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1654694

全球水管理人工智慧市場 - 2025 至 2032 年

Global AI in Water Management Market - 2025-2032

出版日期: 2025年02月13日 | 出版商:

DataM Intelligence | 英文 190 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

2024 年全球水管理人工智慧市場規模達到 75.4 億美元，預計到 2032 年將達到 538.5 億美元，2025-2032 年預測期內的複合年成長率為 27.85%。

全球水管理人工智慧市場正在經歷快速成長，這得益於擴大採用智慧技術來解決水資源短缺問題並提高營運效率。城市化進程加快、基礎設施老化和氣候變遷加劇了水資源管理挑戰，為人工智慧（AI）解決方案創造了機會。據西門子稱，人工智慧技術可以識別 90% 的潛在問題，比傳統方法的效率高出三倍。據聯合國教科文組織稱，這一事實支持了全球永續發展目標，並解決了預計到 2050 年將成長 55% 的用水需求成長問題。

人工智慧系統正在透過預測性維護和自動洩漏檢測徹底改變水基礎設施管理。根據美國環保署（EPA）的數據，因洩漏而損失的水約占美國每年總用水量的 14-18%。智慧感測器和機器學習演算法使公用事業能夠及時檢測和處理洩漏，節省數十億加侖的水。人工智慧綜合灌溉系統可減少高達 25% 的用水量，促進永續農業實踐並減輕水資源短缺對糧食生產的影響。

亞太地區是水管理人工智慧成長最快的市場。印度、中國和印尼等國家的快速城市化、工業擴張和嚴重的水資源短缺正在推動人工智慧技術的應用。根據亞洲開發銀行（ADB）統計，亞洲超過75%的地區面臨水資源短缺，該地區90%以上人口的居住國已經面臨迫在眉睫的水危機。該地區的各國政府正大力投資智慧水利基礎設施，印度的「水力教育運動」正在推動以人工智慧為基礎的水資源保護措施。

動力學

水資源短缺與高效率資源管理

水資源短缺是一場日益嚴重的全球危機。根據聯合國報告，全球有23億人生活在缺水國家，而且由於人口成長、城市化和氣候變化，這一數字預計還會繼續上升。人工智慧最佳化水資源使用和分配的能力提供了一種變革性的解決方案。人工智慧技術與物聯網設備結合，可即時監測水質、壓力和流速。據 IBM 公司稱，即時資料分析可以主動應對供水系統的異常情況，從而減少高達 15% 的水浪費。

採用人工智慧漏水檢測系統，結合雲端資訊管理，對供水管網進行監測，漏水率達全球最低。同樣，機器學習模型可以分析歷史資料來預測用水需求並預防系統故障。例如，歐洲水務與能源署報告稱，95% 的歐洲公民可以獲得高品質的飲用水，其中 82% 的公民已接入廢水處理系統，透過開發數位環境和資料處理能力來最佳化水處理、供水和廢水收集與處理

提高企業永續發展目標

美國水利基礎設施老化是一個嚴重的問題，美國土木工程師學會 (ASCE) 報告稱，由於管道老化，每天約有 60 億加侖的處理過的水流失。為了應對這項挑戰，人們正在利用人工智慧技術，尤其是數位孿生技術來模擬水系統的行為。這使得公用事業公司能夠更有效地優先安排維修和升級，從而提高營運效率和資源管理。此外，人工智慧在洩漏檢測和預測性維護方面發揮關鍵作用，使公用事業公司能夠主動管理基礎設施並減少停機時間，最終延長水資產的使用壽命。

隨著氣候變遷加劇，人工智慧對於調整水管理系統以適應極端天氣事件變得越來越重要。例如，日本國土交通省利用人工智慧預測洪水風險，成功減少了與災害相關的水系統故障。透過利用即時資料和高級分析，人工智慧幫助公用事業最佳化資源配置並提高應對氣候引發的挑戰的能力。人工智慧的整合不僅提高了供水服務的可靠性和永續性，而且還滿足了為應對日益成長的需求和環境壓力而迫切需要實現現代化的需要。

數據品質問題和高實施成本

人工智慧在水管理市場的整合存在一些重大限制，可能會阻礙其有效性和廣泛應用。數據品質問題至關重要；人工智慧系統嚴重依賴高品質的資料輸入，但由於基礎設施過時和監控能力不足，許多水務公司面臨資料集不一致或不完整的挑戰。缺乏可靠資料會嚴重限制人工智慧模型的預測能力，從而降低其最佳化水資源管理的整體效能。

此外，高昂的實施成本對許多公用事業構成了障礙，特別是在預算限制普遍存在的發展中地區。人工智慧技術所需的初始投資（包括硬體、軟體和培訓）可能會阻止組織採用這些解決方案。此外，由於許多現有基礎設施與現代技術不相容，與傳統系統的整合使向人工智慧驅動流程的過渡變得複雜。最後，勞動力往往缺乏有效實施和利用人工智慧系統的技術技能和知識，這進一步加劇了水務公司在利用人工智慧改善管理方面所面臨的挑戰。

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
- Smart Irrigation Management
- Advanced Pressure Management
- Mobile Workforce Management
- Network Management
- CIS and Billing
- Leak Detection
- Other Solutions
Services
- Integration & Deployment
- Support & Maintenance
- 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
- Urban Water Management
- Rural Water Supply Programs
Industrial
- Power Generation
- Food and Beverage
- Pharmaceuticals
- Textiles and Leather
- Pulp and Paper
- Oil and Gas
- Mining and Metals
- Others
Commercial
Others

By Region

North America
- US
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- 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

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