Global Artificial Intelligence in Smart Cities Market Size, Share & Trends Analysis Report, By Component, By Application, By Deployment, By Region Forecasts, 2024 - 2032

Global Artificial Intelligence in Smart Cities Market Size, Share & Trends Analysis Report, By Component, By Application, By Deployment, By Region Forecasts, 2024 - 2032



Global Artificial Intelligence in Smart Cities Market was valued at US $ 24.9 Billion in 2023 and is expected to reach US $ 359.6 Billion by 2032 growing at a CAGR of 34.54% during the forecast period 2024 – 2032.

The market for artificial intelligence (AI) in smart cities is the result of the convergence of AI technology with the ever-changing requirements of urban settings. It includes a wide range of AI-driven programmes and solutions intended to deal with the difficulties and complications that contemporary cities face. The main focus of this sector is the incorporation of AI technology to improve many facets of urban living, resulting in smarter, more sustainable, and efficient cities.

The growing need for smart city solutions is one of the main factors driving the expansion of AI applications in urban environments. Global urbanisation is posing previously unheard-of issues to cities as a result of expanding infrastructure and population. Cities are looking more and more to new technology, with artificial intelligence (AI) taking centre stage, to effectively solve these issues. AI is a vital tool for addressing these issues because of its ability to handle enormous datasets and make choices in real time, allowing cities to effectively adjust to the changing needs of their citizens. In the context of developing smart cities, resource management and efficiency are critical. Urban challenges like traffic congestion and the need for more efficient public transportation systems drive the growing demand for intelligent transportation solutions, which positions AI as a transformative force in mitigating these issues. Additionally, AI empowers cities to optimise the utilisation of vital resources, like energy, water, and waste management, leading to not only sustainability but also cost savings.

“Software segment, by component, to be dominating market from 2023 to 2030.”

Software held a market share of 58.43% in 2023, making it the most popular component in the AI for smart cities market. With a CAGR of 42.5% from 2024 to 2032, it is also the component with the quickest rate of growth. With many attractive benefits, software is the main force driving AI's dominance and quick rise in the smart cities sector. Because of its flexibility and adaptability, it may be used to create customised solutions that can change over time to meet the unique and changing needs of smart cities. Furthermore, the scalability of software effectively tackles the growing volumes of data and the problems that metropolitan environments provide. Cost-effectiveness, needing smaller initial capital inputs compared to hardware-based alternatives, is one of its primary advantages. Because of this, software is a strong option for smart city planners looking to implement cutting-edge technologies on a tight budget. Software's natural ability to develop and deploy quickly assures prompt responses to urgent urban concerns, which is essential in a continually changing urban environment.

“Cloud Based segment, by deployment, to be dominating market from 2023 to 2030.”

With the largest market share of 52.6% in 2023 for AI in smart cities, cloud-based deployments are the most popular kind. With a 36.64% CAGR from 2024 to 2032, it is also the deployment type with the quickest rate of growth. Because of its many benefits, cloud-based deployment in the AI for smart cities sector is becoming more and more popular. Because of its unparalleled scalability, smart cities can quickly adapt their AI systems to accommodate expanding populations and changing urban needs. Cost effectiveness is a major motivator since it does away with the need for large upfront investments in infrastructure and technology by using a pay-as-you-go approach that corresponds with actual usage. Cloud-based solutions' accessibility is essential in smart cities since remote access to data and decision-making is frequently required. This feature speeds up the deployment of AI-driven systems so that smart cities may immediately realise their benefits, especially when combined with cloud platforms' speedy deployment capabilities. Furthermore, cloud service providers give strong analytics and data management capabilities, tackling the difficulty of effectively managing enormous volumes of urban data.

“Transportation segment, by application, to be dominating market from 2023 to 2030.”

With market share of 38% in 2023, transport is the most popular use of artificial intelligence in smart cities. With a CAGR of 36.72% from 2024 to 2032, it is also the application with the quickest rate of growth. The increase in the transportation industry can be attributed to the growing need for intelligent transportation systems (ITS) that are designed to improve traffic efficiency, reduce traffic jams, and increase safety. Autonomous vehicles, adaptive cruise control systems, and intelligent traffic lights are just a few of the ITS improvements made possible by AI. At the same time, the energy sector is growing due to the need to improve energy efficiency and properly handle renewable energy sources. AI solutions play a key role in optimising energy use in different types of facilities and forming energy management systems.

