Role library · aec-roles-v1 · technology
AI Exposure Check
Agile Coach
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 67% average exposure · CHF 91984 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Draft recurring status updates | 74% | Moderate exposure: “Draft recurring status updates” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Summarise meeting transcripts | 78% | High generative AI exposure: “Summarise meeting transcripts” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Reconcile spreadsheet trackers | 68% | Moderate exposure: “Reconcile spreadsheet trackers” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Answer FAQ-style requests | 77% | High generative AI exposure: “Answer FAQ-style requests” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Coordinate cross-team handoffs | 37% | Lower-moderate exposure: “Coordinate cross-team handoffs” requires coordination or domain nuance; AI may summarise inputs but should not own the outcome. |
Sample actions
- Train the team on prompt libraries tied to existing templates.
- Pilot generative drafting on the two highest-exposure tasks with human review.
- Bundle similar low-judgment tasks into one assisted workflow queue.
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.