Embedded AI Market Worth $18.0 billion by 2028, Growing At a CAGR of 14.0% Report by MarketsandMarkets™

Chicago, July 06, 2023 (GLOBE NEWSWIRE) -- The global Embedded AI Market size is estimated to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, with a CAGR of 14.0% during the forecast period, according to a new report by MarketsandMarkets™. Embedded AI encompasses hardware and software components, including AI chips, edge computing platforms, AI frameworks, and development tools specific to embedded AI applications across key industry sectors such as automotive, manufacturing, telecom, and healthcare and life sciences.

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271 - Tables
55 - Figures
320 - Pages

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Scope of the Report

The global embedded AI market is segmented based on offering such as hardware, software, and services. The hardware comprises processors, memory units, accelerators, and other hardware. The services segment is further segregated into training and consulting, system integration and implementation, and support and maintenance. The hardware segment holds the largest market share in 2023 as it plays a critical role by providing the necessary computing power and specialized capabilities to support AI applications directly on embedded systems. It empowers devices to process, analyze, and make intelligent decisions locally without constant connectivity or reliance on cloud-based AI services. This enables real-time and context-aware applications across IoT, automotive, healthcare, manufacturing, and more industries.

The global embedded AI market is segmented based on data type into sensor data, image & video data, numeric data, categorical data, and other data types. Numeric data is growing rapidly during the forecast period owing to the rising need to train AI models used in embedded systems. Numeric data consists of labeled examples that help AI algorithms learn patterns and relationships to make predictions or decisions. Numeric data, such as sensor readings, time series data, or structured data, is often used to train models for tasks like anomaly detection, predictive maintenance, image recognition, or natural language processing.

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The global embedded AI software market is segmented into edge computing platforms, AI and ML frameworks, AI middleware, and other software. The edge computing platform plays a crucial role in the embedded AI market. It provides a comprehensive set of tools, frameworks, and infrastructure that enable organizations to develop, deploy, and manage AI applications directly on edge devices. Edge AI platforms facilitate the deployment of AI models on embedded systems by providing mechanisms for model packaging, optimization, and integration into the target devices. They enable efficient model conversion, quantization, and compression techniques to ensure that the models can run effectively within the resource-constrained environments of edge devices.

The global embedded AI market is segmented based on verticals into automotive, BFSI, retail & eCommerce, healthcare & life sciences, IT & ITES, energy & utilities, manufacturing, media & entertainment, transportation & logistics, telecom, and other verticals (construction & real estate, agriculture, education, aerospace & defense, and travel & hospitality). The automotive industry is adopting embedded AI for applications like advanced driver assistance systems (ADAS), autonomous driving, vehicle control, and infotainment systems. AI algorithms in vehicles help improve safety, efficiency, and user experience. 

The embedded AI market has been segmented into five regions: North America, Europe, Asia Pacific, Middle East & Africa, and Latin America. Among these regions, North America is projected to hold the largest market size during the forecast period. In North America, the rising demand for embedded AI is driven by the increasing adoption of AI technologies in various industries such as healthcare, automotive, and manufacturing. Additionally, major technology companies in this region, such as Google, Microsoft, and AWS, are fueling the development of more sophisticated embedded AI solutions. Asia Pacific is expected to grow rapidly during the forecast period. Countries such as China, Singapore, Japan, South Korea, ANZ, and India are technology-driven and present significant opportunities in terms of investments and revenue.

Top Trends in Global Embedded AI Market

The increasing demand for AI-powered devices in various end-user industries is driving the growth of the market. here are some of the top trends that were prevalent in the global embedded AI market:

• The processing and analysis of data at or close to the network's edge, where the data is generated, is referred to as edge computing. At the edge, embedded AI systems are being used more frequently to enable real-time decision-making, lower latency, and improve privacy and security.
• Massive volumes of data are produced by IoT devices; by integrating AI capabilities, these devices can evaluate local data and perform predictive analytics and intelligent automation. IoT devices are now smarter, more effective, and able to offer customized experiences thanks to embedded AI.
• Specifically created hardware accelerators called NPUs are used for AI tasks. For embedded AI systems, they offer fast processing and low energy consumption. NPUs were becoming more and more popular, and they were being included into a variety of embedded devices, including smartphones, smart home appliances, and driverless vehicles.
• Embedded AI-driven NLP technologies are being included into a variety of consumer electronics products, such as smart speakers, smartphones, and home appliances. By enabling people to communicate with gadgets using voice commands and everyday language, this integration improved user interfaces and made them more fluid.
• Vision systems were using embedded AI technology for computer vision, image and video analysis, and object recognition. In sectors including surveillance, automotive, robotics, and healthcare, this development proved particularly important.
• Machine learning operations could now be carried out directly on edge devices by embedded AI systems without utilizing cloud-based processing. In applications including autonomous vehicles, industrial automation, and smart cities, this method lowered latency, protected data privacy, and enabled real-time decision-making.
• With the rise of embedded AI systems, there has been an increased emphasis on protecting the security and privacy of private data processed at the edge. In order to safeguard data and stop unauthorized access, security features like secure enclaves and encryption were integrated into embedded AI advancements.

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Key Industry Development

The embedded AI market is rapidly growing, and there are a number of key industry developments that are driving this growth. Here are some of the most important ones:

• Chipset producers made research and development investments to develop more potent and effective processors specifically for embedded AI applications. These chipsets included specialised hardware accelerators like GPUs and NPUs to boost embedded AI systems' performance and energy efficiency.
• As part of their efforts to utilise one another's expertise and hasten the development and adoption of embedded AI solutions, numerous businesses from the technology ecosystem were forging partnerships and collaborations. For instance, IoT device manufacturers were working with AI platform providers to improve their product offerings, while hardware manufacturers were engaging with AI software firms to develop integrated solutions.
• Developers can now readily include AI capabilities into their embedded systems because to the accessibility and user-friendliness of AI software development kits. These SDKs included pre-trained models, libraries, and tools that made it simpler to construct AI algorithms, facilitating the use of embedded AI in applications by developers.
• Power consumption is a crucial consideration when using embedded AI systems on devices with limited resources, such as edge devices and IoT devices. To increase energy efficiency without sacrificing performance, industry innovations have been centred on optimising AI algorithms and hardware architectures. Long battery life and lower operating expenses were made possible by doing this.
• Adoption of embedded AI was significantly influenced by the automotive sector. Object identification, gesture recognition, voice assistants, and predictive maintenance are just a few of the functions that embedded AI is enabling in advanced driver assistance systems (ADAS), autonomous vehicles, and in-car infotainment systems. Partnerships were being formed between automakers and suppliers of AI technology to provide AI-driven solutions that would improve user experience, convenience, and safety.
• Regulators and legislators started to care about guaranteeing ethical and responsible usage of AI as embedded AI applications spread throughout industries. Discussions around privacy, data protection, openness, and accountability included the development of standards, guidelines, and rules. For providers of embedded AI solutions, compliance with these rules was increasingly critical.

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