Skráning | Verkefnastjórnunarfélag Íslands (vsf.is)
In this Masterclass, Ricardo Vargas will guide you through the transformative power of Artificial Intelligence in project management, from foundational concepts to practical applications. You'll explore how AI can enhance decision-making, optimize resource allocation, and predict project risks, all while maintaining ethical considerations. By the end of the course, you'll be equipped with the knowledge and tools to lead AI-driven projects and stay ahead in the evolving landscape of project management.
1. Understanding What is Artificial Intelligence (30 min) - This section introduces the foundational concepts of AI, exploring its relationship with machine learning, data science, and other core areas, providing a baseline understanding for deeper exploration.
- AI, Machine Learning, and Data Science: Define AI and its connection to machine learning and data science, outlining how they intersect and differ.
- Supervised, Unsupervised, and Reinforcement Learning: Explain the key types of learning methodologies, with examples of how each is applied in real-world scenarios.
- Generative AI: Discuss the basics of generative models, including how they create new content and their applications in various industries.
- Adversarial Neural Networks: Introduce the concept of GANs (Generative Adversarial Networks), focusing on how they work and their significance in creating realistic data.
2. Developing AI Projects (20 min) - This section covers the lifecycle of AI projects, guiding participants through the stages from conceptualization to deployment, with an emphasis on practical application in project management.
- Project Scoping and Feasibility: Discuss how to define the problem, set objectives, and assess the feasibility of AI projects.
- Data Collection and Preparation: Outline the importance of quality data, data cleaning, and preprocessing steps.
- Model Selection and Development: Explain the process of selecting the right AI model and how to train and validate it.
- Deployment and Monitoring: Cover best practices for deploying AI models in production and monitoring their performance.
3. Implementing AI in Projects (40 min) - Focuses on practical steps to integrate AI tools into project management, including evaluation frameworks and the most relevant AI tools currently available.
- Framework to Evaluate AI Tools: Introduce criteria for assessing AI tools, such as scalability, ease of integration, and cost-effectiveness.
- Most Relevant Tools and Apps to Apply AI into Projects: Provide an overview of top AI tools, including project management software, predictive analytics tools, and AI-based decision support systems.
4. Case Studies of AI and Generative AI in Project Management (40 min) - Real-world examples illustrate how AI and Generative AI are being successfully applied in project management, complemented by live demonstrations to enhance understanding.
- Case Study 1: Evaluation of critical resources.
- Case Study 2: Financial Analysis and project budgeting.
- Case Study 3: Portfolio Management
- Case Study 4: Project Forecasting
5. Using Multimodal ChatGPT to Advise in Project Management (30 min) - This section demonstrates how multimodal AI models like ChatGPT can assist in various project management tasks, from communication to analysis.
- Talking to ChatGPT: Demonstrate how to use ChatGPT for project-related queries, from scheduling to brainstorming solutions.
- Report Analysis: Show how ChatGPT can analyze reports and documents, providing summaries, insights, and recommendations.
6. AI, Emotions, and Human Collaboration (30 min) - Explores the interplay between AI, emotional intelligence, and human collaboration, emphasizing how AI can enhance teamwork and project success.
- AI-Augmented Emotional Intelligence: Discuss how AI can help project managers understand and manage team emotions.
- Collaborative AI Tools: Introduce tools that facilitate better collaboration between AI systems and human teams.
- Balancing AI and Human Intuition: Explore strategies for integrating AI insights with human judgment.
7. Ethical Considerations and AI Governance (30 min) - This section addresses the ethical implications and governance frameworks necessary to ensure responsible AI use in projects.
- Bias and Fairness in AI: Discuss the risks of bias in AI models and strategies to ensure fairness and transparency.
- Privacy and Data Security: Explore the importance of protecting data in AI projects and compliance with data regulations.
- AI Governance Frameworks: Introduce governance models and ethical guidelines for overseeing AI projects.
8. Conclusion and Future Outlook (10 min) - Summarizes the key learnings from the Masterclass and looks ahead to emerging trends and future opportunities in AI-driven project management.