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市场调查报告书
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2007832

人工智慧智慧城市平台市场预测至2034年：按组件、技术、应用、部署模式、最终用户和区域分類的全球分析

AI Smart City Platforms Market Forecasts to 2034 - Global Analysis By Component (Hardware, Software, and Services), Technology, Application, Deployment Mode, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3个工作天内

价格

根据 Stratistics MRC 的数据，预计到 2026 年，全球 AI 智慧城市平台市场规模将达到 907 亿美元，在预测期内将以 37.1% 的复合年增长率增长，到 2034 年将达到 1.1342 兆美元。

人工智慧智慧城市平台是一个整合的数位化框架，它利用人工智慧来管理、分析和优化城市基础设施和服务。这些平台从感测器、物联网设备、摄影机和互联繫统收集数据，涵盖交通、能源、公共、废弃物管理和公共产业等领域。透过应用先进的分析和机器学习技术，它们能够帮助城市管理部门提高营运效率、提升市民服务水准并支援数据驱动的决策。人工智慧智慧城市平台透过实现城市资源的即时监测、预测性洞察和自动化管理，协助建构永续、高效且反应迅速的城市环境。

推动都市化和智慧城市计划

快速的都市化给现有基础设施带来了沉重负担，迫使各国政府采用人工智慧驱动的平台来实现高效的城市管理。全球智慧城市计画正获得巨额公共和私人资金支持，以建立互联互通的交通、公共产业和公共服务系统。对优化资源配置、降低能源消耗和提升公共安全日益增长的需求，正在加速这些平台的普及应用。此外，政府对城市规划数位转型的强制性要求，也为市场成长创造了有利环境，促使地方政府从传统的管理模式转向人工智慧驱动的预测性运作。

较高的初始实施和整合成本

实施人工智慧智慧城市平台需要前期在硬体、软体和大规模网路基础设施方面进行大量前期投资。将人工智慧平台与现有市政系统整合的复杂性往往会导致意想不到的成本和计划延期。许多市政当局，尤其是在发展中地区，面临预算限制，这阻碍了全面智慧城市解决方案的采用。此外，持续升级和专门的网路安全措施的需求推高了总体拥有成本（TCO），使得小规模的城市在没有明确的短期投资回报率（ROI）的情况下难以证明投资的合理性。

官民合作关係（PPP）模式的兴起

官民合作关係（PPP）模式的日益普及，为人工智慧智慧城市平台的资金筹措和部署开闢了新的途径。各国政府正与科技公司合作，共同承担大规模城市数位化所需的财务风险与技术专长。这种伙伴关係能够加快计划执行速度，获取前沿的人工智慧创新成果，并获得长期的维护支援。私营部门的参与也能带来营运效率和商业最佳实践，从而优化平台性能。随着地方政府寻求在不增加公共预算负担的情况下加速智慧城市蓝图，公私合作模式正成为市场扩张的关键驱动力。

资料隐私和网路安全漏洞

城市系统中大量公民资料的收集，使其极易遭受网路攻击和资料外洩。人工智慧智慧城市平台汇集了来自交通系统、监控网路和公共产业网路的敏感讯息，因此成为恶意攻击者的主要目标。对监控和个人资料滥用的担忧可能导致公民抵制和监管机构的介入，进而延缓平台部署。在确保遵守不断变化的资料保护法律的同时，也要维持平台的功能，这对开发者和城市管理者来说都是复杂的挑战，并有可能损害公众对这些工作的信任。

新冠疫情的感染疾病

疫情加速了人工智慧智慧城市的普及，因为城市迫切需要数位化工具来进行人群管理、远端监控和接触者追踪。封锁措施凸显了自动化系统在维持基本服务的同时最大限度地减少人为介入的必要性。投资转向了能够支援医疗物流、远端医疗和非接触式公共介面的人工智慧平台。儘管一些计划最初由于预算重新分配而有所延误，但这场危机最终凸显了具有韧性、数据驱动的城市基础设施的价值，并加速了疫情后公共卫生和紧急应变系统中人工智慧解决方案的应用。

在预测期内，软体产业预计将占据最大的市场份额。

预计在预测期内，软体领域将占据最大的市场份额，因为它构成了人工智慧智慧城市平台的核心智慧层。该领域包括人工智慧演算法、数据分析工具和平台接口，这些工具支援诸如交通优化和预测性维护等城市应用。机器学习和生成式人工智慧的持续进步正在增强软体功能，并推动更高级的城市自动化。

