Automation index
How much of a Labour consultant’s job can AI already do?
A payroll run and a contract template come earlier. Advocacy in labour court and an ERE do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Labour consultant
29%
AI can already do 29% of this job.
#62 / 623
View rankingLabour consultant
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 |
|---|---|---|---|
Payroll, social security and standard contracts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Draft filings, appeals and settlements.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Conciliation, hearing and labour inspectorate visits.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Advise on dismissals, working time and collective agreements.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Day-to-day service to company and worker.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 18% | +3.2 |
Deadlines, records and the firm’s 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
- Draft filings, appeals and settlements
- Day-to-day service to company and worker
- graduado social
- asesor laboral
- labour consultant
Sources for this job· O*NET 13-1071.00· ESCO/ISCO 2423
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