The role of AI in smart water management

Driven by the need to adapt to climate change, demographic and environmental challenges, and to optimise water resource management, Australia and New Zealand are taking strategic steps towards the digitalisation of the integrated water cycle.

Both regions, with advanced regulatory frameworks and growing awareness of sustainability, are implementing digital technologies to improve the management of drinking water, sanitation, wastewater treatment and stormwater. It is worth noting that Australia is not only the driest inhabited continent on Earth, it also has one of the highest per-capita water consumption rates in the world — 493 L per day, according to UN-Habitat.

New Zealand, on the other hand, has a relative abundance of water resources. However, intensive agriculture, excessive water extraction for irrigation, droughts and outdated infrastructure —resulting in water loss through leaks — are among the key challenges it must address to ensure equitable and sustainable access to water. As a result, the sector is undergoing a profound transformation aimed at modernising and unifying the management of drinking water, wastewater and stormwater services through the Three Waters program.

In this context, water resource management — integrating IoT sensors, artificial intelligence (AI) and predictive analytics, among other technologies — is emerging not just as an option but as a key solution to enhance operational efficiency and reduce environmental risks.

According to Joan Carles Guardiola, Business Development Manager, Xylem Vue at Xylem ANZ, “The digital tsunami we are immersed in must be leveraged by all industries and sectors — especially the water sector, which can rely on multiple digital tools to help utilities improve their operations and efficiency.”

The role of digitalisation: predictive management

Digital transformation, therefore, is a tool that helps utilities optimise operations, retain institutional knowledge, reduce the use of natural resources, improve service quality, and anticipate water crises and extreme events — such as the numerous floods that have affected Eastern Australia in recent years or the 2023 floods in Auckland.

Several digital applications being implemented in the region are proving highly useful in facing these challenges:

• Real-time monitoring of water networks: Digitalisation allows for a shift from reactive maintenance to a predictive approach. Smart networks collect real-time infrastructure data through various sensors, which are then analysed using AI algorithms to detect anomalies and anticipate failures.
• Predictive analytics and big data: Advanced data analysis makes it possible to process massive volumes of historical data to identify behavioural patterns, anticipate infrastructure failures, improve responses to extreme events, and plan investments more effectively.
• Digital twins of infrastructure: Digital twins are virtual replicas of plants, networks and treatment systems that simulate different operational scenarios and support decision-making through real-time simulations. These systems can also generate recommendations or even automate certain processes to optimise performance while meeting required service levels.
• Smart agriculture and efficient water use: Agriculture plays a fundamental role in the economy of both countries, which are global agri-exporting powers. (In New Zealand’s case, agriculture is a key economic pillar.) For this reason, digital irrigation — through sensors, satellite imagery and AI — is transforming water management in agriculture, enabling more precise and sustainable use of the resource.
   

Benefits of predictive management are:

• Cost reduction: Data-driven interventions lower operational costs by avoiding unnecessary maintenance and emergencies.
• Improved service: Service continuity is enhanced by preventing network outages or sewer overflows that affect communities.
• Environmental sustainability: By reducing resource consumption and minimising untreated discharges, natural ecosystems are better protected.
• Extended infrastructure lifespan: Detecting failures before they occur increases the durability of assets.
• Knowledge retention: Predictive management allows for the structuring, systematisation and preservation of operational and historical knowledge. Through predictive models fuelled by historical and real-time data, the accumulated expertise of technicians, operators and managers is captured, avoiding its loss due to staff turnover or retirement.
• Data democratisation: With the integration of predictive platforms, data moves out of isolated departmental silos and becomes accessible and understandable to various profiles within the organisation. This breaks down traditional barriers to knowledge management and fosters a more transparent and participatory approach.
• Value generation: Predictive management not only optimises processes but also transforms data into strategic insights, enabling anticipation of critical events, cost reduction and improved asset performance. In doing so, it turns information into a competitive advantage, allowing utilities to offer better service, reduce risks, and maximise the economic, social and environmental value of water.
   

However, the digital transformation of water utilities, in pursuit of smart water management, must consider three key aspects:

• Cybersecurity: With digitalisation come new threats and connected networks must be protected accordingly.
• Corporate culture and digital skills: One of the cornerstones is staff training. As mentioned earlier, digitalisation adds value to society, but achieving this requires developing and encouraging digital skills among operational teams.
• Public–private collaboration: Continuous cooperation between governments, technology companies and water operators is essential to enhance customer service and integrated water cycle management.
   

The integration of AI and predictive maintenance not only optimises the present but also prepares cities for a resilient future in the face of climate change. In regions like Queensland and New South Wales — where extreme weather events are increasingly frequent — these tools will be essential for anticipating risks and ensuring service continuity.

Smart wastewater networks, powered by AI and IoT sensors, represent a quiet yet crucial revolution in water management across Australia and New Zealand. For example, in Adelaide, the implementation of Xylem Vue to detect hydraulic transients in its distribution network has enabled improved asset and operational management, reducing the number of incidents and extending infrastructure lifespan by minimising stress.

This demonstrates that predictive maintenance is not only possible but also highly cost-effective and sustainable. As technology continues to advance and costs decrease, broader adoption is expected — transforming the sanitation system into a central pillar of resilient urban water management.