AI-assisted Project Management: from Planning to Execution
In an increasingly complex and constrained environment (limited resources, donor requirements, multiple stakeholders), project management must evolve to become faster, more accurate and more adaptable. Artificial intelligence (AI) technologies offer concrete opportunities to improve planning, cost and time estimates, early risk detection, field data analysis and resource optimisation.
This two-week practical training course combines recognised project management methodologies (life cycle, logical framework, risk management, monitoring and evaluation) with operational applications of AI tools (automation, predictive analysis, natural language processing, intelligent visualisation) adapted to the context of public administrations, private organisations and projects funded by international donors.
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AI-assisted Project Management: from Planning to Execution
AI-assisted Project Management: from Planning to Execution
AI-assisted Project Management: from Planning to Execution
AI-assisted Project Management: from Planning to Execution
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Practical Objectives:
- Understand the key concepts of artificial intelligence and their practical applications in project management.
- Master the key stages of the project life cycle by integrating AI contributions.
- Use AI techniques to improve estimation, planning (Gantt, PERT), risk detection and prioritisation.
- Automate recurring tasks to free up time for decision-making.
Training Seminar Topics
Introduction to AI and project management
Definition and basic concepts of artificial intelligence; types of AI; fundamental principles of project management.
Applications of AI in the project cycle
AI-assisted planning (forecasts, scenarios); automated monitoring and evaluation; smart dashboards; data analysis and management; decision support tools.
AI-assisted planning and estimation
Estimation techniques; optimisation of planning and resource management.
Risk management
Identification; modelling and prioritisation; scenario analysis.
Data collection, processing and quality
Best practices for structuring data useful for AI; automation; dashboards and intelligent visualisation.
Automation of administrative tasks and reporting
Automatic report generation; information extraction; implementation of automated workflows.
Governance, ethics and data security
Personal data protection, confidentiality and regulatory compliance; ethical issues related to the use of AI (bias, transparency, responsibility).