Training course – Generative AI – Automotive

Generative AI for the Automotive Industry

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
6 hours, one session
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Generative AI for the Automotive Industry” is designed for roles such as Engineering and requirements management staff, Purchasing and supplier management at OEMs and suppliers and Quality management and complaint handling and shows step by step how to use Generative AI productively, safely and transparently in Automotive – from the fundamentals to advanced workflows.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

The automotive industry is under immense pressure to change: electrification, software-defined vehicles, new competitors and demanding regulation are transforming manufacturers, suppliers and dealerships alike. Generative AI supports almost every stage of the value chain – from requirements management in engineering, through supplier communication and complaint handling in purchasing and quality, to sales and aftersales at the dealership. AI assistants help unlock specifications, standards and test reports, structure 8D reports or answer customer enquiries. AI agents connect data from different systems. What matters is protecting engineering know-how, meeting quality and safety standards and having clear approval processes.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Engineering and requirements management staff
  • Purchasing and supplier management at OEMs and suppliers
  • Quality management and complaint handling
  • Sales, service and aftersales at dealerships
  • Production and logistics planning

Typical challenges we address

  • Extensive specifications, standards and requirement documents
  • Heavy documentation workload in quality management and complaints
  • Complex supply chains requiring constant coordination
  • Strict protection of engineering data and intellectual property

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Analyse technical documents such as specifications and standards with AI in a structured way.
  • Accelerate quality, complaint and supplier processes with AI support.
  • Use AI while upholding confidentiality, quality standards and approval processes.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Automotive
  • Analysing specifications
  • Complaints and 8D reports
  • Supplier communication
  • Unlocking standards and processes
  • Sales and aftersales
  • Training and technical documentation
Module 7 Data protection, quality & transfer to daily work
  • Engineering data, design documents and customer specifications are subject to strict confidentiality agreements and industry information security requirements such as TISAX, and may only be processed in approved AI environments. Customer data at dealerships falls under the GDPR, and safety-relevant content always requires expert sign-off.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Analysing specifications

AI extracts requirements from lengthy customer specifications, compares them with the supplier's technical response, flags contradictions and ambiguities and drafts clarification questions for customers or development partners.

Complaints and 8D reports

Complaint descriptions are structured, potential causes gathered using methods such as Ishikawa and 8D reports written in clear language – root cause analysis remains the expert team's job.

Supplier communication

Requests, formal notices, escalations and audit reports are written precisely and in several languages, supplier ratings are commented on and meeting preparation is produced. Agreements are recorded for follow-up.

Unlocking standards and processes

Internal process descriptions, work instructions and quality requirements are made searchable, so staff receive answers with a reference to the governing document. New staff find applicable rules much faster.

Sales and aftersales

At the dealership, AI drafts offers, vehicle descriptions, replies to customer enquiries and service reminders and helps service advisers explain repairs clearly. Customer requests are captured for follow-up in the CRM.

Training and technical documentation

Training materials for assembly, service or new vehicle technologies are created from technical documents, simplified for the audience and translated into other languages. Quiz questions per chapter are included.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. Professional experience at a vehicle manufacturer, supplier or dealership; no programming skills required.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Engineering data, design documents and customer specifications are subject to strict confidentiality agreements and industry information security requirements such as TISAX, and may only be processed in approved AI environments. Customer data at dealerships falls under the GDPR, and safety-relevant content always requires expert sign-off.

Dates & locations

For every location we suggest a date roughly four weeks ahead. Your preferred date is always possible.

  • Munich Wed, 16 December 2026 6 hrs Request
  • Salzburg Thu, 10 December 2026 6 hrs Request
  • Berlin Wed, 16 December 2026 6 hrs Request
  • Augsburg Fri, 11 December 2026 6 hrs Request
  • Hanover Thu, 10 December 2026 6 hrs Request
  • Bremen Tue, 15 December 2026 6 hrs Request
  • Stuttgart Tue, 15 December 2026 6 hrs Request
  • Kaiserslautern Mon, 14 December 2026 6 hrs Request
  • Online Fri, 11 December 2026 6 hrs Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

Can specifications be uploaded to AI tools?

Only into environments approved by your information security team that meet your customers' requirements. Many OEMs regulate this contractually. The course shows how to work with approved enterprise solutions, clean documents beforehand and which content never belongs in external services.

Can AI carry out a root cause analysis?

AI helps gather potential causes systematically, structure methods such as 5 Whys or Ishikawa and write reports. The actual analysis with measurements, tests and process knowledge remains the expert team's task. AI speeds up the path there and improves documentation quality.

How much does “Generative AI for the Automotive Industry” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 6 hours of live training with a trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. For every location we suggest a date roughly four weeks ahead. In the request form you can always describe your preferred date or period in free text – we check availability and get back to you within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run the course exclusively for your team.

Can the course be tailored to our team?

Yes. Tailor-made courses cost approx. €4,500 more, depending on the topic and by arrangement. We agree content, examples and exercises with you in advance.

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