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Digital systems, increasingly strengthened by artificial intelligence, are helping water utilities move from reacting to failures toward anticipating where infrastructure problems are most likely to occur.
Joshua Cantone of SUEZ explains how that shift is changing decisions about pipes, pumps, wells, treatment plants, and long-term capital investment.
Many utilities already collect large amounts of information through control systems, maintenance records, hydraulic models, and asset-management platforms.
The opportunity now is to connect those datasets and use AI-assisted analysis to identify which assets should be cleaned, repaired, rehabilitated, or replaced before they fail.
That preventive approach matters because emergency infrastructure repairs can cost two to three times more than work that is planned and coordinated in advance.
Digital planning tools can test thousands of infrastructure scenarios, helping utilities compare construction costs with the energy and operating expenses that accumulate over decades.
Cantone also argues that growing familiarity with tools such as ChatGPT, Claude, and Microsoft Copilot is making utility employees more comfortable with specialized AI designed for water operations.
Groundwater monitoring offers another example of the shift, with more frequent data helping operators track well performance, maintain production, and reduce the energy required for pumping.
Smart meters and digital network models are also giving utilities a clearer view of non-revenue water, which Cantone says remains near 20 to 30 percent at many U.S. utilities compared with roughly 5 to 15 percent in parts of the United Kingdom.
The next phase of digital water will depend on whether utilities can build reliable data systems—and enough operator trust—to use AI and other digital tools in everyday decisions.
waterloop is a nonprofit news outlet exploring solutions for water sustainability.
