Edge Artificial Intelligence Market Size, Share, Growth Analysis, By Component(Hardware (Device )), By End Use Industry(Consumer Electronics Device, Smart Homes Device ), By Region - Industry Forecast 2024-2031

Edge Artificial Intelligence (AI) Market size was valued at USD 15.60 Billion in 2022 and is projected to grow from USD 20.55 Billion in 2023 to reach USD 185.95 Billion by 2031, growing at a CAGR of 31.7% in the forecast period (2024-2031).

"Edge AI" refers to the implementation of artificial intelligence algorithms on edge devices such as embedded systems, smartphones, and Internet of Things (IoT) devices located near data sources. Unlike traditional cloud-based AI, Edge AI processes data locally, eliminating the need for continuous data transfer to and from a centralized server. This approach offers several advantages, including reduced latency, minimized bandwidth usage, enhanced privacy, and improved operational efficiency. Within the broader AI and IoT sectors, the Edge AI industry is witnessing rapid growth, driven by increasing demand for decentralized AI applications, low-latency processing, and energy-efficient computation. The Edge AI market is poised for significant expansion due to several key factors and opportunities. The proliferation of IoT devices, advancements in AI and machine learning technologies, and rising demand for real-time data processing and analysis are the primary drivers of this growth. The exponential increase in data generated by connected devices necessitates efficient local processing to mitigate latency and bandwidth consumption, particularly crucial for applications requiring real-time decision-making, such as smart city infrastructure, healthcare monitoring, and autonomous vehicles. Additionally, the deployment of 5G technology enhances connectivity and data transmission rates, further enhancing the viability and efficiency of Edge AI systems.

Top-down and bottom-up approaches were used to estimate and validate the size of the Edge Artificial Intelligence (AI) market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.

Edge Artificial Intelligence (AI) Market Segmental Analysis

The edge artificial intelligence (AI) market is segmented based on component, end use industry, and regional type. In terms of components, the market is segmented into Hardware (Device [Smartphone, Surveillance Cameras, Robots, Wearables, Edge Servers, Smart Speakers, Automobiles, Other Devices]). Power Consumption [Less Than 1W, 1-3W, 3-5W, 5-10W, More Than 10W], Processor [CPU, GPU, ASIC, Other]). (Function [Training, Inference]). Software (Offerings [Solutions {By type (Standalone, Integrated), {By Deployment (On-premises, Cloud)}, [Services {Professional Services (Training & consulting, System integration & testing, Support & maintenance}, Managed Services. (Data Type [Video and Image Data Object Detection & Recognition, Facial Recognition, Visual Inspection, Other Video & Image Data Applications], Audio Data Text & Language Data [Speech Recognition, Sound Anomaly Detection, Voice Assistants, Other Audio Data Applications], Text & Language Data [Natural Language Processing, Sentiment Analysis, Chatbots & Virtual Assistants, Other Text & Language Data Applications] Environmental & Location Data [Geospatial Analysis, Weather Prediction , Air Quality Monitoring , Other Environmental & Location Data Applications]Biometric Data [Fingerprint Recognition, Iris & Retina Scanning, Biometric Authentication, Other Biometric Data Applications], Multi-Modal Data [Integration Of Sensors, Integration Of Text & Visual Data, Other Multi-modal Data Applications], Edge Cloud Infrastructure, Services. In terms of end use industry, the market is segmented into Consumer Electronics (By Device [smartphones, wearables, entertainment robots, Smart Homes [By Device {smart speakers, smart cameras, domestic robots, Automotive & Transportation [By Device {automobile parts, surveillance cameras, logistics robots, other automotive & transportation devices], Government [By Device {surveillance cameras, drones}, Healthcare [By Device, {Medical Robots, Wearables, Other Healthcare Devices}, Industrial [By Device {Industrial robots, Drones, Mv Cameras}, Aerospace & Defense [By Device {Service Robots, Other Aerospace & Defense Devices}, Construction [By Device, Service Robots, Drones], Other Verticals [By Device,{Surveillance Cameras, Professional Service Robots, Wearables, Other Devices},[Edge Servers, Drones]. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America, and Middle East & Africa.

Drivers of the Edge Artificial Intelligence (AI) Market

Security vulnerabilities and the threat of malware infiltration pose significant obstacles to the seamless operation of Edge AI applications. Given the sensitivity of the data they manage and store, these apps are susceptible to various security risks. Despite being a relatively newer development, the lack of experience and comprehension across industries exacerbates these vulnerabilities. Moreover, edge computing relies heavily on standardized units and hardware, making the prototype construction labor-intensive and time-consuming to integrate into production. Additionally, substantial investments are required for training, personnel, and research and development efforts. Edge AI encounters challenges such as computing constraints, data privacy concerns, and security issues. The limited computational capacity of edge devices, compared to cloud servers, constrains the complexity and scale of AI models that can be deployed. Processing data locally on distributed edge devices raises significant apprehensions regarding data privacy and security, necessitating robust security measures to safeguard sensitive data.

Restraints in the Edge Artificial Intelligence (AI) Market

The significant expense associated with setting up and maintaining equipment poses challenges for the adoption of technologies such as Edge Artificial Intelligence (AI)s. Procuring Edge Artificial Intelligence (AI)s and the necessary tools incurs substantial costs, leading to overall increased expenses. Consequently, only companies and firms with high turnovers and strong competitiveness in the market can afford to integrate these Edge Artificial Intelligence (AI)s. This situation is expected to dampen the growth of the global Edge Artificial Intelligence (AI) market throughout the forecast period.

Market Trends of the Edge Artificial Intelligence (AI) Market

The escalating reliance on digital technologies, particularly Edge Artificial Intelligence (AI), within Industry 4.0 signifies a pivotal transformation in design and industrial production. Organizations are increasingly integrating advanced technologies like AI, Internet of Things (IoT), cloud computing, and automation to facilitate the creation of smart factories. This shift towards Industry 4.0 emphasizes the importance of Edge AI, which involves processing data closer to its source with minimal reliance on centralized areas like data centers or cloud premises. Edge AI plays a vital role in Industry 4.0 by addressing various needs such as reducing latency, facilitating instantaneous information processing to enhance real-time insights, and automating network activities. The Edge AI market is undergoing a revolutionary phase characterized by notable trends that significantly impact its growth and development. One such trend is the rising demand for rapid and efficient response through real-time analysis and decision-making across diverse organizational sectors. This trend places considerable pressure on technologies like Edge AI solutions, enabling swift reactions with minimal latency. Additionally, the convergence of cloud services and edge computing emerges as a significant trend offering enhanced scalability and resource utilization.