Automation index
How much of a Fleet dispatcher’s job can AI already do?
A GPS and a route come earlier. Covering the job when the truck stops do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Fleet dispatcher
28%
AI can already do 28% of this job.
#93 / 623
View rankingFleet dispatcher
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 |
|---|---|---|---|
Routes, GPS and the day’s plan.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 52% | +9.4 |
The stopped truck, the client and reassigning.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Times, tachograph and not burning the hours.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 36% | +5.8 |
Incidents, warehouse and the delivery that is not there.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The driver, the shortcut and not lying to the client.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Cover the job, and not leaving the route half done.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- dispatch the fleet
- truck GPS
- tachograph
- reassign routes
- driving hours
- delivery incidents
- day plan
- stopped truck
Sources for this job· O*NET 43-5032.00· ESCO/ISCO 4323
IS WORK OVER?
ML engineer
29%
#64 / 623
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