Chapter 9: Data Analysis & Financial Modeling
You have a spreadsheet with six months of transactions. A client wants a financial projection. Your accountant needs a reconciliation report. Each analysis requires combining data from multiple sources, applying domain-specific rules, and presenting results in a format stakeholders can act on.
The problem isn't the math. It's the time spent assembling, cleaning, and formatting data before you can even start thinking.
This chapter teaches you to use your General Agent for data analysis workflows — from connecting to spreadsheets and accounting platforms to building financial models and generating insight reports. You'll combine domain expertise (what questions to ask) with AI capabilities (processing speed and pattern recognition).
Principles Applied
| Principle | How It Applies |
|---|---|
| Code as Universal Interface | Express analysis as reproducible code, not one-off calculations |
| Verification as Core Step | Cross-check AI-generated numbers against known values |
| Persisting State in Files | Save models, templates, and analysis outputs for reuse |
| Constraints and Safety | Validate financial data before acting on it; audit trails |
| Observability | Show your work — transparent calculation chains |
Interface Focus
Primary: Code (data analysis requires precise, reproducible operations) Secondary: Cowork (for interpreting results and planning analyses)
What You'll Learn
By the end of this chapter, you'll be able to:
- Connect your General Agent to spreadsheet data via MCP integrations
- Build financial models collaboratively with AI assistance
- Create reproducible analysis workflows (not one-off calculations)
- Validate AI-generated numbers against known baselines
- Generate formatted reports from raw data
- Apply governance and compliance patterns to financial workflows
Lessons
| Lesson | Title | Focus |
|---|---|---|
| L01 | General Agent for Finance | Why AI-native finance analysis changes the game |
| L02 | Finance Workspace Setup | Configuring your analysis environment |
| L03 | Finance Skills | Building domain-specific agent skills for financial work |
| L04 | Sheets MCP Integration | Connecting to spreadsheet data sources |
| L05 | Intent-Driven Modeling | Expressing what you want to know, not how to calculate it |
| L06 | AI-Native Accounting | Reconciliation, categorization, and reporting |
| L07 | Accounting Platform MCP | Connecting to professional accounting systems |
| L08 | Finance Subagents | Specialized agents for different financial tasks |
| L09 | Governance & Compliance | Audit trails, validation, and regulatory awareness |
| L10 | Capstone: Finance Digital FTE | Complete financial analysis workflow |
| Quiz | Chapter Quiz | Test your understanding |
Connection to AI Employee (Chapter 11)
The data analysis patterns you build here power your AI Employee's analytical capabilities. In Chapter 10, your employee uses these techniques to:
- Track financial metrics for your weekly CEO Briefing
- Analyze patterns in your email volume and response times
- Generate data-driven recommendations from activity logs
- Monitor budgets and flag anomalies automatically
Data analysis is how your AI Employee turns raw information into actionable insight.