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Global AI in IoT Market "(By Technology: Edge AIoT, Cloud AIoT; By Application: Manufacturing, Healthcare, Automotive, Smart Cities, Agriculture; By End-Use Industry: Industrial, Consumer, Commercial, Government; By Region: North America, Europe, Asia Pacific, Latin America, and Middle East and Africa)”- Global Industry Analysis, Size, Share, Growth, Trends, And Forecast, 2024-2032

A prominent research firm, Cognizance Market Research added a cutting-edge industry report on the “Global AI in IoT Market”. The report studies the current and past growth trends and opportunities for the market to gain valuable insights during the forecast period from 2023 to 2032.

Global AI in IoT Market Analysis:

According to cognizance market research, the Global AI in IoT Market was valued at US$ 78.99 Billion in 2023 and is anticipated to reach US$ 144.85 Billion by the end of 2032 with a CAGR of 7.9% from 2023 to 2032.

ai in iot market

What is the Global AI in IoT Market?

The Global AI in IoT Market means the deployment of AI into IoT, to make devices and systems on the IoT platform intelligent, with the ability to process data, make decisions, and control actions. Integrating both AI as well as IoT means of optimizing data acquired through connected devices helps business organizations to devise efficiency, customer experience, and operations. This integration is described as AIoT and has taken a pole position in manufacturing, healthcare, automotive, and smart cities among other industries.

AI in IoT systems enables devices to operate locally, to process data on themselves, and to provide solutions at the local level without cloud support. Because of developments in new technological tools like machine learning and deep learning IoT devices have the ability to predict the future, recognize abnormal data as well as have the capability to make decisions autonomously. He emphasized that this capability would be of great importance for industries ready to adopt AIoT solutions to enhance organizational effectiveness and decision support.

The AI market in IoT is therefore continuing to grow at a fast pace owing to factors such as the rising use of IoT devices, the rising automation need, as well as improvements in Artificial Intelligence and ML. The market continues to experience growth with the advancement in 5G networks, edge computing, and IoT connectivity therefore remains poised to deliver greater value and notable opportunities for new applications and lidiaries across diverse sectors.

Global AI in IoT Market Outlook:

The study titled Global AI in IoT Market: Opportunities and Forecast speaks of the massive growth potential of AI and IoT in the global market. AI IoT systems hence emerge as a critical enabling technology for automating and informing decision-making as organisations look to find better, faster ways of achieving their goals. New generations of algorithms and edge processing, improved connectivity of IoT devices and systems, and strong demand for integration of numerous industries including manufacturing, healthcare, automotive, and agriculture will greatly benefit from AIoT.

The factors that have promoted such an increase include the increasing demand for automation, and data analysis together with the pressure to do more with less in various sectors. AI combined with IoT is making systems more efficient, making maintenance more proactive, and offering better ways of managing resources and offering analysis. Due to the increased realization of AIoT in driving digital transformation, organizations’ buying trends remain a notable force.

It also contributes to market growth the growing trend of connected devices and the emergence of smart technologies. The advancement in 5G networks and edge computing provide scalable ways for AI solutions in IoT to work, therefore fulfilling real-time decision-making.

New markets in developing countries and especially in Asia-Pacific countries are expected to be the main growth drivers in the market. Today, such countries recognize AIoT solutions as capacities for development due to growing digitalization, investments in smart cities, and IoT architecture progress. The need for adopting trends like technologies in certain areas including agriculture, health, and transport will remain key factors that present attractive opportunities to the market players.

However, issues like data security issues, huge capital investments, and human resource specialty continue to act as inhibitors to optimum market exploitation. Nonetheless the growing need for intelligent solutions, along with technology advancement complexity are expected to be a boon to the global market for AI in IoT, and innovations will keep growing in the years to come.

