Four AI Applications Water Utilities Can Compare Before They Commit

Artificial intelligence is already doing practical work at water and wastewater utilities: predicting equipment failures, sharpening water quality control, forecasting demand, and finding leaks that traditional methods miss. But the applications are not interchangeable, and neither are their implementation burdens. This article compares four use cases—predictive maintenance, water quality management, demand forecasting, and leak detection—on their operational benefits and their common challenges, then lays out a low-risk path for choosing where to start.

Introduction to AI in Water Utilities

Industry training and professional guidance now treat AI as a mainstream operational tool rather than a novelty. A utility-sector curriculum from EUCI, for instance, covers AI applications spanning digital twin modeling, water quality monitoring, leak detection, asset management optimization, and predictive emergency management. Writing for VODA.ai, Jim Fitchett frames the same point from the practitioner's side: AI offers practical applications that drive real value when they are tied to well-defined operational problems.

What that means for a utility manager is a selection question, not a technology question. The four applications below differ in what they optimize, what data and staff readiness they demand, and how quickly a pilot can show measurable results. The comparison table later in this article puts those dimensions side by side so you can match an application to your most pressing goal—whether that is cutting losses, protecting assets, or planning resources.

AI Application: Predictive Maintenance

Predictive maintenance applies AI to critical assets—pumps, valves, and other infrastructure whose failure is costly and disruptive. The EUCI curriculum lists asset management optimization among the core AI applications for water and wastewater utilities, alongside predictive emergency management, which extends the same analytics toward anticipating operational emergencies. Fitchett's guidance names predictive maintenance for critical assets as one of the first places a utility should consider piloting AI.

The operational benefit is proactive rather than reactive: maintenance is scheduled before failure instead of after it. The evidence base for water utilities describes this as asset optimization and improved financial efficiency—EUCI's program explicitly ties AI-driven measures to operational and capital expenditure savings and better resource allocation—though specific quantified savings for predictive maintenance are not detailed in the sources here, so treat the benefit as directional rather than guaranteed.

The challenges are shared across most AI deployments in this sector. A discussion of AI adoption at HRSD, the Hampton Roads Sanitation District, reported by Wastewater Digest, highlights cybersecurity and communication reliability as implementation hurdles, and emphasizes the need for dedicated internal champions to carry the effort. Those constraints apply squarely to maintenance analytics, which depends on reliable sensor data flowing from field assets.

AI Application: Water Quality Management

Water quality management is the application with the deepest treatment-process stakes. EUCI's curriculum includes water quality monitoring among the AI applications revolutionizing utility management, and Fitchett lists automated water quality monitoring as a candidate starting point for utilities new to AI. The American Society of Civil Engineers' coverage of a utility AI guidebook adds a concrete example: models that predict membrane fouling and forecast water quality in treatment processes.

The benefit, as described by HRSD's leadership in the Wastewater Digest conversation, is improved control of biological processes and better resource efficiency—AI helps utilities maintain quality standards while using energy and chemicals more precisely. That dual payoff, compliance plus efficiency, is what distinguishes this application from the others.

The challenge is accuracy under change. ASCE's reporting quotes the guidebook's author, Koyama, on the need for careful planning: models must be tested under realistic operating conditions to determine whether their predictions remain accurate as treatment processes and water quality change. Utilities must also weigh cybersecurity, staff training, and ongoing model maintenance. A phased implementation approach, the guidebook encourages, gives organizations time to evaluate model performance and identify issues at each step.

AI Application: Demand Forecasting

Demand forecasting is where AI touches resource planning most directly. Industry writing from Numalis describes how forecast solutions can accurately predict consumption and demand patterns depending on variables such as time of day, season, and socioeconomic factors. That enables correct estimation of the water needed from reservoirs, helping reduce water loss while meeting customer needs.

The operational benefit is better allocation: when a utility knows how much water it will need and when, it can manage reservoirs, pumping schedules, and treatment capacity with less waste. Fitchett's guidance includes energy optimization for major pumping stations as a related AI starting point, which pairs naturally with demand forecasts—pumping to predicted demand rather than to fixed schedules.

The implementation challenge is data breadth. Forecast accuracy depends on the quality and coverage of the consumption data feeding the model, and the same sector-wide hurdles reported elsewhere—cybersecurity, staff training, and ongoing model maintenance—apply here too. As with water quality models, forecasts need validation under realistic conditions before a utility relies on them for operational decisions.

AI Application: Leak Detection

Leak detection has the clearest published success story of the four. EUCI lists it among the core AI applications for water utilities, and a case study published by Oldcastle Infrastructure, written by Matt Allen, documents what happened when a suburban Midwestern utility turned to AI after conventional methods failed.

The utility had a major non-revenue water loss it could not locate. Water was leaking into a storm drain with no visible indication of a leak nearby, and after two years of trying three different leak detection techniques and technologies without success, the utility decided a better approach was needed. It partnered with Oldcastle Infrastructure and used CivilSense AI leak detection to pinpoint the leak's location.

The reported results were substantial: the AI solution located a large leak that had evaded traditional detection methods for years, saving the utility $213,000 a year and preventing the loss of 350,000 gallons of non-revenue water every day. That figure comes from the vendor's own published case study, so it should be read as a reported outcome for one utility rather than a typical result. Still, it illustrates why leak detection is often the easiest application to justify internally: the savings are directly measurable in gallons and dollars.

Comparative Overview of AI Applications for Water Utilities

The table below compares the four applications across the two dimensions that matter most when choosing a starting point: the primary operational benefit each delivers, and the challenges a utility should anticipate during implementation. Use it to match an application to your most pressing goal and to gauge which implementation path your organization can realistically manage.

Read across the rows, a pattern emerges: the challenges are largely shared—cybersecurity, data reliability, staff readiness—while the benefits are distinct. That means the choice of application should be driven less by which is easiest to implement and more by which addresses your utility's most costly operational problem.

Getting Started with AI: A Strategic Adoption Path

Water utilities should strategically adopt AI by identifying specific operational problems, starting with risk-free trials, and carefully selecting technology partners with relevant experience. Fitchett's guidance is explicit on the sequence: successful implementation begins with identifying specific operational challenges where AI can provide clear value, then starting with risk-free trials that address well-defined problems and demonstrate measurable results. The most critical factor in successful AI adoption, he writes, is choosing the right partners.

Concretely, that means a utility should pick one problem—unexplained non-revenue water, a failure-prone pump station, erratic demand at a reservoir—and run a bounded pilot with a defined success metric before scaling. The ASCE-reported guidebook reinforces this with its phased implementation approach, which lets organizations evaluate model performance and identify issues at each step rather than committing enterprise-wide up front. The measurable indicator of success is straightforward: a completed pilot project with measurable results, and an established partnership with an AI provider that has proven experience in the water sector. If a trial cannot demonstrate a measurable improvement against its stated metric, that is the signal to reassess before expanding.

Sources

• Artificial Intelligence for Water and Wastewater Utilities — EUCI
• AI’s Role in Water Utilities: Overcome Common Concerns and Get Started — VODA.ai
• The Future of Water Management: AI, Machine Learning, and Innovation at HRSD — WWDMAG
• AI and machine learning can help water utilities operate more efficiently – here’s how — ASCE
• Beyond Utilities: AI Transformative Role in the Power, Water and Gas Sector - Numalis
• New AI leak detection saves water utility $213,000 — Oldcastle Infrastructure