在预测期内，交通管理部门预计将呈现最高的复合年增长率。

在预测期内，交通管理部门预计将呈现最高的成长率。这些机构正在利用预测分析和电脑视觉技术进行即时交通流量管理、缓解拥塞和公共交通调度。自动驾驶汽车的引入和智慧交通控制系统的进步正在推动平台的应用。透过运用人工智慧，交通管理部门旨在提高通勤者的安全，提升营运效率，并减少整个城市交通生态系统对环境的影响。

市占率最大的地区：

在预测期内，北美预计将占据最大的市场份额，这得益于其强大的技术基础设施和先进的人工智慧解决方案的高普及率。美国和加拿大在将生成式人工智慧和边缘运算融入市政营运方面处于领先地位。联邦政府为城市基础设施现代化提供的巨额资金以及蓬勃发展的科技Start-Ups生态系统正在推动创新。领先的人工智慧平台供应商的存在以及对网路安全和资料管治标准的重视，也促进了该地区市场的快速扩张。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于中国、印度和东南亚各国政府对智慧城市计划的巨额投资。快速的都市化以及高效管理特大城市的需求，正在推动人工智慧平台在交通、公共产业和公共领域的应用。地方政府正积极部署数位基础设施，并与全球技术供应商建立伙伴关係。

免费客製化服务：

所有购买此报告的客户均可享受以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 对主要企业进行SWOT分析（最多3家公司）
  • 区域细分
    • 应客户要求，我们提供主要国家和地区的市场估算和预测，以及复合年增长率（註：需进行可行性检查）。
  • 竞争性标竿分析
    • 根据产品系列、地理覆盖范围和策略联盟对主要企业进行基准分析。

目录

第一章：执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 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
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章 全球人工智慧智慧城市平台市场：按组件划分

  • 硬体
  • 软体
  • 服务

第六章 全球人工智慧智慧城市平台市场：依技术划分

  • 机器学习（ML）
  • 自然语言处理（NLP）
  • 电脑视觉
  • 预测分析
  • 边缘人工智慧
  • 人工智慧世代

第七章 全球人工智慧智慧城市平台市场：按应用划分

  • 智慧运输与交通
  • 公共保障
  • 智慧公用事业和能源管理
  • 智慧医疗
  • 智慧管治与公民服务
  • 智慧基础设施建筑
  • 环境监测

第八章 全球人工智慧智慧城市平台市场：按部署模式划分

  • 基于云端的
  • 现场
  • 杂交种

第九章 全球人工智慧智慧城市平台市场：按最终用户划分

  • 地方政府和地方政府
  • 公共安全机关
  • 交通管理局
  • 公共产业营运商
  • 医疗机构
  • 房地产开发公司

第十章 全球人工智慧智慧城市平台市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他南美国家
  • 中东和非洲
    • 沙乌地阿拉伯
    • UAE
    • 南非
    • 其他中东和非洲国家

第十一章 策略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十二章 产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十三章：公司简介

  • Microsoft Corporation
  • IBM Corporation
  • Cisco Systems, Inc.
  • Siemens AG
  • Hitachi, Ltd.
  • Huawei Technologies Co., Ltd.
  • Intel Corporation
  • NVIDIA Corporation
  • Amazon Web Services（AWS）
  • Google（Alphabet Inc.）
  • Schneider Electric
  • ABB Ltd.
  • NEC Corporation
  • Honeywell International Inc.
  • Thales Group
  • Telensa
  • UrbanLogiq
  • IBI Group
  • Current（GE）
  • Verizon Communications
Product Code: SMRC34707

According to Stratistics MRC, the Global AI Smart City Platforms Market is accounted for $90.7 billion in 2026 and is expected to reach $1,134.2 billion by 2034 growing at a CAGR of 37.1% during the forecast period. AI Smart City Platforms are integrated digital frameworks that use artificial intelligence to manage, analyze, and optimize urban infrastructure and services. These platforms collect data from sensors, IoT devices, cameras, and connected systems across transportation, energy, public safety, waste management, and utilities. By applying advanced analytics and machine learning, they enable city authorities to improve operational efficiency, enhance citizen services, and support data-driven decision-making. AI smart city platforms help create sustainable, efficient, and responsive urban environments by enabling real-time monitoring, predictive insights, and automated management of city resources.

