Embedded Al Market Size, Share, Growth Analysis, By Offering:(Hardware, Software), By Data Type:(Sensor Data, Image and Video Data), By Vertical:(BFSI, IT & ITES), By Region:(North America, US) - Industry Forecast 2024-2031

Embedded Al Market Insights

Market Overview:

According to estimates, the embedded AI market would increase at a CAGR of 14.0% from USD 9.4 billion in 2023 to USD 18.0 billion by 2028.the main factors are rising Embedded AI for Industry-Specific Applications, Proliferation of Connected Devices and IoT Ecosystem for Effective Communications, Growing Demand for Intelligent and Autonomous Systems, Growing Advancements in AI and Ml Technologies for Better and Smarter Decisions.

Embedded Al Market, Forecast & Y-O-Y Growth Rate, 2020 - 2028

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This report is being written to illustrate the market opportunity by region and by segments, indicating opportunity areas for the vendors to tap upon. To estimate the opportunity, it was very important to understand the current market scenario and the way it will grow in future.

Production and consumption patterns are being carefully compared to forecast the market. Other factors considered to forecast the market are the growth of the adjacent market, revenue growth of the key market vendors, scenario-based analysis, and market segment growth.

The market size was determined by estimating the market through a top-down and bottom-up approach, which was further validated with industry interviews. Considering the nature of the market we derived the Technology Hardware, Storage & Peripherals by segment aggregation, the contribution of the Technology Hardware, Storage & Peripherals in Technology Hardware & Equipment and vendor share.

To determine the growth of the market factors such as drivers, trends, restraints, and opportunities were identified, and the impact of these factors was analyzed to determine the market growth. To understand the market growth in detail, we have analyzed the year-on-year growth of the market. Also, historic growth rates were compared to determine growth patterns.

Segmentation Analysis:

The Embedded Al Market is segmented by Offering:, Data Type:, Vertical:, Region:. We are analyzing the market of these segments to identify which segment is the largest now and in the future, which segment has the highest growth rate, and the segment which offers the opportunity in the future.

Embedded Al Market Basis Point Share Analysis, 2021 Vs. 2028

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Based on Offering: the market is segmented as, Hardware, Software, Services
Based on Data Type: the market is segmented as, Sensor Data, Image and Video Data, Numeric Data, Categorial Data, Other Data Types (iris & facial data, text data, time series data, and audio data)
Based on Vertical: the market is segmented as, BFSI, IT & ITES, Retail & Ecommerce, Manufacturing, Energy & Utilities, Transportation & Logistics, Healthcare & Life Sciences, Media & Entertainment, Telecom, Automotive, Other Verticals (government, aerospace and defense, construction & real estate, agriculture, education, and travel & hospitality)
Based on Region: the market is segmented as, North America, US, Canada, Europe, UK, Germany, France, Italy, Spain, Rest of Europe, Asia Pacific, China, India, Japan, Australia and New Zealand (ANZ), South Korea, ASEAN Countries, Rest of Asia Pacific, Middle East and Africa, UAE, Saudi Arabia, South Africa, Israel, Rest of the Middle East and Africa, Latin America, Brazil, Mexico, Argentina, Rest of Latin America, KEY MARKET PLAYERS, Google, IBM, Microsoft, AWS, NVIDIA, Intel, Qualcomm, Arm, AMD, MediaTek, Oracle, Salesforce, NXP, Lattice, Octonion, NeuroPace, Siemens, HPE, LUIS Technology, Code Time Technologies, HiSilicon, VectorBlox, AU-Zone Technologies, STMicroelectronics, SenseTime, Edge Impulse, Perceive, Eta Compute, SensiML, Syntiant, Graphcore, SiMa.ai

Regional Analysis:

Embedded Al Market is being analyzed by North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA) regions. Key countries including the U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, and South Africa among others were analyzed considering various micro and macro trends.

Embedded Al Market Attractiveness Analysis, By Region 2020-2028

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Embedded Al Market : Risk Analysis

SkyQuest's expert analysts have conducted a risk analysis to understand the impact of external extremities on Embedded Al Market. We analyzed how geopolitical influence, natural disasters, climate change, legal scenario, economic impact, trade & economic policies, social & ethnic concerns, and demographic changes might affect Embedded Al Market's supply chain, distribution, and total revenue growth.

Competitive landscaping:

To understand the competitive landscape, we are analyzing key Embedded Al Market vendors in the market. To understand the competitive rivalry, we are comparing the revenue, expenses, resources, product portfolio, region coverage, market share, key initiatives, product launches, and any news related to the Embedded Al Market.

To validate our hypothesis and validate our findings on the market ecosystem, we are also conducting a detailed porter's five forces analysis. Competitive Rivalry, Supplier Power, Buyer Power, Threat of Substitution, and Threat of New Entry each force is analyzed by various parameters governing those forces.

Key Players Covered in the Report:

