Artificial intelligence (AI) in the automotive market is set to record a robust CAGR of 55% during the forecast period. The market holds a share of US$ 9.3 billion in 2023 while it is anticipated to cross a value of US$ 744.39 billion by 2033.
The research report on artificial intelligence (AI) in the automotive market explains that the advent of autonomous vehicles with ADAS and auto-driving modes are adopting artificial intelligence solutions that integrate with automotive technology, leading to services like guided park assist, lane locater, etc.
The restoration of the automotive industry post-pandemic with extensive research and development programs is flourishing the sales of Artificial Intelligence (AI) in automotive. Personalized vehicles with AI features are on high sales, as they deliver ease in the end user’s life. The better customer experience with AI-enabled applications for autonomous operations is expanding the demand for Artificial Intelligence (AI) integrated automotive systems. Also, automotive companies integrate with strategic companies that specifically cater to AI integration. Automotive engineers designing new cars with driving assistance are likely to fuel the market growth.
Artificial intelligence (AI) in the automotive market outlook states that the future of automotive companies using AI for their transformed transmissions is also fueling the sales of AI-integrated automotive systems. The design and production of new vehicles using AI and automation are essentially what is driving the sales of fully digitalized electric vehicles. New vehicles have AI-integrated systems that observe the driver’s driving pattern and keep it in their systems for advanced guidance and assistance. It also provides data about the temperature settings, music, and ambiance. The integration of AI with machine learning, and Natural Language Processing (NLP), is another factor that supports AI in automotive market expansion.
Future vehicles are expected to implement high-end AI technology, as they are supposed to work on autopilot systems. Hence, AI integration becomes important for future automotive manufacturing. Government and authorities adopting sustainable technology while pushing the same agenda over the technology and automotive vendors is fueling the demand for artificial intelligence and machine learning technology. AI technology is not just part of the final automotive product, but becomes an important part of its construction of it. AI-backed robots are helping the manufacturing of vehicles with higher precision. AI along with machine earning also navigates through traffic in an autonomous driving operation.
For instance, Motional, a joint venture between Aptiv and Hyundai Motor Group, delivers advanced autonomous driving technology. It has implemented three sensor types, LiDAR, Radar, and cameras. These AI input sources feed enough information to the AI system to analyze and command. Unlike Motional, which outfits vehicles with autonomous capabilities, some companies are also creating self-driving vehicles from scratch. Application of AI in driver assistance and autonomous delivery of items are the latest fronts added to its applications and are likely to fuel the market growth. These factors are anticipated to transform Artificial Intelligence (AI) in the automotive market.
| Attributes | Details |
|---|---|
| Artificial Intelligence (AI) in Automotive Market CAGR (2023 to 2033) | 55% |
| Artificial Intelligence (AI) in Automotive Market Size (2023) | US$ 9.3 billion |
| Artificial Intelligence (AI) in Automotive Market Size (2033) | US$ 744.39 billion |
Don't pay for what you don't need
Customize your report by selecting specific countries or regions and save 30%!
Short-term Growth: The market was affected by the COVID-19 pandemic and research & development. However, the machine learning processes fueled the demand for Artificial Intelligence in automotive systems. The market has built its base during this phase of its growth with new integration-based programs. Companies with personalized automotive systems also fueled the demand for Artificial Intelligence (AI) in automotive.
Mid-term Growth: The new and advanced project regarding research of AI’s application in different automotive machines. Industries 4.0 with its components brushing up the applications of AI in automobiles. For instance, machine learning plays a crucial role in understanding the driving pattern, while AI analyses it and gives assistance to the driver.
Long-term Growth: Strong marketing campaigns, along with the normalization of EVs and hybrid vehicles, are likely to give the market a large push. People with increased per capita income are investing in the upcoming technology. This is likely to have a positive impact on the market. Artificial intelligence (AI) in the automotive market is anticipated to record a CAGR of 55% between 2023 and 2033.
