
Leading utilities are leveraging AI-powered risk modeling to make confident, data-driven decisions that save millions and improve service. This brief highlights 5 real-world applications of this technology, showcasing how predictive modeling helps organizations stretch budgets, reduce leaks, and maximize resource allocation.
The proven benefits of this technology are significant and measurable. Here are just a few examples of the results utilities are achieving:
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Rock Hill, SC avoided 97% of unnecessary service line inspections in their lead program, saving an estimated $7 million.
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Tucson Water increased pipe failure prediction accuracy to 77% and used Remaining Useful Life (RUL) insights to save $5.4 million in avoided premature pipe replacements.
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Greenville Water layered Al risk data with leak detection to save an estimated 71 million gallons of water annually.
Read how these utilities are stretching budgets, reducing leaks, and maximizing resource allocation with predictive modeling.
