AI in Project Management

Training overview

The first part of the training focuses on an introduction to the world of generative AI: what can it do, how does it work and how can its potential be utilised in a corporate context? It's not just about theory, but about actively trying things out, reflecting and discussing - with a clear practical focus and tangible results.

In the second part, we will address the question of how AI changes - and improves - project management in concrete terms. Participants will analyse their own project methods, identify specific areas of application for AI and try out how artificial intelligence can be trained and integrated as a project-related assistant. The end result is a clear picture: Where, how and with what benefits AI can be used in project management - immediately, efficiently and with added value.

This training module is aimed at companies that not only want to inform their employees, but also actively accompany them into the future. Participants do not leave the training with a slide presentation, but with a toolbox that can be used directly in their next day-to-day project work.

• Understanding generative AI: How a language model works, why it "hallucinates" and where its strengths and limits lie – explained clearly, without technical ballast.
• Prompting that works: Structured instructions with the RTF-Q framework – plus advanced techniques that let you solve demanding tasks reliably.
• AI as a project assistant: With our OPI approach you set up AI so that it holds project context and thinks along across the whole project – instead of starting from scratch with every request.
• Build your own assistants: Without any programming, you create your own CustomGPTs in ChatGPT and Agents in MS Copilot – right inside the chat, for example an assistant that turns your meeting minutes into the structure you want.
• Security, data protection and compliance:  Placing the EU AI Act in context, anonymising and pseudonymising data, spotting and cross-checking hallucinations, embedding AI governance in the project.
• Real project practice: apply AI directly to typical project documents: work breakdown structure, risk analysis, stakeholder matrix, status report, kick-off, meeting minutes and lessons learned.
• Outlook on operational AI.: For orientation, we show how automated, agent-based AI systems are changing project management and which platforms exist for them – so you can place first use cases in your own environment.
• Your own use cases: You identify, assess and test concrete scenarios from your real project work – live and in exchange with the group.

What we deliberately don't do

• We are upfront about where this training does not aim – so you know what to expect:
• No model programming: We don't build AI and we don't write code. You learn to apply AI, not to develop it.
• No development of advanced AI agents: We don't build complex, autonomous agent systems in Copilot Studio, ADK or comparable platforms. Creating CustomGPTs and Agents in the chat is part of this training – professional agent development is the subject of more advanced, specialised formats.
• No legal advice: We place the EU AI Act in a practical context, but we don't replace binding legal counsel.
• No tool lock-in: The training is tool-independent and works with all major models. But we won't rush you through dozens of tools – we focus on the transferable principles.
• No theory seminar.: No lecture, no slide overload – you work with AI yourself from the very first exercise.
• No showcases to watch: You train on realistic cases, not polished demos.
• No prior knowledge required – and no IT basics course: We bring everyone on board while staying firmly focused on project value.

• Phrase AI instructions in a structured way – with the RTF-Q framework.
• Set up AI as a personal project assistant that keeps context (OPI).
• Create your own CustomGPTs in ChatGPT and Agents in MS Copilot Chat – without any programming.
• Use AI safely and compliantly: place the EU AI Act in context, anonymise data, spot and cross-check hallucinations.
• Create and refine project documents with AI: work breakdown structure, risk analysis, stakeholder matrix, status reports, meeting minutes, lessons learned.
• Identify, assess and prioritise your own AI use cases in the project.
• Place the landscape of operational, agent-based AI in context and spot first automation potential in your own environment.
• Assess AI output critically and keep responsibility where it belongs: with you.