“Asia Pacific to be largest and fastest growing region in Artificial Intelligence in Smart Cities market.”

With a 35.7% CAGR, the Asia-Pacific area has become the fastest-growing in the world. Numerous important characteristics that distinguish the area can be combined to explain this pattern. First off, a lot of APAC governments have invested heavily in AI and associated technologies in an effort to promote smart city programmes. These programmes are meant to enhance inhabitants' quality of life while tackling the intricacies of quickly expanding metropolitan areas. A significant fraction of the world's population lives in densely populated cities, and the APAC area is rapidly becoming more urbanised. Because of this change in population, there is an urgent need for sophisticated solutions to handle problems like resource usage, traffic congestion, and environmental pollution—all of which are well-suited for AI-driven interventions.

Artificial Intelligence in Smart Cities Competitive Landscape

The competitive landscape of the Artificial Intelligence in Smart Cities market involves assessing the competitive landscape to understand the strengths, weaknesses, opportunities, and threats of the industry. Key industry players have recognized that the adoption of Artificial Intelligence in Smart Cities technology holds the potential for further growth. The growing desire among producers to optimize their production costs has spurred collaborative efforts among companies to scale up their production capacity. This strategic collaboration not only aims to increase revenue but also seeks to establish dominance in the market.

The Artificial Intelligence in Smart Cities market is highly competitive, with numerous companies vying for market share. Prominent companies in the Artificial Intelligence in Smart Cities Market include:

Cisco, IBM, Google, Amazon Web Services (AWS), Microsoft, Siemens, ABB, Hitachi, Huawei, Schneider Electric, Intel, Nvidia, Qualcomm, Bentley Systems, Dassault Systèmes, PTC, ARM, Autodesk, Hexagon AB, Trimble, Esri, HERE Technologies, TomTom, Mapbox, StreetLight Data, Flowbird, Iteris, Transdev, Cubic, Keolis

Recent Developments:

IBM and Cisco teamed up on November 8, 2023, to work together on creating artificial intelligence (AI) solutions specifically for smart cities. Their collaboration is centred on developing artificial intelligence (AI) solutions for a variety of smart city functions, including security, healthcare, energy management, and transportation.

On November 7, 2023, Microsoft made a big announcement by launching Azure for Smart Cities, a state-of-the-art AI-powered platform for smart cities. This platform provides a full range of tools and services to make it easier to develop and implement AI solutions made especially for smart city projects.

On November 6, 2023, Google announced the release of a unique traffic management system driven by artificial intelligence. This cutting-edge solution uses AI algorithms to improve traffic flow and lessen traffic-related problems. It is now being tested and implemented in several places across the globe.


