Supervisor Wastewater Research Los Angeles County Sanitation Districts Whittier, CA
Presentation Description: Water and wastewater utilities face mounting challenges—aging infrastructure, workforce shortages, and increasing operational complexity. Generative AI is emerging as a transformative tool to support operations and maintenance (O&M) by making institutional knowledge more accessible, improving decision-making, and boosting workforce efficiency.
This session presents the development and deployment of a domain-specific Generative AI platform for utility O&M. The goal is to show how large language models (LLMs), integrated with structured utility data, can assist field crews, operators, and engineers in real time. The system—called a Knowledge Twin—uses retrieval-augmented generation (RAG) to deliver accurate, actionable responses to natural language queries on assets, procedures, alarms, and troubleshooting.
Currently piloted with multiple utilities, the platform accommodates varying levels of data maturity. Data sources such as CMMS records, SCADA logs, SOPs, GIS data, and work order histories are ingested, structured into a knowledge graph, and connected to a fine-tuned LLM trained on the terminology, context, and workflows unique to water and wastewater operations.
Early results show that field staff can resolve issues faster, locate information more easily, and preserve institutional knowledge despite staff turnover. Use cases include alarm response guidance, step-by-step maintenance support, asset history retrieval, and on-demand training. A key finding is the system’s ability to uncover insights from previously siloed or underused data, reducing time-to-resolution and unplanned downtime.
The approach is designed to start small—focusing on high-impact use cases—and scale as more data is curated or digitized. This lowers the barrier to entry, enabling utilities of all sizes to leverage AI without perfect datasets or major system changes.
The session will feature a live demonstration, practical deployment considerations, and lessons from early adopters. Attendees will leave with a clear understanding of how Generative AI can be safely and effectively implemented to enhance daily O&M, improve service reliability, and strengthen workforce capabilities.
As utilities confront aging infrastructure and the demands of digital transformation, Generative AI provides a powerful path to future-proof operations and empower the next generation of utility professionals.
Learning Objectives:
Learning Objective 1 (maps to: Understand what Generative AI is and how it applies to utility O&M)
Distinguish between generic AI tools (e.g., ChatGPT, Copilot) and domain-specific AI built for utility operations, including the key differences in data sources, security, and output reliability.
Learning Objective 2 (maps to: See key operational use cases where Generative AI delivers measurable value)
Identify at least three operational use cases where AI-driven CMMS analysis reduces maintenance cost, surfaces compliance risk, and preserves institutional knowledge using existing utility data.
Learning Objective 3 (maps to: Understand how AI tools use existing utility data regardless of data maturity)
Describe a practical, low-risk approach to evaluating AI for their own utility, starting with existing data of any quality level, without requiring IT integration or system replacement.