Segment Analysis:

ai in iot market by end user

The market segmentation of the Global AI in IoT Market by application has been done for specific industries like manufacturing, health care, automotive, and smart city among others, where IoT solutions integrated with AI are being adopted supportively. In manufacturing AIoT is applied in such uses as predictive maintenance, process optimization, and smart factories whereby real-time monitoring of manufacturing equipment is achieved thus reducing the instances of equipment breakdown. In healthcare, the adoption of IoT solutions helps to monitor patient status, recommend treatments that fit patients’ needs, and deliver healthcare services more efficiently, so that the results would be beneficial for patient’s health and cost-efficient for the services. These sectors are quickly becoming front runners in the adoption of AIoT given their demand for efficient automation, results, and analysis.

ai in iot market by application

It is also categorized by technology type, including cloud-integrated AIoT and Edge-Integrated AIoT, with the latter steadily growing due to increased demand for quicker actionable analytics results. It is based on data processing on the periphery and focused on the device, which is crucial for the effective implementation of such applications as autonomous vehicles and industrial automation. While cloud-based AIoT comes with the advantage of facilitating the management of a complex network of IoT devices, the model supports the management of large networks with high analytical capabilities. This segmentation also depicts the various areas of application as well as the technology that fuels AI in IoT market growth.

ai in iot market by technology

Geographical Analysis:

North America is dominating the market of AI in IoT due to the intensive investment in AI and IoT applications across such sectors as healthcare, automotive, and manufacturing sectors. There is a strong foundation of Science, Technology, Engineering, and Mathematics (STEM), well-developed infrastructure, and early use of Internet of Things (IoT) devices guarantee a broad application of AIoT to power a range of applications by industry leaders such as Microsoft, IBM, Amazon, and others. Canada and the U.S. are also focusing on smart cities and autonomous system investment to drive market growth in the region.

Advanced Industrialised nations of Germany, the United Kingdom, and France are leading AI implementation in IoT amongst sectors like energy, manufacturing, and agriculture in Europe. New projects introduced by governments are regarding digital transformation, sustainability, and Industry 4.0 to grow the AIoT market. Euレ regulations like General data protection regulations also affect the development of secure and compliant AIoT solutions.

Currently, the Asia-Pacific takes the position of the most revolutionary region in AI IoT, with nations like China Japan, and India at the helm. Intelligent city construction, automation of industries, and expanding IoT investment are driving the AIoT market growth. Strengthened again by this industrial background and the governmental encouragement of technological advancement in Asia-Pacific, they will continue to hold an important position in the worldwide promotion of AI in IoT.

ai in iot market by region

The report offers the revenue of the Global AI in IoT Market for the period 2020-2032, considering 2020 to 2022 as a historical year, 2023 as the base year, and 2024 to 2032 as the forecast year. The report also provides the compound annual growth rate (CAGR) for the Global AI in IoT Market for the forecast period. The Global AI in IoT Market report provides insights and in-depth analysis into developments impacting enterprises and businesses on a regional and global level. The report covers the Global AI in IoT Market performance in terms of revenue contribution from several segments and comprises a detailed analysis of key drivers, trends, restraints, and opportunities prompting revenue growth of the Global AI in IoT Market.

The report has been prepared after wide-ranging secondary and primary research. Secondary research included internet sources, numerical data from government organizations, trade associations, and websites. Analysts have also employed an amalgamation of bottom-up and top-down approaches to study numerous phenomena in the Global AI in the IoT Market. Secondary research involved a detailed analysis of significant players’ product portfolios. Literature reviews, press releases, annual reports, white papers, and relevant documents have been also studied to understand the Global AI in the IoT Market. Primary research involved a great extent of research efforts, wherein experts carried out interviews telephonic as well as questioner-based with industry experts and opinion-makers.

The report includes an executive summary, along with a growth pattern of different segments included in the scope of the study. The Y-o-Y analysis with elaborate market insights has been provided in the report to comprehend the Y-o-Y trends in the Global AI in IoT Market. Additionally, the report focuses on altering competitive dynamics in the global market. These indices serve as valued tools for present market players as well as for companies interested in participating in the Global AI in IoT Market. The subsequent section of the Global AI in IoT Market report highlights the USPs, which include key industry events (product launch, research partnership, acquisition, etc.), technology advancements, pipeline analysis, prevalence data, and regulatory scenarios.

