RIYADH: As Saudi Arabia faces rising freshwater demand, the desalination industry is exploring how artificial intelligence could improve efficiency, cut costs, and secure long-term sustainability.

As the world’s largest producer of desalinated water, the Saudi desalination industry is constantly looking for new technologies and innovative solutions to advance the field.

In July 2026, the Saudi Water Authority signed its landmark deal with US-based Energy Recovery for a project expected to contribute SR137 million ($36.54 million) to Saudi Arabia’s gross domestic product by 2033.

Looking ahead, the industry is turning its attention to AI as a way to make the desalination process itself smarter.

A 2025 report published by the Saudi Water Authority, titled “AI Applications in The Desalination Industry,” described AI integration as a vital step toward ensuring a sustainable water supply for future generations.

The report noted that AI’s impact extended beyond individual plants to the entire water value chain, from sourcing and resource management to treatment, distribution, and customer experience.

At King Abdullah University of Science and Technology, pilot work is already underway exploring AI-based tools in desalination.

Najat Amin, a Ph.D. student in environmental science and engineering at KAUST, described the initial results as very promising.

“We are at a genuinely transitional moment in the field,” she said. “It has moved beyond theoretical promise, and there is now substantial research showing that AI/ML can predict membrane behavior, detect fouling in the early stage, reduce energy consumption, optimize variables, and improve process control with measurable accuracy.”

However, she noted that commercial adoption remained limited.

“Most implementations today sit somewhere between well-validated laboratory models and site-specific pilot projects, rather than routine deployment across the industry,” she said.

Broadly speaking, AI can optimize desalination by using data-based models to run processes more efficiently, cut energy use, and reduce waste, while helping integrate renewables and monitor equipment in real time.

“This would improve reliability, reduce energy use, limit chemical waste, and help maintain consistent water quality,” she said.

She pointed to a reported example from a desalination company in Jeddah where AI-based monitoring reduced power consumption by 7 percent, cut chemical use by 15 percent, and increased water production by 8 percent.

Amin explained that AI can analyze data to identify which variables have the greatest influence on energy consumption, giving operators a clear picture of where to focus their efforts.

“AI can also support predictive maintenance by detecting equipment problems early, before they lead to energy waste or serious damage. This could make desalination plants more efficient and reliable,” she said.

AI could also help accelerate the use of renewable energy in desalination.

“In Saudi Arabia, the opportunity goes further,” Amin said. “The Kingdom’s solar resource makes renewable-powered desalination attractive, but solar energy is variable by nature.”

Noreddine Ghaffour, professor of environmental sciences and engineering at KAUST, said AI-integrated desalination could make a noticeable difference in everyday life by helping provide a more reliable supply of drinking water.

“AI-integrated desalination could make a noticeable difference in everyday life. It could help provide a more reliable supply of drinking water by detecting problems early and adjusting the desalination process in real time,” he said.

“Fewer plant breakdowns could mean less risk of interruptions, while better water management could support homes, agriculture, and industry,” he explained.

Ghaffour noted that the question is no longer whether AI can contribute to desalination, and said: “The evidence shows that it can.”

The main challenge now is scaling successful pilots to systems that can run reliably in commercial operation, he added.

Ghaffour said the cost and complexity of upgrading existing desalination plants were among the main factors preventing wider AI integration.

“Many facilities use older equipment and systems that may not easily connect with modern AI tools, and AI requires accurate, consistent data to make reliable predictions,” he said.

Other obstacles include a shortage of specialists who understand both AI and desalination, as well as the intensive energy requirements generally associated with mass AI operations, especially for cooling.

Ghaffour said: “Ultimately, success depends on reliable instrumentation, cybersecurity, and engineers who can challenge model outputs rather than simply accept them.”

Given these foundations, he predicts AI-driven plants could handle most routine decision-making by the end of the decade, while engineers move into a supervisory role.

Deploying AI in desalination advances broader Vision 2030 goals by accelerating technological development in a critical national sector and strengthening sustainability through reduced energy and chemical consumption, lowering the sector’s operational costs and environmental impact.

Ghaffour added: “AI-integrated desalination enhances water security, cultivates Saudi technical talent, encourages research and innovation, and opens opportunities for local companies, directly supporting Vision 2030’s pillars of economic diversification, digital transformation, environmental protection, and sustainable resource management.”