Data and AI Driven Automotive Innovation

Data is the foundation of innovation
The automotive industry is experiencing a data revolution. With the rise of connected vehicles and the Internet of Things (IoT), cars are becoming increasingly intelligent and connected. Industry projections suggest over 40 billion connected devices will soon produce a staggering 175 zettabytes of data (IDC). In the automotive sector alone, connected vehicles have surpassed 400 million globally, with over 30 million equipped with advanced 5G connectivity (EETimes, Hash-studioz).

From real-time navigation and telematics to autonomous driving capabilities, modern vehicles are producing unprecedented volumes of information. However, a critical challenge emerges: Forrester reports that only 1% of this valuable data is actually utilized (Forrester).

Often, this valuable data remains largely trapped in disconnected silos across the automotive value chain. Data from different parts of the business remains disconnected – information from vehicles on the road can’t be easily combined with factory data, and supply chain information isn’t readily available to product designers, sales or marketing. When production issues arise, manufacturers struggle to correlate them with vehicle usage data. When engineers design next-generation features, they lack unified visibility into real-world usage patterns and supply chain constraints.

This fragmentation not only limits the industry’s ability to receive meaningful insights but also slows down vehicle development, lowers operational efficiency and limits personalized, connected experiences that customers increasingly expect. To enable faster innovation, automotive companies need to break down these data silos. Enterprise-wide access to data is a prerequisite to unlock the true value that generative and agentic AI can bring to the industry and is needed for automotive companies to remain competitive.

With data and AI as the foundation, the automotive industry is now entering an era of autonomous AI agents – systems that can independently reason, plan, and execute complex tasks across the automotive value chain. This shift from AI that simply generates outputs to AI that drives outcomes represents a fundamental transformation in how vehicles are designed, manufactured, and operated.

What makes generative and agentic ai essential for automotive success
For years, automotive companies like Honda, Toyota, BMW, Volkswagen, Hyundai and others have used AWS to connect their vehicles to the cloud in order to offer connected services and remote software updates. With generative AI, we see new transformative opportunities emerging. It is fundamentally changing how automotive companies operate, innovate and deliver value. By combining the power of large language models with industry-specific data and expertise, generative AI is unlocking new capabilities.

Accelerating vehicle development
First, generative AI enables automotive engineers and developers to build sophisticated and complex vehicle systems faster and with higher quality. Automotive companies are creating development assistants powered by generative AI trained on internal best practices, proprietary data and documentation. These AI assistants understand natural language, analyze complex data and code, identify opportunities for optimization and can generate new code or synthetic data. Design agents then autonomously iterate through thousands of design possibilities while balancing performance, cost, and manufacturing constraints.

Automakers are using generative AI to transform how they develop autonomous vehicles (AV) and advanced driver assistance systems (ADAS). Rather than manually scanning through hours of video footage, they now use natural language queries like »pedestrians’ crosswalk rainy day« to instantly find relevant scenes needed for model training. This capability allows engineers to focus more time on innovation and accelerate feature delivery to production.

Leading automotive companies are leveraging generative AI to produce synthetic training data for complex, rate traffic and weather scenarios. Synthetic scene generation allows engineers to create scenarios for edge cases on-demand to continually improve perception, prediction and motion planning algorithms.

Improving the customer experience
Additionally, generative AI powers applications that help manufacturers, fleet operators and end users interact naturally with vehicle data to generate actionable insights. AI-powered assistants are making data more accessible for end users and technicians alike. For end users, having a natural language interface allows them to quickly get answers to common questions. Instead of searching through hundreds of pages in a user manual, users receive clear responses about vehicle features and can be directed to service centers for repair and maintenance needs.

At the same time, AI makes it easier for service technicians to resolve issues. Rather than interpreting cryptic diagnostic codes, technicians can simply describe issues such as »check engine light on and reduced fuel efficiency.« The system analyzes these descriptions, cross-references its knowledge base and provides detailed diagnostics with step-by-step repair instructions, enabling faster issue resolution.

For example, Ferrari has achieved remarkable operational improvements with Amazon Bedrock. Custom vehicle configurations increased by 20%, and over 1,000 technical users benefited from an AI-powered knowledge base, and 60% faster simulation times for customized models. Ferrari leveraged multiple foundation models to enhance vehicle design processes and create personalized customer experiences, which includes an immersive virtual configuration platform.

These examples represent just the beginning of generative AI’s potential in automotive. As the technology evolves, we are seeing new applications emerge across design, manufacturing supply chain optimization and customer experience personalization.

How automakers can get started
The path to AI implementation is now simpler than ever before. Instead of building complex systems from scratch, automotive companies can access ready-to-use AI capabilities that integrate with their existing operations. This approach saves time, reduces costs, and allows companies to focus on innovation rather than infrastructure.

Amazon Bedrock embodies this evolution, providing unified access to foundation models through secure APIs, which organizations can implement without managing underlying infrastructure. As organizations scale their AI implementations, they consistently face a critical challenge which is balancing sophisticated AI capabilities with operational costs and performance requirements. Modern industrial applications demand varied forms of AI understanding, from processing technical documentation to analyzing visual data from production lines.

Just as different tasks require different types of expertise, AI solutions need to be tailored to specific business needs. That’s why Amazon introduced Amazon Nova, a new generation of AI models offering different tiers of capability. Nova Micro provides fast processing for text-based tasks, while Nova Pro handles more complex work involving text, images, and video. This range of options lets organizations choose the right solution for their specific needs and budget.

The future: AI agents and autonomous operations
While the advancements in generative AI are impressive, the move towards AI agents will be even more transformative. The current solutions provide responses to commands, and AI agents can execute tasks across workflows. Using solutions like Amazon Bedrock, customers can implement autonomous systems that orchestrate entire processes across the automotive value chain. These AI agents work together across organizational boundaries, connecting previously siloed operations into seamless, intelligent workflows that drive efficiency and innovation.

We see three key trends that will meaningfully drive productivity:
– In product development and engineering, AI agents enhance design processes by simultaneously analyzing CAD designs, simulating aerodynamics and suggesting optimizations. While at the same time continuously learning and adapting their approaches, which accelerate innovation cycles while improving design quality.
– In manufacturing operations, AI agents orchestrate complex workflows that coordinate robots, manage supply chains and ensure quality control. These systems make split-second decisions that once required multiple human interventions, enabling more efficient and resilient production processes.
– In connected vehicle operations, AI agents process vehicle telemetry data to enable real-time diagnostics and predictive maintenance. These systems can interpret complex sensor data, identify potential issues before they become critical and recommend proactive maintenance schedules.
– AI is revolutionizing in-vehicle interactions and advanced natural language processing, which allows for intuitive voice commands and conversations with vehicle systems. This technology delivers personalized user experiences, adapting to individual preferences for things like climate control, entertainment and navigation. This creates a more comfortable and efficient driving environment.

BMW exemplifies this transformation, as they are undergoing an enterprise-wide AI transformation, leveraging Generative AI across its entire value chain to enhance workflows and product development.

A key enabler is the BMW Group AI Assistant, built on AWS, which empowers employees to create AI applications and agents for use in their daily work, and to share them with other colleagues.