Automation index
How much of a Miner’s job can AI already do?
A blast plan and a log come earlier. Going down, drilling, and coming out alive do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Miner
16%
AI can already do 16% of this job.
#616 / 643
View rankingMiner
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 |
|---|---|---|---|
Logs, blast plans and sensors.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Logged maintenance and mine CMMS.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 22% | +3.1 |
Drill, load and extract.EstimateNo public source provides a precise figure. This is our estimate. | 24% | 10% | +2.4 |
Support, ventilation and water.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Safety, gases and evacuation.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 10% | +1.6 |
Shift, partner and not getting casual.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 16% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Logs, blast plans and sensors
- Logged maintenance and mine CMMS
- Drill, load and extract
- Support, ventilation and water
- Safety, gases and evacuation
- Shift, partner and not getting casual
- minero
- miner
- interior de mina
Sources for this job· O*NET 47-5041.00· ESCO/ISCO 8111
IS WORK OVER?
ML engineer
29%
#64 / 643
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