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Global Artificial intelligence (AI) in Internet of Things Market to reach USD 60.61 billion by the end of 2030.

Global Artificial Intelligence (AI) in Internet of Things Market Size Study & Forecast, by Component (Platform, Software, Service), By Technology (Machine Learning and Deep Learning, Natural Language Processing (NLP)), By Industry Vertical (BFSI, IT and Telecom, Retail and E-commerce, Manufacturing, Healthcare, Energy and Utilities, Transportation and Mobility, Others), and Regional Analysis, 2023-2030

Product Code: ICTNGT-36377798
Publish Date: 30-10-2023
Page: 200

Global Artificial intelligence (AI) in Internet of Things Market is valued at approximately USD 10.3 billion in 2022 and is anticipated to grow with a healthy growth rate of more than 24.8% over the forecast period 2023-2030. Artificial intelligence (AI) in Internet of Things (IoT) refers to the application of machine learning and deep learning techniques to analyze huge amounts of data generated by IoT devices. It involves using AI algorithms to IoT data in order to acquire useful data, spot trends, and make predictions or decisions. In addition, automation is another aspect of AI in IoT, where AI-powered solutions optimize processes, enhance corporate operations, and enable autonomous decision-making throughout the IoT ecosystem. Additionally, the combination of artificial intelligence with IoT has the potential to offer a number of advantages for both consumers and enterprises. IoT solutions with AI increase productivity and efficiency for organizations while also lowering expenses. The global market growth is being driven by the factors such as the increasing need for real-time insights to process and analyze data manually, rising big data volume, growing demand for automation and efficiency, coupled with the increasing investment in Industry 4.0 technologies.

In addition, the rising adoption of IoT devices is acting as a catalyzing factor for market growth across the globe. IoT devices generate enormous amounts of data. AI algorithms analyze this data to identify trends, anomalies, and patterns that might not be easily discernible by humans. This analysis leads to actionable insights that can drive better decision-making. As per Statista report, in 2019, there were around 8.6 billion Internet of Things (IoT) connected devices were recorded, which is estimated to reach around 19.1 billion devices by 2025. Thus, these aforementioned factors are propelling the growth of Artificial intelligence (AI) in Internet of Things Market during the estimated period. Moreover, the rise in advancements in AI technologies, as well as growing focus on effective management of data generated from IoT devices to gain valuable insights are presenting various lucrative opportunities over the forecast years. However, the dearth of skilled professionals for AI infrastructure and rising data security and privacy concerns are challenging the market growth throughout the forecast period of 2023-2030.

The key regions considered for the Global Artificial intelligence (AI) in Internet of Things Market study include Asia Pacific, North America, Europe, Latin America, and Middle East & Africa. North America dominated the market in 2022 owing to the widespread implementation of AI and IoT technologies across a variety of industries, including manufacturing, retail, healthcare, and automotive and transportation. Additionally, the market is expanding as a result of several multinational corporations collaborating with industry providers to include AI-based IIoT into their manufacturing processes. For instance, in September 2022, P&G and Microsoft signed a multiyear collaboration deal to upgrade P&G’s digital manufacturing infrastructure. Under this deal, the industrial internet of things (IIoT), digital twin, data, and AI, are accelerating product delivery to consumers and increasing customer satisfaction while boosting productivity and cutting costs.
Whereas, Asia Pacific is expected as a fastest growing region over the forecast years. The rise in digitalization, surge in demand for IoT devices, and higher adoption of advanced technology are significantly propelling the market demand across the region.

Major market players included in this report are:
Salesforce, Inc.
Google LLC
SAP SE
SAS Institute Inc.
Amazon Web Services Inc.
PTC Inc.
IBM Corporation
Oracle Corporation.
Softweb Solutions Inc.
Hitachi Ltd.

Recent Developments in the Market:
Ø In October 2022, Accenture and Google Cloud announced the expansion of their global partnership through a reaffirmed commitment to building their respective talent bases, enhancing their shared capabilities, developing cutting-edge solutions utilizing data and AI, and delivering improved support to help clients create effective digital core and reinvent their businesses on the cloud.

Global Artificial Intelligence (AI) in Internet of Things 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 – Component, Technology, Industry 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 Component:
Platform
Software
Service

By Technology:
Machine Learning and Deep Learning
Natural Language Processing (NLP)

By Industry Vertical:
BFSI
IT and Telecom
Retail and E-commerce
Manufacturing
Healthcare
Energy and Utilities
Transportation and Mobility
Others

By Region:

North America
U.S.
Canada

Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC

Latin America
Brazil
Mexico

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. Artificial intelligence (AI) in Internet of Things Market, by region, 2020-2030 (USD Billion)
1.2.2. Artificial intelligence (AI) in Internet of Things Market, by Component, 2020-2030 (USD Billion)
1.2.3. Artificial intelligence (AI) in Internet of Things Market, by Technology, 2020-2030 (USD Billion)
1.2.4. Artificial intelligence (AI) in Internet of Things Market, by Industry Vertical, 2020-2030 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Artificial intelligence (AI) in Internet of Things 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) in Internet of Things Market Dynamics
3.1. Artificial intelligence (AI) in Internet of Things Market Impact Analysis (2020-2030)
3.1.1. Market Drivers
3.1.1.1. Increasing investment in Industry 4.0 technologies
3.1.1.2. Rise in the adoption of IoT devices
3.1.2. Market Challenges
3.1.2.1. Dearth of skilled professionals for AI infrastructure
3.1.2.2. Rising data security and privacy concerns
3.1.3. Market Opportunities
3.1.3.1. Rise in advancements in AI technologies
3.1.3.2. Growing focus on effective management of data generated from IoT devices to gain valuable insights
Chapter 4. Global Artificial intelligence (AI) in Internet of Things 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. Economic
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) in Internet of Things Market, by Component
5.1. Market Snapshot
5.2. Global Artificial intelligence (AI) in Internet of Things Market by Component, Performance – Potential Analysis
5.3. Global Artificial intelligence (AI) in Internet of Things Market Estimates & Forecasts by Component 2020-2030 (USD Billion)
5.4. Artificial intelligence (AI) in Internet of Things Market, Sub Segment Analysis
5.4.1. Platform
5.4.2. Software
5.4.3. Service
Chapter 6. Global Artificial intelligence (AI) in Internet of Things Market, by Technology
6.1. Market Snapshot
6.2. Global Artificial intelligence (AI) in Internet of Things Market by Technology, Performance – Potential Analysis
6.3. Global Artificial intelligence (AI) in Internet of Things Market Estimates & Forecasts by Technology 2020-2030 (USD Billion)
6.4. Artificial intelligence (AI) in Internet of Things Market, Sub Segment Analysis
6.4.1. Machine Learning and Deep Learning
6.4.2. Natural Language Processing (NLP)
Chapter 7. Global Artificial intelligence (AI) in Internet of Things Market, by Industry Vertical
7.1. Market Snapshot
7.2. Global Artificial intelligence (AI) in Internet of Things Market by Industry Vertical, Performance – Potential Analysis
7.3. Global Artificial intelligence (AI) in Internet of Things Market Estimates & Forecasts by Industry Vertical 2020-2030 (USD Billion)
7.4. Artificial intelligence (AI) in Internet of Things Market, Sub Segment Analysis
7.4.1. BFSI
7.4.2. IT and Telecom
7.4.3. Retail and E-commerce
7.4.4. Manufacturing
7.4.5. Healthcare
7.4.6. Energy and Utilities
7.4.7. Transportation and Mobility
7.4.8. Others
Chapter 8. Global Artificial intelligence (AI) in Internet of Things Market, Regional Analysis
8.1. Top Leading Countries
8.2. Top Emerging Countries
8.3. Artificial intelligence (AI) in Internet of Things Market, Regional Market Snapshot
8.4. North America Artificial intelligence (AI) in Internet of Things Market
8.4.1. U.S. Artificial intelligence (AI) in Internet of Things Market
8.4.1.1. Component breakdown estimates & forecasts, 2020-2030
8.4.1.2. Technology breakdown estimates & forecasts, 2020-2030
8.4.1.3. Industry Vertical breakdown estimates & forecasts, 2020-2030
8.4.2. Canada Artificial intelligence (AI) in Internet of Things Market
8.5. Europe Artificial intelligence (AI) in Internet of Things Market Snapshot
8.5.1. U.K. Artificial intelligence (AI) in Internet of Things Market
8.5.2. Germany Artificial intelligence (AI) in Internet of Things Market
8.5.3. France Artificial intelligence (AI) in Internet of Things Market
8.5.4. Spain Artificial intelligence (AI) in Internet of Things Market
8.5.5. Italy Artificial intelligence (AI) in Internet of Things Market
8.5.6. Rest of Europe Artificial intelligence (AI) in Internet of Things Market
8.6. Asia-Pacific Artificial intelligence (AI) in Internet of Things Market Snapshot
8.6.1. China Artificial intelligence (AI) in Internet of Things Market
8.6.2. India Artificial intelligence (AI) in Internet of Things Market
8.6.3. Japan Artificial intelligence (AI) in Internet of Things Market
8.6.4. Australia Artificial intelligence (AI) in Internet of Things Market
8.6.5. South Korea Artificial intelligence (AI) in Internet of Things Market
8.6.6. Rest of Asia Pacific Artificial intelligence (AI) in Internet of Things Market
8.7. Latin America Artificial intelligence (AI) in Internet of Things Market Snapshot
8.7.1. Brazil Artificial intelligence (AI) in Internet of Things Market
8.7.2. Mexico Artificial intelligence (AI) in Internet of Things Market
8.8. Middle East & Africa Artificial intelligence (AI) in Internet of Things Market
8.8.1. Saudi Arabia Artificial intelligence (AI) in Internet of Things Market
8.8.2. South Africa Artificial intelligence (AI) in Internet of Things Market
8.8.3. Rest of Middle East & Africa Artificial intelligence (AI) in Internet of Things 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. Salesforce, Inc.
9.3.1.1. Key Information
9.3.1.2. Overview
9.3.1.3. Financial (Subject to Data Availability)
9.3.1.4. Product Summary
9.3.1.5. Recent Developments
9.3.2. Google LLC
9.3.3. SAP SE
9.3.4. SAS Institute Inc.
9.3.5. Amazon Web Services Inc.
9.3.6. PTC Inc.
9.3.7. IBM Corporation
9.3.8. Oracle Corporation.
9.3.9. Softweb Solutions Inc.
9.3.10. Hitachi Ltd.
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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