Edge AI Software Market Research Report: Information by Component(Solution [Software Tools and Platform] and Services [Training & Consultation Services, System Integration & Testing, and Support & Maintenance]), Data Source (Video and Image Recognition, Speech Recognition, Biometric Data, Sensor Data, and Mobile Data), Application (Autonomous Vehicle, Access Management, Video Surveillance, Remote Monitoring & Predictive Maintenance, Telemetry, Energy Management, and Others), Vertical (Government & Public, Manufacturing, Automotive, Energy &

Edge AI Software Market Overview:

The edge AI software market is expected to reach USD 2271.73 million growing at a CAGR of 21.3% during the forecast, 2021–2027.

The term "edge computing" refers to processing that takes into account a specific viewpoint. It brings data closer to the device or information source where it is needed most often.Edge Computing allows IoT data to be processed close to its source rather than being sent over long distances to data centers or the cloud. It has to do with dealing with persistent data near the data source, which is considered the association's 'edge.' Instead of a cloud or data gathering zone, it is connected to active applications as close as feasible to the place where the data is created.

Edge AI is a system that processes data generated at the local level by hardware devices using machine learning techniques. It refers to AI algorithms that are proposed locally on hardware devices and utilize data generated locally. They save the results locally on the devices before sending them to the cloud to be processed and stored. One of the most significant advantages of edge AI is its speed. Integrating smart devices and functionality can detect faults and deliver AI at the edge for insights. Edge AI is noted for its adaptability, allowing smart devices to support a variety of businesses. Edge AI-powered gadgets also give a high level of safety and security with additional security features, reducing the danger. Edge AI also lowers costs and reduces latency for a better user experience. It enables the integration of technologies that are centered on the user's experience, allowing you to engage in real-time and make payments.

COVID-19 Analysis

The COVID-19 epidemic has left industries and businesses with little time to prepare or defend themselves from potential damages. The market situation is uncertain, and it can go sharply up or down, depending on the activities performed and the outcomes achieved by the businesses. The outbreak has impacted a wide range of sectors all across the world. Most industrial units throughout the world have had to close or suspend their manufacturing operations due to it. The COVID-19 epidemic has had a huge impact on industries such as aircraft, automotive, manufacturing, and food & beverage.

The rapid spread of COVID-19 across the globe has had a significant impact on the IT sector. Changing consumer choices and behavior due to the evolving global pandemic scenario has had a significant impact on the IT sector over the forecasted timeframe. For example, the World Health Organization (WHO) declared COVID-19 a global epidemic in March 2020, prompting some governments to declare lockdowns. Because of the manufacturing sector's downturn and factory closures, the pandemic has had an impact on the economy.During the Covid-19 pandemic, however, AI has discovered several key applications. AI can quickly assess unusual symptoms and other "red flags," alerting patients and healthcare officials. It aids in cost-effective decision-making by allowing for speedier decision-making. Through relevant algorithms, it aids in the development of a novel diagnosis and management strategy for COVID 19 cases. With the use of medical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) scans of human body parts, AI can assist in diagnosing infected cases.

Market Dynamics

The major factors driving the growth of the edge AI software market are the increasing need for real-time operations, including data creation, increasing demand for edge AI-enabled devices, and the growing emergence of 5G network connectivity. However, the data scarcity at edge and data consistency on edge devices are hindering market growth. Although increased levels of security, reduced costs & latency for data communication, and edge AI overcoming cloud computing challenges are providing opportunities in the global market.

Drivers

• Increasing Demand for Edge AI-Enabled Devices

There is a broad list of edge AI applications. Facial recognition and real-time traffic reports on smartphones and semi-autonomous vehicles or intelligent devices. Video games, smart speakers, robots, drones, security cameras, and wearable health monitoring devices are among the other Edge AI-enabled items. The security camera detection procedure will benefit from edge AI. Traditional surveillance cameras capture images for hours before storing and using them as needed. With Edge AI, however, the algorithmic procedures will be carried out in real-time in the system itself, allowing the cameras to detect and process suspicious activity in real-time, resulting in more efficient and cost-effective services. The capacity of autonomous vehicles to process data and images in real-time to identify traffic signs, pedestrians, other cars, and roads will rise through Edge AI, enhancing transportation security.In terms of industrial IoT, Edge AI will lower costs and increase safety (IIoT). Machine Learning will recompile data in real-time of the entire process, while AI will watch machinery for probable defects or faults in the production chain.

Edge AI technology has no limitations in terms of applications. Following the Covid-19 crisis, firms' innovation has pushed them to develop Artificial Intelligence-based systems that deliver precise information in real-time.

Restraints

• Data Consistency on Edge Devices

Speech recognition, activity recognition, emotion recognition, and other edge intelligence-based applications typically collect data from a large number of sensors dispersed around the edge network. Nonetheless, the information gathered may be inconsistent. Different sensing contexts and sensor heterogeneity are two variables that contribute to this issue. The surrounding environment (e.g., street and library) and its characteristics (e.g., rain, windy conditions) introduce background noise to the sensordata, affecting model accuracy.

