Automation index
How much of a Mixer-truck driver’s job can AI already do?
A route and a slump come earlier. Arriving fresh and pouring without blocking the pump do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Mixer-truck driver
21%
AI can already do 21% of this job.
#412 / 633
View rankingMixer-truck driver
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 |
|---|---|---|---|
Order, mix and mixing time.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Drive, site and the tight access.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 10% | +2.2 |
Pour, pump and the one who wants more water.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Wash, leftovers and not leaving a hard drum.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The jam, the delay and the concrete that sets.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Arrive fresh, and not adding extra water.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 12% | +1.7 |
| Total | 100% | 21% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- drive mixers
- batch
- pour concrete
- wash the drum
- slump
- concrete pump
- mix order
- site access
Sources for this job· O*NET 53-3032.00· ESCO/ISCO 8332
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