Artificial Intelligence (AI) in Edge Computing Market

Artificial Intelligence (AI) in Edge Computing Market Report, By Component (Hardware, Software, Services), By Technology (Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Others), By Deployment (Cloud, On-Premises), By Application (Autonomous Vehicles, Industrial Automation, Healthcare, Smart Cities, Consumer Electronics, Others), and Regions 2024-2032

Market Overview

"The artificial intelligence (AI) in edge computing market size reached US$ 11.2 billion in 2023. Looking forward, Reports and Insights expects the market to reach US$ 63.2 billion by 2032, exhibiting a growth rate (CAGR) of 21.2% during 2024-2032."

Report Attributes

Details

Base Year

2023

Forecast Years

2024-2032

Historical Years

2021-2023

Market Growth Rate (2024-2032)

21.2%

Artificial Intelligence in edge computing entails integrating AI algorithms in edge devices to enable processing of data in real-time, analysis, and decision-making at source. This is a crucial synergy for applications that operate on rapid response times and the solution reduces data transmission to central servers. AI in edge computing is crucial in integrating Internet of Things (IoT), autonomous vehicles, remote monitoring solutions and technologies, and in smart manufacturing, and improves operational efficiency, minimizes latency, and conserves network bandwidth.

The global Artificial Intelligence (AI) in edge computing market continues to register significantly robust revenue growth owing to increasing integration and functionality of cognitive capabilities of artificial intelligence with real-time processing capabilities of edge computing.

Comprehensive analysis provides insights into market evolution and potential, and extensive details and outlooks are available in the market research report to help businesses make informed decisions in this evolving industry. Also, clarity and understanding of market trends helps companies tailor their products to meet customer demands more effectively.

Artificial Intelligence (AI) in Edge Computing Market Report, By Component (Hardware, Software, Services), By Technology (Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Others), By Deployment (Cloud, On-Premises), By Application (Autonomous Vehicles, Industrial Automation, Healthcare, Smart Cities, Consumer Electronics, Others), and Regions 2024-2032

Artificial Intelligence (AI) in Edge Computing Market Drivers & Trends:

Expanding Integration of IoT: The Internet of Things (IoT) continues to garner traction and increasing deployment has been resulting in substantial incline in data volumes being generated from various processes and operations. Need to store and process such data volumes locally is a key factor driving integration of AI with edge computing. Also, need for processing and analysis of data locally is due to transmission of these streams from a variety of smart devices and sensors onsite, and criticality of swift response times to address potential issues.

Latency Reduction: Edge computing has capability to process and analyze data close to source and this is a major strategic advantage offered. Besides significantly diminishing latency, which is a critical consideration for applications such as autonomous vehicles and mission-critical remote operations, integration offers enhanced accuracy and operational efficiency.

Bandwidth Efficiency: AI applications generate vast volumes of data, and transmitting this information to centralized servers can strain network bandwidth. Edge computing addresses this challenge by processing data locally, sending only relevant insights to the cloud. This bandwidth efficiency is a crucial factor supporting the widespread adoption of AI in edge computing and supporting market revenue growth.

Preserving Data Privacy: Integration of AI in edge computing enables leveraging of potential to address data privacy concerns. AI enhances data privacy by minimizing exposure of sensitive information to centralized servers by conducting data processing at the edge. Also, protection of data enables adherence to relevant regulations and enhances user confidence.

Advancements in AI: A major factor driving adoption of this technology integration is continuous advancements of AI algorithms which enable edge devices to execute increasingly intricate computational tasks and complex analytics. These advances enable edge devices to execute local decision-making autonomously, and adds to intelligence and self-sufficiency.

Customized Industry Implementation: The combined capabilities of AI and edge computing are effectively utilized in various fields, including healthcare, manufacturing, energy, and others. Utilizing AI for predictive maintenance, continuous health monitoring, and streamlining production processes plays a crucial role in boosting productivity and, consequently, drives traction and revenue growth.

Deployment of 5G Networks: The emergence of 5G networks brings a transformative element to the realm of AI and edge computing, with increased network speeds and reduced latency aligning with edge processing. This collaboration offers smoother data transmission, and widens scope of opportunities for real-time data insights and innovation.

