Product Code: 3199
Global AI in Automotive Market is poised to witness significant growth through 2032, primarily due to the increasing integration of AI in car manufacturing. Car manufacturers are trying to employ AI-powered robots, which help streamline their manufacturing process and boost their production capabilities.
Besides, the increased cognizance of the benefits of the CaaP (car as a platform) business model, including its ability to offer scalable and affordable service models and minimize overall capital investments, will further bolster market growth in the foreseeable future.
Overall, the AI in automotive market has been segmented on the basis of component, technology, process, application, and region.
In terms of the components, the market size from the services segment was valued at USD 1 billion in 2022 and is poised to expand at a noticeable growth rate through 2032. The demand for third-party service providers has gained significant momentum in recent years, which, in association with the lack of skilled professionals in the automotive industry will propel segment revenues during the analysis period.
Based on technology, the computer vision segment amassed more than 15% market share in 2022 and is set to record a sizeable valuation by 2032. The consistent rise in demand for improving the navigation of autonomous vehicles is fueling the adoption of computer vision technology across the automotive sector.
Similarly, the natural language processing segment is also slated to exhibit significant growth through 2032. NLP helps passengers interact with autonomous vehicles using voice commands for different applications, including route changing, virtual valet parking, and accessing in-vehicle infotainment systems. The ongoing increase in demand for improved in-vehicle experience will stimulate industry revenues between 2023 and 2032.
In the context of the process, the AI in automotive market is likely to be defined by the image/signal recognition segment. The increasing demand for semi-autonomous automobiles that deploy computer vision systems to enhance the night vision mode is likely to foster market growth through the analysis timeframe.
With respect to the application landscape, the market is likely to be characterized by the semi-autonomous vehicles segment. The growth of this segment is attributed to the current trend associated with the development of semi-autonomous vehicles from level 1 to level 3 by major automotive solution providers such as Audi, Tesla, and Cadillac.
From the regional context, the North America AI in automotive market will grow exponentially from 2023 to 2032. The regional growth is attributed to the flourishing automotive manufacturing sector in countries such as Canada. Besides, the rapid adoption of AI technology across the automotive industry in the U.S. will also facilitate market growth.
Table of Contents
Chapter 1 Methodology & Scope
- 1.1 Market Definitions
- 1.2 Base estimates & calculations
- 1.3 Forecast calculation
- 1.4 Data sources
- 1.4.1 Primary
- 1.4.2 Secondary
- 1.4.2.1 Paid sources
- 1.4.2.2 Public sources
Chapter 2 Executive Summary
- 2.1 AI in Automotive market 360 degree synopsis, 2018 - 2032
- 2.2 Business Trends
- 2.2.1 Total addressable market(TAM)
- 2.3 Regional trends
- 2.4 Component trends
- 2.5 Technology trends
- 2.6 Process trends
- 2.7 Application trends
Chapter 3 AI in Automotive Market Insights
- 3.1 Introduction
- 3.2 Impact of COVID-19 outbreak
- 3.2.1 North America
- 3.2.2 Europe
- 3.2.3 Asia Pacific
- 3.2.4 South America
- 3.2.5 MEA
- 3.3 Russia- Ukraine war impact on AI in Automotive market
