Sample Report
This is a demonstration fragment of an AI audit structure. It does not describe a specific client and does not contain confidential data.
1. Executive Summary
The company has 4–6 processes where AI and automation may reduce manual workload. The main effect is expected not from a “chatbot”, but from the combination of process mapping, data normalization, automation of repeatable steps and quality metrics.
2. Current-State Map
| Process | Pain point | Metric | Data readiness |
|---|---|---|---|
| Inbound request handling | Manual classification and routing | 8–14 hours per week | Medium |
| Management reporting | Excel summaries from several systems | 2–3 working days per month | High |
| Document flow | Repeated data entry and field validation | Errors in 3–5% of documents | Medium |
3. AI Opportunity Prioritization
| Initiative | Expected effect | Complexity | First step |
|---|---|---|---|
| Request classifier | Less manual triage | Low | Pilot on historical requests |
| Knowledge-base assistant | Faster access to policies and procedures | Medium | Document inventory |
| Document data extraction | Less manual entry and fewer errors | Medium | Select 3 document types |
4. 90-Day Roadmap
- Weeks 1–2: clarify process owners, success criteria and data sources.
- Weeks 3–6: build a pilot around one process with a limited data scope.
- Weeks 7–10: measure quality, refine exception handling and improve UX.
- Weeks 11–12: prepare a scale-or-stop decision for the pilot.
5. Risks
- No process owner is accountable for the effect after the pilot.
- Data is spread across systems and lacks a single reference model.
- Success metrics are not agreed before development starts.