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.
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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.
AI in IoT Market:
Attributes | AI in IoT Market |
---|---|
CAGR (2023 to 2033) | 6.4% |
Market Value (2024) | USD 89 billion |
Growth Factor | Reduction of maintenance costs and downtime while processing huge volumes of data generated by the Internet of Things devices are key growth factors in the market. |
Opportunity | Technology advancements, 5G networks, and sensors are expected to create new opportunities for artificial intelligence in IoT markets. |
Key Trends | The devices connected to the Internet of Things are expected to accelerate, increasing the demand for AI devices in the market. Energy efficiency may be a key focus in the coming years, according to manufacturers. This may spur market demand for AI in IoT in future markets. |
Platforms AI in IoT Market:
Attributes | Platforms AI in IoT Market |
---|---|
CAGR (2023 to 2033) | 5.7% |
Market Value (2024) | USD 47 billion |
Growth Factor | Market growth can be largely attributed to the adoption of AI-based solutions and the proliferation of large amounts of data being generated across various sectors are key growth factors for the AI platform market. |
Opportunity | Market growth in the construction and infrastructure development sectors, particularly in commercial space development, smart home design, and energy-saving data centers, may provide opportunities for AI in IoT markets. |
Key Trends | Several IoT platform vendors are building AI and machine learning technologies to boost their operations management capabilities aimed at reining in sprawling IoT infrastructures which may further grow the market for AI in IoT devices. |
ML and Deep Learning Market:
Attributes | ML and Deep Learning Market |
---|---|
CAGR (2023 to 2033) | 5.7% |
Market Value (2024) | USD 66 billion |
Growth Factor | With the IoT market's growing reliance on mobile networks and the large amount of data captured, machine learning is growing quickly. |
Opportunity | To increase the accuracy and predictability of AI and to make the output independent of programming behaviors, ML and deep learning should work in conjunction. This may further provide ML or deep learning with an opportunity. |
Key Trends | Using real-time data training algorithms to build real-time user experiences through tools. Such as bots and recommendation engines may positively influence the overall user experience in the market |
Digital transformation is enabled by IoT technology, which equips organizations with the tools they need to upgrade their existing processes by forming and executing new business structures.
As more and more businesses see IoT as imperative to business success, the adoption of this technology is increasing. Growth in the performance management appliance market is primarily driven by the growing demand for monitoring tools. The requirement is to minimize the cost of maintenance and downtime.
As several sensors operate continuously, AI in IoTs provides parallel execution, which is a key benefit of AI in the IoT technological market. IoTs can be made smarter and more efficient by developing AI by incorporating 5G technology into them. Various companies are manufacturing this technology to be used in the IoT market.
Data training algorithms and machine learning strive to mimic real-life customer service situations through text analysis. The result is that technological innovations like chatbots and user recommendations are available, providing real-time assistance and tailored user experiences. Plus, these tools offer an additional benefit to companies, as they continue to learn as they interact with customers.
The United States is likely to hold the largest market for AI in the IoT market. Huge investments, AI innovations, and data collection technologies to further boost the demand for AI in the IoT market. The United States market demand for 5G-enabled IoT connections is expected to rise further. Growing government spending on IoT and AI, as well as the growing presence of AI in IoT vendors, is expected to drive market growth during the forecast period.
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.
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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.
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
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