From software to hardware and chipsets, the role of AI in modern vehicles is prominent. AI has proved itself to be the transforming technology of the future, and its integration with any smart device becomes a necessity. Vehicles with smart AC controls, lighting, park guide assist systems, and autonomous steering systems demand software and programming support. The AI-integrated transmission comes into play here with its enhanced machine learning system and active memory. AI remembers actions and utilizes memory for helpful decision-making during the drive. It comes with features like automatic lane-shift, overtaking, and more. Hence, the growing requirement for autonomous cars is fueling market growth. The rapidly changing trends of the Advance Driver Assist System (ADAS) are another driving factor for the market. Increasing awareness around these vehicles and the importance of CaaP business models are anticipated to fuel the sales of Artificial Intelligence (AI) in automotive.
Key restrictions for the market can be explained as the limited application of sensors and equipment that strengthens AI and ML systems. Another roadblock to the market’s success is software and hardware malfunctioning, which makes the end user skeptical about its application in the first place.
Principal Consultant
The United States Artificial Intelligence (AI) in the automotive market is recording a significant CAGR between 2023 and 2033.
The United States is expected to dominate the North America artificial intelligence (AI) in automotive market, attributed to the sale trends of autonomous vehicles and electric vehicles with fully automatic programs. The new businesses designing vehicles based on self-driving prospects are another factor that thrives the regional growth. The programs for substantial human growth and environment preservation are also supporting this trend of adopting EVs. The presence of EV giant Tesla in the United States also fuels the demand for AI in automotive solutions.
The increased per-capita income, highly advanced automotive engineering, and collaboration between vehicle companies and AI technological vendors are creating new opportunities for the market while increasing the overall demand for Artificial Intelligence (AI) in automotive solutions.
China plays an important role in the thriving market space. The rapid adoption of AI and ML technologies in electric vehicles is likely to fuel the demand for AI software and hardware tools. Chinese automotive giants have extended their research and development programs to analyze the autonomous driving concept, so that it can be launched on a bigger scale in the future. The application of AI during vehicle manufacturing as OEM implants are another factor driving the use and sales of AI in automotive.
In the countries like Germany, France, Spain, and Poland, the leading automotive manufacturing spaces are trying their best to integrate AI systems in their vehicle transmission. From fossil fuel-based vehicles to EVs, the plan is to digitize the wheels while putting the driver at ease. Thus, the demand for artificial intelligence in automobiles is at the boom, supporting the global market. Germany itself is a vehicle manufacturing hub and is extending its research facilities to implement AI and ML-based technologies in its full manner.
| Segment | Top Component |
|---|---|
| Top Sub-segment | Software |
| Expected Value in 2033 | US$ 200 billion |
| Segment | Top Application |
|---|---|
| Top Sub-segment | Fully Autonomous |
| Expected Value in 2033 | US$ 30 billion |
The software segment leads in the component category, with a leading anticipated value of US$ 200 billion in 2033. The increased application of autonomous vehicle services like paring support, self-driving, AC controls, and advanced music systems are all controlled by the software. The OEM software and the third-party software are the available options. While companies don’t experiment with their pre-installed, outside vendor support and personalize the AI platform according to the need.
By application, the fully autonomous segment thrives at an anticipated value of US$ 30 billion by 2033. The growth is attributed to the trending vehicles with driving assistance or autonomous control. The parameters for self-sustaining driving, to put it simply, are determined by how much control the AI is given. Advanced AI systems are being produced by technology companies and automakers, particularly for driverless vehicles.
Get the data you need at a Fraction of the cost
Personalize your report by choosing insights you need
and save 40%!
The global artificial intelligence (AI) in automotive market is highly fragmented, where players are advancing their systems through the integration of sensors and other components. The AI vendors are anticipated to introduce software support to vehicles that make them fully autonomous.
Market Developments
The market is valued at US$ 9.3 million in 2023.
The software segment is expected to reach US$ 200 billion in 2033.
The growth potential of the market is 55% through 2033.
The increasing significance of the CaaP business model is the key trend in the market.
Malfunctions in software and hardware limit market growth.
