Automation index
How much of a Switchboard operator’s job can AI already do?
A switchboard comes earlier. Putting the call through without hanging up on the one who waits do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Switchboard operator
27%
AI can already do 27% of this job.
#187 / 623
View rankingSwitchboard operator
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 |
|---|---|---|---|
Board, destinations and the shift.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
The call, the one who is out and the message.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Emergencies, the anger and not losing it.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Directory, changes and not putting through wrong.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
The peak, the queues and not hanging up.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Put it through, and not hanging up on the one who waits.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
- switchboard
- destinations
- messages
- directory
- call queues
- emergencies
- telephony shifts
- extension changes
Sources for this job· O*NET 43-2021.00· ESCO/ISCO 4223
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