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AI in Project Management – practical applications of GenAI in the work of a Project Manager

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Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

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Stronger project planning - You will learn how to use GenAI to refine project goals, scope, and WBS structure, so you can build a more consistent project plan faster and spot gaps much earlier.

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Better risk assessment - You will see how AI can help you identify risks, dependencies, and alternative scenarios, allowing you to react to threats earlier and make more informed project decisions.

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Faster project documentation - You will practice creating a Project Charter, communication plans, risk registers, and lessons learned, helping you reduce documentation effort without lowering quality.

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Smarter use of project knowledge - You will learn how to organize project materials, find critical information quickly, and answer questions based on documents, making scattered project knowledge easier to use.

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Less admin work - You will discover how to automate task updates, checklists, and meeting summaries, so you can free up time for leading the team and keeping delivery on track.

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More effective backlog management - You will explore how AI can help you structure a backlog, group tasks, detect blockers, and assess team workload, so you can prioritize work with greater confidence.

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Clearer project communication - You will learn how to create messages, status updates, and summaries tailored to different audiences, helping you report progress clearly and support international teamwork.

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Sharper reports and presentations - You will practice preparing management reports, decision-making materials, and status presentations, so you can communicate key project insights clearly and persuasively.

Training programme

1. Introduction to AI in Project Management

  • the role of AI in the work of a Project Manager,
  • the place of AI in the project life cycle,
  • areas of the greatest business value,
  • limitations and risks of using AI.

2. AI in project planning

  • defining the project objective,
  • clarifying the project scope,
  • creating the WBS structure,
  • identifying dependencies and milestones,
  • estimating time, costs and resources,
  • analysis of project risks,
  • alternative scenarios and „what-if”.

3. AI in project documentation

  • Project Charter,
  • description of the project scope and assumptions,
  • project schedule,
  • risk and issue register,
  • communication plan,
  • change management plan,
  • lessons learned.

4. AI in project knowledge management

  • organizing project documentation,
  • searching for information in project materials,
  • answering questions based on documents,
  • analysis of decisions and project changes,
  • building a project knowledge repository.

5. Automation of the daily work of a Project Manager

  • preparation and updating of tasks,
  • prioritization of activities,
  • meeting summaries,
  • preparation of checklists,
  • reduction of administrative work.

6. AI in task and backlog management

  • analysis and organizing of the backlog,
  • grouping of tasks and work areas,
  • identification of blockers and dependencies,
  • priority suggestions,
  • analysis of team workload.

7. AI in team collaboration and visual planning

  • AI-supported brainstorming,
  • project and process mapping,
  • schedule visualization,
  • stakeholder analysis,
  • joint planning of the scope of work.

8. AI in project communication

  • creating project communications,
  • preparing project statuses,
  • adapting communication to audiences,
  • supporting international communication,
  • managing information in the project.

9. AI in project reporting

  • analysis of project progress,
  • identification of deviations and risks,
  • creation of management summaries,
  • reports for the management board and PMO,
  • interpretation of project data.

10. AI in preparing project presentations

  • structure of a project presentation,
  • status presentations,
  • materials for decision-making meetings,
  • project summary presentations,
  • project storytelling.

11. Standardization of the Project Manager's work with AI

  • PM's work models with AI,
  • consistency of documentation and communication,
  • repeatable action patterns,
  • example end-to-end scenarios,
  • best practices for using AI in projects.

What are the prerequisites for participating in the training?

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Project management basics - You should understand core project terms such as goals, scope, schedule, risk, stakeholders, and tasks, so you can follow the training examples without difficulty.

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Project work experience - You should have hands-on exposure to projects as a Project Manager, team lead, or team member, so you can relate the AI use cases to your own day-to-day work.

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Working with documentation - You should be able to read and create basic project documents such as plans, status updates, task lists, or risk registers to get the most value from the exercises.

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Comfort with office tools - You should be comfortable using a computer and standard tools for writing, communication, and file handling, as the training is based on practical AI usage examples.