Embedded AI Market Latest Trends, Size, Share, Growth Outlook, Scope, Future Demand, Key Segments and Forecast to 2028

Embedded AI Market by Offering (Hardware, Software, Services), Data Type (Numerical Data, Categorical Data, Image & Video Data), Vertical (Automotive, Manufacturing, Healthcare & Life Sciences, Telecom), and Region - Global Forecast to 2028.
The embedded AI market [https://www.marketsandmarkets.com/Market-Reports/embedded-ai-market-261207150.html?utm_campaign=embeddedaimarket&utm_source=abnewswire.com&utm_medium=paidpr] is expected to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, with a compound annual growth rate (CAGR) of 14.0%. Embedded AI refers to the integration of artificial intelligence (AI) technologies into embedded systems designed for specialized tasks or functions. This involves implementing AI algorithms, models, and software directly onto embedded devices, such as microcontrollers, system-on-chips (SoCs), and other hardware platforms.

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By offering, Services to register for the highest CAGR during the forecast period

The scope of the services segment comprises training and consulting, system integration and implementation, and support and maintenance. The services segment of the Embedded AI market is growing rapidly as businesses increasingly look to outsource the development and deployment of AI solutions. This is due to a number of factors, including the complexity of developing AI-powered applications, the need for specialized expertise, and the high cost of in-house development. Businesses looking to adopt AI should consider the services segment as a viable option for developing and deploying AI solutions.

By data type, numeric data to register for the highest market size during the forecast period

The increasing demand for real-time data processing and analysis boosts the demand for numeric data type in the embedded AI market. Numeric data plays a crucial role in the embedded AI market, forming the foundation for training, optimizing, and deploying AI models on embedded systems. Numeric data captured by sensors, cameras, or other sources in real-time serves as input to the deployed AI models, allowing them to process the data and generate actionable outputs promptly. This is crucial for robotics, autonomous vehicles, or industrial automation applications, where quick decision-making is required.

By region, Asia Pacific to account for the highest growth rate during the forecast period

Embedded AI adoption in the Asia Pacific region is experiencing significant growth. It is driven by several factors, including the region's strong manufacturing base, rapid urbanization, increasing demand for IoT applications, and advancements in AI technologies. With strong government support, a thriving startup ecosystem, and investments in AI infrastructure, the Asia Pacific market presents significant growth opportunities for Embedded AI technologies and solutions.

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Unique Features in the Embedded AI Market

Embedded AI systems are designed to perform real-time data processing, enabling immediate decision-making without relying on cloud infrastructure. This feature is essential for applications like autonomous vehicles, industrial automation, and robotics, where low latency is critical.

A key highlight of embedded AI is its ability to operate efficiently on resource-constrained devices. Optimized algorithms and specialized hardware, such as AI accelerators, ensure minimal power consumption while maintaining high performance.

Unlike traditional AI systems that require cloud connectivity, embedded AI executes AI models directly on devices. This ensures faster processing, reduced dependency on network connections, and enhanced privacy and security by keeping data local.

Embedded AI systems are highly customizable and tailored for specific applications. Whether for facial recognition in security cameras or predictive maintenance in industrial equipment, these systems are optimized for their intended tasks.

Embedded AI works hand-in-hand with Internet of Things (IoT) devices, enabling intelligent data processing and decision-making at the edge. This synergy supports advanced use cases like smart homes, wearables, and connected healthcare devices.

Major Highlights of the Embedded AI Market

The shift toward edge computing is a key driver of the embedded AI market. By enabling real-time data processing directly on devices, embedded AI supports applications requiring low latency, such as autonomous vehicles, industrial automation, and smart cities.

Significant progress in hardware technologies, including neural processing units (NPUs), system-on-chips (SoCs), and AI-specific accelerators, has revolutionized the capabilities of embedded AI systems, making them more powerful and efficient.

