AI in Smart Cities Market Size, Share & Trends Analysis Report, By Application: Smart Mobility, Energy Management, Healthcare, Public Safety and Security, Waste Management, Environmental Monitoring, Water Management, Others), By Deployment Mode, By Component, By End User, By Region, Forecasts, 2024-2031

Report Id: 2761 Pages: 170 Last Updated: 25 September 2024 Format: PDF / PPT / Excel / Power BI
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Segmentation of AI in the Smart Cities Market-

AI in the Smart Cities Market- By Component

  • Hardware
  • Software
  • Services (Consulting, Maintenance, Training)

ai in smart cities

AI in the Smart Cities Market- By Application

  • Smart Mobility
  • Energy Management
  • Healthcare
  • Public Safety and Security
  • Waste Management
  • Environmental Monitoring
  • Water Management
  • Others

AI in the Smart Cities Market- By Deployment Mode

  • Cloud-based
  • On-premises

AI in the Smart Cities Market- By End-User

  • Government
  • Utilities
  • Transportation Companies
  • Healthcare Providers
  • Real Estate Developers
  • Others

AI in the Smart Cities Market- By Region

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa
  • Rest of the Middle East and Africa

Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in Smart Cities Market Snapshot

Chapter 4. Global AI in Smart Cities Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis

Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2019 & 2031
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Type:

5.2.1. Machine Learning
5.2.2. Natural Language Processing
5.2.3. Computer Vision
5.2.4. Deep Learning
5.2.5. Expert Systems

Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2019 & 2031
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Application:

6.2.1. Smart Mobility
6.2.2. Urban Planning and Infrastructure
6.2.3. Energy Management
6.2.4. Healthcare
6.2.5. Public Safety and Security
6.2.6. Waste Management
6.2.7. Environmental Monitoring
6.2.8. Water Management
6.2.9. Governance and Civic Engagement

Chapter 7. Market Segmentation 3: by Component Estimates & Trend Analysis
7.1. by Component & Market Share, 2019 & 2031
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Component:

7.2.1. Hardware
7.2.2. Software
7.2.3. Services (Consulting, Maintenance, Training)

Chapter 8. Market Segmentation 4: by Deployment Mode Estimates & Trend Analysis
8.1. by Deployment Mode & Market Share, 2019 & 2031
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by Deployment Mode:

8.2.1. Cloud-based
8.2.2. On-premises

Chapter 9. Market Segmentation 4: by End User Estimates & Trend Analysis
9.1. by End User & Market Share, 2019 & 2031
9.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2019 to 2031 for the following by End User:

9.2.1. Utilities
9.2.2. Transportation Companies
9.2.3. Healthcare Providers
9.2.4. Real Estate Developers
9.2.5. Others (Education, Retail, etc.)

Chapter 10. AI in Smart Cities Market Segmentation 5: Regional Estimates & Trend Analysis

10.1. North America
10.1.1. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
10.1.2. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
10.1.3. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
10.1.4. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2024-2031
10.1.5. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
10.1.6. North America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

10.2. Europe
10.2.1. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
10.2.2. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
10.2.3. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
10.2.4. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2024-2031
10.2.5. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
10.2.6. Europe AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

10.3. Asia Pacific
10.3.1. Asia Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
10.3.2. Asia Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
10.3.3. Asia-Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
10.3.4. Asia-Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2024-2031
10.3.5. Asia-Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
10.3.6. Asia Pacific AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

10.4. Latin America
10.4.1. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
10.4.2. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
10.4.3. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
10.4.4. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2024-2031
10.4.5. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
10.4.6. Latin America AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

10.5. Middle East & Africa
10.5.1. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Type, 2024-2031
10.5.2. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Application, 2024-2031
10.5.3. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Component, 2024-2031
10.5.4. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2024-2031
10.5.5. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by End User, 2024-2031
10.5.6. Middle East & Africa AI in Smart Cities Market Revenue (US$ Million) Estimates and Forecasts by country, 2024-2031

Chapter 11. Competitive Landscape
11.1. Major Mergers and Acquisitions/Strategic Alliances
11.2. Company Profiles