1 Introduction Of Global Artificial Intelligence In Smart Cities Market
1.1 Overview Of The Market
1.2 Scope Of Report
1.3 Assumptions
2 Executive Summary
3 Research Methodology
3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List Of Data Sources
4 Global Artificial Intelligence In Smart Cities Market Outlook
4.1 Overview
4.2 Market Dynamics
4.2.1 Drivers
4.2.2 Restraints
4.2.3 Opportunities
4.3 Porters Five Force Model
4.3.1. Bargaining Power Of Suppliers
4.3.2. Threat Of New Entrants
4.3.3. Threat Of Substitutes
4.3.4. Competitive Rivalry
4.3.5. Bargaining Power Among Buyers
4.4 Value Chain Analysis
5 Global Artificial Intelligence In Smart Cities Market, By Component
5.1 Overview
5.2 Hardware
5.3 Software
5.4 Services
6 Global Artificial Intelligence In Smart Cities Market, By Application
6.1 Overview
6.2 Transportation
6.3 Energy
6.4 Security
6.5 Healthcare
6.6 Education
6.7 Others
7 Global Artificial Intelligence In Smart Cities Market, By Deployment
7.1 Overview
7.2 On Premise
7.3 Cloud Based.
7.4 Hybrid
8 Global Artificial Intelligence In Smart Cities Market, By Region
8.1 North America
8.1.1 U.S.
8.1.2 Canada
8.2 Europe
8.2.1 Germany
8.2.3 U.K.
8.2.4 France
8.2.5 Rest Of Europe
8.3 Asia Pacific
8.3.1 China
8.3.2 Japan
8.3.3 India
8.3.4 South Korea
8.3.5 Singapore
8.3.6 Malaysia
8.3.7 Australia
8.3.8 Thailand
8.3.9 Indonesia
8.3.10 Philippines
8.3.11 Rest Of Asia Pacific
8.4 Others
8.4.1 Saudi Arabia
8.4.2 U.A.E.
8.4.3 South Africa
8.4.4 Egypt
8.4.5 Israel
8.4.6 Rest Of Middle East And Africa (Mea)
8.4.7 Brazil
8.4.8 Argentina
8.4.9 Mexico
8.4.10 Rest Of South America
9 Company Profiles
9.1 Cisco
9.1.1. Company Overview
9.1.2. Key Executives
9.1.3. Operating Business Segments
9.1.4. Product Portfolio
9.1.5. Financial Performance (As Per Availability)
9.1.6 Key News
9.2 Siemens
9.2.1. Company Overview
9.2.2. Key Executives
9.2.3. Operating Business Segments
9.2.4. Product Portfolio
9.2.5. Financial Performance (As Per Availability)
9.2.6. Key News
9.3 Schneider Electric
9.3.1. Company Overview
9.3.2. Key Executives
9.3.3. Operating Business Segments
9.3.4. Product Portfolio
9.3.5. Financial Performance (As Per Availability)
9.3.6. Key News
9.4 Google
9.4.1. Company Overview
9.4.2. Key Executives
9.4.3. Operating Business Segments
9.4.4. Product Portfolio
9.4.5. Financial Performance (As Per Availability)
9.4.6. Key News
9.5 Microsoft
9.5.1. Company Overview
9.5.2. Key Executives
9.5.3. Operating Business Segments
9.5.4. Product Portfolio
9.5.5. Financial Performance (As Per Availability)
9.5.6. Key News
9.6 Google
9.6.1. Company Overview
9.6.2. Key Executives
9.6.3. Operating Business Segments
9.6.4. Product Portfolio
9.6.5. Financial Performance (As Per Availability)
9.6.6. Key News
9.7 Ibm
9.7.1. Company Overview
9.7.2. Key Executives
9.7.3. Operating Business Segments
9.7.4. Product Portfolio
9.7.5. Financial Performance (As Per Availability)
9.7.6. Key News
9.8 Amazon Web Services
9.8.1. Company Overview
9.8.2. Key Executives
9.8.3. Operating Business Segments
9.8.4. Product Portfolio
9.8.5. Financial Performance (As Per Availability)
9.8.6. Key News
9.9 Abb
9.9.1. Company Overview
9.9.2. Key Executives
9.9.3. Operating Business Segments
9.9.4. Product Portfolio
9.9.5. Financial Performance (As Per Availability)
9.9.6. Key News
9.10 Hitachi
9.10.1. Company Overview
9.10.2. Key Executives
9.10.3. Operating Business Segments
910.4. Product Portfolio
9.10.5. Financial Performance (As Per Availability)
9.10.6. Key News
9.11 Huawei
9.11.1. Company Overview
9.11.2. Key Executives
9.11.3. Operating Business Segments
9.11.4. Product Portfolio
9.11.5. Financial Performance (As Per Availability)
9.11.6. Key News
9.12 Intel