Embedded AI is finding applications in diverse sectors such as healthcare (e.g., diagnostic tools), automotive (e.g., advanced driver-assistance systems), and manufacturing (e.g., predictive maintenance), showcasing its versatility and industry-wide relevance.

With the growing emphasis on sustainability, embedded AI solutions are designed for energy-efficient operations. This feature makes them suitable for IoT devices and wearables, where power consumption is a critical factor.

The seamless integration of embedded AI with IoT devices has unlocked new possibilities in connected systems, enabling intelligent decision-making and automation in smart homes, industrial IoT, and healthcare monitoring systems.

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Top Companies in the Embedded AI Market

Some major players in the Embedded AI market include Google (US), IBM (US), Microsoft (US), AWS (US), NVIDIA (US), Intel (US), Qualcomm (US), Arm (UK), AMD (US), MediaTek (Taiwan), Oracle (US), Salesforce (US), NXP (The Netherlands), Lattice (US), Octonion (Switzerland), NeuroPace (US), Siemens (Germany), HPE (US), LUIS Technology (Germany), Code Time Technologies (Canada), HiSilicon (China), VectorBlox (Canada), AU-Zone Technologies (Canada), STMicroelectronics (Switzerland), SenseTime (Hong Kong), Edge Impulse (US), Perceive (US), Eta Compute (US), SensiML (US), Syntiant (US), Graphcore (UK), and SiMa.ai (US).

IBM (US)

IBM is a leading cloud solution providersthat engages in the establishment of integrated solutions that leverage information technology and knowledge of business processes. The company operates through the following segments: Cloud & Cognitive Software, Global Business Services, Global Technology Services, Systems, and Global Financing. The Cloud & Cognitive Software segment provides combined and secure cloud, data, and solutions to customers. The Global Business Services segment provides clients consulting, application management, and global process services. The Global Technology Services segment offers comprehensive IT infrastructure and platform services that create business value for clients. The Systems segment provides clients with innovative infrastructure platforms to help meet the requirements of hybrid cloud and enterprise AI workload. The Global Financing segment encompasses two primary businesses: financing, primarily conducted through IBM Credit LLC. IBM offers a range of Embedded AI offerings in the market, providing organizations with the tools and technologies to incorporate artificial intelligence capabilities directly into their devices and systems. .. IBM offers companies with the tools they need to incorporate AI capabilities into their embedded systems, such as IBM Watson Assistant, IBM Power Ai Vision, IBM Watson Studio, and IBM Watson IoT Platform. Moreover, these offerings from IBM provide organizations with the tools, platforms, and infrastructure necessary to develop, deploy, and manage embedded AI solutions. IBM's Embedded AI offerings empower organizations to leverage AI capabilities directly within their devices and systems, enabling real-time inferencing, local data processing, and intelligent decision-making at the edge.

Google (US)

Google continues to make significant research and development investments and seeks to develop new, innovative offerings and improve existing offerings across its businesses. The company also invested in technical infrastructure, including servers, network equipment, and data centers, to support the growth of the business in support of AI. In addition, acquisitions and strategic investments contribute to the breadth and depth of the company's offerings, expand its expertise in engineering and other functional areas, and allow the company to build strong partnerships. For example, in September 2022, Google closed the acquisition of Mandiant to help expand its offerings in dynamic cyber defense and response. The company has expanded its offerings to include TensorFlow Lite, Coral, and ML Kit products. Additionally, Google has invested in artificial intelligence and machine learning technologies, which have the potential to transform multiple industries. Google has been investing in hardware development, such as the Coral Dev Board, to provide more powerful and efficient processing capabilities for AI. Google has also collaborated with industry partners and developers to create a more open and accessible ecosystem for embedded AI.

Microsoft (US)

Microsoft is a major player in the Embedded AI market through its Azure for Edge platform. This cloud solution extends AI capabilities to devices with limited resources, offering both hardware and software tools to run AI models directly on those devices. This empowers real-time decision-making and increased efficiency for applications across various industries.

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