Excavating/Trench digging machine operator
AI displacement risk
3%
Range 1.74–3.56%
Excavating/Trench digging machine operator has 9% AI task overlap and 49% human bottleneck protection — lower risk than 88% of occupations in the live market. Current AI capabilities have limited overlap with core tasks.
Plant & Machine Operators & Assemblers·SGD 3,098/mo (2,000–4,998)·~6.2K 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
9% of tasks overlap with current AI
49% human advantage from judgment & presence
78% demand buffer from the local labour market
AI usage 5pp 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 9% AI task overlap (based on Felten AIOE, Anthropic Economic Index, Eloundou GPT exposure, and ILO occupational exposure), the Excavating/Trench digging machine operator tasks most exposed include: predictive maintenance scheduling, safety checklist automation, inventory management, and remote monitoring via sensors.
- • Move levers, depress foot pedals, and turn dials to operate power machinery, such as power shovels, stripping shovels, scraper loaders, or backhoes.
- • Set up or inspect equipment prior to operation.
- • Become familiar with digging plans, machine capabilities and limitations, and efficient and safe digging procedures in a given application.
O*NET tasks for this occupation with the most observed AI usage (Anthropic task data).
What AI can't do here
At 49% human bottleneck protection, the tasks that remain hardest to automate for Excavating/Trench digging machine operator include: physical dexterity on job sites, real-time environmental adaptation, operating heavy equipment safely, and handling unexpected on-site conditions.
Main insulation channels: High-stakes decisions + Accountability for others — 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.
Singapore Now
Current labour market conditions and how they affect this role.
Still healthy locally. Hiring remains positive and retrenchment stays low, even if demand is not accelerating.
Vacancy
2.8%
↑ 16.7% YoY
Hiring
2.4%
vs 1.5% resign
Retrenchment
1.5
per 1,000 · low
Re-entry
78.1%
find work in 12mo· -4.5pp
Production & Transport Operators, Cleaners & Labourers · 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
Excavating/Trench digging machine operator 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 Plant & Machine Operators & Assemblers
See how this compares to similar occupations
Compare with... →Classification
Higher risk than 12% of occupations · V7 structural score
Raw scores
AIOE -1.024 · θ 0.673 · C-AIOE -0.831
Stability
watch · Optimistic 0% · Pessimistic 6%
Score range (best/worst case)
Exposure 7–12% · Net risk 1.74–3.56%
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 2,000 · Median 3,098 · 75th 4,998
Evidence & sources
Data matching
direct · SSOC 83421
Real-world AI usage: +5% vs estimated
Data quality
85% · Matching 0.88 · Market data 0.70 · Freshness 0.84
100% weighted task match · 0% effective coverage
AI overlap by data source
Weights: aioe 24% · anthropic 26% · eloundou 25% · ilo 26%
Tools & offset factors
What helps
- Nearby moves and published transition support look reasonably strong.
Worker profile & local context
- Vacancy rate is 2.8% and rose by 0.8 points from last quarter.
- Hiring read: recruitment is running above resignation (2.4% vs 1.5%).
- Retrenchment was low at 1.5 per 1,000 employees.
- 78.1% of retrenched workers re-entered employment within 12 months.
- Employer pressure is low, based on 1 recent Singapore-relevant company signals.
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.
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 Excavating/Trench digging machine operator?
Excavating/Trench digging machine operator has 9% AI task overlap and 49% human bottleneck protection — lower risk than 88% of occupations in the live market. Current AI capabilities have limited overlap with core tasks. Net displacement risk: 3% (Very Low). Median wage: SGD 3,098/month.
What is the AI risk score for Excavating/Trench digging machine operator?
Excavating/Trench digging machine operator has an AI displacement risk of 3%, rated Very Low. AI task overlap: 9%. Human advantage: 49%. Local demand buffer: 78%.
What career transitions are available for Excavating/Trench digging machine operator?
Excavating/Trench digging machine operator has modeled transition pathways to related occupations. The strongest adjacent pathway is Crane/Hoist operator (excluding port), based on skill and wage similarity (model-estimated). Transition scoring accounts for wage preservation, training ease, and destination quality.
How does Excavating/Trench digging machine operator salary compare in the live market?
Excavating/Trench digging machine operator earns a median gross wage of SGD 3,098/month in the live market (25th-75th percentile: SGD 2,000-4,998). This is 31% below median across all 562 scored occupations, and 16% above group median within Plant & Machine Operators & Assemblers occupations.