Generative AI in automotive

A recent McKinsey survey of automotive and manufacturing executives revealed that more than 40% of respondents are investing up to almost USD 6 million in gen AI research and development. More than 10% of respondents are investing more than USD 23 million.¹

AI technologies support a range of technologies in the automotive industry, including predictive analytics and self-driving capabilities. This page focuses specifically on generative AI and its growing impact on vehicle design, engineering and customer experience.

At a broader level, gen AI refers to AI systems that can produce original content—such as text, images, video or software—based on patterns it has learned from large datasets. 

Gen AI is powered by advanced machine learning (ML) techniques, especially deep learning, which mimics how the human brain recognizes and processes information. Many generative systems rely on large language models (LLMs) and natural language processing (NLP), which allow them to understand prompts and generate text that resembles human communication.

Generative AI models learn to predict what content should come next. Areas that currently see the most use of generative AI include product development, customer engagement, operational efficiency and technology modernization.²

In the automotive sector, generative AI is reshaping how vehicles are imagined and built. Designers can input rough sketches or technical constraints into AI tools that generate polished visualizations, propose aerodynamic shapes or suggest structural improvements—all much faster than traditional methods. These AI-driven outputs reflect the iterative nature of modern vehicle design. Many tools can also simulate crash tests, airflow and weather conditions virtually, reducing the need for physical prototypes and speeding up development.

Behind the scenes, generative AI is benefiting engineering and manufacturing teams. For example, it helps identify the best materials and layouts to balance strength and weight. It can detect quality issues on the production line by using computer vision and improve supply chain planning by predicting disruptions and managing inventory more precisely. These applications reduce errors and uncover hidden vulnerabilities in production processes.

75% of auto industry executives say that the software-defined experience will be the core of brand value by 2035.³ Generative AI in software development is now used by automakers to write, review and refactor code, especially for embedded systems that power safety features and infotainment.

Gen AI tools use learning models and algorithms to streamline development and save time on documentation, prototyping and compliance checks. Still, integrating AI into safety-critical systems requires new validation processes and skilled oversight.

Generative AI is also reshaping how automakers interact with both potential and current customers. For potential buyers, AI can create personalized content along the entire buyer’s journey—from targeted ads to tailored landing pages. Brands like Mercedes-Benz and BMW are exploring these kinds of personalized AI use cases to improve marketing and outreach.

Inside the vehicle, generative AI enhances the driving experience itself. In-car assistants learn driver preferences and automatically adjust routes, climate and entertainment. For example, Mercedes-Benz has integrated ChatGPT into more than 900,000 vehicles as part of a beta program, offering more advanced and personalized voice interactions.⁴ Some systems can even generate immersive visual or audio experiences tailored to individual passengers.

Outside the vehicle, AI-powered chatbots and virtual assistants are improving the customer experience. They answer questions, recommend financing options and schedule service appointments. These tools streamline interactions across websites, apps and dealership systems.

Generative AI also powers predictive maintenance systems, which monitor vehicle data in real time to alert drivers before a part fails, helping reduce downtime and build long-term trust.

Generative AI has moved from experimentation to execution in the automotive world; it’s streamlining operations, unlocking new ideas and enabling faster and more adaptive responses. As the industry evolves, this technology plays a central role in how vehicles are designed, developed and delivered.

Generative AI is important to the automotive industry because it fundamentally aligns with the industry's transformation into a software-driven, customer-centric ecosystem.

As the industry shifts from mechanical engineering to digital and data-led operations, generative AI plays a growing role. It becomes a strategic enabler that helps companies move faster. It also supports deeper personalization and unlocks value from increasingly complex systems. AI helps manage this complexity by accelerating everything from code generation to simulation.

For example, McKinsey found that integrating generative AI into development environments can cut time spent on coding tasks like writing, translating and documenting by up to 40%.¹ This allows companies to efficiently scale their digital ambitions and gain a competitive advantage in speed, cost and innovation.

Generative AI is helping reinvent the car—not just as a product, but as a service and an experience. Electric vehicles, autonomous vehicles and connectivity are changing what mobility means. Gen AI allows automakers to adapt quickly. It supports personalized in-car experiences, faster design cycles and smarter customer support that feels human, all driven by rapid advancements in AI technology. This kind of real-time adaptation was nearly impossible at scale just a few years ago.

In this context, generative AI is not just a tool—it’s a force multiplier. It helps legacy automakers compete with agile, tech-native challengers. It also gives startups a way to enter the automotive space with less capital. As the industry shifts toward software, generative AI becomes essential. It supports competitiveness, helps meet growing customer expectations and enables the creation of new vehicles and business models.

