Automation index
How much of a Chief executive’s job can AI already do?
A dashboard and a board note write. Deciding a layoff, a buyer, and looking the workforce in the eye do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Chief executive
20%
AI can already do 20% of this job.
#447 / 633
View rankingChief executive
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 |
|---|---|---|---|
Reports, dashboards and board notes.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 42% | +7.6 |
Prep committees, agendas and follow-ups.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Decide strategy, investment and cuts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 12% | +2.2 |
Lead the exec team and conflicts.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 10% | +1.8 |
Talk to investors, unions and press.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
Represent the company and own the miss.EstimateNo public source provides a precise figure. This is our estimate. | 14% | 15% | +2.1 |
| Total | 100% | 20% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- Reports, dashboards and board notes
- Prep committees, agendas and follow-ups
- Decide strategy, investment and cuts
- Lead the exec team and conflicts
- Talk to investors, unions and press
- Represent the company and own the miss
- directivo
- ceo
- director general
- consejero delegado
Sources for this job· O*NET 11-1011.00· ESCO/ISCO 1120
IS WORK OVER?
ML engineer
29%
#64 / 633
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