Training course · Make · Energy & Utilities

Make for the Energy Sector

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 the Energy Sector” is designed for roles such as Staff at municipal utilities and energy suppliers, Grid operators in planning, connections and operations and Customer service and billing teams and shows step by step how to use Make productively, safely and transparently in Energy & Utilities – 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 energy sector is undergoing a deep transformation: distributed generation, grid expansion, sector coupling, new market roles and dense regulation are reshaping municipal utilities, grid operators, suppliers and energy service providers alike. Generative AI helps above all in managing complexity. It summarises regulatory decisions and legislative changes, answers customer enquiries about billing, tariffs or grid connections efficiently, unlocks technical documentation and prepares project documents for permits. AI agents can support market communication processes, pre-sort fault reports or automate reporting. Because critical infrastructure is involved, requirements for IT security, data quality and traceability are especially high.

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?

  • Staff at municipal utilities and energy suppliers
  • Grid operators in planning, connections and operations
  • Customer service and billing teams
  • Regulatory affairs and energy trading
  • Renewable energy project developers

Typical challenges we address

  • Dense regulation that changes frequently
  • Growing numbers of customer enquiries on tariffs, bills and grid connections
  • Extensive technical and permitting documentation
  • Skills shortages alongside rising investment volumes

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.
  • Navigate regulatory requirements and technical documents faster and more reliably with AI.
  • Relieve customer service and grid connection processes through AI-supported pre-checks and draft replies.
  • Assess AI use cases with due regard to IT security and critical infrastructure requirements.

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: Energy & Utilities
  • Making regulation understandable
  • Relieving customer service
  • Pre-checking connection requests
  • Unlocking technical documentation
  • Permitting and project documents
  • Reporting and market analysis
Module 7 Data protection, quality & transfer to daily work
  • As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.
  • 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

Making regulation understandable

Regulatory decisions, legislative changes and industry guidelines are summarised, their impact on internal processes derived and prepared as task lists for departments – reviewed by regulatory affairs.

Relieving customer service

Enquiries about instalments, annual bills, tariff changes or registering wallboxes and PV systems are classified, draft replies created and missing documents requested specifically. Recurring issues feed into FAQs and self-service content.

Pre-checking connection requests

Incoming applications are checked for completeness, technical details extracted and passed to grid planning in a structured format, with follow-up questions to installers drafted. Processing times shrink noticeably.

Unlocking technical documentation

Operating manuals, maintenance logs and excerpts from standards are made searchable so teams quickly get answers to technical questions, including a reference to the source.

Permitting and project documents

For wind, solar or grid projects, application documents are structured, comments from authorities and the public analysed and project reports prepared for boards. Deadlines and conditions are tracked clearly.

Reporting and market analysis

AI helps write commentary on sales, generation and grid KPIs, summarises market reports and drafts mandatory disclosures and internal reports. Figures always come from verified source systems, never from the AI.

Prerequisites

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. Professional experience in an energy sector company; 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

As operators of critical infrastructure, utilities and grid operators face heightened IT security requirements, for example under NIS2 and sector-specific security catalogues; grid and asset data must not flow uncontrolled into external AI services. Consumption and metering data is personal data under the GDPR and metering regulations, and unbundling rules must also be observed.

Suggested dates

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

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Berlin Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Tue, 12 January 2027 – Wed, 13 January 2027 Stuttgart Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Hanover 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.

May a grid operator use cloud AI services?

In principle yes, but only for suitable data and after approval by IT security and data protection. Operational and grid data from critical assets does not belong in external services. In the course we carefully distinguish non-critical use cases such as text work from sensitive areas that require special safeguards.

Can AI reliably interpret regulatory changes?

AI summarises regulatory decisions and legislation well and helps identify affected processes. Binding interpretation, however, remains the job of regulatory affairs or the legal department. The course shows how to back up AI summaries with source references and avoid misinterpretation.

How much does “Make for the Energy Sector” 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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