Industry
Energy, Resources & Utilities
Rebalance the energy equation
Highlights
- Water utilities worldwide are operating in an era of unprecedented complexity. Climate volatility, increasing water scarcity, tightening regulatory scrutiny, rising customer expectations, and escalating cyber risks are converging to challenge the resilience of traditional operating models.
- Against this backdrop, artificial intelligence for water articulates a clear and actionable vision for transformation, showing how data, artificial intelligence, and human-centric design can be orchestrated to drive safer operations, smarter decision-making, and more sustainable outcomes across the end-to-end water value chain.
- How can utilities move from isolated pilots to scalable, enterprise-wide impact and accelerate their journey towards a resilient, intelligent, and future-ready water business?
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Why resilience, real-time data, and AI are becoming essential for the future of water utilities
Across regions, water utilities are facing a converging set of structural and systemic pressures. Increasing water scarcity and prolonged drought conditions are projected to place severe stress on the United Kingdom, Europe, the Middle East and North Africa, India, and parts of the Americas over the coming decades. These challenges are compounded by climate change, driving more frequent heatwaves, floods, and runoff-related pollution. Rising levels of environmental contamination, including persistent and difficult-to-remediate chemicals, add to the challenge.
At the same time, water utilities, as operators of critical yet ageing infrastructure, are becoming increasingly exposed to cybersecurity threats. On the other hand, regulatory expectations continue to intensify around environmental, social, and governance performance, water reuse, and transparent reporting.
The clear implication is that resilience, transparency, real-time data, and artificial intelligence are no longer optional capabilities. They are becoming foundational requirements for a sustainable, secure, and future-ready water sector.
How AI and real-time data are enabling zero outages, zero emissions, and smarter water ecosystems
The digital and AI transformation imperative is to enable a smarter, safer and sustainable future across the water value chain.
| Zero boundaries | Zero outages, leakages and spills | Zero complaints | Zero emissions |
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Why AI is becoming critical for managing water scarcity and building resilient, sustainable utilities
Freshwater accounts for only around 2.5 per cent of global water resources, while groundwater, frozen reserves, and surface water are coming under increasing pressure. In this context, the adoption of AI in the water sector is no longer discretionary. It is a strategic necessity to strengthen resilience and deliver sustainable outcomes:
- AI enables water utilities to build resilience to droughts, floods, and extreme weather events,
- Optimise the management of scarce water resources across surface, ground, frozen, and recycled supplies
- Enhance regulatory compliance while strengthening trust with customers and regulators.
- Help reduce operational risk and cost while accelerating the realisation of business value
Driving the “Four Zero” outcomes through AI-powered decision intelligence and automation
How AI improves decision-making: faster, safer, and more efficient outcomes
| Themes | Zero complaints | Zero emissions | Zero outage | Zero leakage |
| Usecase | Flow to full treatment (FFT) regulatory reporting | Wastewater treatment Digital twin |
Sensor fingerprinting Anomaly detection |
Smart metering Leakage detection |
| Objectives | Automate compliance reports for thresholds and alerts in near real-time to act quickly on water quality | Optimise wastewater treatment by simulating changes before implementing them for the waste catchment | Quick detection of sensor drift/malfunction for predictive maintenance | Early detection of water loss or abnormal usage patterns |
| Expected outcome | Increase water quality | Reduce carbon emissions | Reduce downtime | Reduce water leaks |
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Why unified, governed, and real-time data is the backbone of AI in water utilities
Some of the key considerations for AI and data foundation are:
Unified across the data value chain
A cohesive and integrated approach across the full data management life cycle, encompassing ingest, storage, analysis, processing, access, consumption, and governance. This consolidation enables a streamlined architecture, reducing complexity while improving scalability and performance.
Human and machine co-creation
Designing for both the existing human workforce and an emerging digital workforce, including intelligent agents. These agents create the opportunity to accelerate data strategy execution with greater speed, scale, quality, and control, while enhancing collaboration between people and intelligent systems.
Real-time and up to date
The integration of real-time data to deliver accurate, relevant, and timely insights for decision-making. This foundation supports personalised recommendations and an improved experience for data consumers across the organisation.
