AI Automation Index

Will AI Replace Solar Energy Systems Engineers?

Task-level automation exposure for Solar Energy Systems Engineers: 13 O*NET tasks scored against their work activities, plus the simulator.

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Developed & audited by Gini Loh

Automation Exposure

50% weighted task index

13 of 13 tasks scored

Median Salary

$122,930 / year

BLS OEWS, May 2025 — this exact occupation

Employment Level

High

154,070 people hold this job — size, not growth

Projected 2025–35

+3.7% employment change

8,800 openings a year · BLS line item

Pay by state

BLS OEWS publishes 53 state estimates for this occupation. Each is its own survey estimate, not the national median adjusted for local prices.

$76,100 – $162,070

Highest medians

New Mexico $162,070

Alabama $152,550

District of Columbia $151,920

Virginia $148,160

Alaska $142,390

Lowest medians

Kansas $76,100

Arkansas $76,940

Puerto Rico $81,370

Nebraska $88,490

Tennessee $96,640

State figures come from the 17-2199 row in the May 2025 state panel. States where the estimate was suppressed or too small to publish are absent.

What the score is made of

Each task is mapped to its work activities, scored for AI automation potential, and weighted by how important those activities are to the job. These are the components.

Automation potential 50%

Importance-weighted mean of the scored tasks. This is the headline index.

Physical work 0%

Share of weighted tasks in hands-on or machine-operating work activities.

Judgment & influence 63%

Share in analysis, decisions, teaching, directing, advising, or caring for others.

Routine administration 0%

Share in documenting, recording, and administrative activities.

Rewritten by O*NET 0%

Share of weighted tasks O*NET rewrote in a recent update. Reported, not folded into the score.

Consequence of error 32/100 Moderate

O*NET's own rating of how serious an uncorrectable mistake would be here — respondents, not us. Deliberately not in the index: the score measures what AI can do with the task content, and multiplying it by stakes would be our judgment dressed as data.

Constituent Task Mix

13 tasks for this occupation, weighted by work-activity importance. Shares below are of total task weight, not task count.

High exposure (≥ 60% of the task is automatable) 51%

6 tasks

Medium exposure (≥ 35%) 28%

4 tasks

Low exposure (below 35%) 20%

3 tasks

How would you survive?

Move your week away from the high-exposure tasks and toward the ones that need judgment. The simulator recalculates the index as you shift the mix.

Simulate Upskilling →

Constituent Tasks

#16575 · Core · 18% automatable

Conduct engineering site audits to collect structural, electrical, and related site information for use in the design of residential or commercial solar power systems.

physical manipulation — On-site data collection needs physical presence and instruments

Collect data about project sites.

#16571 · Core · 72% automatable

Create plans for solar energy system development, monitoring, and evaluation activities.

text generation — Detailed work plans drafted from specifications

Prepare detailed work plans.

#16574 · Core · 35% automatable

Design or coordinate design of photovoltaic (PV) or solar thermal systems, including system components, for residential and commercial buildings.

decision support — AI assists system design; engineering and site validation human

Design alternative energy systems.

#16568 · Core · 65% automatable

Provide technical direction or support to installation teams during installation, start-up, testing, system commissioning, or performance monitoring.

decision support — Technical guidance needs context-specific human judgment

Provide technical guidance to other personnel.

#16572 · Core · 45% automatable

Create electrical single-line diagrams, panel schedules, or connection diagrams for solar electric systems, using computer-aided design (CAD) software.

text generation — Energy system diagrams need engineering validation

Create graphical representations of energy production systems.

#16569 · Core · 60% automatable

Perform computer simulation of solar photovoltaic (PV) generation system performance or energy production to optimize efficiency.

data analysis — AI builds computational models; engineer validates assumptions

Create models of engineering designs or methods.

#16564 · Core · 63% automatable

Review specifications and recommend engineering or manufacturing changes to achieve solar design objectives.

decision support — Proposes design changes; human validates feasibility

Evaluate plans or specifications to determine technological or environmental implications. Recommend technical design or process changes to improve efficiency, quality, or performance.

#16570 · Core · 65% automatable

Develop design specifications and functional requirements for residential, commercial, or industrial solar energy systems or components.

decision support — Design criteria drafted; human owns decisions

Determine design criteria or specifications.

#16566 · Core · 50% automatable

Develop standard operation procedures and quality or safety standards for solar installation work.

decision support — Operational methods aided; human decides

Determine operational methods.

#16573 · Core · 25% automatable

Create checklists for review or inspection of completed solar installation projects.

visual inspection — Flaw location on physical products needs presence

Inspect finished products to locate flaws.

#16565 · Core · 64% automatable

Perform thermal, stress, or cost reduction analyses for solar systems.

data analysis — Cost-benefit analysis from given inputs; human reviews

Analyze green technology design requirements. Analyze costs and benefits of proposed designs or projects.

#16563 · Supplemental · 30% automatable

Test or evaluate photovoltaic (PV) cells or modules.

physical manipulation — Testing green tech installations requires physical setup

Test green technologies or processes.

#16567 · Supplemental · 35% automatable

Design or develop vacuum tube collector systems for solar applications.

decision support — AI assists system design; engineering and site validation human

Design alternative energy systems.

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Community Sentiment

Do you agree?

Do you agree with the AI automation risk score calculated for this occupation?

Method

How this was scored

1. Tasks. O*NET task statements for this occupation, from a release whose newest record is dated 08/2026.
2. Work activities. Each task maps to 1–5 Detailed Work Activities; each activity belongs to one of 41 Generalized Work Activities that O*NET rates for importance (1–5) in this occupation.
3. Automation potential. Every work activity carries one score for how far current AI can perform it end-to-end. Tasks inherit the mean of their activities.
4. Weighting. Task weight = 0.35 + 0.65 × (importance − 1)/4, scaled by how often the task is performed and by 0.7 for Supplemental tasks. Importance is the task's own O*NET rating where it exists (18,369 of 18,838 tasks); the rest inherit their work activities' importance. The index is the weighted mean.

Check: across the 910 occupations where O*NET also rates how far machine speed sets the pace, this index moves opposite to it (Spearman ρ = -0.49). The more a job is paced by equipment, the less of its task content AI can address — the relationship you would expect if the scoring is working.

Sources · O*NET 31.0 (task records to 08/2026) · BLS OEWS May 2025 · BLS projections 2025-2035 · rubric v2.1 · tasks for O*NET, not a forecast

See the full method →

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