AI Edge Computing Market by Component, Data Source, Application, End-User

List of Tables

[187 Pages Report] The AI Edge Computing Market size was estimated at USD 16.25 billion in 2023 and expected to reach USD 19.52 billion in 2024, at a CAGR 20.63% to reach USD 60.43 billion by 2030.

The AI edge computing includes the application of artificial intelligence (AI) and machine learning (ML) technologies within edge computing systems. These systems enable real-time processing and analysis of data at the local level rather than relying on centralized cloud-based servers. The market encompasses hardware, software, and services that facilitate efficient processing of large-scale data close to its source, thereby offering enhanced performance, reduced latency, and improved privacy for various end-use applications. Increased IoT device adoption rates and advancement in AI/ML algorithms capable of performing complex tasks at a faster pace with lower power consumption requirements is driving the usage of AI edge computing. Growing demand for low-latency applications, rising concerns about data security & privacy due to stricter regulatory oversight, and increasing focus on Industry 4.0 initiatives amidst various industries globally are creating a platform for AI edge computing. High initial investment costs associated with implementing edge infrastructure and the complexity of integrating multiple data sources from different IoT devices into a coherent system are hampering market growth. The growing development of energy-efficient processors, memory units, and other components specifically designed to handle AI tasks at the edge is expected to create opportunities for market growth.

KEY MARKET STATISTICS
Base Year [2023]	USD 16.25 billion
Estimated Year [2024]	USD 19.52 billion
Forecast Year [2030]	USD 60.43 billion
CAGR (%)	20.63%

Component: Expanding usage of software components for real-time analytics

Hardware components play a crucial role in AI edge computing systems. They provide the necessary infrastructure to support advanced computations at the edge of networks, enabling real-time decision-making and reducing latency. When it comes to hardware selection, factors such as performance, power efficiency, and compatibility with existing infrastructure are key considerations. Services encompass various aspects of AI edge computing adoption, including deployment consulting, system integration, maintenance support, and training. Organizations often seek service providers that offer expertise in edge computing architectures tailored to their specific needs. Software forms the backbone of AI edge computing systems, enabling data processing, machine learning algorithms, and real-time analytics. Selecting the right software ensures seamless integration with hardware components and efficiently utilizes computational resources. When comparing these subcategories, hardware selection should prioritize performance and compatibility. At the same time, services play a vital role in assisting organizations with system integration and support for edge computing architectures, and software providers facilitate AI model deployment and optimization for edge devices.

Data Source: Increasing demand for mobile data to creat

Biometric data refers to individuals' physical or behavioral characteristics that are used for identification and authentication purposes. It includes fingerprints, facial recognition, iris scanning, voice patterns, and gait analysis. Organizations across various industries are adopting biometric technology based on security needs and convenience factors. Mobile data encompasses various information generated by smartphones and other portable devices. It includes user-generated content, location-based services data, usage patterns, and app analytics data. Companies leverage this data source for targeted marketing campaigns or optimizing customer engagement. Sensor data is generated by a variety of electronic devices, such as IoT gadgets, industrial equipment, and wearable health monitors. It encompasses information about physical properties like temperature, pressure, humidity, or movement. Companies use this data source for predictive maintenance, remote monitoring, and process optimization.

Application:

Access management is crucial for ensuring secure access to critical resources and maintaining data integrity in organizations. AI edge computing providers offer advanced AI-powered edge computing solutions that enhance access control processes through real-time monitoring and anomaly detection. The rise of autonomous vehicles has driven significant demand for AI edge computing solutions to facilitate real-time decision-making capabilities. Innovative energy management systems are leveraging AI edge computing technologies to optimize energy consumption and reduce costs associated with power generation. AI edge computing has revolutionized remote monitoring and predictive maintenance by enabling rapid data processing at the source, reducing latency and network load. The telemetry sector is experiencing significant growth with the adoption of AI edge computing to enable efficient data collection from disparate sources. The video surveillance industry is leveraging AI edge computing technologies to enhance security systems' efficiency and accuracy by implementing real-time analytics on edge devices.

