Artificial Intelligence in Automotive Companies
We analyzed the enterprise AI use cases of 10 automotive companies to understand trends, impact, and insights.
Automotive companies' adoption of AI
Overview
General Motors has significantly increased its adoption of AI from 2021 through 2025, evolving from licensing cutting-edge AI systems like MENNDL for pattern recognition and collaboration on autonomous vehicle perception systems, to appointing Barak Turovsky as Chief AI Officer in Q1 2025, indicating a strategic commitment to AI-driven innovation.
Ford Motor Company has steadily increased its AI adoption from 2018 through 2025, evolving from early interactive marketing and quality assurance AI applications to advanced autonomous driving initiatives and AI-enhanced manufacturing and training systems.
Tesla has significantly advanced its AI capabilities from 2021 through 2025, focusing on autonomous driving with Full Self-Driving (FSD), humanoid robots (Optimus), and smart factory automation leveraging vision-based neural networks and AI-driven energy management.
Stellantis has progressively integrated AI into multiple facets of its business from 2021 to 2025, evolving from strategic revenue ambitions of €20 billion by 2030 via connected technologies to company-wide autonomous generative AI deployments.
Toyota has progressively expanded its AI initiatives from internal factory efficiency and autonomous driving systems to advanced generative AI techniques for vehicle design, culminating in a substantial $3.3 billion joint investment with NTT in 2024 to develop AI platforms by 2030.
Honda has progressively integrated AI technology across its operations from 2018 through 2025, with significant advancements in autonomous driving, safety systems, patent portfolio management, manufacturing, and marketing for electric vehicles (EVs).
BMW has steadily expanded its AI adoption from initial manufacturing quality control and monotone task automation in 2020 to advanced integration in vehicle assembly, AI-driven quality inspections, and AI-powered smart cockpits by 2025, showing a clear trend of increasing and deepening AI utilization across operations and products.
Mercedes-Benz has steadily integrated advanced AI technologies from 2023 through 2025 across manufacturing, user experience, and vehicle functionalities, evolving from digital factory optimizations to sophisticated AI-powered virtual assistants and production robots.
Hyundai Motor Group has steadily embraced AI from 2020 onwards, initially improving automatic transmission performance, and evolving into large-scale investments, research institutes (Boston Dynamics AI Institute 2022), and extensive AI integration in manufacturing, R&D, marketing, and autonomous mobility by 2025.
Rivian has progressively integrated AI technology across vehicle software, manufacturing, and autonomous driving from Q2 2024 through Q3 2025, demonstrating increased focus and investments in AI-driven capabilities.
53 Use Cases in Automotive
| Company | Use Case |
|---|---|
| Stellantis | Enterprise Automation By late 2025, Stellantis is expanding the application of generative AI technologies developed with Mistral AI from initial pilots to full enterprise-wide deployment, automating and optimizing operations at scale across manufacturing, vehicle development, and customer service. agentic |
| Ford | Augmented Reality Ford uses AI-augmented reality tools to assist service technicians in truck maintenance, improving repair accuracy and addressing labor shortages in skilled trades. generative |
| Toyota | Robotic Automation Toyota collaborates with Boston Dynamics to develop humanoid robots equipped with AI, enhancing automation capabilities in challenging environments for industrial and mobility applications. agentic |
| Hyundai | Mobility Ecosystem Forming public-private alliances such as the Next Urban Mobility Alliance (NUMA) to develop AI-powered transportation and smart city infrastructure transforming urban mobility. agentic |
| Rivian | AI Talent Acquisition Rivian is expanding its AI and autonomous driving capabilities by establishing a research hub in London, targeting recruitment of top AI engineering talent to accelerate innovation. traditional |
| Tesla | Safety Enhancement Tesla's AI systems improve vehicle safety by reducing crash rates via Autopilot’s autonomous features, analyzing vast driving datasets to enhance decision-making and responding in real-time to reduce accidents and improve road safety. agentic |
| Tesla | Predictive Maintenance Tesla applies AI-powered predictive maintenance in factories and vehicles using sensor and operational data to preemptively detect wear or issues, minimizing downtime and improving asset reliability through early interventions. traditional |
| Tesla | Energy Optimization Tesla leverages AI to control HVAC and energy systems within Gigafactories, modeling factory dynamics using sensor data to reduce thermal and fan energy demand, achieving multi-megawatt-hour annual savings and advancing sustainability goals in manufacturing. traditional |
| General Motors | Customer Targeting GM uses data-driven AI to deliver timely marketing and personalized vehicle feature suggestions, enhancing customer experience and driving revenue growth. traditional |
| General Motors | Production Optimization AI enables GM to analyze data in real-time to adjust production lines dynamically, improving efficiency and responding swiftly to changing demands or quality signals. traditional |