There have been several market characteristics that have emerged that drive demand for smart and autonomous products resulting into significant growth in embedded AI market in recent years.
The rise of Internet of Things (IoT) is one of the key market trends driving the growth in embedded AI market. With increasing numbers of connected devices and growing demands for real-time data analysis, there has been an increase in demand for embedded artificial intelligence solutions capable processing and analyzing data locally. Unlike a server based or cloud-based computing system where these decisions would be made at, edge computing contains intelligent IoT endpoints that can make decisions independently without relying on backhaul connection to a central site.
Additionally, there is a shift towards edge computing being observed in the market currently. It refers to carrying out data processing as well as analysis right from where they were obtained rather than sending it back to any centralized cloud or even a data center The importance played by Embedded AI in edge computing includes reduction latency performing complex tasks locally improving privacy/security among others. Consequently this factors has pushed industries such as automotive manufacturing health care as well as smart homes into adopting embedded AI solutions that require real-time and local data analysis.
Consequently, there is growing need for energy efficient embedded AI system. This can cause an increase in power consumption when the devices become more intelligent and autonomous since they require much processing power. Embedded AI technologies have been developed to deal with this problem by consuming less power while still ensuring high performance. As a result, Energy efficiency and environmental friendliness are now becoming important consideration in the design and development of embedded AI since the energy required to run these machines is very high.
Another market trend in the embedded AI market is hardware acceleration and specialized chips. Often CPUs do not have good performance when it comes to AI tasks, thus having limited computing ability and consuming much power. These limitations made designing specialized chips such as GPUs, FPGAs or even ASICs specifically for artificial intelligence workloads possible.
On top of this, there has been increasing integration of embedded AI into consumer electronics and smart devices. In order to offer personalized intelligent experiences, smartphones, smart speakers wearables among other consumers’ electronic gadgets increasingly include embedded artificial intelligence capabilities that allows them to understand consumer preferences adapt accordingly and operate autonomously.
Moreover, the market is facing increased adoption of autonomous vehicles and robotics. Embedded AI helps in aiding self-driving cars and robots to sense their environment, reason, make intelligent decisions, and negotiate with complex situations. These applications need localized AI processing for real-time functions such as object detection, path planning or decision-making. This trend has led to development of embedded AI solutions that target autonomous systems specifically hence propelling growth in automotive and robotics sectors.
Report Attribute/Metric | Details |
---|---|
Market Opportunities | IoT integration, and automotive advancements |
Market Dynamics | Rapid technological advancements |
Embedded AI Market Size was valued at USD 9.1 Billion in 2022. The embedded AI market industry is projected to grow from USD 10.41 Billion in 2023 to USD 30.78 Billion by 2032, exhibiting a compound annual growth rate (CAGR) of 14.50% during the forecast period (2023 - 2032). Growing demand for automation and smart devices is the key market drivers enhancing the market growth.
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
The growing integration with IoT is driving the market CAGR for embedded AI. IoT devices' intelligence and capacities are increased through the integration of AI. These devices have the capacity to process and analyze data locally, which enables them to make decisions in the present without the need for a constant internet connection or centralized cloud servers. IoT devices with AI capabilities, such as thermostats, can expertly learn user preferences, optimize energy use, and fine-tune settings for improved comfort, for instance, in smart homes. The user experience is significantly improved by this additional intelligence, thus increasing the value of IoT items.
Additionally, IoT devices generate a lot of data, which makes quick data analysis necessary for many applications. IoT devices can handle localized data locally thanks to embedded AI, which improves response times and lowers latency. Take industrial settings as an example, where AI-driven sensors may quickly identify anomalies or equipment breakdowns and urge fast action to save expensive downtime. Furthermore, real-time data processing has important ramifications for mission-critical applications like autonomous vehicles and healthcare monitoring, where a split-second choice could mean the difference between life and death.
In addition, energy economy is a major challenge for many IoT applications, particularly those that depend on battery power or are installed in remote areas. Intelligent power management is an area where embedded AI excels in improving device performance. AI systems, for instance, can determine when it is necessary to turn on sensors or send data, thus reducing overall energy use. This is particularly useful in fields like environmental monitoring and precision agriculture, where equipment may need to run over extended periods of time in remote locations.
The regular collection of sensitive data by IoT devices makes data privacy and security of the utmost significance. These issues are effectively addressed by embedded AI by enabling on-device data processing and encryption. This means that private information does not need to be sent to the cloud for analysis, reducing the possibility of data breaches or unwanted access. The IoT security architecture can be strengthened by using AI for device authentication, anomaly detection, and the identification of possible threats. As a result, the need for embedded AI solutions is anticipated to increase as IoT spreads across industries, thus boosting market growth over the course of the research period. Thus, driving the Web3 in E-Commerce & Retail market revenue.
The Embedded AI Market segmentation, based on Offering includes hardware, software and services. The software segment dominated the market in the Embedded AI Market. This is owing to the continuous advancements in AI algorithms, including developments in deep learning and neural networks.
Figure 1: Embedded AI Market, by Distribution channel, 2022 & 2032 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
The Embedded AI Market segmentation, based on data type, includes Sensor Data, Image and Video Data, Numeric Data, Categorical Data And Other Data Types (Iris & Facial Data, Time Series Data and Audio Data). The numeric data type generated the most revenue. Numerical data serves as the fundamental building block for training, enhancing, and implementing AI models within embedded systems.