Leading providers are supporting this shift. For example, Microsoft is investing heavily in automotive NLP and LLM tools. AWS is partnering with BMW and Toyota for connected car platforms and voice assistants. And Alphabet’s Android Automotive OS includes Google Assistant integration for voice-controlled infotainment and navigation.

Some key generative AI use cases in the automotive industry include:

• Advanced driver assistance systems (ADAS)
• Customer support and service
• Digital twins and autonomous driving simulation
• Manufacturing and supply chain optimization
• Marketing and localization
• Personalized in-vehicle experiences 
• Predictive maintenance and diagnostics
• Software development
• Training automation
• Vehicle design and prototyping

Generative AI plays an emerging role in enhancing ADAS by improving how vehicles interpret their surroundings. It is expected to reduce software-defined vehicle testing and simulation workloads by nearly 40% over the next three years, improving the efficiency of autonomous system development.⁵

By training on large image datasets, gen AI can accurately detect, recognize and track objects like pedestrians, vehicles and traffic signs in real time. It also enables semantic segmentation, helping ADAS systems understand and differentiate between road elements such as lanes, sidewalks and signs. These capabilities support features like collision warnings, lane departure alerts and emergency braking that make driving safer and more predictable.

For example, BMW is building a new driver assistance system for its 2025 Neue Klasse vehicles by using Amazon Web Services (AWS). The system uses cloud-based tools, including generative AI, to improve safety and features.⁶

Generative AI is making customer interactions with dealerships and service centers more accessible, personalized and efficient. Instead of waiting on hold or digging through manuals, drivers can get real-time, intelligent assistance from AI-powered tools.

At dealerships and OEM service centers, gen AI-powered virtual agents handle topics like vehicle recommendations, financing questions and service scheduling. These assistants understand natural language, respond instantly and adapt their tone and detail to the customer’s intent. All this improves customer satisfaction and lowers support costs.

AI is also transforming traditional car manuals into interactive, conversational guides. Drivers can ask specific questions and get precise, vehicle-specific answers without searching through pages of documentation.

For roadside assistance, gen AI-enabled agents analyze vehicle data or interpret the driver's description of an issue, helping pinpoint problems quickly and accurately. This AI initiative speeds up resolution, reduces unnecessary service calls and improves fleet maintenance planning.

Data from customer interactions helps automakers understand common vehicle issues and improve future designs, while deepening trust between brands and drivers.

Generative AI produces detailed digital twins of vehicles, production lines or traffic environments. These virtual models can run thousands of virtual miles of road tests, including edge-case scenarios like sudden pedestrian crossings or extreme weather. This is valuable in training and validating autonomous driving software, reducing reliance on real-world testing alone.

In production environments, generative AI helps forecast supply chain needs, spot potential disruptions and optimize inventory. It enables real-time tracking and management of materials to help ensure that the right parts are available at the right time to avoid manufacturing delays.

It also improves quality control by analyzing images or sensor data to detect defects on the assembly line. These capabilities streamline manufacturing while reducing waste and cost.

Automotive companies are using gen AI to produce hypertargeted content at scale, from personalized video ads and landing pages to localized vehicle brochures. It supports localization by translating and adapting materials for different regions or demographics and tailoring messages to specific customer profiles. This allows for faster, more relevant marketing across global markets.

Brands are also using it to optimize pricing strategies based on customer behavior. For example, Toyota and Ford use these tools to customize messages for different customer segments.

Automotive industry executives anticipate productivity gains of 7% in customer support and a 5% improvement in overall marketing budgets and customer acquisition metrics due to AI integration.⁵

Generative AI-powered chatbots and voice assistants enable adaptive, human-like interactions between passengers and their vehicles. These assistants understand context and intent, respond to voice commands and interact with the driver in a conversational way. They allow drivers to stay focused on the road while activating navigation, playing music, sending messages and controlling other vehicle functions with their voice.

Gen AI also supports dynamic infotainment systems that curate entertainment based on mood and location. Gen AI tools are incorporated into smart features that adjust climate, lighting and navigation preferences. These tools are the next step toward hyperpersonalization and “software-defined mobility” that’s designed to improve the user experience and make the car feel more like a digital companion than a machine.

Using telematics—technology that uses GPS and on-board diagnostics to collect vehicle data—and real-time sensor data, generative AI models can predict when a part is likely to fail before issues occur. This helps reduce downtime and extend vehicle life. It also helps with managing car fleets.

These systems generate personalized service alerts, analyze vehicle data for better decision-making and can even recommend the best time and location for repairs. They are increasingly integrated with over the air (OTA) update systems. For example, Tesla vehicles use AI-driven diagnostics to detect early signs of battery or motor issues.