End-User: Growing utilization by IT and telecommunication sector to optimize network operations and enhance customer experience

AI edge computing is crucial in the automotive sector, particularly in advanced driver-assistance systems (ADAS) and autonomous vehicles. The need for real-time processing, low-latency communication, and data security drives the adoption of AI edge solutions within the segment. The energy and utilities sector benefits from AI edge computing by improving grid management, energy efficiency, and predictive maintenance of assets. Governments are adopting AI edge computing to enhance public safety through surveillance systems while maintaining data privacy adherence. AI edge computing transforms healthcare through real-time patient monitoring systems, diagnostics tools, and telemedicine applications. AI-driven edge computing enables IT and telecom companies to optimize network operations, enhance data analytics capabilities, and improve customer experience through personalized services. The manufacturing sector adopts AI-powered edge computing technologies to enable predictive equipment maintenance, improve production efficiency, and lower operational costs.

Regional Insights

In the Americas, North American countries such as the United States and Canada are leading in technological advancements with strong investment in research & development activities. Factors driving growth in this region include higher adoption of IoT devices and increased cloud-based services among businesses. Moreover, there is a rising demand for real-time data processing solutions to improve operational efficiency across various industries such as healthcare and automotive, which further accelerates the adoption of AI edge computing technologies. Europe is currently at the forefront of adopting advanced AI technologies with numerous initiatives undertaken by governments to support research on artificial intelligence across European Union countries. Industry 4.0 enablers are also promoting AI-powered automation solutions in manufacturing sectors, leading to a growing demand for edge computing capabilities. In the Middle East and Africa, although the adoption of AI technologies is still in its nascent stage, high growth potential is anticipated due to increasing government support for digital transformation initiatives and rising investment in smart city projects. The Asia-Pacific region is witnessing a prompt growth rate in the AI edge computing market during the forecast period, owing to rapid industrialization, raised smartphone penetration, and advancements in communication infrastructure. China, Japan, and South Korea are driving strong AI development with government policies supporting research & development activities and focusing on IoT applications across industries. Furthermore, emerging countries such as Australia and Singapore focus on creating smart city frameworks incorporating AI-driven solutions, thus promoting growth opportunities within this sector.

FPNV Positioning Matrix

The FPNV Positioning Matrix is pivotal in evaluating the AI Edge Computing Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).

Market Share Analysis

The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the AI Edge Computing Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.

Key Company Profiles

The report delves into recent significant developments in the AI Edge Computing Market, highlighting leading vendors and their innovative profiles. These include ABB Ltd., ADLINK Technology Inc., Advantech Co., Ltd., Akamai Technologies, Inc., Amazon Web Services, Inc., Atos SE, Broadcom Inc., Cisco Systems, Inc., Cloudera, Inc., Dell Inc., EdgeConneX, Inc., EdgeCortix, Inc., Fastly, Inc., General Electric Company, Hewlett Packard Enterprise Development LP, Honeywell International Inc., Huawei Technologies Co., Ltd., Intel Corporation, International Business Machines Corporation, Lenovo Group Limited, Microsoft Corporation, NVIDIA Corporation, Robert Bosch GmbH, Rockwell Automation, Inc., SAP SE, Schneider Electric SE, Siemens AG, Sterlite Technologies Limited, Tata Elxsi Limited, Teksun Inc., Telefonaktiebolaget LM Ericsson, and Unigen Corporation.

Market Segmentation & Coverage

This research report categorizes the AI Edge Computing Market to forecast the revenues and analyze trends in each of the following sub-markets:

Component
- Hardware
- Services
- Software

Data Source
- Biometric Data
- Mobile Data
- Sensor Data

Application
- Access Management
- Autonomous Vehicles
- Energy Management
- Remote Monitoring & Predictive Maintenance
- Telemetry
- Video Surveillance

End-User
- Automotive
- Energy & Utilities
- Government & Public
- Healthcare
- IT & Telecom
- Manufacturing

Region
- Americas
  - Argentina
  - Brazil
  - Canada
  - Mexico
  - United States
    - California
    - Florida
    - Illinois
    - New York
    - Ohio
    - Pennsylvania
    - Texas
- Asia-Pacific
  - Australia
  - China
  - India
  - Indonesia
  - Japan
  - Malaysia
  - Philippines
  - Singapore
  - South Korea
  - Taiwan
  - Thailand
  - Vietnam

Europe, Middle East & Africa
- Denmark
- Egypt
- Finland
- France
- Germany
- Israel
- Italy
- Netherlands
- Nigeria
- Norway
- Poland
- Qatar
- Russia
- Saudi Arabia
- South Africa
- Spain
- Sweden
- Switzerland
- Turkey
- United Arab Emirates
- United Kingdom

The report offers valuable insights on the following aspects:

1. Market Penetration: It presents comprehensive information on the market provided by key players.

2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.

