Role library · aec-roles-v1 · finance
AI Exposure Check
Finance Manager
Illustrative task-level Exposure from the offline sample model — not a market survey.
Exposure 71% average exposure · CHF 113635 illustrative time value · 5 tasks
Tasks
Exposure and reasoning
| Task | Exposure % | Reasoning |
|---|---|---|
| Compile recurring management packs | 81% | High generative AI exposure: “Compile recurring management packs” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Reconcile ledger exceptions | 66% | Moderate exposure: “Reconcile ledger exceptions” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Draft variance commentary | 72% | Moderate exposure: “Draft variance commentary” mixes templateable steps with contextual judgment; AI can draft or triage, but an accountable human should decide. |
| Build ad-hoc Excel models | 83% | High generative AI exposure: “Build ad-hoc Excel models” is largely pattern-matching, drafting, or structured data work that models can assist with human review. |
| Answer stakeholder data requests | 54% | Lower-moderate exposure: “Answer stakeholder data requests” requires coordination or domain nuance; AI may summarise inputs but should not own the outcome. |
Sample actions
- Set an audit sample rate for AI outputs before customer or regulator exposure.
- Instrument time saved for 30 days before expanding automation scope.
- Protect relationship-critical and judgment-heavy tasks from full automation.
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.