Summary

Overview
Global AI in Water Management Market reached US$ 7.54 billion in 2024 and is expected to reach US$ 53.85 billion by 2032, growing with a CAGR of 27.85% during the forecast period 2025-2032.
The global AI in water management market is experiencing rapid growth, driven by the increasing adoption of smart technologies to address water scarcity and improve operational efficiencies. Rising urbanization, aging infrastructure and climate change exacerbate water management challenges, creating opportunities for artificial intelligence (AI) solutions. According to Siemens AI-driven technologies identify 90% of potential issues, which makes it three times more effective than traditional methods. This fact supports global sustainability goals and addresses the growing water demand projected to rise by 55% by 2050, according to UNESCO.
AI-enabled systems are revolutionizing water infrastructure management through predictive maintenance and automated leak detection. According to the U.S. Environmental Protection Agency (EPA), water loss from leaks accounts for approximately 14-18% of total water usage annually in US. Smart sensors and machine learning algorithms enable utilities to detect and address leaks promptly, saving billions of gallons of water. AI-integrated irrigation systems can reduce water consumption by up to 25%, promoting sustainable agricultural practices and mitigating the impacts of water scarcity on food production.
Asia-Pacific is the fastest-growing market for AI in water management. Rapid urbanization, industrial expansion and severe water scarcity in countries like India, China and Indonesia are driving the adoption of AI technologies. According to the Asian Development Bank (ADB), over 75% of Asia is water insecure, with countries that are home to more than 90% of the region's population already facing an imminent water crisis. Governments in the region are investing heavily in smart water infrastructure, with India’s "Jal Shakti Abhiyan" promoting AI-based water conservation initiatives.
Dynamics
Water Scarcity and Efficient Resource Management
Water scarcity is a growing global crisis. The United Nations reports that 2.3 billion people live in water-stressed countries, with the figure expected to rise due to population growth, urbanization and climate change. AI’s ability to optimize water usage and distribution offers a transformative solution. AI technologies integrated with IoT devices allow for real-time monitoring of water quality, pressure and flow rates. According to the IBM Corporation, real-time data analytics can reduce water waste by up to 15% by enabling proactive responses to irregularities in water systems.
AI-based water leak detection system with cloud information management for monitoring its water distribution network, achieving a leakage rate, among the lowest globally. Similarly, machine learning models analyze historical data to predict water demand and preempt system failures. For instance, the EurEau reports that 95% of European citizens have access to high-quality drinking water while 82% of them are connected to wastewater treatment, by developing a digital environment and data treatment capabilities to optimize water treatment, water distribution and wastewater collection and treatment
Increasing Corporate Sustainability Goals
The aging water infrastructure in the U.S. is a significant concern, with the American Society of Civil Engineers (ASCE) reporting that approximately 6 billion gallons of treated water are lost daily due to deteriorating pipelines. To address this challenge, AI technologies, particularly digital twins, are being utilized to simulate the behavior of water systems. This allows utilities to prioritize repairs and upgrades more effectively, enhancing operational efficiency and resource management. Additionally, AI plays a critical role in leak detection and predictive maintenance, enabling utilities to proactively manage infrastructure and reduce downtime, ultimately extending the lifespan of water assets.
As climate change intensifies, AI is increasingly vital for adapting water management systems to extreme weather events. For instance, Japan’s Ministry of Land, Infrastructure, Transport and Tourism employs AI to predict flood risks, successfully reducing disaster-related failures in water systems. By leveraging real-time data and advanced analytics, AI helps utilities optimize resource allocation and improve resilience against climate-induced challenges. This integration of AI not only enhances the reliability and sustainability of water services but also addresses the urgent need for modernization in response to growing demands and environmental pressures.
Data Quality Issues with High Implementation Costs
The integration of AI in the water management market presents several significant restraints that can hinder its effectiveness and widespread adoption. Data Quality Issues are paramount; AI systems rely heavily on high-quality data inputs, yet many water utilities face challenges with inconsistent or incomplete datasets due to outdated infrastructure and insufficient monitoring capabilities. This lack of reliable data can severely limit the predictive capabilities of AI models, reducing their overall effectiveness in optimizing water resource management.
