COURSES

Goals

The objective of the course is to provide general and advanced knowledge in the field of business intelligence and business analytics, with a particular emphasis on the use of artificial intelligence and large language models as core technologies of modern decision-support systems for business and strategic (especially marketing) decision-making. Business intelligence is addressed as an application framework in which AI and LLMs enable automated data analysis, generation of insights, scenario forecasting, and support for complex decision processes. In the introductory part, the conceptual and technological foundations of business intelligence, business analytics, and AI-native approaches are presented, along with the objectives, purposes, and key technical, organizational, and methodological challenges related to their practical adoption. Best practices in the design, implementation, and evaluation of AI- and LLM-enabled BI solutions are also discussed. Students who complete this course will acquire an in-depth understanding of the role of artificial intelligence and large language models in business intelligence and will be capable of applying advanced analytical, machine learning, and generative methods to solve complex business problems. They will be able to critically assess the results produced by AI and LLM models, evaluate their suitability in business contexts, and effectively transfer analytical and model-based solutions into strategic decision-making practice.

Curriculum

Scientific Method: Structure of scientific knowledge, scientific activities, and processes. Application of the scientific method in the development, training, validation, and evaluation of artificial intelligence models and LLMs in the context of business intelligence and decision-making. Introduction: Definition of intelligence, artificial intelligence, and large language models (LLMs), and their role in modern BI systems. Overview of the evolution of BI from traditional reporting systems to AI- and LLM-enabled decision-support systems. Reasons, criteria, and application areas for AI-driven BI, typical limitations, pitfalls, and best practices. Relationship between business intelligence, business analytics, and AI-native approaches. Data Management: Data warehouses and modern data architectures for AI and LLM applications. Data quality, data preparation, cleansing, and enrichment, data migration, and data delivery to support model training and deployment. Data as the foundation of analytical, machine learning, and LLM-enabled BI solutions, with an overview of common errors and risks. Business Analytics: Definition of business problems as inputs to analytical and AI-driven solutions. Analytical and model-based approaches to solving business and market problems using machine learning methods, predictive modeling, and LLMs. Evaluation of results, model interpretability, and transfer of outcomes into business practice. Marketing Strategies and Direct Marketing: Application of AI and LLMs in the development of business and marketing strategies. Market and customer analysis using advanced analytical and generative approaches, contact strategies, marketing channels, and integration challenges. Content personalization, customer behavior monitoring, marketing performance management, event-based marketing, and real-time marketing. Game Theory and Its Application: Fundamental concepts of game theory and their connection to decision-making algorithms and AI. Nash equilibrium, pure and mixed strategies, and business applications in negotiations, auctions, and strategic interactions. Computer simulation and decision-support applications. Challenges in Software System Development and Project Implementation: Development and deployment of AI- and LLM-enabled BI systems in organizations. Technical, organizational, and project-related challenges, integration of models into existing systems, and management of complexity in large-scale projects. Use of Generative Artificial Intelligence in BI: LLMs as a central mechanism of modern BI: automation of data analysis, interpretation of results, generation of reports and scenarios, and decision support. Use of LLMs in combination with other AI methods, along with a discussion of limitations, risks, and responsible use. Tools and Solutions: Overview of contemporary AI- and LLM-enabled tools and platforms for business intelligence and analytics, as well as an outlook on emerging technologies and future development trends.