Automation index
How much of a Alarm installer’s job can AI already do?
A diagram and an app come earlier. Wiring without false alarms and training the neighbour do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Alarm installer
23%
AI can already do 23% of this job.
#300 / 633
View rankingAlarm 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 |
|---|---|---|---|
Diagram, zones and programming the panel.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Wire, sensors and the detector that trips alone.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
ARC, tests and the connection note.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Train the user and the fob that gets lost.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
The lived-in flat, the brick and not breaking extra.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
No false alarms, and a real call-out.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 23% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- fit alarms
- program panels
- wire sensors
- ARC
- avoid false alarms
- train the user
- detectors
- alarm zones
Sources for this job· O*NET 49-2098.00· ESCO/ISCO 7412
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