UC19: Narrative Reporting Assistant (MD&A)

The Problem

Management Discussion & Analysis (MD&A) sections of financial reports require synthesizing period-over-period changes, explaining key drivers, and contextualizing results. Drafting MD&A consumes 20 to 40 FTE hours per quarter for a mid-cap company, pulling senior accountants away from other work. MD&A quality varies based on drafter skill, creating inconsistent stakeholder communication.

What the Agent Does

Retrieves current and prior-period financial statements (P&L, Balance Sheet, Cash Flow)
Analyzes period-over-period changes by line item (variance analysis)
Identifies material variances (>5% or >$5M)
Integrates context:
Budget vs. actual: pulls budget variances and management commentary
Operational data: pulls headcount, transaction volume, or unit sales for context
External factors: incorporates economic/industry indicators (e.g., "Q3 revenue decline driven by 12% industry contraction")
Prior commentary: reviews prior-quarter MD&A for context/patterns
Generates narrative draft organized by section:
Overview: 2 to 3 sentence executive summary
Segment analysis: Revenue, margin, and EBITDA drivers by business unit
Liquidity & resources: Working capital, debt, and cash flow analysis
Key metrics and ratios: Highlights important non-GAAP metrics
Flags items requiring management discussion (e.g., "Gross margin declined 150 bps; confirm if driven by mix or price/cost dynamics")
Routes draft to CFO/Controller for review and customization

Data Requirements

Data Sources:

ERP GL , P&L, balance sheet, cash flow statements (current + prior periods)
Budgeting system , Budget data, budget variance analysis
Operational systems , Headcount, transaction volumes, unit sales data
CRM/Business systems , Segment revenue, customer metrics
External feeds , Economic indicators, industry benchmarks
Prior MD&A , Prior-period MD&A narrative for consistency/pattern matching

Data Classification:

Public: Economic indicators, industry benchmarks
Internal Structured: Financial statements, budget, operational data, segment data
Internal Unstructured: Prior MD&A, management commentary, email discussions
External Third-Party: Economic data, industry reports
Sensitive/Regulated: Financial statements (pre-release), forward-looking statements (regulatory)

Data Quality Requirements:

Timeliness: Financial statement data finalized and audited
Completeness: All relevant operational and segment data must be available
Accuracy: Budget and operational data must be reconciled to GL

Integration Complexity: High , Requires ERP financials integration, budget system integration, operational data integration, external data feeds, and NLP for narrative generation

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%	3	0.15
Data Availability	15%	3	0.45
Process Clarity	15%	3	0.45
Ease of Implementation	10%	2	0.20
Fallback Available	10%	4	0.40
Audience (Internal)	10%	4	0.40
Composite	100%		3.95

Why It Scores Well

Time savings: Reducing MD&A drafting from 30 hours to 10 hours per quarter × 8 to 10 reporting cycles/year = 160 to 200 FTE hours annually. Consistency improves: standardized structure and reasoning improve stakeholder communication. Turnaround time improves: draft available within days of financial close.

Regulatory Alignment

SEC (for public companies): MD&A must comply with Item 7 disclosure requirements
SOX 404: MD&A accuracy is part of financial reporting controls

Sprint Factory Fit

Sprint 1 (4 weeks)

Sprint 1 + build sprints. Requires financial data integration, NLP narrative generation, and regulatory compliance review.

Comparable Implementations

Workiva Connected Data Cloud , Integrated financial reporting and MD&A generation
Donnelley Financial Solutions (DFIN) eSpeed , Regulatory filing and narrative generation

Deploy This Use Case with the Sprint Factory

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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.