Market Dynamics:

Driver:

Growing urbanization and smart city initiatives

Rapid urbanization is placing immense pressure on existing infrastructure, compelling governments to adopt AI-driven platforms for efficient city management. Smart city initiatives worldwide are receiving substantial public and private funding to deploy interconnected systems for traffic, utilities, and public services. The need to optimize resource allocation, reduce energy consumption, and improve citizen safety is accelerating the adoption of these platforms. Furthermore, government mandates for digital transformation in urban planning are creating a conducive environment for market growth, pushing municipalities to move from traditional management to predictive, AI-enabled operations.

Restraint:

High initial deployment and integration costs

Implementing AI smart city platforms requires significant upfront investment in hardware, software, and extensive network infrastructure. The complexity of integrating AI platforms with legacy municipal systems often leads to unforeseen costs and project delays. Many municipalities, particularly in developing regions, face budget constraints that hinder the adoption of comprehensive smart city solutions. Additionally, the need for continuous upgrades and specialized cybersecurity measures adds to the total cost of ownership, making it difficult for smaller cities to justify the investment without clear short-term return on investment.

Opportunity:

Rise of public-private partnerships (PPPs)

The growing trend of public-private partnerships is opening new avenues for funding and deploying AI smart city platforms. Governments are collaborating with technology firms to share the financial risk and technical expertise required for large-scale urban digitalization. These partnerships enable faster project execution, access to cutting-edge AI innovations, and long-term maintenance support. Private sector involvement also brings in operational efficiencies and commercial best practices that help optimize platform performance. As cities seek to accelerate their smart city roadmaps without straining public budgets, PPPs are becoming a critical enabler for market expansion.

Threat:

Data privacy and cybersecurity vulnerabilities

The extensive collection of citizen data across urban systems creates significant vulnerabilities to cyberattacks and data breaches. AI smart city platforms aggregate sensitive information from traffic systems, surveillance networks, and utility grids, making them prime targets for malicious actors. Concerns over surveillance and misuse of personal data can lead to public resistance and regulatory scrutiny, slowing down implementation. Ensuring compliance with evolving data protection laws while maintaining platform functionality poses a complex challenge for developers and city administrators, threatening to undermine public trust in these initiatives.

Covid-19 Impact

The pandemic acted as a catalyst for AI smart city adoption, as cities urgently needed digital tools for crowd management, remote monitoring, and contact tracing. Lockdowns highlighted the necessity of automated systems for maintaining essential services with reduced human intervention. Investment shifted toward AI platforms that could support healthcare logistics, telemedicine, and touchless public interfaces. While budget reallocations initially slowed some projects, the crisis ultimately underscored the value of resilient, data-driven urban infrastructure, leading to accelerated procurement of AI solutions for public health and emergency response systems post-pandemic.

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

The software segment is expected to account for the largest market share during the forecast period, as it forms the core intelligence layer of AI smart city platforms. This segment includes AI algorithms, data analytics tools, and platform interfaces that enable urban applications like traffic optimization and predictive maintenance. Continuous advancements in machine learning and generative AI are enhancing software capabilities, allowing for more sophisticated urban automation.

The transportation authorities segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the transportation authorities segment is predicted to witness the highest growth rate. These agencies utilize predictive analytics and computer vision for real-time traffic flow management, congestion reduction, and public transit scheduling. The push for autonomous vehicle integration and intelligent traffic control systems is driving platform adoption. By harnessing AI, transportation authorities aim to enhance commuter safety, improve operational efficiency, and reduce environmental impact across urban transportation ecosystems.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by strong technological infrastructure and high adoption rates of advanced AI solutions. The U.S. and Canada are at the forefront of integrating generative AI and edge computing into municipal operations. Substantial federal funding for modernizing urban infrastructure and a robust ecosystem of technology startups are fueling innovation. The presence of major AI platform vendors and a focus on cybersecurity and data governance standards are also contributing to rapid market expansion in this region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by massive government investments in smart city projects across China, India, and Southeast Asia. Rapid urbanization and the need to manage megacities efficiently are fueling the adoption of AI platforms for traffic, utilities, and public safety. Local governments are aggressively deploying digital infrastructure and fostering partnerships with global technology providers.

Key players in the market

Some of the key players in AI Smart City Platforms Market include Microsoft Corporation, IBM Corporation, Cisco Systems, Inc., Siemens AG, Hitachi, Ltd., Huawei Technologies Co., Ltd., Intel Corporation, NVIDIA Corporation, Amazon Web Services (AWS), Google (Alphabet Inc.), Schneider Electric, ABB Ltd., NEC Corporation, Honeywell International Inc., Thales Group, Telensa, UrbanLogiq, IBI Group, Current (GE), and Verizon Communications.