Edge-Aware Devices Proliferation: The proliferation of edge-aware devices, such as IoT sensors and smart cameras, contributes significantly to the adoption of AI in edge computing. These devices generate data at the source, and the integration of AI algorithms at the edge allows for quick and context-aware decision-making. Increasing deployment of such edge-aware devices across industries is a key factor driving growth of the global AI in edge computing market.

Artificial Intelligence (AI) in Edge Computing Market Restraints

Infrastructure Challenges: A key factor restraining potential revenue growth of the global AI in edge computing market is limitations in existing infrastructure. Many organizations, especially in developing economies, encounter challenges and complexities in efforts to upgrade existing infrastructure to support the high computing power required for AI applications at the edge. This is a challenge faced by over 60% of global enterprises, and infrastructure-related barriers are mentioned as major restraints to integration of AI in edge computing.

Security Concerns: Heightened security concerns pose a considerable restraint on the adoption of AI in edge computing. Edge devices are more vulnerable to physical tampering and unauthorized access compared to centralized cloud servers. As per statistics, over 70% of IT executives express concerns about the security of edge computing, leading to hesitancy in implementing AI at the edge. This apprehension dampens urgency to deploy and significantly impacts growth potential of the global market.

Limited Standardization: Lack of standardized protocols and frameworks for AI in edge computing is another key factor hampering interoperability and seamless integration across diverse devices and platforms. The absence of standardization results in increased development costs and complexity for businesses, and this reason is cited as a major impediment, restricting the growth of the AI in edge computing market by over 30% in certain sectors.

Data Privacy and Compliance Challenges: Stringent data privacy regulations and compliance requirements present challenges for wider adoption of AI in edge computing. Organizations need to navigate complex regulatory landscapes, ensuring that data processing at the edge complies with various privacy laws. As per the study, approximately 45% of businesses cite concerns about data privacy and compliance as a significant barrier to deploying AI at the edge, and this is a major factor affecting potential market growth.

Skills Gap and Talent Shortage: Shortage of skilled professionals with expertise in both AI and edge computing is a critical factor restraining market growth. Study results indicate a 40% gap remaining in the demand and supply of AI and edge computing skills. The lack of qualified personnel hampers successful implementation and management of AI in edge computing solutions, and is also slowing down potential for market expansion.

Artificial Intelligence (AI) in Edge Computing Market Opportunities

AI-Driven Edge Analytics Services: Companies can capitalize on the growing demand for AI-driven analytics at the edge. Offering services that provide actionable insights in real-time for industries such as manufacturing, healthcare, and retail presents a significant revenue opportunity. This includes predictive maintenance analytics, quality control, and personalized customer experiences based on edge-collected data.

Edge-Optimized AI Hardware: Developing and providing specialized hardware optimized for edge computing applications opens up significant revenue streams. As the demand for efficient edge processing increases, companies can design and market AI hardware solutions that enhance computational performance, energy efficiency, and scalability, catering to the unique requirements of edge deployments.

Edge Security Solutions: With rising concerns about security in edge computing, companies can leverage opportunities by offering robust edge security solutions, including developing AI-powered security algorithms that can detect and mitigate threats at the edge. By addressing the specific security needs of edge environments, companies can attract clients across various industries, including finance, healthcare, and smart cities.

Edge-Enabled IoT Platforms: As the Internet of Things (IoT) continues to expand, companies can create revenue streams by providing comprehensive edge-enabled IoT platforms. These platforms can integrate AI algorithms at the edge to process and analyze data from IoT devices, enabling clients to derive valuable insights. This approach is particularly lucrative for sectors such as agriculture, logistics, and smart infrastructure.

Customized Edge Computing Solutions: Offering tailored edge computing solutions for specific industries or use cases provides a significant revenue opportunity. Companies can provide consulting services to assess the unique needs of clients and then design and implement AI-powered edge solutions accordingly. This approach allows companies to establish themselves as experts in niche markets, fostering long-term partnerships and revenue growth.

