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市場調查報告書
商品編碼
1787935

2032 年智慧城市 AI 市場預測：按組件、部署模式、技術、應用、最終用戶和地區進行的全球分析

AI in Smart Cities Market Forecasts to 2032 - Global Analysis By Component (Hardware, Software and Services), Deployment Mode, Technology, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球智慧城市人工智慧市場規模預計在 2025 年達到 459 億美元，到 2032 年將達到 1,579 億美元，預測期內的複合年成長率為 19.3%。

智慧城市中的人工智慧是指將人工智慧技術融入城市基礎設施和服務，以最佳化能源使用、交通流量、廢棄物管理、安全和公民參與。人工智慧能夠即時分析來自物聯網設備和感測器的數據，從而改善決策、自動化和永續性。這種轉變有助於促進高效管治，提高公共安全，並降低營運成本。智慧城市中的人工智慧應用還支援預測性維護、智慧運輸和個人化公共服務，與城市的長期發展目標一致。

根據 451 Research 的“企業之聲：物聯網、OT 視角、用例和成果 2023”，50% 的政府受訪者選擇確保公共安全作為其智慧城市計劃的主要驅動力，其次是改善整體生活品質（44%）和改善城市服務（42%）。

政府越來越重視數位轉型

世界各國政府日益重視數位轉型，以改善城市生活和業務效率。政府的大力推動也包括對利用人工智慧的智慧城市計劃進行大量投資。這些努力旨在改善公共服務、最佳化資源管理並增強公民參與。支持人工智慧等先進技術融合的政策正在為市場成長創造肥沃的土壤。公共機構的這些專注努力，正成為智慧城市人工智慧市場發展的關鍵催化劑。

資料安全和隱私問題

對資料安全和公民隱私的擔憂，嚴重限制了智慧城市人工智慧市場的擴張。人工智慧系統廣泛收集和分析個人數據，引發了倫理問題和公民焦慮。確保強而有力的網路安全措施，保護敏感的城市資料免遭洩露，是一項複雜的挑戰。公民對其資訊的收集、儲存和使用方式日益警惕，這引發了對更嚴格監管的呼聲。數據濫用的可能性和監控風險，也對市場成長構成了障礙。

人工智慧驅動的交通運輸和廢棄物管理的成長

對高效城市基礎設施日益成長的需求，為人工智慧驅動的交通和廢棄物管理解決方案創造了巨大的機會。透過即時數據分析，人工智慧演算法可以最佳化交通流量，減少堵塞，並提高公共運輸效率。以人工智慧為基礎的智慧廢棄物管理系統可以最佳化收集路線，預測廢棄物產生量，並加強回收。這些應用為城市政府帶來了實際的效益，包括成本節約和環境改善。隨著城市人口的持續成長，對此類最佳化解決方案的需求預計將持續成長。

智慧電網面臨的網路安全威脅

智慧城市基礎設施，尤其是智慧電網，互聯互通，因此易受高級網路安全威脅的影響，對城市發展構成重大威脅。針對關鍵城市系統的惡意攻擊可能導致大規模中斷，影響電力供應和基本服務。資料外洩和基礎設施破壞的可能性為智慧城市部署創造了高風險環境。隨著對數位網路依賴的增加，此類安全漏洞的潛在影響也隨之放大。這些固有的漏洞需要強大的防禦機制來確保智慧城市運作的韌性。

COVID-19的影響

新冠疫情顯著加速了人工智慧在智慧城市的應用，並凸顯了韌性和適應性城市管理的必要性。在疫情期間，各城市已利用人工智慧進行即時公共衛生監測、接觸者追蹤和資源配置。對數位服務和遠端系統管理解決方案的需求激增，促使地方政府加速推動智慧城市計畫。這場突如其來的全球事件凸顯了智慧城市基礎設施在危機應變和未來防備方面的重要性。因此，疫情也成為推動人工智慧技術在城市環境中投資與整合的催化劑。

預計硬體部分將成為預測期內最大的部分

由於智慧城市部署對實體基礎設施的基本需求，預計硬體領域將在預測期內佔據最大的市場佔有率。這包括大量感測器、攝影機、物聯網設備以及資料收集和連接所必需的網路設備。此外，邊緣運算和5G網路的日益普及也推動了對強大處理單元和通訊模組的需求。因此，全球智慧城市計劃的持續擴張將直接轉化為硬體領域佔據主導地位。

