Automation index
How much of a Data protection officer’s job can AI already do?
A processing record and a clause come earlier. Calling an incident and answering the regulator do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Data protection officer
29%
AI can already do 29% of this job.
#58 / 623
View rankingData protection 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 |
|---|---|---|---|
Processing records, clauses and DPIAs.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Policies, processor contracts and transfers.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Data-subject rights, internal queries and training.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Incidents, notification and the call to the regulator.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Vendors, cookies and new processing.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Independence, board report and liability.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 28% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- data protection
- apply GDPR
- run DPIAs
- handle subject rights
- notify breaches
- draft clauses
- processor contracts
- answer the regulator
Sources for this job· O*NET 13-1041.00· ESCO/ISCO 2422
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