11.2.1. IBM Corporation
11.2.2. Microsoft Corporation
11.2.3. Google LLC
11.2.4. Intel Corporation
11.2.5. Cisco Systems, Inc.
11.2.6. Siemens AG
11.2.7. Huawei Technologies Co., Ltd.
11.2.8. NVIDIA Corporation
11.2.9. Hitachi Vantara
11.2.10. NEC Corporation
11.2.11. Other Prominent Players

Research Design and Approach

This study employed a multi-step, mixed-method research approach that integrates:

  • Secondary research
  • Primary research
  • Data triangulation
  • Hybrid top-down and bottom-up modelling
  • Forecasting and scenario analysis

This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.

Secondary Research

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.

Sources Consulted

Secondary data for the market study was gathered from multiple credible sources, including:

  • Government databases, regulatory bodies, and public institutions
  • International organizations (WHO, OECD, IMF, World Bank, etc.)
  • Commercial and paid databases
  • Industry associations, trade publications, and technical journals
  • Company annual reports, investor presentations, press releases, and SEC filings
  • Academic research papers, patents, and scientific literature
  • Previous market research publications and syndicated reports

These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.

Secondary Research

Primary Research

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.

Stakeholders Interviewed

Primary interviews for this study involved:

  • Manufacturers and suppliers in the market value chain
  • Distributors, channel partners, and integrators
  • End-users / customers (e.g., hospitals, labs, enterprises, consumers, etc., depending on the market)
  • Industry experts, technology specialists, consultants, and regulatory professionals
  • Senior executives (CEOs, CTOs, VPs, Directors) and product managers

Interview Process

Interviews were conducted via:

  • Structured and semi-structured questionnaires
  • Telephonic and video interactions
  • Email correspondences
  • Expert consultation sessions

Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.

Data Processing, Normalization, and Validation

All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.

The data validation process included:

  • Standardization of units (currency conversions, volume units, inflation adjustments)
  • Cross-verification of data points across multiple secondary sources
  • Normalization of inconsistent datasets
  • Identification and resolution of data gaps
  • Outlier detection and removal through algorithmic and manual checks
  • Plausibility and coherence checks across segments and geographies

This ensured that the dataset used for modelling was clean, robust, and reliable.

Market Size Estimation and Data Triangulation

Bottom-Up Approach

The bottom-up approach involved aggregating segment-level data, such as:

  • Company revenues
  • Product-level sales
  • Installed base/usage volumes
  • Adoption and penetration rates
  • Pricing analysis

This method was primarily used when detailed micro-level market data were available.

Bottom Up Approach

Top-Down Approach

The top-down approach used macro-level indicators:

  • Parent market benchmarks
  • Global/regional industry trends
  • Economic indicators (GDP, demographics, spending patterns)
  • Penetration and usage ratios

This approach was used for segments where granular data were limited or inconsistent.

Hybrid Triangulation Approach

To ensure accuracy, a triangulated hybrid model was used. This included:

  • Reconciling top-down and bottom-up estimates
  • Cross-checking revenues, volumes, and pricing assumptions
  • Incorporating expert insights to validate segment splits and adoption rates

This multi-angle validation yielded the final market size.

Forecasting Framework and Scenario Modelling

Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.

Forecasting Methods

  • Time-series modelling
  • S-curve and diffusion models (for emerging technologies)
  • Driver-based forecasting (GDP, disposable income, adoption rates, regulatory changes)
  • Price elasticity models
  • Market maturity and lifecycle-based projections

Scenario Analysis

Given inherent uncertainties, three scenarios were constructed:

  • Base-Case Scenario: Expected trajectory under current conditions
  • Optimistic Scenario: High adoption, favourable regulation, strong economic tailwinds
  • Conservative Scenario: Slow adoption, regulatory delays, economic constraints

Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.

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Frequently Asked Questions

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

The AI in Smart Cities Market is expected to grow at a 18.1% CAGR during the forecast period for 2024-2031.

IBM Corporation, Microsoft Corporation, Google LLC, Intel Corporation, Cisco Systems, Inc., Siemens AG, Huawei Technologies Co., Ltd., NVIDIA Corporat

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