Training course · Generative AI · Marketing

Generative AI for Marketing 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 Marketing Teams” is a hands-on course for roles such as Marketing managers and heads of marketing, Performance and digital marketing specialists and Product and brand managers. Rather than learning Generative AI in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Marketing.

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.

Marketing teams now cover more channels, audiences and formats than ever, often on the same budget. Generative AI is fundamentally changing the work: AI assistants support market research, persona development and positioning, draft campaign concepts, landing page copy and ad variants and analyse campaign data. AI agents and automation take over routines such as adapting content for different channels, reporting or enriching leads. The value does not come from mass production but from faster testing, better personalisation and more time for strategy and creativity. Clear brand guidelines as the foundation for prompts, careful handling of customer data and editorial review keep content accurate, legally sound and on brand.

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?

  • Marketing managers and heads of marketing
  • Performance and digital marketing specialists
  • Product and brand managers
  • Marketing operations and CRM owners
  • Agency staff

Typical challenges we address

  • Delivering a consistent brand message across many channels and formats
  • Testing campaigns quickly and drawing meaningful conclusions
  • Sharpening target groups and personas with data
  • Organising content production and approvals with limited resources

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 audience analysis, campaign concepts and copy variants within your brand guidelines.
  • Analyse campaign data with AI support and make well-founded decisions.
  • Automate marketing routines in a privacy-compliant way with clear approval steps.

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: Marketing
  • Audiences and personas
  • Campaign concepts
  • Ad and copy variants
  • Campaign reporting analysis
  • Competitor monitoring
  • Marketing automation
Module 7 Data protection, quality & transfer to daily work
  • Marketing use of AI must comply with the GDPR and ePrivacy rules on tracking, consent and personalisation; customer data belongs only in approved tools. Unfair competition law applies to advertising claims, copyright and trademark law to generated content, and the EU AI Act's transparency duties to AI-generated media.
  • 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

Audiences and personas

An AI assistant condenses market studies, customer feedback and sales notes into personas with needs, objections and buying criteria, then derives key messages and value propositions for campaigns.

Campaign concepts

From the brief, goals and budget, AI develops several campaign ideas with a central idea, channel mix, messages and KPIs, which the team evaluates, combines and refines.

Ad and copy variants

AI creates variants of headlines, ad copy, subject lines and calls to action within brand guidelines and prepares A/B tests whose results are then analysed.

Campaign reporting analysis

Exports from ad platforms, web analytics and CRM are combined, anomalies explained and recommendations drafted, so reporting meetings can focus on decisions rather than data gathering.

Competitor monitoring

AI summarises publicly available information on competitors, such as website messaging, product launches and pricing communication, and highlights differences from your own positioning.

Marketing automation

Workflows enrich new leads, segment contacts, draft personalised nurture emails and notify sales as soon as a lead meets defined qualification criteria.

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 marketing or at an agency and understand the basics of campaign planning and digital channels.

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

Marketing use of AI must comply with the GDPR and ePrivacy rules on tracking, consent and personalisation; customer data belongs only in approved tools. Unfair competition law applies to advertising claims, copyright and trademark law to generated content, and the EU AI Act's transparency duties to AI-generated media.

Suggested dates

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

  • Mon, 23 November 2026 Online Request
  • Tue, 1 December 2026 Hanover Request
  • Wed, 16 December 2026 Online Request
  • Fri, 8 January 2027 Munich Request
  • Tue, 12 January 2027 Online Request
  • Fri, 22 January 2027 Stuttgart 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.

How do we keep AI-generated content on brand?

Start with well-documented brand guidelines covering tone of voice, sample copy and banned terms, and provide them as context to AI assistants. Combined with reusable prompt templates and editorial approval, this produces consistent copy. In the course we build such templates together for your channels.

Who owns the rights to AI-generated content?

Purely machine-generated content is generally not protected by copyright in many jurisdictions, including Germany, whereas works substantially shaped by people can be. You also need to check that outputs do not infringe third-party rights such as trademarks or well-known imagery. We discuss practical rules for approval and labelling.

How much does “Generative AI for Marketing 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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