“Technology, when guided by purpose, becomes more than a tool, it becomes an act of stewardship for the generations that follow.” – MJ Martin
Introduction
In Canada’s water utility sector, the emergence of artificial intelligence (AI) offers a transformative opportunity to enhance resilience, efficiency and service to communities. The challenges faced by many Canadian utilities, including aging infrastructure, climate-driven extremes, rising energy costs, regulatory demands and growing expectations of transparency, call for innovative responses. As noted by the global advisory firm Arcadis, AI is becoming the backbone of how water utilities can “cut carbon, make decisions faster and empower and protect communities.” This paper presents a structured overview of how AI is being applied within water utilities, tailored to a Canadian context, and explores practical use cases, enabling conditions and considerations for operators.
AI Focus Areas
There are five core focus areas for AI in Canadian water utilities, they are:
- Customer Experience
- Safety
- Predictive Maintenance
- Demand and Consumption Reconciliation and Remediation
- Distribution Optimization
Leak and Asset-Failure Prediction
One of the most compelling use cases for AI in water utilities is predicting leaks and asset failures before they become major disruptions. Traditional asset management often relies on reactive or scheduled maintenance; by contrast, predictive analytics draw on sensor data (pressure, flow, vibration, acoustic), historical failure records and environmental parameters to identify anomalies. For example, the technology providers reports that AI-driven platforms have enabled utilities abroad to reduce visible leaks by 57 % and pipeline repairs by 30 %. In a Canadian setting, where many municipalities manage long-buried networks and face water loss and non-revenue water issues, applying AI-based leak detection can yield significant water savings, lower operational costs and reduce the carbon footprint of lost treated water. The utility training and upskilling dimension is key: some studies indicate that 39 % of professionals believe AI will help automate routine inspections and free staff for higher-value work.
Optimized Treatment and Energy Use
Water treatment plants and distribution systems are energy-intensive, particularly for pumping, chemical dosing and monitoring. AI offers the capability to optimize these processes dynamically. For example, a manufacturer overview identifies energy-optimization as a primary AI trend: predictive models adapt operations according to demand, real-time climate data and consumption patterns, with realized energy reductions of up to 25 %. In Canada, with its variable climate zones and complex water sources, AI-enabled systems can adjust pump schedules, treatment intensities and storage levels in real time, thus aligning operations with both cost-control and sustainability priorities. Moreover, regulatory frameworks in many provinces encourage utilities to reduce energy consumption and carbon emissions, making these AI applications aligned with broader policy objectives.
Demand Forecasting and Network Management
Beyond infrastructure and treatment, AI excels at forecasting demand and managing distribution systems in an intelligent manner. Leveraging Internet of Things (IoT) sensors, supervisory-control systems (SCADA), geographic information systems (GIS) and historical consumer behaviour data, AI models can anticipate usage peaks, detect irregular patterns (such as fraud or leaks), and optimize network hydraulics. In the utilities sector more broadly, capacity-building firm Capacity reports use cases such as demand forecasting and load balancing among the top applications of AI in utilities. For a Canadian utility operator serving a diversified service area, from remote communities to dense urban centres, AI-driven demand forecasting assists both operational planning and capital investment decisions. In conjunction with digital twin technologies, this leads to a more agile, resilient network.
Customer Engagement and Service Optimization
AI’s role is also expanding in the realm of customer engagement and service. From virtual customer assistants to proactive notification of irregular water usage (indicating possible plumbing faults) or billing anomalies, AI enhances the customer-utility relationship. Although many of the current examples stem from energy utilities, the lessons are transferable: AI-powered conversational agents help manage high call volumes during disruptions, reduce response times and improve customer satisfaction. Within Canada’s context of transparency, regulatory accountability and customer expectations, water utilities can deploy AI tools to personalize outreach (e.g., conservation tips), segment customers according to risk (e.g., high usage or leak-prone properties) and deliver real-time insights to end users. This ultimately strengthens public trust and aligns with the ethos of diversity, equity and inclusion that drives Canadian community-focused utilities.
