Automation index
How much of a Tourist-office officer’s job can AI already do?
A map and a leaflet come earlier. The visitor leaving happy, not lost, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Tourist-office officer
27%
AI can already do 27% of this job.
#184 / 623
View rankingTourist-office officer
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 |
|---|---|---|---|
Map, diary and the counter.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
The one with no language, the one who argues and the one with no booking.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Events, capacity and the one that cancels.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Data, survey and not inflating visitors.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The council, the shops and not only selling the centre.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Happy, and not lost.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- tourist counter
- maps
- events diary
- capacity
- surveys
- bookings
- visitors
- the council
Sources for this job· O*NET 41-3041.00· ESCO/ISCO 4221
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