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AI Automation Index
Will AI Replace Wind Energy Engineers?
Task-level automation exposure for Wind Energy Engineers: 16 O*NET tasks scored against their work activities, plus the simulator.
E-E-A-T Verified
Developed & audited by Gini Loh Automation Exposure
50%
weighted task index
16 of 16 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 55%
Share in analysis, decisions, teaching, directing, advising, or caring for others.
Routine administration 5%
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 8/100
Low
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
16 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) 36%
6 tasks
Medium exposure (≥ 35%) 42%
6 tasks
Low exposure (below 35%) 22%
4 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 →Emerging & rewritten tasks
Tasks O*NET added or rewrote for this occupation in its recent updates (05/2025 – 08/2026), with who proposed them. Additions are not in the scored task inventory — they are new work O*NET is still measuring, so they appear here and nowhere in the index. A Revision replaces a task that is scored, which is why only those move the "Rewritten by O*NET" share.
- New 07/2012 · Incumbent
Analyze meteorological data.
- New 07/2012 · Incumbent
Design electrical interconnections.
- New 07/2012 · Incumbent
Design wind turbine components.
- New 07/2012 · Incumbent
Estimate energy production by analyzing wind data.
Constituent Tasks
#16559 · Core · 45% automatable
Create or maintain wind farm layouts, schematics, or other visual documentation for wind farms.
text generation — Energy system diagrams need engineering validation
Create graphical representations of energy production systems.
#16549 · Core · 68% automatable
Recommend process or infrastructure changes to improve wind turbine performance, reduce operational costs, or comply with regulations.
decision support — Proposes design changes; human validates feasibility
Recommend technical design or process changes to improve efficiency, quality, or performance.
#16560 · Core · 45% automatable
Create models to optimize the layout of wind farm access roads, crane pads, crane paths, collection systems, substations, switchyards, or transmission lines.
text generation — Energy system diagrams need engineering validation
Create graphical representations of energy production systems.
#16553 · Supplemental · 65% automatable
Provide engineering technical support to designers of prototype wind turbines.
decision support — Technical guidance needs context-specific human judgment
Provide technical guidance to other personnel.
#16550 · Supplemental · 45% automatable
Investigate experimental wind turbines or wind turbine technologies for properties such as aerodynamics, production, noise, and load.
information retrieval — Green tech literature aided; engineering validation human
Research design or application of green technologies.
#16558 · Supplemental · 45% automatable
Develop active control algorithms, electronics, software, electromechanical, or electrohydraulic systems for wind turbines.
decision support — AI assists energy system design; engineer accountable
Design energy production or management equipment or systems.
#16557 · Supplemental · 65% automatable
Develop specifications for wind technology components, such as gearboxes, blades, generators, frequency converters, or pad transformers.
decision support — Design criteria drafted; human owns decisions
Determine design criteria or specifications.
#16552 · Supplemental · 30% automatable
Test wind turbine components, using mechanical or electronic testing equipment.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#16548 · Supplemental · 28% automatable
Oversee the work activities of wind farm consultants or subcontractors.
decision support — Energy operations direction needs real-time human control
Direct energy production or management activities.
#16551 · Supplemental · 30% automatable
Test wind turbine equipment to determine effects of stress or fatigue.
physical manipulation — Testing green tech installations requires physical setup
Test green technologies or processes.
#16555 · Supplemental · 55% automatable
Monitor wind farm construction to ensure compliance with regulatory standards or environmental requirements.
decision support — AI flags compliance deviations; human confirms in the world
Monitor processes for compliance with standards.
#16556 · Supplemental · 28% automatable
Direct balance of plant (BOP) construction, generator installation, testing, commissioning, or supervisory control and data acquisition (SCADA) to ensure compliance with specifications.
decision support — Energy operations direction needs real-time human control
Direct energy production or management activities.
#16562 · Supplemental · 65% automatable
Analyze operation of wind farms or wind farm components to determine reliability, performance, and compliance with specifications.
information retrieval — Green tech characteristics from literature, human judges
Evaluate the characteristics of green technologies.
#16554 · Supplemental · 68% automatable
Perform root cause analysis on wind turbine tower component failures.
data analysis — Quantitative failure analysis; domain judgment needed
Conduct quantitative failure analyses of operational data.
#16561 · Supplemental · 45% automatable
Design underground or overhead wind farm collector systems.
decision support — AI assists energy system design; engineer accountable
Design energy production or management equipment or systems.
#16547 · Supplemental · 82% automatable
Write reports to document wind farm collector system test results.
text generation — Document test results from provided data
Document design or operational test results.
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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
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