Training course · Make · IT Service Desk & Support

Make for IT Service Desk and Support Teams

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Make for IT Service Desk and Support Teams” is designed for roles such as First- and second-line service desk analysts, Team leads and service desk managers and IT administrators and end-user support and shows step by step how to use Make productively, safely and transparently in IT Service Desk & Support – from the fundamentals to advanced workflows.

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. Over two days you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within your organisation.

The IT service desk is the first point of contact for incidents, requests and questions about devices, software and access. Rising ticket volumes, more complex IT estates and hybrid working put support teams under growing pressure. Generative AI and AI agents are changing this field particularly strongly: they classify and prioritise tickets, suggest fixes from the knowledge base, write clear replies for users, turn resolved cases into knowledge articles and answer standard requests through self-service around the clock. Scripts for recurring admin tasks can also be produced faster with AI. That leaves specialists more time for complex incidents, problem management and service improvement.

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-line service desk analysts
  • Team leads and service desk managers
  • IT administrators and end-user support
  • IT service management and knowledge management leads
  • Internal and external support providers

Typical challenges we address

  • High ticket volumes with many repetitive requests
  • Incomplete or outdated knowledge bases
  • Users with very different levels of IT literacy
  • Meeting service levels with limited resources

What you will be able to do

  • You can build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • Handle tickets faster, more consistently and more user-friendly with AI support.
  • Build an up-to-date, well-structured knowledge base with AI.
  • Design self-service and automation solutions with a sensible handover to people.

Course outline

Day 1

Module 1 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team

Day 2

Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
Module 6 Practice lab: IT Service Desk & Support
  • Classifying and prioritising tickets
  • Fix suggestions from knowledge
  • Clear user communication
  • Writing knowledge articles
  • Self-service and chatbots
  • Scripts for routine tasks
Module 7 Data protection, quality & transfer to daily work
  • Tickets often contain personal data, access details and information about IT infrastructure that must not flow into unapproved AI services; passwords and keys never belong in prompts. Using ticket data to evaluate staff performance requires compliance with the GDPR and employee representation rules, and AI-generated scripts must be reviewed according to IT security policies.
  • 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

Classifying and prioritising tickets

Incoming tickets are categorised by type, urgency and affected service, missing information is detected and follow-up questions to users are prepared automatically. Duplicate tickets and related incidents are detected too.

Fix suggestions from knowledge

Drawing on the knowledge base, past tickets and vendor documentation, AI suggests resolution steps, which the analyst checks and adapts to the specific situation. Sources are always cited.

Clear user communication

Technical fixes are turned into clear, friendly step-by-step guides, incident status updates are drafted and replies are provided in several languages. Fewer follow-up questions arise, and users can solve simple issues themselves.

Writing knowledge articles

Resolved tickets and notes become structured knowledge articles with symptom, cause and resolution; outdated articles are identified and flagged for review. Team knowledge keeps growing and stays current.

Self-service and chatbots

AI agents answer standard questions such as password resets, VPN setup or printer issues in the self-service portal and hand complex cases to the team with a summary.

Scripts for routine tasks

Administrators use AI to create PowerShell or Bash scripts for recurring tasks such as user provisioning or reports, and test them carefully before use. A review before production use is mandatory.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. Experience in IT support or IT service management; basic scripting skills are helpful but not required.

Technical requirements

You will need a laptop with an up-to-date browser and a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

Data protection & compliance

Tickets often contain personal data, access details and information about IT infrastructure that must not flow into unapproved AI services; passwords and keys never belong in prompts. Using ticket data to evaluate staff performance requires compliance with the GDPR and employee representation rules, and AI-generated scripts must be reviewed according to IT security policies.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Berlin Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Stuttgart Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Online Request

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

Will an AI chatbot replace first-line support?

An AI agent can resolve many standard requests on its own and pre-qualify tickets. Complex incidents, exceptions and users who need personal help stay with the team. Success depends on a clear handover with a summary. In the course you design such handovers and define which cases are suitable for automation.

How do I prevent incorrect fix suggestions from AI?

Ground the AI in verified knowledge such as your knowledge base and vendor documentation, ask for sources and check critical steps before executing them. For changes to systems, the four-eyes principle applies. The course shows how to build quality assurance into the support process.

How much does “Make for IT Service Desk and Support Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 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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