Controlling Forecast Chat
Use Case Family
Business Domain
Finance
Processes
Planning & Reporting
Challenge
Teams must constantly reconcile forecasts with budget and actuals, explain variances, and recommend actions. Iterating across SQL/Excel and scattered reports is slow and error-prone. AI-based forecasts often face low adoption due to limited explainability of drivers and lineage. This delays month-end close and rolling forecasts—exactly when speed, auditability and consistent metric logic are critical.
Solution
A GenAI based forcast assistant accepts natural-language questions (“Why is Q3 EBIT −6% vs. budget?”), securely queries plan/actual/forecast data, runs variance, driver and sensitivity analyses, and returns explainable answers with citations (e.g., link to SAC/Power BI report, cell-level provenance). A RAG layer holds KPI definitions; role-based views for CFO/analysts; auto-narratives for management reports; optional Copilot-assisted reconciliation and collections.
Benefits
- Faster time-to-insight across forecast/budget/actuals; fewer manual reconciliations.
- Higher trust via driver breakdowns, source citations and reproducible steps → better forecast adoption.
- More self-service, less dependency on data experts; controllers focus on actions.
- Consistent KPIs via RAG; governance & audit trail support close and audit processes.
Sources: SAP Cloud; Microsoft Copilot for Finance
Target Group
Potential Industries
Risk Classification (EU AI Act)