Additionally, high implementation costs pose a barrier for many utilities, particularly in developing regions where budget constraints are prevalent. The initial investments required for AI technologies, including hardware, software and training, can deter organizations from adopting these solutions. Furthermore, integration with legacy systems complicates the transition to AI-driven processes, as many existing infrastructures are not compatible with modern technologies. Lastly, there is often a lack of technical skills and knowledge within the workforce to effectively implement and utilize AI systems, which further exacerbates the challenges faced by water utilities in leveraging AI for improved management.
Segment Analysis
The global AI in water management market is segmented based on offering, technology, capacity, end-user and region.
Rising Demand for AI-based Water Management in Power Generation Industry
The smart water utilities sector is rapidly evolving, driven by the need for enhanced operational efficiency and reduced water losses. Artificial Intelligence (AI) plays a crucial role in optimizing water treatment and distribution processes. Wastewater treatment plant uses AI to reduce aeration energy use by 30%. Furthermore, AI systems are instrumental in addressing the challenge of non-revenue water (NRW), which represents substantial losses in the water sector.
A notable example is a pilot project in South Africa's eThekwini Municipality, where AI helped decrease NRW in two years, demonstrating the technology's effectiveness in detecting unauthorized usage and leaks. Additionally, AI enhances customer engagement through smart billing systems that provide real-time insights into water consumption. AI-driven portals have improved customer satisfaction significantly while minimizing billing discrepancies.
The integration of AI into water management not only fosters operational improvements but also facilitates data-driven decision-making for long-term resource planning and allocation. By leveraging advanced analytics and predictive capabilities, utilities can optimize their operations, respond to demand fluctuations and ultimately contribute to sustainable water management practices..
Geographical Penetration
Availability of Advanced Technological Eco-Systemin North America
North America is leading the global AI-driven water management market, primarily due to its advanced technological ecosystem and supportive regulatory environment. The AI-integrated water systems have enhanced efficiency in urban utilities. Cities such as Las Vegas and Los Angeles are utilizing AI to optimize water distribution, resulting in significant reductions in per capita water consumption.
Furthermore, government initiatives like the EPA’s Water Infrastructure Finance and Innovation Act (WIFIA) are providing essential funding for smart water projects, facilitating the adoption of AI technologies. In Canada, the "Smart Water Canada" initiative has contributed to an improvement in municipal water management efficiency since 2020.
The industrial sector is also witnessing robust AI adoption in water management. For example, GM's new system can recycle and reuse 85% of wastewater, cutting the complex's daily groundwater extraction in half while increasing operations across its North American facilities. This trend is indicative of a broader movement towards sustainable practices, driven by increasing concerns over water scarcity and aging infrastructure.
As North America continues to invest heavily in smart water solutions, it is expected to maintain its dominance in the market, with projected growth fueled by advancements in digital technologies and regulatory support aimed at enhancing efficiency and sustainability in water resource management.
Competitive Landscape
The major global players in the market include Honeywell International Inc, Schneider Electric, IBM Corp., ABB, TATA Consultancy Services Limited oracle, SUEZ, Landis+Gyr, Siemens and Larsen & Toubro.
Sustainable Analysis
The integration of artificial intelligence (AI) in water management is significantly contributing to the achievement of the United Nations Sustainable Development Goal 6, which focuses on clean water and sanitation. AI systems enhance water conservation by optimizing usage, leading to a reported 22% reduction in water wastage across various pilot projects globally, according to the International Institute for Sustainable Development (IISD).
Additionally, these systems improve energy efficiency in water treatment processes; the AI-enabled wastewater treatment solutions saved millions of kWh of water annually in pilot initiatives. By utilizing predictive analytics, AI also enhances pollution control by improving wastewater treatment efficiency, with the European Commission's Horizon 2020 initiative indicating a 15% improvement in water quality standards through AI-driven monitoring. Beyond efficiency gains, AI technologies are reshaping how utilities manage water resources. They enable real-time data analysis to predict water demand, optimize supply and reduce waste effectively.
Furthermore, AI can identify leaks and other infrastructure issues before they escalate into crises, allowing for timely repairs and maintenance. This proactive approach not only conserves water but also minimizes operational costs and environmental impacts. As these technologies continue to evolve, their role in ensuring sustainable water management becomes increasingly vital, particularly in addressing global challenges such as climate change and population growth that threaten water security.
