[PMI accredited course with 16 PDUs]
This 2 day instructor-led course equips project managers with the knowledge and skills required to successfully manage Artificial Intelligence (AI) initiatives across the project lifecycle. The course covers AI fundamentals, use case identification, business case development, governance structures, and ethical considerations, enabling participants to align AI initiatives with organizational strategy.
Participants will learn how to assess AI impacts, manage stakeholders, estimate AI projects, and identify and mitigate risks specific to AI systems. The course also addresses AI lifecycle management, including model monitoring, bias and drift detection, incident management, and performance tracking.
Through case studies, simulations, and hands-on exercises, participants will develop practical skills in roadmap prioritization, dashboard creation, supplier management, and change management for AI adoption. The course concludes with project closure practices and a self-assessment of AI project management competencies.
This program is aligned with the PMI Talent Triangle and provides balanced coverage across Business Acumen, Power Skills, and Ways of Working.
Learning Objectives:
By the end of this 2 day course, participants will be able to:
- Explain key AI concepts (ML, LLMs, agents) and their impact on project management.
- Identify and prioritize AI use cases aligned with business strategy.
- Develop and justify AI business cases, including ROI considerations.
- Design AI governance structures, including roles and responsibilities (RACI).
- Assess organizational, stakeholder, and societal impacts of AI initiatives.
- Estimate AI project effort and apply appropriate planning techniques.
- Identify, analyze, and mitigate risks specific to AI systems.
- Apply AI lifecycle frameworks, including ethical and regulatory guardrails.
- Monitor AI systems using key parameters such as bias, drift, and performance metrics.
- Manage incidents, reporting, and compliance requirements in AI projects.
- Define AI performance goals and track execution using dashboards.
- Evaluate suppliers and conduct due diligence for AI solutions.
- Address AI adoption challenges through change management strategies.
- Apply project closure practices including lessons learned and financial closure.
- Assess and improve personal competencies in AI project management.
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.
For more details please write to us at customer_relations@qaiglobal.com
- 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.
