A foundational layer for data-driven water systems 1% Water receives less than 1% of global climate-tech investment — far below other critical infrastructure sectors The water investment gap Advanced Metering Infrastructure is often framed as a billing tool, but at scale it functions as the data backbone of modern water systems. By establishing a continuous transmission of near-real-time data across the network, AMI enables utilities to move beyond static, asset-level management toward a more dynamic understanding of system behaviour. This data foundation enables more coordinated, system-level management of network performance. Rather than relying on isolated data points, utilities can develop a more integrated view of performance, supporting more informed and adaptive operations. It also enables integration with broader digital tools and platforms, allowing water systems to be managed as interconnected, data driven networks that link operations with longer term planning.
Strengthening resilience: Supporting emergency response and disaster readiness This shift in visibility has immediate implications for how utilities manage risk and disruption. Water system resilience is not a standalone function, but a direct outcome of improved system awareness. With AMI in place, utilities can detect anomalies earlier and monitor system behaviour in near real time. While these capabilities are already improving day-to- day risk management, their application in extreme event response, such as wildfires, floods or contamination events is still emerging³. As utilities expand their use of high-frequency data, these systems could support more dynamic monitoring during disruptions, enabling more targeted responses to outages, infrastructure damage and potential contamination risks. These capabilities may be particularly valuable during high-impact events. In wildfire or extreme heat conditions, enhanced visibility could help utilities identify areas experiencing elevated demand or operational strain, improving coordination with emergency services. In large urban networks, it may also support earlier detection of emerging issues and more effective mobilisation of response efforts. For example, Houston Public Works plans to deploy stormwater sensors and use data analytics to predict flooding, helping improve resilience and operational response during extreme weather events⁴.
In this way, AMI has the potential to strengthen resilience by making water systems more responsive. As utilities translate system awareness into timely action and communication, they will be better positioned to protect people, infrastructure and essential services during disruption. From visibility to intelligence: Enabling predictive and integrated systems AMI is often described as a way to improve visibility, from more frequent reads to faster leak detection and better customer insights. Its strategic value lies in what that enables. As meter data becomes a continuous input into connected systems, utilities can move from understanding what has happened to making forward looking decisions about risk, performance and investment. One way this shift is realised is through predictive analytics. When AMI data is combined with asset information such as age, material and break history, advanced analytics and AI can identify patterns that periodic monitoring often misses. This allows utilities to anticipate issues earlier and reduce the cost and disruption associated with reactive responses. For example, Thames Water reports that its smart metering program has helped detect more than 28,000 leaks on customer supply lines, with repairs saving approximately 43 million litres of water per day. In more advanced applications, these capabilities can extend further through digital models. Connected to core operational systems, AMI data can support digital twins that reflect how systems behave over time. This allows utilities to test scenarios, refine pressure zones and assess how networks respond under stress. For example, the Public Utility Board, Singapore’s national water agency, has developed a digital twin of its Changi Water Reclamation Plant that integrates operational data with simulation models to improve system performance and support decision making. The result is a shift in how water systems are managed, from time-based programs and reactive interventions toward more condition-based, predictive approaches. AMI does not simply provide more data; it enables more proactive, forward-looking decisions. Expanding value: Unlocking broader system and customer benefits In addition to these operational gains, AMI can unlock further value by improving everyday service and strengthening system oversight.
Turning usage data into daily decisions
On the customer side, AMI can connect to mobile apps and online portals that provide visibility into water use, issue leak alerts and support more informed decision- making. Evidence suggests that when households have access to this information alongside conservation- focused messaging, they can meaningfully reduce water use, with savings driven largely by earlier leak detection and repairs⁵. This transparency helps customers respond earlier to issues and better manage water use and costs. In Toronto, these tools are being considered as part of a planned AMI rollout, with a focus on improving transparency and enabling future integration with broader smart city systems. At the system level, AMI data can be aggregated to analyse demand patterns across service areas, helping utilities identify peak usage and better understand how consumption varies over time. This can support more informed planning and help align system capacity with actual usage. Palm Beach County Water Utilities, which serves approximately 635,000 residents, has incorporated hourly AMI data into its rate-setting processes to better reflect observed demand patterns. More broadly, improving how water demand is understood and managed can have implications beyond the water system itself. For example, because pumping, treating and heating water are energy-intensive processes, even incremental reductions in water demand can translate into lower energy use over time.
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