預計機器學習領域在預測期內將實現最高的複合年成長率

機器學習領域預計將在預測期內實現最高成長率，這得益於其在智慧城市中實現智慧決策和預測能力的關鍵作用。機器學習演算法對於處理來自各種城市來源的複雜數據以及實現即時分析和最佳化回應至關重要。預測性基礎設施維護、智慧交通管理和自適應公共系統等應用嚴重依賴先進的機器學習模型。這種變革潛力正在推動機器學習領域的快速擴張。

比最大的地區

由於快速的都市化和特大城市的激增，亞太地區預計將在預測期內佔據最大的市場佔有率，這迫切需要高效的城市管理解決方案。中國、印度和韓國等國家政府對智慧城市計劃的大力投資正在推動市場成長。該地區正廣泛採用人工智慧、物聯網和5G等先進技術。該地區也是技術創新和製造業的中心，為智慧城市的發展提供了有利的環境。

複合年成長率最高的地區

預計北美地區在預測期內的複合年成長率最高，這得益於其完善的技術基礎設施和對尖端人工智慧解決方案的早期採用。主要參與企業的高水準研發投入正在推動智慧城市應用的持續技術創新。政府的支持以及旨在增強城市韌性和永續性的舉措也促進了這一成長。此外，對資料隱私和安全的高度重視，加上不斷完善的監管框架，正在推動負責任的人工智慧部署。

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  • 區域細分
    • 根據客戶興趣對主要國家進行的市場估計、預測和複合年成長率（註：基於可行性檢查）
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目錄

第1章執行摘要

第2章 前言

  • 概述
  • 相關利益者
  • 調查範圍
  • 調查方法
    • 資料探勘
    • 數據分析
    • 數據檢驗
    • 研究途徑
  • 研究材料
    • 主要研究資料
    • 二手研究資料
    • 先決條件

第3章市場走勢分析

  • 介紹
  • 驅動程式
  • 抑制因素
  • 機會
  • 威脅
  • 技術分析
  • 應用分析
  • 最終用戶分析
  • 新興市場
  • COVID-19的影響

第4章 波特五力分析

  • 供應商的議價能力
  • 買方的議價能力
  • 替代品的威脅
  • 新進入者的威脅
  • 競爭對手之間的競爭

5. 全球智慧城市人工智慧市場（按組成部分）

  • 介紹
  • 硬體
  • 軟體
  • 服務

6. 全球智慧城市人工智慧市場（依部署模式）

  • 介紹
  • 雲端基礎
  • 本地

7. 全球智慧城市人工智慧市場（按技術）

  • 介紹
  • 機器學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 物聯網整合
  • 巨量資料分析

8. 全球智慧城市人工智慧市場（按應用）

  • 介紹
  • 交通管理
  • 公共和保障
  • 能源管理
  • 基礎設施管理
  • 環境監測
  • 智慧管治

9. 全球智慧城市人工智慧市場（按最終用戶分類）

  • 介紹
  • 公共產業
  • 航運公司
  • 醫療保健提供者
  • 房地產開發商
  • 其他

第 10 章：全球智慧城市人工智慧市場（按地區）

  • 介紹
  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 德國
    • 英國
    • 義大利
    • 法國
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 日本
    • 中國
    • 印度
    • 澳洲
    • 紐西蘭
    • 韓國
    • 其他亞太地區
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地區
  • 中東和非洲
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 卡達
    • 南非
    • 其他中東和非洲地區

第11章 重大進展

  • 協議、夥伴關係、合作和合資企業
  • 收購與合併
  • 新產品發布
  • 業務擴展
  • 其他關鍵策略

第12章 公司概況

  • IBM Corporation
  • Microsoft Corporation
  • Google LLC
  • Intel Corporation
  • Cisco Systems, Inc.
  • Siemens AG
  • Huawei Technologies Co., Ltd.
  • NVIDIA Corporation
  • Hitachi Vantara
  • NEC Corporation
  • Oracle Corporation
  • SAP SE
  • Schneider Electric
  • General Electric（GE）
  • Thales Group
  • Bosch
Product Code: SMRC30200

According to Stratistics MRC, the Global AI in Smart Cities Market is accounted for $45.9 billion in 2025 and is expected to reach $157.9 billion by 2032 growing at a CAGR of 19.3% during the forecast period. AI in Smart Cities refers to the integration of artificial intelligence technologies into urban infrastructure and services to optimize energy use, traffic flow, waste management, security, and citizen engagement. AI enables real-time data analysis from IoT devices and sensors, improving decision-making, automation, and sustainability. This transformation promotes efficient governance, enhances public safety, and reduces operational costs. AI applications in smart cities also support predictive maintenance, smart mobility, and personalized public services, aligning with long-term urban development goals.

