Role library · aec-roles-v1 · operations
AI Exposure Check
Logistics Manager
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 58% average exposure · CHF 71712 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Reconcile vendor invoices | 79% | High generative AI exposure: “Reconcile vendor invoices” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Forecast capacity load | 64% | Moderate exposure: “Forecast capacity load” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Draft SOP refresh notes | 57% | Moderate exposure: “Draft SOP refresh notes” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Chase overdue purchase orders | 56% | Moderate exposure: “Chase overdue purchase orders” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Coordinate live incident bridges | 32% | Low exposure: “Coordinate live incident bridges” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
Sample actions
- Map data-classification rules before connecting internal systems to models.
- Rewrite SOPs so AI-assisted steps keep an accountable human owner.
- Train the team on prompt libraries tied to existing templates.
What these figures are not
- Illustrative role library from the offline AEC sample model (aec-ai-v1), not a surveyed occupation census.
- Task packs and exposure scores are deterministic samples for self-serve triage — Faethm-style ontology breadth, not Pearson proprietary data.
- CHF time-value estimates use illustrative Swiss mid-market rates and annual hours.