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Natural Language Financial Query and Ad-Hoc Analysis
"Most companies invest in expensive BI tools that end up being used by the same three people who could have just used Excel—because everyone else finds them too complicated."
The Problem
Business partners constantly request custom analyses requiring FP&A to manually query data and create reports. Each request requires data extraction, Excel manipulation, and formatting—consuming 30-90 minutes per request. FP&A becomes bottleneck; business users wait days for simple questions.
Current State
Each request requires analyst to extract data from ERP/data warehouse, manipulate in Excel, format for presentation. Similar questions asked repeatedly. Business users cannot self-serve because query languages are technical.
GenAI Solution
AI reads: Database schemas, financial data, business definitions, user context and permissions. AI generates: SQL queries from natural language, visualizations, formatted reports, answer narratives. AI decides: Appropriate data sources, aggregation levels, visualization types. CRITICAL PREREQUISITE: Requires clean, well-modeled financial data. Companies with messy ERP data, multiple unintegrated systems, or no semantic layer will fail. 70% of implementation effort is data preparation, not AI.
Key Differentiator
Must understand natural language intent and translate to data queries. Must present results in business-friendly format with context. Rules-based reporting requires predefined formats; LLMs generate answers to questions that were never anticipated.
Example Workflow
- 1 Regional manager asks via chat: 'How did our French subsidiary perform versus budget last quarter?'
- 2 AI translates to appropriate SQL query against financial data warehouse
- 3 Retrieves revenue, costs, EBITDA vs. budget and prior year
- 4 Generates response with visualization: 'France Q3: Revenue €2.1M (+4% vs budget), EBITDA €340K (-8% vs budget). YoY growth: 12%.'
- 5 User asks follow-up: 'What drove the marketing increase?'
- 6 AI drills down: 'Marketing spend €180K vs €120K budget. €45K from unbudgeted trade show, €15K from agency fees.'
Prerequisites
- Clean data warehouse or data lake with defined schemas
- Single source of truth for financial data (or will be built as part of project)
- Consistent, well-documented chart of accounts
- Clean dimension hierarchies (cost centers, products, regions)
- Permission controls (users should only see authorized data)
- LLM access to metadata describing available data
Red Flags
- Very small organization where everyone can ask finance directly
- Poor data quality or no unified data model
- No ERP or data warehouse in place
- Business users have no appetite for self-service (cultural issue)
- Already deployed Power BI/Tableau with high adoption
- IT controls all analytics and won't allow finance-led initiative
Complexity Drivers
Data quality remediation, semantic layer creation, security/permissions, user adoption
Risk Factors
Data quality (primary cause of failure), user adoption, hallucination on financial numbers, governance complexity
Value Metrics
Query response time, self-service adoption, FP&A ad-hoc request volume
4-24 hour query response, 20-30% self-service, FP&A bottleneck
Seconds-to-minutes response, 40-70% self-service, FP&A freed for strategic work
Industry Perspectives
Specific pain points, solutions, and regulatory factors for 6 industries.
C-Suite Relevance
Key Metrics
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