The Market Opportunity—and How OEMs Can Capture It
In-vehicle AI is entering a rapid growth phase. Frost & Sullivan projects the AI addressable market across key automotive use cases to grow from $43B in 2025 to over $238B by 2030.
The challenge isn’t identifying AI use cases—it’s scaling them across production vehicles, diverse ECUs, and global platforms without increasing cost or complexity.
This Frost & Sullivan white paper examines where the value is emerging and how automakers can operationalize AI at scale.
What You’ll Learn
- Where in-vehicle AI delivers the highest impact
Prognostics, sensor virtualization, battery health, cybersecurity, UBI, and AI companions—backed by market sizing and TAM analysis. - Why edge AI is critical for mass-market vehicles
Lower latency, reduced cloud costs, stronger data privacy, and compatibility with widely deployed ECUs. - How OEMs can scale AI across vehicles
Unified orchestration across data, models, deployment, optimization, and monitoring. - How Sonatus enables closed-loop vehicle intelligence
Using an Observe–Analyze–Act model powered by Collector AI, AI Director, AI Technician, and Automator AI.
Real-World Use Case Examples Featured
The white paper includes key examples demonstrating how in-vehicle AI is being applied across key domains, including:
- Tire load, wear, and remaining-life prediction
- Virtual sensor implementations such as software-based headlight leveling
- AI-driven battery health and safety diagnostics
- Edge-based, generative AI cybersecurity for intrusion detection
- Embedded, behavior-based usage-based insurance models.
How Sonatus Helps OEMs Capture This Opportunity
Sonatus enables scalable, production-ready AI through a closed-loop Observe–Analyze–Act model:
- Collector AI – Policy-driven, event-triggered vehicle data
- AI Director – Deploys and optimizes AI models across ECUs
- AI Technician – AI-driven diagnostics and aftersales intelligence
- Automator AI – Safely acts on AI insights with low-code orchestration
