Training course · Generative AI · Customer Service

Generative AI for Customer Service 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

This course connects Generative AI with the everyday processes of Customer Service. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

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.

Customer service teams are the voice of the company: they answer questions, solve problems and have a major influence on customer satisfaction. Generative AI is changing service in many ways. AI assistants suggest replies to tickets and emails, summarise long conversation histories, translate enquiries and search knowledge bases in seconds. AI agents and chatbots answer routine requests around the clock and hand complex cases over to agents with full context. Automation classifies requests, sets priorities and detects sentiment. This gives service staff more time for demanding cases and empathetic conversations. The prerequisites are a well-maintained knowledge base, clear escalation rules, transparency towards customers and careful handling of the personal data in every ticket.

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?

  • First and second-level service agents
  • Team leads and service managers
  • Knowledge managers and service trainers
  • Customer success managers
  • Contact centre and service process owners

Typical challenges we address

  • Handling high ticket volumes while maintaining response quality
  • Keeping knowledge up to date and quickly accessible for everyone
  • Onboarding new staff quickly
  • Handling customer requests consistently across languages and channels

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.
  • Use AI for reply suggestions, summaries and ticket routing in your service process.
  • Build an AI-ready knowledge base and define clear escalation rules for chatbots.
  • Deploy AI in customer service transparently and in compliance with data protection rules.

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: Customer Service
  • Reply suggestions
  • Ticket classification and routing
  • Case summaries
  • Knowledge base building
  • Self-service assistant
  • Quality analysis and coaching
Module 7 Data protection, quality & transfer to daily work
  • Tickets contain personal data, so processing them in AI tools requires a GDPR-compliant data processing agreement; under the EU AI Act chatbots must be clearly identifiable as AI. If conversations are recorded or agent performance data analysed, works councils must be involved, for example under the German Works Constitution Act.
  • 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

Reply suggestions

An AI assistant analyses the request, searches knowledge articles and past resolutions and suggests a reply in the company's tone of voice, which agents adapt and send.

Ticket classification and routing

Incoming requests are automatically categorised by topic, product, urgency and sentiment and routed to the right team, with critical cases prioritised.

Case summaries

Long email threads, chat logs or call notes are condensed into a short summary of the problem, steps taken and open issue, ideal for handovers and escalations.

Knowledge base building

From resolved tickets and expert knowledge, AI drafts knowledge articles and FAQs, spots documentation gaps and keeps existing articles up to date with suggested changes.

Self-service assistant

An AI-powered chat answers frequent questions based on approved content, collects structured information for complex cases and hands them over to agents with context.

Quality analysis and coaching

AI reviews ticket samples against defined quality criteria, identifies recurring customer issues and gives team leads pointers for coaching and process improvement.

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 customer service or customer success and know the ticketing or CRM systems your team uses.

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

Tickets contain personal data, so processing them in AI tools requires a GDPR-compliant data processing agreement; under the EU AI Act chatbots must be clearly identifiable as AI. If conversations are recorded or agent performance data analysed, works councils must be involved, for example under the German Works Constitution Act.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Mon, 7 December 2026 Berlin Request
  • Thu, 17 December 2026 Online Request
  • Thu, 7 January 2027 Online Request
  • Tue, 12 January 2027 Stuttgart Request
  • Wed, 27 January 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.

Will an AI chatbot replace our service team?

No. Good chatbots take care of routine questions, but complex, emotional or unusual requests need people. What matters is a smooth handover with full context. In the course we show how to define which requests are automated and when a human takes over.

How do we prevent chatbots from giving wrong answers?

Restrict the chatbot to verified knowledge content, show sources and define topics that are always handed to agents, such as legal questions or cancellations. Regular reviews and tests with real requests keep quality high over time. Clear feedback loops from agents help close knowledge gaps quickly.

How much does “Generative AI for Customer Service 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.

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