According to 451 Research's Voice of the Enterprise: Internet of Things, the OT Perspective, Use Cases and Outcomes 2023, 50% of government respondents selected ensuring public safety as the main driver for their smart city initiatives, followed by improving overall quality of life (44%) and improving city services (42%).

Market Dynamics:

Driver:

Increased government focus on digital transformation

Governments worldwide are increasingly prioritizing digital transformation initiatives to enhance urban living and operational efficiency. This strong governmental push includes significant investments in smart city projects that leverage artificial intelligence. These initiatives aim to improve public services, optimize resource management, and enhance citizen engagement. Policies supporting the integration of advanced technologies like AI are creating a fertile ground for market growth. This concentrated effort by public authorities is a key catalyst for the AI in smart cities market.

Restraint:

Data security and privacy concerns

Significant concerns surrounding data security and citizen privacy pose a notable restraint on the expansion of the AI in smart cities market. The extensive collection and analysis of personal data by AI systems raise ethical questions and public apprehension. Ensuring robust cybersecurity measures to protect sensitive urban data from breaches is a complex challenge. Citizens are increasingly wary about how their information is collected, stored, and utilized, leading to calls for stricter regulations. The potential for misuse of data and the risk of surveillance create hurdles for market growth.

Opportunity:

Growth of AI-powered traffic and waste management

The increasing demand for efficient urban infrastructure is presenting significant opportunities in AI-powered traffic and waste management solutions. AI algorithms can optimize traffic flow, reduce congestion, and improve public transit efficiency through real-time data analysis. Smart waste management systems utilizing AI can optimize collection routes, predict waste generation, and enhance recycling efforts. These applications offer tangible benefits to city administrations, including cost savings and environmental improvements. As urban populations continue to grow, the need for such optimized solutions will only intensify.

Threat:

Cybersecurity threats targeting smart grids

The interconnected nature of smart city infrastructure, particularly smart grids, makes them vulnerable to sophisticated cybersecurity threats, posing a significant threat to market development. Malicious attacks on critical urban systems could lead to widespread disruptions, impacting power supply and essential services. The potential for data breaches and infrastructure sabotage creates a high-risk environment for smart city deployments. The increasing reliance on digital networks amplifies the potential impact of such security compromises. This inherent vulnerability necessitates robust defense mechanisms to ensure the resilience of smart city operations.

Covid-19 Impact:

The COVID-19 pandemic significantly accelerated the adoption of AI in smart cities, highlighting the need for resilient and adaptive urban management. Cities leveraged AI for real-time monitoring of public health, contact tracing, and resource allocation during the crisis. The demand for digital services and remote management solutions surged, pushing municipalities to fast-track their smart city initiatives. This unforeseen global event underscored the value of intelligent urban infrastructure for crisis response and future preparedness. Consequently, the pandemic acted as a catalyst for greater investment and integration of AI technologies in urban environments.

The hardware segment is expected to be the largest during the forecast period

The hardware segment is expected to account for the largest market share during the forecast period, owing to the foundational requirement for physical infrastructure in smart city deployments. This includes a vast array of sensors, cameras, IoT devices, and network equipment essential for data collection and connectivity. Furthermore, the increasing adoption of edge computing and 5G networks drives the demand for robust processing units and communication modules. Therefore, the continuous expansion of smart city projects globally directly translates into a dominant share for the hardware segment.

The machine learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the machine learning segment is predicted to witness the highest growth rate impelled by, its pivotal role in enabling intelligent decision-making and predictive capabilities within smart cities. Machine learning algorithms are crucial for processing complex data from various urban sources, allowing for real-time analysis and optimized responses. Applications such as predictive maintenance of infrastructure, intelligent traffic management, and adaptive public safety systems heavily rely on advanced machine learning models. This transformative potential drives the rapid expansion of the machine learning segment.

Region with largest share:

During the forecast period, the Asia Pacific region is expected to hold the largest market share, driven by rapid urbanization and the proliferation of mega-cities, leading to an urgent need for efficient urban management solutions. Significant government investments in smart city projects across countries like China, India, and South Korea are fueling market growth. The increasing adoption of advanced technologies like AI, IoT, and 5G is widespread in this region. This region is also a hub for technological innovation and manufacturing, providing a conducive environment for smart city development.

Region with highest CAGR:

Over the forecast period, the North America region is anticipated to exhibit the highest CAGR attributed to, to its well-established technological infrastructure and early adoption of cutting-edge AI solutions. High levels of R&D investment by key market players are driving continuous innovation in smart city applications. Government support and initiatives aimed at enhancing urban resilience and sustainability also contribute to this growth. Additionally, a strong focus on data privacy and security, combined with advanced regulatory frameworks, encourages responsible AI deployment.

