Infrastructure for AI-Enhanced Financial Planning & Forecasting

How explicitly business rules and processes are documented

• Documented forecasting methodology with formalized definitions for revenue recognition events, expense accrual timing, and intercompany elimination rules as machine-readable policy

How data is organized into queryable, relational formats

• Standardized chart-of-accounts taxonomy with consistent cost center hierarchies, product line codes, and reporting period definitions applied uniformly across business units

Whether operational knowledge is systematically recorded

• Systematic capture of actuals, budget revisions, and driver metric updates into structured time-series records with version-stamped submission identifiers

Whether systems expose data through programmatic interfaces

• Cross-system query access to operational metrics, HR headcount data, and economic indicator feeds via standardized interfaces without manual spreadsheet aggregation

Whether systems share data bidirectionally

• Integration middleware connecting forecasting model outputs to planning platform and ERP for scenario version management and variance report generation

How frequently and reliably information is kept current

• Scheduled monitoring of forecast accuracy by horizon and business unit with drift detection on bias trends across consecutive forecast cycles