AI in Edge Computing Market Segmentation

Artificial Intelligence (AI) in Edge Computing Market Report, By Component (Hardware, Software, Services), By Technology (Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Others), By Deployment (Cloud, On-Premises), By Application (Autonomous Vehicles, Industrial Automation, Healthcare, Smart Cities, Consumer Electronics, Others), and Regions 2024-2032

By Component:

  • Hardware
  • Software
  • Services

The report is segmented on the basis of component into hardware, software, and services. The software segment accounted for largest revenue share in 2022, and is expected to maintain dominate over other component segments throughout the forecast period. This prevalence is linked to the growing need for AI and edge computing solutions, which depend on advanced software platforms to facilitate operations. Also, increasing complexity of AI algorithms and rising demand for sophisticated software integration in edge devices are expected to contribute to steady rise in the demand for software solutions. In addition, software plays a crucial role in ensuring the smooth integration, customization, and management of AI and edge computing applications, establishing it as a key component for businesses seeking to leverage the advantages of these technologies.

By Technology:

  • Machine Learning
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Others

Among the technology segments, the Machine Learning (ML) segment accounted for largest revenue share in 2022, and is expected to continue to register faster growth rate than the deep learning, Natural Language Processing (NLP), and computer vision segments over the forecast period. This can be attributed to versatility, established implementation, and broad industry adoption of ML across various sectors.

By Application:

  • Autonomous Vehicles
  • Industrial Automation
  • Healthcare
  • Smart Cities
  • Consumer Electronics
  • Agriculture
  • Others

Revenue from the autonomous vehicles segment among the application segments is expected to register a faster growth rate compared to other application segments over the forecast period. This can be attributed to key factors such as increasing interest in autonomous or self-driving vehicles and the imperative for real-time processing and decision-making capacities at the edge. The integration of AI-powered edge computing becomes pivotal in augmenting the safety and efficiency of autonomous vehicles, facilitating real-time data processing, swift decision-making, and adept navigation through intricate environments. With the automotive industry steadily progressing toward the realization of autonomous driving, the demand for AI and edge computing solutions in this specific sector is expected to register a substantial incline over the forecast period.

By Region

Artificial Intelligence (AI) in Edge Computing Market Report, By Component (Hardware, Software, Services), By Technology (Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), Others), By Deployment (Cloud, On-Premises), By Application (Autonomous Vehicles, Industrial Automation, Healthcare, Smart Cities, Consumer Electronics, Others), and Regions 2024-2032

North America:

  • United States
  • Canada

Asia Pacific:

  • China
  • India
  • Japan
  • Australia & New Zealand
  • Association of Southeast Asian Nations (ASEAN)
  • Rest of Asia Pacific

Europe:

  • Germany
  • The U.K.
  • France
  • Spain
  • Italy
  • Russia
  • Poland
  • BENELUX (Belgium, the Netherlands, Luxembourg)
  • NORDIC (Norway, Sweden, Finland, Denmark)
  • Rest of Europe

Latin America:

  • Brazil
  • Mexico
  • Argentina
  • Rest of Latin America

The Middle East & Africa:

  • Saudi Arabia
  • United Arab Emirates
  • South Africa
  • Egypt
  • Israel
  • Rest of MEA (Middle East & Africa)

The United States (U.S.) is at the forefront of a number of new and advanced technologies, and remains the largest adopter of AI in edge computing for a number of reasons. The US is a leader in technological innovation, with a robust ecosystem of companies and research institutions actively contributing to the development of AI and edge computing. Also, availability and continuous addition of more advanced infrastructure and investments in cutting-edge technologies creates an environment conducive to early adoption in the country.

In addition, a vibrant entrepreneurial ecosystem that encourages startups to explore and implement emerging technologies is another major factor positioning the US as a market leader. A number of startups in the country focus on AI and edge computing solutions, thereby driving innovation and contributing to the overall adoption landscape. The US government is proactive in formulating policies and initiatives that support the development and adoption of AI technologies. This includes fostering a regulatory environment that encourages experimentation and deployment of AI and edge computing solutions across various sectors. Moreover, the diverse range of industries in the US, including healthcare, finance, manufacturing, and technology, provides ample opportunities for AI and edge computing adoption. Each sector finds unique applications for these technologies, contributing to the overall growth of adoption across the country.

A number of key trends are expected to drive steady adoption of AI in edge computing in Europe. These include: Industries in Europe, including manufacturing, healthcare, and transportation, are increasingly recognizing the potential of AI at the edge. Specific applications and needs specifically tailored to these industries, such as predictive maintenance in manufacturing or AI-assisted healthcare diagnostics, are expected to drive adoption as these sectors prioritize efficiency and innovation. Strong emphasis on data privacy and sovereignty are existent in countries in the region, as perceived according to regulations such as General Data Protection Regulation (GDPR).