d AI market is estimated to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, at a CAGR of 14.0% during the forecast period. Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms and integration with cloud-based AI services for better scalability to offer opportunities to the end users to leverage embedded AI solutions. Moreover, the growing demand for intelligent and autonomous systems for a personalized experience and the proliferation of connected devices and IoT ecosystems for effective communications will boost market growth worldwide.
Embedded AI Market Technology Roadmap till 2030
The embedded AI market report covers the embedded AI technology roadmap till 2030, with insights around the initiation, development, and commercialization of technologies across AI-driven autonomous systems, AI-driven intelligent devices, and next-gen embedded AI systems. Some of the key findings from the technology roadmap include:
Embedded AI Market Short-term Technology Roadmap (2023-2025)
Advancements in edge AI platforms to provide enhanced processing power, reduce latency, and flexibility
Commercialization of Embedded AI enhancing human intelligence in a wide range of applications
Embedded AI Market Mid-term Technology Roadmap (2026-2028)
Development in hardware accelerators empowers embedded AI solutions by improving performance, energy efficiency, compactness, real-time responsiveness, and cost-effectiveness
Next-gen embedded AI systems will continue to push the boundaries of what is possible at the edge to enable intelligent, autonomous, and context-aware applications across various industries
Embedded AI Market Long-term Technology Roadmap (2029-2030)
Advanced AI-driven autonomous systems heavily rely on embedded AI for advanced sensing and perception capabilities
AI-driven intelligent devices continue to evolve and become more pervasive in homes, workplaces, and other environments, embedded AI will play a vital role in enabling their intelligent and context-aware capabilities.
Driver: Growing demand for intelligent and autonomous systems for a personalized experience
The increasing need for advanced technologies that can provide personalized and adaptive experiences to users to boost the adoption of embedded AI solutions in the market. The demand for personalized experiences has led to the integration of AI capabilities into various embedded systems. By leveraging embedded AI solutions, devices and applications can analyze user data, preferences, and behavior to provide tailored recommendations, suggestions, and responses. This enhances user satisfaction and engagement. Moreover, embedded AI solutions can enable autonomous behavior in devices and systems, reducing the need for constant user interference. This is particularly relevant in applications such as autonomous vehicles, smart home automation, and industrial automation, where embedded AI algorithms can enable intelligent decision-making and automated actions. Embedded AI solutions can leverage machine learning algorithms to analyze data patterns and make predictions about user preferences, behavior, or system performance. This helps in anticipating user needs, optimizing resource allocation, and enhancing the overall efficiency of embedded systems. Nowadays, the demand for voice-controlled and natural language interfaces is soaring. Embedded AI solutions can incorporate natural language processing (NLP) and voice recognition capabilities, allowing users to interact with devices and applications using voice commands, making the experience more intuitive and user-friendly. Overall, the growing demand for intelligent and autonomous systems for personalized experience is driving the development and adoption of embedded AI solutions. These solutions enable devices and systems to understand user preferences, adapt to changing contexts, make intelligent decisions, and provide personalized experiences, ultimately enhancing user satisfaction and driving market growth.
Restraint: Concerns related to data privacy and security
Data privacy and security concerns can erode trust between users and embedded AI solutions. Users may hesitate to share their data or engage with AI-powered systems if they are not confident in the security measures. Lack of transparency about how embedded AI solutions collect, store, and use data can further contribute to mistrust and hinder adoption. Embedded AI solutions may have access to a wide range of data, including personal information and user behavior. Concerns arise regarding the ethical use of this data and the potential for misuse or biased decision-making. Ensuring fairness, transparency, and accountability in AI algorithms and data processing becomes crucial to address these concerns. Failure to address ethical considerations can result in resistance to adopting embedded AI solutions. To overcome these challenges and boost the adoption of embedded AI solutions, vendors and organizations need to prioritize data privacy and security. This includes implementing robust security measures, complying with data protection regulations, ensuring transparency and accountability in data handling, and promoting ethical use of data. Building trust among users by addressing privacy concerns and communicating the steps to secure data can help alleviate barriers to adoption and drive wider acceptance of embedded AI solutions.
Opportunity : Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms
The rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms provides more significant opportunities for embedded AI solution providers in the market. As AI algorithms become increasingly complex and resource-intensive, there is a growing need for processors that can handle computational demands efficiently. The demand for more powerful processors, such as high-performance CPUs, GPUs, and specialized AI accelerators, opens opportunities for embedded AI solution providers to offer advanced hardware solutions. By developing and offering processors specifically optimized for AI workloads, providers can cater to the increasing demand for enhanced performance and enable more sophisticated embedded AI applications. Furthermore, traditional processors may need help to handle the computational requirements of AI algorithms while maintaining energy efficiency. Energy-efficient processors, including low-power CPUs, specialized AI chips, and edge computing solutions, are in high demand to enable embedded AI solutions in resource-constrained environments. Embedded AI solution providers can capitalize on this opportunity by developing energy-efficient processors that deliver high-performance computing while minimizing power consumption. These processors can be integrated into various devices and systems, enabling AI capabilities without compromising energy efficiency. Henceforth, the rise in demand for more powerful and energy-efficient processors to handle complex AI algorithms offers significant opportunities for embedded AI solution providers. By focusing on developing advanced processors, energy-efficient solutions, edge computing capabilities, and fostering partnerships, providers can capitalize on the growing market demand and deliver high-performance embedded AI solutions that meet customers’ evolving needs.
Challenge: Inadequate computational resources and model optimization
Embedded AI solutions often operate on resource-constrained devices with limited processing power, memory, and energy. Inadequate computational resources can limit the performance of AI algorithms, leading to slower inference times, reduced accuracy, and compromised user experience. When AI models cannot be efficiently executed on embedded devices due to computational limitations, it hinders the adoption of embedded AI solutions as they may not meet the performance requirements of the intended applications. Model optimization involves techniques like quantization, pruning, and model compression to reduce the model size and computational requirements without significant loss of accuracy. However, optimizing models for embedded devices can be complex and time-consuming. Inadequate computational resources can limit the ability to optimize models effectively, resulting in suboptimal performance and hindering the widespread adoption of embedded AI solutions. Addressing the challenge of inadequate computational resources and model optimization requires a combination of hardware advancements, algorithmic optimizations, and software frameworks tailored for embedded AI. As the industry continues to innovate in these areas, overcoming these challenges will help accelerate the adoption of embedded AI solutions in various domains and enable deploying more powerful and efficient AI applications on resource-constrained devices.
By offering software to register at the highest CAGR during the forecast period
Embedded AI software plays a crucial role in the market by providing the necessary algorithms, frameworks, and libraries to enable AI capabilities on embedded systems. Embedded AI software unlocks the potential of AI on embedded systems, enabling intelligent decision-making, real-time data analysis, and enhanced functionality across various industries. Embedded AI software allows embedded devices to process and interpret data locally, leading to increased autonomy, improved performance, and enhanced user experiences.
By data type, numeric data to account for the largest market size during the forecast period
Numeric data forms the foundation for training, optimizing, and deploying AI models on embedded systems. Embedded AI systems can leverage numeric data to optimize operations and resource utilization. By analyzing historical data and patterns, AI models embedded in the system can make data-driven decisions to optimize energy consumption, scheduling, routing, or resource allocation. This data-driven optimization can improve efficiency, and cost savings, to enhance performance across various sectors such as energy & utilities, transportation & logistics, manufacturing, and many more.
By Services, training and consulting to register at the highest CAGR during the forecast period
Training and consulting services play a significant role in the market for embedded AI solutions by providing expertise, guidance, and support to organizations adopting embedded AI technologies. Training and consulting services assist organizations in developing and optimizing AI models for embedded systems. They offer guidance in selecting appropriate algorithms, data preprocessing techniques, and well-suited model architectures for the embedded environment. By leveraging their expertise, these services ensure that AI models are efficiently trained, optimized, and fine-tuned to achieve optimal performance on embedded devices.
North America to account for the largest market size during the forecast period
North America is a leading region in adopting and growing embedded AI solutions. The presence of advanced AI technology companies, robust R&D capabilities, and a mature market ecosystem contribute to the rapid growth of embedded AI solutions in this region. Embedded AI adoption in North America has been growing steadily in recent years, driven by advancements in AI technologies, increasing demand for intelligent edge devices, and the proliferation of IoT applications. Overall, embedded AI adoption in North America is gaining momentum across industries, driven by technological advancements, the rise of IoT, a supportive ecosystem, and increasing awareness of its benefits.
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.
KEY MARKET SEGMENTS
By Offering:
Hardware
Software
Services
By Data Type:
Sensor Data
Image and Video Data
Numeric Data
Categorial Data
Other Data Types (iris & facial data, text data, time series data, and audio data)
By Vertical:
BFSI
IT & ITES
Retail & Ecommerce
Manufacturing
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences
Media & Entertainment
Telecom
Automotive
Other Verticals (government, aerospace and defense, construction & real estate, agriculture, education, and travel & hospitality)
By Region:
North America
US
Canada
Europe
UK
Germany
France
Italy
Spain
Rest of Europe
Asia Pacific
China
India
Japan
Australia and New Zealand (ANZ)
South Korea
ASEAN Countries
Rest of Asia Pacific
Middle East and Africa
UAE
Saudi Arabia
South Africa
Israel
Rest of the Middle East and Africa
Latin America
Brazil
Mexico
Argentina
Rest of Latin America
KEY MARKET PLAYERS
Google
IBM
Microsoft
AWS
NVIDIA
Intel
Qualcomm
Arm
AMD
MediaTek
Oracle
Salesforce
NXP
Lattice
Octonion
NeuroPace
Siemens
HPE
LUIS Technology
Code Time Technologies
HiSilicon
VectorBlox
AU-Zone Technologies
STMicroelectronics
SenseTime
Edge Impulse
Perceive
Eta Compute
SensiML
Syntiant
Graphcore
SiMa.ai

