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市场调查报告书
商品编码
1787935

2032 年智慧城市 AI 市场预测：按组件、部署模式、技术、应用、最终用户和地区进行的全球分析

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个工作天内

价格

根据 Stratistics MRC 的数据，全球智慧城市人工智慧市场规模预计在 2025 年达到 459 亿美元，到 2032 年将达到 1,579 亿美元，预测期内的复合年增长率为 19.3%。

智慧城市中的人工智慧是指将人工智慧技术融入城市基础设施和服务，以优化能源使用、交通流量、废弃物管理、安全和公民参与。人工智慧能够即时分析来自物联网设备和感测器的数据，从而改善决策、自动化和永续性。这种转变有助于促进高效管治，提高公共安全，并降低营运成本。智慧城市中的人工智慧应用还支援预测性维护、智慧运输和个人化公共服务，与城市的长期发展目标一致。

根据 451 Research 的“企业之声：物联网、OT 视角、用例和成果 2023”，50% 的政府受访者选择确保公共安全作为其智慧城市计划的主要驱动力，其次是改善整体生活品质（44%）和改善城市服务（42%）。

政府越来越重视数位转型

世界各国政府日益重视数位转型，以改善城市生活和业务效率。政府的大力推动也包括对利用人工智慧的智慧城市计划进行大量投资。这些努力旨在改善公共服务、优化资源管理并增强公民参与。支持人工智慧等先进技术融合的政策正在为市场成长创造肥沃的土壤。公共机构的这些专注努力，正成为智慧城市人工智慧市场发展的关键催化剂。

资料安全和隐私问题

对资料安全和公民隐私的担忧，严重限制了智慧城市人工智慧市场的扩张。人工智慧系统广泛收集和分析个人数据，引发了伦理问题和公民焦虑。确保强而有力的网路安全措施，保护敏感的城市资料免遭洩露，是一项复杂的挑战。公民对其资讯的收集、储存和使用方式日益警惕，这引发了对更严格监管的呼声。数据滥用的可能性和监控风险，也对市场成长构成了障碍。

人工智慧驱动的交通运输和废弃物管理的成长

对高效城市基础设施日益增长的需求，为人工智慧驱动的交通和废弃物管理解决方案创造了巨大的机会。透过即时数据分析，人工智慧演算法可以优化交通流量，减少拥堵，并提高公共运输效率。以人工智慧为基础的智慧废弃物管理系统可以优化收集路线，预测废弃物产生量，并加强回收。这些应用为城市政府带来了实际的效益，包括成本节约和环境改善。随着城市人口的持续增长，对此类最佳化解决方案的需求预计将持续增长。

智慧电网面临的网路安全威胁

智慧城市基础设施，尤其是智慧电网，互联互通，因此易受高级网路安全威胁的影响，对城市发展构成重大威胁。针对关键城市系统的恶意攻击可能导致大规模中断，影响电力供应和基本服务。资料外洩和基础设施破坏的可能性为智慧城市部署创造了高风险环境。随着对数位网路依赖的增加，此类安全漏洞的潜在影响也随之放大。这些固有的漏洞需要强大的防御机制来确保智慧城市运作的韧性。

COVID-19的影响

新冠疫情显着加速了人工智慧在智慧城市的应用，并凸显了韧性和适应性城市管理的必要性。在疫情期间，各城市已利用人工智慧进行即时公共卫生监测、接触者追踪和资源配置。对数位服务和远端系统管理解决方案的需求激增，促使地方政府加速推动智慧城市计画。这场突如其来的全球事件凸显了智慧城市基础设施在危机应变和未来防备方面的重要性。因此，疫情也成为推动人工智慧技术在城市环境中投资与整合的催化剂。

预计硬体部分将成为预测期内最大的部分

由于智慧城市部署对实体基础设施的基本需求，预计硬体领域将在预测期内占据最大的市场占有率。这包括大量感测器、摄影机、物联网设备以及资料收集和连接所必需的网路设备。此外，边缘运算和5G网路的日益普及也推动了对强大处理单元和通讯模组的需求。因此，全球智慧城市计划的持续扩张将直接转化为硬体领域占据主导地位。

