AC26 Generative AI for Operations and Maintenance for Utilities

CWEA Members: $35.00
Non-Members $45.00
CWEA Contact Hours: 1.0 contact hours towards CWEA Certifications: CSM, MT, LAB, EIT

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:
1. Understand what Generative AI is and how it applies specifically to utility operations and maintenance.
4. To see key operational use cases where Generative AI delivers measurable value.
2. To understand how AI tools using existing utility data—regardless of current data maturity.

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Registrants who view the live webinar to see the slides and hear the audio and then enter the correct attention check code (directions below)1.0  contact hours towards CWEA's Contact Hours.CSM,MT LAB, EIT

To receive your contact hours for viewing the live webinar, please note the one (1) attention check code that will be displayed during the webinar in the top left or right corner of the presentation for approximately 90 seconds.  Please enter this code in the Attention Check Code component under the "Contents" tab.  

Please note, all user activity of CWEA certification holders on the Online Wastewater Education Network is subject to the CWEA Code of Ethics standards for professional conduct and ethics. Certification holders should receive credit for a training only once within the same contact hour period. Any attempt to undermine the certification process may be subject to ethics procedures and possible sanctions. It is not possible to receive contact hours for both attending the live webinar and viewing the recording.  

Once you have entered the correct attendance check code, you will be able to create and download an electronic "Certificate of Completion" under the "Contents" tab.