SkyQuest's Expertise:

The Embedded Al Market is being analyzed by SkyQuest's analysts with the help of 20+ scheduled Primary interviews from both the demand and supply sides. We have already invested more than 250 hours on this report and are still refining our date to provide authenticated data to your readers and clients. Exhaustive primary and secondary research is conducted to collect information on the market, peer market, and parent market.

Our cross-industry experts and revenue-impact consultants at SkyQuest enable our clients to convert market intelligence into actionable, quantifiable results through personalized engagement.

Scope Of Report

Report Attribute	Details
The base year for estimation	2021
Historical data	2016 – 2022
Forecast period	2022 – 2028
Report coverage	Revenue forecast, volume forecast, company ranking, competitive landscape, growth factors, and trends, Pricing Analysis
Segments covered	By Offering: - Hardware, Software, Services By Data Type: - Sensor Data, Image and Video Data, Numeric Data, Categorial Data, Other Data Types (iris & facial data, text data, time series data, and audio data) By Vertical: - BFSI, IT & ITES, Retail & Ecommerce, Manufacturing, Energy & Utilities, Transportation & Logistics, Healthcare & Life Sciences, Media & Entertainment, Telecom, Automotive, Other Verticals (government, aerospace and defense, construction & real estate, agriculture, education, and travel & hospitality) By Region: - North America, US, Canada, Europe, UK, Germany, France, Italy, Spain, Rest of Europe, Asia Pacific, China, India, Japan, Australia and New Zealand (ANZ), South Korea, ASEAN Countries, Rest of Asia Pacific, Middle East and Africa, UAE, Saudi Arabia, South Africa, Israel, Rest of the Middle East and Africa, Latin America, Brazil, Mexico, Argentina, Rest of Latin America, KEY MARKET PLAYERS, Google, IBM, Microsoft, AWS, NVIDIA, Intel, Qualcomm, Arm, AMD, MediaTek, Oracle, Salesforce, NXP, Lattice, Octonion, NeuroPace, Siemens, HPE, LUIS Technology, Code Time Technologies, HiSilicon, VectorBlox, AU-Zone Technologies, STMicroelectronics, SenseTime, Edge Impulse, Perceive, Eta Compute, SensiML, Syntiant, Graphcore, SiMa.ai
Regional scope	North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA)
Country scope	U.S., Canada, Germany, France, UK, Italy, Spain, China, India, Japan, Brazil, GCC Countries, South Africa
Key companies profiled	d AI market is estimated to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, at a CAGR of 14.0% during the forecast period. Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms and integration with cloud-based AI services for better scalability to offer opportunities to the end users to leverage embedded AI solutions. Moreover, the growing demand for intelligent and autonomous systems for a personalized experience and the proliferation of connected devices and IoT ecosystems for effective communications will boost market growth worldwide. Embedded AI Market Technology Roadmap till 2030 The embedded AI market report covers the embedded AI technology roadmap till 2030, with insights around the initiation, development, and commercialization of technologies across AI-driven autonomous systems, AI-driven intelligent devices, and next-gen embedded AI systems. Some of the key findings from the technology roadmap include: Embedded AI Market Short-term Technology Roadmap (2023-2025) Advancements in edge AI platforms to provide enhanced processing power, reduce latency, and flexibility Commercialization of Embedded AI enhancing human intelligence in a wide range of applications Embedded AI Market Mid-term Technology Roadmap (2026-2028) Development in hardware accelerators empowers embedded AI solutions by improving performance, energy efficiency, compactness, real-time responsiveness, and cost-effectiveness Next-gen embedded AI systems will continue to push the boundaries of what is possible at the edge to enable intelligent, autonomous, and context-aware applications across various industries Embedded AI Market Long-term Technology Roadmap (2029-2030) Advanced AI-driven autonomous systems heavily rely on embedded AI for advanced sensing and perception capabilities AI-driven intelligent devices continue to evolve and become more pervasive in homes, workplaces, and other environments, embedded AI will play a vital role in enabling their intelligent and context-aware capabilities. Driver: Growing demand for intelligent and autonomous systems for a personalized experience The increasing need for advanced technologies that can provide personalized and adaptive experiences to users to boost the adoption of embedded AI solutions in the market. The demand for personalized experiences has led to the integration of AI capabilities into various embedded systems. By leveraging embedded AI solutions, devices and applications can analyze user data, preferences, and behavior to provide tailored recommendations, suggestions, and responses. This enhances user satisfaction and engagement. Moreover, embedded AI solutions can enable autonomous behavior in devices and systems, reducing the need for constant user interference. This is particularly relevant in applications such as autonomous vehicles, smart home automation, and industrial automation, where embedded AI algorithms can enable intelligent decision-making and automated actions. Embedded AI solutions can leverage machine learning algorithms to analyze data patterns and make predictions about user preferences, behavior, or system performance. This helps in anticipating user needs, optimizing resource allocation, and enhancing the overall efficiency of embedded systems. Nowadays, the demand for voice-controlled and natural language interfaces is soaring. Embedded AI solutions can incorporate natural language processing (NLP) and voice recognition capabilities, allowing users to interact with devices and applications using voice commands, making the experience more intuitive and user-friendly. Overall, the growing demand for intelligent and autonomous systems for personalized experience is driving the development and adoption of embedded AI solutions. These solutions enable devices and systems to understand user preferences, adapt to changing contexts, make intelligent decisions, and provide personalized experiences, ultimately enhancing user satisfaction and driving market growth. Restraint: Concerns related to data privacy and security Data privacy and security concerns can erode trust between users and embedded AI solutions. Users may hesitate to share their data or engage with AI-powered systems if they are not confident in the security measures. Lack of transparency about how embedded AI solutions collect, store, and use data can further contribute to mistrust and hinder adoption. Embedded AI solutions may have access to a wide range of data, including personal information and user behavior. Concerns arise regarding the ethical use of this data and the potential for misuse or biased decision-making. Ensuring fairness, transparency, and accountability in AI algorithms and data processing becomes crucial to address these concerns. Failure to address ethical considerations can result in resistance to adopting embedded AI solutions. To overcome these challenges and boost the adoption of embedded AI solutions, vendors and organizations need to prioritize data privacy and security. This includes implementing robust security measures, complying with data protection regulations, ensuring transparency and accountability in data handling, and promoting ethical use of data. Building trust among users by addressing privacy concerns and communicating the steps to secure data can help alleviate barriers to adoption and