UC36: Scenario Planning & Sensitivity Analysis

The Problem

Management requests rapid what-if analysis to support strategic decisions: "What if pricing increases 5%?", "What if FX strengthens 10%?", "What if volume declines 20%?" Finance teams currently build point-in-time spreadsheet models that are difficult to maintain, require manual assumption updates, and take weeks to deliver results. Without parameterized models, sensitivity analysis is performed ad-hoc and inconsistently, limiting decision-making speed and quality.

What the Agent Does

Ingests financial model and assumption parameters:
Income statement model (revenue, COGS, operating expenses, taxes)
Key driver assumptions: volumes (units), pricing ($/unit), COGS margin, FX rates, discount rates
Constraints and business rules: minimum/maximum pricing, production capacity, tax rate bounds
Builds parameterized financial model:
Organizes model into input assumptions (drivers), calculations, and outputs (P&L, cash flow metrics)
Creates assumption parameter table with ranges (low, base, high) for each driver
Implements transparent calculation logic with clear audit trail (formula references)
Generates scenario impacts:
Single-variable sensitivity: Tests one driver at a time (e.g., pricing +5%, +10%, +15%) while holding others constant
Multi-variable scenarios: Named scenarios (e.g., "Bull Case", "Bear Case", "FX Shock") with coordinated assumption changes
Stress tests: Extreme scenarios (e.g., "Pricing down 20% + Volume down 10% + FX weakens 15%") to test business resilience
Produces output tables:
Sensitivity table: Impact on Net Income, EBITDA, cash flow, or key ratio for each assumption variation
Tornado chart: Ranks assumptions by magnitude of impact on output metric
Scenario comparison: Side-by-side financials for Bull, Base, Bear cases with key metrics highlighted
Waterfall chart: Shows contribution of each assumption change to total impact
Enables rapid iteration:
Finance user can adjust assumptions on parameterized model; agent re-calculates and refreshes output tables within minutes
Tracks assumption history: "Q3 scenario assumed pricing +2%; Q4 scenario assumes pricing +0% due to competitive pressure"

Data Requirements

Data Sources:

Financial model template , Income statement, cash flow statement with formula structure
Historical data , Prior period financials, volumes, pricing, margins to inform ranges
Assumption parameters , Approved ranges for key drivers (min/max, probability distribution)
Market data feeds , FX rates, commodity prices, discount rates (WACC) for scenario application

Data Classification:

Internal Structured: Financial models, historical data, assumption parameters
Internal Unstructured: Management guidance on scenarios, market commentary
External Third-Party: FX rate feeds, commodity prices, interest rate curves
Sensitive/Regulated: Pricing and margin data (confidential), strategic assumptions (confidential)

Data Quality Requirements:

Timeliness: Historical data and financial model current within 1 period
Completeness: All drivers in model must have defined ranges; formula references must be clear
Accuracy: Historical data reconciles to audited statements; assumption ranges reflect business reality and market conditions

Integration Complexity: Low , Requires parameterized spreadsheet (Excel) or planning system (Anaplan) with scenario management features. Standard financial formulas and sensitivity logic. Straightforward to implement with Excel macros or planning system native functionality.

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	3	0.30
Cost Avoidance	10%	2	0.20
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	3	0.45
Ease of Implementation	10%	4	0.40
Fallback Available	10%	3	0.30
Audience (Internal)	10%	4	0.40
Composite	100%		3.70

Why It Scores Well

Decision speed: Parameterized model delivers what-if results in minutes vs. weeks of manual modeling. Decision quality: Systematic sensitivity analysis reveals key drivers and thresholds. Consistency: All scenarios use same formula logic and assumption parameters, eliminating model errors.

Regulatory Alignment

SOX: Documented assumption parameters and scenario methodology support IT controls
Auditor alignment: Parameterized models with audit trails simplify audit reviews

Sprint Factory Fit

Sprint 2 (2 weeks)

Low-to-medium complexity. Standard financial modeling using Excel or planning system features. 2-week sprint with financial analyst oversight.

Comparable Implementations

Anaplan modeling and scenario planning , Native parameterized model support
Planful , Scenario comparison and sensitivity analysis
Workday Planning , What-if analysis for workforce and financial planning

Deploy This Use Case with the Sprint Factory

From zero to a governed, production agent in 6 weeks.

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Governance Risks to Consider

Before deploying this use case, review these agentic AI risks from the Corvair Risk Catalogue. Each is scored on the DAMAGE framework and mapped to regulatory expectations.