Automation index
How much of a Key account manager’s job can AI already do?
A QBR comes earlier. Growing the account without losing it at renewal do not.
This percentage does not predict how many people will lose their jobs.
Task comparison
IS WORK OVER?
Key account manager
27%
AI can already do 27% of this job.
#166 / 623
View rankingKey account 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 |
|---|---|---|---|
Account, plan and the QBR.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 48% | +8.6 |
Buyer, objections and the one who changes.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 40% | +6.4 |
Forecast, the gap and not inflating.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 22% | +3.5 |
Incident, the SLA and not hiding the fail.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 28% | +4.5 |
Internal, the one who does not deliver and fighting it.EstimateNo public source provides a precise figure. This is our estimate. | 18% | 14% | +2.5 |
Grow it, and not losing it at renewal.EstimateNo public source provides a precise figure. This is our estimate. | 16% | 12% | +1.9 |
| Total | 100% | 27% |
Modelled workweek: 100% · Public evidence: 0% · Estimate: 100% · Confidence: Low
Skills and activities
- QBR
- account plans
- account forecast
- SLA
- renewal
- buyers
- objections
- gaps
Sources for this job· O*NET 41-4012.00· ESCO/ISCO 2433
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