drive wider acceptance of embedded AI solutions. Opportunity : Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms The rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms provides more significant opportunities for embedded AI solution providers in the market. As AI algorithms become increasingly complex and resource-intensive, there is a growing need for processors that can handle computational demands efficiently. The demand for more powerful processors, such as high-performance CPUs, GPUs, and specialized AI accelerators, opens opportunities for embedded AI solution providers to offer advanced hardware solutions. By developing and offering processors specifically optimized for AI workloads, providers can cater to the increasing demand for enhanced performance and enable more sophisticated embedded AI applications. Furthermore, traditional processors may need help to handle the computational requirements of AI algorithms while maintaining energy efficiency. Energy-efficient processors, including low-power CPUs, specialized AI chips, and edge computing solutions, are in high demand to enable embedded AI solutions in resource-constrained environments. Embedded AI solution providers can capitalize on this opportunity by developing energy-efficient processors that deliver high-performance computing while minimizing power consumption. These processors can be integrated into various devices and systems, enabling AI capabilities without compromising energy efficiency. Henceforth, the rise in demand for more powerful and energy-efficient processors to handle complex AI algorithms offers significant opportunities for embedded AI solution providers. By focusing on developing advanced processors, energy-efficient solutions, edge computing capabilities, and fostering partnerships, providers can capitalize on the growing market demand and deliver high-performance embedded AI solutions that meet customers’ evolving needs. Challenge: Inadequate computational resources and model optimization Embedded AI solutions often operate on resource-constrained devices with limited processing power, memory, and energy. Inadequate computational resources can limit the performance of AI algorithms, leading to slower inference times, reduced accuracy, and compromised user experience. When AI models cannot be efficiently executed on embedded devices due to computational limitations, it hinders the adoption of embedded AI solutions as they may not meet the performance requirements of the intended applications. Model optimization involves techniques like quantization, pruning, and model compression to reduce the model size and computational requirements without significant loss of accuracy. However, optimizing models for embedded devices can be complex and time-consuming. Inadequate computational resources can limit the ability to optimize models effectively, resulting in suboptimal performance and hindering the widespread adoption of embedded AI solutions. Addressing the challenge of inadequate computational resources and model optimization requires a combination of hardware advancements, algorithmic optimizations, and software frameworks tailored for embedded AI. As the industry continues to innovate in these areas, overcoming these challenges will help accelerate the adoption of embedded AI solutions in various domains and enable deploying more powerful and efficient AI applications on resource-constrained devices. By offering software to register at the highest CAGR during the forecast period Embedded AI software plays a crucial role in the market by providing the necessary algorithms, frameworks, and libraries to enable AI capabilities on embedded systems. Embedded AI software unlocks the potential of AI on embedded systems, enabling intelligent decision-making, real-time data analysis, and enhanced functionality across various industries. Embedded AI software allows embedded devices to process and interpret data locally, leading to increased autonomy, improved performance, and enhanced user experiences. By data type, numeric data to account for the largest market size during the forecast period Numeric data forms the foundation for training, optimizing, and deploying AI models on embedded systems. Embedded AI systems can leverage numeric data to optimize operations and resource utilization. By analyzing historical data and patterns, AI models embedded in the system can make data-driven decisions to optimize energy consumption, scheduling, routing, or resource allocation. This data-driven optimization can improve efficiency, and cost savings, to enhance performance across various sectors such as energy & utilities, transportation & logistics, manufacturing, and many more. By Services, training and consulting to register at the highest CAGR during the forecast period Training and consulting services play a significant role in the market for embedded AI solutions by providing expertise, guidance, and support to organizations adopting embedded AI technologies. Training and consulting services assist organizations in developing and optimizing AI models for embedded systems. They offer guidance in selecting appropriate algorithms, data preprocessing techniques, and well-suited model architectures for the embedded environment. By leveraging their expertise, these services ensure that AI models are efficiently trained, optimized, and fine-tuned to achieve optimal performance on embedded devices. North America to account for the largest market size during the forecast period North America is a leading region in adopting and growing embedded AI solutions. The presence of advanced AI technology companies, robust R&D capabilities, and a mature market ecosystem contribute to the rapid growth of embedded AI solutions in this region. Embedded AI adoption in North America has been growing steadily in recent years, driven by advancements in AI technologies, increasing demand for intelligent edge devices, and the proliferation of IoT applications. Overall, embedded AI adoption in North America is gaining momentum across industries, driven by technological advancements, the rise of IoT, a supportive ecosystem, and increasing awareness of its benefits. 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. KEY MARKET SEGMENTS By Offering: Hardware Software Services By Data Type: Sensor Data Image and Video Data Numeric Data Categorial Data Other Data Types (iris & facial data, text data, time series data, and audio data) By Vertical: BFSI IT & ITES Retail & Ecommerce Manufacturing Energy & Utilities Transportation & Logistics Healthcare & Life Sciences Media & Entertainment Telecom Automotive Other Verticals (government, aerospace and defense, construction & real estate, agriculture, education, and travel & hospitality) By Region: North America US Canada Europe UK Germany France Italy Spain Rest of Europe Asia Pacific China India Japan Australia and New Zealand (ANZ) South Korea ASEAN Countries Rest of Asia Pacific Middle East and Africa UAE Saudi Arabia South Africa Israel Rest of the Middle East and Africa Latin America Brazil Mexico Argentina Rest of Latin America KEY MARKET PLAYERS Google IBM Microsoft AWS NVIDIA Intel Qualcomm Arm AMD MediaTek Oracle Salesforce NXP Lattice Octonion NeuroPace Siemens HPE LUIS Technology Code Time Technologies HiSilicon VectorBlox AU-Zone Technologies STMicroelectronics SenseTime Edge Impulse Perceive Eta Compute SensiML Syntiant Graphcore SiMa.ai
Customization scope	Free report customization (15% Free customization) with purchase. Addition or alteration to country, regional & segment scope.
Pricing and purchase options	Reap the benefits of customized purchase options to fit your specific research requirements.

