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 sustainable.

Some of the benefits of designing a Artificial Intelligence in Smart Cities Market include:

• Optimized Resource Utilization: AI plays a pivotal role in streamlining the allocation of resources like energy, water, and waste management, resulting in diminished wastage, reduced expenditures, and enhanced sustainability.
• Enhanced Traffic Control: AI-driven systems contribute to the optimization of traffic flow, alleviating congestion and refining transportation networks. This translates to more seamless commutes, decreased travel times, and lower fuel consumption.
• Energy Conservation: AI takes a central role in the efficient oversight and fine-tuning of energy usage within structures and facilities. Additionally, it facilitates the integration of renewable energy sources into the power grid, leading to diminished energy expenses and a reduced carbon footprint.
• Augmented Public Safety: AI-based security systems are instrumental in real-time threat identification and response, elevating safety levels in urban environments and fortifying defenses against criminal activities and acts of terrorism.
• Elevated Healthcare Services: AI applications enhance the quality of healthcare offerings by delving into areas such as medical image analysis, the formulation of individualized treatment regimens, telemedicine, and remote patient monitoring, all of which amplify healthcare accessibility and improve health outcomes.

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.

COVID -19 Impact

The COVID-19 pandemic left an indelible mark on the AI in smart cities market, ushering in both challenges and opportunities. In response to the crisis, cities worldwide embarked on accelerated digital transformations, turning to AI-driven solutions to navigate the uncertainties of the pandemic era. These challenges ranged from remote healthcare and contactless services to online education and the need for improved public safety.

AI found its place in the healthcare sector, assisting in contact tracing, remote patient monitoring, and predictive modeling of the virus's spread. Public safety was enhanced through AI-powered thermal imaging and facial recognition systems for temperature checks and mask compliance in public spaces.

The surge in remote work and online education spurred the demand for AI-driven communication and collaboration tools, as well as virtual learning environments. Moreover, the disruption of transportation systems prompted a renewed focus on AI for optimizing traffic flow, public transportation, and reducing congestion to ensure safe mobility during the pandemic. Supply chain resilience became paramount, leading to a greater reliance on AI to enhance supply chain management and logistics, ensuring the uninterrupted delivery of essential goods during times of crisis. However, the economic challenges brought about by the pandemic temporarily slowed AI investments in many cities as fiscal constraints were felt. Over time, the need for cost-effective and innovative solutions gradually drove further AI adoption.

The pandemic generated a deluge of data, emphasizing the importance of AI-driven analytics to make sense of this vast information. AI proved crucial in interpreting pandemic-related data and providing actionable insights for decision-makers. Additionally, the pandemic prompted a renewed focus on safety and security. AI applications were employed to monitor social distancing, mask-wearing compliance, and even implement contactless access control in public spaces.

Remote monitoring and predictive maintenance played a vital role in managing critical infrastructure, ensuring its continuous operation with minimal physical presence. Furthermore, the pandemic heightened discussions surrounding data privacy and public acceptance of AI-driven surveillance and contact tracing. This prompted cities to grapple with the delicate balance between health and privacy concerns, resulting in ongoing deliberations and regulations.

Factors Driving the Market

Drivers

Increasing demand for smart city solutions

The growing demand for smart city solutions serves as a powerful catalyst for the market growth of AI in smart cities. As urbanization continues to accelerate globally, cities face unprecedented challenges in managing their expanding populations and infrastructure. In response to these challenges, there is an increasing need for advanced technologies to optimize resource allocation, enhance energy efficiency, and improve transportation systems. AI, with its ability to analyze vast amounts of data and make real-time decisions, plays a pivotal role in addressing these issues, ensuring that cities can efficiently adapt to the changing needs of their residents.

Efficiency and resource management are paramount in smart city development. AI empowers cities to manage resources like energy, water, and waste more effectively, contributing to sustainability and cost reduction. Furthermore, the growing demand for smart transportation solutions is driven by traffic congestion and the need for efficient public transportation systems, where AI can alleviate these urban pains.

Smart energy management is essential as cities consume substantial energy. The demand for AI-driven solutions is motivated by the desire to reduce energy consumption and environmental impact. Security is another critical area, with increasing demand for AI-powered security systems to ensure the safety of city residents. The demand for quality healthcare and education services in urban areas is also on the rise, leading to the adoption of AI applications that improve healthcare delivery and enhance educational experiences. Moreover, the demand for data-driven decision-making in urban governance is increasing, and AI provides the analytical tools necessary to harness the insights generated by smart city solutions.

Challenges

High initial investment costs

The high initial investment costs associated with AI in smart cities can significantly impact its growth and adoption. Cities often operate within constrained budgets, and these substantial upfront expenses can strain financial resources, slowing down or even preventing the implementation of AI solutions. The delay in project deployment can hinder the timely resolution of urban challenges like traffic congestion, energy inefficiency, and public safety.

