Power networks, water systems, roads and railways hold years of operating history, yet information about them is often scattered across sensors, software, spreadsheets and paper. Applying AI to these assets is harder than deploying it in a new project: Equipment cannot simply be switched off, and poor recommendations can affect safety and reliability.
But better forecasts, earlier fault detection and more informed maintenance can make existing systems work better. AI in infrastructure is not one technology: Machine-learning models can forecast demand or equipment failure, computer vision can inspect assets, and analytical tools can help operators interpret data.
AI can also aid project planning by estimating schedules, resource needs and cost overruns. At construction sites, computer vision can flag hazards and check progress and quality; drones can inspect hard-to-reach areas. In operating systems, predictive maintenance can help teams act before equipment fails. Weather models can improve wind and solar forecasts, while traffic analytics can support more responsive urban transport.
India’s power-distribution network shows the potential. Under the Revamped Distribution Sector Scheme, India is installing nearly 200 million prepaid smart meters, more than 5.2 million distribution-transformer meters and around 210,000 feeder meters. Together, these devices can provide a continuous view of network conditions.
Combining meter, feeder, weather and asset data can reveal unusual consumption, overloaded feeders, technical losses or early signs of equipment failure. Analytics could flag a vulnerable transformer, recommend load rebalancing and help position a field crew before a fault becomes an outage. Ideally, customers would never notice the interruption that was prevented.
Water is another promising application. The Jal Jeevan Mission aims to connect 190 million rural households to piped drinking water. Sensors can help monitor the quantity, quality and regularity of supply. AI could identify leaks, detect unusual pump behaviour and show where service is falling short from the source through the network to household taps.
International examples offer practical lessons. In Sweden, water utility VA SYD trained AI to monitor 2,000 kilometres of pipes. Siemens reports that water loss fell from 10 per cent to 8 per cent, while smaller leaks could be detected before bursts.
In Britain, Network Rail is developing its INSIGHT decision-support tool to help frontline teams diagnose track problems earlier; the organisation says it could save more than £30 million a year. Italy’s Enel uses AI and computer vision to identify grid components and anomalies, helping teams distinguish routine inspections from urgent maintenance.
The International Energy Agency estimates that AI-based fault detection could reduce outage durations by 30-50 per cent, while digital tools could unlock up to 175 Gw of transmission capacity without building new lines. These are estimates of potential, not guaranteed outcomes; results depend on the assets, data and operational response.
The challenge is that brownfield information is rarely ready for AI. Asset records may be incomplete, identifiers inconsistent and data trapped in disconnected systems. Infrastructure must keep running while improvements are made. Operators, therefore, need dependable, secure and usable data, not simply a large “data lake.”
Clear asset registers, reliable records, interoperable systems and accountable data ownership are more useful starting points. Organisations should select the method that fits the task: A forecasting or anomaly-detection model may solve an equipment problem more simply than a large language model. Language models can help staff search manuals or maintenance histories, but they cannot substitute for accurate operational data or engineering judgement.
Cybersecurity must be designed in from the start. Connecting systems can widen the attack surface, particularly where (IT meets operational technology. Strong access controls, network separation, monitoring and recovery plans are essential. So is frontline involvement: Engineers and field workers can identify operating conditions that may be invisible in the data.
A sensible path is to start with one high-value use case, establish a baseline and run a controlled pilot. Measure results that matter — outage time, water loss, equipment failures, inspection effort, false alarms and repair costs. Keep people responsible for consequential decisions, define a safe fallback and expand only when the pilot demonstrates reliable value.
Procurement should also specify who owns the data, how systems interoperate, and how performance can be audited. Models must be tested regularly as assets, operating patterns and weather conditions change. An alert has value only when a named team can verify it and act within a timeframe.
The countries that benefit most from AI will connect intelligence to productive assets at scale. India should continue building new infrastructure while making existing systems more reliable, efficient and responsive. The AI dividend will not be won only in data centres. It will also be won in the control room that catches a fault early, beneath the road where a leak is found before a burst, and at the bus stop where services arrive when expected.
The writer is an infrastructure expert. He is also the founder & managing trustee of The Infravision Foundation. With research inputs by Srinivas Gutta