Also, AI at the edge allows for localized data processing, reducing the need to transmit sensitive information to centralized servers. This aligns with Europe's data privacy concerns, potentially driving adoption due to enhanced security and compliance. Rollout of 5G networks across Europe will significantly impact adoption of AI in edge computing. The high-speed, low-latency capabilities of 5G networks complement edge computing, enabling faster data processing and real-time decision-making. This synergy is expected to drive wider adoption, especially in sectors that benefit from rapid data transmission.

In addition, governments in countries in the region are investing in AI research and development, often through public-private partnerships. Initiatives aimed at promoting AI innovation and adoption in various sectors, including agriculture, smart cities, and energy, will likely drive increased adoption of AI at the edge across Europe over the forecast period. Moreover, rapid proliferation of IoT devices and edge-aware sensors is another trend expected to drive steady AI adoption in Europe. These devices generate vast volumes of data that can be processed at the edge, leading to more efficient and contextually aware decision-making. As these devices become more prevalent, the demand for AI-driven edge solutions is expected to rise. Collaborations between tech companies, startups, and research institutions in the region are also accelerating the development and adoption of AI in edge computing. Partnerships allow for shared expertise, leading to the creation of innovative edge solutions and their subsequent adoption across various country-level markets in the region.

Companies stand to gain immensely by strategically aligning with various scenarios and establishing a strong foothold in markets in China and India, and this enables tapping into vast potential for growth and contributing to the overall expansion of the global AI in edge computing market as a result. Both China and India have substantially large populations, presenting vast and untapped consumer markets, and companies investing in AI in edge computing can tailor solutions for diverse consumer needs, from smart cities and healthcare to manufacturing and agriculture, leading to increased market penetration and revenue generation. Also, governments in China and India are actively promoting AI and technology adoption through strategic initiatives and policies. Companies can also benefit from favorable regulatory environments, financial incentives, and collaborative opportunities, accelerating the development and deployment of AI in edge computing solutions.

In addition, China and India are rapidly integrating emerging technologies, such as 5G, IoT, and smart infrastructure. Companies investing in AI in edge computing can capitalize on the integration of these technologies to create innovative and advanced solutions. This can result in a competitive edge, and drive revenue growth and market leadership. Both countries have diverse industries, ranging from manufacturing and agriculture to healthcare and finance, and companies can develop industry-specific AI in edge computing applications to address unique challenges in sectors prevalent in these markets. This targeted approach enhances the relevance of solutions, fostering adoption and revenue generation.

Moreover, China and India play major roles in the global supply chain, with extensive manufacturing capabilities. Companies can leverage AI in edge computing to optimize supply chain processes, enhance efficiency, and reduce costs. This not only benefits local operations, but also positions companies as valuable contributors to the global supply chain, attracting international partnerships and creating additional revenue streams.

Leading Companies in the Global AI in Edge Computing Market & Competitive Landscape:

The competitive landscape in the global AI in edge computing market is dynamic and characterized by the presence of both established technology giants and innovative startups. The market is evolving rapidly, driven by increasing adoption of edge computing solutions across various industries. Some key aspects of the competitive landscape include presence of major technology companies, emergence of startups, partnerships and collaborations, focus on industry-specific solutions, hardware and software integration, global expansion efforts, acquisitions and mergers, and open-source initiatives.

Established technology companies, including Microsoft, IBM, Google, Intel, and NVIDIA, have a significant presence in the market. These companies provide a range of solutions, from hardware components to cloud-based platforms, targeting diverse applications of AI in edge computing. The market is also witnessing the emergence of startups specializing in edge computing and AI solutions, and these companies focus on niche applications, innovative technologies, or specific industry verticals.

Also, companies are actively engaging in partnerships and collaborations to enhance their capabilities and expand their market reach. Partnerships may involve technology integrations, joint product development, or collaborations to address specific industry challenges. Another major trend is focus on industry-specific solutions and companies tailoring their AI in edge computing solutions for specific industries. This approach involves understanding the unique requirements and challenges of sectors such as healthcare, manufacturing, energy, and transportation.

