AI for automotive and mobility: Better quality, safer decisions, more resilient processes.
What makes AI successful in automotive and mobility
Companies in the automotive and mobility industry, from suppliers to machine builders and engineering service providers to vehicle manufacturers and technical mobility operators, face similar challenges: high quality requirements, complex supply chains, increasing variety, cost pressure, and the need to make decisions faster and based on data. AI can create real added value in many areas: in manufacturing and quality assurance, in logistics and planning, in service and aftermarket processes, or in engineering, testing, and validation.
However, in projects with companies of different sizes, we often see the same stumbling blocks: scattered or inconsistent data, unclear responsibilities between production, engineering, IT, and commercial departments, and uncertainty regarding traceability requirements or the evaluation of economically viable AI use cases. Promising ideas then remain stuck in pilot status without any noticeable effect on quality, costs, or process stability.
AI becomes successful when companies prioritize in a structured manner, take data realities into account, involve specialist departments at an early stage, and start with manageable use cases – whether in production, logistics, service, or technical development. This is exactly where the AI Innovation Lab of CyberForum, a cooperation project within the AI Alliance, comes in: We provide guidance, develop viable concepts, and support you in using AI in a way that improves quality, strengthens planning, and stabilizes processes in the long term.
Challenges in AI projects
- Strict quality and traceability requirements: AI must be stable, traceable, and auditable.
- Complex supply chains and volatile demand make forecasting and planning difficult.
- Inconsistent data and systems hinder robust AI models, especially when there is a wide variety of variants.
- Uncertainty in selecting meaningful AI use cases: technical, economic, and organizational.
Our approaches to solutions
- Develop AI strategy and governance in line with industry quality and process requirements.
- Define viable AI use cases: e.g., in quality assurance, predictive maintenance, planning, or service.
- Evaluate data quality and system landscapes: for reliable, scalable AI projects.
- Bringing together IT, production, and specialist departments for clear decisions and responsibilities.
- Bring in suitable implementation partners with expertise in automotive, mechanical engineering, and engineering.
Our formats for automotive & mobility
Free initial consultation & AI readiness check
Together, we analyze where AI can deliver real benefits in your company, e.g., in development, manufacturing, quality assurance, service, or administration. In doing so, we take into account typical challenges such as product variety, complex supply chains, and high quality requirements, clarify open questions, and develop the first steps for your AI roadmap.
AI strategy and governance
We develop an AI strategy that combines economic potential, technical feasibility, and industry-specific requirements. This includes roles, responsibilities, data and process requirements, and the integration of relevant quality and audit standards. The result is a robust, practical roadmap for your AI entry.
Development of AI use cases
We identify realistic use cases, such as automated quality checks, anomaly detection, production and demand forecasts, variant and complexity management, supply chain forecasts, or AI-based document and knowledge tools. We evaluate benefits, data availability, and feasibility, and prioritize the use cases that fit your processes, resources, and market requirements.
Matching suitable implementation partners
We connect you with implementation partners who have experience in complex production environments, engineering processes, quality standards, and supply chain requirements—from specialized AI service providers to system integrators, software providers, and research institutions.
Employee training & enablement
We offer target group-specific training courses on AI basics, data quality, process integration, variant and complexity management, governance, and the EU AI Act. For developers, we teach technical skills such as machine learning, MLOps, computer vision, and RAG systems. AI literacy programs help all employees use AI safely and productively in their everyday work.
Startup cooperations & venture clienting
Through CyberLab and our community, you gain access to startups with AI solutions, e.g., for computer vision, process and quality optimization, predictive maintenance, or supply chain analyses—often with on-premise options and industry-specific integration. We select suitable partners and support pilot projects in a structured and low-risk manner.
Funding advice
We identify suitable programs for your projects. Whether digitization, AI development, process automation, or research projects: we support you in finding suitable funding programs and aligning them optimally with your project goals.
Your contact person
Inspiration & practical examples from the automotive and mobility sectors
Voices from the field
As I walked out of the cell through the door towards freedom, I knew I had to leave my bitterness and hatred behind or I would remain a prisoner for life.
Those who give up freedom in order to gain security will end up losing both.
Freedom is like the sea: the individual waves are not very powerful, but the force of the surf is irresistible.
Marlies Schwarz: Let's work together to find out how AI can bring concrete benefits to your company.
- Customer Success Manager
- marlies.schwarz@cyberforum.de
- +49 721 602 897 664