Artificial intelligence is not just about algorithms — it's about data readiness. For utilities, implementing AI in finance begins with assembling data from multiple systems, cleansing it for consistency, and formatting it so the AI can "see" relationships across accounting, operations, and engineering. The steps below outline how to prepare your organization's data so that AI models can produce accurate and actionable insights.
Five-Stage Data Pipeline for AI Implementation
1. Assembling the Data
The first step is to bring together all data influencing financial performance — often spread across different utility systems.
| System | Examples of Data Needed | Typical Source Format |
|---|---|---|
| ERP / Accounting System | General ledger transactions, journal entries, cost centers, budgets | CSV export, SQL query, or API (SAP, Munis, Tyler, etc.) |
| Work Management System (WMS) | Work orders, labor and materials, project status | Excel/CSV, API, or database |
| Customer Information System (CIS) | Billing, usage, payment history, rate class | SQL, CSV, or JSON |
| Asset Management System (AMS) | Asset ID, installation date, cost, depreciation schedule | CSV, EAM export, or integration feed |
| Operational Systems (SCADA, OMS, AMI) | Energy output, outage durations, meter data, temperature | CSV, XML, or API |
| Regulatory / Grant Records | FEMA project numbers, reimbursement documentation, RUS forms | PDF + structured index (OCR or metadata extraction) |
Once assembled, merge data around common keys such as work order number, GL account number, asset or project ID, and customer or service location number. These identifiers connect engineering activity with accounting outcomes — for instance, linking a feeder upgrade work order to depreciation and CIAC accounting entries.
2. Cleansing and Normalizing the Data
AI performance depends on data quality. Cleansing ensures your data is consistent, complete, and ready for model training.
| Data Issue | Common Example | Cleansing Action |
|---|---|---|
| Inconsistent account names | "Plant Additions" vs "Plant Addition" | Apply controlled vocabulary (FERC/RUS USoA) |
| Duplicate work orders | Same project entered twice | De-duplicate using unique work order number |
| Missing or invalid dates | "1/0/2020" or blank | Infer missing data using nearest valid entry |
| Mis-categorized costs | Engineering labor coded to materials | Rule-based or ML-based reclassification |
| Non-numeric fields | "$1,000 (est.)" | Convert to numeric and remove special characters |
The goal is to output every dataset in a machine-readable, tabular format — typically CSV, Parquet, or structured database tables. Think of the end product as a data model, where tables such as "Work Orders," "GL Transactions," and "Assets" share defined relationships.
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Explore the AI Skills Learning Path3. Preferred Formats for AI Training and Analysis
After cleansing, structure your data in a consistent, relational format for long-term use. Common storage and integration formats include:
| Format / Platform | Best For | Notes |
|---|---|---|
| CSV / Excel Tables | Initial model training, simple datasets | Ideal for pilot projects and POCs |
| SQL Database (PostgreSQL, SQL Server) | Continuous model training and dashboards | Enables queries and version control |
| Parquet Files (Data Lake) | Large data storage for AI/ML | Scalable, efficient, cloud-ready |
| Power BI Dataflows / Models | Visualization + Copilot/AI integration | Works natively with Microsoft AI tools |
| JSON / API Feeds | Real-time integration with live systems | Supports continuous AI retraining |
4. Automating and Updating the Data
AI delivers the best results when fed regular, automated data updates. Set up scheduled feeds and automations using nightly or weekly exports from ERP, WMS, or CIS; SQL connectors or Power BI dataflows; Robotic Process Automation (RPA) for legacy systems; and cloud data pipelines such as Azure Data Factory or AWS Glue.
These processes evolve into a financial data lake — a living repository feeding AI dashboards, forecasts, and automated variance explanations.
Key Takeaway
AI implementation succeeds when utilities treat data as a strategic asset, not just a byproduct of accounting and operations. Consistent formats, validated records, and unified identifiers create the foundation for reliable automation, predictive modeling, and intelligent financial reporting.