Training course – n8n – Media & Journalism

n8n for Media and Journalism

Workflow automation with n8n and AI: master nodes, the AI Agent node, integrations and GDPR-friendly self-hosting in practice.

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

This course connects n8n with the everyday processes of Media & Journalism. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

n8n is a flexible workflow automation platform that combines visual flows with language models and hundreds of integrations. Unlike many cloud services, n8n can also be self-hosted, so your data can stay in your own data centre or with a European hosting provider. In this compact course you will learn how triggers, nodes and data flows fit together, how to transform data between applications and how to automate recurring tasks from your day-to-day work reliably.

The core of the course is the combination of automation and AI. Using the AI Agent node, chat models, memory and tools, you build workflows that classify emails, summarise documents, enrich records or answer enquiries. You learn to catch errors, test workflows and document them properly. Finally, you look at hosting options, credential management and data protection, so that your automations run stably, securely and in line with GDPR once they go into everyday operation.

Newsrooms, publishers, broadcasters and content teams are caught between economic pressure and the demands of quality journalism. Generative AI is fundamentally changing research, production and distribution. It helps reporters work through documents and datasets faster, transcribe and summarise interviews, develop headline and teaser variants, adapt stories for different channels and make archives searchable. AI agents can sort wire copy or handle monitoring tasks. At the same time, AI raises core questions: how do you safeguard accuracy, how do you make AI use transparent to your audience and how do you respect copyright? Media professionals therefore need competence in both productive use and critical verification.

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?

  • Reporters and editors in print, online and broadcast
  • News desk chiefs and section editors
  • Social media and audience teams
  • Freelance journalists
  • Content and corporate publishing teams

Typical challenges we address

  • Intense time pressure alongside a growing number of channels and formats
  • Large volumes of documents, data and wire material
  • Safeguarding accuracy and credibility
  • Unresolved questions about copyright and labelling of AI content

What you will be able to do

  • You can build n8n workflows independently using triggers, logic nodes and data transformations.
  • You can connect n8n to the applications you use every day and fall back on the HTTP Request node where needed.
  • You can use the AI Agent node and other AI building blocks for classification, extraction and summarisation.
  • You can safeguard workflows with error handling, validation and human approval steps.
  • Use AI for research, transcription and production efficiently and faithfully to sources.
  • Check AI output systematically for accuracy and avoid typical pitfalls.
  • Apply rules on copyright, labelling and press ethics confidently when using AI.

Course outline

Module 1 Fundamentals: workflows, triggers and nodes
  • Getting to know the n8n interface, canvas and execution view
  • Trigger types: schedules, webhooks, incoming email and app events
  • Understanding items and JSON data and working with expressions
  • Using core nodes such as Edit Fields, IF, Switch, Merge and Loop Over Items
Module 2 Integrations and data processing
  • Connecting common applications: email, spreadsheets, CRM, collaboration and cloud storage
  • Using the HTTP Request node for APIs without a ready-made integration
  • Creating, managing and sharing credentials securely within the team
  • Filtering, merging and reshaping data and writing it to target systems
Module 3 AI in n8n: the AI Agent node and LLM building blocks
  • Connecting chat models from different providers as well as locally hosted models
  • Configuring the AI Agent node with a system prompt, memory and tools
  • Automating classification, structured data extraction and summarisation
  • Using your own documents via vector stores for retrieval-augmented generation
Module 4 Error handling, testing and maintainability
  • Setting up error workflows, retries and failure notifications
  • Validating AI output and defining fallbacks for unusable responses
  • Working with pinned test data and tracing past executions
  • Modularising, naming and documenting workflows for colleagues
Module 5 Self-hosting, data protection and operations
  • Cloud versus self-hosted: effort, control and licensing model compared
  • Assessing data flows to AI providers against GDPR requirements
  • Managing access rights, logging and retention of execution data
  • Adding human-in-the-loop approval steps to automated processes
Module 6 Practice lab: Media & Journalism
  • Document research
  • Transcribing and analysing interviews
  • Headlines and teasers
  • Adapting formats
  • Unlocking the archive
  • Supporting fact-checking
Module 7 Data protection, quality & transfer to daily work
  • Press codes and journalistic due diligence apply unchanged: AI output must be verified and AI use made transparent to the audience in line with in-house guidelines. Copyright and related rights in source material and generated content must be respected, and source protection rules out entering confidential source information into unapproved tools.
  • 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

Document research

Lengthy reports, court rulings, budgets or leaked datasets are searched and summarised with AI and checked for relevant passages – every statement is then verified against the source.

Transcribing and analysing interviews

Audio and video interviews are transcribed, key statements and quotes are highlighted and sorted by topic, so stories can be written faster and quotes attributed precisely.

Headlines and teasers

For a finished story, AI generates variants for headlines, standfirsts, social posts and push alerts that match the brand's tone and from which the editors choose.

Adapting formats

An article is turned into newsletter summaries, explainers, podcast scripts or easy-read versions without changing what it says. Editors check every version for accuracy and house style before publication.

Unlocking the archive

Archive material is tagged, summarised and made searchable so that background, timelines and earlier coverage can be retrieved quickly. Dossiers on people, places and topics stay up to date with little effort.

Supporting fact-checking

AI helps identify checkable claims in texts, formulate verification questions and point out contradictions; the verification itself is done with primary sources. Due diligence becomes more systematic.

Prerequisites

You should be confident with common office applications and have basic experience with AI assistants. No programming is required, although a basic grasp of structured data is helpful. Professional experience in journalism or editorial work; no technical background required.

Technical requirements

You will need a laptop with an up-to-date browser. We provide an n8n environment for the exercises, or you can use your own instance; API keys for a language model are set up together during the course.

Data protection & compliance

Press codes and journalistic due diligence apply unchanged: AI output must be verified and AI use made transparent to the audience in line with in-house guidelines. Copyright and related rights in source material and generated content must be respected, and source protection rules out entering confidential source information into unapproved tools.

Dates & locations

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

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

Frequently asked questions

Do I need to code to work with n8n?

No. Most workflows are built visually by connecting nodes. During the course you also learn simple expressions to reference and reshape data precisely. A Code node is available for special cases but is not essential. A logical understanding of processes and data fields is entirely sufficient to get started.

Why is n8n attractive from a European data protection perspective?

n8n can be self-hosted, for instance on your own servers or with a European hosting provider. This keeps you in control of where workflow and execution data is stored. What still matters is which data you send to external AI services. On the course you learn to assess these data flows and, where appropriate, connect locally hosted models.

How does n8n differ from Make or Power Automate?

All three automate processes across applications. n8n stands out for its self-hosting option, its fair-code licensing and particularly flexible AI building blocks such as the AI Agent node. Make offers a very approachable cloud interface, while Power Automate excels at deep Microsoft integration. The course puts these differences into context for typical scenarios.

Does AI-generated content need to be labelled?

That is determined by legislation, voluntary commitments and your organisation's guidelines. The EU AI Act sets transparency obligations for certain AI-generated content, and many newsrooms go further with their own policies. In the course we discuss when labelling is required and how to document editorial responsibility.

Can I use AI for fact-checking?

AI helps identify checkable claims and plan verification steps, but it is not a reliable source in itself. Language models can state false facts plausibly. Verification always relies on primary sources, documents and conversations. The course demonstrates a workflow that uses AI as an assistant without compromising rigour.

How much does “n8n for Media and Journalism” 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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