Key players in the market

Some of the key players in AI in Smart Cities Market include IBM Corp, Microsoft, Google LLC, Intel Corp, Cisco Systems, Siemens AG, Huawei Tech, NVIDIA Corp, Hitachi Vantas, NEC Corp, Oracle Corp, SAP SE, Schneider Electric, General Electric, Thales Group, and Bosch.

Key Developments:

In June 2025, IBM Corporation released the IBM Maximo for Smart Cities, an AI-driven asset management tool for urban utilities, improving predictive maintenance for water and power systems with a reported 10% reduction in downtime.

In May 2025, NVIDIA Corporation announced the Metropolis AI Framework update, enabling real-time video analytics for smart city applications like traffic management and public safety. The framework supports edge AI deployments for faster processing.

In April 2025, Cisco Systems, Inc. introduced the Cisco Smart City Connect, an AI-powered IoT solution for urban infrastructure monitoring. It enhances public safety and waste management through predictive analytics, deployed in select U.S. cities.

Components Covered:

  • Hardware
  • Software
  • Services

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises

Technologies Covered:

  • Machine Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • IoT Integration
  • Big Data Analytics

Applications Covered:

  • Traffic Management
  • Public Safety & Security
  • Energy Management
  • Infrastructure Management
  • Environmental Monitoring
  • Smart Governance

End Users Covered:

  • Utilities
  • Transportation Companies
  • Healthcare Providers
  • Real Estate Developers
  • Other End Users

Regions Covered:

  • North America
    • US
    • Canada
    • Mexico
  • Europe
    • Germany
    • UK
    • Italy
    • France
    • Spain
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia
    • New Zealand
    • South Korea
    • Rest of Asia Pacific
  • South America
    • Argentina
    • Brazil
    • Chile
    • Rest of South America
  • Middle East & Africa
    • Saudi Arabia
    • UAE
    • Qatar
    • South Africa
    • Rest of Middle East & Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2024, 2025, 2026, 2028, and 2032
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

2 Preface

  • 2.1 Abstract
  • 2.2 Stake Holders
  • 2.3 Research Scope
  • 2.4 Research Methodology
    • 2.4.1 Data Mining
    • 2.4.2 Data Analysis
    • 2.4.3 Data Validation
    • 2.4.4 Research Approach
  • 2.5 Research Sources
    • 2.5.1 Primary Research Sources
    • 2.5.2 Secondary Research Sources
    • 2.5.3 Assumptions

3 Market Trend Analysis

  • 3.1 Introduction
  • 3.2 Drivers
  • 3.3 Restraints
  • 3.4 Opportunities
  • 3.5 Threats
  • 3.6 Technology Analysis
  • 3.7 Application Analysis
  • 3.8 End User Analysis
  • 3.9 Emerging Markets
  • 3.10 Impact of Covid-19

4 Porters Five Force Analysis

  • 4.1 Bargaining power of suppliers
  • 4.2 Bargaining power of buyers
  • 4.3 Threat of substitutes
  • 4.4 Threat of new entrants
  • 4.5 Competitive rivalry

5 Global AI in Smart Cities Market, By Component

  • 5.1 Introduction
  • 5.2 Hardware
  • 5.3 Software
  • 5.4 Services

6 Global AI in Smart Cities Market, By Deployment Mode

  • 6.1 Introduction
  • 6.2 Cloud-Based
  • 6.3 On-Premises

7 Global AI in Smart Cities Market, By Technology

  • 7.1 Introduction
  • 7.2 Machine Learning
  • 7.3 Natural Language Processing (NLP)
  • 7.4 Computer Vision
  • 7.5 IoT Integration
  • 7.6 Big Data Analytics

8 Global AI in Smart Cities Market, By Application

  • 8.1 Introduction
  • 8.2 Traffic Management
  • 8.3 Public Safety & Security
  • 8.4 Energy Management
  • 8.5 Infrastructure Management
  • 8.6 Environmental Monitoring
  • 8.7 Smart Governance

9 Global AI in Smart Cities Market, By End User

  • 9.1 Introduction
  • 9.2 Utilities
  • 9.3 Transportation Companies
  • 9.4 Healthcare Providers
  • 9.5 Real Estate Developers
  • 9.6 Other End Users

