The global sales of edge AI is estimated to be worth USD 8,901.9 million in 2024 and anticipated to reach a value of USD 71,131.3 million by 2034. Sales are projected to rise at a CAGR of 23.1% over the forecast period between 2024 and 2034. The revenue generated by emergency lighting in 2023 was USD 3,149.1 million. The application is anticipated to exhibit a Y-o-Y growth of 22.7% in 2024.
Edge AI means direct deployment of AI algorithms directly on the edge, such as smartphones, sensors, cameras, IoT devices, or any other embedded systems instead of relying on centralized cloud servers.
Thus, by processing data locally, Edge AI minimizes constant data transmission to and from the cloud, which allows faster decision-making, improved data privacy, and lower bandwidth usage. It uses compact AI models that are optimized for edge devices and usually use technologies such as machine learning, computer vision, & natural language processing.
The benefits of Edge AI are several and transformative across industries. A major benefit is the capability to process data in real-time since the edge can support analysis and action against the data with minimal latency. Examples include autonomous vehicles, industrial automation, and monitoring healthcare.
Enhanced data privacy and security is another important advantage. As sensitive data is processed locally on the device, there is a lesser threat of exposure through its transmission, thereby making edge AI perfect for applications in diverse sectors.
Remote or underserved areas where reliable cloud access is a challenge would particularly be beneficial due to reduced reliance on internet connectivity. Edge AI can operate without interruption in an offline scenario. It is very important for disaster response and remote surveillance applications.
In addition, fewer amounts of data are transmitted to the cloud, reducing bandwidth and cloud storage costs, driving edge AI market growth. Thus, business savings can be highly realized without compromising analytic and decision-making performance.
Edge AI also fosters energy efficiency as it uses resources in an optimized way, particularly for low-power devices. This is particularly important in an IoT where devices have to operate on extremely low energy budgets over a long period of time.
Global Edge AI Industry Assessment
| Attributes | Key Insights |
|---|---|
| Historical Size, 2023 | USD 3,149.1 million |
| Estimated Size, 2024 | USD 8,901.9 million |
| Projected Size, 2034 | USD 71,131.3 million |
| Value-based CAGR (2024 to 2034) | 23.1% |
Edge AI is transforming distribution services by enabling faster, more efficient, and secure operations through localized data processing. One significant advantage is its ability to manage inventory in real time. With AI-powered sensors & devices in warehouses and distribution centers, businesses can track stock levels, monitor product movement, & ensure accurate inventory management.
This minimizes issues such as overstocking or stockouts, enhancing supply chain efficiency. In Edge AI, data processing occurs on-site rather than over the cloud, which means that no latency is caused to halt an operation in low-connectivity environments.
Another area where Edge AI plays key role is route optimization. A delivery vehicle may, with AI systems deployed, be analyzing traffic patterns, weather conditions, and prioritized deliveries on the go to create dynamically updated routes, further reducing fuel consumption, delivery time and even operational costs.
Edge AI also improves predictive maintenance capabilities for delivery fleets and distribution equipment to avoid potential issues in real- time in order to prevent pricey downtime. The abilities are helpful especially in last-mile delivery, where the efficiency and accuracy speed up customer satisfaction.
In addition, Edge AI increases overall quality assurance and security in distribution services. AI-powered devices can inspect products for defects during the distribution process, ensuring that only high-quality goods reach the customer, reducing returns and improving brand trust.
Furthermore, sensitive customer and delivery data can be processed directly on edge devices, reducing the risks associated with cloud-based transmissions and enhancing data security. This enables edge AI to provide the needed tools to help the distribution network address modern challenges whilst meeting customer demands for reliability and speed, supporting the growth of edge AI market.
Don't pay for what you don't need
Customize your report by selecting specific countries or regions and save 30%!
The below table presents the anticipated CAGR for the global edge AI market over several semi-annual periods spanning from 2024 to 2034. This assessment outlines changes in the Edge AI industry and identify revenue trends, offering key decision makers an understanding about market performance throughout the year.
