Programme overview
Introduction:
Project teams already use AI assistants informally, yet charters, estimates, risk logs and status updates produced this way are rarely checked, reused or governed, and confidential project data leaks into public tools. AI project management turns these ad hoc habits into a repeatable delivery practice. This Core Concept course shows project managers how to apply AI assistants and the AI features of work-management platforms from initiation to closure, without writing code, while keeping human judgement in charge. Participants build an AI Project Manager's Playbook of tested prompts, templates and review checkpoints.
Course Objectives:
- Draft project charters, scope statements and work breakdown structures with an AI assistant and validate them against sponsor requirements
- Generate activity lists, dependencies and three-point estimates with AI support and challenge them with team expertise
- Identify project risks and define early-warning indicators using AI-assisted risk workshops and issue log analysis
- Automate status updates, meeting summaries and action tracking while checking every AI output for accuracy
- Forecast resource demand and team capacity with AI features in work-management tools and rebalance allocations
- Apply data privacy rules and human oversight checkpoints to every AI-supported project decision
Target Audience:
- Project managers who plan and deliver projects across functions
- Project coordinators who maintain plans, logs and meeting records
- PMO officers who set templates, reporting cycles and delivery standards
- Delivery leads who manage team workload and stakeholder updates
- Product and change managers who run cross-functional initiatives
Course Outline:
Day 1: AI in the Project Lifecycle and Readiness Check
- Generative AI Assistants, Copilots and Agents: What Each Can Do for a Project Manager
- Project Lifecycle Map of AI Use Cases from Initiation to Closure
- PMI Guidance on AI in Project Work and the Changing Project Manager Role
- Personal AI Readiness Self-Assessment and Tool Inventory
- Project Data Classification: Public, Internal and Confidential Inputs
Day 2: Prompt Patterns and AI-Assisted Initiation and Planning
- Role-Context-Task-Format Prompt Pattern for Project Artefacts
- Few-Shot Prompting with Organisational Templates and Past Project Examples
- AI-Drafted Project Charter and Stakeholder Register
- Scope Statement and Work Breakdown Structure Generation with Decomposition Checks
- Activity Sequencing, Milestone Plans and Dependency Mapping with AI Assistance
Day 3: Estimation, Risk Early Warning and Resource Planning
- Analogous and Three-Point Estimates Using AI with Team Calibration
- AI-Facilitated Risk Identification: Prompted Risk Breakdown Structures and Pre-Mortems
- Early-Warning Indicators from Issue Logs, Change Requests and Sentiment Signals
- Resource Demand Forecasting and Capacity Heatmaps in Work-Management Platforms
- RACI Matrix Drafting and Workload Rebalancing Scenarios
Day 4: Automated Reporting, Meetings and Oversight Controls
- Automated RAG Status Reports from Plan and Task Data
- AI Meeting Transcription, Summaries and Action Item Registers
- Stakeholder Messaging: Audience-Tailored Updates and Escalation Notes
- Hallucination, Bias and Over-Reliance: Verification Checklists for AI Outputs
- NIST AI RMF and ISO/IEC 42001 Principles for Human Review Gates and Data Privacy
Day 5: Lab Work and the AI Project Manager's Playbook
- IT Implementation Lab: Charter, WBS and Estimate Built with an AI Assistant
- Facilities Relocation Lab: Risk Register, Early-Warning Triggers and Status Report
- Prompt Library Assembly with Tested Templates and Version Notes
- AI Use Policy and Approval Checkpoints for a Project Team
- AI Project Manager's Playbook Presentation and Peer Review
Skills You Will Gain:
- Prompt Design for Project Artefacts
- AI-Assisted Scope Definition
- Estimate Validation
- Risk Early-Warning Design
- Status Report Automation
- Capacity Planning
- AI Output Verification
- Project Data Privacy
Why Attend This Course:
- Leave with an AI Project Manager's Playbook of prompts, templates and review checkpoints tested on realistic project cases
- Save hours each reporting cycle by producing status updates and meeting records in minutes, then checking them before release
- Spot slipping tasks and overloaded team members earlier through indicators set up during the course
- Explain to sponsors and auditors how AI was used in each project decision and who approved it
Conclusion:
AI assistants add value to project delivery only when their drafts are checked, their data is protected and their role in decisions is clear. This course moves from lifecycle use cases and readiness, through prompt patterns for initiation and planning, to AI-supported estimation, risk warning signals and capacity planning, and then to automated reporting, meeting records and oversight controls. The final day applies these methods in hands-on labs, producing an AI Project Manager's Playbook that participants can use on their next project.