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Global Embedded AI Market to reach USD 14.6 billion by the end of 2030

Global Embedded AI Market Size study & Forecast, by Offering (Hardware, Software, Services), by Data Type (Sensor Data, Image and Video Data, Numeric Data, Categorical Data, Other Data), by Vertical (BFSI, IT & ITES, Retail & E-commerce, Manufacturing, Energy & Utilities, Transportation & Logistics, Healthcare & Life Sciences, Media & Entertainment, Telecom, Automotive, Other Verticals), and Regional Analysis, 2023-2030

Product Code: ICTNGT-58510640
Publish Date: 10-08-2023
Page: 200

Global Embedded AI Market is valued approximately USD 8.2 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 14% over the forecast period 2023-2030. Embedded AI refers to the integration of artificial intelligence (AI) capabilities directly into devices, systems, or applications, enabling them to process data, make intelligent decisions, and perform tasks without relying on cloud-based resources. By embedding AI algorithms and models into edge devices, such as sensors, appliances, or industrial machinery, embedded AI enables real-time data analysis, local inference, and autonomous operation, enhancing efficiency, responsiveness, and intelligence at the edge of the network. The driving factors boosting the market growth are increasing demand for Artificial Intelligence (AI) and growing demand for Real-Time Decision-Making.

According to Statista, the global AI market estimated to be worth USD 142.3 billion as of 2023, continues to grow by the influx of investments. The overall annual corporate investment in AI startups climbed by five billion dollars between 2020 and 2022, roughly double the amount invested previously. Another driving factor is growing demand for Real-Time Decision-Making. Many applications require real-time decision-making capabilities, such as autonomous vehicles, robotics, and industrial automation. Embedded AI allows for instant data analysis and decision-making at the edge, eliminating the need for data transmission to a centralized system and enabling faster response times. Moreover, deployment of 5G networks coupled with Edge AI and rising emphasis on ethical AI is expected to create lucrative opportunities in the market. However, the high cost of Embedded AI solutions and limited availability of skilled talent stifles market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Embedded AI Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 due to the presence of companies with cutting-edge AI technology, strong R&D skills, rising demand for intelligence edge services, and an established market ecosystem. However, Asia Pacific is expected to become the fastest growing during the forecast period, owing to factors such as growing urbanization, a robust manufacturing base, rising IoT application demand, and improvements in AI technology. The Asia Pacific market offers enormous development prospects for Embedded AI technologies and solutions thanks to strong government support, a robust startup ecosystem, and investments in AI infrastructure.

Major market player included in this report are:
Qualcomm Technologies
Google LLC
Microsoft Corporation
International Business Machines Corporation (IBM)
Amazon Web services
Code Time Technologies
LUIS Technology
Oracle Corporation
Intel Corporation
Eigen Technologies

Recent Developments in the Market:
Ø In April 2023, IBM launched Watson Edge for Financial Services, a solution that can be used by financial institutions to enhance customer service, fraud detection, and risk management.
Ø In March 2023, IBM acquired Instana, an application performance monitoring software provider, in order to expand its edge AI capabilities and offers customers a holistic perspective on their applications. This move enhances IBM’s ability to deliver comprehensive solutions for application monitoring and optimization.
Global Embedded AI Market Report Scope:
ü Historical Data – 2020 – 2021
ü Base Year for Estimation – 2022
ü Forecast period – 2023-2030
ü Report Coverage – Revenue forecast, Company Ranking, Competitive Landscape, Growth factors, and Trends
ü Segments Covered – Offering, Data Type, Vertical, Region
ü Regional Scope – North America; Europe; Asia Pacific; Latin America; Middle East & Africa
ü Customization Scope – Free report customization (equivalent up to 8 analyst’s working hours) with purchase. Addition or alteration to country, regional & segment scope*

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within countries involved in the study.

The report also caters detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, it also incorporates potential opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Offering:
By Data Type:
Sensor Data
Image and Video Data
Numeric Data
Categorical Data
Other Data
By Vertical:
Retail & E-commerce
Energy & Utilities
Transportation & Logistics
Healthcare & Life Sciences
Media & Entertainment
Other Verticals

By Region:

