Utility operations are becoming more complex as data volumes surge, regulatory scrutiny intensifies, and customer expectations for reliability and transparency grow. Utilities need a way to accelerate insight generation without sacrificing governance, auditability, or statistical rigor.

This presentation introduces a hybrid approach that combines proven analytics platforms with Large Language Models (LLMs) such as Claude or Copilot. The result is a governed, scalable, and defensible analytics environment that is simultaneously more accessible and productive.

The synergy works by layering LLM capabilities (natural language interaction, automated documentation, rapid summarization, and ad hoc query response) on top of traditional platforms that enforce lineage, governance, and repeatable math.
 
Attendees will see how this hybrid model transforms analytics from a code heavy, siloed process into a governed yet agile system. Real world use cases will demonstrate how utilities can improve visibility, reduce token costs, and deliver faster, verifiable insights - supporting better decisions around reliability, asset health, maintenance prioritization, and investment planning.

Session Take-aways:

• Accelerate productivity by reducing the learning curve and speeding time to value.
• Maintain governance with traceable, auditable, and statistically defensible results.
• Enhance decision support through conversational access to complex model outputs.
• Reduce manual work by automating reporting, documentation, and workflow integration.