Water utilities have long known the value of analytics for reducing non-revenue water and managing assets. The challenge? Turning complex data into action. Until now, that required tech-savvy teams and hours of interpretation. AI changes the game. By bringing natural language queries and smart automation into advanced analytics, AI makes insights accessible and actionable, so utilities can move from detection to resolution faster than ever.
Tackling Water Loss With Prediction And Prescription
AI-powered analytics enable utilities to transition from reactive to proactive maintenance, using predictive approaches.
This is most apparent when considering AI as a tool for a utility’s asset management and capital planning needs. For example, mitigating real losses (i.e., physical leakage) requires predictive management of a utility’s pipe assets. Using the utility’s own data, such as asset ages, leak history, location, and geographic information system (GIS), combined with publicly available data like soil type, weather patterns, and satellite imagery, AI can analyze extensive data sets to identify the assets most likely to fail. This information aids utilities in prioritizing capital investments by detecting the most critical replacements needed, i.e., the top 1% of pipes that truly need replacement.
Focusing on these key assets also prevents premature repair of assets that are likely to have several years of remaining useful life, which is possible if replacement decisions are not based on hard data. AI modeling can also provide prescriptive recommendations by analyzing the financial impact of a potential pipe failure, including the cost of replacement versus the expected net gains from increased revenue, as well as the overall effect on the rest of the distribution network. Using these models, utilities can confidently create long-term capital improvement plans spanning five to 10 years.
AI can also be deployed to monitor and analyze pressure and flow data in near real-time. Pressure optimization is one of the most critical first steps for managing NRW, because reducing pressure minimizes the impact of existing leaks. When combined with live sensor data, pressure and flow monitoring allows AI to localize a utility’s leaks using a hydraulic model. By running thousands of simulated leak scenarios, AI can compare these simulations against current sensor data to accurately predict the real-world location of a leak, streamlining the identification and repair process.
Augmenting The Human Element
The topic of AI inevitably invokes fears that software will replace human jobs. Thankfully, the technology is designed to augment existing staff and resources, not supplant them. AI automates tedious tasks such as pattern recognition, reducing the hours spent manually reviewing large data sets. It provides resources in areas where manpower is lacking, making existing personnel more effective at their jobs without changing headcounts. Additionally, AI delivers a prioritized start-of-the-day report based on alerts and offers prompts associated with regulatory thresholds. However, in all cases, a human makes the final judgment call. Moreover, people will always remain “at the wheel,” as complex physical work in the field and on-the-spot troubleshooting cannot be replaced by current AI or robotics.
Data Readiness And Adoption
Maximizing the benefits of AI-powered solutions requires data readiness. AI models benefit from integrating multimodal data sets, including time series consumption data, static GIS data, billing information, and work order data. External sources like weather, soil type, and satellite images can also be layered in to improve predictive analysis for pipe asset management.
As such, utilities need good governance processes to ensure they are feeding the best quality data into the system. For utilities that lack expertise in this area, the right vendor can offer guidance, as well as perform data assessments and help create a collaborative roadmap for adoption. Furthermore, before investigating AI-powered analytics, utilities must start by asking what problems they are trying to solve. In other words, they must clearly define what challenges they are addressing before engaging vendors.
Overall, AI is rapidly reshaping how water utilities understand, manage, and reduce water loss. By transforming complex data into actionable insights, AI empowers utilities of all sizes to shift from reactive repairs to proactive predictive management of both real and apparent losses — all while strengthening, rather than replacing, the human workforce. With the right data foundation and a clear understanding of operational challenges, utilities can leverage AI-powered analytics to drive smarter investments, improve efficiency, and build more resilient distribution networks.