North America


Asia Pacific
South Korea

Latin America

Middle East & Africa
Saudi Arabia
South Africa
Rest of Middle East & Africa

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2020-2030 (USD Billion)
1.2.1. Embedded AI Market, by Region, 2020-2030 (USD Billion)
1.2.2. Embedded AI Market, by Offering, 2020-2030 (USD Billion)
1.2.3. Embedded AI Market, by Data Type, 2020-2030 (USD Billion)
1.2.4. Embedded AI Market, by Vertical, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Embedded AI Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Industry Evolution
2.2.2. Scope of the Study
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Embedded AI Market Dynamics
3.1. Embedded AI Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Increasing demand for Artificial Intelligence (AI) Growing demand for Real-Time Decision-Making
3.1.2. Market Challenges High cost of Embedded AI solutions Limited availability of skilled talent
3.1.3. Market Opportunities Deployment of 5G Networks with Edge AI Rising emphasis on ethical AI
Chapter 4. Global Embedded AI Market Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.2. Porter’s 5 Force Impact Analysis
4.3. PEST Analysis
4.3.1. Political
4.3.2. Economical
4.3.3. Social
4.3.4. Technological
4.3.5. Environmental
4.3.6. Legal
4.4. Top investment opportunity
4.5. Top winning strategies
4.6. COVID-19 Impact Analysis
4.7. Disruptive Trends
4.8. Industry Expert Perspective
4.9. Analyst Recommendation & Conclusion
Chapter 5. Global Embedded AI Market, by Offering
5.1. Market Snapshot
5.2. Global Embedded AI Market by Offering, Performance – Potential Analysis
5.3. Global Embedded AI Market Estimates & Forecasts by Offering 2020-2030 (USD Billion)
5.4. Embedded AI Market, Sub Segment Analysis
5.4.1. Hardware
5.4.2. Software
5.4.3. Services
Chapter 6. Global Embedded AI Market, by Data Type
6.1. Market Snapshot
6.2. Global Embedded AI Market by Data Type, Performance – Potential Analysis
6.3. Global Embedded AI Market Estimates & Forecasts by Data Type 2020-2030 (USD Billion)
6.4. Embedded AI Market, Sub Segment Analysis
6.4.1. Sensor Data
6.4.2. Image and Video Data
6.4.3. Numeric Data
6.4.4. Categorical Data
6.4.5. Other Data
Chapter 7. Global Embedded AI Market, by Vertical
7.1. Market Snapshot
7.2. Global Embedded AI Market by Vertical, Performance – Potential Analysis
7.3. Global Embedded AI Market Estimates & Forecasts by Vertical 2020-2030 (USD Billion)
7.4. Embedded AI Market, Sub Segment Analysis
7.4.1. BFSI
7.4.2. IT & ITES
7.4.3. Retail & E-commerce
7.4.4. Manufacturing
7.4.5. Energy & Utilities
7.4.6. Transportation & Logistics
7.4.7. Healthcare & Life Sciences
7.4.8. Media & Entertainment
7.4.9. Telecom
7.4.10. Automotive
7.4.11. Other Verticals
Chapter 8. Global Embedded AI Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Embedded AI Market, Regional Market Snapshot
8.4. North America Embedded AI Market
8.4.1. U.S. Embedded AI Market Offering breakdown estimates & forecasts, 2020-2030 Data Type breakdown estimates & forecasts, 2020-2030 Vertical breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Embedded AI Market
8.5. Europe Embedded AI Market Snapshot
8.5.1. U.K. Embedded AI Market
8.5.2. Germany Embedded AI Market
8.5.3. France Embedded AI Market
8.5.4. Spain Embedded AI Market
8.5.5. Italy Embedded AI Market
8.5.6. Rest of Europe Embedded AI Market
8.6. Asia-Pacific Embedded AI Market Snapshot
8.6.1. China Embedded AI Market
8.6.2. India Embedded AI Market
8.6.3. Japan Embedded AI Market
8.6.4. Australia Embedded AI Market
8.6.5. South Korea Embedded AI Market
8.6.6. Rest of Asia Pacific Embedded AI Market
8.7. Latin America Embedded AI Market Snapshot
8.7.1. Brazil Embedded AI Market
8.7.2. Mexico Embedded AI Market
8.8. Middle East & Africa Embedded AI Market
8.8.1. Saudi Arabia Embedded AI Market
8.8.2. South Africa Embedded AI Market
8.8.3. Rest of Middle East & Africa Embedded AI Market

Chapter 9. Competitive Intelligence
9.1. Key Company SWOT Analysis
9.1.1. Company 1
9.1.2. Company 2
9.1.3. Company 3
9.2. Top Market Strategies
9.3. Company Profiles
9.3.1. Qualcomm Technologies Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.3.2. Google LLC
9.3.3. Microsoft Corporation
9.3.4. International Business Machines Corporation (IBM)
9.3.5. Amazon Web services
9.3.6. Code Time Technologies
9.3.7. LUIS Technology
9.3.8. Oracle Corporation
9.3.9. Intel Corporation
9.3.10. Eigen Technologies
Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

At Bizwit Research and Consultancy, we employ a thorough and iterative research methodology with the goal of minimizing discrepancies, ensuring the provision of highly accurate estimates and predictions over the forecast period. Our approach involves a combination of bottom-up and top-down strategies to effectively segment and estimate quantitative aspects of the market, utilizing our proprietary data & AI tools. Our Proprietary Tools allow us for the creation of customized models specific to the research objectives. This enables us to develop tailored statistical models and forecasting algorithms to estimate market trends, future growth, or consumer behavior. The customization enhances the accuracy and relevance of the research findings.
We are dedicated to clearly communicating the purpose and objectives of each research project in the final deliverables. Our process begins by identifying the specific problem or challenge our client wishes to address, and from there, we establish precise research questions that need to be answered. To gain a comprehensive understanding of the subject matter and identify the most relevant trends and best practices, we conduct an extensive review of existing literature, industry reports, case studies, and pertinent academic research.
Critical elements of methodology employed for all our studies include:
Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
After conducting the research, our experts analyze the findings in relation to the research objectives and the specific needs of the client. They generate valuable insights and recommendations that directly address the client’s business challenges. These insights are carefully connected to the research findings to provide a comprehensive understanding.
Next, we create a well-structured research report that effectively communicates the research findings, insights, and recommendations to the client. To enhance clarity and comprehension, we utilize visual aids such as charts, graphs, and tables. These visual elements are employed to present the data in an engaging and easily understandable format, ensuring that the information is accessible and visually appealing to the client. Our aim is to deliver a clear and concise report that conveys the research findings effectively and provides actionable recommendations to meet the client’s specific needs.

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