Znanstvena metoda: Struktura znanstvenega védenja, znanstvene aktivnosti in procesi. Uporaba znanstvene metode pri razvoju, učenju, preverjanju in vrednotenju umetno-inteligenčnih modelov ter velikih jezikovnih modelov v kontekstu poslovne inteligence in odločanja.
Uvod: Definicija inteligence, umetne inteligence in velikih jezikovnih modelov ter njihova vloga v sodobnih BI sistemih. Pregled razvoja BI od klasičnih poročilnih sistemov do AI- in LLM-podprtih odločilnih sistemov. Razlogi, kriteriji in področja uvajanja AI-podprte BI, tipične omejitve, pasti in najboljše prakse. Razmerje med poslovno inteligenco, poslovno analitiko in AI-native pristopi.
Upravljanje s podatki: Podatkovna skladišča in sodobne podatkovne arhitekture za AI in LLM. Kakovost podatkov, priprava, čiščenje in oplemenitenje podatkov, migracija in posredovanje podatkov za potrebe učenja in uporabe modelov. Podatki kot temelj delovanja analitičnih, strojno-učnih in LLM-podprtih BI rešitev ter pregled najpogostejših napak in tveganj.
Poslovna analitika: Opredelitev poslovnih problemov kot vhod v analitične in AI-podprte rešitve. Analitično in modelno podprto reševanje poslovnih in tržnih problemov z uporabo metod strojnega učenja, napovednega modeliranja in LLM. Ovrednotenje rezultatov, razložljivost modelov in prenos rezultatov v poslovno prakso.
Strategije trženja in neposredno trženje: Uporaba AI in LLM pri oblikovanju poslovnih in trženjskih strategij. Analiza trga in strank z uporabo naprednih analitičnih in generativnih pristopov, kontaktne strategije, tržni kanali in integracijski izzivi. Personalizacija vsebin, spremljanje vedenja strank, upravljanje tržne učinkovitosti ter trženje na osnovi dogodkov in v realnem času.
Teorija iger in njena uporaba: Osnovni koncepti teorije iger in njihova povezava z algoritmi odločanja in AI. Nashevo ravnovesje, čiste in mešane strategije ter poslovne uporabe pri pogajanjih, dražbah in strateških interakcijah. Računalniška simulacija in podpora odločanju.
Izzivi pri razvoju programskih sistemov in implementacija projektov: Razvoj in uvajanje AI- in LLM-podprtih BI sistemov v organizacije. Tehnični, organizacijski in projektni izzivi, integracija modelov v obstoječe sisteme ter obvladovanje kompleksnosti večjih projektov.
Uporaba generativne umetne inteligence v BI: Veliki jezikovni modeli kot osrednji mehanizem sodobne BI: avtomatizacija analize podatkov, interpretacija rezultatov, generiranje poročil in scenarijev ter podpora odločanju. Uporaba LLM v kombinaciji z drugimi AI metodami ter obravnava omejitev, tveganj in odgovorne rabe.
Orodja in rešitve: Pregled sodobnih AI- in LLM-podprtih orodij ter platform za poslovno inteligenco in analitiko ter pregled razvojnih smeri in prihajajočih tehnologij.
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.