H1 symbolizes first half of the year from January to June, H2 spans from July to December, which is the second half. In the first half (H1) of the year from 2023 to 2033, the business is expected to grow at a CAGR of 22.8%, followed by an increased growth rate of 23.3% in the second half (H2) of the same year.
| Particular | Value CAGR |
|---|---|
| H1, 2023 | 22.8% (2023 to 2033) |
| H2, 2023 | 23.3% (2023 to 2033) |
| H1, 2024 | 22.7% (2024 to 2034) |
| H2, 2024 | 23.8% (2024 to 2034) |
Moving into the subsequent period, from H1 2024 to H2 2034, the CAGR is projected to decrease slightly to 22.7% in the first half and remain higher at 23.8% in the second half. In the first half (H1) the market witnessed a decrease of 10 BPS while in the second half (H2), the market witnessed an increase of 50 BPS.
Adoption of AI-Enabled Internet of Things (IoT) Devices and Rollout of 5G Networks are Emerging Trend in Edge AI Market
Integration of artificial intelligence in the IoT devices is significantly transforming the market for Edge AI. IoT devices is generates massive data, for which, edge AI allows localized processing within devices such as sensors, cameras, and gateways.
This reduces latency and improves responsiveness since raw data does not have to be transmitted to cloud servers. For instance, in smart manufacturing, Edge AI enables IoT sensors to identify anomalies in the machinery instantly thereby enabling predictive maintenance and avoiding costly downtime.
This trend is accompanied by the rising demand for real-time decision-making in industrial automation applications, smart home appliances, and health monitoring equipment.
Further, AI-enabled IoT devices with Edge AI find applications not only in retail for tracking inventory but also in agriculture for precision farming & in energy management for optimizing resource use. As companies look to become more efficient and reduce operational costs, business adoption of Edge AI in IoT ecosystems is picking up speed dramatically.
Further, the 5G network roll-out makes Edge AI adoption much faster due to quick data transfer and ultra-low latency which is critical for real-time applications. Edge computing processes data close to its source, and the combination of 5G with Edge AI creates a seamless ecosystem for handling complex tasks that demand immediate responses, such as autonomous vehicles, remote healthcare, & industrial automation.
Rising Need for Energy Efficiency and Advancements in AI Models is driving the Edge AI Market Growth
Edge AI market witnessing high growth owing to is rising demand for energy-efficient technologies. Traditional AI solutions, relying on heavy use of cloud computing, consume substantial amounts of energy for data transmission and processing.
Edge AI minimizes reliance on energy-intensive cloud infrastructure by processing data locally on edge devices, thereby significantly minimizing power consumption. This makes Edge AI the preferred solution for industries that are emphasizing sustainability, aligning with green initiatives.
For instance, in IoT applications, smart thermostats, surveillance cameras, and industrial sensors utilize Edge AI to carry out real-time analysis of data without constantly sending communications to the cloud. This approach not only minimizes the energy usage but also allows prolongation of the operational life of batteries of devices, which is especially important in remote and resource-poor environments.
Further, lightweight AI models have been another key factor driving the growth of Edge AI. Unlike traditional AI models, which are heavy with computation and need a lot of resources, the AI model has been optimized to run efficiently on resource-constrained edge devices such as sensors, drones, and smartphones. This has allowed deploying capabilities directly on devices without compromising performance.
These models use techniques such as model quantization, pruning, and knowledge distillation to reduce computational complexity & memory requirements. In smart agriculture, for instance, edge devices analyze soil conditions, crop health, using compact AI models, providing actionable insights in real-time without depending on cloud connectivity.
In healthcare, similar models serve as lightweights, enabling wearable devices to monitor one's vitals and detect anomalies locally before actual interventions can be made in time.
Growth in Smart City Initiatives and Healthcare Applications is Creating Ample Opportunities for Edge AI Market
The Edge AI market presents a big opportunity in the increasing focus on the development of smart cities. Edge AI plays a very important part by processing at the local end, thereby making faster decisions without cloud computing. This is very important in applications like traffic management, utilities, and surveillance where responses are critical.
For instance, Edge AI supports traffic management systems that analyze live feeds from cameras and sensors for optimizing signal timings to reduce congestion & raise the security of public areas.
