 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 Delivery man using motorised personal mobility aids/devices?
Delivery man using motorised personal mobility aids/devices has an AI Exposure Rank of 22/100, meaning its work is more exposed to current AI capabilities than approximately 22% 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: 22/100 (Low). Median wage: SGD 1,985/month.
What is the AI exposure rank for Delivery man using motorised personal mobility aids/devices?
Delivery man using motorised personal mobility aids/devices has an AI Exposure Rank of 22/100, rated Low. It ranks higher than approximately 22% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Delivery man using motorised personal mobility aids/devices?
Delivery man using motorised personal mobility aids/devices has modeled transition pathways to related occupations. The strongest adjacent pathway is Motorcycle delivery man, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Delivery man using motorised personal mobility aids/devices salary compare in the live market?
Delivery man using motorised personal mobility aids/devices earns a median gross wage of SGD 1,985/month in the live market (25th-75th percentile: SGD 1,700-2,000). This is 56% below median across all 562 scored occupations, and 26% below group median within Plant & Machine Operators & Assemblers occupations.