The AI in Smart Cities Market Size was valued at USD 36.9 Bn in 2023 and is predicted to reach USD 138.8 Bn by 2031 at a 18.1% CAGR during the forecast period for 2024-2031.
AI in smart cities integrates artificial intelligence technology to improve efficiency, sustainability, and quality of living, thus enhancing urban living. Using data analysis and automation, artificial intelligence helps smart cities manage better resources, control traffic flow, lower energy consumption, and improve public safety. Key factors driving the industry ahead are the building of smart transportation systems to reduce traffic congestion, enhancing public safety with cutting-edge monitoring and response systems, and optimizing sustainable energy usage, which are all factors that are expected to drive the market. The application of AI in smart cities is expected to increase demand in the future due to its ability to improve water distribution efficiency, waste management, and predictive infrastructure maintenance, all of which contribute to smarter, more resilient communities with greater quality of life. In addition, the market is anticipated to be propelled by increased government investments in research and development to optimize better smart city development.
However, the market growth is hindered by data privacy worries, expensive implementation expenses, a shortage of trained AI experts, and problems with regulation and compliance. Several variables can hinder adoption in this market. Global markets expanded during the coming years due to technological developments, heightened globalization, increasing consumer demand, and expanding infrastructure investments. Global market growth is being propelled by these elements, which improve productivity and open up new company opportunities in this market growth.
The AI in smart cities market is segmented based on application, component, deployment mode, and end-user. The market is segmented by application into smart mobility, energy management, healthcare, public safety and security, waste management, environmental monitoring, water management, etc. The market is segmented by components into hardware, software, and services. By deployment mode, the market is segmented into cloud-based and on-premises. The market is segmented by end-user government, utilities, transportation companies, healthcare providers, real estate developers, and others.
Software segment in the AI in the smart cities market are expected to hold a major global market share in 2023 because increasing demand for data analytics, automation, and real-time decision-making to optimize city functions like traffic management, energy usage, and public safety. AI software enables scalable, flexible solutions that process vast amounts of data from IoT devices and sensors, allowing cities to improve efficiency and sustainability. Additionally, advancements in cloud computing, machine learning, and AI algorithms further propel the adoption of software solutions in smart city initiatives.
The real estate developer segment is projected to grow rapidly in the global AI in smart cities market because developers are increasingly using AI to create smart, environmentally friendly structures. Additionally, with AI’s predictive maintenance and energy optimization features, property management is made easier, which appeals to purchasers who are looking for contemporary, environmentally conscious homes. In addition, developers can benefit from data-informed decisions made possible by AI-driven insights, which enhances urban project planning and execution.
The North American AI in smart cities market is expected to register the highest market share in revenue in the near future. This can be attributed to the fact that the government is heavily invested in smart infrastructure, prioritizes innovation, strongly backs sustainable urban development, and improves urban living standards while simultaneously cutting carbon emissions. In addition, Asia Pacific is projected to grow rapidly in the AI in smart cities market because the use of artificial intelligence to improve the effectiveness of public services, sophisticated technical infrastructure, substantial governmental funding for smart city initiatives, and a growing need for effective urban solutions will boost the market's growth.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 36.9 Bn |
| Revenue Forecast In 2031 | USD 138.8 Bn |
| Growth Rate CAGR | CAGR of 18.1% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Deployment Mode, By Application, By Component, By End User |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | IBM Corporation, Microsoft Corporation, Google LLC, Intel Corporation, Cisco Systems, Inc., Siemens AG, Huawei Technologies Co., Ltd., NVIDIA Corporation, Hitachi Vantara, and NEC Corporation. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in the Smart Cities Market- By Component
AI in the Smart Cities Market- By Application
AI in the Smart Cities Market- By Deployment Mode
AI in the Smart Cities Market- By End-User
AI in the Smart Cities Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.