Automation index
How much of a Solar installer’s job can AI already do?
A roof study and a quote come earlier. Climbing the roof, anchoring, and not falling do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Solar installer
21%
AI can already do 21% of this job.
#425 / 633
View rankingSolar installer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Roof study, shade and quote.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Permits, reports and orders.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Fit structure, modules and cabling on roofs.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Inverter, commissioning and measurements.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
Heights, anchors and weather.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Client, community and easements.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Roof study, shade and quote
- Permits, reports and orders
- Inverter, commissioning and measurements
- Heights, anchors and weather
- Client, community and easements
- placas solares
- instalador fotovoltaico
- solar installer
Sources for this job· O*NET 47-2231.00· ESCO/ISCO 7411
IS WORK OVER?
ML engineer
29%
#64 / 633
View rankingML engineer
Where the time goes
Task weights are our estimates based on O*NET frequency data, not time shares published by O*NET.
The time saving for each task is estimated separately.
| Task | Share | AI | Points |
|---|---|---|---|
Train models, features and notebooks.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 48% | +10.6 |
Evaluate, document and compare runs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Deploy, monitor and retrain.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Talk to product about what a good error is.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Dirty data, bias and model limits.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 18% | +2.5 |
On-call, incidents and rollback.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
| Total | 100% | 29% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Train models, features and notebooks
- Evaluate, document and compare runs
- Deploy, monitor and retrain
- Talk to product about what a good error is
- Dirty data, bias and model limits
- On-call, incidents and rollback
- machine learning engineer
- ingeniero ml
- mlops
Sources for this job· O*NET 15-1221.00· ESCO/ISCO 2511