The AI in IoT market is expected to expand its roots at a steady CAGR of 6.4% during the forecast period. The market is likely to hold a revenue of USD 82.1 billion in 2023 while it is anticipated to cross a value of USD 153.1 billion by 2033.
With the increasing demand for tracking of assets and performance management appliances by both transportation and connected devices, the market for dedicated services is expected to flourish. In addition, the increasing global operators of mobile network services for increasing connectivity platforms are also driving the market upward.
The advancements in artificial intelligence, coupled with ubiquitous network access, and real-time data exchange, are accelerating IoT deployments. To an unprecedented level of efficiency, which may boost the market value of IoT products in the coming decade.
Artificial Intelligence has proved to be an effective instrument in the collection of big data. This platform provides the foundation and tools necessary to automate and implement real-time decisions in IoT-related applications. For IoT devices to reach their full potential, substantial investment in new technology may have to be made to make full use of them.
| Attributes | Details |
|---|---|
| AI in IoT Market CAGR (2023 to 2033) | 6.4% |
| AI in IoT Market Size (2023) | USD 82.1 billion |
| AI in IoT Market Size (2033) | USD 153.1 billion |
In combining AI in conjunction with the Internet of Things, new possibilities can be created that may change the face and structure of industries, businesses, and economies. Through the IoT, artificial intelligence provides intelligent technologies for mimicking intelligent behavior and helping make decisions without human intervention.
During pandemic, the pharmaceutical companies have been exponentially increasing the use of AI to track spread of the virus and develop vaccines. The companies must access cost-effective, consistent, and highly secure computing power to support medical work, students academic research, and maintain the productivity of remote employees. All of these are key factors are driving the market growth during the period.
Due to an increase in manufacturing activity in 2021 and the rise in the adoption of autonomous vehicles in the automation market, the demand for AI in IoT components increased.
A novel coronavirus has further demonstrated the importance of businesses being able to cope with digital disruption. Companies starting to look for technologies that make them more sustainable and able to adapt to new technologies. Moreover, AI and automation adoption may continue to grow at an exponential rate.
Don't pay for what you don't need
Customize your report by selecting specific countries or regions and save 30%!
The growing IoT market has exponentially formed demand for managing and securing data generated from the smart devices. The AI integrated IoT technology enables the devices to record and track insights and offer a real-time visibility thereby enhancing customer experience. With decreasing delivery time and low latency, along with real-time tracking of the products, the market is likely to experience considerable growth in the forecast period.
In the retail sector, cloud AI is being used mainly for enhancing the customer experience of their programs and creating IoT-based products that focus on customers. A new generation of legacy vendors offer software to gain a stronghold throughout the value chain. This has formed as a result of intense competition in industrial IoT and market maturation.
Recently their has been a growing demand among the manufacturers to deploy computer based 100% automated data management system (DMS). As part of an AI-enabled IoT application for manufacturing, IoT-enabled applications can also be used to effectively monitor and optimize the performance of equipment, produce quality control, and collaborate between machines and humans.
Manufacturing and supply chain operations that are faster and more efficient can significantly reduce the time required for a product to reach its market.
As a result of the rising demand for advanced artificial intelligence applications in the BFSI sector, IoT may grow to become a more secure way to conduct business transactions and help to close data breaches. The development of technological and scientific advancement in IoT and the development of artificial intelligence in automation devices may ensure the market may thrive and develop further.
Edge chips are facing the challenge of power consumption and size constraints in automotive and consumer electronics. As there is a rising demand for real-time, low-latency response edge AI in these markets. With its SoC, the company hopes to resolve this issue with a computing platform that uses less power and is compact while delivering sufficient computing power for biometrics and AI.
The high cost of artificial intelligence and the lack of knowledge among consumers may slow its growth in the IoT sector. The growing concerns about privacy and security of personal data are one of the key factors leading to the decline of this market.
