(MENAFN- GlobeNewsWire - Nasdaq) The Edge AI Software market is booming due to its ability to process real-time data at the Network edge, enabling faster decisions and efficiency across sectors like healthcare, manufacturing, and smart cities.Pune, Dec. 12, 2024 (GLOBE NEWSWIRE) -- Edge AI Software Market Size Analysis:
“ The Edge AI Software Market Size was valued at USD 1.33 Billion in 2023 and is expected to reach USD 13.67 Billion by 2032, growing at a CAGR of 29.58% over the forecast period 2024-2032. ”
The Rapid Growth and Impact of the Edge AI Software Market
The Edge AI Software Market is experiencing rapid growth, driven by the rising demand for real-time data processing closer to the source. This technology is transforming industries by enhancing operational efficiency through intelligent, on-device decision-making, reducing dependency on cloud connectivity. Key sectors such as healthcare, manufacturing, and telecommunications are leading the adoption of edge AI solutions. 38% of medical providers now leverage AI for diagnostics, and more than half of telecom companies utilize AI-powered chatbots to enhance customer service.
The market's financial impact is equally impressive, with AI-enabled marketing and sales tools boosting lead generation by 50% and Netflix's AI-based recommendation engine contributing USD 1 billion annually to its revenue. In healthcare, wearable devices integrated with edge AI enable real-time patient monitoring, allowing healthcare professionals to respond promptly to critical conditions.
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Major Players Analysis Listed in this Report are:
NVIDIA Corporation (NVIDIA Jetson, DeepStream SDK) Intel Corporation (OpenVINO, Movidius VPU) Qualcomm Technologies (Snapdragon Neural Processing Engine, Qualcomm AI Stack) Google LLC (TensorFlow Lite, Edge TPU) Microsoft Corporation (Azure IoT Edge, Custom Vision AI) Amazon Web Services (AWS) (AWS IoT Greengrass, AWS SageMaker Edge) Xilinx, Inc. (Vitis AI, Zynq UltraScale+ MPSoC) Huawei Technologies Co., Ltd. (Ascend AI Processors, MindSpore) Arm Holdings (Arm Cortex-M, Arm NN SDK) Texas Instruments Incorporated (TDA4VM Processors, Edge AI Studio) IBM Corporation (IBM Watson IoT, IBM Maximo Visual Inspection) Cisco Systems, Inc. (Cisco Edge Intelligence, Cisco Kinetic) Siemens AG (MindSphere, Industrial Edge) Edge Impulse, Inc. (Edge Impulse Studio, EON Tuner) Rockwell Automation (FactoryTalk, Connected Services) Samsung Electronics (Samsung ARTIK, SmartThings Cloud) Fujitsu Limited (Fujitsu Edge AI, Fujitsu Human Centric AI Zinrai) SAP SE (SAP Leonardo, SAP Edge Services) Palo Alto Networks (Cortex XSOAR, Prisma Cloud) C3.ai (C3 AI Suite, C3 AI Ex Machina)
Edge AI Software Market Report Scope:
Report Attributes | Details |
Market Size in 2023 | USD 1.33 Billion |
Market Size by 2032 | USD 13.67 Billion |
CAGR | CAGR of 29.58% From 2024 to 2032 |
Base Year | 2023 |
Forecast Period | 2024-2032 |
Historical Data | 2020-2022 |
Key Regional Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe [Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) |
Key Growth Drivers | . The need for immediate data processing is increasing, especially in industries such as healthcare, autonomous vehicles, manufacturing, and telecommunications. . The advancement of 5G technology is greatly enhancing edge AI through facilitating quicker, more dependable, and reduced-latency connectivity. |
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Edge AI | Revolutionizing Real-Time Processing in the IoT Era
The rapid proliferation of IoT devices is driving the widespread adoption of edge AI software. As billions of devices generate vast amounts of data, edge AI offers a solution by processing data locally rather than relying on cloud servers. This approach significantly reduces latency, ensuring faster responses critical for time-sensitive applications. For example, autonomous vehicles depend on split-second decision-making, which is made possible through edge AI's ability to process data in real time. Similarly, smart cities benefit from enhanced efficiency in traffic management, energy optimization, and security monitoring. In industrial automation, edge AI ensures seamless operations by analyzing machine data locally to detect anomalies and optimize performance. By reducing data transfer to the cloud, edge AI also enhances privacy and lowers bandwidth costs.
Solutions Segment Leads with Over 72% Share in 2023, Driven by Advanced Real-Time Processing and Video Recognition
Solutions segment dominated with the market share over 72% in 2023. These solutions provide companies with sophisticated software frameworks, platforms, and tools designed for real-time AI processing on edge devices. For example, NVIDIA's Jetson AI platform allows developers to integrate AI capabilities into edge devices for industries like healthcare, automotive, and retail. These cutting-edge solutions significantly reduce latency and improve operational productivity, making them indispensable for modern businesses.
Video and Image Recognition segment dominated with the market share over 32% in 2023. This growth is attributed to its extensive use across industries such as security, healthcare, and retail. Real-time solutions for image analysis, object detection, and facial recognition have become indispensable. Companies like Amazon (AWS DeepLens) and Google (Vision AI) leverage video and image recognition technologies to enhance customer experiences and operational efficiency.
Edge AI Software Market Segmentation:
By Type:
Simple Programmable Logic Devices Complex Programmable Logic Devices
By Product Type:
By Application:
Telecommunication Automotive Consumer Electronics Healthcare Industrial Others
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North America Leads with Innovation, Asia-Pacific Emerges as a Growth Powerhouse in Edge AI Market
North America region dominated with the market share over 35% in 2023. The region's leadership is attributed to robust IT infrastructure, significant investments by tech giants like IBM, Google, and Microsoft, and strong adoption across industries such as automotive and healthcare. Tesla's use of edge AI for autonomous vehicles and GE Healthcare's application of AI for predictive diagnostics underscore the region's innovative approach. Additionally, government initiatives such as the U.S. National AI Initiative further bolster market growth.
The Asia-Pacific region is expected to grow at the fastest CAGR during 2024-2032. Factors driving this growth include rapid digitalization, widespread IoT adoption, and investments in smart city projects in countries like China, Japan, and South Korea. Companies like Huawei and Samsung are leading advancements in edge AI for telecommunications and consumer electronics. In China, edge AI is increasingly utilized in manufacturing automation and retail, further propelling the region's growth.
Recent Developments:
In November 2023: AWS launched IoT Greengrass 3.0, an upgrade to its edge AI software for IoT devices. This version supports advanced machine learning models and seamless integration with cloud AI services, enabling industries like logistics and manufacturing to harness real-time intelligence.
In February 2024: Microsoft introduced an enhanced Azure Percept platform, featuring improved computer vision and voice recognition models. The update simplifies AI model deployment to edge devices, with built-in support for Azure Cognitive Services.
Table of Contents – Major Key Points
1. Introduction
2. Executive Summary
3. Research Methodology
4. Market Dynamics Impact Analysis
5. Statistical Insights and Trends Reporting
6. Competitive Landscape
7. Edge AI Software Market Segmentation, By Offering
8. Edge AI Software Market Segmentation, By Data Type
9. Edge AI Software Market Segmentation, by Vertical
10. Regional Analysis
11. Company Profiles
12. Use Cases and Best Practices
13. Conclusion
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