Objectives of the Study

To forecast the market size, in terms of value, for various segments with respect to five main regions, namely, North America, Europe, Asia-Pacific (APAC), Latin America (LATAM), Middle East & Africa (MEA)
To provide detailed information regarding the major factors influencing the growth of the Market (drivers, restraints, opportunities, and challenges)
To strategically analyze the micro markets with respect to the individual growth trends, future prospects, and contribution to the total market
To provide a detailed overview of the value chain and analyze market trends with the Porter's five forces analysis
To analyze the opportunities in the market for various stakeholders by identifying the high-growth Segments
To identify the key players and comprehensively analyze their market position in terms of ranking and core competencies, along with detailing the competitive landscape for the market leaders
To analyze competitive development such as joint ventures, mergers and acquisitions, new product launches and development, and research and development in the market

What does this Report Deliver?

Market Estimation for 20+ Countries
Historical data coverage: 2016 to 2022
Growth projections: 2022 to 2028
SkyQuest's premium market insights: Innovation matrix, IP analysis, Production Analysis, Value chain analysis, Technological trends, and Trade analysis
Customization on Segments, Regions, and Company Profiles
100+ tables, 150+ Figures, 10+ matrix
Global and Country Market Trends
Comprehensive Mapping of Industry Parameters
Attractive Investment Proposition
Competitive Strategies Adopted by Leading Market Participants
Market drivers, restraints, opportunities, and its impact on the market
Regulatory scenario, regional dynamics, and insights of leading countries in each region
Segment trends analysis, opportunity, and growth
Opportunity analysis by region and country
Porter's five force analysis to know the market's condition
Pricing analysis
Parent market analysis
Product portfolio benchmarking

Table Of Content

Executive Summary

Market overview

Exhibit: Executive Summary – Chart on Market Overview
Exhibit: Executive Summary – Data Table on Market Overview
Exhibit: Executive Summary – Chart on Embedded Al Market Characteristics
Exhibit: Executive Summary – Chart on Market by Geography
Exhibit: Executive Summary – Chart on Market Segmentation
Exhibit: Executive Summary – Chart on Incremental Growth
Exhibit: Executive Summary – Data Table on Incremental Growth
Exhibit: Executive Summary – Chart on Vendor Market Positioning

Parent Market Analysis

Market overview

Market size

Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
  - Drivers
  - Opportunities
  - Restraints
  - Challenges
SWOT Analysis

