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.

1. Assembling the Data

The first step is to bring together all data influencing financial performance — often spread across different utility systems. 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. 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.

3. 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 machine-readable, tabular structures that allow AI models to perform predictive analysis and trend identification with high accuracy.

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.