- 3.4 Evolution of AI in Automotive
- 3.5 Industry ecosystem analysis
- 3.5.1 Hardware suppliers
- 3.5.2 Software providers
- 3.5.3 Third-party party service providers
- 3.5.4 Automotive manufacturers
- 3.5.5 Marketing & Distribution
- 3.5.6 Peripheral stakeholders
- 3.5.7 Profit margin
- 3.5.8 Vendor matrix
- 3.5.8.1 Hardware suppliers
- 3.5.8.2 Software providers
- 3.5.8.3 Third-party party service providers
- 3.5.8.4 Automotive manufacturers
- 3.5.8.5 Marketing & Distribution
- 3.5.8.6 Peripheral stakeholders
- 3.6 Technology & innovation landscape
- 3.6.1 Machine learning and neural networks
- 3.6.2 Multiple sensor fusion technologies
- 3.7 Patent analysis
- 3.8 Investment portfolio
- 3.9 Key initiatives and news
- 3.10 Regulatory landscape
- 3.10.1 North America
- 3.10.2 Europe
- 3.10.3 Asia Pacific
- 3.10.4 Latin America
- 3.10.5 MEA
- 3.11 Industry impact forces
- 3.11.1 Growth drivers
- 3.11.1.1 Growing adoption of AI in automotive value chain
- 3.11.1.2 Growing trend of Advance Driver Assist System (ADAS) level 2 technology
- 3.11.1.3 Growing AI implementation through ROI
- 3.11.1.4 Rising importance of ‘Car as a Platform' business model
- 3.11.1.5 Rising demand for enhanced driver convenience
- 3.11.2 Industry pitfalls & challenges
- 3.11.2.1 Limitation of sensors and equipment
- 3.11.2.2 Issues related to hardware and software reliability
- 3.12 Growth potential analysis
- 3.13 Porter's analysis
- 3.14 PESTEL analysis
Chapter 4 Competitive Landscape, 2022
- 4.1 Introduction
- 4.2 Company market share, 2022
- 4.3 Competitive analysis of major market players, 2022
- 4.3.1 Amazon Web Services (AWS)
- 4.3.2 Alphabet Inc
- 4.3.3 International Business Machines Corporation(IBM)
- 4.3.4 Tencent
- 4.3.5 Microsoft
- 4.3.6 NVIDIA Corporation
- 4.3.7 Baidu Core
- 4.3.8 SAP SE
- 4.3.9 Salesforce
- 4.4 Competitive analysis of innovative market players, 2022
- 4.4.1 Argo AI
- 4.4.2 Cruise Automation
- 4.4.3 Appen
- 4.4.4 Interactions LLC
- 4.4.5 Lexalytics
- 4.4.6 Waymo
- 4.4.7 Nuro
- 4.5 Competitive positioning matrix
- 4.6 Stratgic outlook matrix
Chapter 5 AI in Automotive Market, By Component
- 5.1 Key trends, by component
- 5.2 Hardware
- 5.2.1 Market estimates and forecast, 2018 - 2032
- 5.3 Software
- 5.3.1 Market estimates and forecast, 2018 - 2032
- 5.4 Services
- 5.4.1 Market estimates and forecast, 2018 - 2032
Chapter 6 AI in Automotive Market, By Technology
- 6.1 Key trends, by Technology
- 6.2 Computer Vision
- 6.2.1 Market estimates and forecast, 2018 - 2032
- 6.3 Context Awareness
- 6.3.1 Market estimates and forecast, 2018 - 2032
- 6.4 Deep Learning
- 6.4.1 Market estimates and forecast, 2018 - 2032
- 6.5 Machine Learning
- 6.5.1 Market estimates and forecast, 2018 - 2032
- 6.6 Natural Language Processing
- 6.6.1 Market estimates and forecast, 2018 - 2032
Chapter 7 AI in Automotive Market, By Process
- 7.1 Key trends, by process
- 7.2 Data mining
- 7.2.1 Market estimates and forecast, 2018 - 2032
- 7.3 Image/signal Recognition
- 7.3.1 Market estimates and forecast, 2018 - 2032
Chapter 8 AI in Automotive Market, By Application
- 8.1 Key trends, by application
- 8.2 Semi-Autonomous Vehicle
- 8.2.1 Market estimates and forecast, 2018 - 2032
- 8.3 Fully Autonomous Vehicle
- 8.3.1 Market estimates and forecast, 2018 - 2032
Chapter 9 AI in Automotive Market, By Region
- 9.1 Key trends, by region
- 9.2 North America
- 9.2.1 Market estimates and forecast, by component, 2018 - 2032
- 9.2.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.2.3 Market estimates and forecast, by process, 2018-2032
- 9.2.4 Market estimates and forecast, by application, 2018 - 2032
- 9.2.5 U.S.
