Training course · Generative AI · Project Management

Generative AI for Project Managers

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 Project Management. 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.

Project management is largely information work: clarifying requirements, building plans, assessing risks, reporting status and keeping a wide range of stakeholders aligned. Generative AI speeds up exactly these activities. It turns meeting notes into minutes and action lists, structures project charters and work breakdown structures, writes status reports for different audiences and supports risk workshops with well-grounded suggestions. AI agents connect project management tools, inbox and calendar and send automatic reminders about overdue tasks. Whether you work waterfall, agile with Scrum or hybrid, project leads gain time for what AI cannot do: making decisions, resolving conflicts and leading the team.

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?

  • Project managers
  • PMO staff and portfolio managers
  • Scrum Masters and agile coaches
  • Workstream and work package leads
  • Business teams with occasional project responsibility

Typical challenges we address

  • Heavy documentation workload for minutes, reports and plans
  • Many stakeholders with different information needs
  • Risks and dependencies spotted too late
  • Information scattered across tools, emails and chats

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.
  • Produce project documents such as charters, plans, minutes and reports faster and more consistently with AI.
  • Use AI as a sparring partner for risk analysis, planning and stakeholder communication.
  • Design simple automations for follow-up and reporting in your project environment.

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: Project Management
  • Meeting minutes and actions
  • Project charter and WBS
  • Audience-specific status reports
  • Identifying risks
  • Agile artefacts
  • Automated follow-up
Module 7 Data protection, quality & transfer to daily work
  • Project documents often contain confidential client data, contract details and personal data about team members, which may only be processed in approved AI environments. Analysing employee performance or workload data requires compliance with the GDPR and, where applicable, works council co-determination rights.
  • 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

Meeting minutes and actions

Notes or transcripts become structured minutes with decisions, open points and actions including owners and due dates, ready to be transferred into the project tool.

Project charter and WBS

AI helps turn a project idea into a charter with objectives, scope boundaries and success criteria, plus an initial work breakdown structure with work packages and milestones.

Audience-specific status reports

From raw data such as task progress, budget and risks, AI drafts status reports at different levels of detail – concise for the steering committee, detailed for the core team.

Identifying risks

In risk workshops AI suggests typical risks for the project type and industry, helps assess likelihood and impact and drafts mitigation actions. Owners and review dates are assigned straight away.

Agile artefacts

User stories with acceptance criteria, backlog refinement, sprint goals and retrospective formats are prepared with AI and then sharpened together by the team. Definitions of done can be drafted too.

Automated follow-up

AI agents check task progress regularly, remind owners about overdue items, summarise the week's changes and prepare a short project newsletter from them. The team stays informed without extra effort.

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. Experience leading or contributing to projects, whether traditional, agile or hybrid.

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

Project documents often contain confidential client data, contract details and personal data about team members, which may only be processed in approved AI environments. Analysing employee performance or workload data requires compliance with the GDPR and, where applicable, works council co-determination rights.

Suggested dates

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

  • Thu, 26 November 2026 Online Request
  • Mon, 30 November 2026 Munich Request
  • Mon, 7 December 2026 Online Request
  • Wed, 13 January 2027 Hanover Request
  • Fri, 22 January 2027 Online Request
  • Fri, 29 January 2027 Berlin 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.

Can AI create a project plan?

AI provides a solid first draft of the work breakdown structure, milestones and dependencies if you describe objectives and constraints clearly. Effort estimates, resources and priorities still need to be agreed with the team. The course shows how to review AI drafts systematically and transfer them into your project tool.

Does this work in agile projects too?

Yes. In Scrum or Kanban, AI helps write user stories and acceptance criteria, prepare refinements and retrospectives and summarise sprint results for stakeholders. Decisions about the backlog and delivery naturally remain with the team and the Product Owner. AI supports, the team decides.

How much does “Generative AI for Project Managers” 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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