Merchandising/Category executive
AI Exposure Rank
93/100
Range 89–96/100 across source-weight sensitivity checks
Merchandising/Category executive has an AI Exposure Rank of 93/100, meaning its work is more exposed to current AI capabilities than approximately 93% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss.
Associate Professionals & Technicians·SGD 3,197/mo (2,468–4,000)·~7.6K 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
Workflow redesign
Current demand context
Weak 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 88% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Merchandising/Category executive tasks most exposed include: technical documentation, standard testing procedures, data logging, routine diagnostics, and equipment monitoring.
- • Make travel arrangements for executives.
- • Prepare invoices, reports, memos, letters, financial statements, and other documents, using word processing, spreadsheet, database, or presentation software.
- • Conduct research, compile data, and prepare papers for consideration and presentation by executives, committees, and boards of directors.
O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).
What AI can't do here
At 43% human bottleneck protection, the tasks that remain hardest to automate for Merchandising/Category executive include: hands-on troubleshooting, interpreting non-standard test results, calibrating instruments, and bridging communication between engineers and operators.
Main insulation channels: Deep preparation + 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 · 22% effective coverage
Human advantage
Coordination and judgment still matter
At 43% human bottleneck protection, the tasks that remain hardest to automate for Merchandising/Category executive include: hands-on troubleshooting, interpreting non-standard test results, calibrating instruments, and bridging communication between engineers and operators.
Hiring and demand
3.4% vacancy rate · 2026 Q1
PMET vacancies increased from Q4 and remained below their year-ago level. Hiring stayed net positive but slowed, while retrenchment incidence remained the highest of the three broad occupation groups. In 2025, this broad occupation group had 2.3% time-related underemployment and 8.7% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -7.7% to -2.1% from 2018 to 2023.
Career transitions
International market agent/representative (e.g. junket operator)
moderate modeled transition; outcomes still depend on skills, wages and openings.
Evidence strength
high confidence
4 exposure sources · sensitivity 89–96/100 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.
Local conditions are under more strain. Vacancies have softened and displacement signals are less forgiving.
Vacancy
3.4%
↓ 2.9% YoY
Hiring
1.2%
vs 0.7% resign
Retrenchment
2.6
per 1,000 · moderate
Re-entry
69.1%
find work in 12mo· +1.4pp
Professionals, Managers, Executives & Technicians · 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
Merchandising/Category executive 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
Exposure-reducingHigher AI exposure, but comparatively credible exposure-reducing moves exist — the strongest scores 71% match. Escape-route quality and labour demand matter alongside exposure.
Compare within Associate Professionals & Technicians
See how this compares to similar occupations
Compare with... →Classification
More exposed than approximately 92% of occupations · V8 AI Exposure Rank· University Degree
Raw scores
AIOE 1.341 · θ 0.667 · C-AIOE 1.097
Stability
watch · Optimistic 39% · Pessimistic 51%
Score range (best/worst case)
Exposure sensitivity 84–90% · Rank sensitivity 89–96/100 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 2,468 · Median 3,197 · 75th 4,000
Evidence & sources
Data matching
direct · SSOC 33225
Real-world AI usage: -2% vs estimated
Data quality
high evidence · 4 exposure sources · direct mapping
100% weighted task match · 22% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Conflicting data signals
Tools & offset factors
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 3.4% and rose by 0.3 points from last quarter.
- Hiring read: recruitment is running above resignation (1.2% vs 0.7%).
- Retrenchment was moderate at 2.6 per 1,000 employees.
- 69.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 2 captured Singapore-relevant company signals through 2025-03-17.
Worker profile
Gender mix
39% male / 61% femalePublished Singapore worker composition for the detailed occupation family 33 Business & Administration Associate Professionals.
Employment structure
Employee-heavy87% employees, 13% employers or self-employed workers.
Work arrangement
Mostly full-time9% part-time and 91% full-time in 2025.
Age profile
Mid-career heavy17% aged 15 to 29, 52% aged 30 to 49, and 31% aged 50 or older.
Qualification mix
Diploma-heavyDegree 37%; Diploma / professional qualification 35%.
Gross wage by sex
Female median 15% lowerPublished June 2024 gross wage medians: male $3,650, female $3,100.
Where this work is concentrated
Top planning areas
Jurong West, Woodlands, Tampines22% 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
Longer commutesEstimated average commute 38.7 minutes. 36% 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 Merchandising/Category executive?
Merchandising/Category executive has an AI Exposure Rank of 93/100, meaning its work is more exposed to current AI capabilities than approximately 93% of Singapore occupations. The evidence currently points to workflow redesign; this is a relative rank, not a probability of job loss. AI Exposure Rank: 93/100 (Very High). Median wage: SGD 3,197/month.
What is the AI exposure rank for Merchandising/Category executive?
Merchandising/Category executive has an AI Exposure Rank of 93/100, rated Very High. It ranks higher than approximately 93% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.
What career transitions are available for Merchandising/Category executive?
Merchandising/Category executive has modeled transition pathways to related occupations. The strongest adjacent pathway is Sports coach, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Merchandising/Category executive salary compare in the live market?
Merchandising/Category executive earns a median gross wage of SGD 3,197/month in the live market (25th-75th percentile: SGD 2,468-4,000). This is 29% below median across all 562 scored occupations, and 23% below group median within Associate Professionals & Technicians occupations.