Key Developments:

In March 2026, IBM completed its acquisition of Confluent, Inc., the data streaming platform that more than 6,500 enterprises, including 40% of the Fortune 500, rely on to power real-time operations. Together, IBM and Confluent deliver a smart data platform that gives every AI model, agent, and automated workflow the real-time, trusted data needed to operate across on-premises and hybrid cloud environments at scale.

In March 2026, NVIDIA and Emerald AI announced that they are working with AES, Constellation, Invenergy, NextEra Energy, Nscale Energy & Power and Vistra to power and advance a new class of AI factories that connect to the grid faster, generate valuable AI tokens and intelligence, and operate as flexible energy assets that can support the grid.

Components Covered:

  • Hardware
  • Software
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Computer Vision
  • Predictive Analytics
  • Edge AI
  • Generative AI

Applications Covered:

  • Smart Mobility & Transportation
  • Public Safety & Security
  • Smart Utilities & Energy Management
  • Smart Healthcare
  • Smart Governance & Citizen Services
  • Smart Infrastructure & Buildings
  • Environmental Monitoring

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid

End Users Covered:

  • Municipalities & Local Governments
  • Public Safety Agencies
  • Transportation Authorities
  • Utility Providers
  • Healthcare Institutions
  • Real Estate & Developers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of 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 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • 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

  • 1.1. Market Snapshot and Key Highlights
  • 1.2. Growth Drivers, Challenges, and Opportunities
  • 1.3. Competitive Landscape Overview
  • 1.4. Strategic Insights and Recommendations

2 Research Framework

  • 2.1. Study Objectives and Scope
  • 2.2. Stakeholder Analysis
  • 2.3. Research Assumptions and Limitations
  • 2.4. Research Methodology
    • 2.4.1. Data Collection (Primary and Secondary)
    • 2.4.2. Data Modeling and Estimation Techniques
    • 2.4.3. Data Validation and Triangulation
    • 2.4.4. Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1. Market Definition and Structure
  • 3.2. Key Market Drivers
  • 3.3. Market Restraints and Challenges
  • 3.4. Growth Opportunities and Investment Hotspots
  • 3.5. Industry Threats and Risk Assessment
  • 3.6. Technology and Innovation Landscape
  • 3.7. Emerging and High-Growth Markets
  • 3.8. Regulatory and Policy Environment
  • 3.9. Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1. Porter's Five Forces Analysis
    • 4.1.1. Supplier Bargaining Power
    • 4.1.2. Buyer Bargaining Power
    • 4.1.3. Threat of Substitutes
    • 4.1.4. Threat of New Entrants
    • 4.1.5. Competitive Rivalry
  • 4.2. Market Share Analysis of Key Players
  • 4.3. Product Benchmarking and Performance Comparison

5 Global AI Smart City Platforms Market, By Component

  • 5.1. Hardware
  • 5.2. Software
  • 5.3. Services

6 Global AI Smart City Platforms Market, By Technology

  • 6.1. Machine Learning (ML)
  • 6.2. Natural Language Processing (NLP)
  • 6.3. Computer Vision
  • 6.4. Predictive Analytics
  • 6.5. Edge AI
  • 6.6. Generative AI

7 Global AI Smart City Platforms Market, By Application

  • 7.1. Smart Mobility & Transportation
  • 7.2. Public Safety & Security
  • 7.3. Smart Utilities & Energy Management
  • 7.4. Smart Healthcare
  • 7.5. Smart Governance & Citizen Services
  • 7.6. Smart Infrastructure & Buildings
  • 7.7. Environmental Monitoring

8 Global AI Smart City Platforms Market, By Deployment Mode

  • 8.1. Cloud-Based
  • 8.2. On-Premises
  • 8.3. Hybrid

9 Global AI Smart City Platforms Market, By End User

  • 9.1. Municipalities & Local Governments
  • 9.2. Public Safety Agencies
  • 9.3. Transportation Authorities
  • 9.4. Utility Providers
  • 9.5. Healthcare Institutions
  • 9.6. Real Estate & Developers