KEY MARKET INSIGHTS

Technology Analysis
- (Exhibit: Data Table: Name of technology and details)
Pricing Analysis
- (Exhibit: Data Table: Name of technology and pricing details)
Supply Chain Analysis
- (Exhibit: Detailed Supply Chain Presentation)
Value Chain Analysis
- (Exhibit: Detailed Value Chain Presentation)
Ecosystem Of the Market
- Exhibit: Parent Market Ecosystem Market Analysis
- Exhibit: Market Characteristics of Parent Market
IP Analysis
- (Exhibit: Data Table: Name of product/technology, patents filed, inventor/company name, acquiring firm)
Trade Analysis
- (Exhibit: Data Table: Import and Export data details)
Startup Analysis
- (Exhibit: Data Table: Emerging startups details)
Raw Material Analysis
- (Exhibit: Data Table: Mapping of key raw materials)
Innovation Matrix
- (Exhibit: Positioning Matrix: Mapping of new and existing technologies)
Pipeline product Analysis
- (Exhibit: Data Table: Name of companies and pipeline products, regional mapping)
Macroeconomic Indicators

COVID IMPACT

Introduction
Impact On Economy—scenario Assessment
- Exhibit: Data on GDP - Year-over-year growth 2016-2022 (%)
Revised Market Size
- Exhibit: Data Table on Embedded Al Market size and forecast 2021-2027 ($ million)
Impact Of COVID On Key Segments
- Exhibit: Data Table on Segment Market size and forecast 2021-2027 ($ million)
COVID Strategies By Company
- Exhibit: Analysis on key strategies adopted by companies

MARKET DYNAMICS & OUTLOOK

Market Dynamics
- Exhibit: Impact analysis of DROC, 2021
  - Drivers
  - Opportunities
  - Restraints
  - Challenges
Regulatory Landscape
- Exhibit: Data Table on regulation from different region
SWOT Analysis
Porters Analysis
- Competitive rivalry
  - Exhibit: Competitive rivalry Impact of key factors, 2021
- Threat of substitute products
  - Exhibit: Threat of Substitute Products Impact of key factors, 2021
- Bargaining power of buyers
  - Exhibit: buyers bargaining power Impact of key factors, 2021
- Threat of new entrants
  - Exhibit: Threat of new entrants Impact of key factors, 2021
- Bargaining power of suppliers
  - Exhibit: Threat of suppliers bargaining power Impact of key factors, 2021
Skyquest special insights on future disruptions
- Political Impact
- Economic impact
- Social Impact
- Technical Impact
- Environmental Impact
- Legal Impact

Market Size by Region

Chart on Market share by geography 2021-2027 (%)
Data Table on Market share by geography 2021-2027(%)
North America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- USA
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Canada
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
Europe
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Germany
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Spain
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- France
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- UK
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Europe
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
Asia Pacific
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- China
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- India
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Japan
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- South Korea
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of Asia Pacific
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
Latin America
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- Brazil
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of South America
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
Middle East & Africa (MEA)
- Chart on Market share by country 2021-2027 (%)
- Data Table on Market share by country 2021-2027(%)
- GCC Countries
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- South Africa
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)
- Rest of MEA
  - Exhibit: Chart on Market share 2021-2027 (%)
  - Exhibit: Market size and forecast 2021-2027 ($ million)

