Technology-driven trends, new customer demands, the Internet of Things (IoT), and new advances in artificial intelligence support digital transformation (DX) in the automotive industry. This transformation involves the product design process, manufacturing, maintenance, operations, and sales and marketing.
The competition in the automotive industry has changed in the last twenty years. Tesla, founded in 2003, has become the most valuable car manufacturer since June 2020. Its investments in R&D, focus on electric vehicles, and self-driving cars put Tesla forward in the industry.
What are the use cases?
1. Product Design
Designing digitally enabled services & products
Digital transformation in consumers’ lives is pushing automotive companies to change their product design processes and start to design automobiles with new features and services, following consumer requests and new trends:
- Autonomous driving/self-driving technology
- Alternative fuels/compliance with environmental regulations (e.g., electric vehicles, hybrid vehicles)
- Connected vehicles: integration of other software and hardware for better customer experience. For example, intelligent components that can identify the need for their maintenance and provide notifications in advance
- Vehicular communication systems
- Consumer expectations about higher energy efficiency and maintenance costs
- New habits and services such as ride-sharing and car-sharing
Designing with ML
New technologies also enable companies to use machine learning in design and leverage historical data from vehicles and simulations to improve the design process.
Case study: Hyundai’s Bluelink System
- Technology Used: 5G connectivity and BLE (Bluetooth Low Energy).
- Use Case: Hyundai’s Bluelink enables features like remote start, real-time diagnostics, and parking fee payments via the car’s dashboard. Over 80% of users report improved satisfaction. 1
2. Manufacturing
Digital transformation enables companies to construct smart plants. Capgemini Research claims that automotive manufacturing companies will transform 24% of their plants into smart factories by 2022, and 49% of companies have already made >$250 million investments in this transformation.
Increased use of manufacturing robots
Companies in the automotive industry have been using robots on assembly lines to increase their productivity since the 1970s, but this is becoming more common.
Manufacturing optimization with ML
Factories use data from IoT and machine learning advanced algorithms to manage their production lines, such as planning and revising schedules, planning maintenance operations of factories, or detecting problematic parts.
Case study: BMW’s Smart Factories
- Technology Used: NVIDIA-powered material handling robots and IoT-enabled neural networks.
- Use Case: BMW uses autonomous robots to detect and transport components in factories, optimizing assembly line efficiency. These robots adapt in real-time using IoT data. 2
3. Predictive Maintenance
Vehicle Maintenance/Service
Intelligent components like sensors gather data about vehicle performance and analyze it in the cloud to:
- Identify potential sources of failure in the future.
- Notifying drivers to prevent incidents
These advanced features may transform the automotive service sector, and we may have a next-generation service that includes new services such as software upgrades.
Maintenance in manufacturing
Deep learning solutions are used to improve maintenance operations by processing production data from equipment and machines.
Supply Chain Optimization
Real-time data from multiple sources is used to manage inventory and purchasing processes. Digitalization in supply chain operations allows companies to optimize their processes. So, they can respond to market changes more quickly. Low costs, supply chain transparency, and minimizing defects can be advantages that digital transformation provides to companies. For example, Bosch can gather data from sensors from its plants and third-party logistics firms to have a complete view of its shipments.
Case study: Tesla’s Proactive Alerts
- Technology Used: IoT sensors and cloud-based machine learning (ML).
- Use Case: Tesla uses real-time sensor data to predict battery cooling system failures days in advance. Drivers receive notifications to schedule maintenance, preventing breakdowns and enabling remote software updates via over-the-air (OTA) fixes. 3
4. Sales
Interactive showrooms
Customers want to make virtual tests before buying a car. Therefore, companies need to set up digital platforms with virtual reality capabilities.
Chatbots
Chatbots can help qualify users, answer their pressing questions, and boost sales.
Case study: Audi City London
- Technology Used: Virtual reality (VR) and interactive displays.
- Use Case: Audi’s virtual showroom lets customers explore car models in full scale via VR, including sound effects and interior views. This reduced physical inventory costs while boosting sales by 60%. 4
5. Generative AI and LLMs
The automotive industry is increasingly leveraging generative AI (GenAI) and large language models (LLMs) to drive innovation across design, manufacturing, customer engagement, and decision-making. While traditional AI focuses on rule-based systems for tasks like predictive analytics, GenAI and LLMs enable creative problem-solving, personalized interactions, and rapid prototyping. Below are key applications and real-world examples.
Generative AI in Design and Simulation
GenAI accelerates vehicle design by generating synthetic data, simulating scenarios, and optimizing components. For instance, automakers use GenAI tools to create thousands of virtual prototypes for aerodynamics, battery efficiency, or crash testing, reducing physical prototyping costs by up to 50%.
Case Study: General Motors + Autodesk
Technology Used: Autodesk’s GenAI-powered Fusion 360 for generative design.
Use Case: GM used generative design algorithms to reimagine EV battery brackets, producing lightweight yet durable components. The AI-generated designs reduced material use by 35% while maintaining structural integrity, accelerating GM’s Ultium EV platform development. 5
LLMs for Customer Experience and Documentation
LLMs like GPT-4 enhance customer support, vehicle manuals, and voice assistants. They interpret natural language queries, troubleshoot issues, and dynamically update documentation based on real-time data.
Case Study: Toyota’s Interactive Repair Manuals
Technology Used: Fine-tuned LLM integrated with vehicle telematics.
