India’s water sector stands at a pivotal juncture. Mounting water stress, ageing infrastructure, and increasingly extreme weather events are converging with rapid urbanisation and rising demand. Nearly 600 million Indians face acute water stress, and with 70 per cent of resources contaminated, the country ranks 120th out of 122 countries in global water quality, according to NITI Aayog.1 Millions remain without access to safe water, while climate change and population growth intensify pressure on already strained systems, amplifying risks to growth and stability.
For decades, water management in India has relied on manual monitoring, scheduled sampling, and reactive maintenance. Drinking water networks continue to lose non-revenue water due to undetected leaks and pressure anomalies. Treatment plants often lack real-time insights into efficiency and sludge management, while sewer systems depend on outdated telemetry that leads to blockages and overflows during monsoons. Flood and stormwater management still relies on static models and delayed alerts. The absence of predictive, data-driven strategies significantly undermines resilience and responsiveness.
That paradigm is beginning to shift, as India lays the foundation for scalable AI adoption in the water sector through digital solutions designed to transform service delivery. The Integrated Management Information System (IMIS) aggregates data across the water sector value chain, creating a robust platform for AI to analyse trends, predict risks, and optimise planning. Supervisory Control and Data Acquisition (SCADA) systems, now widely used in urban utilities, enable continuous monitoring of pumps, reservoirs, and treatment units, thereby generating high-frequency data essential for AI-driven anomaly detection and predictive maintenance. Similarly, IoT sensors are increasingly deployed to track water quantity and quality, feeding directly into AI systems for early alerts and demand forecasting. AI is transforming water operations by providing real-time situational awareness and predictive insights at every level of the system.
In drinking water networks, AI detects pressure anomalies and leaks in real time, cutting water loss and optimising energy use. AI-powered biosensors enable more accurate detection of toxic contaminants than traditional methods. In treatment plants, AI analytics fine-tune chemical dosing, track sludge dynamics, and enhance energy efficiency while upholding high water quality standards. Sewer systems use machine learning (ML) to predict blockages and overflows, enabling proactive maintenance, and reducing health risks. In flood and stormwater management, AI-driven hydrological models, powered by remote sensing and real-time data, deliver earlier and more reliable warnings, strengthening emergency preparedness.
Several states are already piloting AI to address water challenges, exploring its potential to optimise water-use efficiency. These initiatives are gaining momentum across India, particularly in operations, maintenance, and data-driven decision making to improve service delivery and ensure long-term sustainability.
AI gives India an opportunity to reshape water governance, not merely improving service delivery but redefining how water is managed as a national resource: intelligently, inclusively, and sustainably. By closing gaps in decision making and equipping institutions with actionable insights, AI enables a decisive shift from reactive fixes to proactive strategies.
As India advances towards the vision of Viksit Bharat, harnessing AI is poised to be central to securing every drop, strengthening every system, and safeguarding the nation’s water future for generations to come.