For utilities, smart meters and grids adopt Edge AI to monitor patterns of consumption, predict demand patterns, and improve energy distribution. Similarly, surveillance systems are equipped with Edge AI, which can identify anomalies or potential threats in real time to enhance security without overburdening network infrastructure.
The healthcare sector is offering wide opportunities for the Edge AI market, driven by the rising demand for real-time diagnostics, patient monitoring, and wearable devices. Edge AI enables localized data processing so that medical devices can offer timely insights without dependency on connectivity with the cloud. This could be particularly valuable in critical scenarios where speed and accuracy might directly impact patient outcomes.
Healthcare devices like wearable devices and health monitors, depends on Edge AI to process real-time analytics of vitals such as heart rate, blood pressure, and oxygen levels. This helps both patients & clinicians detect anomalies early, thereby providing preventive care. Diagnostic tools with Edge AI support the analysis of images, allowing healthcare providers to make quicker and more informed decisions.
Technical Complexity and Interoperability Issues may affect Edge AI Market Growth in the Projected Timeframe
Technical challenges associated with the deployment of AI models on edge devices is a restraint to the growth of the Edge AI market. These edge devices include IoT sensors, cameras, and microcontrollers, which are often resource-constrained and possess low computing capability, memory, & energy. Designing AI models that perform well within these constraints requires new techniques like model pruning, quantization, and compression, which demand specialized skillsets.
Training AI models for edge deployment is another challenge. As opposed to training on cloud, the computational resources needed for heavy data processing on edge devices are hardly available, making it tougher to develop or adapt models.
Besides this, maintaining the same level of accuracy and performance in such compactly-sized models adds additional challenges to their development. Deployment can also be very complicated as different hardware architectures require varied solutions for the inclusion of AI models with hopefully seamless functionality.
Lack of standardization and interoperability across devices, platforms, & ecosystems is another major restraint in the Edge AI market. The deployment of Edge AI solutions mostly takes place on heterogeneous hardware and software environments that each has its own protocol, APIs, and communication standards. This diversity discourages the construction of AI models to smoothly integrate and execute on a variety of systems.
Principal Consultant
The global Edge AI market registered a CAGR of 22.4% during the historic period between 2019 and 2023. The growth of Edge AI industry was progressive as it reached a value of USD 3,149.1 million in 2023 from USD 3,149.1 million in 2019.
Edge AI marked growth through 2019 to 2023, primarily due progressions in technology, the explosion of IoT devices, and increasing demand for real-time analytics. In 2019, the market experienced expansion with remarkable adoption in sectors like manufacturing, automotive, & retail.
However, the COVID-19 pandemic, which started in early 2020, brought mixed impacts. Early on, supply chain disruptions and delays in hardware production were passed down to the market. On the other hand, accelerated digital transformation in industries opening up new opportunities for Edge AI has been realized in healthcare, logistics, and smart city applications.
From 2021, 5G rollout and upgraded hardware related to AI, including energy-efficient chips, improved the position of the Edge AI market. By 2023, Edge AI became a cornerstone of technologies in automotive for autonomous driving, industrial automation for predictive maintenance, and in smart cities for traffic management & surveillance.
It had to navigate some of the initial difficult stages before the pandemic unveiled the necessity for decentralized low latency and efficient AI services.
The market was compounded annually, showing growth in its role of powering innovative, real-time applications in various sectors. This period established Edge AI as a pivotal technology for modernization and enhancing resilience in a fast-changing business environment.
Tier 1 companies holds noteworthy market share up to 40% to 45% in the global market. Such companies have significant financial strengths, massive production capabilities, and wide global reach.
Tier 1 players develop comprehensive, end-to-end solutions in hardware, software, and cloud, providing deep support for integration, scalability, and global deployment. Companies primarily belonging to Tier 1 are Microsoft Corporation, Google, LLC, IBM Corporation, Nvidia Corporation, Intel Corporation, and Qualcomm.
Tier 2 companies are mid-sized companies that hold a strong position. However, they may not have the same widespread resources and global presence as Tier 1 players. While they may lack financial power or market share as Tier 1 players, these companies may lead in niche applications.