This artificial intelligence is expected to produce unmemorable results where this technology may result in mundane jobs for low-income-level people. All of these factors contribute to a decline in the market.
Principal Consultant
Platforms account for a leading market share of AI in IoT, with a CAGR of 5.7% during the forecast period. AI capabilities, such as machine learning-based analytics, are increasingly being incorporated with IoT platforms and solution vendors' solutions. To tap into the massive amount of data generated by IoT devices. As enterprises across numerous vertical industry segments integrate IoT solutions with AI capabilities.
They are becoming nimble while reducing reaction time and relying less on traditional tools for analysis of IoT data, and depending more on advanced technological solutions. Additionally, these solutions improve human-machine interactions by enhancing operational prediction accuracy and agility.
Platforms were created to enable developers to connect, manage, and integrate data collected from IoT devices into various applications and services. Platforms like these are designed to reduce the development time and cost of IoT solutions by setting up standard components upon which enterprises can build. These factors have contributed to the market growth of platforms in the IoT market.
| Countries | Revenue Share % (2023) |
|---|---|
| United States | 19.4% |
| Germany | 9.4% |
| Japan | 6.8% |
| Australia | 2.8% |
| North America | 29.4% |
| Europe | 22.9% |
| Countries | CAGR % (2023 to 2033) |
|---|---|
| China | 7.1% |
| India | 6.8% |
| United Kingdom | 5.5% |
North America is anticipated to hold the largest market for AI in IoT market. The market for artificial intelligence in IoT in the United States is expected to account for a 5.8% CAGR during the forecast period. By 2032, the market is expected to be worth USD 49 billion in the future years.
Businesses in North America are adopting the AI technology thereby propelling the demand for te market. This growing adoption of technology along wiith cloud services is mainly owing to the presence of key players operating in the region. Increased investments for developing new technologies have significantly contributed to the growth of this sector.
Asia Pacific is anticipated to dominate the AI in IoT market during the forecast period. In the upcoming years, the market is anticipated to increase significantly due to the continued investments and growth of the IT business in China. In China, the company is anticipated to grow at a 5.6% CAGR throughout the forecast period.
As a result of the growth of Internet penetration, advances in technology, the proliferation of mobile devices, and networking infrastructure. As well as the influx of consumers utilizing technologically advanced and connected devices. All are factors contributing to the development of the market in this region. Increasing industrial automation in the region is also driving the demand for artificial intelligence in IoTs in China .
According to the forecast, Japan may reach a CAGR of 5.2% in 2032. Increasing industrial production and the expansion of mobile technologies are expected to boost market demand for AI in IoT in this region. Market value in South Korea is expected to reach USD 5.3 billion during the forecast period. A CAGR of 5% is predicted for the market during the forecast period.
As a result of the growing revolution in the consumer shopping experience and the implementation of smart building infrastructures, AI in IoT has become an integral part of the growing IoT market in this region. Besides boosting the economy, this region is focusing on digitization post-Covid-19 by using AI and 5G networks.
During the forecast period, the artificial intelligence market for IoT in the United Kingdom is expected to thrive at a CAGR of 4.6% during the period. Because of the emergence of IoT and Machine-to-Machine technologies, AI in IoTs is expected to see a high degree of growth in this region. With the increasing demand to increase research and industrial capacity in this region, the market demand for AI in IoT is growing.
Get the data you need at a Fraction of the cost
Personalize your report by choosing insights you need
and save 40%!
| Category | By Component |
|---|---|
| Leading Segment | Platform |
| Market Share (2022) | 47.3% |
| Category | By Technologies |
|---|---|
| Leading Segment | ML and Deep Learning |
| Market Share (2022) | 79.4% |
Machine learning holds a substantial market share in the AI in IoT segment. The market is anticipated to record a CAGR of 5.7% during the forecast period. With massive data volumes, machine learning is becoming a powerful tool for data analysis. In conjunction with ML and edge computing, IoT devices can filter out most background noise and allow cloud analytics engines and edge devices to analyze the relevant data.
