The global generative AI In automotive market size was valued at USD 389.47 million in 2023 and it is projected to be worth around USD 3,146.10 million by 2033, poised to grow at a CAGR of 23.21% during the forecast period 2024 to 2033.
To Access our Exclusive Data Intelligence Tool with 15000+ Database, Visit: Precedence Statistics
The U.S. generative AI In automotive market size reached USD 160.21 million in 2023 and is anticipated to be worth around USD 1,337.09 million by 2033, poised to grow at a CAGR of 23.62% from 2024 to 2033.
North America is expected to dominate the market during the forecast period. In the automobile sector, the region is leading generative AI research and development. The development of generative AI technologies for car design, manufacturing optimization, autonomous driving systems, and other applications is supported by partnerships between renowned research institutes, universities, and the automotive industry.
This emphasis on R&D encourages innovation and the creation of state-of-the-art generative AI technologies. In addition, the creation and application of generative AI in the automobile industry are significantly impacted by the regulatory environment in North America. The National Highway Traffic Safety Administration (NHTSA) in the United States and Transport Canada are two examples of government organizations that have a significant impact on the regulatory environment for autonomous driving systems and associated technology. For generative AI to be successfully used in the North American automobile industry, compliance with laws and safety norms is crucial. Therefore, driving the market growth in the region.
Asia Pacific is expected to experience a considerable growth rate over the forecast period. A substantial rate of technological improvements in most industries in the Asia Pacific highlights the growth of the market in the region. Countries such as China and Japan are observed to be the largest contributors to the growth of the market in the region. Moreover, the regional growth is attributed to the increasing product launches by the market players operating in the region. For instance, in June 2023, Toyota Research Institute (TRI) announced the release of a generative artificial intelligence (AI) method to help automotive designers. Designers may already use freely accessible text-to-image generative AI technologies as a first stage in their creative process. With TRI's innovative approach, designers may speed up this process by reducing the limitations with sketches and engineering allowing with lowering the number of iterations required to balance the design.
The Asia Pacific generative AI In automotive market size surpassed USD 87.16 million in 2023 and is projected to reach USD 755.06 million by 2033.
| Year | Market Size (USD Million) |
| 2023 | 87.16 |
| 2024 | 108.35 |
| 2025 | 135.36 |
| 2026 | 169.08 |
| 2027 | 211.19 |
| 2028 | 263.79 |
| 2029 | 329.46 |
| 2030 | 411.47 |
| 2031 | 513.86 |
| 2032 | 641.70 |
| 2033 | 755.06 |
Generative AI has made significant advancements in several industries, including the automobile industry. Generative AI is the technology that allows machines to produce new content, such as images, text, or even full designs, based on patterns and data that the machine has been trained on. In the automotive sector, this technology has the potential to transform vehicle design, production and autonomous systems. Vehicle design is one of the most prominent applications of generative AI in the automobile sector. Traditionally, creating a new automobile entails multiple iterations and prototypes, which takes a significant amount of time and resources. To produce new and inventive automotive designs, generative AI algorithms may examine existing vehicle designs, consumer preferences, market trends, and even engineering restrictions. This technology can help automobile designers explore new options, speed up the design process, and perhaps create more aesthetically appealing and functional vehicles. Furthermore, this technology has the potential to improve the production process in the automotive sector.
Generative AI algorithms can find areas for improvement, decrease faults, and enhance production efficiency by evaluating massive volumes of data associated with manufacturing processes, supply chain logistics, and quality control. This technology can assist car manufacturers in lowering costs, increasing productivity, and improving overall product quality. The generative AI in the automotive market is driven by several factors including the growing automotive sector, increasing investment in advanced technology, growing product launches, rising government initiatives, increasing trend of connected cars and growing R&D for autonomous vehicles.
| Report Coverage | Details |
| Growth Rate from 2024 to 2033 | CAGR of 23.21% |
| Market Size in 2023 | USD 389.47 Million |
| Market Size by 2033 | USD 3,146.10 Million |
| Largest Market | North America |
| Fastest Growing Market | Asia Pacific |
| Base Year | 2023 |
| Forecast Period | 2024 To 2033 |
| Segments Covered | By Vehicle Type, By Technology, and By Application |
| Regions Covered | North America, Europe, Asia-Pacific, Latin America, and Middle East & Africa |
Driver:
Increasing utilization of generative AI in the supply chain
For automakers, generative AI can improve every step of the supply chain, including distribution, inventory management, and purchasing. By examining data from suppliers, production methods, and consumer demand, AI models may produce insights and recommendations for enhancing supply chain efficiency. By studying data on supplier performance, manufacturing capacity, and transit durations, for instance, lead times throughout the supply chain may be improved.
AI models are capable of producing suggestions to shorten lead times at each level of the supply chain by taking these variables into account. Similar to this, generative AI can estimate future demand for automobiles or components by examining historical data, market patterns, and other relevant criteria. This may assist manufacturers with demand forecasting and inventory optimization. Recently, generative AI has been employed by Mahindra & Mahindra and CEAT Tyres to provide precise inventory visibility, data transparency, and inventory optimization. Thus, this is expected to drive market growth during the forecast period.
