Governing Artificial Intelligence for Effective Utility Management
Wednesday, September 30, 2026 9:30 AM to 9:45 AM · 15 min. (US/Central)
New Orleans Convention Center
Interactive Technical Session
Room 238
Information
Water and wastewater utilities are increasingly deploying artificial intelligence (AI) enabled technologies to support asset management, cybersecurity monitoring, predictive maintenance, and operational decision-making. These tools offer measurable benefits, including improved fault detection, optimized maintenance scheduling, and enhanced situational awareness across operational technology (OT) and enterprise environments. However, many utilities are adopting AI faster than their governance, risk, and resilience programs can mature to support operational decision-making, creating new organizational and operational risks that often remain unrecognized until an incident occurs.
The objective of this session is to provide utility leaders with practical management structures to govern AI as an operational capability rather than treating it solely as an information technology enhancement. The presentation focuses on how Effective Utility Management frameworks must evolve to ensure AI-enabled insights align with service reliability, regulatory obligations, and executive accountability. The session reflects the current state of AI adoption in the water sector, where AI tools are increasingly embedded within asset management platforms, cybersecurity monitoring systems, and digital decision-support tools, yet frequently lack clearly defined ownership, validation criteria, and escalation thresholds.
The methodology presented is based on applied governance, risk, and resilience practices drawn from utility programs addressing cybersecurity, incident response, disaster recovery, and continuity of operations. Using real-world utility program data, the session examines how AI-driven alerts and recommendations intersect with existing operational workflows, including maintenance planning, incident escalation, and emergency response activation. Examples include AI-supported asset prioritization influencing capital planning decisions, AI-enabled cyber monitoring generating operational alarms, and predictive analytics informing staffing and outage response strategies. These examples illustrate how AI outputs, when not governed, can unintentionally increase operational risk through false confidence, alert fatigue, or unclear accountability.
Findings from these programs indicate utilities with defined management structures, such as decision ownership, risk acceptance thresholds, and formal escalation pathways are better positioned to realize AI benefits while maintaining operational resilience. Utilities integrating AI governance into existing management frameworks demonstrate clearer executive decision-making, improved coordination between IT and OT teams, reduced ambiguity during incident response, and stronger alignment between cyber risk, operational risk, and service continuity planning. These practices have been applied across utilities of varying size and technological maturity.
The significance of this session lies in providing utilities with repeatable, vendor-neutral management practices supporting responsible AI adoption while preserving service reliability and public trust. Rather than prescribing specific technologies, the presentation equips attendees with practical governance approaches that can be applied regardless of platform, system architecture, or level of automation maturity, strengthening long-term resilience in an increasingly data-driven operational environment.
The objective of this session is to provide utility leaders with practical management structures to govern AI as an operational capability rather than treating it solely as an information technology enhancement. The presentation focuses on how Effective Utility Management frameworks must evolve to ensure AI-enabled insights align with service reliability, regulatory obligations, and executive accountability. The session reflects the current state of AI adoption in the water sector, where AI tools are increasingly embedded within asset management platforms, cybersecurity monitoring systems, and digital decision-support tools, yet frequently lack clearly defined ownership, validation criteria, and escalation thresholds.
The methodology presented is based on applied governance, risk, and resilience practices drawn from utility programs addressing cybersecurity, incident response, disaster recovery, and continuity of operations. Using real-world utility program data, the session examines how AI-driven alerts and recommendations intersect with existing operational workflows, including maintenance planning, incident escalation, and emergency response activation. Examples include AI-supported asset prioritization influencing capital planning decisions, AI-enabled cyber monitoring generating operational alarms, and predictive analytics informing staffing and outage response strategies. These examples illustrate how AI outputs, when not governed, can unintentionally increase operational risk through false confidence, alert fatigue, or unclear accountability.
Findings from these programs indicate utilities with defined management structures, such as decision ownership, risk acceptance thresholds, and formal escalation pathways are better positioned to realize AI benefits while maintaining operational resilience. Utilities integrating AI governance into existing management frameworks demonstrate clearer executive decision-making, improved coordination between IT and OT teams, reduced ambiguity during incident response, and stronger alignment between cyber risk, operational risk, and service continuity planning. These practices have been applied across utilities of varying size and technological maturity.
The significance of this session lies in providing utilities with repeatable, vendor-neutral management practices supporting responsible AI adoption while preserving service reliability and public trust. Rather than prescribing specific technologies, the presentation equips attendees with practical governance approaches that can be applied regardless of platform, system architecture, or level of automation maturity, strengthening long-term resilience in an increasingly data-driven operational environment.