Embedded AI Market worth $18.0 billion by 2028 - Exclusive Report by MarketsandMarkets™

CHICAGO, June 22, 2023 /PRNewswire/ -- Because of rising demand for smart devices and IoT, advancements in AI technology, the rise of edge computing, the development of autonomous systems, applications across various sectors, miniaturisation and energy efficiency, and the importance of data privacy and security, the future of the Embedded AI market appears promising. As AI becomes an intrinsic aspect of embedded systems, boosting their functionality and decision-making skills, this industry is likely to increase significantly.

The global Embedded AI Market is projected to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028 at a compound annual growth rate (CAGR) of 14.0% during the forecast period, according to a new report by MarketsandMarkets™. The Embedded AI Market is expected to grow significantly during the forecast period, owing to various business drivers like growing demand for intelligent and autonomous systems for a personalized experience and increasing advancements in AI and ML technologies for better and smart decisions. Furthermore, the proliferation of connected devices and IoT ecosystem for effective communications and the rising usage of Embedded AI for industry-specific applications drive market growth.

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271 - Tables
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320 - Pages

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Scope of the Report

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.

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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.

Top Key Companies in 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).

Recent Developments:

• In April 2023, IBM announced the launch of Watson Edge for Financial Services, a solution that helps financial institutions deploy AI at the edge to improve customer service, fraud detection, and risk management.
• In April 2023, Qualcomm Technologies partnered with eInfochips, an Arrow company, to launch Edge Labs. Edge Labs is a program that will help developers and innovators accelerate the development and deployment of AI applications for embedded devices. This partnership will help developers and innovators accelerate developing and deploying AI applications for embedded devices. Edge Labs will provide developers with access to Qualcomm's expertise in AI and eInfochips' development and deployment services.
• In March 2023, Arm partnered with Google Cloud to bring Arm-based solutions to the Google Cloud Platform (GCP). The partnership is expected to help Arm customers take advantage of GCP's AI and machine learning capabilities and to help Google Cloud customers deploy Arm-based solutions.
• In March 2023, IBM acquired Instana, an application performance monitoring software provider. The acquisition will help IBM to expand its edge AI capabilities and provide customers with a more comprehensive view of their applications.
• In March 2023, the AI-powered tool has been designed to assist Microsoft 365 users in performing various tasks, such as troubleshooting, training, and onboarding. Microsoft 365 Copilot, as an embedded AI technology, is integrated within a broader software ecosystem and has been created to function seamlessly with other Microsoft 365 products and services.
• In October 2022, Intel partnered with Amazon Web Services (AWS) to bring Intel-based solutions to AWS. The partnership is expected to help Intel customers use AWS's AI and machine learning capabilities to help AWS customers deploy Intel-based solutions.
• In June 2022, Microsoft announced a partnership with NVIDIA to accelerate the development and deployment of edge AI applications. The partnership will combine Microsoft's Azure platform with NVIDIA's AI hardware and software to create a more comprehensive solution for edge computing.
• In Jan 2022, IBM partnered with Google Cloud to accelerate the development and deployment of edge AI applications. The partnership will combine IBM's AI and ML expertise with Google Cloud's infrastructure and AI capabilities.

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Embedded AI Market Advantages:

• Sensitive data no longer needs to be sent to external servers or the cloud for processing thanks to embedded AI. Data can be maintained on the device or on a private network by performing AI computations locally, boosting privacy and lowering the possibility of data breaches or unauthorised access. For applications that deal with sensitive data, such those in healthcare or banking, this is essential.
• Data communication to remote servers is not necessary because embedded AI processes data on the device itself. This allows for quicker response times and considerably lowers latency. When compared to conventional computing architectures, embedded AI also enables optimised hardware-software integration by utilising specialised chips or accelerators to achieve improved speed and energy efficiency.
• By incorporating AI capabilities directly into devices, no additional infrastructure or cloud-based resources are required. This saves operational costs while simplifying deployment. Furthermore, because the intelligence is dispersed across several devices rather than depending on a centralised server architecture, embedded AI systems grow readily. Because of its scalability and low cost, embedded AI is an appealing solution for both small-scale and large-scale deployments.
• Embedded AI enables edge devices to make intelligent decisions on their own without relying on ongoing contact with central servers. This provides edge intelligence and autonomy, allowing devices to analyse and respond to data locally, resulting in faster and more efficient operations. Edge intelligence is especially useful in settings requiring real-time judgements, such as autonomous drones or smart manufacturing.
• By analysing data locally and customising the experience to each user's preferences and behaviours, embedded AI can enhance user interactions. This makes it possible for devices to offer personalised recommendations, predictive ideas, and customised interactions, improving user engagement and pleasure.
• Through resource management and local computing, embedded AI systems can reduce their overall energy usage. Embedded AI devices can work effectively with lower energy requirements by minimising the need for data transmission and decreasing dependency on power-hungry cloud servers. For devices like wearables or IoT sensors that run on batteries or have limited resources, this is especially advantageous.
• Devices with inbuilt AI may carry out complicated calculations locally without largely relying on cloud resources or constant network connectivity. As a result, embedded AI solutions are more readily available and reasonably priced for both organisations and consumers. This decreases network dependence and associated costs. It also makes applications possible in rural or unconnected settings where cloud connectivity can be expensive or scarce.

Report Objectives

• To define, describe, and predict the Embedded AI Market by offering (hardware, software, and services), data type, vertical, and region
• To provide detailed information related to major factors (drivers, restraints, opportunities, and industry-specific challenges) influencing the market growth
• To analyze the micro markets with respect to individual growth trends, prospects, and their contribution to the total market
• To analyze the opportunities in the market for stakeholders by identifying the high-growth segments of the Embedded AI Market
• To analyze opportunities in the market and provide details of the competitive landscape for stakeholders and market leaders
• To forecast the market size of segments for five main regions: North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
• To profile key players and comprehensively analyze their market rankings and core competencies
• To analyze competitive developments, such as partnerships, new product launches, and mergers and acquisitions, in the Embedded AI Market
• To analyze the impact of recession across all the regions across the Embedded AI Market

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