Restraint:
Regulatory and safety concerns
The automotive industry is subject to rigorous regulatory standards and safety requirements. Introducing generative AI technologies, especially in safety-critical areas such as autonomous driving, necessitates compliance with regulations and ensuring that the generated outputs meet the required safety standards. Meeting these standards and obtaining regulatory approvals can be time-consuming and resource-intensive. Thus, the regulatory and safety concerns consider as a major restraint for the market revenue growth.
Opportunity:
Growing uses in autonomous driving and ADAS
The advancement of autonomous driving systems and advanced driver assistance systems (ADAS) presents significant opportunities for generative AI in the automotive market. Generative AI algorithms can assist in the development and training of autonomous driving systems, improving perception, decision-making, and control capabilities. This opportunity allows manufacturers to enhance safety, reliability, and overall performance in autonomous vehicles. For instance, Alphabet subsidiary Waymo is leading the way in this field in terms of the application of generative AI. To train their self-driving algorithms, they use models to generate thousands of different scenarios that each replicate a different real-world situation. Waymo can expose its autonomous driving systems to a variety of driving conditions using AI, enhancing its safety and robustness.
Based on the vehicle type, the global generative AI in the automotive market is segmented into passenger vehicles and commercial vehicles. Passenger vehicles are expected to dominate the market during the forecast period. Automakers have integrated AI into passenger vehicles including automobiles and motorbikes. AI has the potential to significantly enhance the comfort and safety of passengers in any type of vehicle. To interpret data from numerous sensors, including cameras, radars, and LiDARs, ADAS frequently uses artificial intelligence (AI).
On the other hand, the commercial vehicles segment is expected to grow substantially during the projected period. Commercial vehicles are progressively using AI to provide intelligent logistics and routing, fleet management, predictive maintenance, and autonomous capabilities. These applications have increased safety, improved vehicle performance and economy, and improved logistics, making the development of self-driving commercial cars more viable. As a result, when AI develops and improves further, it is positioned to make huge advancements in commercial vehicles, increasing total productivity. Thus, this is expected to drive segment expansion over the forecast period.
Based on the technology, the global generative AI in the automotive market is segmented into machine learning, natural language processing, computer vision, context-aware computing and others. The machine learning segment is expected to grow significantly during the forecast period. Machine learning algorithms are utilized to generate training data for generative AI models in the automotive industry. For instance, in the context of autonomous driving, machine learning algorithms can analyze large amounts of real-world driving data to create artificial datasets that mimic different driving scenarios. These synthetic datasets can be used to train generative models, enhancing their ability to generate accurate and realistic responses to various situations. In addition, these algorithms are instrumental in vehicle design and styling using generative AI. Therefore, the utilization of machine learning in different applications of the automotive sector is expected to drive segment growth during the forecast period.
Based on the application, the global generative AI in the automotive industry is divided into vehicle design, manufacturing optimization, transportation & logistics, autonomous driving and ADAS. The vehicle design segment is expected to dominate the market over the forecast period. The simulation and 3D depiction of vehicles are one of the most effective uses of generative AI in the automotive sector. BMW's usage of AI in its design process is a prime example. BMW built an AI-based system that uses the concepts of generative design. The system creates a wide range of design options based on certain design criteria including weight optimization, connecting locations, and load capacity. This significantly cuts the time it takes to produce new design concepts and produces inventive, effective, and visually beautiful vehicle parts that meet the design standards.
On the other hand, the ADAS segment is expected to grow at the fastest CAGR during the forecast period. Advanced driver-assistance systems (ADAS) are being improved in a big way because of generative AI. These systems include features like collision detection and avoidance, lane-keeping aid, adaptive cruise control, and parking assistance that are intended to help drivers and increase safety. Tesla, a well-known brand in the automotive sector, has improved its ADAS capabilities by using generative AI. Tesla cars equipped with AI-powered Autopilot technology employ generative models to comprehend and pick up on different driving scenarios. By continually learning from the enormous volumes of data produced by Tesla's everyday operations, the system develops safer and more effective ADAS functions. Thus, the adoption of generative AI by key market players in ADAS is expected to drive market growth during the forecast period.
Furthermore, the autonomous driving segment is expected to witness a noticeable growth rate during the forecast period. This segment focuses on improving the accuracy and reliability of object detection, recognition, and tracking in autonomous vehicles. Furthermore, generating accurate and up-to-date maps is critical for autonomous vehicles to understand their position and navigate the environment. Generative AI algorithms can generate high-definition maps by analyzing sensor data and incorporating various environmental factors. Additionally, generative AI can aid in localization by generating synthetic sensor data that match real-world conditions, facilitating the precise positioning of autonomous vehicles within the generated maps. Thereby, driving the segment growth during the forecast period.
Segments Covered in the Report:
By Vehicle Type
By Technology
By Application
By Geography
PROCEED TO BUY :
ASK FOR SAMPLE
No cookie-cutter, only authentic analysis – take the 1st step to become a Precedence Research client
