Training course · Generative AI · Quality Management

Generative AI for Quality Management Teams

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

Duration
1 day (7 hrs)
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 Quality Management Teams” is designed for roles such as Quality managers and management representatives, Quality engineers and quality planners and Internal auditors and shows step by step how to use Generative AI productively, safely and transparently in Quality Management – 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.

Quality management depends on clean documentation, systematic analysis and rigorous follow-up of actions. Generative AI can relieve quality teams exactly there: AI assistants create and revise procedures, prepare audits, analyse complaints, support 8D reports and FMEA and compare documents with standard requirements such as ISO 9001 or IATF 16949. Automation sends reminders about action deadlines, evaluates inspection data and consolidates supplier ratings. That leaves more time for root cause work, process improvement and collaboration with production and engineering. AI output must not replace conformity with standards; it needs expert review and must enter the management system through controlled processes. When document control and approval workflows are respected, AI becomes a reliable tool for quality and efficiency.

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?

  • Quality managers and management representatives
  • Quality engineers and quality planners
  • Internal auditors
  • Complaint and supplier quality managers
  • Process owners in management systems

Typical challenges we address

  • Keeping extensive quality documentation current, consistent and compliant with standards
  • Analysing and resolving complaints and nonconformities quickly
  • Preparing internal and external audits efficiently
  • Reliably tracking actions from audits, 8D reports and management reviews

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.
  • Create and maintain quality documentation efficiently and in line with standards using AI.
  • Use AI for complaint analysis, 8D, FMEA and audit preparation.
  • Integrate AI output into your management system in a controlled and traceable way.

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: Quality Management
  • Writing procedures
  • Standards gap analysis
  • 8D report support
  • FMEA preparation
  • Audit preparation
  • Complaint data analysis
Module 7 Data protection, quality & transfer to daily work
  • AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.
  • 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

Writing procedures

An AI assistant turns process descriptions and interviews into procedures and work instructions with a consistent structure, checks them for contradictions with other documents and suggests improvements.

Standards gap analysis

AI compares existing documents with the requirements of ISO 9001, IATF 16949 or ISO 13485, highlights possible gaps and produces an action list for expert evaluation.

8D report support

For complaints, AI structures the problem description, suggests containment actions and possible root causes and writes the 8D report in a form customers can understand.

FMEA preparation

Based on process steps and experience, AI suggests failure modes, effects and causes and helps the team facilitate and document FMEA sessions.

Audit preparation

From standard clauses and the process map, AI creates audit questions, checklists and audit plans and condenses audit notes into a structured draft report with findings.

Complaint data analysis

Complaints and inspection results are analysed by defect type, product and supplier, trends identified and priorities for improvement projects proposed.

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. You work in quality management and know the requirements of your management system, for example ISO 9001.

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

AI-generated documents must meet the standard's requirements for controlling documented information and be reviewed by experts before release. Customer data from complaints and confidential product information may only be processed in approved, GDPR-compliant AI environments.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Mon, 23 November 2026 Online Request
  • Mon, 7 December 2026 Berlin Request
  • Tue, 15 December 2026 Online Request
  • Tue, 12 January 2027 Online Request
  • Wed, 27 January 2027 Stuttgart Request
  • Wed, 3 February 2027 Online 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.

Do auditors accept AI-generated documents?

What matters is not who drafted a document but whether it is technically correct, controlled, approved and applied in practice. If you review AI drafts, release them through your document control and keep responsibility clear, they meet the standard's requirements. We show how to integrate AI cleanly into these processes.

Can AI replace an FMEA?

No. An FMEA relies on the team's cross-functional knowledge of product and process. AI can prepare, make suggestions and document, which makes sessions more efficient. Ratings and decisions on actions remain with the team, which is accountable for the result.

How much does “Generative AI for Quality Management Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm 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 or Kaiserslautern. We can also run the course exclusively for your team.

More courses for Quality Management Teams

More Generative AI courses

All 45 Generative AI courses

Generative AI · MunichGenerative AI · SalzburgGenerative AI · BerlinGenerative AI · AugsburgGenerative AI · HanoverGenerative AI · BremenGenerative AI · StuttgartGenerative AI · KaiserslauternGenerative AI · Online