Generative AI automates critical parts of software development for vehicles, especially embedded systems like ADAS, infotainment and battery management. It can write, refactor and document code, generate test cases and draft technical requirements.

A McKinsey survey indicated a 44% improvement in productivity when using gen AI with software quality assurance measures, such as creating and automating tests to enhance efficiency and code reliability.¹

These abilities significantly accelerate development while streamlining software development workflows and reducing the workload for engineers. However, most systems still require rigorous validation to meet safety standards.

Automakers are using gen AI to generate training guides, service walkthroughs and technical documentation for employees. These tools can simplify complex procedures, simulate real-world support questions and localize content quickly. This helps frontline staff stay current with evolving vehicle technologies such as electric vehicles (EVs) and connected cars.

Generative AI is reshaping vehicle design by helping engineers explore more efficient and innovative structures. By inputting constraints like weight, materials and safety requirements, teams can generate optimized designs—often lighter, stronger or more cost-effective than human-created versions. This includes topology optimization (improving material layout and structure within a particular 3D geometrical design space), weight reduction strategies and 3D-printed components. These tools also speed up virtual trials, making prototyping faster and less expensive while supporting sustainability.

Several benefits of generative AI in automotive have been described previously, such as faster design cycles, more efficient software development and more personalized in-vehicle experiences. Those examples show how generative AI can speed up workflows, reduce costs and enhance customer experience.

In addition to those areas, generative AI offers several broader and emerging advantages:

• Enhanced metrics and performance tracking: AI-generated dashboards and analytics summaries give stakeholders faster visibility into KPIs across engineering, sales and support teams. These insights help improve alignment and accountability and support more informed decision-making.
• Faster onboarding: As described above, generative AI helps capture institutional knowledge and turn it into searchable, interactive content for employee training. This automated system of training helps new employees ramp up faster and reduces the strain on more experienced staff.
• Proactive cybersecurity support: AI models can help identify potential vulnerabilities in connected vehicle systems, flagging suspicious patterns or weak code that might otherwise go undetected.
• Smarter pricing strategies: AI can analyze everything from customer behavior to regional trends to help automakers fine-tune their pricing strategies. It’s especially useful when launching new models across global markets.
• Streamlined compliance and documentation: Generative tools can automatically generate or update technical documentation, certification reports and regulatory submissions. This ability helps teams keep pace with fast-changing standards, especially in EV and ADAS development.

Manufacturers must take a strategic and structured approach to unlock the full potential of generative AI in automotive.

65% of auto OEM executives say that they have a clear approach to integrate AI into long-term innovation strategy. 79% also say that senior leadership strongly supports AI investment.⁵

Some best practices to help ensure safe, scalable and value-driven generative AI implementation include:

Focus on generative AI applications that address specific automotive needs, such as software generation, vehicle design optimization or customer support. McKinsey notes that organizations succeed by “selecting the right use cases for gen AI,” balancing impact with feasibility.¹

Generative AI can help streamline every phase of the automotive software stack, from embedded systems like battery management to advanced systems like infotainment and ADAS. Teams can use it to draft functional requirements, design architecture diagrams for ECU networks, write and review embedded code and generate documentation that complies with safety standards. Embedding gen AI across the lifecycle enhances speed and traceability in a heavily regulated development environment.

Generative models need access to high-quality training data and purpose-built ML infrastructure to be effective, especially when used for predictive maintenance, simulation or vehicle personalization. Strive for clean, labeled datasets from domains like sensor fusion, vehicle diagnostics and customer interactions.

Gen AI requires new workflows, collaborative structures and digital talent. OEMs and Tier 1 suppliers will need to invest in upskilling engineers who work on systems like ADAS, cybersecurity or human-machine interface (HMI) design to understand and oversee gen AI outputs responsibly.

McKinsey emphasizes adapting operating models and upskilling employees to succeed with gen AI—especially in safety-critical software.¹

In automotive, especially for embedded systems and ADAS, rigorous validation and human-in-the-loop checks are essential. Gen AI integration must align with regulatory guidelines like ISO 26262 (an international standard for electrical and electronic systems in road vehicles) and internal safety standards.

Design gen AI solutions on secure, scalable cloud infrastructure. AWS, Azure and custom solutions support flexibility. Designing gen AI applications with modularity enables integration with existing automotive development environments like model-based design tools or product lifecycle management (PLM) systems. This foresight allows future upgrades without disrupting the full software pipeline.

In automotive, more than just productivity needs to be tracked. KPIs can include, for example, time saved in ECU calibration, accuracy of software-generated diagnostic reports or reduction in dealership support tickets. These metrics can help teams decide how and where gen AI tools are deployed.