Integration of IoT and AI in AI in Water Management
The integration of IoT (Internet of Things) and AI (Artificial Intelligence) in water management systems is transforming how utilities operate by providing real-time data and actionable insights. According to the World Economic Forum, IoT-connected sensors can reduce operational inefficiencies by up to 30% in water systems. These technologies enable automation in critical areas such as irrigation, leak detection and water quality monitoring. For instance, Israel's Mekorot National Water Company successfully reduced irrigation water usage, through the integration of AI and IoT.
Furthermore, AI enhances decision-making capabilities by analyzing large datasets, with a significant percent reduction in response times to water crises in pilot implementations. The benefits of these technologies extend beyond efficiency gains; they also facilitate better resource management and sustainability. IoT systems provide continuous monitoring that allows for early leak detection and optimized water use, which can significantly lower maintenance costs.
Additionally, AI-driven analytics help predict infrastructure failures, enabling proactive maintenance and minimizing downtime. As a result, water utilities can respond more effectively to challenges such as aging infrastructure and changing environmental conditions, ultimately leading to improved service delivery and conservation efforts across the sector.
Key Developments
• In October 2024, FIDO AI Ltd, a software company dedicated to reducing non-revenue water (NRW) through AI-led solutions and curated water data insights, announced the successful closing of its Series B funding round. This investment will enable FIDO AI to scale its operations and continue its mission to support global water resilience.
• In July 2023, SUEZ, a leader in sustainable water and waste services, joined forces with Schneider Electric to drive the adoption of digital solutions focused on energy efficiency, resource conservation and minimizing the carbon footprint of water cycle management systems. This collaboration emphasizes the importance of digital transformation in addressing water management challenges.
• In June 2023, SUEZ also partnered with Northumbrian Water Ltd. (NWL) on the Hydro Powered Concentric Smart Meter Project, which was awarded the Ofwat Innovation Fund’s Catalyst Stream prize. This groundbreaking project aims to revolutionize water usage monitoring and management through innovative smart metering technology.
By Offering
• Water Meter
• Solutions
o Smart Irrigation Management
o Advanced Pressure Management
o Mobile Workforce Management
o Network Management
o CIS and Billing
o Leak Detection
o Other Solutions
• Services
o Integration & Deployment
o Support & Maintenance
o Consulting
By Capacity
• Less than 25,000L,
• 25,001 TO 50,000 L
• 50,001 L TO 100,000 L
• More than 100,001 L
By Technology
• Internet of Things (IoT) Devices
• Remote Sensing and Satellite Imagery
• Data Analytics and Big Data
• Cloud Computing Solutions
• Others
By End-User
• Municipal
o Urban Water Management
o Rural Water Supply Programs
• Industrial
o Power Generation
o Food and Beverage
o Pharmaceuticals
o Textiles and Leather
o Pulp and Paper
o Oil and Gas
o Mining and Metals
o Others
• Commercial
• Others
By Region
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o France
o Italy
o Spain
o Rest of Europe
• South America
o Brazil
o Argentina
o Rest of South America
• Asia-Pacific
o China
o India
o Japan
o Australia
o Rest of Asia-Pacific
• Middle East and Africa
Why Purchase the Report?
• To visualize the global AI in water management market segmentation based on offering, technology, capacity, end-user and region.
• Identify commercial opportunities by analyzing trends and co-development.
• Excel data sheet with numerous data points at the AI in water management market level for all segments.
• PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
• Product mapping available as excel consisting of key products of all the major players.
The global AI in water management market report would provide approximately 70 tables, 66 figures and 190 pages.
