Water utilities around the world are at a critical juncture. Climate change, urban growth and aging infrastructure are putting unprecedented strain on already limited and precious resources, of which, water is key. Research from the World Resources Institute’s Aqueduct Water Risk Atlas puts at least 50% of the world’s population, about 4 billion people, at high risk of facing water stress each year1. These risks are not theoretical or in the distant future - they are evidenced today through failing pipelines, service disruptions and escalating operational and maintenance costs.
This blog is part of our 'AI for Water' series, where we explore how artificial intelligence can transform water utilities. In this perspective, we delve into the practical applications of predictive AI and how it can address some of the water sector’s most pressing challenges, from aging infrastructure to operational inefficiencies.
In Europe, systems built over a century ago are grappling with meeting modern infrastructure demands. In the United States, the American Society of Civil Engineers (ASCE) has consistently graded the nation's drinking water infrastructure at a "C-" level, indicating significant deficiencies. Further, in India, water demand is projected to double by 2030 due to population growth.
Despite these pressures, many water utilities still continue to rely on outdated systems that are costly, inefficient and vulnerable. Failure to innovate could mean widespread disruptions, and moreover, result in huge economic and social costs.
Met stedelijke verdichting, klimaatadaptatie en woningtekorten aan de orde van de dag, moeten we ons sterker richten op een duurzame, gezonde en toekomstbestendige inrichting van onze steden. Aan de basis hiervan liggen drie aspecten:
Artificial Intelligence (AI) offers a way forward. If applied effectively, AI could revolutionize how water utilities manage limited resources and optimize operations. While much of the conversation today evolves around generative AI - a technology that creates content and has sparked debates around ethics and risks - predictive AI is a more promising solution, operating quietly in the background, delivering tangible results.
Unlike generative AI, predictive AI has been around for decades and has matured significantly over the past 10-15 years. Its purpose is clear: to forecast, optimize and improve systems based on data-driven insights. What’s more, the risks associated with predictive AI are far less, making it a more practical and reliable solution for water utilities. And, it focuses on critical applications needed today like pipe condition assessment, forecasting future system states and optimizing resource utilization.
With the urgency to act, now is the time to move from theory to practical application.