Workforce Enablement and Ethical Considerations
Implementing AI is as much about people as it is about technology. As Arcadis emphasizes, one of the top concerns in AI adoption is having staff or consultants equipped to leverage and manage AI systems. For Canadian water utilities, many of which operate in small or mid-sized municipalities, investing in staff training, redefining roles and fostering an AI-positive culture is vital. Equally, ethical considerations must be addressed: data privacy, algorithmic transparency, bias mitigation and regulatory compliance all play a role. A research project by the Water Research Foundation titled “The Role of Generative AI (GenAI) for the Global Water Sector” underscores the need to develop guidelines and best practices for municipal water and wastewater utilities. For Canadian utilities working within federal and provincial water regulation frameworks, this means creating transparent AI governance, involving Indigenous and community partners meaningfully and ensuring equitable service delivery.
Barriers and Enabling Factors
Despite the promise, several barriers remain. Canadian statistics indicate that AI integration among firms is still relatively low, with only 12 % of Canadian businesses reportedly having implemented AI by mid-2025. In the water utility context this may be compounded by legacy systems, data silos, limited budgets, uneven data quality and concerns about regulatory or cybersecurity risks. To overcome these, utilities must build strong data infrastructures, clean, interoperable, accessible, and develop clear business cases for AI programmes. Industry commentators recommend beginning with targeted pilots (for example, a district-metered area leak detection) and scaling gradually. Collaboration also matters: Canadian utilities can partner with research networks such as the Canadian Water Network (CWN), which convenes utilities, regulators and academia to address water system challenges.
Realizing Canadian-Specific Value
For Canadian water utility operators, leveraging AI means aligning technology investments with local realities. For instance, utilities in British Columbia, Alberta, or Ontario face unique hydro-climatic variability, aging infrastructure and diverse service geographies from urban to rural and Indigenous communities. AI-enabled leak detection will therefore need to consider freezing/thaw cycles, remote sensor connectivity and smaller customer bases. Energy optimization must reflect cold-climate pumping and treatment loads. Customer engagement programmes must be accessible in both official languages and culturally responsive. By embracing AI but tailoring it to the Canadian context, regulations, funding models, climate zones and community expectations, utilities can maximize value while upholding service equity.
Summary
In summary, the integration of AI into water utility operations offers considerable value across asset management, energy optimization, network management, customer service and workforce enablement. As the water sector enters a digital transformation era, Canadian utility operators stand to gain from deploying these technologies, provided they invest in data infrastructure, staff skills, governance frameworks and community-centred approaches. With the right strategy, AI will not only enhance operational performance and cost-effectiveness but also strengthen the role of water utilities as trusted community anchors delivering safe, sustainable and equitable water services for all Canadians.
About the Author:
Michael Martin is the Vice President of Technology with Metercor Inc., a Smart Meter, IoT, and Smart City systems integrator based in Canada. He has more than 40 years of experience in systems design for applications that use broadband networks, optical fibre, wireless, and digital communications technologies. He is a business and technology consultant. He was a senior executive consultant for 15 years with IBM, where he worked in the GBS Global Center of Competency for Energy and Utilities and the GTS Global Center of Excellence for Energy and Utilities. He is a founding partner and President of MICAN Communications and before that was President of Comlink Systems Limited and Ensat Broadcast Services, Inc., both divisions of Cygnal Technologies Corporation (CYN: TSX).
Martin served on the Board of Directors for TeraGo Inc (TGO: TSX) and on the Board of Directors for Avante Logixx Inc. (XX: TSX.V). He has served as a Member, SCC ISO-IEC JTC 1/SC-41 – Internet of Things and related technologies, ISO – International Organization for Standardization, and as a member of the NIST SP 500-325 Fog Computing Conceptual Model, National Institute of Standards and Technology. He served on the Board of Governors of the University of Ontario Institute of Technology (UOIT) [now Ontario Tech University] and on the Board of Advisers of five different Colleges in Ontario – Centennial College, Humber College, George Brown College, Durham College, Ryerson Polytechnic University [now Toronto Metropolitan University]. For 16 years he served on the Board of the Society of Motion Picture and Television Engineers (SMPTE), Toronto Section.
He holds three master’s degrees, in business (MBA), communication (MA), and education (MEd). As well, he has three undergraduate diplomas and seven certifications in business, computer programming, internetworking, project management, media, photography, and communication technology. He has completed over 60 next generation MOOC (Massive Open Online Courses) continuous education in a wide variety of topics, including: Economics, Python Programming, Internet of Things, Cloud, Artificial Intelligence and Cognitive systems, Blockchain, Agile, Big Data, Design Thinking, Security, Indigenous Canada awareness, and more.