Training course · Make · Product Management

Make for Product Managers

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 Product Managers” is a hands-on course for roles such as Product managers, Product Owners in agile teams and Heads of Product and product directors. Rather than learning Make in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Product Management.

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.

Product managers connect customer needs, business goals and technical delivery. Their day revolves around discovery, prioritisation, specification and alignment with engineering, design, sales and leadership. Generative AI speeds up almost every one of these steps: it analyses customer feedback, interviews and support tickets, helps write product requirements and user stories, structures competitor analyses and prepares roadmap communication for different stakeholders. With vibe coding, product managers can build clickable prototypes themselves and test ideas before committing engineering capacity. AI agents bring together signals from analytics, CRM and feedback tools. That leaves more time for product vision, decisions and conversations with customers.

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?

  • Product managers
  • Product Owners in agile teams
  • Heads of Product and product directors
  • Product marketing teams
  • UX researchers and product designers

Typical challenges we address

  • Large amounts of unstructured feedback from many channels
  • Heavy effort for specifications and stakeholder alignment
  • Prioritising under uncertainty and competing interests
  • Long lead times from idea to testable prototype

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.
  • Synthesise customer feedback and market information with AI into well-founded product decisions.
  • Produce specifications, user stories and roadmap communication faster and more precisely with AI.
  • Build clickable prototypes through vibe coding to validate ideas early with users.

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: Product Management
  • Synthesising customer feedback
  • PRDs and user stories
  • Preparing prioritisation
  • Competitor and market analysis
  • Prototypes via vibe coding
  • Roadmap and release communication
Module 7 Data protection, quality & transfer to daily work
  • Customer feedback and usage data often contain personal information and are subject to the GDPR; they should be pseudonymised before being processed in AI tools. Roadmaps and unreleased product information are trade secrets, and AI features in your own product must be assessed against the requirements of the EU AI 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

Synthesising customer feedback

Interviews, support tickets, app reviews and surveys are grouped by theme, problem and request and turned into an opportunity overview with supporting quotes for discovery.

PRDs and user stories

Problem statement, target users and goals become drafts of product requirement documents, user stories with acceptance criteria and edge cases, refined with engineering and design.

Preparing prioritisation

AI helps score initiatives using frameworks such as RICE or Kano, surface assumptions and prepare arguments for and against options ahead of prioritisation discussions. Disagreements become easier to resolve.

Competitor and market analysis

Feature comparisons, positioning and pricing models of competitors are structured and updated regularly; claims are backed by sources and checked critically. You keep track of market moves without lengthy manual research.

Prototypes via vibe coding

Product managers use AI coding tools to build clickable prototypes and simple functional demos to test concepts with users and make requirements tangible for the team.

Roadmap and release communication

Roadmaps, release notes, internal enablement material and customer communication are tailored to the audience – technical for engineering, benefit-led for sales and customers. Every audience gets exactly what it needs.

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 product management or as a Product Owner; no programming skills 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

Customer feedback and usage data often contain personal information and are subject to the GDPR; they should be pseudonymised before being processed in AI tools. Roadmaps and unreleased product information are trade secrets, and AI features in your own product must be assessed against the requirements of the EU AI Act.

Suggested dates

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

  • Mon, 23 November 2026 – Tue, 24 November 2026 Online Request
  • Thu, 3 December 2026 – Fri, 4 December 2026 Augsburg Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Kaiserslautern Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Bremen 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.

Can AI make product decisions?

No, but it improves the basis for them. AI condenses feedback, surfaces assumptions and prepares options. Product vision, weighing trade-offs and accountability for decisions remain with product management. In the course you use AI deliberately as an analysis and sparring partner without giving up critical judgement.

How far can I get with vibe-coded prototypes?

Very far for clickable interfaces, simple logic and user testing – often within a few hours. Production-ready software with security, scalability and maintainability remains engineering's job. The course shows how prototypes improve collaboration with the team rather than replacing it.

How much does “Make for Product Managers” 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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