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2007832

人工智慧智慧城市平台市場預測至2034年：按組件、技術、應用、部署模式、最終用戶和區域分類的全球分析

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個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球 AI 智慧城市平台市場規模將達到 907 億美元，在預測期內將以 37.1% 的複合年成長率成長，到 2034 年將達到 1.1342 兆美元。

人工智慧智慧城市平台是一個整合的數位化框架，它利用人工智慧來管理、分析和最佳化城市基礎設施和服務。這些平台從感測器、物聯網設備、攝影機和互聯系統收集數據，涵蓋交通、能源、公共、廢棄物管理和公共產業等領域。透過應用先進的分析和機器學習技術，它們能夠幫助城市管理部門提高營運效率、提升市民服務水準並支援數據驅動的決策。人工智慧智慧城市平台透過實現城市資源的即時監測、預測性洞察和自動化管理，協助建構永續、高效且反應迅速的城市環境。

推動都市化和智慧城市計劃

快速的都市化給現有基礎設施帶來了沉重負擔，迫使各國政府採用人工智慧驅動的平台來實現高效的城市管理。全球智慧城市計畫正獲得巨額公共和私人資金支持，以建立互聯互通的交通、公共產業和公共服務系統。對最佳化資源配置、降低能源消耗和提升公共安全日益成長的需求，正在加速這些平台的普及應用。此外，政府對城市規劃數位轉型的強制性要求，也為市場成長創造了有利環境，促使地方政府從傳統的管理模式轉向人工智慧驅動的預測性運作。

較高的初始實施和整合成本

實施人工智慧智慧城市平台需要前期在硬體、軟體和大規模網路基礎設施方面進行大量前期投資。將人工智慧平台與現有市政系統整合的複雜性往往會導致意想不到的成本和計劃延期。許多市政當局，尤其是在發展中地區，面臨預算限制，這阻礙了全面智慧城市解決方案的採用。此外，持續升級和專門的網路安全措施的需求推高了總體擁有成本（TCO），使得小規模的城市在沒有明確的短期投資回報率（ROI）的情況下難以證明投資的合理性。

官民合作關係（PPP）模式的興起

官民合作關係（PPP）模式的日益普及，為人工智慧智慧城市平台的資金籌措和部署開闢了新的途徑。各國政府正與科技公司合作，共同承擔大規模城市數位化所需的財務風險與技術專長。這種夥伴關係能夠加快計劃執行速度，獲取前沿的人工智慧創新成果，並獲得長期的維護支援。私營部門的參與也能帶來營運效率和商業最佳實踐，從而最佳化平台性能。隨著地方政府尋求在不增加公共預算負擔的情況下加速智慧城市藍圖，公私合作模式正成為市場擴張的關鍵驅動力。

資料隱私和網路安全漏洞

城市系統中大量公民資料的收集，使其極易遭受網路攻擊和資料外洩。人工智慧智慧城市平台匯集了來自交通系統、監控網路和公共產業網路的敏感訊息，因此成為惡意攻擊者的主要目標。對監控和個人資料濫用的擔憂可能導致公民抵制和監管機構的介入，進而延緩平台部署。在確保遵守不斷變化的資料保護法律的同時，也要維持平台的功能，這對開發者和城市管理者來說都是複雜的挑戰，並有可能損害公眾對這些工作的信任。

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，因為城市迫切需要數位化工具來進行人群管理、遠端監控和接觸者追蹤。封鎖措施凸顯了自動化系統在維持基本服務的同時最大限度地減少人為介入的必要性。投資轉向了能夠支援醫療物流、遠端醫療和非接觸式公共介面的人工智慧平台。儘管一些計劃最初由於預算重新分配而有所延誤，但這場危機最終凸顯了具有韌性、數據驅動的城市基礎設施的價值，並加速了疫情後公共衛生和緊急應變系統中人工智慧解決方案的應用。

在預測期內，軟體產業預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率，因為它構成了人工智慧智慧城市平台的核心智慧層。該領域包括人工智慧演算法、數據分析工具和平台介面，這些工具支援諸如交通最佳化和預測性維護等城市應用。機器學習和生成式人工智慧的持續進步正在增強軟體功能，並推動更高級的城市自動化。

在預測期內，交通管理部門預計將呈現最高的複合年成長率。

在預測期內，交通管理部門預計將呈現最高的成長率。這些機構正在利用預測分析和電腦視覺技術進行即時交通流量管理、緩解擁塞和公共交通調度。自動駕駛汽車的引入和智慧交通控制系統的進步正在推動平台的應用。透過運用人工智慧，交通管理部門旨在提高通勤者的安全，提升營運效率，並減少整個城市交通生態系統對環境的影響。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這得益於其強大的技術基礎設施和先進的人工智慧解決方案的高普及率。美國和加拿大在將生成式人工智慧和邊緣運算融入市政營運方面處於領先地位。聯邦政府為城市基礎設施現代化提供的巨額資金以及蓬勃發展的科技Start-Ups生態系統正在推動創新。領先的人工智慧平台供應商的存在以及對網路安全和資料管治標準的重視，也促進了該地區市場的快速擴張。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度和東南亞各國政府對智慧城市計劃的巨額投資。快速的都市化以及高效管理特大城市的需求，正在推動人工智慧平台在交通、公共產業和公共領域的應用。地方政府正積極部署數位基礎設施，並與全球技術供應商建立夥伴關係。

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目錄

第1章：執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 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
    • 競爭公司之間的競爭
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章 全球人工智慧智慧城市平台市場：按組件分類

  • 硬體
  • 軟體
  • 服務

第6章 全球人工智慧智慧城市平台市場：依技術分類

  • 機器學習（ML）
  • 自然語言處理（NLP）
  • 電腦視覺
  • 預測分析
  • 邊緣人工智慧
  • 人工智慧世代

第7章 全球人工智慧智慧城市平台市場：按應用分類

  • 智慧運輸與交通
  • 公共保障
  • 智慧公用事業和能源管理
  • 智慧醫療
  • 智慧管治與公民服務
  • 智慧基礎設施建築
  • 環境監測

第8章 全球人工智慧智慧城市平台市場：按部署模式分類

  • 基於雲端的
  • 現場
  • 混合

第9章 全球人工智慧智慧城市平台市場：按最終用戶分類

  • 地方政府和地方政府
  • 公共安全機關
  • 交通管理局
  • 公共產業營運商
  • 醫療機構
  • 房地產開發公司

第10章 全球人工智慧智慧城市平台市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 其他南美國家
  • 中東和非洲
    • 沙烏地阿拉伯
    • UAE
    • 南非
    • 其他中東和非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • 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.