In addition, companies are strategically integrating hardware and software solutions to offer comprehensive AI in edge computing packages. This integration aims to provide end-to-end solutions for clients, ensuring compatibility and optimization of both hardware and software components. A number of companies are also actively pursuing global expansion strategies to tap into emerging markets and diversify their customer base. This involves adapting solutions to regional needs and addressing specific challenges in different geographic areas.

Moreover, mergers and acquisitions are prevalent in the competitive landscape as companies seek to strengthen their portfolios and acquire specialized capabilities. These strategic moves aim to consolidate market share and enhance overall competitiveness.

Companies are also leveraging the advantages offered by open source initiatives, which are contributing to open standards and frameworks for edge computing. This collaborative approach aims to establish interoperability and industry-wide standards.

Company List:

  • International Business Machines Corporation (IBM)
  • Microsoft Corporation
  • Intel Corporation
  • NVIDIA Corporation
  • Alphabet Inc. (Google)
  • Cisco Systems, Inc.
  • Dell Technologies Inc.
  • Hewlett Packard Enterprise Company (HPE)
  • Amazon Web Services, Inc. (AWS)
  • Huawei Technologies Co., Ltd.
  • ARM Holdings plc
  • Oracle Corporation
  • SAP SE
  • Accenture plc
  • Fujitsu Limited

Research Scope:

Report Metric

Report Details

Market size available for the years   

2021-2032

Base Year

2023

Forecast Period       

2024-2032

Compound Annual Growth Rate (CAGR)

21.2%

Segment covered 

Component, Technology, Deployment, Application, and Region

Regions Covered

North America:  The U.S. & Canada

Latin America: Brazil, Mexico, Argentina, & Rest of Latin America

Asia Pacific: China, India, Japan, Australia & New Zealand, ASEAN, & Rest of Asia Pacific

Europe: Germany, The U.K., France, Spain, Italy, Russia, Poland, BENELUX, NORDIC, & Rest of Europe

The Middle East & Africa:  Saudi Arabia, United Arab Emirates, South Africa, Egypt, Israel, and Rest of MEA 

Fastest Growing Market in Europe

Germany

Largest Market

North America

Key Players

International Business Machines Corporation (IBM), Microsoft Corporation, Intel Corporation, NVIDIA Corporation, Alphabet Inc. (Google), Cisco Systems, Inc., Dell Technologies Inc., Hewlett Packard Enterprise Company (HPE), Amazon Web Services, Inc. (AWS), Huawei Technologies Co., Ltd., ARM Holdings plc, Oracle Corporation, SAP SE, Accenture plc, Fujitsu Limited



Frequently Asked Question

What is the current size of the global AI in edge computing market?

The global AI in edge computing market size is continually evolving, as the market is registering robust growth, driven by increasing adoption of AI at the edge across various industries.


What products and services are offered in the AI in edge computing market?

Products include edge computing hardware components, AI processors, and software platforms enabling real-time data processing. Services encompass consulting, deployment, and maintenance of AI-powered edge solutions. The market caters to a variety of industry-specific applications, providing end-to-end solutions for enhanced efficiency and innovation.


Among the component segments, which one accounts for the largest revenue share in the AI in edge computing market?

The software segment is expected to account for the largest revenue share among the component segments in the AI in edge computing market. This is attributed to increasing demand for advanced software platforms that drive the functionalities of AI and edge computing solutions.


Which application segment is registering fastest revenue growth rate in the AI in edge computing market?

The autonomous vehicles segment is expected to register fastest revenue growth rate among the s in the AI in edge computing market. The growing interest in self-driving vehicles and the need for real-time processing capabilities at the edge contribute to the prominence of this segment, driving substantial revenue growth.


What is a key driving factor contributing to the market's revenue growth?

A key factor contributing to market revenue growth is increasing demand for real-time data processing and decision-making capabilities, particularly in applications like predictive maintenance and healthcare monitoring.


Which key factor is restraining revenue growth in the AI in edge computing market?

A key restraint to revenue growth is existing infrastructure challenges faced by many organizations, hindering the seamless integration of AI in edge computing due to infrastructure-related barriers.


What is the market size of AI in edge computing market in the year 2023?

The AI in edge computing market size reached US$ 11.2 billion in 2023.


At what CAGR will the AI in edge computing market expand?

The market is anticipated to rise at 21.2% CAGR through 2032.


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