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Global Artificial Intelligence (AI) Sensor Market to reach USD XX million by the end of 2030

Global Artificial Intelligence (AI) Sensor Market Size study & Forecast, by Technology (NLP, Machine Learning, Computer Vision, Context-aware Computing), by Sensor Type (Pressure, Temperature, Optical, Others), by Application (Automotive, Consumer Electronic, Manufacturing, Others), and Regional Analysis, 2023-2030

Product Code: EESC-90096404
Publish Date: 5-12-2023
Page: 200

Global Artificial Intelligence (AI) Sensor Market is valued at approximately USD XX million in 2022 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2023-2030. Artificial intelligence (AI) sensors are sensors that use AI to collect, process, and analyze data. They are also known as smart sensors or intelligent sensors. AI sensors are able to learn from data and adapt to new situations, which makes them more powerful and versatile than traditional sensors to enhance their functionality and capabilities. These sensors are designed to collect data and process it using AI techniques for various applications. The Artificial Intelligence (AI) Sensor Market is being driven by factors such as increasing demand for automation and robotics, growing investments in artificial intelligence, emerging trend of manufacturing automation, and rising awareness of the benefits of AI sensors.

In addition, the growing proliferation of IoT devices and connected systems is playing a vital role in the market growth on a global scale. Globally, the number of Internet of Things (IoT) devices is projected to nearly double, increasing from 15.1 billion in 2020 to surpass 29 billion by 2030. By 2030, China is expected to have the largest number of IoT devices, reaching approximately 8 billion consumer devices. These sensors utilize machine learning algorithms and advanced analytics to make sense of the collected data and make intelligent decisions or predictions. AI sensors are used in various applications such as autonomous vehicles, smart home systems, industrial automation, environmental monitoring, and healthcare. According to NetBase Quid via AI Index Report, Annual global corporate investment in artificial intelligence in 2020 was, USD 125.15 billion and in 2021 was USD 176.47 billion along with that according to same source annul investment in industrial automation in 2020 was 3.79 billion and in 2021 was 5.85 billion. Thus, increasing investment in AI and automation is fueling the growth of the market. In addition to rising government initiatives for the adoption and development of AI sensor and increasing demand for personalized medicine & fitness trackers create lucrative opportunities for the market. However, the concern about data privacy hinders the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Artificial Intelligence (AI) Sensor Market study includes Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the presence of strong technological infrastructure and increasing investment in AI technology in the region. Asia Pacific is considered the fastest growing region over the forecasted period due to the increasing adoption of AI and increasing automation in industries in the region.

Major market player included in this report are:
Baidu, Inc.
BAE Systems
LexisNexis Risk Solutions
Oracle Corporation
Robert Bosch GmbH
Sensata Technologies, Inc.
Sensirion AG
Silicon Sensing Systems Limited
Sony Corporation
Teledyne Technologies Incorporated

Recent Developments in the Market:
Ø In January 2023, Elliptic Labs, a leading global AI software company specializing in AI Virtual Smart Sensors, unveiled its latest innovation, the AI Virtual Distance Sensor. As part of the AI Virtual Smart Sensor Platform, this software-only solution enables devices to dynamically measure the distance between themselves, facilitating relative location detection and connection capabilities, whether it’s a one-to-one or one-to-many device scenario. By utilizing the AI Virtual Distance Sensor, devices can identify when another device or user is in proximity and respond accordingly.

Global Artificial Intelligence (AI) Sensor 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 – Technology, Sensor Type, Application, 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 Technology:
Machine Learning
Computer Vision
Context-aware Computing

By Sensor Type:

