AI In The Water Sector: Navigating Innovation Responsibly
By Ashwin Dhanasekar and Sandy Moskovitz
How utilities can balance efficiency, resilience, and ethics in the age of intelligent infrastructure.
Mounting Pressures On The Water Sector
Globally, the water sector is facing mounting challenges. The situation is particularly acute in the U.S., where nearly half of utilities report that aging infrastructure presents their most significant obstacle (Source: ASCE). Climate-related phenomena, such as floods, droughts, and wildfires, are compounding these pressures, threatening both water supply and service reliability. Demand is not abating, either; by 2050, global water consumption is expected to rise by more than 20%, placing additional strain on already vulnerable systems (Source: World Economic Forum). Utilities must manage this complex interplay of escalating demand, deteriorating assets, limited financial resources, regulatory scrutiny, and workforce transitions.
The Acceleration Of Artificial Intelligence (AI) Adoption
According to Stanford’s 2024 AI Index Report, AI adoption is accelerating across industries: 78% of organizations reported using AI in 2024, up from 55% in 2023. Use of generative AI more than doubled in the same period, rising from 33% to 71%. This rapid growth underscores the need for governance frameworks to keep pace. The water sector, facing escalating demands and aging infrastructure, cannot afford to be left behind. Embracing AI is not just about efficiency; it's about ensuring future resilience and continued service delivery in a world increasingly reliant on intelligent systems.
Barriers To AI Adoption in Utilities
Despite its promise, AI adoption in the water sector is slowed by practical and organizational barriers. Data quality is one of the most persistent issues. Many utilities still rely on legacy systems that store incomplete or incompatible information, undermining model accuracy. Limited budgets and staffing capacity further complicate matters, especially for smaller utilities that may lack the resources to scale projects. Cultural readiness is also critical; without training and trust, staff may hesitate to act on AI insights, leaving valuable recommendations underutilized. Addressing these barriers upfront is essential if AI is to deliver sustainable value.
Cybersecurity and Data Privacy: A Barrier of Its Own
Cybersecurity and data privacy add another layer of complexity. Utilities face a strategic choice between local and cloud hosting, each with trade-offs. Local hosting provides tighter control over security and sovereignty but places the burden of managing robust protections entirely on in-house teams. Cloud platforms often offer stronger compliance frameworks and advanced safeguards, however raise questions around vendor dependence, data sovereignty, and jurisdictional oversight. Increasingly, utilities are exploring hybrid approaches that balance the control of local hosting with the scalability and protections of cloud solutions. How utilities navigate this choice will significantly influence not only the success of their AI deployments but also public trust in these systems.
Opportunities Across The Water Cycle
Within this context, AI is emerging as a transformative tool. Projections indicate that the global AI market for water and wastewater management will expand by nearly 12% each year through 2030, driven by AI’s capacity to process large datasets, identify subtle patterns, and generate predictive insights. Utilities that have adopted AI are already experiencing improvements, ranging from expedited leak detection and lower energy consumption to enhanced resilience planning. Technology alone does not deliver transformation; it’s how insights are applied across people, processes, and policy that drives lasting impact.
Operational Efficiency: From Reactive to Predictive
AI is increasingly applied across the full water cycle, helping utilities move from reactive responses to proactive, and even predictive, operations.
Detecting Leaks and Predictive Maintenance
Sydney Water’s AI-driven leak detection program demonstrates the benefits of combining sensors with machine learning. Real-time acoustic and pressure data are analyzed to detect anomalies that might escape human review. Field crews can act quickly to prevent bursts, saving millions of liters of water and reducing maintenance costs.
Seattle Public Utilities has taken a similar approach with its AI-assisted CCTV inspections. Automated systems highlight potential defects in wastewater footage, which are then verified by operators before repair orders are issued. This “human-in-the-loop” model improves both the speed and accuracy of inspections while maintaining oversight on high-stakes decisions.
Explainability is central to these successes. As the SWAN AI Community emphasizes: “Operators need to trust AI outputs to make high-stakes decisions. Model transparency is as important as technical performance.”
Optimizing Operations and Energy Use
Predictive modeling is also helping utilities cut energy consumption, a key step toward decarbonization. By integrating weather data, customer usage trends, and reservoir levels, AI enables more accurate demand forecasting. Utilities can then fine-tune pump and treatment plant operations, avoiding unnecessary energy use while balancing supply security, flood mitigation, and environmental needs.
Strategic Planning and Resilience
Beyond daily operations, AI is influencing the strategic decisions that shape future resilience.
In Australia and Singapore, utilities are combining demographic projections, climate risk data, and asset condition assessments into AI models that guide capital planning. These insights identify which assets are most critical and most at risk, helping utilities prioritize upgrades and stretch limited budgets further.
Responsible AI: Key Considerations For Utilities
While opportunities are substantial, AI in the water sector must be deployed responsibly to avoid introducing new risks or inequalities. Four principles stand out:
- Governance and Oversight: AI should augment human expertise, not replace it. Clear data principles, risk management processes, and human review at decision points are essential. Utilities should also track data lineage for auditability and document how AI outputs are generated.
- Transparency and Explainability: Dashboards that show both recommendations and the reasoning behind them allow operators to explore an AI’s decision pathway. This transparency builds trust and reduces misinterpretation, particularly in safety-critical operations.
- Equity and Accessibility: AI must serve all communities, not just those with advanced infrastructure. Without deliberate planning, tools may be deployed first in well-served areas, leaving others at greater risk of leaks, contamination, or service disruptions. Equity should be a design requirement from the outset.
- Robustness and Reliability: From leak detection to flood forecasting, AI failures can have major consequences. Ongoing monitoring, validation, and scenario testing are critical to ensure systems remain reliable under both routine and extreme conditions.
This EY Responsible AI framework, presented during the July 31 SWAN AI Community of Practice call by Kavi Pather, Africa AI Leader (Ernst & Young), breaks responsible deployment into actionable pillars, forming a checklist for utilities.
The Path Forward: Collaboration And Workforce Development
The most successful AI deployments in the water sector, like those seen in various utilities, integrate technological capabilities with human insight, operational context, and ethical governance. Utilities are increasingly collaborating with peers, researchers, and technology providers to share lessons and accelerate adoption, directly impacting how these systems are deployed and managed. Workforce development is crucial for this transformation within utilities.
As AI shifts roles from “doing” to “reviewing” for water sector staff, utilities will need employees skilled in oversight, interpretation, and governance of AI-driven systems. Simultaneously, AI can enhance knowledge transfer within utilities, capturing the expertise of an aging workforce and making critical institutional knowledge more widely available to new generations of operators and engineers.
By embedding ethical principles specific to water management, fostering transparency in AI’s decision-making processes, and ensuring equitable access to these technological advancements, the water sector can responsibly harness AI’s potential. This will build smarter, more resilient, and inclusive water services for the future, directly benefiting communities and infrastructure.
Ashwin Dhanasekar is Principal of Research, Innovation, and Digital Solutions at Brown and Caldwell.
Sandy Moskovitz is Programme Director for the Smart Water Networks Forum (SWAN), the leading global hub for the smart water sector.