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.
- Prepare and apply weather-stripping, glazing, caulking, or door sweeps to reduce energy losses.69
- Maintain activity logs, financial transaction logs, or other records of weatherization work performed.69
- Apply insulation materials, such as loose, blanket, board, and foam insulation to attics, crawl spaces, basements, or walls.69
- Explain recommendations, policies, procedures, requirements, or other related information to residents or building owners.69
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.
- Install and seal air ducts, combustion air openings, or ventilation openings to improve heating and cooling efficiency.01
- Apply spackling, compounding, or other materials to repair holes in walls.02
- Make minor repairs using basic hand or power tools and materials, such as glass, lumber, and drywall.03
- Determine amount of air leakage in buildings, using a blower door machine.04
- Prepare and apply weather-stripping, glazing, caulking, or door sweeps to reduce energy losses.05
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Occupations O*NET links to this one. Relatedness reflects shared work, not a claim that these roles are safer.
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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.