10 Global AI Smart City Platforms Market, By Geography

  • 10.1. North America
    • 10.1.1. United States
    • 10.1.2. Canada
    • 10.1.3. Mexico
  • 10.2. Europe
    • 10.2.1. United Kingdom
    • 10.2.2. Germany
    • 10.2.3. France
    • 10.2.4. Italy
    • 10.2.5. Spain
    • 10.2.6. Rest of Europe
  • 10.3. Asia Pacific
    • 10.3.1. China
    • 10.3.2. Japan
    • 10.3.3. India
    • 10.3.4. South Korea
    • 10.3.5. Australia
    • 10.3.6. Rest of Asia Pacific
  • 10.4. South America
    • 10.4.1. Brazil
    • 10.4.2. Argentina
    • 10.4.3. Rest of South America
  • 10.5. Middle East & Africa
    • 10.5.1. Saudi Arabia
    • 10.5.2. UAE
    • 10.5.3. South Africa
    • 10.5.4. Rest of MEA

11 Strategic Market Intelligence

  • 11.1. Industry Value Network and Supply Chain Assessment
  • 11.2. White-Space and Opportunity Mapping
  • 11.3. Product Evolution and Market Life Cycle Analysis
  • 11.4. Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1. Mergers and Acquisitions
  • 12.2. Partnerships, Alliances, and Joint Ventures
  • 12.3. New Product Launches and Certifications
  • 12.4. Capacity Expansion and Investments
  • 12.5. Other Strategic Initiatives

13 Company Profiles

  • 13.1. Microsoft Corporation
  • 13.2. IBM Corporation
  • 13.3. Cisco Systems, Inc.
  • 13.4. Siemens AG
  • 13.5. Hitachi, Ltd.
  • 13.6. Huawei Technologies Co., Ltd.
  • 13.7. Intel Corporation
  • 13.8. NVIDIA Corporation
  • 13.9. Amazon Web Services (AWS)
  • 13.10. Google (Alphabet Inc.)
  • 13.11. Schneider Electric
  • 13.12. ABB Ltd.
  • 13.13. NEC Corporation
  • 13.14. Honeywell International Inc.
  • 13.15. Thales Group
  • 13.16. Telensa
  • 13.17. UrbanLogiq
  • 13.18. IBI Group
  • 13.19. Current (GE)
  • 13.20. Verizon Communications

List of Tables

  • Table 1 Global AI Smart City Platforms Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI Smart City Platforms Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI Smart City Platforms Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 4 Global AI Smart City Platforms Market Outlook, By Software (2023-2034) ($MN)
  • Table 5 Global AI Smart City Platforms Market Outlook, By ervices (2023-2034) ($MN)
  • Table 6 Global AI Smart City Platforms Market Outlook, By Technology (2023-2034) ($MN)
  • Table 7 Global AI Smart City Platforms Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 8 Global AI Smart City Platforms Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 9 Global AI Smart City Platforms Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 10 Global AI Smart City Platforms Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 11 Global AI Smart City Platforms Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 12 Global AI Smart City Platforms Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 13 Global AI Smart City Platforms Market Outlook, By Application (2023-2034) ($MN)
  • Table 14 Global AI Smart City Platforms Market Outlook, By Smart Mobility & Transportation (2023-2034) ($MN)
  • Table 15 Global AI Smart City Platforms Market Outlook, By Public Safety & Security (2023-2034) ($MN)
  • Table 16 Global AI Smart City Platforms Market Outlook, By Smart Utilities & Energy Management (2023-2034) ($MN)
  • Table 17 Global AI Smart City Platforms Market Outlook, By Smart Healthcare (2023-2034) ($MN)
  • Table 18 Global AI Smart City Platforms Market Outlook, By Smart Governance & Citizen Services (2023-2034) ($MN)
  • Table 19 Global AI Smart City Platforms Market Outlook, By Smart Infrastructure & Buildings (2023-2034) ($MN)
  • Table 20 Global AI Smart City Platforms Market Outlook, By Environmental Monitoring (2023-2034) ($MN)
  • Table 21 Global AI Smart City Platforms Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 22 Global AI Smart City Platforms Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 23 Global AI Smart City Platforms Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 24 Global AI Smart City Platforms Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 25 Global AI Smart City Platforms Market Outlook, By End User (2023-2034) ($MN)
  • Table 26 Global AI Smart City Platforms Market Outlook, By Municipalities & Local Governments (2023-2034) ($MN)
  • Table 27 Global AI Smart City Platforms Market Outlook, By Public Safety Agencies (2023-2034) ($MN)
  • Table 28 Global AI Smart City Platforms Market Outlook, By Transportation Authorities (2023-2034) ($MN)
  • Table 29 Global AI Smart City Platforms Market Outlook, By Utility Providers (2023-2034) ($MN)
  • Table 30 Global AI Smart City Platforms Market Outlook, By Healthcare Institutions (2023-2034) ($MN)
  • Table 31 Global AI Smart City Platforms Market Outlook, By Real Estate & Developers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.