Moreover, the high costs may limit AI adoption to only the largest and most economically well-off cities, creating disparities in smart city development. Smaller or less financially robust cities may find it challenging to afford the initial investments, leading to a digital divide. Cities may seek external funding sources to cover these costs, but this approach can introduce complexities in securing funding and meeting the expectations of external stakeholders. Additionally, risk aversion and uncertainty about the return on investment can deter cities from committing to AI projects, slowing down the overall growth of AI in smart cities.

The long payback period of some AI projects may further discourage cities, as it can take time to demonstrate the full value of the investment. Resource reallocation, required to fund AI initiatives, may affect other essential services and infrastructure. To mitigate these challenges and promote AI adoption in smart cities, it is essential to explore cost-effective implementation strategies, innovative financing models, and to emphasize the long-term benefits and returns on investment that AI can offer. Collaborative efforts involving governments, the private sector, and technology providers are vital in addressing the barriers imposed by high initial investment costs, fostering the growth of AI in smart cities and ensuring urban environments can harness the advantages of advanced technologies.

Trends

Improved quality of life for residents

The enhancement of residents' quality of life stands as a potent driving force behind the market growth of AI in smart cities. A smart city that provides a higher quality of life becomes an enticing destination for individuals, attracting population growth and stimulating economic activity. A satisfied resident populace, experiencing the conveniences and efficiencies brought about by AI solutions, becomes more supportive of investments in smart city technologies.

The economic implications of an improved quality of life are significant. Such cities often witness increased productivity and consumer spending, establishing a positive feedback loop of investment and development. Moreover, public support for AI projects is bolstered when residents directly experience the benefits, such as reduced traffic congestion, enhanced healthcare access, and improved public services. A smart city that offers an improved quality of life is more likely to attract and retain a skilled workforce, fostering innovation and entrepreneurship. This dynamic contributes to the growth of a thriving technology ecosystem and an increase in AI-related initiatives. Additionally, AI applications addressing urban challenges lead to reduced stress, improved well-being, and better physical and mental health for residents, further enhancing the overall quality of life and reducing healthcare costs.

Environmental sustainability is also integral to a higher quality of life, with AI-powered solutions promoting cleaner air, reduced energy consumption, and more sustainable practices. This not only betters the environment but also contributes to the well-being of residents, creating a more appealing and livable urban setting. Furthermore, AI-powered security systems that enhance public safety make residents feel more secure, fostering a sense of well-being and overall contentment. AI-driven public services, such as waste management and healthcare, streamline processes, reduce waiting times, and enhance convenience, further elevating residents' satisfaction.

Market Segmentation

By Component

By Component, the global Artificial Intelligence in Smart Cities Market is divided into Hardware, Software, Services.

Software is the leading component in the AI in smart cities market, with a market share of 58.43% in 2023. It is also the fastest-growing component, with a CAGR of 42.5% from 2024 to 2032.

Software stands as the primary driver behind the leadership and rapid growth of AI in the smart cities market, owing to a multitude of compelling advantages. Its adaptability and flexibility allow for customized solutions that can evolve to meet the dynamic and specific requirements of smart cities. Moreover, software's scalability efficiently addresses the expanding data volumes and intricate challenges that urban environments present. One of its standout benefits is cost-effectiveness, requiring lower upfront capital investments compared to hardware-based alternatives. This makes software a compelling choice for smart city planners aiming to deploy advanced technologies within budget constraints. Rapid development and deployment, a characteristic inherent to software, ensures timely responses to pressing urban challenges, vital in a rapidly evolving landscape.

Furthermore, software seamlessly integrates with existing hardware and infrastructure, leveraging prior investments while enhancing capabilities with AI-driven solutions. Data analytics and insights are fundamental in urban decision-making, and software plays a central role in processing and interpreting data, enabling informed actions in traffic management, energy efficiency, and public safety. Software interfaces can be designed to be user-friendly, promoting wider adoption among city officials, residents, and businesses. It supports remote accessibility, facilitating real-time monitoring and response to changing urban dynamics. A thriving ecosystem of software developers, startups, and tech companies actively contributes to the innovation and continuous evolution of software applications tailored for smart cities.

Software is the foundation of AI algorithms and machine learning models, making it indispensable in predictive maintenance, traffic optimization, and security threat detection, among other use cases. Its ease of customization and adaptability render it suitable for a wide range of applications within smart cities, from transportation to healthcare and education. Software is the key to the development of AI in smart cities, ensuring that these applications remain current, secure, and able to address the ever-evolving urban challenges. In essence, software-driven AI applications are at the forefront of enhancing urban living, sustainability, and efficiency in smart cities.

By Deployment

By Deployment, the global Artificial Intelligence in Smart Cities Market is divided into On premise, Cloud Based, Hybrid.