AI is part of the overall value of the Internet of Things, as analytics plays a key role in increasing the overall benefit of IoT. By adopting deep learning and machine learning and AI, companies can use behavioral insights. To predict the demands of their customers and networks and automatically alert them to potential problems, and customize their products.
Targeting the sensors to capture certain things, deep learning, and machine learning tools may help fuse the layers to share reports in real time with the authorities. Thus, deep learning and machine learning are gaining popularity in the market.
Through strategic partnerships, manufacturers can increase production and meet consumer demand, increasing both their revenues and market share. The introduction of new products and technologies may allow end-users to reap the benefits of new technologies. Increasing the company's production capacity is one of the potential benefits of a strategic partnership.
Market Developments
The Asia-Pacific region generated the highest demand for AI in IoT in 2023.
The Asia-Pacific market is expected to grow at the highest CAGR of 7.1% through 2033.
The semiconductor material segment is the preferred material for making AI in IoT.
The embedded AI segment is in the highest demand for AI in the IoT market.
The global AI in IoT market exhibited a Market Value of USD 89 billion in 2024.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2018 to 2022 and Forecast, 2023 to 2033
4.1. Historical Market Size Value (US$ Million) Analysis, 2018 to 2022
4.2. Current and Future Market Size Value (US$ Million) Projections, 2023 to 2033
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Components
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Components, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Components, 2023 to 2033
5.3.1. Platform
5.3.2. Software Solutions
5.3.3. Services
5.3.3.1. Deployment and Integration
5.3.3.2. Support and Maintenance
5.3.3.3. Training and Consulting
5.3.3.4. Managed Services
5.4. Y-o-Y Growth Trend Analysis By Components, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Components, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Technologies
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Technologies, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Technologies, 2023 to 2033
6.3.1. ML and Deep Learning
6.3.2. NLP
6.4. Y-o-Y Growth Trend Analysis By Technologies, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Technologies, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Verticals
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Verticals, 2018 to 2022
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Verticals, 2023 to 2033
7.3.1. Manufacturing
7.3.2. Energy and Utilities
7.3.3. Transportation and Mobility
7.3.4. BFSI
7.3.5. Government and Defense
7.3.6. Retail
7.3.7. Healthcare and Life Sciences
7.3.8. Telecom
7.3.9. Others
7.4. Y-o-Y Growth Trend Analysis By Verticals, 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Verticals, 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
