Drinking water utilities worldwide are facing a perfect storm of challenges—tighter regulations, erratic climate patterns, shrinking freshwater supplies, and surging demand. Traditional administrative systems, often bogged down by siloed data and slow decision-making, are struggling to keep pace. But what if artificial intelligence could act as the missing link, turning raw data into actionable insights that drive efficiency, reduce waste, and improve service reliability?
That’s the bold proposition put forward by Enneffah Z. of Ibn Tofail University, whose new research published in the *E3S Web of Conferences* (translated: *E3S Web of Conferences*) explores how AI-based Decision Support Systems (DSS) can transform drinking water management from the ground up. “We’re not just talking about automation,” Enneffah explains. “We’re talking about a paradigm shift—where AI becomes a strategic partner in governance, helping utilities navigate complexity with greater agility and foresight.”
The core of the innovation lies in integrating machine learning models—such as Random Forest algorithms—into the operational heart of water utilities. These models don’t just crunch numbers; they learn from them. By analyzing vast streams of data on water flow, pressure, and infrastructure health, the system can predict leaks before they happen, optimize pump schedules in real time, and even forecast demand shifts tied to weather or population trends. “It’s like having a 24/7 operations center that never sleeps,” says Enneffah. “But instead of humans watching screens, the system is watching the system—and intervening when necessary.”
The commercial implications for the energy sector are particularly compelling. Water and energy are intrinsically linked; pumping, treating, and distributing water consumes significant electricity. AI-driven efficiency in water management doesn’t just save water—it reduces energy demand across the entire supply chain. A utility that cuts non-revenue water (leakage) by 15% could, in turn, lower its electricity bill by millions annually. That’s a direct line to improved EBITDA and sustainability metrics, especially for energy-intensive regions or utilities under decarbonization pressure.
Yet the path to implementation is not without hurdles. Enneffah underscores the need for robust data governance, interoperable systems, and transparent AI—so that decisions aren’t black boxes, but accountable tools. “We can’t afford to trade one inefficiency for another,” he warns. “AI must be explainable, auditable, and aligned with public trust.”
As water utilities and energy providers increasingly collaborate under climate and regulatory pressures, AI-based DSS could become the backbone of next-generation infrastructure. The research by Enneffah and his team isn’t just academic—it’s a blueprint for how data, when wielded wisely, can turn operational challenges into competitive advantages. And in a world where every drop and every kilowatt counts, that kind of foresight isn’t just smart—it’s essential.