Cloud-based is the leading deployment type in the AI in smart cities market, with a market share of 52.6% in 2023. It is also the fastest-growing deployment type, with a CAGR of 36.64% from 2024 to 2032

Cloud-based deployment in the AI for smart cities market is gaining prominence due to its numerous advantages. It offers unmatched scalability, enabling smart cities to easily adjust their AI applications to meet evolving urban needs and growing populations. Cost efficiency is a key driver as it eliminates the requirement for substantial upfront investments in hardware and infrastructure, following a pay-as-you-go model that aligns with actual usage. The accessibility of cloud-based solutions is vital in smart cities, where data and decision-making often need to be accessed remotely. This attribute, coupled with the rapid deployment capabilities of cloud platforms, accelerates the implementation of AI-driven systems, ensuring smart cities can harness the benefits quickly. Additionally, cloud providers offer robust data management and analytics tools, addressing the challenge of managing vast amounts of urban data efficiently.

Security is another compelling factor, with leading cloud providers investing heavily in safeguarding data. This not only secures sensitive information but also enhances the overall security of critical infrastructure in smart cities. Cloud providers assume the responsibility of updates and maintenance, alleviating the burden on city IT departments and ensuring that AI systems remain current and secure.

Cloud platforms support interoperability, enabling the development of comprehensive and interconnected smart city solutions that can seamlessly communicate with various devices and sensors. Furthermore, global cloud data centers facilitate data access from diverse regions and global collaborations. Cloud deployment comes with built-in data backup and disaster recovery, assuring data resilience in unforeseen circumstances. Elasticity is a standout feature, with cloud resources automatically adapting to changes in demand, ensuring AI applications remain responsive even during peak usage times. Beyond these advantages, cloud providers' increasing focus on sustainability and green computing aligns with the environmental goals of smart cities, contributing to a reduced carbon footprint. In essence, cloud-based deployment simplifies AI implementation, offers cost savings, and provides the scalability and accessibility needed for smart cities to effectively manage and leverage AI solutions in their urban landscapes.

By Application

By Application the Artificial Intelligence in Smart Cities Market is divided by Transportation, Energy, Security, Healthcare, Education, Others.

Transportation is the leading application in the AI in smart cities market, with a market share of 38% in 2023. It is also the fastest-growing application, with a CAGR of 36.72% from 2024 to 2032

The surge in the transportation sector is a response to the escalating demand for intelligent transportation systems (ITS) aimed at enhancing traffic flow, mitigating congestion, and bolstering safety. AI is harnessed to create ITS innovations, including intelligent traffic signals, adaptive cruise control systems, and autonomous vehicles. Concurrently, the energy domain is experiencing expansion, driven by the imperative to enhance energy efficiency and effectively manage renewable energy sources. AI solutions are pivotal in shaping energy management systems, optimizing energy consumption in various facilities. Additionally, smart grid management systems utilizing AI play a crucial role in integrating renewable energy sources into the grid and enhancing its dependability. Likewise, the security sector is on an upward trajectory, responding to the escalating necessity to fortify cities against criminal activities and acts of terrorism. AI is instrumental in the development of security solutions encompassing facial recognition systems, video surveillance systems, and intrusion detection systems.

The healthcare field, too, is witnessing substantial growth, predominantly attributed to the aspiration to enhance healthcare services' quality and efficiency. AI-driven healthcare innovations span medical imaging analysis systems, drug discovery platforms, and personalized treatment plans. Education, in parallel, is advancing as a result of the demand for elevated quality and personalized learning experiences. AI is the driving force behind educational solutions, encompassing intelligent tutoring systems, adaptive learning platforms, and virtual reality learning environments.

Furthermore, the 'others' category is marked by diverse applications, such as intelligent waste management systems, efficient parking solutions, and environmentally-aware monitoring systems. In a holistic perspective, the AI in smart cities market is poised for rapid expansion in the forthcoming years, underpinned by the increasing desire for AI-infused solutions that foster urban efficiency, sustainability, and an improved quality of life.

By Region

By region, the global Artificial Intelligence in Smart Cities Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.

The Asia-Pacific (APAC) region has emerged as a global leader and the fastest-growing with 35.7% CAGR. This trend can be attributed to a combination of key factors that set the region apart. Firstly, 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.

Additionally, the APAC region is experiencing rapid urbanization, with a substantial portion of the world's population residing in densely populated cities. This demographic shift has created a pressing need for advanced solutions to manage challenges such as traffic congestion, environmental pollution, and resource utilization, all of which are ideally suited to AI-driven interventions. Economic growth in the APAC region has further fueled its leadership in this domain, as the increased financial resources have facilitated substantial investments in cutting-edge technologies like AI. The thriving technology ecosystem in the region, marked by numerous startups, tech companies, and research institutions, has fostered a culture of innovation and accelerated the development and adoption of AI in smart cities.

