Automation index
How much of a Compliance officer’s job can AI already do?
A matrix and a course come earlier. Telling a director no, and writing it down, do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Compliance officer
28%
AI can already do 28% of this job.
#89 / 623
View rankingCompliance 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 |
|---|---|---|---|
Policies, matrices and controls.EstimateNo public source provides a precise figure. This is our estimate. | 22% | 50% | +11 |
Reports, desk KYC and tracking.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Investigate a case and talk to people.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 18% | +2.9 |
Train, persuade and take pressure.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 15% | +2.4 |
Regulator, audit and evidence.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 16% | +2.2 |
Say no and leave a record.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 14% | +2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Policies, matrices and controls
- Reports, desk KYC and tracking
- Investigate a case and talk to people
- Train, persuade and take pressure
- Regulator, audit and evidence
- Say no and leave a record
- compliance
- cumplimiento normativo
- oficial de cumplimiento
Sources for this job· O*NET 13-1041.00· ESCO/ISCO 2619
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