Use Case: Toyota deployed an AI-driven manual that answers mechanics’ verbal questions (e.g., “How do I reset the hybrid powertrain error code?”). The LLM cross-references live sensor data with repair histories, cutting diagnostic time by 40% and reducing dealership visits. 6
Synthetic Data for Training and Personalization
GenAI creates synthetic datasets to train AI models where real-world data is scarce. For example, automakers generate virtual driver scenarios to test ADAS (Advanced Driver Assistance Systems) under rare conditions (e.g., snowstorms).
Case Study: BMW’s AI-Driven Marketing
Technology Used: GenAI tools like DALL-E and Stable Diffusion for personalized ads.
Use Case: BMW’s marketing team uses GenAI to generate hyper-personalized ads. By inputting customer preferences (e.g., “adventure travel” or “urban commuting”), the AI produces tailored video ads featuring vehicles in customized settings, boosting click-through rates by 25%. 7
What is digital transformation in the automotive sector?
Like all businesses, automotive companies also want to digitize their businesses (e.g., in terms of data, connectivity, and cybersecurity) to gain numerous benefits such as productivity and observability. Numerous new technologies like process mining and deep learning enable this digitization. These technologies include deep learning algorithms, process mining, task mining, and robotic process automation (RPA).
Why is it important?
Competition in the industry for manufacturers and service providers is tough, and companies must adapt to rapidly changing customer demands. Like in other industries, digital transformation is an unavoidable requirement to:
- Meet customer demands and improve customer experience
- Strengthen position in the marketplace
- Get ahead in the competition
This is especially critical for established manufacturing giants like Daimler, VW, Fiat, etc., given that newcomers like Tesla have been valued at significantly higher valuations despite having limited commercial impact. As a result of the premium that the market places on Tesla, it is the most valuable car manufacturer with a $882 billion market cap in March 2022.
What are the benefits?
With the latest advances in AI, IoT, and data analytics, digital transformation has gained momentum in the automotive industry over the last 20 years. Digital transformation provides important benefits for businesses:
- Launching new digitally enabled services or businesses
- Design of customer-centric products
- Optimization of supply chains
- Increasing productivity (i.e., decreasing operational costs)
- Improvement in quality management
What are the key trends that enable DX?
Environment: As environmental awareness gains importance and environmental policies are made on climate change, people want to use environmentally friendly cars. Automotive companies are trying to build more sustainable vehicles.
Connectivity: Companies in the industry have started to design and produce new cars with connectivity features. Drivers want to change their experience with their cars and expect their vehicles to:
- Connect to apps and social media (e.g., music apps)
- Have customized media content and navigation
- Provide digital assistance for driving
- Enable paying parking fees from the vehicle’s dashboard
Autonomous Driving: Self-driving technology will be real and common in the near future. Advanced algorithms analyze data from cameras and sensors, allowing people to drive safely. However, data privacy and cybersecurity can be tough challenges in this area.
Besides Tesla, many companies are making investments in autonomous vehicles, such as Apple, Audi, Bosch, and Huawei. Autonomous cars are expected to have a 15% market share of passenger vehicles sold worldwide in 2030.
Other trends include:
- Increased process awareness via
- Digital Twins
- Process Mining
- Task Mining
- IoT has numerous applications, but when used on the manufacturing shop floor, it increases awareness about the production process
- Improved analytics via automated machine learning
- Increased back office automation via Robotic Process Automation (RPA)
For example, Cobmax, a sales call center, implemented an RPA solution, which reduced back-office operations by 50%, sold 20,000 products each month, and produced client reports in 1 day vs 2-3 days. 8
What is the future of digital transformation in automotive?
The whole value chain of automotive companies may be digitalized in the future. As a result:
- Companies’ operations can have higher software/technology dependency with the expansion of digital transformation applications.
- More partnerships can be seen between technology vendors and automotive companies.
- New consumer value definitions and business models, data protection, feature upgrades, and cybersecurity will continue to be important subjects shaping digital transformation in the automotive industry.
What are the challenges?
- Investment: Digital transformation investments require significant capital. Return on Investment (ROI) of these digitalization projects can be uncertain for some investments.
- Impact analysis: The expected benefits of digital transformation on business performance and objectives are not easy to see in a short time. This can slow down new projects and investments in the automotive industry.
- Data privacy: Companies in the industry collect consumer, driver, and vehicle data to improve vehicle features and design new products and mobility services. Like other industries, concerns about data security arise in the automotive industry.
- Complexity: Digital transformation means changing the business model and customer definition. It includes major changes, and all operations can affect this transformation. Therefore, the complexity of DX projects may be a challenge for companies. The transformation process should be managed meticulously, and organizational culture and talent change should also be prioritized.
Feel free to read our other articles about digital transformation:
- Digital Transformation
- Digital Transformation Frameworks
- Digital Transformation Consultants
- Reasons for digital transformation (DX) project failure
If you need help in digital transformation for your business, we can help you get started. Let us know:
External Links
- 1. Bluelink Connectivity | Hyundai Motor Europe.
- 2. BMW Outfits Robots with Artificial Intelligence | Automation World. Automation World
- 3. Revamping Vehicle Health for Optimal Predictive Maintenance Insights .
- 4. A showcase becomes a reality: Audi brings VR experience platform to CES 2023 | Audi MediaCenter.
- 5. Lightweight Construction: GM combines generative design and 3D printing.
- 6. Toyota and Generative AI: It’s Here, and This is How We’re Using It - Toyota USA Newsroom.
- 7. Artificial Intelligence at BMW Group.
- 8. Cobmax Sales Center | IBM.
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