- 9.2.5.1 Market estimates and forecast, by component, 2018 - 2032
- 9.2.5.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.2.5.3 Market estimates and forecast, by process, 2018-2032
- 9.2.5.4 Market estimates and forecast, by application 2018 - 2032
- 9.2.6 Canada
- 9.2.6.1 Market estimates and forecast, by component, 2018 - 2032
- 9.2.6.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.2.6.3 Market estimates and forecast, by process, 2018-2032
- 9.2.6.4 Market estimates and forecast, by application 2018 - 2032
- 9.2.7 Mexico
- 9.2.7.1 Market estimates and forecast, by component, 2018 - 2032
- 9.2.7.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.2.7.3 Market estimates and forecast, by process, 2018-2032
- 9.2.7.4 Market estimates and forecast, by application 2018 - 2032
- 9.3 Europe
- 9.3.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.3 Market estimates and forecast, by process, 2018-2032
- 9.3.4 Market estimates and forecast, by application, 2018 - 2032
- 9.3.5 UK
- 9.3.5.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.5.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.5.3 Market estimates and forecast, by process, 2018-2032
- 9.3.5.4 Market estimates and forecast, by application 2018 - 2032
- 9.3.6 Germany
- 9.3.6.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.6.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.6.3 Market estimates and forecast, by process, 2018-2032
- 9.3.6.4 Market estimates and forecast, by application 2018 - 2032
- 9.3.7 France
- 9.3.7.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.7.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.7.3 Market estimates and forecast, by process, 2018-2032
- 9.3.7.4 Market estimates and forecast, by application 2018 - 2032
- 9.3.8 Italy
- 9.3.8.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.8.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.8.3 Market estimates and forecast, by process, 2018-2032
- 9.3.8.4 Market estimates and forecast, by application 2018 - 2032
- 9.3.9 Spain
- 9.3.9.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.9.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.9.3 Market estimates and forecast, by process, 2018-2032
- 9.3.9.4 Market estimates and forecast, by application 2018 - 2032
- 9.3.10 Russia
- 9.3.10.1 Market estimates and forecast, by component, 2018 - 2032
- 9.3.10.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.3.10.3 Market estimates and forecast, by process, 2018-2032
- 9.3.10.4 Market estimates and forecast, by application 2018 - 2032
- 9.4 Asia Pacific
- 9.4.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.3 Market estimates and forecast, by process, 2018-2032
- 9.4.4 Market estimates and forecast, by application, 2018 - 2032
- 9.4.5 China
- 9.4.5.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.5.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.5.3 Market estimates and forecast, by process, 2018-2032
- 9.4.5.4 Market estimates and forecast, by application 2018 - 2032
- 9.4.6 India
- 9.4.6.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.6.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.6.3 Market estimates and forecast, by process, 2018-2032
- 9.4.6.4 Market estimates and forecast, by application 2018 - 2032
- 9.4.7 Japan
- 9.4.7.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.7.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.7.3 Market estimates and forecast, by process, 2018-2032
- 9.4.7.4 Market estimates and forecast, by application 2018 - 2032
- 9.4.8 Australia
- 9.4.8.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.8.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.8.3 Market estimates and forecast, by process, 2018-2032
- 9.4.8.4 Market estimates and forecast, by application 2018 - 2032
- 9.4.9 South Korea
- 9.4.9.1 Market estimates and forecast, by component, 2018 - 2032
- 9.4.9.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.4.9.3 Market estimates and forecast, by process, 2018-2032
- 9.4.9.4 Market estimates and forecast, by application 2018 - 2032
- 9.5 LAMEA
- 9.5.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.3 Market estimates and forecast, by process, 2018-2032
- 9.5.4 Market estimates and forecast, by application, 2018 - 2032
- 9.5.5 Brazil