Furthermore, the APAC region benefits from its vast and diverse population, serving as a valuable testing ground for a wide range of smart city applications. The rich tapestry of urban environments and demographics allows for comprehensive testing and refinement of AI solutions. Data availability is another critical aspect of the APAC region's prominence in this field. The widespread use of digital services and the internet generates copious amounts of data, which are essential for data-driven AI applications in smart cities.

Collaboration between governments, academic institutions, and businesses is a common practice in the APAC region, fostering knowledge sharing and innovation in the AI domain. These collaborative efforts result in more robust and effective smart city projects. Moreover, transportation challenges, environmental concerns, and the region's cultural and societal acceptance of technology have all contributed to the rapid adoption of AI solutions in smart cities throughout the APAC region. These multifaceted factors combine to position the Asia-Pacific as a leader in the global AI in smart cities market, poised to shape the future of urban living and innovation.

Competitive Landscape

The global Artificial Intelligence in Smart Cities Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified solutions to end use industries.

• Cisco

Cisco Systems, Inc., widely recognized as Cisco, is a multinational technology conglomerate based in San Jose, California, in the United States. Cisco specializes in the development, production, and distribution of telecommunications equipment and networking hardware, software, as well as telecommunications services. Notably, Cisco stands as one of the largest and most influential technology companies on a global scale. Cisco's inception dates back to 1984 when it was founded by Leonard Bosack and Sandy Lerner, both of whom were former computer scientists at Stanford University. Originally known as Crescendo Systems, the company underwent a name change in 1987 to become Cisco Systems, inspired by the city of San Francisco. The company's initial product was a groundbreaking router designed to interconnect two dissimilar Ethernet networks.

• Siemens

Siemens AG, headquartered in Munich, Germany, is a prominent German multinational technology conglomerate. The company's operations are distributed across six distinct business sectors: Digital Industries, Smart Infrastructure, Process Industries and Drives, Building Technologies, Siemens Healthineers, and Mobility. Siemens boasts a comprehensive portfolio of products and services, encompassing industrial automation, power generation and transmission, medical imaging, transportation systems, and building technologies. Founded in 1847 by Werner von Siemens, this company boasts a rich legacy of pioneering innovation. Siemens has played a pivotal role in the advancement of numerous technologies that underpin modern society, including the electric railway, the telephone, and the X-ray machine.

• Schneider Electric

Schneider Electric SE, a French multinational corporation, specializes in the fields of digital automation and energy management. The company's expertise extends to various domains, including residences, buildings, data centers, infrastructure, and industrial operations. Schneider Electric achieves its mission by integrating energy technologies, real-time automation, software solutions, and a spectrum of services. Notably, Schneider Electric holds a place in the prestigious Fortune Global 500 list and is publicly traded on the Euronext Exchange. It's a prominent constituent of the Euro Stoxx 50 stock market index. In the fiscal year 2022, the company reported revenues of €34.2 billion. Schneider Electric also serves as the parent company for well-known brands like Square D, APC, among others. In addition to its corporate identity, Schneider Electric actively engages in research activities.

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

Recent Developments

• On November 8, 2023, IBM and Cisco joined forces to collaborate on the development of artificial intelligence (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.
• Microsoft made a significant move on November 7, 2023, by introducing 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.
• In an announcement made on November 6, 2023, Google unveiled a novel AI-powered traffic management system. This innovative system employs AI algorithms to optimize traffic flow and mitigate congestion issues. Currently, it is undergoing testing and implementation in multiple cities worldwide.
• Amazon Web Services (AWS) took a step forward on November 5, 2023, by launching a new AI-powered energy management service called AWS IoT Energy Management. This service is designed to assist businesses in reducing their energy consumption and, consequently, lowering energy costs.
• On November 4, 2023, Siemens introduced a state-of-the-art AI-powered security platform named MindSphere Security. This platform harnesses AI capabilities to swiftly identify and respond to security threats in real time. It is already in use by several cities and businesses across the globe.

Artificial Intelligence in Smart Cities Market Scope

Table of Contents

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

Global Artificial Intelligence in Smart Cities Market Segmentation

Artificial Intelligence in Smart Cities by Offering: Market Size & Forecast 2023-2032

• Hardware
• Software
• Services

Artificial Intelligence in Smart Cities by Deployment: Market Size & Forecast 2023-2032

• On premise
• Cloud
• Hybrid

Artificial Intelligence in Smart Cities by Application: Market Size & Forecast 2023-2032

• Transportation
• Energy
• Security
• Healthcare
• Education
• Others

Artificial Intelligence in Smart Cities by Geography: Market Size & Forecast 2023-2032

• North America (USA, Canada, Mexico)
• Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
• Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
• South America (Brazil, Argentina, Columbia, Rest of South America)
• Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

Major Players:

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