Murali Mantravadi Writes on How AI Can Change the Future of Water Management

Murali Mantravadi, Joint Managing Director at Flosenso, writes that predictive systems, real time monitoring and data led planning can help address rising water stress and infrastructure challenges

Water scarcity is no longer a distant concern. It is emerging as a significant economic and social challenge, driven by rapid urbanisation, climate variability and ageing infrastructure.
According to the United Nations, nearly two billion people already live in water stressed regions, and global demand is expected to rise by more than 30 percent by 2050. In India, where groundwater supports a large share of irrigation and drinking water supply, the pressure on resources is particularly visible.

However, the issue is not limited to availability. It is also linked to how efficiently existing water systems are managed. In many cases, water management continues to follow a reactive approach. Utilities respond after failures occur, cities act when reservoirs decline, and households often remain unaware of wastage caused by leaks or inefficient usage.
A shift toward predictive systems is increasingly being seen as necessary.
Advances in artificial intelligence and data analytics are enabling more informed planning and decision making. Demand forecasting, for instance, is becoming more precise by combining historical consumption data with real time inputs such as weather patterns, population trends and seasonal cycles. This allows utilities to better plan supply and reduce the risk of sudden shortages.

Infrastructure monitoring is also improving with the use of sensors and data analysis. Water losses due to leaks and pipeline failures remain a significant issue in many cities. With connected systems, changes in flow and pressure can be detected early, allowing timely intervention and reducing both water loss and repair costs.
Climate variability adds another layer of complexity. Changing rainfall patterns and increasing frequency of droughts require better planning tools. Data driven models can help estimate reservoir inflows, groundwater recharge and potential stress periods, enabling more informed decisions around storage and distribution.

At the same time, technology is extending beyond large infrastructure into residential spaces. Smart water systems in homes and buildings can regulate usage, prevent overflows and monitor consumption patterns. When adopted at scale, such systems can contribute to reducing overall wastage.
The larger takeaway is the growing role of data in water governance. As seen in the energy sector, digital systems can improve efficiency, visibility and long term planning. Similar approaches in water management could help address both supply and distribution challenges.

Water will remain central to economic stability, public health and environmental sustainability. The available technologies offer new ways to manage it more effectively, but their impact will depend on how widely they are adopted and integrated into existing systems.

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