- 9.5.5.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.5.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.5.3 Market estimates and forecast, by process, 2018-2032
- 9.5.5.4 Market estimates and forecast, by application 2018 - 2032
- 9.5.6 Mexico
- 9.5.6.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.6.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.6.3 Market estimates and forecast, by process, 2018-2032
- 9.5.6.4 Market estimates and forecast, by application 2018 - 2032
- 9.5.7 Saudi Arabia
- 9.5.7.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.7.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.7.3 Market estimates and forecast, by process, 2018-2032
- 9.5.7.4 Market estimates and forecast, by application 2018 - 2032
- 9.5.8 UAE
- 9.5.8.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.8.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.8.3 Market estimates and forecast, by process, 2018-2032
- 9.5.8.4 Market estimates and forecast, by application 2018 - 2032
- 9.5.9 South Africa
- 9.5.9.1 Market estimates and forecast, by component, 2018 - 2032
- 9.5.9.2 Market estimates and forecast, by technology, 2018 - 2032
- 9.5.9.3 Market estimates and forecast, by application 2018 - 2032
Chapter 10 Company Profiles
- 10.1 Amazon Web Services (AWS)
- 10.1.1 Business Overview
- 10.1.2 Financial Data
- 10.1.3 Product Landscape
- 10.1.4 Strategic Outlook
- 10.1.5 SWOT Analysis
- 10.2 Alphabet Inc
- 10.2.1 Business Overview
- 10.2.2 Financial Data
- 10.2.3 Product Landscape
- 10.2.4 Strategic Outlook
- 10.2.5 SWOT Analysis
- 10.3 IBM Corporation
- 10.3.1 Business Overview
- 10.3.2 Financial Data
- 10.3.3 Product Landscape
- 10.3.4 Strategic Outlook
- 10.3.5 SWOT Analysis
- 10.4 NVIDIA Corporation
- 10.4.1 Business Overview
- 10.4.2 Financial Data
- 10.4.3 Product Landscape
- 10.4.4 Strategic Outlook
- 10.4.5 SWOT Analysis
- 10.5 Tencent
- 10.5.1 Business Overview
- 10.5.2 Financial Data
- 10.5.3 Product Landscape
- 10.5.4 Strategic Outlook
- 10.5.5 SWOT Analysis
- 10.6 Microsoft
- 10.6.1 Business Overview
- 10.6.2 Financial Data
- 10.6.3 Product Landscape
- 10.6.4 Strategic Outlook
- 10.6.5 SWOT Analysis
- 10.7 Audi AG
- 10.7.1 Business Overview
- 10.7.2 Financial Data
- 10.7.3 Product Landscape
- 10.7.4 Strategic Outlook
- 10.7.5 SWOT Analysis
- 10.8 BMW AG
- 10.8.1 Business Overview
- 10.8.2 Financial Data
- 10.8.3 Product Landscape
- 10.8.4 Strategic Outlook
- 10.8.5 SWOT Analysis
- 10.9 Daimler AG
- 10.9.1 Business Overview
- 10.9.2 Financial Data
- 10.9.3 Product Landscape
- 10.9.4 Strategic Outlook
- 10.9.5 SWOT Analysis
- 10.10 Didi Chuxing
- 10.10.1 Business Overview
- 10.10.2 Financial Data
- 10.10.3 Product Landscape
- 10.10.4 Strategic Outlook
- 10.10.5 SWOT Analysis
- 10.11 Ford Motor Company
- 10.11.1 Business Overview
- 10.11.2 Financial Data
- 10.11.3 Product Landscape
- 10.11.4 Strategic Outlook
- 10.11.5 SWOT Analysis
- 10.12 General Motors Company
- 10.12.1 Business Overview
- 10.12.2 Financial Data
- 10.12.3 Product Landscape
- 10.12.4 Strategic Outlook
- 10.12.5 SWOT Analysis
- 10.13 Harman International Industries, Inc
- 10.13.1 Business Overview
- 10.13.2 Financial Data
- 10.13.3 Product Landscape
- 10.13.4 Strategic Outlook
- 10.13.5 SWOT Analysis
- 10.14 Honda Motors
- 10.14.1 Business Overview
- 10.14.2 Financial Data
- 10.14.3 Product Landscape
- 10.14.4 Strategic Outlook
- 10.14.5 SWOT Analysis
- 10.15 Intel Corporation
- 10.15.1 Business Overview
- 10.15.2 Financial Data
- 10.15.3 Product Landscape
- 10.15.4 Strategic Outlook
- 10.15.5 SWOT Analysis
- 10.16 Qualcomm Inc
- 10.16.1 Business Overview
- 10.16.2 Financial Data
- 10.16.3 Product Landscape
- 10.16.4 Strategic Outlook
- 10.16.5 SWOT Analysis
- 10.17 Tesla Inc
- 10.17.1 Business Overview
- 10.17.2 Financial Data
- 10.17.3 Product Landscape
- 10.17.4 Strategic Outlook
- 10.17.5 SWOT Analysis
- 10.18 Uber Technologies, Inc.
- 10.18.1 Business Overview
- 10.18.2 Financial Data
- 10.18.3 Product Landscape
- 10.18.4 Strategic Outlook
- 10.18.5 SWOT Analysis
- 10.19 Volvo Car Coroporation
- 10.19.1 Business Overview
- 10.19.2 Financial Data
- 10.19.3 Product Landscape
- 10.19.4 Strategic Outlook
- 10.19.5 SWOT Analysis
- 10.20 Xilinx Inc.
- 10.20.1 Business Overview
- 10.20.2 Financial Data
- 10.20.3 Product Landscape
- 10.20.4 Strategic Outlook
- 10.20.5 SWOT Analysis