1. Executive Summary | Artificial Intelligence (AI) in Automotive Market
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 Component
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Component, 2018 to 2022
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Component, 2023 to 2033
5.3.1. Hardware
5.3.2. Software
5.3.3. Service
5.4. Y-o-Y Growth Trend Analysis By Component, 2018 to 2022
5.5. Absolute $ Opportunity Analysis By Component, 2023 to 2033
6. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Technology
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Technology, 2018 to 2022
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Technology, 2023 to 2033
6.3.1. Computer Vision
6.3.2. Context Awareness
6.3.3. Deep Learning
6.3.4. Machine Learning
6.3.5. Natural Language Processing
6.4. Y-o-Y Growth Trend Analysis By Technology, 2018 to 2022
6.5. Absolute $ Opportunity Analysis By Technology, 2023 to 2033
7. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Application
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Application , 2018 to 2022
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Application , 2023 to 2033
7.3.1. Autonomous Driving
7.3.2. Human-Machine Interface
7.3.3. Semi-autonomous Driving
7.4. Y-o-Y Growth Trend Analysis By Application , 2018 to 2022
7.5. Absolute $ Opportunity Analysis By Application , 2023 to 2033
8. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Process
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Million) Analysis By Process, 2018 to 2022
8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Process, 2023 to 2033
8.3.1. Signal Recognition
8.3.2. Image Recognition
8.3.3. Voice Recognition
8.3.4. Data Mining
8.4. Y-o-Y Growth Trend Analysis By Process, 2018 to 2022
8.5. Absolute $ Opportunity Analysis By Process, 2023 to 2033
9. Global Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Region
9.1. Introduction
9.2. Historical Market Size Value (US$ Million) Analysis By Region, 2018 to 2022
9.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2023 to 2033
9.3.1. North America
9.3.2. Latin America
9.3.3. Europe
9.3.4. South Asia
9.3.5. East Asia
9.3.6. Oceania
9.3.7. Middle East & Africa (MEA)
9.4. Market Attractiveness Analysis By Region
10. North 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. The USA
10.2.1.2. Canada
10.2.2. By Component
10.2.3. By Technology
10.2.4. By Application
10.2.5. By Process
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Component
10.3.3. By Technology
10.3.4. By Application
10.3.5. By Process
10.4. Key Takeaways
11. Latin America 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. Brazil
11.2.1.2. Mexico
11.2.1.3. Rest of Latin America
11.2.2. By Component
11.2.3. By Technology
11.2.4. By Application
11.2.5. By Process
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Component
11.3.3. By Technology
11.3.4. By Application
11.3.5. By Process
11.4. Key Takeaways
12. Europe 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. Germany
12.2.1.2. United Kingdom (UK)
12.2.1.3. France
12.2.1.4. Spain
12.2.1.5. Italy
12.2.1.6. Rest of Europe
12.2.2. By Component
12.2.3. By Technology
12.2.4. By Application
12.2.5. By Process
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Component
12.3.3. By Technology
12.3.4. By Application
12.3.5. By Process
12.4. Key Takeaways
13. South 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. India
13.2.1.2. Malaysia
13.2.1.3. Singapore
13.2.1.4. Thailand
13.2.1.5. Rest of South Asia
13.2.2. By Component
13.2.3. By Technology
13.2.4. By Application
13.2.5. By Process
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Component
13.3.3. By Technology
13.3.4. By Application
