Automation index
How much of a Project manager’s job can AI already do?
Today an AI can already knock out copy, outlines, reports, and a lot of the prep. Deciding what matters, and what moves the business, is another story.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Project manager
38%
AI can already do 38% of this job.
#10 / 623
View rankingProject manager
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 |
|---|---|---|---|
Plans and statusEstimateNo public source provides a precise figure. This is our estimate. | 30% | 65% | +19.5 |
Templated risksEstimateNo public source provides a precise figure. This is our estimate. | 20% | 50% | +10 |
People and conflictEstimateNo public source provides a precise figure. This is our estimate. | 30% | 15% | +4.5 |
Client managementEstimateNo public source provides a precise figure. This is our estimate. | 20% | 20% | +4 |
| Total | 100% | 38% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- sustainable finance
- corporate social responsibility
- strategic planning
- data analytics
- promote sustainability
- promote human rights implementation
- monitor social impact
- define the corporate structure
- conduct quantitative research
- promote environmental awareness
Sources for this job· O*NET 13-1082.00· ESCO/ISCO 1219
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