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.
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.
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.
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.
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.
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.
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.
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.
The opening section places AI within the day-to-day responsibilities of a Project Manager and across the project lifecycle. It also examines where business value is highest, alongside the main limitations and risks that need to be understood before applying AI in project work.
Effective planning starts with sharpening the project objective and defining scope boundaries. This module uses AI to structure WBS, identify dependencies and milestones, estimate time, cost, and resources, assess project risks, and compare alternative what-if scenarios before execution begins.
From Project Charter to lessons learned, AI can support the preparation of core project documents. Participants work on scope and assumptions, schedules, risk and issue registers, communication plans, and change management plans, with emphasis on producing coherent documentation across the project lifecycle.
Project knowledge is often scattered across files, notes, and decisions made over time. The section shows how AI can organize documentation, retrieve information from project materials, answer questions based on documents, analyze decisions and changes, and support creation of a reusable knowledge repository.
Reducing administrative effort is one of the most direct AI use cases for a Project Manager. This section concentrates on preparing and updating tasks, prioritizing activities, producing meeting summaries, and generating checklists so daily coordination work becomes more systematic and less time-consuming.
Managing backlog and tasks becomes easier when AI helps reveal structure in complex workstreams. The module addresses backlog analysis and organization, grouping tasks by work areas, identifying blockers and dependencies, suggesting priorities, and assessing team workload to support execution decisions.
Team collaboration benefits from AI when ideas, plans, and relationships need to be made visible. Here the focus is on AI-assisted brainstorming, process and project mapping, schedule visualization, stakeholder analysis, and collaborative definition of the work scope in shared planning sessions.
Creating project communication with AI involves more than drafting messages faster. The section covers status updates, tailoring content to different audiences, supporting international communication, and organizing information flow so stakeholders receive relevant, consistent, and usable project information.
When project data grows, the challenge is turning it into concise management insight. Participants work with AI methods for progress analysis, detecting deviations and risks, preparing management summaries, and shaping reports for the board and PMO with clear interpretation of project indicators.
Clear project presentations require both structure and narrative. This part shows how AI can support status decks, materials for decision-making meetings, and final project summaries, while strengthening project storytelling so that key messages are matched to the context and purpose of each presentation.
AI-supported work models help Project Managers standardize how recurring activities are handled across the project. The section focuses on consistency in documentation and communication, repeatable operating patterns, end-to-end scenarios, and practical rules for using AI reliably in project environments.
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.
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.
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.
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.