9.12.1. Company Overview
9.12.2. Key Executives
9.12.3. Operating Business Segments
9.12.4. Product Portfolio
9.12.5. Financial Performance (As Per Availability)
9.12.6. Key News
9.13 Nvidia
9.13.1. Company Overview
9.13.2. Key Executives
9.13.3. Operating Business Segments
9.13.4. Product Portfolio
9.13.5. Financial Performance (As Per Availability)
9.13.6. Key News
9.14 Qualcomm
9.14.1. Company Overview
9.14.2. Key Executives
9.14.3. Operating Business Segments
9.14.4. Product Portfolio
9.14.5. Financial Performance (As Per Availability)
9.14.6. Key News
9.15 Arm
9.15.1. Company Overview
9.15.2. Key Executives
9.15.3. Operating Business Segments
9.15.4. Product Portfolio
9.15.5. Financial Performance (As Per Availability)
9.15.6. Key News
9.16 Autodesk
9.16.1. Company Overview
9.16.2. Key Executives
9.16.3. Operating Business Segments
9.16.4. Product Portfolio
9.16.5. Financial Performance (As Per Availability)
9.16.6. Key News
9.17 Dassault Systèmes
9.17.1. Company Overview
9.17.2. Key Executives
9.17.3. Operating Business Segments
9.17.4. Product Portfolio
9.17.5. Financial Performance (As Per Availability)
9.17.6. Key News
9.18 Ptc
9.18.1. Company Overview
9.18.2. Key Executives
9.18.3. Operating Business Segments
9.18.4. Product Portfolio
9.18.5. Financial Performance (As Per Availability)
9.18.6. Key News
9.19 Hexagon Ab
9.19.1. Company Overview
9.19.2. Key Executives
9.19.3. Operating Business Segments
9.19.4. Product Portfolio
9.19.5. Financial Performance (As Per Availability)
9.19.6. Key News
9.20 Trimble
9.20.1. Company Overview
9.20.2. Key Executives
9.20.3. Operating Business Segments
9.20.4. Product Portfolio
9.20.5. Financial Performance (As Per Availability)
9.20.6. Key News
9.21 Esri
9.21.1. Company Overview
9.21.2. Key Executives
9.21.3. Operating Business Segments
9.21.4. Product Portfolio
9.21.5. Financial Performance (As Per Availability)
9.21.6. Key News
9.22 Here Technologies
9.22.1. Company Overview
9.22.2. Key Executives
9.22.3. Operating Business Segments
9.22.4. Product Portfolio
9.22.5. Financial Performance (As Per Availability)
9.22.6. Key News
9.23 Mapbox
9.23.1. Company Overview
9.23.2. Key Executives
9.23.3. Operating Business Segments
9.23.4. Product Portfolio
9.23.5. Financial Performance (As Per Availability)
9.23.6. Key News
9.24 Tom Tom
9.24.1. Company Overview
9.24.2. Key Executives
9.24.3. Operating Business Segments
9.24.4. Product Portfolio
9.24.5. Financial Performance (As Per Availability)
9.24.6. Key News
9.25 Streetlight Data
9.25.1. Company Overview
9.25.2. Key Executives
9.25.3. Operating Business Segments
9.25.4. Product Portfolio
9.25.5. Financial Performance (As Per Availability)
9.20.6. Key News
9.26 Flowbird
9.26.1. Company Overview
9.26.2. Key Executives
9.26.3. Operating Business Segments
9.26.4. Product Portfolio
9.26.5. Financial Performance (As Per Availability)
9.26.6. Key News
9.27 Cubic
9.27.1. Company Overview
9.27.2. Key Executives
9.27.3. Operating Business Segments
9.27.4. Product Portfolio
9.27.5. Financial Performance (As Per Availability)
9.27.6. Key News
9.28 Iteris
9.28.1. Company Overview
9.28.2. Key Executives
9.28.3. Operating Business Segments
9.28.4. Product Portfolio
9.28.5. Financial Performance (As Per Availability)
9.28.6. Key News
9.29 Transdev
9.29.1. Company Overview
9.29.2. Key Executives
9.29.3. Operating Business Segments
9.29.4. Product Portfolio
9.29.5. Financial Performance (As Per Availability)
9.29.6. Key News
9.30 Keolis
9.30.1. Company Overview
9.30.2. Key Executives
9.30.3. Operating Business Segments
9.30.4. Product Portfolio
9.30.5. Financial Performance (As Per Availability)
9.30.6. Key News

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