Automation index
How much of a Billing clerk’s job can AI already do?
An ERP comes earlier. Issuing the right invoice without blocking the client do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Billing clerk
27%
AI can already do 27% of this job.
#152 / 623
View rankingBilling clerk
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 |
|---|---|---|---|
Orders, delivery notes and the invoice.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
VAT, withholdings and the one that does not square.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Credit notes, the credit and the client who argues.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Close, the immediate supply and the deadline.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Sales, the extra and not over-billing.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
The right invoice, and not blocking the client.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
- delivery notes
- invoices
- VAT
- credit notes
- immediate supply
- billing close
- withholdings
- orders
Sources for this job· O*NET 43-3021.00· ESCO/ISCO 4311
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