KEY COMPANY PROFILES

Competitive Landscape
- Total number of companies covered
  - Exhibit: companies covered in the report, 2021
- Top companies market positioning
  - Exhibit: company positioning matrix, 2021
- Top companies market Share
  - Exhibit: Pie chart analysis on company market share, 2021(%)
d AI market is estimated to grow from USD 9.4 billion in 2023 to USD 18.0 billion by 2028, at a CAGR of 14.0% during the forecast period. Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms and integration with cloud-based AI services for better scalability to offer opportunities to the end users to leverage embedded AI solutions. Moreover, the growing demand for intelligent and autonomous systems for a personalized experience and the proliferation of connected devices and IoT ecosystems for effective communications will boost market growth worldwide.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI Market Technology Roadmap till 2030
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
The embedded AI market report covers the embedded AI technology roadmap till 2030, with insights around the initiation, development, and commercialization of technologies across AI-driven autonomous systems, AI-driven intelligent devices, and next-gen embedded AI systems. Some of the key findings from the technology roadmap include:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI Market Short-term Technology Roadmap (2023-2025)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Advancements in edge AI platforms to provide enhanced processing power, reduce latency, and flexibility
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Commercialization of Embedded AI enhancing human intelligence in a wide range of applications
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI Market Mid-term Technology Roadmap (2026-2028)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Development in hardware accelerators empowers embedded AI solutions by improving performance, energy efficiency, compactness, real-time responsiveness, and cost-effectiveness
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Next-gen embedded AI systems will continue to push the boundaries of what is possible at the edge to enable intelligent, autonomous, and context-aware applications across various industries
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI Market Long-term Technology Roadmap (2029-2030)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Advanced AI-driven autonomous systems heavily rely on embedded AI for advanced sensing and perception capabilities
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
AI-driven intelligent devices continue to evolve and become more pervasive in homes, workplaces, and other environments, embedded AI will play a vital role in enabling their intelligent and context-aware capabilities.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Driver: Growing demand for intelligent and autonomous systems for a personalized experience
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
The increasing need for advanced technologies that can provide personalized and adaptive experiences to users to boost the adoption of embedded AI solutions in the market. The demand for personalized experiences has led to the integration of AI capabilities into various embedded systems. By leveraging embedded AI solutions, devices and applications can analyze user data, preferences, and behavior to provide tailored recommendations, suggestions, and responses. This enhances user satisfaction and engagement. Moreover, embedded AI solutions can enable autonomous behavior in devices and systems, reducing the need for constant user interference. This is particularly relevant in applications such as autonomous vehicles, smart home automation, and industrial automation, where embedded AI algorithms can enable intelligent decision-making and automated actions. Embedded AI solutions can leverage machine learning algorithms to analyze data patterns and make predictions about user preferences, behavior, or system performance. This helps in anticipating user needs, optimizing resource allocation, and enhancing the overall efficiency of embedded systems. Nowadays, the demand for voice-controlled and natural language interfaces is soaring. Embedded AI solutions can incorporate natural language processing (NLP) and voice recognition capabilities, allowing users to interact with devices and applications using voice commands, making the experience more intuitive and user-friendly. Overall, the growing demand for intelligent and autonomous systems for personalized experience is driving the development and adoption of embedded AI solutions. These solutions enable devices and systems to understand user preferences, adapt to changing contexts, make intelligent decisions, and provide personalized experiences, ultimately enhancing user satisfaction and driving market growth.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Restraint: Concerns related to data privacy and security
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Data privacy and security concerns can erode trust between users and embedded AI solutions. Users may hesitate to share their data or engage with AI-powered systems if they are not confident in the security measures. Lack of transparency about how embedded AI solutions collect, store, and use data can further contribute to mistrust and hinder adoption. Embedded AI solutions may have access to a wide range of data, including personal information and user behavior. Concerns arise regarding the ethical use of this data and the potential for misuse or biased decision-making. Ensuring fairness, transparency, and accountability in AI algorithms and data processing becomes crucial to address these concerns. Failure to address ethical considerations can result in resistance to adopting embedded AI solutions. To overcome these challenges and boost the adoption of embedded AI solutions, vendors and organizations need to prioritize data privacy and security. This includes implementing robust security measures, complying with data protection regulations, ensuring transparency and accountability in data handling, and promoting ethical use of data. Building trust among users by addressing privacy concerns and communicating the steps to secure data can help alleviate barriers to adoption and drive wider acceptance of embedded AI solutions.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Opportunity : Rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
The rise in demand for more powerful and energy-efficient processors to effectively handle complex AI algorithms provides more significant opportunities for embedded AI solution providers in the market. As AI algorithms become increasingly complex and resource-intensive, there is a growing need for processors that can handle computational demands efficiently. The demand for more powerful processors, such as high-performance CPUs, GPUs, and specialized AI accelerators, opens opportunities for embedded AI solution providers to offer advanced hardware solutions. By developing and offering processors specifically optimized for AI workloads, providers can cater to the increasing demand for enhanced performance and enable more sophisticated embedded AI applications. Furthermore, traditional processors may need help to handle the computational requirements of AI algorithms while maintaining energy efficiency. Energy-efficient processors, including low-power CPUs, specialized AI chips, and edge computing solutions, are in high demand to enable embedded AI solutions in resource-constrained environments. Embedded AI solution providers can capitalize on this opportunity by developing energy-efficient processors that deliver high-performance computing while minimizing power consumption. These processors can be integrated into various devices and systems, enabling AI capabilities without compromising energy efficiency. Henceforth, the rise in demand for more powerful and energy-efficient processors to handle complex AI algorithms offers significant opportunities for embedded AI solution providers. By focusing on developing advanced processors, energy-efficient solutions, edge computing capabilities, and fostering partnerships, providers can capitalize on the growing market demand and deliver high-performance embedded AI solutions that meet customers’ evolving needs.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Challenge: Inadequate computational resources and model optimization
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI solutions often operate on resource-constrained devices with limited processing power, memory, and energy. Inadequate computational resources can limit the performance of AI algorithms, leading to slower inference times, reduced accuracy, and compromised user experience. When AI models cannot be efficiently executed on embedded devices due to computational limitations, it hinders the adoption of embedded AI solutions as they may not meet the performance requirements of the intended applications. Model optimization involves techniques like quantization, pruning, and model compression to reduce the model size and computational requirements without significant loss of accuracy. However, optimizing models for embedded devices can be complex and time-consuming. Inadequate computational resources can limit the ability to optimize models effectively, resulting in suboptimal performance and hindering the widespread adoption of embedded AI solutions. Addressing the challenge of inadequate computational resources and model optimization requires a combination of hardware advancements, algorithmic optimizations, and software frameworks tailored for embedded AI. As the industry continues to innovate in these areas, overcoming these challenges will help accelerate the adoption of embedded AI solutions in various domains and enable deploying more powerful and efficient AI applications on resource-constrained devices.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By offering software to register at the highest CAGR during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Embedded AI software plays a crucial role in the market by providing the necessary algorithms, frameworks, and libraries to enable AI capabilities on embedded systems. Embedded AI software unlocks the potential of AI on embedded systems, enabling intelligent decision-making, real-time data analysis, and enhanced functionality across various industries. Embedded AI software allows embedded devices to process and interpret data locally, leading to increased autonomy, improved performance, and enhanced user experiences.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By data type, numeric data to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Numeric data forms the foundation for training, optimizing, and deploying AI models on embedded systems. Embedded AI systems can leverage numeric data to optimize operations and resource utilization. By analyzing historical data and patterns, AI models embedded in the system can make data-driven decisions to optimize energy consumption, scheduling, routing, or resource allocation. This data-driven optimization can improve efficiency, and cost savings, to enhance performance across various sectors such as energy & utilities, transportation & logistics, manufacturing, and many more.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By Services, training and consulting to register at the highest CAGR during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Training and consulting services play a significant role in the market for embedded AI solutions by providing expertise, guidance, and support to organizations adopting embedded AI technologies. Training and consulting services assist organizations in developing and optimizing AI models for embedded systems. They offer guidance in selecting appropriate algorithms, data preprocessing techniques, and well-suited model architectures for the embedded environment. By leveraging their expertise, these services ensure that AI models are efficiently trained, optimized, and fine-tuned to achieve optimal performance on embedded devices.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
North America to account for the largest market size during the forecast period
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
North America is a leading region in adopting and growing embedded AI solutions. The presence of advanced AI technology companies, robust R&D capabilities, and a mature market ecosystem contribute to the rapid growth of embedded AI solutions in this region. Embedded AI adoption in North America has been growing steadily in recent years, driven by advancements in AI technologies, increasing demand for intelligent edge devices, and the proliferation of IoT applications. Overall, embedded AI adoption in North America is gaining momentum across industries, driven by technological advancements, the rise of IoT, a supportive ecosystem, and increasing awareness of its benefits.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Recent Developments:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key 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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
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.
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
KEY MARKET SEGMENTS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By Offering:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Hardware
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Software
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Services
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By Data Type:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Sensor Data
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Image and Video Data
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Numeric Data
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Categorial Data
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Other Data Types (iris & facial data, text data, time series data, and audio data)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By Vertical:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
BFSI
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
IT & ITES
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Retail & Ecommerce
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Manufacturing
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Energy & Utilities
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Transportation & Logistics
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Healthcare & Life Sciences
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Media & Entertainment
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Telecom
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Automotive
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Other Verticals (government, aerospace and defense, construction & real estate, agriculture, education, and travel & hospitality)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
By Region:
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
North America
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
US
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Canada
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Europe
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
UK
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Germany
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
France
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Italy
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Spain
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Rest of Europe
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Asia Pacific
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
China
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
India
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Japan
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Australia and New Zealand (ANZ)
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
South Korea
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
ASEAN Countries
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Rest of Asia Pacific
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Middle East and Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
UAE
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Saudi Arabia
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
South Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Israel
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Rest of the Middle East and Africa
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Latin America
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Brazil
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Mexico
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Argentina
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Rest of Latin America
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
KEY MARKET PLAYERS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Google
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
IBM
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Microsoft
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
AWS
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
NVIDIA
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Intel
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Qualcomm
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Arm
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
AMD
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
MediaTek
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
Oracle
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Salesforce
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
NXP
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Lattice
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Octonion
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
NeuroPace
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Siemens
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
HPE
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
LUIS Technology
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Code Time Technologies
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
HiSilicon
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
VectorBlox
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
AU-Zone Technologies
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
STMicroelectronics
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
SenseTime
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
Edge Impulse
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
Perceive
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Eta Compute
- Exhibit Company Overview
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- Exhibit Financial Updates
- Exhibit Key Developments
SensiML
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments
Syntiant
- Exhibit Company Overview
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- Exhibit Key Developments
Graphcore
- Exhibit Company Overview
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- Exhibit Key Developments
SiMa.ai
- Exhibit Company Overview
- Exhibit Business Segment Overview
- Exhibit Financial Updates
- Exhibit Key Developments

