Automation index
How much of a Irrigation technician’s job can AI already do?
A controller comes earlier. Watering the block without drowning the field do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Irrigation technician
22%
AI can already do 22% of this job.
#393 / 633
View rankingIrrigation technician
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 |
|---|---|---|---|
Flow, controller and the sector.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Dripper, valve and the leak you cannot see.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Filters, fertigation and the blockage.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Field, pressure and the one that does not reach the end.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The drought, the water turn and not fighting the flow.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
The block watered, and not drowning it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 22% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- irrigation controllers
- drippers
- sectors
- fertigation
- valves
- filters
- field pressure
- water turns
Sources for this job· O*NET 45-2091.00· ESCO/ISCO 6114
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