Training course · Generative AI · Controlling

Generative AI for Controllers

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
1 day (7 hrs)
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 Generative AI with the everyday processes of Controlling. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

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 explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • 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

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
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

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. 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 internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

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.

  • Wed, 25 November 2026 Online Request
  • Mon, 30 November 2026 Kaiserslautern Request
  • Tue, 8 December 2026 Online Request
  • Wed, 20 January 2027 Augsburg Request
  • Mon, 25 January 2027 Online Request
  • Thu, 11 February 2027 Salzburg Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

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 “Generative AI for Controllers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 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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