By Application:
Consumer Electronic
Aerospace & Defense
Smart Home Automation

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 Million)
1.2.1. Artificial Intelligence (AI) Sensor Market, by Region, 2020-2030 (USD Million)
1.2.2. Artificial Intelligence (AI) Sensor Market, by Technology, 2020-2030 (USD Million)
1.2.3. Artificial Intelligence (AI) Sensor Market, by Sensor Type, 2020-2030 (USD Million)
1.2.4. Artificial Intelligence (AI) Sensor Market, by Application, 2020-2030 (USD Million)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Artificial Intelligence (AI) Sensor 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 Artificial Intelligence (AI) Sensor Market Dynamics
3.1. Artificial Intelligence (AI) Sensor Market Impact Analysis (2020-2030)
3.1.1. Market Drivers Growing proliferation of IoT devices and connected systems Increasing adoption of automation in manufacturing industry
3.1.2. Market Challenges Concern about data privacy and security Dearth of skilled professionals
3.1.3. Market Opportunities Rising government initiatives for the adoption and development of AI sensor Increasing demand of personalized medicine & fitness trackers
Chapter 4. Global Artificial Intelligence (AI) Sensor 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 Artificial Intelligence (AI) Sensor Market, by Technology
5.1. Market Snapshot
5.2. Global Artificial Intelligence (AI) Sensor Market by Technology, Performance – Potential Analysis
5.3. Global Artificial Intelligence (AI) Sensor Market Estimates & Forecasts by Technology 2020-2030 (USD Million)
5.4. Artificial Intelligence (AI) Sensor Market , Sub Segment Analysis
5.4.1. NLP
5.4.2. Machine Learning
5.4.3. Computer Vision
5.4.4. Context-aware Computing
Chapter 6. Global Artificial Intelligence (AI) Sensor Market, by Sensor Type
6.1. Market Snapshot
6.2. Global Artificial Intelligence (AI) Sensor Market by Sensor Type, Performance – Potential Analysis
6.3. Global Artificial Intelligence (AI) Sensor Market Estimates & Forecasts by Sensor Type 2020-2030 (USD Million)
6.4. Artificial Intelligence (AI) Sensor Market , Sub Segment Analysis
6.4.1. Pressure
6.4.2. Temperature
6.4.3. Optical
6.4.4. Position
6.4.5. Ultrasonic
6.4.6. Motion
6.4.7. Navigation
6.4.8. Others
Chapter 7. Global Artificial Intelligence (AI) Sensor Market, by Application
7.1. Market Snapshot
7.2. Global Artificial Intelligence (AI) Sensor Market by Application, Performance – Potential Analysis
7.3. Global Artificial Intelligence (AI) Sensor Market Estimates & Forecasts by Application 2020-2030 (USD Million)
7.4. Artificial Intelligence (AI) Sensor Market , Sub Segment Analysis
7.4.1. Automotive
7.4.2. Consumer Electronic
7.4.3. Manufacturing
7.4.4. Aerospace & Defense
7.4.5. Robotics
7.4.6. Smart Home Automation
7.4.7. Healthcare
7.4.8. Others
Chapter 8. Global Artificial Intelligence (AI) Sensor Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Artificial Intelligence (AI) Sensor Market , Regional Market Snapshot
8.4. North America Artificial Intelligence (AI) Sensor Market
8.4.1. U.S. Artificial Intelligence (AI) Sensor Market Technology breakdown estimates & forecasts, 2020-2030 Sensor Type breakdown estimates & forecasts, 2020-2030 Application breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Artificial Intelligence (AI) Sensor Market
8.5. Europe Artificial Intelligence (AI) Sensor Market Snapshot
8.5.1. U.K. Artificial Intelligence (AI) Sensor Market
8.5.2. Germany Artificial Intelligence (AI) Sensor Market
8.5.3. France Artificial Intelligence (AI) Sensor Market
8.5.4. Spain Artificial Intelligence (AI) Sensor Market
8.5.5. Italy Artificial Intelligence (AI) Sensor Market
8.5.6. Rest of Europe Artificial Intelligence (AI) Sensor Market
8.6. Asia-Pacific Artificial Intelligence (AI) Sensor Market Snapshot
8.6.1. China Artificial Intelligence (AI) Sensor Market
8.6.2. India Artificial Intelligence (AI) Sensor Market
8.6.3. Japan Artificial Intelligence (AI) Sensor Market
8.6.4. Australia Artificial Intelligence (AI) Sensor Market
8.6.5. South Korea Artificial Intelligence (AI) Sensor Market
8.6.6. Rest of Asia Pacific Artificial Intelligence (AI) Sensor Market
8.7. Latin America Artificial Intelligence (AI) Sensor Market Snapshot
8.7.1. Brazil Artificial Intelligence (AI) Sensor Market
8.7.2. Mexico Artificial Intelligence (AI) Sensor Market
8.8. Middle East & Africa Artificial Intelligence (AI) Sensor Market
8.8.1. Saudi Arabia Artificial Intelligence (AI) Sensor Market
8.8.2. South Africa Artificial Intelligence (AI) Sensor Market
8.8.3. Rest of Middle East & Africa Artificial Intelligence (AI) Sensor 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. Baidu, Inc. Key Information Overview Financial (Subject to Data Availability) Product Summary Recent Developments
9.3.2. BAE Systems
9.3.3. LexisNexis Risk Solutions
9.3.4. Oracle Corporation
9.3.5. Robert Bosch GmbH
9.3.6. Sensata Technologies, Inc.
9.3.7. Sensirion AG
9.3.8. Silicon Sensing Systems Limited
9.3.9. Sony Corporation
9.3.10. Teledyne Technologies Incorporated
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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