3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.

4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.

5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.

The report addresses key questions such as:

1. What is the market size and forecast of the AI Edge Computing Market?

2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the AI Edge Computing Market?

3. What are the technology trends and regulatory frameworks in the AI Edge Computing Market?

4. What is the market share of the leading vendors in the AI Edge Computing Market?

5. Which modes and strategic moves are suitable for entering the AI Edge Computing Market?

Product Code: MRR-742BD517A299

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Growing customer inclination toward smart devices
  - 5.1.1.2. Increasing adoption of predictive maintenance in digital infrastructure worldwide
  - 5.1.1.3. Need for cost and operational efficiency in enterprises
- 5.1.2. Restraints
  - 5.1.2.1. Latency issues due to massive data generation
- 5.1.3. Opportunities
  - 5.1.3.1. Investments to integrate AI edge computing in various end-user industries
  - 5.1.3.2. Adoption of AI edge computing in smart cities for intelligent traffic controls and parking communications
- 5.1.4. Challenges
  - 5.1.4.1. Data privacy and security concerns associated with AI Edge computing
5.2. Market Segmentation Analysis
- 5.2.1. Component: Expanding usage of software components for real-time analytics
- 5.2.2. Data Source: Increasing demand for mobile data to creat

5.2.3. Application:

5.2.4. End-User: Growing utilization by IT and telecommunication sector to optimize network operations and enhance customer experience

5.3. Market Disruption Analysis
5.4. Porter's Five Forces Analysis
- 5.4.1. Threat of New Entrants
- 5.4.2. Threat of Substitutes
- 5.4.3. Bargaining Power of Customers
- 5.4.4. Bargaining Power of Suppliers
- 5.4.5. Industry Rivalry
5.5. Value Chain & Critical Path Analysis
5.6. Pricing Analysis
5.7. Technology Analysis
5.8. Patent Analysis
5.9. Trade Analysis
5.10. Regulatory Framework Analysis

6. AI Edge Computing Market, by Component

6.1. Introduction
6.2. Hardware
6.3. Services
6.4. Software

7. AI Edge Computing Market, by Data Source

7.1. Introduction
7.2. Biometric Data
7.3. Mobile Data
7.4. Sensor Data

8. AI Edge Computing Market, by Application

8.1. Introduction
8.2. Access Management
8.3. Autonomous Vehicles
8.4. Energy Management
8.5. Remote Monitoring & Predictive Maintenance
8.6. Telemetry
8.7. Video Surveillance

9. AI Edge Computing Market, by End-User

9.1. Introduction
9.2. Automotive
9.3. Energy & Utilities
9.4. Government & Public
9.5. Healthcare
9.6. IT & Telecom
9.7. Manufacturing

10. Americas AI Edge Computing Market

10.1. Introduction
10.2. Argentina
10.3. Brazil
10.4. Canada
10.5. Mexico
10.6. United States

11. Asia-Pacific AI Edge Computing Market

11.1. Introduction
11.2. Australia
11.3. China
11.4. India
11.5. Indonesia
11.6. Japan
11.7. Malaysia
11.8. Philippines
11.9. Singapore
11.10. South Korea
11.11. Taiwan
11.12. Thailand
11.13. Vietnam

12. Europe, Middle East & Africa AI Edge Computing Market

12.1. Introduction
12.2. Denmark
12.3. Egypt
12.4. Finland
12.5. France
12.6. Germany
12.7. Israel
12.8. Italy
12.9. Netherlands
12.10. Nigeria
12.11. Norway
12.12. Poland
12.13. Qatar
12.14. Russia
12.15. Saudi Arabia
12.16. South Africa
12.17. Spain
12.18. Sweden
12.19. Switzerland
12.20. Turkey
12.21. United Arab Emirates
12.22. United Kingdom

13. Competitive Landscape

13.1. Market Share Analysis, 2023
13.2. FPNV Positioning Matrix, 2023
13.3. Competitive Scenario Analysis
- 13.3.1. EdgeCortix Raises USD 20 Million for Technology Development and Global Expansion
- 13.3.2. Lenovo Delivers AI at the Edge, Bringing Next Generation Intelligence to Data
- 13.3.3. Unigen Launches Compact Edge AI Computing Solutions
13.4. Strategy Analysis & Recommendation

14. Competitive Portfolio

14.1. Key Company Profiles
14.2. Key Product Portfolio