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Global Ai in Smart Cities Market to reach USD XX billion by the end of 2030.

Global Ai in Smart Cities Market Size study & Forecast, by Component (Hardware, Software, Service), by Application (Transportation, Energy, Security, Healthcare, Education, Others), by Deployment (On-Premise, Cloud Based, Hybrid), and Regional Analysis, 2023-2030

Product Code: ICTICTI-82873779
Publish Date: 10-01-2024
Page: 200

Global Ai in Smart Cities Market is valued at approximately USD XX billion in 2022 and is anticipated to grow with a healthy growth rate of more than XX% during the forecast period 2023-2030. The AI in Smart Cities Market represents the intersection of Artificial Intelligence (AI) technology and the dynamic needs of urban environments. It encompasses a diverse range of AI-driven solutions and applications that are designed to address the complexities and challenges faced by modern cities. This market revolves around the integration of AI technology to enhance various aspects of urban living, making cities smarter, more efficient, and more sustainable. The market growth is driven by increasing demand for smart city solutions, growing urbanization, and growing capabilities of AI technologies.

The growing demand for smart city solutions serves as a powerful catalyst for the market growth of AI in smart cities. According to the Indian Government, the Smart Cities Mission, is a program initiated by the Government of India focused on urban renewal and retrofitting. The primary objective is to enhance the livability and sustainability of 100 cities nationwide. The key components of the Smart Cities Mission include city improvement (retrofitting), city renewal (redevelopment), and city extension (greenfield development). Additionally, there is a Pan-city initiative, implementing Smart Solutions across broader city areas to create a more citizen-friendly and sustainable urban environment. On the other side, the Infrastructure Investment and Jobs Act, signed into law by U.S. President Biden on November 15, marks a significant milestone by allocating USD 1.2 trillion in federal spending. This funding is dedicated to fortifying both the digital and physical infrastructure of the United States. Widely regarded as a “once-in-a-generation investment in our nation’s infrastructure and competitiveness,” the Act aims to bring about substantial enhancements in various key areas. However, high initial investment costs, privacy and security concerns, and lack of skilled workforce stifle market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Ai in Smart Cities Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. The Asia-Pacific (APAC) region has emerged as a global leader and the fastest-growing with XX% CAGR over the forecast period. Initially, many APAC governments have taken proactive measures to drive smart city initiatives, making significant investments in AI and related technologies. These initiatives aim to address the complexities of rapidly growing urban centers and improve the quality of life for citizens.

Major market player included in this report are:
Qualcomm Incorporated
International Business Machines Corporation
Amazon Web Services (AWS)
Microsoft Corporation
Google LLC
ABB Ltd.
Hitachi Ltd.
Huawei Technologies Co., Ltd.
Intel Corporation
Nvidia

Recent Developments in the Market:
Ø In November 2023, IBM and Cisco joined forces to collaborate on the development of AI solutions tailored for smart cities. Their partnership focuses on creating AI solutions for a wide range of smart city applications, spanning transportation, energy management, security, and healthcare.
Ø In Nov. 2023, Microsoft introduced a cutting-edge AI-powered platform for smart cities, known as Azure for Smart Cities. This platform offers a comprehensive suite of tools and services to facilitate the creation and deployment of AI solutions specifically designed for smart city initiatives.
Ø In November 2023, Google unveiled a new AI-powered traffic management system. This innovative system employs AI algorithms to optimize traffic flow and mitigate congestion issues. Presently, it is undergoing testing and implementation in multiple cities worldwide.

Global Ai in Smart Cities Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Component, Application, Deployment, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:
Hardware
Software
Service

By Application:
Transportation
Energy
Security
Healthcare
Education
Others

By Deployment:
On-Premise
Cloud
Hybrid

By Region:

North America
U.S.
Canada

Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC

Latin America
Brazil
Mexico

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. AI in Smart Cities Market, by Region, 2020-2030 (USD Billion)
1.2.2. AI in Smart Cities Market, by Component, 2020-2030 (USD Billion)
1.2.3. AI in Smart Cities Market, by Application, 2020-2030 (USD Billion)
1.2.4. AI in Smart Cities Market, by Deployment, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global AI in Smart Cities Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global AI in Smart Cities Market Dynamics
3.1. AI in Smart Cities Market Impact Analysis (2020-2030)
3.1.1. Market Drivers
3.1.1.1. Increasing demand for smart city solutions
3.1.1.2. Growing urbanization
3.1.1.3. Growing capabilities of AI technologies
3.1.2. Market Challenges
3.1.2.1. High initial investment costs
3.1.2.2. Privacy and security concerns
3.1.2.3. Lack of skilled workforce
3.1.3. Market Opportunities
3.1.3.1. Improved efficiency and productivity
3.1.3.2. Reduced environmental impact
3.1.3.3. Improved quality of life for residents
Chapter 4. Global AI in Smart Cities Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global AI in Smart Cities Market, by Component
5.1. Market Snapshot
5.2. Global AI in Smart Cities Market by Component, Performance – Potential Analysis
5.3. Global AI in Smart Cities Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. AI in Smart Cities Market, Sub Segment Analysis
5.4.1. Hardware
5.4.2. Software
5.4.3. Service
Chapter 6. Global AI in Smart Cities Market, by Application
6.1. Market Snapshot
6.2. Global AI in Smart Cities Market by Application, Performance – Potential Analysis
6.3. Global AI in Smart Cities Market Estimates & Forecasts by Application 2020-2030 (USD Billion)
6.4. AI in Smart Cities Market, Sub Segment Analysis
6.4.1. Transportation
6.4.2. Energy
6.4.3. Security
6.4.4. Healthcare
6.4.5. Education
6.4.6. Others
Chapter 7. Global AI in Smart Cities Market, by Deployment
7.1. Market Snapshot
7.2. Global AI in Smart Cities Market by Deployment, Performance – Potential Analysis
7.3. Global AI in Smart Cities Market Estimates & Forecasts by Deployment 2020-2030 (USD Billion)
7.4. AI in Smart Cities Market, Sub Segment Analysis
7.4.1. On-Premise
7.4.2. Cloud
7.4.3. Hybrid
Chapter 8. Global AI in Smart Cities Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. AI in Smart Cities Market, Regional Market Snapshot
8.4. North America AI in Smart Cities Market
8.4.1. U.S. AI in Smart Cities Market
8.4.1.1. Component breakdown estimates & forecasts, 2020-2030
8.4.1.2. Application breakdown estimates & forecasts, 2020-2030
8.4.1.3. Deployment breakdown estimates & forecasts, 2020-2030
8.4.2. Canada AI in Smart Cities Market
8.5. Europe AI in Smart Cities Market Snapshot
8.5.1. U.K. AI in Smart Cities Market
8.5.2. Germany AI in Smart Cities Market
8.5.3. France AI in Smart Cities Market
8.5.4. Spain AI in Smart Cities Market
8.5.5. Italy AI in Smart Cities Market
8.5.6. Rest of Europe AI in Smart Cities Market
8.6. Asia-Pacific AI in Smart Cities Market Snapshot
8.6.1. China AI in Smart Cities Market
8.6.2. India AI in Smart Cities Market
8.6.3. Japan AI in Smart Cities Market
8.6.4. Australia AI in Smart Cities Market
8.6.5. South Korea AI in Smart Cities Market
8.6.6. Rest of Asia Pacific AI in Smart Cities Market
8.7. Latin America AI in Smart Cities Market Snapshot
8.7.1. Brazil AI in Smart Cities Market
8.7.2. Mexico AI in Smart Cities Market
8.8. Middle East & Africa AI in Smart Cities Market
8.8.1. Saudi Arabia AI in Smart Cities Market
8.8.2. South Africa AI in Smart Cities Market
8.8.3. Rest of Middle East & Africa AI in Smart Cities Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. Qualcomm Incorporated
9.3.1.1. Key Information
9.3.1.2. Overview
9.3.1.3. Financial (Subject to Data Availability)
9.3.1.4. Product Summary
9.3.1.5. Recent Developments
9.3.2. International Business Machines Corporation
9.3.3. Amazon Web Services (AWS)
9.3.4. Microsoft Corporation
9.3.5. Google LLC
9.3.6. ABB Ltd.
9.3.7. Hitachi Ltd.
9.3.8. Huawei Technologies Co., Ltd.
9.3.9. Intel Corporation
9.3.10. Nvidia
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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