Most of America’s water infrastructure is invisible. Roughly 2.2 million miles of pipe carry drinking water to homes and businesses across the country, almost all of it buried and much of it well past its prime. Washington, D.C. knows the problem firsthand. DC Water runs one of the oldest systems in the nation, with some pipes in the District in the ground for more than a century. It maintains about 1,300 miles of water main and more than 1,800 miles of sewers, and it operates Blue Plains, the largest advanced wastewater treatment plant in the world.

The strain on systems like these is no longer hypothetical. Mains break. Budgets are tight. Weather is more volatile, some regions face real water scarcity, and a wave of retirements is carrying decades of hard-won knowledge out the door. Running this kind of system on reactive repairs and partial information gets harder every year.

Utilities need better ways to see what is happening underground, decide sooner, and stretch limited resources further. Artificial intelligence will not rebuild a pipe network or replace a treatment plant, and it is no substitute for the engineers and operators who keep water flowing. But it is becoming a practical tool for running, maintaining, and protecting these systems. DC Water’s own experience shows where AI earns its keep.

Turning data into decisions

Over the last decade, utilities have invested heavily in sensors, smart meters, geographic information systems, and modern SCADA. They are collecting more data than ever. Much of it, though, sits in separate systems and rarely gets used, and few utilities have enough engineers or data scientists to make sense of it all. Useful signals get buried in the noise.

This is where machine learning is useful. It can read large, mismatched streams of data in real time, from pressure and flow to water chemistry and maintenance records and surface the patterns and early warning signs that people tend to miss.

DC Water built its answer around a real-time, cloud-based digital twin of its water distribution system, developed on Bentley’s OpenFlows WaterSight platform. The model pulls GIS, SCADA, CMMS and more than 160 IoT sensors into one operational picture. Instead of stitching together disconnected datasets, operators can see the network as a whole, run “what-if” scenarios before they act, and understand how the system behaves under stress.

From break-fix to predict-and-prevent

For decades, water utilities have run mostly in break-fix mode. A pipe bursts, a pump fails, or a customer reports low pressure, and a crew goes out to fix it. That model is expensive once you add up emergency repairs, service disruptions, and lost water. The EPA puts the average U.S. non-revenue water rate near 16 percent, which means roughly one of every six gallons of treated water never reaches a paying customer. Nationally, that is billions of dollars a year slipping out through leaks no one has found yet.

Analytics can move that work earlier in the timeline. By reading flow, pressure, and acoustic data, models can flag the early signs of a leak and narrow down where it is, turning a reactive scramble into something closer to scheduled maintenance. The same approach applies to other critical assets. Patterns in vibration, temperature, and energy use can show when a pump or valve is trending toward failure, so it gets serviced before it quits.

DC Water has put this to work in its sewers. Inspecting more than 1,800 miles of pipe generates an enormous amount of CCTV footage, which engineers once reviewed by hand. That was slow, costly, and inconsistent, since every reviewer brings a slightly different eye. The utility’s AI tool, PipeSleuth, scores against industry standards and flags defects with color-coded detail, identifying close to 50 distinct defect types at better than 90 percent accuracy on vitrified clay pipe. It has cut video-analysis time by about 90 percent and freed engineers to fix problems instead of just finding them.

That kind of asset-condition picture changes planning, too. Rather than reacting to the worst break of the week, a utility can prioritize rehabilitation and replacement across the whole system based on what the data shows is deteriorating.