8.1. Introduction
8.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
8.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
8.3.1. North America
8.3.2. Latin America
8.3.3. Europe
8.3.4. South Asia
8.3.5. East Asia
8.3.6. Oceania
8.3.7. MEA
8.4. Market Attractiveness Analysis By Region
9. North America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
9.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
9.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
9.2.1. By Country
9.2.1.1. U.S.
9.2.1.2. Canada
9.2.2. By Components
9.2.3. By Technologies
9.2.4. By Verticals
9.3. Market Attractiveness Analysis
9.3.1. By Country
9.3.2. By Components
9.3.3. By Technologies
9.3.4. By Verticals
9.4. Key Takeaways
10. Latin America Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
10.2.1. By Country
10.2.1.1. Brazil
10.2.1.2. Mexico
10.2.1.3. Rest of Latin America
10.2.2. By Components
10.2.3. By Technologies
10.2.4. By Verticals
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Components
10.3.3. By Technologies
10.3.4. By Verticals
10.4. Key Takeaways
11. Europe Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
11.2.1. By Country
11.2.1.1. Germany
11.2.1.2. U.K.
11.2.1.3. France
11.2.1.4. Spain
11.2.1.5. Italy
11.2.1.6. Rest of Europe
11.2.2. By Components
11.2.3. By Technologies
11.2.4. By Verticals
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Components
11.3.3. By Technologies
11.3.4. By Verticals
11.4. Key Takeaways
12. South Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
12.2.1. By Country
12.2.1.1. India
12.2.1.2. Malaysia
12.2.1.3. Singapore
12.2.1.4. Thailand
12.2.1.5. Rest of South Asia
12.2.2. By Components
12.2.3. By Technologies
12.2.4. By Verticals
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Components
12.3.3. By Technologies
12.3.4. By Verticals
12.4. Key Takeaways
13. East Asia Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
13.2.1. By Country
13.2.1.1. China
13.2.1.2. Japan
13.2.1.3. South Korea
13.2.2. By Components
13.2.3. By Technologies
13.2.4. By Verticals
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Components
13.3.3. By Technologies
13.3.4. By Verticals
13.4. Key Takeaways
14. Oceania Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
14.2.1. By Country
14.2.1.1. Australia
14.2.1.2. New Zealand
14.2.2. By Components
14.2.3. By Technologies
14.2.4. By Verticals
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Components
14.3.3. By Technologies
14.3.4. By Verticals
14.4. Key Takeaways
15. MEA Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
15.2.1. By Country
15.2.1.1. GCC Countries
15.2.1.2. South Africa
15.2.1.3. Israel
15.2.1.4. Rest of MEA
15.2.2. By Components
15.2.3. By Technologies
15.2.4. By Verticals
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Components
15.3.3. By Technologies
15.3.4. By Verticals
15.4. Key Takeaways
16. Key Countries Market Analysis
16.1. U.S.
16.1.1. Pricing Analysis
16.1.2. Market Share Analysis, 2022
16.1.2.1. By Components
16.1.2.2. By Technologies
16.1.2.3. By Verticals
16.2. Canada
16.2.1. Pricing Analysis
16.2.2. Market Share Analysis, 2022
16.2.2.1. By Components
16.2.2.2. By Technologies
16.2.2.3. By Verticals
16.3. Brazil
16.3.1. Pricing Analysis
16.3.2. Market Share Analysis, 2022
16.3.2.1. By Components
16.3.2.2. By Technologies
16.3.2.3. By Verticals
16.4. Mexico
16.4.1. Pricing Analysis
16.4.2. Market Share Analysis, 2022
16.4.2.1. By Components
16.4.2.2. By Technologies