13.3.5. By Process
13.4. Key Takeaways
14. East Asia 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. China
14.2.1.2. Japan
14.2.1.3. South Korea
14.2.2. By Component
14.2.3. By Technology
14.2.4. By Application
14.2.5. By Process
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Component
14.3.3. By Technology
14.3.4. By Application
14.3.5. By Process
14.4. Key Takeaways
15. Oceania 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. Australia
15.2.1.2. New Zealand
15.2.2. By Component
15.2.3. By Technology
15.2.4. By Application
15.2.5. By Process
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Component
15.3.3. By Technology
15.3.4. By Application
15.3.5. By Process
15.4. Key Takeaways
16. MEA Market Analysis 2018 to 2022 and Forecast 2023 to 2033, By Country
16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2018 to 2022
16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2023 to 2033
16.2.1. By Country
16.2.1.1. GCC Countries
16.2.1.2. South Africa
16.2.1.3. Israel
16.2.1.4. Rest of MEA
16.2.2. By Component
16.2.3. By Technology
16.2.4. By Application
16.2.5. By Process
16.3. Market Attractiveness Analysis
16.3.1. By Country
16.3.2. By Component
16.3.3. By Technology
16.3.4. By Application
16.3.5. By Process
16.4. Key Takeaways
17. Key Countries Market Analysis
17.1. USA
17.1.1. Pricing Analysis
17.1.2. Market Share Analysis, 2022
17.1.2.1. By Component
17.1.2.2. By Technology
17.1.2.3. By Application
17.1.2.4. By Process
17.2. Canada
17.2.1. Pricing Analysis
17.2.2. Market Share Analysis, 2022
17.2.2.1. By Component
17.2.2.2. By Technology
17.2.2.3. By Application
17.2.2.4. By Process
17.3. Brazil
17.3.1. Pricing Analysis
17.3.2. Market Share Analysis, 2022
17.3.2.1. By Component
17.3.2.2. By Technology
17.3.2.3. By Application
17.3.2.4. By Process
17.4. Mexico
17.4.1. Pricing Analysis
17.4.2. Market Share Analysis, 2022
17.4.2.1. By Component
17.4.2.2. By Technology
17.4.2.3. By Application
17.4.2.4. By Process
17.5. Germany
17.5.1. Pricing Analysis
17.5.2. Market Share Analysis, 2022
17.5.2.1. By Component
17.5.2.2. By Technology
17.5.2.3. By Application
17.5.2.4. By Process
17.6. United Kingdom
17.6.1. Pricing Analysis
17.6.2. Market Share Analysis, 2022
17.6.2.1. By Component
17.6.2.2. By Technology
17.6.2.3. By Application
17.6.2.4. By Process
17.7. France
17.7.1. Pricing Analysis
17.7.2. Market Share Analysis, 2022
17.7.2.1. By Component
17.7.2.2. By Technology
17.7.2.3. By Application
17.7.2.4. By Process
17.8. Spain
17.8.1. Pricing Analysis
17.8.2. Market Share Analysis, 2022
17.8.2.1. By Component
17.8.2.2. By Technology
17.8.2.3. By Application
17.8.2.4. By Process
17.9. Italy
17.9.1. Pricing Analysis
17.9.2. Market Share Analysis, 2022
17.9.2.1. By Component
17.9.2.2. By Technology
17.9.2.3. By Application
17.9.2.4. By Process
17.10. India
17.10.1. Pricing Analysis
17.10.2. Market Share Analysis, 2022
17.10.2.1. By Component
17.10.2.2. By Technology
17.10.2.3. By Application
17.10.2.4. By Process
17.11. Malaysia
17.11.1. Pricing Analysis
17.11.2. Market Share Analysis, 2022
17.11.2.1. By Component
17.11.2.2. By Technology
17.11.2.3. By Application
17.11.2.4. By Process
17.12. Singapore
17.12.1. Pricing Analysis
17.12.2. Market Share Analysis, 2022
17.12.2.1. By Component
17.12.2.2. By Technology
17.12.2.3. By Application
17.12.2.4. By Process
17.13. Thailand
17.13.1. Pricing Analysis
17.13.2. Market Share Analysis, 2022
17.13.2.1. By Component
17.13.2.2. By Technology
17.13.2.3. By Application
17.13.2.4. By Process
17.14. China
17.14.1. Pricing Analysis
17.14.2. Market Share Analysis, 2022
17.14.2.1. By Component
17.14.2.2. By Technology
17.14.2.3. By Application
17.14.2.4. By Process
