AI in automotive
AI in automotive: ADAS and autonomy, manufacturing and quality, connected-car services, and the safety/validation bar.
This block builds practical AI fluency for the automotive sector, spanning the full value chain from design and manufacturing to connected vehicles, autonomous driving, and aftersales. You will learn how core AI concepts map to real automotive problems such as predictive maintenance, computer vision for quality inspection, ADAS perception, and demand forecasting. The block clarifies where AI genuinely creates value versus hype, how to evaluate vendor solutions and build-versus-buy decisions, and how to assess adoption and ROI with realistic expectations. It also covers automotive-specific AI regulation, functional safety, model risk, and the concrete guardrails and validation checks required before deploying AI into vehicles and plants.
What you'll master
- Identify high-value AI use cases across the automotive value chain and distinguish them from low-return or hype-driven initiatives
- Evaluate AI solutions and vendors using sector-relevant criteria and build a realistic ROI and adoption case
- Apply automotive AI regulation and functional safety frameworks to a proposed deployment
- Run pre-deployment guardrails and model risk checks for vehicle and manufacturing AI systems