This course introduces project managers to the transformative potential of Artificial Intelligence (AI) across the entire project lifecycle (planning, execution, monitoring, and communication). Learners will explore how AI tools automate scheduling, optimize resource allocation, and assess risks through predictive analytics. Real-world examples illustrate how AI can dynamically adjust timelines based on project data, team capacity, and shifting priorities. Participants will also gain practical experience using AI-driven assistants—such as chatbots for reporting and dashboards for data visualization—to streamline workflows and decision-making. By the end of the course, project managers will be equipped to integrate AI responsibly and effectively into their project management strategies.

Introduction to AI for PM

• AI Fundamentals for PM
• AI/ML (Machine Learning) basics
• Use cases in project contexts
• ML workflow for project data/reports

Task & Resource Automation

• AI tools for automation
• Scheduling and resource allocation
• AI-enabled PM platforms
• Progress tracking

Risk Management with AI

• AI-driven risk modelling
• Historical data and simulations
• Identification of bottlenecks

Collaboration & Communication

• AI transcriptions
• Intelligent dashboards
• Integrating AI chatbots
• Agentic AIs

Implementation & Case Study

• Hands-on project: use AI tools (e.g. GPTs, AgenticAIs, Automation)
• AI scheduling script (e.g. Atlassian API)
• Evaluating AI risk scoring model
• Analyze results and best practices

Upon completion, learners will be able to:

• Understand the fundamentals of AI and machine learning in the context of project management.
• Identify key AI use cases across project planning, execution, monitoring, and closure phases.
• Use AI tools to automate task scheduling, timeline optimization, and resource allocation.
• Apply predictive analytics to identify potential risks and bottlenecks before they impact project timelines.
• Leverage AI platforms (e.g., Jira, Asana) for real-time progress tracking and status reporting.
• Integrate AI chatbots and assistants to support team communication and reduce manual reporting workloads.
• Analyze project data to detect patterns and improve forecasting accuracy using AI techniques.
• Evaluate ethical, practical, and organizational considerations when deploying AI in project workflows.
• Implement and assess AI tools in a guided hands-on case study scenario.
• Develop strategies for responsible adoption and scaling of AI across project portfolios.

Lectures, learning with no-coding tools, interactive demos and business case simulations, peer discussions, and live Q&A session.