Global AI in IoT Market Competitive Landscape:

There are several small and major firms participating in the highly fragmented Global AI in IoT Market. The new strategies formed by companies revolve around accuracy and precision. The following are some of the major market participants:

  • IBM Corporation
  • Microsoft Corporation
  • Intel Corporation
  • Google LLC
  • Cisco System Inc.
  • Amazon Web Services
  • PTC Inc.
  • Qualcomm Technologies Inc.
  • Siemens AG
  • Oracle Corporation
  • SAP SE
  • Honeywell International Inc.
  • Arm Holdings
  • Rockwell Automation Inc

The report explores the competitive scenario of Global AI in the IoT Market. Major players working in the Global AI in IoT Market have been named and profiled for unique commercial attributes. Company overview (company description, product portfolio, geographic presence, employee strength, Key management, etc.), financials, SWOT analysis, recent developments, and key strategies are some of the features of companies profiled in the Global AI in IoT Market report.

Segmentation:

Global AI in IoT Market, By Technology:

  • Edge AIoT
  • Cloud AIoT

Global AI in IoT Market, By Application:

  • Manufacturing
  • Healthcare
  • Automotive
  • Smart Cities
  • Agriculture

Global AI in IoT Market, By End-use Industry:

  • Industrial
  • Consumer
  • Commercial
  • Government

Global AI in IoT Market, by Region:

  • North America
    • U.S.
    • Canada
  • Europe
    • Germany
    • U.K.
    • France
    • Spain
    • Italy
    • Rest of Europe
  • Asia Pacific
    • Japan
    • China
    • India
    • Australia & New Zealand
    • South Korea
    • Rest of Asia Pacific
  • Latin America
    • Brazil
    • Mexico
    • Rest of Latin America
  • Middle East & Africa
    • GCC
    • South Africa
    • Rest of the Middle East & Africa
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Table of Content

Research Methodology: Aspects

Market research is a crucial tool for organizations aiming to navigate the dynamic landscape of customer preferences, business trends, and competitive landscapes. At Cognizance Market Research, acknowledging the importance of robust research methodologies is vital to delivering actionable insights to our clientele. The significance of such methodologies lies in their capability to offer clarity in complexity, guiding strategic management with realistic evidence rather than speculation. Our clientele seek insights that excel superficial observations, reaching deep into the details of consumer behaviours, market dynamics, and evolving opportunities. These insights serve as the basis upon which businesses craft tailored approaches, optimize product offerings, and gain a competitive edge in an ever-growing marketplace.

The frequency of information updates is a cornerstone of our commitment to providing timely, relevant, and accurate insights. Cognizance Market Research adheres to a rigorous schedule of data collection, analysis, and distribution to ensure that our reports reflect the most current market realities. This proactive approach enables our clients to stay ahead of the curve, capitalize on emerging trends, and mitigate risks associated with outdated information.

Our research process is characterized by meticulous attention to detail and methodological rigor. It begins with a comprehensive understanding of client objectives, industry dynamics, and research scope. Leveraging a combination of primary and secondary research methodologies, we gather data from diverse sources including surveys, interviews, industry reports, and proprietary databases. Rigorous data analysis techniques are then employed to derive meaningful insights, identify patterns, and uncover actionable recommendations. Throughout the process, we remain vigilant in upholding the highest standards of data integrity, ensuring that our findings are robust, reliable, and actionable.

Key phases involved in in our research process are mentioned below:

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Understanding Clients’ Objectives:

Extensive Discussions and Consultations:

  • We initiate in-depth discussions and consultations with our clients to gain a comprehensive understanding of their objectives. This involves actively listening to their needs, concerns, and aspirations regarding the research project.
  • Through these interactions, we aim to uncover the underlying motivations driving their research requirements and the specific outcomes they hope to achieve.

Industry and Market Segment Analysis:

  • We invest time and effort in comprehensively understanding our clients’ industry and market segment. This involves conducting thorough research into market trends, competitive dynamics, regulatory frameworks, and emerging opportunities or threats.
  • By acquiring a deep understanding of the broader industry landscape, we can provide context-rich insights that resonate with our clients’ strategic objectives.