Trusted and governed data and AI
Building trust through well-governed data and AI, enabled by simplified governance models, robust policy management, effective access controls, and strong standards for accuracy and integrity. This approach reduces risk while strengthening compliance and confidence across the enterprise.
AI adoption barriers
The following outlines some of the key challenges that limit the adoption of AI for water utilities:
Siloed water operational technology (OT) systems
- Water utilities often operate disconnected SCADA, telemetry, asset management, GIS, and enterprise systems.
- These silos restrict visibility across the network and limit digital transformation initiatives.
- As a result, insights remain local, reactive, and difficult to scale.
Implication: Data cannot easily flow from the field to enterprise analytics or AI models.
Decentralised decision-making and fragmented cloud strategies
- Different teams adopt different cloud platforms, tools, and data models.
- Decisions are made independently across operations, engineering, finance, and customer teams.
- This creates duplication, inconsistent metrics, and governance challenges.
Implication: No single source of truth, making enterprise-wide optimisation difficult.
Disconnected data and AI systems
- Data platforms and AI initiatives evolve independently.
- AI models struggle due to poor data quality, lack of context, and limited operational integration.
- Innovation slows because each new use case requires bespoke engineering.
Implication: AI value is trapped in pilots rather than scaled into production.
Key AI and data foundations for water utilities: Cloud-native, secure, observable, and governed at scale
An AI-powered approach built on a stronger data foundation and faster insights to drive smarter water innovation.
4C framework: Connect. Collect. Curate. Consume. A secure, observable, cloud-native foundation driving data and AI innovation.
Common foundational elements
Successful AI for water utilities depends on a robust foundation spanning flexible cloud native infrastructure, zero trust security, observability, and cross-platform governance, enabling innovation to scale securely and consistently.
1. Cloud-native infrastructure
Water utilities operate across diverse and often multi-platform environments. Architectures must support hybrid, multi-cloud, and interoperable ecosystems to enable seamless integration across utilities, partners, and regulators.
2. Security and trust (Zero trust)
Given the sensitivity of operational and customer data (e.g., SCADA/OT, GIS, ERP, CRM), a zero trust model is essential. Controls must extend across identity, network, data, devices, and applications—ensuring continuous verification, least-privilege access, and controlled actions always.
3. Reliability and observability
AI effectiveness relies on trusted data. Utilities must establish end-to-end observability, including real-time monitoring, automated incident management, and intelligent remediation—progressively evolving towards self-healing systems to support agentic AI.
4. Cross-platform governance
A federated, domain-driven governance model is critical. AI and data products should operate within domain hubs (e.g., asset, customer, commercial), while central guardrails ensure consistency in security, compliance, observability, and reuse—balancing local agility with enterprise control.
Key AI for water approach should include the following aspects:
1. Connect
Integrate intelligent, interoperable data and AI ecosystems by breaking down traditional silos and bringing IT and OT together in a more unified operating environment.
2. Collect
Support diverse water data sources and ingestion scenarios, including high-volume IoT data flows that require ultra-low-latency, real-time processing.
3. Curate
Establish a comprehensive data architecture that enables both data and AI-driven water use cases and supports complex, high-volume, time-sensitive workflows tied to operational and regulatory outcomes.
4. Consume
Deliver data and AI products that support collaboration between people and intelligent systems, guided by FAIROE principles: findability, accessibility, interoperability, reusability, openness, and ethics
The payoff
Data platforms powered by AI models are designed to help water utilities unlock strategic value from data and AI with tangible business benefits below.
Up to 10× reduced risk
- Informed decisions through real-time data and predictive insights
- Improved operational resilience and compliance posture
- Reduced likelihood of failures, leakages, and service disruptions
Up to 50% improvement in cost-to-value efficiency
- Reduced operational costs through automation and efficiency
- Faster realisation of value from data and AI investments
- Potential to unlock new revenue streams through advanced analytics
Up to 10x speed-to-value
- Rapid deployment of use cases
- Accelerated insights from real-time data
- Intelligent automation driving faster business outcomes