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AI Work Index

Marine superintendent

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AI Exposure Rank

33/100

Low

Range 31–35/100 across source-weight sensitivity checks

Marine superintendent has an AI Exposure Rank of 33/100, meaning its work is more exposed to current AI capabilities than approximately 33% of Singapore occupations. The evidence currently points to limited direct change; this is a relative rank, not a probability of job loss.

Limited direct changeIn demand (SOL 2026)

Professionals·SGD 9,600/mo (6,580–14,426)·~3.0K 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.

Wage 48% above group median Exposure 35pp below group median #167 of 182 in Professionals →
01

How This Rank Is Built

The evidence behind this occupation's AI exposure, with human-work and demand context shown separately. How this works

Tasks AI can handle

With 34% AI task overlap (based on Felten AIOE, Anthropic Economic Index, and Eloundou GPT exposure), the Marine superintendent tasks most exposed include: running standard statistical analyses, generating charts, cleaning data, writing SQL queries, and producing summary reports from structured data.

  • • Resolve customer complaints.
  • • Monitor employees' work schedules and attendance for payroll purposes.
  • • Organize and supervise activities, such as the processing of incoming and outgoing mail.

O*NET tasks for this occupation with the most observed Claude usage (Anthropic task data).

What AI can't do here

At 84% human bottleneck protection, the tasks that remain hardest to automate for Marine superintendent include: framing the right question, identifying data quality issues, interpreting results in business context, communicating insights to non-technical stakeholders, and making judgment calls on methodology.

Main insulation channels: Non-routine work + High-stakes decisions — the work-context dimensions behind this occupation's human bottleneck.

Skills to focus on

Problem FramingStatistical ReasoningStorytelling with DataDomain Contextualization

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.

01

Workplace adoption

Occupation-level adoption unknown

MOM does not publish a directly mappable adoption rate for the listed industries.

02

Task structure

Task evidence available

100% weighted task match · 11% effective coverage

03

Human advantage

Coordination and judgment still matter

At 84% human bottleneck protection, the tasks that remain hardest to automate for Marine superintendent include: framing the right question, identifying data quality issues, interpreting results in business context, communicating insights to non-technical stakeholders, and making judgment calls on methodology.

04

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 0.9% time-related underemployment and 6.9% non-permanent employment. Across the published male and female series, CPI-adjusted median income changed -0.8% to +2.3% from 2018 to 2023.

05

Career transitions

Chief engineer/Second engineer (ship)

moderate modeled transition; outcomes still depend on skills, wages and openings.

06

Evidence strength

medium confidence

3 exposure sources · sensitivity 31–35/100 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.

02

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

Public Administration & Education Services
18%
Financial & Insurance Services
16%
Professional Services
13%

Industry vacancy overlays use the latest published detailed cross-tab, which can lag the main labour monitor.

03

What You Can Do

Frequently asked questions

Will AI replace Marine superintendent?

Marine superintendent has an AI Exposure Rank of 33/100, meaning its work is more exposed to current AI capabilities than approximately 33% 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: 33/100 (Low). Median wage: SGD 9,600/month.

What is the AI exposure rank for Marine superintendent?

Marine superintendent has an AI Exposure Rank of 33/100, rated Low. It ranks higher than approximately 33% of Singapore occupations for exposure to current AI capabilities; it is not a job-loss probability.

What career transitions are available for Marine superintendent?

Marine superintendent has modeled transition pathways to related occupations. The strongest adjacent pathway is Commercial airline pilot, based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.

How does Marine superintendent salary compare in the live market?

Marine superintendent earns a median gross wage of SGD 9,600/month in the live market (25th-75th percentile: SGD 6,580-14,426). This is 113% above median across all 562 scored occupations, and 48% above group median within Professionals occupations.