Target Audience Understanding:

  • We analyze our clients’ target audience demographics, behaviors, preferences, and needs to align our research efforts with their consumer-centric objectives. This entails segmenting the audience based on various criteria such as age, gender, income level, geographic location, and psychographic factors.
  • By understanding the nuances of the target audience, we can tailor our research methodologies to gather relevant data that illuminates consumer perceptions, attitudes, and purchase intent.

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Identifying Challenges and Opportunities:

  • We proactively identify the challenges and opportunities facing our clients within their respective industries. This involves conducting SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses and competitive benchmarking exercises.
  • By identifying potential obstacles and growth drivers, we can provide strategic recommendations that help our clients navigate complexities and capitalize on emerging opportunities effectively.

Grasping Specific Goals:

  • We delve into the intricacies of our clients’ objectives to gain clarity on the specific goals they aim to accomplish through the research. This entails understanding their desired outcomes, such as market expansion, product development, or competitive analysis.
  • By gaining a nuanced understanding of our clients’ goals, we can tailor our research approach to address their unique challenges and opportunities effectively.

Data Collection:

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Primary Research Process:

  • Surveys: We design and administer surveys tailored to capture specific information relevant to our clients’ objectives. This may involve employing various survey methodologies, such as online, telephone, or face-to-face interviews, to reach target audiences effectively.
  • Interviews: We conduct structured or semi-structured interviews with key stakeholders, industry experts, or target consumers to gather in-depth insights and perspectives on relevant topics. These interviews allow us to probe deeper into specific issues and uncover valuable qualitative data.
  • Focus Groups: We organize focus group discussions with carefully selected participants to facilitate interactive discussions and gather collective opinions, attitudes, and preferences. This qualitative research method provides rich contextual insights into consumer behaviors and perceptions.
  • Observations: We conduct observational research by directly observing consumer behaviors, interactions, and experiences in real-world settings. This method enables us to gather objective data on consumer actions and reactions without relying on self-reported information.

Secondary Research Process:

  • Literature Review: We conduct comprehensive literature reviews to identify existing studies, academic articles, and industry reports relevant to the research topic. This helps us gain insights into previous research findings, theoretical frameworks, and best practices.
  • Industry Reports: We analyze industry reports published by reputable trade associations (whitepapers, research studies, etc.), and government agencies (U.S. Census Bureau, Bureau of Labor Statistics, and Securities and Exchange Commission etc.) to obtain macro-level insights into market trends, competitive landscapes, and industry dynamics.
  • Government Publications: We review government publications, such as economic reports, regulatory documents, and statistical databases, to gather relevant data on demographics, market size, consumer spending patterns, and regulatory frameworks.
  • Online Databases: We leverage online databases, such as industry portals, and academic repositories (PubMed Central (PMC), ScienceDirect, SSRN (Social Science Research Network), Directory of Open Access Journals (DOAJ), NCBI, etc.), to access a wide range of secondary data sources, including market statistics, financial data, and industry analyses.

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Data Analysis:

The data analysis phase serves as a critical juncture where raw data is transformed into actionable insights that inform strategic decision-making. Through the utilization of analytical methods such as statistical analysis and qualitative techniques like thematic coding, we uncover patterns, correlations, and trends within the data. By ensuring the integrity and validity of our findings, we strive to provide clients with accurate and reliable insights that accurately reflect the realities of the market landscape.

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Transformation of Raw Data:

  • Upon collecting the necessary data, we transition into the data analysis phase, where raw data is processed and transformed into actionable insights. This involves organizing, cleaning, and structuring the data to prepare it for analysis.

Utilization of Analytical Methods:

  • Depending on the research objectives, we employ a diverse range of analytical methods to extract meaningful insights from the data. These methods include statistical analysis, trend analysis, regression analysis, and qualitative coding.

Statistical Analysis:

  • Statistical tools are instrumental in uncovering patterns, correlations, and trends within the data. By applying statistical techniques such as descriptive statistics, hypothesis testing, and multivariate analysis, we can discern relationships and derive valuable insights.

