The growing demand for 5G in areas such as real-time virtual reality experiences, self-driving cars, and mission-critical applications drives further edge AI innovation. The rising demand for IoT-based edge computing services is expected to propel the market growth. IoT generates voluminous amounts of data that can be difficult or impossible to collect. Moreover, edge AI enables full utilisation of IoT data by locally analysing massive amounts of sensor data and automating operational decisions.

Edge computing allows for the transfer of AI processing tasks from the cloud to near-end devices, thereby overcoming the inherent problems of traditional cloud computing, such as high latency and a lack of security. Moreover, edge AI is gaining popularity in technical advances owing to low latency and bandwidth requirements. The researchers offer a for instance by way of example. In June 2020, Adlink Technology Inc., a company manufacturing edge computing products based in Taiwan, collaborated with Tier IV, a Japan-based deep-tech startup, and Taiwan-based Industrial Technology Research Institute (ITRI) to enable autonomous driving with the help of edge AI. This alliance aimed to accelerate the development of open-source self-driving technology to help develop intelligent transportation systems.

Integration of edge AI in to AI devices

The integration of edge AI into AI devices enables data processing on the machine without sending additional data elsewhere. This significantly decreases processing time which can tremendously impact cultivating positive user experiences. Relocating AI computations to the network edge create opportunities for various applications, including new products and services. Again as a for instance, in July 2022, Innodisk Corporation, a computer hardware manufacturing company in Taiwan, released an edge computing solid-state drive product line. Edge AI SSDs within edge servers are intended to process data at high speeds at the source, improving latency and lowering costs.

Edge AI computing and 5G enhance network efficiency to support and deploy various real-time AI applications, including AI-based real-time video analytics for intelligent surveillance and security, smart farming, and industrial manufacturing automation. Many businesses are developing and upgrading hardware to improve the performance of cameras to satisfy the growing demand for surveillance dimensions offered by advanced video surveillance analytics. As an example, in March 2023, Hailo AI, a semiconductor manufacturer in Israel, announced a Hailo-15, an AI-centric vision processor. This processor is intended to be directly integrated into intelligent cameras to enhance video processing and analytics at the edge.

Some of the report highlights in figures include:

• The hardware segment dominates the edge artificial intelligence (AI) industry, with a revenue share of 51.8% in 2025.

• The software segment is projected to grow at the fastest CAGR over the forecast period, primarily driven by the rising demand for real-time analytics and machine learning capabilities deployed at the edge.

• The consumer electronics segment accounted for the largest market revenue share in 2025.

• North America dominated the global edge AI market with the largest revenue share of 36% in 2025, driven by early technology adoption and strong digital infrastructure.