17.15. Japan
17.15.1. Pricing Analysis
17.15.2. Market Share Analysis, 2022
17.15.2.1. By Component
17.15.2.2. By Technology
17.15.2.3. By Application
17.15.2.4. By Process
17.16. South Korea
17.16.1. Pricing Analysis
17.16.2. Market Share Analysis, 2022
17.16.2.1. By Component
17.16.2.2. By Technology
17.16.2.3. By Application
17.16.2.4. By Process
17.17. Australia
17.17.1. Pricing Analysis
17.17.2. Market Share Analysis, 2022
17.17.2.1. By Component
17.17.2.2. By Technology
17.17.2.3. By Application
17.17.2.4. By Process
17.18. New Zealand
17.18.1. Pricing Analysis
17.18.2. Market Share Analysis, 2022
17.18.2.1. By Component
17.18.2.2. By Technology
17.18.2.3. By Application
17.18.2.4. By Process
17.19. GCC Countries
17.19.1. Pricing Analysis
17.19.2. Market Share Analysis, 2022
17.19.2.1. By Component
17.19.2.2. By Technology
17.19.2.3. By Application
17.19.2.4. By Process
17.20. South Africa
17.20.1. Pricing Analysis
17.20.2. Market Share Analysis, 2022
17.20.2.1. By Component
17.20.2.2. By Technology
17.20.2.3. By Application
17.20.2.4. By Process
17.21. Israel
17.21.1. Pricing Analysis
17.21.2. Market Share Analysis, 2022
17.21.2.1. By Component
17.21.2.2. By Technology
17.21.2.3. By Application
17.21.2.4. By Process
18. Market Structure Analysis
18.1. Competition Dashboard
18.2. Competition Benchmarking
18.3. Market Share Analysis of Top Players
18.3.1. By Regional
18.3.2. By Component
18.3.3. By Technology
18.3.4. By Application
18.3.5. By Process
19. Competition Analysis
19.1. Competition Deep Dive
19.1.1. Intel Corporation
19.1.1.1. Overview
19.1.1.2. Product Portfolio
19.1.1.3. Profitability by Market Segments
19.1.1.4. Sales Footprint
19.1.1.5. Strategy Overview
19.1.1.5.1. Marketing Strategy
19.1.2. Waymo, LLC.
19.1.2.1. Overview
19.1.2.2. Product Portfolio
19.1.2.3. Profitability by Market Segments
19.1.2.4. Sales Footprint
19.1.2.5. Strategy Overview
19.1.2.5.1. Marketing Strategy
19.1.3. IBM Corporation
19.1.3.1. Overview
19.1.3.2. Product Portfolio
19.1.3.3. Profitability by Market Segments
19.1.3.4. Sales Footprint
19.1.3.5. Strategy Overview
19.1.3.5.1. Marketing Strategy
19.1.4. Microsoft Corporation
19.1.4.1. Overview
19.1.4.2. Product Portfolio
19.1.4.3. Profitability by Market Segments
19.1.4.4. Sales Footprint
19.1.4.5. Strategy Overview
19.1.4.5.1. Marketing Strategy
19.1.5. Nvidia Corporation
19.1.5.1. Overview
19.1.5.2. Product Portfolio
19.1.5.3. Profitability by Market Segments
19.1.5.4. Sales Footprint
19.1.5.5. Strategy Overview
19.1.5.5.1. Marketing Strategy
19.1.6. Xilinx, Inc.
19.1.6.1. Overview
19.1.6.2. Product Portfolio
19.1.6.3. Profitability by Market Segments
19.1.6.4. Sales Footprint
19.1.6.5. Strategy Overview
19.1.6.5.1. Marketing Strategy
19.1.7. Micron Technology, Inc.
19.1.7.1. Overview
19.1.7.2. Product Portfolio
19.1.7.3. Profitability by Market Segments
19.1.7.4. Sales Footprint
19.1.7.5. Strategy Overview
19.1.7.5.1. Marketing Strategy
19.1.8. Tesla, Inc.
19.1.8.1. Overview
19.1.8.2. Product Portfolio
19.1.8.3. Profitability by Market Segments
19.1.8.4. Sales Footprint
19.1.8.5. Strategy Overview
19.1.8.5.1. Marketing Strategy
19.1.9. General Motors Company
19.1.9.1. Overview
19.1.9.2. Product Portfolio
19.1.9.3. Profitability by Market Segments
19.1.9.4. Sales Footprint
19.1.9.5. Strategy Overview
19.1.9.5.1. Marketing Strategy
19.1.10. Ford Motor Company
19.1.10.1. Overview
19.1.10.2. Product Portfolio
19.1.10.3. Profitability by Market Segments
19.1.10.4. Sales Footprint
19.1.10.5. Strategy Overview
19.1.10.5.1. Marketing Strategy
20. Assumptions & Acronyms Used
21. Research Methodology
Explore Technology Insights
View Reports
Artificial Intelligence (AI) in Automotive Market