Qualitative Analysis Techniques:

  • In addition to quantitative analysis, we leverage qualitative analysis techniques to gain deeper insights from qualitative data sources such as interviews or open-ended survey responses. One such technique is thematic coding, which involves systematically categorizing and interpreting themes or patterns within qualitative data.

Integrity and Validity Maintenance:

  • Throughout the analysis process, we maintain a steadfast commitment to upholding the integrity and validity of our findings. This entails rigorous adherence to established methodologies, transparency in data handling, and thorough validation of analytical outcomes.

Data Validation:

The final phase of our research methodology is data validation, which is essential for ensuring the reliability and credibility of our findings. Validation involves scrutinizing the collected data to identify any inconsistencies, errors, or biases that may have crept in during the research process. We employ various validation techniques, including cross-referencing data from multiple sources, conducting validity checks on survey instruments, and seeking feedback from independent experts or peer reviewers. Additionally, we leverage internal quality assurance protocols to verify the accuracy and integrity of our analysis. By subjecting our findings to rigorous validation procedures, we instill confidence in our clients that the insights they receive are robust, reliable, and trustworthy.

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Importance of Data Validation:

  • Data validation is the final phase of the research methodology, crucial for ensuring the reliability and credibility of the findings. It involves a systematic process of reviewing and verifying the collected data to detect any inconsistencies, errors, or biases.

Scrutiny of Collected Data:

  • The validation process begins with a thorough scrutiny of the collected data to identify any discrepancies or anomalies. This entails comparing data points, checking for outliers, and verifying the accuracy of data entries against the original sources.

Validation Techniques:

  • Various validation techniques are employed to ensure the accuracy and integrity of the data. These include cross-referencing data from multiple sources to corroborate findings, conducting validity checks on survey instruments to assess the reliability of responses, and seeking feedback from independent experts or peer reviewers to validate the interpretation of results.

Internal Quality Assurance Protocols:

  • In addition to external validation measures, internal quality assurance protocols are implemented to further validate the accuracy of the analysis. This may involve conducting internal audits, peer reviews, or data validation checks to ensure that the research process adheres to established standards and guidelines.

Report Scope:

Attribute

Description

Market Size

US$ 144.85 Billion (2032)

Compound Annual Growth Rate (CAGR)

7.9%

Base Year

2023

Forecast Period

2024-2032

Forecast Units

Value (US$ Billion)

Report Coverage

Revenue Forecast, Competitive Landscape, Growth Factors, and Trends

Geographies Covered

North America, Europe, Asia Pacific, Latin America, Middle East & Africa

Countries Covered

U.S., Canada, Germany, U.K., France, Spain, Italy, Rest of Europe, Japan, China, India, Australia & New Zealand, South Korea, Rest of Asia Pacific, Brazil, Mexico, Rest of Latin America, GCC, South Africa, Rest of Middle East & Africa

Key Companies Profiled

IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cisco System Inc., Amazon Web Services, PTC Inc., Qualcomm Technologies Inc., Siemens AG, Oracle Corporation, SAP SE, Honeywell International Inc., Arm Holdings, Rockwell Automation Inc

Key Questions Answered in AI in IoT Market Report

It was Valued at US$ 78.99 Billion in 2023.

It is projected to reach more than US$ 144.85 Billion by 2032.

It is anticipated to be 7.9% from 2024 to 2032.

Trend: Integrating edge computing with AIoT for real-time decision-making and reduced delay time.

Driver: Expansion of automation needs, increase in the efficiency, and need for using data for decision-making in various sectors.

Opportunity: New application areas are emerging due to the expansion of smart cities, self-driving cars and cars, manufacturing automation, etc.

Challenge: Challenges such as inadequate data privacy considerations and costly first investments demanded in integrating IoT solutions.

IBM Corporation, Microsoft Corporation, Intel Corporation, Google LLC, Cisco System Inc., Amazon Web Services, PTC Inc., Qualcomm Technologies Inc., Siemens AG, Oracle Corporation, SAP SE, Honeywell International Inc., Arm Holdings, Rockwell Automation Inc.

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