预计机器学习领域在预测期内将实现最高的复合年增长率

机器学习领域预计将在预测期内实现最高成长率，这得益于其在智慧城市中实现智慧决策和预测能力的关键作用。机器学习演算法对于处理来自各种城市来源的复杂数据以及实现即时分析和优化回应至关重要。预测性基础设施维护、智慧交通管理和自适应公共系统等应用严重依赖先进的机器学习模型。这种变革潜力正在推动机器学习领域的快速扩张。

比最大的地区

由于快速的都市化和特大城市的激增，亚太地区预计将在预测期内占据最大的市场占有率，这迫切需要高效的城市管理解决方案。中国、印度和韩国等国家政府对智慧城市计划的大力投资正在推动市场成长。该地区正广泛采用人工智慧、物联网和5G等先进技术。该地区也是技术创新和製造业的中心，为智慧城市的发展提供了有利的环境。

复合年增长率最高的地区

预计北美地区在预测期内的复合年增长率最高，这得益于其完善的技术基础设施和对尖端人工智慧解决方案的早期采用。主要参与企业的高水准研发投入正在推动智慧城市应用的持续技术创新。政府的支持以及旨在增强城市韧性和永续性的倡议也促进了这一成长。此外，对资料隐私和安全的高度重视，加上不断完善的监管框架，正在推动负责任的人工智慧部署。

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  • 公司简介
    • 对最多三家其他市场参与企业进行全面分析
    • 主要企业的SWOT分析（最多3家公司）
  • 区域细分
    • 根据客户兴趣对主要国家进行的市场估计、预测和复合年增长率（註：基于可行性检查）
  • 竞争基准化分析
    • 透过产品系列、地理分布和策略联盟对主要企业基准化分析

目录

第一章执行摘要

第二章 前言

  • 概述
  • 相关利益者
  • 调查范围
  • 调查方法
    • 资料探勘
    • 数据分析
    • 数据检验
    • 研究途径
  • 研究材料
    • 主要研究资料
    • 二手研究资料
    • 先决条件

第三章市场走势分析

  • 介绍
  • 驱动程式
  • 抑制因素
  • 机会
  • 威胁
  • 技术分析
  • 应用分析
  • 最终用户分析
  • 新兴市场
  • COVID-19的影响

第四章 波特五力分析

  • 供应商的议价能力
  • 买方的议价能力
  • 替代品的威胁
  • 新进入者的威胁
  • 竞争对手之间的竞争

5. 全球智慧城市人工智慧市场（按组成部分）

  • 介绍
  • 硬体
  • 软体
  • 服务

6. 全球智慧城市人工智慧市场（依部署模式）

  • 介绍
  • 云端基础
  • 本地

7. 全球智慧城市人工智慧市场（按技术）

  • 介绍
  • 机器学习
  • 自然语言处理（NLP）
  • 电脑视觉
  • 物联网集成
  • 巨量资料分析

8. 全球智慧城市人工智慧市场（按应用）

  • 介绍
  • 交通管理
  • 公共和保障
  • 能源管理
  • 基础设施管理
  • 环境监测
  • 智慧管治

9. 全球智慧城市人工智慧市场（按最终用户划分）

  • 介绍
  • 公共产业
  • 航运公司
  • 医疗保健提供者
  • 房地产开发商
  • 其他的

第 10 章：全球智慧城市人工智慧市场（按地区）

  • 介绍
  • 北美洲
    • 美国
    • 加拿大
    • 墨西哥
  • 欧洲
    • 德国
    • 英国
    • 义大利
    • 法国
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 日本
    • 中国
    • 印度
    • 澳洲
    • 纽西兰
    • 韩国
    • 其他亚太地区
  • 南美洲
    • 阿根廷
    • 巴西
    • 智利
    • 南美洲其他地区
  • 中东和非洲
    • 沙乌地阿拉伯
    • 阿拉伯聯合大公国
    • 卡达
    • 南非
    • 其他中东和非洲地区

第十一章 重大进展

  • 协议、伙伴关係、合作和合资企业
  • 收购与合併
  • 新产品发布
  • 业务扩展
  • 其他关键策略

第十二章 公司概况

  • 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.