16.4.2.3. By Verticals
16.5. Germany
16.5.1. Pricing Analysis
16.5.2. Market Share Analysis, 2022
16.5.2.1. By Components
16.5.2.2. By Technologies
16.5.2.3. By Verticals
16.6. U.K.
16.6.1. Pricing Analysis
16.6.2. Market Share Analysis, 2022
16.6.2.1. By Components
16.6.2.2. By Technologies
16.6.2.3. By Verticals
16.7. France
16.7.1. Pricing Analysis
16.7.2. Market Share Analysis, 2022
16.7.2.1. By Components
16.7.2.2. By Technologies
16.7.2.3. By Verticals
16.8. Spain
16.8.1. Pricing Analysis
16.8.2. Market Share Analysis, 2022
16.8.2.1. By Components
16.8.2.2. By Technologies
16.8.2.3. By Verticals
16.9. Italy
16.9.1. Pricing Analysis
16.9.2. Market Share Analysis, 2022
16.9.2.1. By Components
16.9.2.2. By Technologies
16.9.2.3. By Verticals
16.10. India
16.10.1. Pricing Analysis
16.10.2. Market Share Analysis, 2022
16.10.2.1. By Components
16.10.2.2. By Technologies
16.10.2.3. By Verticals
16.11. Malaysia
16.11.1. Pricing Analysis
16.11.2. Market Share Analysis, 2022
16.11.2.1. By Components
16.11.2.2. By Technologies
16.11.2.3. By Verticals
16.12. Singapore
16.12.1. Pricing Analysis
16.12.2. Market Share Analysis, 2022
16.12.2.1. By Components
16.12.2.2. By Technologies
16.12.2.3. By Verticals
16.13. Thailand
16.13.1. Pricing Analysis
16.13.2. Market Share Analysis, 2022
16.13.2.1. By Components
16.13.2.2. By Technologies
16.13.2.3. By Verticals
16.14. China
16.14.1. Pricing Analysis
16.14.2. Market Share Analysis, 2022
16.14.2.1. By Components
16.14.2.2. By Technologies
16.14.2.3. By Verticals
16.15. Japan
16.15.1. Pricing Analysis
16.15.2. Market Share Analysis, 2022
16.15.2.1. By Components
16.15.2.2. By Technologies
16.15.2.3. By Verticals
16.16. South Korea
16.16.1. Pricing Analysis
16.16.2. Market Share Analysis, 2022
16.16.2.1. By Components
16.16.2.2. By Technologies
16.16.2.3. By Verticals
16.17. Australia
16.17.1. Pricing Analysis
16.17.2. Market Share Analysis, 2022
16.17.2.1. By Components
16.17.2.2. By Technologies
16.17.2.3. By Verticals
16.18. New Zealand
16.18.1. Pricing Analysis
16.18.2. Market Share Analysis, 2022
16.18.2.1. By Components
16.18.2.2. By Technologies
16.18.2.3. By Verticals
16.19. GCC Countries
16.19.1. Pricing Analysis
16.19.2. Market Share Analysis, 2022
16.19.2.1. By Components
16.19.2.2. By Technologies
16.19.2.3. By Verticals
16.20. South Africa
16.20.1. Pricing Analysis
16.20.2. Market Share Analysis, 2022
16.20.2.1. By Components
16.20.2.2. By Technologies
16.20.2.3. By Verticals
16.21. Israel
16.21.1. Pricing Analysis
16.21.2. Market Share Analysis, 2022
16.21.2.1. By Components
16.21.2.2. By Technologies
16.21.2.3. By Verticals
17. Market Structure Analysis
17.1. Competition Dashboard
17.2. Competition Benchmarking
17.3. Market Share Analysis of Top Players
17.3.1. By Regional
17.3.2. By Components
17.3.3. By Technologies
17.3.4. By Verticals
18. Competition Analysis
18.1. Competition Deep Dive
18.1.1. Google
18.1.1.1. Overview
18.1.1.2. Product Portfolio
18.1.1.3. Profitability by Market Segments
18.1.1.4. Sales Footprint
18.1.1.5. Strategy Overview
18.1.1.5.1. Marketing Strategy
18.1.2. Microsoft
18.1.2.1. Overview
18.1.2.2. Product Portfolio
18.1.2.3. Profitability by Market Segments
18.1.2.4. Sales Footprint
18.1.2.5. Strategy Overview
18.1.2.5.1. Marketing Strategy
18.1.3. IBM
18.1.3.1. Overview
18.1.3.2. Product Portfolio
18.1.3.3. Profitability by Market Segments
18.1.3.4. Sales Footprint
18.1.3.5. Strategy Overview
18.1.3.5.1. Marketing Strategy
