Automation index
How much of a Mountain guide’s job can AI already do?
An avalanche report comes earlier. Taking the group and coming down whole do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Mountain guide
19%
AI can already do 19% of this job.
#498 / 633
View rankingMountain guide
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 |
|---|---|---|---|
Report, route and the kit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Walk, pace and the one who drops.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 8% | +1.8 |
Ground, snow and the yes or no to go on.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Bivouac, the surprise and not improvising anchors.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The client, the ego and not pushing the summit.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Come down whole, and not trusting the report alone.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 19% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- avalanche reports
- mountain routes
- group pace
- bivouac
- anchors
- ground
- summit
- mountain kit
Sources for this job· O*NET 39-7011.00· ESCO/ISCO 5113
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