Executive Master’s in Artificial Intelligence

📄 General Description of the Mini Executive Master in Artificial Intelligence

The Mini Executive Master in Artificial Intelligence is an advanced, fast-paced program designed for executives, consultants, entrepreneurs, and decision-makers seeking a comprehensive and practical understanding of AI and its strategic applications. The program blends technical foundations, analytical skills, leadership competencies, and governance frameworks to equip participants with the capabilities required to lead AI-driven transformation within their organizations.

Delivered through seven core courses, the program follows an integrated learning path that begins with AI fundamentals and data science, progresses through machine learning and generative AI, and culminates in leadership applications, governance principles, and hands-on project development.

Participants also complete a Capstone Project, developing a real-world AI solution that is applicable within their professional context.

This executive program is highly practical, emphasizing case studies, real datasets, hands-on tools, and the development of prototype solutions using modern AI and no-code technologies. The program enables participants to:

  • Deeply understand AI technologies without requiring advanced technical backgrounds
  • Make informed, data-driven decisions
  • Lead digital transformation initiatives powered by AI
  • Develop practical, cost-effective AI solutions using Generative AI and no-code platforms
  • Design and implement AI initiatives with measurable organizational impact

The Mini Executive Master is an ideal choice for leaders aiming to strengthen their future readiness, enhance

Mini Executive Master in Artificial Intelligence

7 Courses – Each course: 12–15 hours – Includes a Final Applied Project

📘 Course 1: Fundamentals of Artificial Intelligence & Digital Transformation

Objective:

Understand the foundations of AI, its evolution, and its strategic role in organizations.

Content:

  • Definition and types of AI
  • Differences between AI, ML, and Deep Learning
  • Applications in education, healthcare, government, and business
  • AI project lifecycle
  • Requirements for successful digital transformation with AI

Learning Outcomes:

  • Build a comprehensive overview of an AI ecosystem in an organization
  • Assess organizational readiness for AI adoption

📗 Course 2: Data Science & Data Analytics for Managers

Objective:

Equip leaders with essential data skills to support evidence-based decision-making.

Content:

  • Types of data
  • Applied statistics for decision-making
  • Analytics tools (Python basics, advanced Excel, PowerBI)
  • Designing smart dashboards
  • Data quality and data governance

Learning Outcomes:

  • Perform basic data analyses
  • Participate effectively in data-driven discussions

📕 Course 3: Machine Learning for Executives

Objective:

Develop a practical understanding of machine-learning algorithms and their use in real projects.

Content:

  • Supervised vs. unsupervised learning
  • Core models:
    • Classification
    • Regression
    • Clustering
    • Recommendation systems
  • Model evaluation techniques
  • ML model development workflow

Learning Outcomes:

  • Interpret ML model reports
  • Identify the right algorithm for different business problems

📙 Course 4: Generative AI

Objective:

Master the use of next-generation AI tools (ChatGPT, Claude, Copilot, Gemini) and build GenAI-powered solutions.

Content:

  • Foundations of LLMs
  • Prompt engineering
  • AI in media and content production
  • GenAI applications in:
    • HR
    • Marketing
    • Education
    • Strategic analysis
  • Building AI assistants and no-code GenAI solutions

Learning Outcomes:

  • Produce high-quality content using AI
  • Automate tasks with GenAI tools
  • Build an internal AI assistant for an organization

📒 Course 5: AI Governance & Ethics

Objective:

Ensure safe, ethical, and compliant use of AI in organizational settings.

Content:

  • Principles of fairness, transparency, and accountability
  • Global AI regulations (EU AI Act, NIST, UNESCO)
  • Risk management for AI systems
  • AI usage policies
  • Developing an AI governance framework

Learning Outcomes:

  • Draft organizational AI policies
  • Assess legal and ethical risks

📓 Course 6: AI for Leadership & Executive Decision-Making

Objective:

Enable leaders to integrate AI into strategic planning, operations, and decision-making.

Content:

  • AI-enabled leadership
  • Data-driven decision-making
  • Managing digital transformation
  • Organizational AI roadmap
  • AI in:
    • Strategic planning
    • Project management
    • Process analysis
    • Talent management

Learning Outcomes:

  • Build an AI-enabled digital transformation roadmap
  • Make decisions using deeper analytical insights

📔 Course 7: Advanced AI Applications & Project Development

Objective:

Empower participants to build a full AI project from concept to implementation.

Content:

  • Identifying suitable AI problems
  • AI project scope development
  • Building an MVP using no-code tools
  • Writing business requirements documents (BRD)
  • AI economic feasibility & ROI
  • Managing AI project teams

Learning Outcomes:

  • Deliver an actionable AI project
  • Manage AI projects at an executive level

🎯 Final Capstone Project

Participants will develop a complete applied AI project such as:

  • An AI knowledge-management assistant
  • A recommendation system
  • A smart decision-support dashboard
  • Any organization-specific AI solution