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.
- Promote awareness or use of alternative or renewable energy sources.76
- Identify and recommend energy savings strategies to achieve more energy-efficient operation.75
- Analyze, interpret, or create graphical representations of energy data, using engineering software.75
- Prepare energy-related project reports or related documentation.75
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.
- Write or install energy management routines for building automation systems.01
- Inspect or monitor energy systems, including heating, ventilating, and air conditioning (HVAC) or daylighting systems to determine energy use or potential energy savings.02
- Monitor energy related design or construction issues, such as energy engineering, energy management, or sustainable design.03
- Collect data for energy conservation analyses, using jobsite observation, field inspections, or sub-metering.04
- Identify and recommend energy savings strategies to achieve more energy-efficient operation.05
Related occupations
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.