They provide tailored solutions that address specific industry needs or technological gaps. Some the key players in this tier include; Samsung Electronics, Hewlett Packard Enterprise, NXP Semiconductors, EdgeQ, and Aaeon Technology.
Tier 3 companies are typically smaller or emerging companies that focus on niche or regional markets. These companies focus on specialized, localized, or cost-effective Edge AI solutions but may lack the resources, scalability, or widespread brand recognition of Tier 1 or Tier 2 companies. Tier 3 players can bring innovation and agility to the market, providing customized, lower-cost options for small to medium-sized enterprises or specific verticals.
Get the data you need at a Fraction of the cost
Personalize your report by choosing insights you need
and save 40%!
The section below covers the market analysis for the edge AI market for different countries. Market demand analysis on key countries in several regions of the globe, including the USA, Germany, China, India, Brazil, and UK is provided.
The United States is anticipated to remain at the forefront in North America, with a value share of 75.2% through 2034. In East Asia, South Korea is projected to witness a CAGR of 22.5% by 2034.
| Countries | Value CAGR (2024 to 2034) |
|---|---|
| USA | 22.8% |
| Germany | 22.1% |
| China | 24.3% |
| India | 23.5% |
| UK | 21.5% |
North America, spearheaded by the USA which currently holds around 81.2% share of the North America edge AI markets in 2023. USA Edge AI market is anticipated to grow at a CAGR of 22.8% throughout the forecast period.
Leading in AI innovation, the USA houses major companies such as NVIDIA, Intel, Google, and IBM, which innovate into the frontiers of AI & edge computing solutions. These companies have created advanced platforms for hardware and software that enable AI models to operate within edge comfort. This is seen in healthcare, automotive, manufacturing, and retail sector.
The adoption of Edge AI technology is enhanced in the USA by 5G technology, as it enables ultra-low latency and very high speed in the connectivity capabilities of edge devices. In particular, autonomous vehicles, smart cities, and industrial automation have gained from this synergy of Edge AI and 5G.
China’s Edge AI market is poised to exhibit a CAGR of 24.3% between 2024 and 2034. Currently, it holds the significant market share in the East Asia market, and the dominance is expected to continue through the forecast period.
China has quickly moved up in the ranks to become a world leader in the Edge AI market, highly encouraged by significant investments into AI research and development along with the extensive embrace of IoT and 5G technologies. The companies in the country are focusing on developing advance Ai solutions to decrease their dependency on Western countries and increase Ai adoption in the region.
For instance, in October 2024, China Telecom, a state-owned carrier, has developed two large language models (LLMs) that were entirely trained on domestically produced processors. This is a significant achievement for China, as it demonstrates the country's progress towards chip independence, particularly in the field of AI.
India’s Edge AI market is expected to witness a CAGR of 23.5% in the forecast period and hold considerable market share in South Asia & Pacific region through 2034.
The edge AI market in India is quite stable, driven by digitalization, increasing IoT devices, and notable young, tech-savvy population. The "Digital India" push by the government and 4G/5G rollout are further driving the growth of edge AI across sectors. India's technology ecosystem, is accelerating due to growing number of AI startups and innovations.
In healthcare, applications include diagnostics, remote patient monitoring, and management of medical devices. In addition, other key drivers of Edge AI adoption in urban infrastructure include the Indian government's initiatives such as National AI Strategy and smart cities.
The section contains information about the leading segments in the market. By component, services segment is estimated to grow at a CAGR of 24.1% throughout 2034. Additionally, the by industry, retail & e-commerce segment is projected to expand at 25.4% till 2034.
| Component | Hardware |
|---|---|
| Value Share (2024) | 56.3% |
Hardware segment is expected to acquire share of 56.3% in the market in terms of component in 2024. Edge AI relies on specialized hardware that can process data locally at the edge and not on cloud-based servers.
