Artificial Intelligence In The Automotive Industry - M&A Trend Analysis
Artificial Intelligence (AI) is redefining the automotive industry, especially in the development of innovative hardware and software stack for level 5 autonomy and intelligent ADAS systems. As AI adoption peaks, organizations are realizing the need to understand and leverage advanced algorithms and computational structures, innovative testing and validation platforms, integrated cockpit solutions, and 5G network adoption and application deployment for building their next generation mobility services. AI is thus driving merger and acquisition trend in the automotive sector.
Further, state of the art customizations are increasingly replacing traditional automotive platforms – a trend that is strengthening in the automotive industry. This is fueling the development of a large number of intelligent platforms covering in-vehicle experiences, supply chains, automotive sales and marketing, insurance tools, vehicle ecommerce and other applications – that are detailed in the report.
Organizations are adopting the merger and acquisition (M&A) route as a key strategy for acquiring AI skills, technologies, and relevant portfolios for leveraging organic and inorganic growth opportunities in the mobility market. Such initiatives are poised to create high potential for these acquirers, by helping them launch new AI services and increase their customer base.
Acquisition Trends
The report includes an analysis of more than 60 deals with a detailed technology overview, deal amount, and the purpose of acquisition. The acquisitions listed in the report capture the intricate requirements of automotive sector to upgrade its value in the marketplace.
Our M&A analysis section offers a comprehensive view of the transactions in the automotive sector, spanning AI, machine learning, and deep learning technologies. The different target technology areas highlighted include intelligent vision systems, data analytics, cloud computing, AI hardware and software, localization, security and other related software stacks.
Some of the prominent deals observed include:
Further, state of the art customizations are increasingly replacing traditional automotive platforms – a trend that is strengthening in the automotive industry. This is fueling the development of a large number of intelligent platforms covering in-vehicle experiences, supply chains, automotive sales and marketing, insurance tools, vehicle ecommerce and other applications – that are detailed in the report.
Organizations are adopting the merger and acquisition (M&A) route as a key strategy for acquiring AI skills, technologies, and relevant portfolios for leveraging organic and inorganic growth opportunities in the mobility market. Such initiatives are poised to create high potential for these acquirers, by helping them launch new AI services and increase their customer base.
Acquisition Trends
The report includes an analysis of more than 60 deals with a detailed technology overview, deal amount, and the purpose of acquisition. The acquisitions listed in the report capture the intricate requirements of automotive sector to upgrade its value in the marketplace.
Our M&A analysis section offers a comprehensive view of the transactions in the automotive sector, spanning AI, machine learning, and deep learning technologies. The different target technology areas highlighted include intelligent vision systems, data analytics, cloud computing, AI hardware and software, localization, security and other related software stacks.
Some of the prominent deals observed include:
- Intel
- Mobileye
- Analog Devices
- OtoSense
- Baidu
- xPerception
- Continental
- Argus Cyber security
- Xilinx
- Deephi
- Daimler
- Torc
- Zurich Insurance
- Brightbox
- Blackberry
- Cylance
- DoorDash
- IvI5
- WPCS International
- DropCar
- Verizon
- Telogis
- Automotive suppliers are leading the AI acquisition trend with a focus on acquiring complementary skills such as computer vision, gesture recognition, and AI-based analytics.
- In terms of technology, computer vision has attracted the largest number of acquires due to its capabilities for object detection, classification, and semantic segmentation.
- AI-based data analytics, conversational AI, intelligent hardware and software solutions, and annotation techniques are the other key technology areas that are prominent in the acquisition trend.
- Acquirers are targeting AI companies for tuck-ins, business line expansion, and vertical and horizontal integration.
- Ford, Intel, and Baidu have made three acquisitions each for gaining AI technologies for the mobility market.
- Uber has made two acquisitions related to data-training capabilities.
- Acquisition trends suggest that automotive retail is undergoing a transformation, with dealers and automotive suppliers focusing on customer-centric solutions.
- What are the key trends driving AI adoption in the automotive industry?
- How new business models are driving the need to acquire AI capabilities in the automotive industry?
- What is the span of M&A activity in the automotive value chain?
- How is the acquisition trend changing over the years?
- How are target companies leading the acquisition of different AI technologies in the US, Europe, Asia, and other regions?
- Who are the different acquirers actively involved in the acquisition scenario?
- What are the different AI technology capabilities acquired in the automotive space?
- How are the acquired AI capabilities such as computer vision, AI-based analytics, data training, and other technologies strengthening the portfolio of the acquirers?
- What are the post-acquisition scenarios for the major deals?
- What are the untapped technology areas that can be considered as potential targets in the future?
1. KEY TRENDS DRIVING AI ADOPTION IN THE AUTOMOTIVE INDUSTRY
1.1. The Need for Automotive Industry to Invest in AI
1.2. Transformation in Business Models with the Introduction of AI
2. AI-RELATED M&A ACROSS AUTOMOTIVE INDUSTRY VALUE CHAIN
3. METHODOLOGY FOR M&A ANALYSIS
4. M&A ANALYSIS OF AI IN AUTOMOTIVE: TECHNOLOGY TRENDS
4.1. Acquirer Landscape
4.2. Technology Breakdown
4.3. Acquisition Trend #1: Computer Vision
4.4. Acquisition Trend #2: AI-based Data Analytics
4.5. Acquisition Trend #3: Conversational AI
4.6. Acquisition Trend #4: Cloud-based Services
4.7. Acquisition Trend #5: AI Hardware and Software
4.8. Acquisition Trend #6: Data Training
4.9. Acquisition Trend #7: Gesture Recognition
4.10. Acquisition Trend #8: Self-driving Software Stack
4.11. Acquisition Trend #9: Simulation Software
4.12. Acquisition Trend #10: Security
4.13. Acquisition Trend #11: Mapping Technologies
4.14. Acquisition Trend #12: Other Technology Trends
5. INSIGHTS & RECOMMENDATIONS
5.1. Acquisition Gap Analysis and Future Growth Opportunities
5.2. Concluding Remarks
6. REFERENCES
1.1. The Need for Automotive Industry to Invest in AI
1.2. Transformation in Business Models with the Introduction of AI
2. AI-RELATED M&A ACROSS AUTOMOTIVE INDUSTRY VALUE CHAIN
3. METHODOLOGY FOR M&A ANALYSIS
4. M&A ANALYSIS OF AI IN AUTOMOTIVE: TECHNOLOGY TRENDS
4.1. Acquirer Landscape
4.2. Technology Breakdown
4.3. Acquisition Trend #1: Computer Vision
4.4. Acquisition Trend #2: AI-based Data Analytics
4.5. Acquisition Trend #3: Conversational AI
4.6. Acquisition Trend #4: Cloud-based Services
4.7. Acquisition Trend #5: AI Hardware and Software
4.8. Acquisition Trend #6: Data Training
4.9. Acquisition Trend #7: Gesture Recognition
4.10. Acquisition Trend #8: Self-driving Software Stack
4.11. Acquisition Trend #9: Simulation Software
4.12. Acquisition Trend #10: Security
4.13. Acquisition Trend #11: Mapping Technologies
4.14. Acquisition Trend #12: Other Technology Trends
5. INSIGHTS & RECOMMENDATIONS
5.1. Acquisition Gap Analysis and Future Growth Opportunities
5.2. Concluding Remarks
6. REFERENCES
