Automation index
How much of a SAP specialist’s job can AI already do?
A customising and a report come earlier. Payroll coming out on the 30th does not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
SAP specialist
25%
AI can already do 25% of this job.
#224 / 623
View rankingSAP specialist
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 |
|---|---|---|---|
Customising, specs and module tests.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Integrations, IDocs and interface faults.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 32% | +5.1 |
Key user, training and resistance.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Go-lives and windows.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Documentation, authorisations and audit.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 16% | +2.9 |
Not leaving a company unable to invoice.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 25% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Customising, specs and module tests
- Integrations, IDocs and interface faults
- Key user, training and resistance
- Go-lives and windows
- Documentation, authorisations and audit
- Not leaving a company unable to invoice
- especialista sap
- consultor sap
- erp specialist
Sources for this job· O*NET 15-1211.00· ESCO/ISCO 2511
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