Methodology

For the Embedded Al Market, our research methodology involved a mixture of primary and secondary data sources. Key steps involved in the research process are listed below:

1. Information Procurement: This stage involved the procurement of Market data or related information via primary and secondary sources. The various secondary sources used included various company websites, annual reports, trade databases, and paid databases such as Hoover's, Bloomberg Business, Factiva, and Avention. Our team did 45 primary interactions Globally which included several stakeholders such as manufacturers, customers, key opinion leaders, etc. Overall, information procurement was one of the most extensive stages in our research process.

2. Information Analysis: This step involved triangulation of data through bottom-up and top-down approaches to estimate and validate the total size and future estimate of the Embedded Al Market.

3. Report Formulation: The final step entailed the placement of data points in appropriate Market spaces in an attempt to deduce viable conclusions.

4. Validation & Publishing: Validation is the most important step in the process. Validation & re-validation via an intricately designed process helped us finalize data points to be used for final calculations. The final Market estimates and forecasts were then aligned and sent to our panel of industry experts for validation of data. Once the validation was done the report was sent to our Quality Assurance team to ensure adherence to style guides, consistency & design.

Analyst Support

Customization Options

With the given market data, our dedicated team of analysts can offer you the following customization options are available for the Embedded Al Market:

Product Analysis: Product matrix, which offers a detailed comparison of the product portfolio of companies.

Regional Analysis: Further analysis of the Embedded Al Market for additional countries.

Competitive Analysis: Detailed analysis and profiling of additional Market players & comparative analysis of competitive products.

Go to Market Strategy: Find the high-growth channels to invest your marketing efforts and increase your customer base.

Innovation Mapping: Identify racial solutions and innovation, connected to deep ecosystems of innovators, start-ups, academics, and strategic partners.

Category Intelligence: Customized intelligence that is relevant to their supply Markets will enable them to make smarter sourcing decisions and improve their category management.

Public Company Transcript Analysis: To improve the investment performance by generating new alpha and making better-informed decisions.

Social Media Listening: To analyze the conversations and trends happening not just around your brand, but around your industry as a whole, and use those insights to make better Marketing decisions.

Embedded Al Market Report Snapshots

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Embedded Al Market Size, Share, Growth Analysis, By Offering:(Hardware, Software), By Data Type:(Sensor Data, Image and Video Data), By Vertical:(BFSI, IT & ITES), By Region:(North America, US) - Industry Forecast 2024-2031

Embedded Al Market Insights

Market Overview:

Segmentation Analysis:

Regional Analysis:

Embedded Al Market : Risk Analysis

Competitive landscaping:

Key Players Covered in the Report:

SkyQuest's Expertise:

Scope Of Report

Objectives of the Study

What does this Report Deliver?

Table Of Content

Methodology

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Embedded Al Market Report Snapshots

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Embedded Al Market Size, Share, Growth Analysis, By Offering:(Hardware, Software), By Data Type:(Sensor Data, Image and Video Data), By Vertical:(BFSI, IT & ITES), By Region:(North America, US) - Industry Forecast 2024-2031

Embedded Al Market Insights

Market Overview:

Segmentation Analysis:

Regional Analysis:

Embedded Al Market : Risk Analysis

Competitive landscaping:

Key Players Covered in the Report:

SkyQuest's Expertise:

Scope Of Report

Objectives of the Study

What does this Report Deliver?

Table Of Content

Methodology

Analyst Support

Embedded Al Market Report Snapshots

Want to customize this report?

FAQs

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