Value Chain Analysis

Due to technology advancements, the global edge AI software market has grown significantly over the last decade and is likely to continue growing steadily in the future years. The Edge AI software market's value chain is divided into four levels: hardware/software vendors, system integrators, and end users.

Market Segmentation

The global edge AI software market has been segmented based on component, data source, vertical, and region.

By component, the edge AI software market has been segmented into solution and service. The solutions segment is further bifurcated into software tools and platforms. The service segment has been further segmented into training & consultation services, system integration & testing services, and support & maintenance services.

By data sources, the edge AI software market has been segmented into video and image recognition, speech recognition, biometric data, sensor data, and mobile data.

By application, the edge AI software market has been segmented into autonomous vehicles, access management, video surveillance, remote monitoring & predictive maintenance, telemetry, energy management, and others.

By vertical, the edge AI software market has been segmented into government & public, manufacturing, automotive, energy & utilities, telecom, healthcare, and others.

Regional Analysis

Geographically, the global edge AI software market has been categorized as North America, Europe, the Asia-Pacific, the Middle East & Africa, and South America. North Americaaccounted for the largest market share in 2019, and it is expected to register strong growth during the forecast period. However, the APAC area is experiencing tremendous growth due to various factors, including expanding local businesses and government programs aimed towards AI developments. The major APAC countries are technologically advanced and provide significant investment and income potential.

Increasing Technological Advancements in the Asia-Pacific Region to Bolster the Edge AI Software Market

Asia-Pacific is expected to be the fastest-growing regional market during the forecast period. The regional market has been segmented into China, Japan, India, and the rest of Asia-Pacific. The region is experiencing tremendous growth due to various factors, including expanding local businesses and government programs aimed towards AI developments. The major APAC countries are technologically advanced and provide significant investment and income potential. The growing volume of data generated by edge devices across multiple industrial verticals and increased consumer spending on smart solutions in countries such as China, Japan, Australia, and India are driving the growth of the edge AI software market in APAC.

Competitive Landscape

The global edge AI software market is characterized by the presence of several regional and local providers. There are several domestic, regional, and global players operating in the edge AI software market who continuously strive to gain a significant share of the overall market.

Key Players

• Oracle Corporation


• Microsoft Corporation


• Hewlett Packard Enterprise Company

Oracle’s corporate strategy is to continue investing in incorporating innovation in its products and services offered through its cloud and on-premises software, hardware, and services businesses. It focuses on acquisitions that help in enhancing its products and services, expand its customer base, and accelerate innovation on a larger scale. The company invests heavily in acquiring several companies, products, services, and technologies that add to, are complementary to or have otherwise enhanced its existing offerings. Oracle also focuses on developing its product suite with advanced capabilities to cater to a wide Payment Mode area and increase its customer base.

Some of the key players in the market are Microsoft Corporation (US), IBM Corporation (US), Amazon Web Services (US), Nutanix Inc. (US), Synaptics (US), TIBCO Software (US), Octonion SA (Switzerland), Intel Corporation (US), HPE (US), Oracle Corporation (US), Foghorn Systems (US), Gorilla Technology Group (Taiwan), Azion Technologies (US), ClearBlade (US), TACT.Ai Technologies (US), SIXSQ (Geneva), ADAPDIX (US), and ALEF EDGE (US).

Recent Developments

• In December 2020, Synaptics and Eta Compute collaborated to introduce the Katana Edge AI Platform, which blends Synaptics' low-power SoC architecture with energy-efficient AI algorithms.


• In May 2020, IBM introduced new edge computing services and solutions backed by a large ecosystem of partners to help organizations and telecommunications firms accelerate their transition to the 5G future.


• In October 2019, FogHorn Systems had unveiled new lightning-edge AI platform features. Tools and advancements for empowering operations technology (OT) professionals are among the highlights. The new drag-and-drop analytic programming capabilities and rich visualization dashboards allow OT personnel to derive insights from real-time data more quickly without the requirement for data science expertise.

Report Overview

This study estimates revenue growth at global, regional, and country levels and offers an overview of the latest developments in each of the sub-sectors from 2018 to 2027. For this analysis, MRFR segmented the global Edge AI softwaremarket has been segmented based on component, data source, vertical, and region.

By Component

• 
  Solution
  
  
  
  • Software Tools
  
  
  • Platform
  
  
  
  


• 
  Services
  
  
  
  • Training & Consultation Services
  
  
  • System Integration & Testing Services
  
  
  • Support & Maintenance Services
  
  
  
  

By Data Source

• Video and Image Recognition


• Speech Recognition


• Biometric Data


• Sensor Data


• Mobile Data

By Application

• Autonomous Vehicles


• Access Management


• Video Surveillance


• Remote Monitoring & Predictive Maintenance


• Telemetry


• Energy Management


• Others

By Vertical

• Government & Public


• Manufacturing


• Automotive


• Energy & Utilities


• Telecom


• Healthcare


• Others

By Region

• North America


• Europe


• Asia-Pacific


• Middle East & Africa


• South America