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.
Key Industry Highlights
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.
Country-wise Insights
Advanced Technological Infrastructure and Increasing Adoption of IoT Devices is Boosting Edge AI Market in USA
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.
Significant Investment in Development of AI Solutions is creating robust opportunities for Edge AI Market in China
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.
Increasing Private & Public Investment in Digitization is propelling Edge AI Market in India
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.
Recent Industry Developments in Edge AI Market
- In November 2024, electronics company, Vecow, Co., Ltd. introduced its next-generation edge AI server platforms designed to enhance AI development across various applications. These platforms aim to provide robust computing power and efficiency, catering to industries such as manufacturing, healthcare, and smart cities.
- In October 2024, semiconductor company, Infineon Technologies AG has introduced the DEEPCRAFT™ brand, focusing on Edge AI and Machine Learning software solutions to enhance its AI portfolio. This initiative aims to support the growing demand for Edge AI in various consumer and industrial applications. The DEEPCRAFT brand will encompass existing products like DEEPCRAFT Studio and DEEPCRAFT Ready Models, with plans for rapid expansion to include a wider range of tools and solutions for customers
- In July 2024, IT Company, NTT DATA has launched its Ultralight Edge AI platform, aimed at enhancing AI capabilities at the edge for various applications. This platform is optimized for low-power devices, making it suitable for deployment in environments with limited resources.
These insights are based on a report Edge AI Market by Future Market Insights.