Target Audience 2024
• Manufacturers/ Buyers
• Industry Investors/Investment Bankers
• Research Professionals
• Emerging Companies

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Table of Contents

1. Methodology and Scope
1.1. Research Methodology
1.2. Research Objective and Scope of the Report
2. Definition and Overview
3. Executive Summary
3.1. Snippet by Offering
3.2. Snippet by Capacity
3.3. Snippet by Technology
3.4. Snippet by End-User
3.5. Snippet by Region
4. Dynamics
4.1. Impacting Factors
4.1.1. Drivers
4.1.1.1. Adoption of the Water Reuse and Recycle from Diverse Industries
4.1.1.2. Increasing Corporate Sustainability Goals
4.1.2. Restraints
4.1.2.1. High Initial Investment
4.1.3. Opportunity
4.1.4. Impact Analysis
5. Industry Analysis
5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Sustainable Analysis
5.6. DMI Opinion
6. By Offering
6.1. Introduction
6.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
6.1.2. Market Attractiveness Index, By Offering
6.2. Water Meter*
6.2.1. Introduction
6.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
6.3. Solutions
6.3.1. Smart Irrigation Management
6.3.2. Advanced Pressure Management
6.3.3. Mobile Workforce Management
6.3.4. Network Management
6.3.5. CIS and Billing
6.3.6. Leak Detection
6.3.7. Other Solutions
6.4. Services
6.4.1. Integration & Deployment
6.4.2. Support & Maintenance
6.4.3. Consulting
7. By Capacity
7.1. Introduction
7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
7.1.2. Market Attractiveness Index, By Capacity
7.2. Less than 25,000L*
7.2.1. Introduction
7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. 25,001 TO 50,000 L
7.4. 50,001 L TO 100,000 L
7.5. More than 100,001 L
8. By Technology
8.1. Introduction
8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
8.1.2. Market Attractiveness Index, By Technology
8.2. Internet of Things (IoT) Devices*
8.2.1. Introduction
8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Remote Sensing and Satellite Imagery
8.4. Data Analytics and Big Data
8.5. Cloud Computing Solutions
8.6. Others
9. By End-User
9.1. Introduction
9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
9.1.2. Market Attractiveness Index, By End-User
9.2. Municipal*
9.2.1. Introduction
9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.2.2.1. Urban Water Management
9.2.2.2. Rural Water Supply Programs
9.3. Industrial
9.3.1. Power Generation
9.3.2. Food and Beverage
9.3.3. Pharmaceuticals
9.3.4. Textiles and Leather
9.3.5. Pulp and Paper
9.3.6. Oil and Gas
9.3.7. Mining and Metals
9.3.8. Others
9.4. Commercial
9.5. Others
10. Sustainability Analysis
10.1. Environmental Analysis
10.2. Economic Analysis
10.3. Governance Analysis
11. By Region
11.1. Introduction
11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
11.1.2. Market Attractiveness Index, By Region
11.2. North America
11.2.1. Introduction
11.2.2. Key Region-Specific Dynamics
11.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
11.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
11.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
11.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.2.7.1. US
11.2.7.2. Canada
11.2.7.3. Mexico
11.3. Europe
11.3.1. Introduction
11.3.2. Key Region-Specific Dynamics
11.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
11.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
11.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
11.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.3.7.1. Germany
11.3.7.2. UK
11.3.7.3. France
11.3.7.4. Italy
11.3.7.5. Spain
11.3.7.6. Rest of Europe
11.4. South America
11.4.1. Introduction
11.4.2. Key Region-Specific Dynamics
11.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
11.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
11.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
11.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.4.7.1. Brazil
11.4.7.2. Argentina
11.4.7.3. Rest of South America
11.5. Asia-Pacific
11.5.1. Introduction
11.5.2. Key Region-Specific Dynamics
11.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
11.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
11.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
11.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
11.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
11.5.7.1. China
11.5.7.2. India
11.5.7.3. Japan
11.5.7.4. Australia
11.5.7.5. Rest of Asia-Pacific
11.6. Middle East and Africa
11.6.1. Introduction
11.6.2. Key Region-Specific Dynamics
11.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Components
11.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Capacity
11.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
11.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
12. Competitive Landscape
12.1. Competitive Scenario
12.2. Market Positioning/Share Analysis
12.3. Mergers and Acquisitions Analysis
13. Company Profiles
13.1. Honeywell International Inc*
13.1.1. Company Overview
13.1.2. Product Portfolio and Description
13.1.3. Financial Overview
13.1.4. Key Developments
13.2. Schneider Electric
13.3. IBM Corp.
13.4. ABB
13.5. TATA Consultancy Services Limited
13.6. Oracle
13.7. SUEZ
13.8. Landis+Gyr
13.9. Siemens
13.10. Larsen & Toubro
LIST NOT EXHAUSTIVE
14. Appendix
14.1. About Us and Services
14.2. Contact Us