Some examples include AI chips, processors, GPUs, FPGAs, and sensors-all designed to handle computationally intensive tasks in real time without hanging latency. Real-time decision-making, in industries such as autonomous vehicles, industrial automation, and healthcare, makes it hardware important in low-latency and high-performance computing.
| Industry | IT & Telecommunications |
|---|---|
| Value Share (2024) | 26.5% |
The IT & telecommunications segment is expected to capture share of 26.5% in 2024. Edge AI in telecommunications allows operators to process data locally on the network edge, enabling immediate insights into network performance, traffic management, and customer experience. All this is essential for resource optimization, handling the quantity of data generated by 5G networks, and optimizing the overall quality of service.
In IT, Edge AI is used to increase the security level, automate monitoring, and even data processing across various distributed environments, such as in data centers, cloud networks, or nodes that constitute edge computing. Proactive identification of anomalies, real-time analysis of threats, and automated response to incidents can keep sensitive data and applications secure from cyber threats.
Key players operating in the edge AI market are investing in advanced technologies and also entering into partnerships. Key edge AI providers have also been acquiring smaller players to grow their presence to further penetrate the market across multiple regions.
Recent Industry Developments in Edge AI Market
In terms of component, the industry is divided into hardware, software, and services.
In terms of application, it is divided video surveillance, remote monitoring, predictive maintenance, and others.
The industry is classified as IT & telecommunications, consumer electronics, healthcare, manufacturing, government, energy & utilities, retail & E-commerce, automotive, and others.
Key countries of North America, Latin America, East Asia, South Asia & Pacific, Western Europe, Eastern Europe, and Middle East & Africa (MEA) have been covered in the report.
The global Edge AI industry is projected to witness CAGR of 23.1% between 2024 and 2034.
The global Edge AI industry stood at USD 3,149.1 million in 2023.
The global Edge AI industry is anticipated to reach USD 71,131.3 million by 2034 end.
East Asia is set to record the highest CAGR of 23.9% in the assessment period.
The key players operating in the global edge AI industry include Microsoft Corporation, Google, LLC, IBM Corporation, Nvidia Corporation, Intel Corporation, and Qualcomm.
1. Executive Summary 2. Consortiums Introduction, including Taxonomy and Market Definition 3. Market Trends and Success Factors, including Macro-economic Factors, Market Dynamics, and Recent Consortiums Developments 4. Global Market Demand Analysis 2019 to 2023 and Forecast 2024 to 2034 4.1. Historical Analysis 4.2. Future Projections 5. Pricing Analysis 6. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Component 6.1. Hardware 6.2. Software 6.3. Services 7. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, by Application 7.1. Video Surveillance 7.2. Remote Monitoring 7.3. Predictive Maintenance 7.4. Others 8. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, by Industry 8.1. IT & Telecommunications 8.2. Consumer Electronics 8.3. Healthcare 8.4. Manufacturing 8.5. Government 8.6. Energy & Utilities 8.7. Retail & E-commerce 8.8. Automotive 8.9. Others 9. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, by Region 9.1. North America 9.2. Latin America 9.3. Western Europe 9.4. Eastern Europe 9.5. East Asia 9.6. South Asia and Pacific 9.7. Middle East and Africa 10. North America Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 11. Latin America Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 12. Western Europe Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 13. Eastern Europe Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 14. East Asia Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 15. South Asia & Pacific Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 16. Middle East and Africa Sales Analysis 2019 to 2023 and Forecast 2024 to 2034, by Key Segments and Countries 17. Sales Forecast 2024 to 2034 by Component, Application, and Consortiums for 30 Countries 18. Competition Outlook, including Market Structure Analysis, Company Share Analysis by Key Players, and Competition Dashboard 19. Company Profile 19.1. Microsoft Corporation 19.2. Google, LLC 19.3. IBM Corporation 19.4. Nvidia Corporation 19.5. Intel Corporation 19.6. Qualcomm 19.7. Samsung Electronics 19.8. Hewlett Packard Enterprise 19.9. NXP Semiconductors 19.10. EdgeQ 19.11. Aaeon Technology 19.12. Samsara 19.13. Kneron 19.14. Adlink Technology 19.15. Greenwave Systems
Explore Technology Insights
View Reports
Edge AI Market
