Automation index
How much of a IT auditor’s job can AI already do?
A log sample and a stock finding come earlier. Judging the control and signing the report do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
IT auditor
32%
AI can already do 32% of this job.
#27 / 623
View rankingIT auditor
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 |
|---|---|---|---|
Plan scope, IT risks and sampling.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Review access, changes, logs and configs.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 52% | +8.3 |
Document findings, evidence and recommendations.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Interview, challenge the auditee and apply judgement.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Follow up action plans and retest.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Report, committee and liability for the opinion.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 14% | +2.2 |
| Total | 100% | 32% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- audit systems
- review logs
- assess IT controls
- test access
- document findings
- audit changes
- retest controls
- sign the audit report
Sources for this job· O*NET 15-1212.00· ESCO/ISCO 2519
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