The Embedded AI Market segmentation, based on vertical, includes BFSI, IT & ITLES, Retail & Ecommerce, Manufacturing, Energy & Utilities, Transportation & Logistics, Healthcare & Life Sciences, Media & Entertainment, Telecom, Automotive and Other Vertical (Government, Aerospace And Defense, Construction & Real Estate, Agriculture, Education and Travel & Hospitality). The automotive segment dominated the market in 2022. The incorporation of embedded AI to support features like adaptive cruise control and automated parking has been prompted by the growing interest in advanced driver assistance systems (ADAS), which has increased car safety and convenience.
By region, the study provides the market insights into North America, Europe, Asia-Pacific and Rest of the World. The North America embedded AI Market dominated this market in 2022 (45.80%). This is due to the region's strong technology ecosystem and high levels of innovation, which have provided a favorable environment for the creation and uptake of embedded AI solutions across a variety of industries. Further, the U.S. embedded AI market held the largest market share, and the Canada embedded AI market was the fastest growing market in the North America region.
Further, the major countries studied in the market report are The U.S., Canada, German, France, the UK, Italy, Spain, China, Japan, India, Australia, South Korea, and Brazil.
Figure 2: EMBEDDED AI MARKET SHARE BY REGION 2022 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database and Analyst Review
Europe embedded AI market accounts for the second-largest market share. This is because efforts like Industry 4.0 and industrial automation are receiving so much attention. Further, the German embedded AI market held the largest market share, and the UK embedded AI market was the fastest growing market in the European region
The Asia-Pacific embedded AI Market is expected to grow at the fastest CAGR from 2023 to 2032. This is due to the region's rising demand for applications involving automation, smart manufacturing, and the Internet of Things (IoT). Moreover, China’s embedded AI market held the largest market share, and the Indian embedded AI market was the fastest growing market in the Asia-Pacific region.
Leading market players are investing heavily in research and development in order to expand their product lines, which will help the embedded AI market, grow even more. Market participants are also undertaking a variety of strategic activities to expand their global footprint, with important market developments including new product launches, contractual agreements, mergers and acquisitions, higher investments, and collaboration with other organizations. To expand and survive in a more competitive and rising market climate, embedded AI industry must offer cost-effective items.
Manufacturing locally to minimize operational costs is one of the key business tactics used by manufacturers in the global embedded AI industry to benefit clients and increase the market sector. In recent years, the embedded AI industry has offered some of the most significant advantages to medicine. Major players in the embedded AI market, including 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 (Netherlands), Lattice (Oregon), 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), SiMa.ai (US), and others, are attempting to increase market demand by investing in research and development operations.
The International Business Machines Corporation, or simply IBM, is a global technology company with its headquarters in Armonk, New York. With operations in more than 175 nations, IBM is present all over the world. The business offers a range of services, including infrastructure, hosting, and consulting, in addition to manufacturing and selling system hardware and software. Artificial intelligence (AI), analytics, automation, cloud computing, blockchain, IT infrastructure, cybersecurity, and software development are all included in IBM's broad range of products. IBM offers services such cloud solutions, networking, security, technology consulting, business resilience services, application services, and technology support services as a complement to this. Its clientele comes from a variety of industries, including the automotive, banking, financial, energy, electronics, utilities, and life sciences; as well as the manufacturing, consumer goods, retail, and telecommunications industries. The corporation operates throughout the Asia-Pacific area, the Americas, Europe, the Middle East, and Africa.
Since its founding in 1968, Intel has played a crucial role in advancing computing technology. As a pioneer in its field, the business has contributed significantly to the development of revolutionary technology that promotes societal advancement and improves quality of life. Artificial intelligence (AI), the 5G network revolution, and the rise of the intelligent edge are just a few of the technical inflection points that Intel is currently on the verge of, all of which will collectively alter the direction of technology. These changes are mostly fueled by a combination of silicon and software, with Intel at the center of these revolutionary advancements. The business' broad range of products provides all-encompassing solutions that meet the changing needs of a data-centric world. Intel continuously creates innovative technologies and goods to serve a variety of markets, from edge computing and 5G networks to cloud computing, artificial intelligence, and driverless vehicles. These goods act as the cornerstones of a world that is becoming more intelligent and interconnected.
May 2023: The newest Jetson AGX Orin Industrial module from NVIDIA enhances computing capabilities for demanding situations. The NVIDIA Jetson AGX Xavier Industrial and the commercial Jetson AGX Orin modules were this advanced module's predecessors, and they both built on their successes. It significantly improves computing performance and was created for ruggedized systems. The Jetson AGX Orin Industrial module has a configurable power range of 15–75 watts and boasts remarkable 248 TOPS of AI performance.
October 2022: IBM introduced three new libraries as part of the expansion of its embeddable AI software lineup. These libraries were designed to make it easier and faster for IBM Ecosystem partners, customers, and developers to construct their AI-driven solutions and sell them in a more effective and economical way.
Hardware
Software
Services
Sensor Data
Image and Video Data
Numeric Data
Categorical Data
Other Data Types
Iris & Facial Data
Time Series Data
Audio Data
BFSI
IT & ITLES
Retail & Ecommerce
Manufacturing
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences
Media & Entertainment
Telecom
Automotive
Other Vertical
North America
U.S.
Canada
Europe
Germany
France
UK
Italy
Spain
Rest of Europe
Asia-Pacific
China
Japan
India
Australia
South Korea
Australia
Rest of Asia-Pacific
Rest of the World
Middle East
Africa
Latin America
© 2024 Market Research Future ® (Part of WantStats Reasearch And Media Pvt. Ltd.)