Training course · AI Agents · Controlling

AI Agents for Controllers

Agentic AI for business teams: understand AI agents, automate workflows safely and keep control with human-in-the-loop and governance.

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

This course connects AI Agents with the everyday processes of Controlling. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

AI agents go a step beyond conventional chat assistants: they plan sub-tasks, use tools such as search, files or business applications and complete multi-step work largely on their own. Over two days you will learn how agents are built, how they differ from chatbots and traditional automation, and which tasks genuinely justify their use. Widely used platforms serve as practical examples, including Microsoft Copilot Studio, custom GPTs and Claude with projects and tool connections.

In the hands-on sessions you design agent workflows for tasks from your own area of work, from triaging requests through research and drafting to handing results over to business systems. You decide where a person reviews and approves, which permissions an agent receives and how errors are detected. A dedicated block covers governance, data protection and accountability, so that your agents not only work but remain controllable, traceable and defensible 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 explain how AI agents are structured and clearly distinguish them from chatbots and classic automation.
  • You can select a suitable agent platform for your requirements using transparent criteria.
  • You can design an agent workflow for a real process in your area and build a working prototype.
  • You can define human-in-the-loop checkpoints, test cases and escalation rules.
  • 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 What makes an AI agent
  • Agent building blocks: language model, instructions, knowledge, tools and memory
  • How agents differ from chatbots, macros and classic workflow automation
  • Levels of autonomy: from suggestion-making assistants to agents that act independently
  • Realistically judging which tasks suit agents and which do not
Module 2 Platform overview: Copilot Studio, GPTs and Claude
  • Microsoft Copilot Studio: agents grounded in company data and connected to Microsoft 365
  • Custom GPTs: configuring instructions, knowledge files and actions
  • Claude with projects, knowledge bases and tool access via the Model Context Protocol
  • Selection criteria: data access, integrations, administration and licensing
Module 3 Designing and building agent workflows
  • Breaking tasks into steps and defining hand-offs between agent and human
  • Writing system instructions, examples and knowledge sources for consistent output
  • Connecting tools: search, documents, email, spreadsheets and business applications
  • Building a prototype agent for a real process from your own area
Module 4 Human-in-the-loop, quality and error handling
  • Setting approval steps, escalation rules and thresholds for human review
  • Test cases, evaluation criteria and spot checks for quality assurance
  • Common failure modes: wrong tool choice, endless loops and fabricated facts
  • Using structured user feedback to improve the agent over time

Day 2

Module 5 Governance, security and roll-out
  • Roles and responsibilities across business units, IT, data protection and works council
  • Least-privilege permissions, logging and protection against prompt injection
  • Positioning under the EU AI Act and GDPR: transparency, documentation and data flows
  • From pilot agent to production: operation, maintenance and measuring success
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 already use an AI assistant such as ChatGPT, Copilot or Claude on a regular basis. 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. Ideally you have access to at least one agent platform such as Copilot Studio, ChatGPT with GPTs or Claude; practice accounts can be provided if needed.

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.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Berlin Request
  • Tue, 15 December 2026 – Wed, 16 December 2026 Online Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Stuttgart Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Mon, 15 February 2027 – Tue, 16 February 2027 Hanover Request

Frequently asked questions

Do I need programming skills to build AI agents?

No. The platforms used on the course rely on visual interfaces, plain-language instructions and ready-made connectors. You learn to specify agents precisely from a business perspective, connect tools and test workflows. Where deeper technical integration would be needed, you will know which requirements to put to your IT team.

Which platform will we use during the course?

The course is vendor-neutral and uses Microsoft Copilot Studio, custom GPTs and Claude as examples. You build your prototype on the platform that is available or planned in your organisation. The principles of workflow design, control and governance you learn carry over to other agent solutions as well.

How do I stop an agent from acting without control?

Through deliberate design: agents receive only the minimum permissions they need, critical actions such as sending, booking or deleting require human approval, and every step is logged. In addition, you define test cases and escalation rules. These are exactly the control mechanisms you develop for your own use case during the course.

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 “AI Agents 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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