AI-Powered Project Management Training in Switzerland

The purpose of this program is to help participants integrate AI-driven approaches into project management processes to achieve faster analysis, smarter decisions, and more efficient resource management.

Participants will learn how to leverage AI tools for planning, tracking, forecasting, and risk management, experiencing real-world scenarios and platforms.

By the end of the course, attendees will enhance their data literacy, automation, and digital leadership skills to create a measurable impact on project success.

• Project Managers

• Program / Portfolio Managers

• Business Analysts

• PMO Specialists

• Operations Managers

• Agile / Scrum Practitioners

• Digital Transformation Leaders

• Executives aiming to integrate AI into projects

By the end of this course, participants will:

• Integrate AI into project management workflows
• Use predictive analytics for planning and risk management
• Optimize processes with data-driven decision making
• Apply NLP to automate documentation and reporting
• Lead digital transformation projects with AI-driven insights

Introduction to AI in Project Management

• The role of AI in modern project management

• Data-driven decision making

• AI-enabled project lifecycle and methodologies

• Project maturity and AI capability model

AI-Enhanced Planning & Forecasting

• Schedule prediction techniques

• Task prioritization algorithms

• Resource optimization using AI

• Predicting project delays before they occur

Intelligent Risk Management

• Automated risk identification & recommendation engines

• AI models for risk scoring

• Predictive analytics and scenario modeling

• AI-based decision support systems

NLP for Project Communication

• Automatic meeting summaries and reports

• Project documentation generation with AI

• Task extraction and action planning

• Using LLMs (like ChatGPT) for quick analysis

Process Mining & Optimization

• Bottleneck detection and process mining techniques

• Workflow optimization and automation

• Measuring process performance with AI

No-Code / Low-Code AI Integration

• Building AI-powered prototypes without coding

• Integration with project tools (MS Project, Asana, Jira, Monday.com)

• Reporting automation with AI plugins

Performance Metrics & Decision Support

• KPI interpretation using AI analytics

• Decision recommendation systems

• Model accuracy and performance evaluation

Ethics, Data Privacy & Human Factor

• Ethical principles in AI use

• Data protection and GDPR compliance

• Balancing automation and human judgment