Module 1: Embracing AI in Project Management (Fundamentals)

• Enter AI – world of project management is changing – managing people and machines.
• AI fundamentals (ML, LLM, AI agent, Agentic AI) .
• Discussion: Key impacts on project management.
• Discussions: Traditional vs AI project management.

Module 2: AI Portfolio Management

• AI use case identification.
• Building a business case for AI.
• Use case prioritization and roadmap.
• Setting up AI governance – teams, RACI.
• Case study on business case for AI.
• Case study on building AI roadmap with prioritization.

Module 3: AI Impact Assessment, Stakeholders and Dependencies

• Discussion and examples – impact assessment (individuals, group, society) .
• Exercise – AI impact assessment and identifying impacted stakeholders and dependencies.

Module 4: Estimation and Planning

• Estimation methodology – people, hardware, software, use of LLMs.
• Case study on estimation with assumptions.

Module 5: Risk and Opportunity Management in AI Projects

• Risk and opportunity identification, analysis, response actions (controls), monitoring and updating.
• Case study on AI risk management.

Module 6: AI Lifecycle

• Phases and activities – problem definition & scoping, data collection & preparation, model development & validation, deployment & monitoring, evaluation of AI outcomes.
• Implementing guardrails for ethical and trustworthy AI.
• Quiz on guardrails.

Module 7: AI Project Execution and Monitoring

• Monitoring parameters – bias, hallucination, model drift.
• Discussion – corrective and preventive actions.
• Reporting and disclosures.
• Incident/issue management.
• Role play – tabletop exercise for issue handling.

Module 8: AI Metrics

• Defining AI performance goals (SMART goals) .
• Monitoring and actions.
• Exercise – setting up project dashboard.

Module 9: Managing Suppliers

• Sourcing, selection, contracting, supplier performance monitoring.
• Group discussion – supplier due diligence parameters.

Module 10: Change Management

• Identify bottlenecks for AI adoption.
• Discussion – driving behaviour change initiatives to increase acceptance of AI.
• Case study – resistance to AI.

Module 11: Project Closure

• Lessons learnt.
• Stakeholder survey.
• Financial closure.

Module 12: Competencies Needed for AI Project Manager

• Understanding the AI domain.
• Conflict management.
• Governance skills.
• Risk management.
• Data management.
• Information security and privacy.
• Understanding of regulation.
• Stakeholder management.
• Change management.
• Self-assessment questionnaire – power skills.

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More details

• Case studies and real-world scenarios.
• Group discussions and collaborative exercises.
• Hands-on activities (dashboard creation, stakeholder mapping) .
• Simulations (incident management, prioritization exercises) .
• Self-assessment and reflection.

Learning Outcomes

• 16 Hours of PMI PDUs will be issued post Training completion.
• Understand and apply AI concepts (ML, LLMs, agents) within the project management context.
• Identify, prioritize and justify AI use cases aligned with business strategy and value realization.
• Plan and execute AI projects including estimation, lifecycle management, and delivery frameworks.
• Manage risks, governance, and ethical considerations specific to AI systems.
• Monitor performance and control outcomes using metrics, dashboards, and incident management practices.
• Lead AI adoption and stakeholder engagement, including change management, collaboration, and continuous improvement.