How much of this occupation’s work can be materially affected by current AI systems.
How likely exposure is to translate into reduced human demand.
Includes provisional estimates for AI adoption pressure and labour-market resilience. How this is measured
Confidence reflects task coverage, mapping and capability-evidence quality, and how much of the score rests on provisional inputs.
What is driving the score?
Occupation scores are built from the task mix—not a single prediction about a job title.
Where AI can do more
Routine, digitized, and highly repeatable tasks face the greatest pressure.
- Evaluate construction project materials for compliance with environmental standards.74
- Investigate or test specific construction project materials to determine compliance to specifications or standards.73
- Present data, maps, or other information at construction-related public hearings or meetings.73
- Prepare project budgets, schedules, or specifications for labor or materials.72
Where people still matter
These tasks score lowest on automation feasibility—physical presence, judgement, accountability and real-world variability all resist end-to-end automation.
- Plan alteration or modification of existing transportation structures to improve safety or function.01
- Supervise the maintenance or repair of transportation systems or system components.02
- Inspect completed transportation projects to ensure safety or compliance with applicable standards or regulations.03
- Design or engineer drainage, erosion, or sedimentation control systems for transportation projects.04
- Analyze environmental impact statements for transportation projects.05
Related occupations
Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
Civil Engineers
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Related work
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Related work
Compare these careers →Adoption and labour-market outlook
Structural factors are kept separate from raw capability so you can see what actually resists automation. Adoption pressure and labour-market resilience are still provisional models—25% of this occupation’s replacement-risk weight rests on them.
O*NET 30.3 occupational data interpreted through the JobsVsAI capability, automation and structural-constraint models.