18.1.4. AWS
18.1.4.1. Overview
18.1.4.2. Product Portfolio
18.1.4.3. Profitability by Market Segments
18.1.4.4. Sales Footprint
18.1.4.5. Strategy Overview
18.1.4.5.1. Marketing Strategy
18.1.5. Oracle
18.1.5.1. Overview
18.1.5.2. Product Portfolio
18.1.5.3. Profitability by Market Segments
18.1.5.4. Sales Footprint
18.1.5.5. Strategy Overview
18.1.5.5.1. Marketing Strategy
18.1.6. SAP
18.1.6.1. Overview
18.1.6.2. Product Portfolio
18.1.6.3. Profitability by Market Segments
18.1.6.4. Sales Footprint
18.1.6.5. Strategy Overview
18.1.6.5.1. Marketing Strategy
18.1.7. PTC
18.1.7.1. Overview
18.1.7.2. Product Portfolio
18.1.7.3. Profitability by Market Segments
18.1.7.4. Sales Footprint
18.1.7.5. Strategy Overview
18.1.7.5.1. Marketing Strategy
18.1.8. GE
18.1.8.1. Overview
18.1.8.2. Product Portfolio
18.1.8.3. Profitability by Market Segments
18.1.8.4. Sales Footprint
18.1.8.5. Strategy Overview
18.1.8.5.1. Marketing Strategy
18.1.9. Salesforce
18.1.9.1. Overview
18.1.9.2. Product Portfolio
18.1.9.3. Profitability by Market Segments
18.1.9.4. Sales Footprint
18.1.9.5. Strategy Overview
18.1.9.5.1. Marketing Strategy
18.1.10. Hitachi
18.1.10.1. Overview
18.1.10.2. Product Portfolio
18.1.10.3. Profitability by Market Segments
18.1.10.4. Sales Footprint
18.1.10.5. Strategy Overview
18.1.10.5.1. Marketing Strategy
18.1.11. Uptake
18.1.11.1. Overview
18.1.11.2. Product Portfolio
18.1.11.3. Profitability by Market Segments
18.1.11.4. Sales Footprint
18.1.11.5. Strategy Overview
18.1.11.5.1. Marketing Strategy
18.1.12. SAS
18.1.12.1. Overview
18.1.12.2. Product Portfolio
18.1.12.3. Profitability by Market Segments
18.1.12.4. Sales Footprint
18.1.12.5. Strategy Overview
18.1.12.5.1. Marketing Strategy
18.1.13. Autoplant Systems Pvt Ltd
18.1.13.1. Overview
18.1.13.2. Product Portfolio
18.1.13.3. Profitability by Market Segments
18.1.13.4. Sales Footprint
18.1.13.5. Strategy Overview
18.1.13.5.1. Marketing Strategy
18.1.14. Kairos
18.1.14.1. Overview
18.1.14.2. Product Portfolio
18.1.14.3. Profitability by Market Segments
18.1.14.4. Sales Footprint
18.1.14.5. Strategy Overview
18.1.14.5.1. Marketing Strategy
18.1.15. Softweb Solutions
18.1.15.1. Overview
18.1.15.2. Product Portfolio
18.1.15.3. Profitability by Market Segments
18.1.15.4. Sales Footprint
18.1.15.5. Strategy Overview
18.1.15.5.1. Marketing Strategy
18.1.16. Arundo
18.1.16.1. Overview
18.1.16.2. Product Portfolio
18.1.16.3. Profitability by Market Segments
18.1.16.4. Sales Footprint
18.1.16.5. Strategy Overview
18.1.16.5.1. Marketing Strategy
18.1.17. C3 IoT
18.1.17.1. Overview
18.1.17.2. Product Portfolio
18.1.17.3. Profitability by Market Segments
18.1.17.4. Sales Footprint
18.1.17.5. Strategy Overview
18.1.17.5.1. Marketing Strategy
18.1.18. Anagog
18.1.18.1. Overview
18.1.18.2. Product Portfolio
18.1.18.3. Profitability by Market Segments
18.1.18.4. Sales Footprint
18.1.18.5. Strategy Overview
18.1.18.5.1. Marketing Strategy
18.1.19. Imagimob
18.1.19.1. Overview
18.1.19.2. Product Portfolio
18.1.19.3. Profitability by Market Segments
18.1.19.4. Sales Footprint
18.1.19.5. Strategy Overview
18.1.19.5.1. Marketing Strategy
18.1.20. Thingstel
18.1.20.1. Overview
18.1.20.2. Product Portfolio
18.1.20.3. Profitability by Market Segments
18.1.20.4. Sales Footprint
18.1.20.5. Strategy Overview
18.1.20.5.1. Marketing Strategy
19. Assumptions & Acronyms Used
20. Research Methodology
Explore Technology Insights
View Reports
AI in IoT Market
