Role library · aec-roles-v1 · sales
AI Exposure Check
Business Development Manager
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 62% average exposure · CHF 94248 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Personalise outbound sequences | 75% | High generative AI exposure: “Personalise outbound sequences” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Prep discovery call briefs | 69% | Moderate exposure: “Prep discovery call briefs” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Update CRM opportunity notes | 63% | Moderate exposure: “Update CRM opportunity notes” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Draft proposal first cuts | 73% | Moderate exposure: “Draft proposal first cuts” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Negotiate commercial terms | 28% | Low exposure: “Negotiate commercial terms” is relationship-, safety-, or judgment-critical; keep a human owner and use AI only as a narrow aid. |
Sample actions
- Instrument time saved for 30 days before expanding automation scope.
- Protect relationship-critical and judgment-heavy tasks from full automation.
- Map data-classification rules before connecting internal systems to models.
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.