Master in AI
Master in AI is a modern, practical, and strategically designed program intended for managers, executives, entrepreneurs, and professionals who want to understand, lead, and implement artificial intelligence projects within a business environment. The program combines a managerial perspective with contemporary technological insights — without requiring any programming skills.
The selection of topics is based on best practices from international AI programs, the market needs of companies in the region, and the approaches of leading global organizations integrating AI into their operations.
Through 10 integrated modules, participants gain competencies in global economics, finance, leadership, project management, and digital marketing, and then progressively move into the domains of AI strategy, use cases, machine learning algorithms, data management, and exponential technologies.
Master in AI Modules
This module provides participants with an understanding of the current global economic, geopolitical, and market environment. The focus is on macroeconomic trends, global risks, and changes in value chains that directly influence business decisions. Special attention is given to the role of AI and digital technologies in shaping international business and new market opportunities.
This module explores how AI can be aligned with business objectives and transformed into measurable business value. Participants learn how to define an AI strategy, identify relevant use cases, and assess ROI and associated risks. Through a practical workshop, participants develop an AI strategy for a specific business scenario.
This module empowers participants to make better decisions through a clear understanding of financial statements and key performance indicators. The focus is on profitability, liquidity, investment metrics, and risk management. Special emphasis is placed on financial analytics as a foundation for informed decision-making.
This module develops leadership skills required to lead people and teams through periods of change and technological transformation. It covers adaptive leadership, emotional intelligence, team management, and effective communication. The goal is to prepare leaders to navigate organizational complexity in AI-driven environments.
This module explains how marketing evolves in the era of digital technologies, data, and artificial intelligence. Participants explore the use of big data insights, marketing automation, and AI tools for personalization and predictive consumer behavior. The objective is to design data-driven marketing strategies that deliver measurable results.
This module aims to provide participants with concepts and tools covering the basics of traditional PM and familiarizing students with the latest approaches to PM and operations. After attending the class and studying, students should be able to apply their knowledge in project and operations management, understand the importance of aligning project management practices with a company's strategy, critically examine project management information and data to make critical decisions and analyses, examine the types of transformation processes that occur within operations and define the roles and responsibilities of operations managers and the challenges they must face.
This module presents real-world AI applications through examples from industries such as healthcare, manufacturing, FMCG, and finance. Participants analyze successful projects as well as implementation challenges and common pitfalls. An industry panel provides direct insights from hands-on, real-life experience.
This module offers a business-oriented understanding of core machine learning algorithms. Participants are introduced to predictive analytics, NLP, computer vision solutions, and recommendation systems. The focus is on understanding capabilities, limitations, and model interpretability - without programming.
This module explains the technological infrastructure required for successful AI initiatives. Participants gain insight into cloud platforms, data architectures, AI integration within IT ecosystems, and vendor selection. Particular emphasis is placed on data security, privacy, and governance models.
This module explores the convergence of AI with other exponential technologies such as IoT, RPA, AR/VR, and edge computing. The focus is on sustainable and responsible AI adoption through ESG principles, ethics, and regulation, including the EU AI Act. Participants develop an understanding of how to apply AI in a long-term, responsible, and sustainable manner.
The Capstone Project represents the final integration of all knowledge acquired throughout the program into a concrete AI solution or strategy. Participants work on a real business challenge, define an AI approach, assess business value and risks, and develop an implementation plan. The project concludes with a final presentation of the solution to an evaluation committee.