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.
- Contract or oversee craft work, such as painting or plumbing.71
- Investigate damage, accidents, or delays at construction sites to ensure that proper construction procedures are being followed.71
- Requisition supplies or materials to complete construction projects.71
- Implement training programs on environmentally responsible building topics to update employee skills and knowledge.71
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.
- Inspect or review projects to monitor compliance with environmental regulations.01
- Plan, schedule, or coordinate construction project activities to meet deadlines.02
- Contract or oversee craft work, such as painting or plumbing.03
- Confer with supervisory personnel, owners, contractors, or design professionals to discuss and resolve matters, such as work procedures, complaints, or construction problems.04
- Study job specifications to determine appropriate construction methods.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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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.
