In our module of technical implementation, students learn how to plan, implement, and evaluate artificial intelligence (AI) solutions in organizational settings. The course focuses on emerging AI technologies, their managerial applications, and best practices for developing and deploying AI-enabled solutions.
Quick Info: Master | Lecture (Fachspezifische Grundlagen, FSG) | English | Winter Semester | 9 ECTS
| Course | Digital Innovations (FSG) - Part 2B: Technical Implementation |
|---|---|
| Chair | Institute of AI in Management |
| Lecturer | Prof. Dr. Stefan Feuerriegel |
| Assistants | Annually changing |
| Weekly Hours | Weekly lectures, on Tuesdays & Fridays |
| Target Group | M.Sc. BWL |
| Examination | Exam on site, in person. |
| Prerequisites | No specific requirements |
| Course Material | Course material will be shared via Moodle, students are required to self-enrol to the course through Moodle, self-enrolment key can be accessed via LSF. |
| Rotation | Annual rotation, winter semester |
| Language | English |
| ECTS | 9 |
this course follows a Y-model and is divided into two parts. In part 1, all students complete a shared foundations module "Foundations of Innovation" taught by Prof. Dr. Anne-Sophie Mayer.
In part two, students choose one of two specialization tracks:
In Part 2B, students learn how to plan, implement, and evaluate artificial intelligence (AI) solutions in organizational settings. The course focuses on emerging AI technologies, their managerial applications, and best practices for developing and deploying AI-enabled solutions.
In recent years, artificial intelligence has rapidly become an important technology for organizations and managers. AI systems are increasingly used to support decision-making, automate processes, generate and analyze content, interact with customers, and develop new products, services, and business models. The course focuses on understanding these developments and on the effective use of AI in management. Real-world case studies will complement the course by illustrating how organizations implement and manage AI in practice.
Topics include machine learning, generative AI and large language models, AI-supported decision-making, intelligent automation, and the organizational implementation of AI systems. Students will also examine the opportunities, limitations, and risks associated with the use of AI in organizations.
The goal of the course is to help students understand how AI can be used to improve managerial effectiveness and create value in organizations. Students will learn:
A further emphasis is on developing a methodological understanding of modern AI systems. Students will learn the core ideas behind machine learning and neural networks, including model training, generalization, overfitting, regularization, and model evaluation. The aim is not only to use AI tools, but also to understand the key methodological principles that determine when and why AI models perform well or fail.
The exercise session will be arranged around smaller guided, hands-on implementation sessions using R, which will train students to apply selected concepts in practice. These practical elements form part of the examination.