Training course · Make · Controlling

Make for Controllers

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 Controllers” is designed for roles such as Controllers and finance business partners, Heads of controlling and FP&A analysts and shows step by step how to use Make productively, safely and transparently in Controlling – 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.

Controlling depends on reliable figures, fast analysis and clear communication with management. Generative AI supports controllers at exactly these points: AI assistants explain variances in the monthly report, suggest formulas and pivot structures, sanity-check planning assumptions and turn findings into plain-language management commentary. AI agents and automation pull data from the ERP, BI tools and spreadsheets, prepare forecasts and generate standard reports. This accelerates the shift from number cruncher to business partner who explains root causes and prepares decisions. A clean data foundation, clearly defined KPIs and expert review of every AI result remain essential before anything reaches a report or a board paper.

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?

  • Controllers and finance business partners
  • Heads of controlling
  • FP&A analysts
  • Cost centre and project controllers
  • Sales and manufacturing controllers

Typical challenges we address

  • Commenting on monthly reports and explaining variances under time pressure
  • Consolidating plans and forecasts from many spreadsheets and departments
  • Defining KPIs consistently and presenting them for different audiences
  • Answering ad-hoc management questions quickly and reliably

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.
  • Use AI for variance analysis, forecasting and management commentary with traceable results.
  • Have AI build formulas, queries and small reporting automations and validate them confidently.
  • Establish rules for quality assurance and confidential use of controlling data.

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: Controlling
  • Variance commentary
  • Formulas and queries
  • Challenging forecast assumptions
  • Management reporting
  • Reporting automation
  • KPI glossary
Module 7 Data protection, quality & transfer to daily work
  • Controlling data contains trade secrets and often personal data such as payroll costs per employee; only use approved AI environments and pseudonymise data in line with GDPR principles. Listed companies must also protect inside information under MAR.
  • 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

Variance commentary

An AI assistant analyses budget-to-actual and prior-year comparisons, identifies the biggest drivers by cost type, product or region and drafts commentary for the monthly report for controllers to review.

Formulas and queries

Describe the analysis you need in plain words and receive suggested spreadsheet formulas, pivot logic, Power Query steps or SQL queries with explanations that you can test straight away.

Challenging forecast assumptions

AI questions planning assumptions on volumes, prices and costs, highlights inconsistencies between departmental plans and builds best, base and worst-case scenarios as a basis for discussion.

Management reporting

KPIs and raw data are turned into concise executive summaries, slide outlines and traffic-light commentary tailored to executives, divisional heads or cost centre owners.

Reporting automation

A workflow pulls ERP and BI exports, checks completeness, refreshes standard reports and sends drafts to cost centre owners, who add their comments before the pack is finalised.

KPI glossary

An AI assistant helps document KPI definitions, calculation logic and data sources consistently, so that business units and management speak the same language and discussions about figures become more objective.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. You work in controlling or FP&A and are comfortable with spreadsheets and core cost accounting metrics.

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

Controlling data contains trade secrets and often personal data such as payroll costs per employee; only use approved AI environments and pseudonymise data in line with GDPR principles. Listed companies must also protect inside information under MAR.

Suggested dates

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

  • Tue, 24 November 2026 – Wed, 25 November 2026 Online Request
  • Mon, 30 November 2026 – Tue, 1 December 2026 Munich Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Online Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Hanover Request
  • Wed, 3 February 2027 – Thu, 4 February 2027 Online Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Berlin 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 AI replace our BI system?

No. BI systems and data warehouses remain the reliable data foundation. AI adds a layer for interpretation, commentary and fast ad-hoc analysis. In the course we show how to pass exports to AI assistants sensibly, check results against the source and automate recurring steps without creating shadow IT.

How reliable are figures calculated by AI?

Language models can miscalculate or mismatch data. Prefer having calculations run through generated, reviewable code or formulas, and reconcile totals with the source. We practise checking routines that let you confirm plausibility quickly before any number makes it into a report.

How much does “Make for Controllers” 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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