AI in Business: Interdisciplinary Tandem Project
Supervising Professor/Lecturer
Prof. Dr. Kawa Nazemi (Department of Computer Science)
Prof. Dr. Anna Altmann (Department of Business), Prof. Dr. Christopher Almeling (Department of Business)
Initial situation and objectives
Knowledge and skills relating to the design, implementation, and evaluation of AI-supported systems are becoming increasingly important in practice. In this project module, students in tandem groups from computer science and economics develop AI-supported software solutions for real economic and organizational fields of application. Specific topics are assigned within the module as part of an overarching thematic block. The projects are research-oriented and combine technical design work, prototypical implementation, and methodologically comprehensible evaluation.
Topic block
AI applications in an economic context with a focus on software implementation, for example, assistance systems, information and knowledge management, intelligent search and retrieval, document and text analysis, decision support, service automation, process support, and quality and risk analyses. The exact details will be assigned in the module and specified in consultation with the instructors.
Working method and interdisciplinarity
The work is carried out in tandem groups with clear cooperation between the implementation perspective (computer science) and the domain perspective (economics). Computer science students are particularly responsible for translating the technical requirements into technical specifications, selecting and integrating suitable AI methods, implementing a working prototype, and technical evaluation and documentation. Collaboration with business students is an integral part of the assessment, particularly in defining objectives, establishing success criteria, and interpreting results in the application context.
Procedure
The project is divided into phases. The first phase comprises project planning. Based on the objectives outlined above, the project team develops detailed project goals, a schedule, a project structure plan, and defines the responsibilities within the project. This is followed by the project implementation phase. In this phase, the previously created plan is to be executed. Project progress is to be presented to the project supervisors in regular, e.g., biweekly progress reports. The final phase is the project completion phase. The project results are documented in a project report and presented in a final workshop.
A potential analysis system combines state-of-the-art artificial intelligence and large language models with interactive visualizations to make new and previously little-researched technologies accessible in an intuitive and understandable way. Thanks to a modular architecture of neural networks, such a system can be flexibly adapted to different areas of application. Possible applications range from technology and trend analysis to data-based decision support and the evaluation of complex research and market data. In industry and product development, it could be used for process optimization and to identify innovation potential. In education and training, such systems also offer the opportunity to present complex technological relationships in a clear and concise manner.
Learning objectives
- Translate technical requirements into technical specifications, architectural decisions, and a feasible prototype design.
- Select and implement suitable AI methods and integrate them into an application, including data preparation and interface design.
- Plan and carry out a methodologically comprehensible evaluation, including metrics, baselines, and documentation of test conditions.
- Consider aspects of robustness, traceability, bias, data protection, and security in the design and implementation
- Document project results in a reproducible manner and communicate them clearly within the team, in the report, and in the presentation
Assessment
Concept (30%), project report on the concept and implementation (50%), project presentation (20%)