10 Global AI in Smart Cities Market, By Geography

  • 10.1 Introduction
  • 10.2 North America
    • 10.2.1 US
    • 10.2.2 Canada
    • 10.2.3 Mexico
  • 10.3 Europe
    • 10.3.1 Germany
    • 10.3.2 UK
    • 10.3.3 Italy
    • 10.3.4 France
    • 10.3.5 Spain
    • 10.3.6 Rest of Europe
  • 10.4 Asia Pacific
    • 10.4.1 Japan
    • 10.4.2 China
    • 10.4.3 India
    • 10.4.4 Australia
    • 10.4.5 New Zealand
    • 10.4.6 South Korea
    • 10.4.7 Rest of Asia Pacific
  • 10.5 South America
    • 10.5.1 Argentina
    • 10.5.2 Brazil
    • 10.5.3 Chile
    • 10.5.4 Rest of South America
  • 10.6 Middle East & Africa
    • 10.6.1 Saudi Arabia
    • 10.6.2 UAE
    • 10.6.3 Qatar
    • 10.6.4 South Africa
    • 10.6.5 Rest of Middle East & Africa

11 Key Developments

  • 11.1 Agreements, Partnerships, Collaborations and Joint Ventures
  • 11.2 Acquisitions & Mergers
  • 11.3 New Product Launch
  • 11.4 Expansions
  • 11.5 Other Key Strategies

12 Company Profiling

  • 12.1 IBM Corporation
  • 12.2 Microsoft Corporation
  • 12.3 Google LLC
  • 12.4 Intel Corporation
  • 12.5 Cisco Systems, Inc.
  • 12.6 Siemens AG
  • 12.7 Huawei Technologies Co., Ltd.
  • 12.8 NVIDIA Corporation
  • 12.9 Hitachi Vantara
  • 12.10 NEC Corporation
  • 12.11 Oracle Corporation
  • 12.12 SAP SE
  • 12.13 Schneider Electric
  • 12.14 General Electric (GE)
  • 12.15 Thales Group
  • 12.16 Bosch

List of Tables

  • Table 1 Global AI in Smart Cities Market Outlook, By Region (2024-2032) ($MN)
  • Table 2 Global AI in Smart Cities Market Outlook, By Deployment Mode (2024-2032) ($MN)
  • Table 3 Global AI in Smart Cities Market Outlook, By Cloud-Based (2024-2032) ($MN)
  • Table 4 Global AI in Smart Cities Market Outlook, By On-Premises (2024-2032) ($MN)
  • Table 5 Global AI in Smart Cities Market Outlook, By Technology (2024-2032) ($MN)
  • Table 6 Global AI in Smart Cities Market Outlook, By Machine Learning (2024-2032) ($MN)
  • Table 7 Global AI in Smart Cities Market Outlook, By Natural Language Processing (NLP) (2024-2032) ($MN)
  • Table 8 Global AI in Smart Cities Market Outlook, By Computer Vision (2024-2032) ($MN)
  • Table 9 Global AI in Smart Cities Market Outlook, By IoT Integration (2024-2032) ($MN)
  • Table 10 Global AI in Smart Cities Market Outlook, By Big Data Analytics (2024-2032) ($MN)
  • Table 11 Global AI in Smart Cities Market Outlook, By Application (2024-2032) ($MN)
  • Table 12 Global AI in Smart Cities Market Outlook, By Traffic Management (2024-2032) ($MN)
  • Table 13 Global AI in Smart Cities Market Outlook, By Public Safety & Security (2024-2032) ($MN)
  • Table 14 Global AI in Smart Cities Market Outlook, By Energy Management (2024-2032) ($MN)
  • Table 15 Global AI in Smart Cities Market Outlook, By Infrastructure Management (2024-2032) ($MN)
  • Table 16 Global AI in Smart Cities Market Outlook, By Environmental Monitoring (2024-2032) ($MN)
  • Table 17 Global AI in Smart Cities Market Outlook, By Smart Governance (2024-2032) ($MN)
  • Table 18 Global AI in Smart Cities Market Outlook, By End User (2024-2032) ($MN)
  • Table 19 Global AI in Smart Cities Market Outlook, By Utilities (2024-2032) ($MN)
  • Table 20 Global AI in Smart Cities Market Outlook, By Transportation Companies (2024-2032) ($MN)
  • Table 21 Global AI in Smart Cities Market Outlook, By Healthcare Providers (2024-2032) ($MN)
  • Table 22 Global AI in Smart Cities Market Outlook, By Real Estate Developers (2024-2032) ($MN)
  • Table 23 Global AI in Smart Cities Market Outlook, By Other End Users (2024-2032) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.