Software lives in a world of pure logic, but AI in physical products has to survive the laws of physics. This is where organizational silos, rigid safety standards, and cutting-edge tech stacks collide in a high-stakes environment. We have the blueprint—the Product Development Process Framework for AI Products (PDPF4AIP)—but we need an ambitious student to evaluate whether this framework is actually accepted by industry, to enhance it, and to develop a practice-relevant guide for analyzing the AI readiness of a companies PDP to adjust it to the most critical components of the PDPF4AIP. 

Problem Description

Previous research examined how companies must adapt their product development processes to integrate AI capabilities. Through this process, we identified organizational changes (e.g., new roles and structural changes), procedural changes (e.g., new process steps and safety and quality requirements), and technological changes (e.g., infrastructure and data management changes). We then developed a concept representing these solutions – the PDPF4AIP. However, this concept has yet to be evaluated. Futhermore, while our PDPF4AIP framework provides the theoretical blueprint, the 'industrial reality check' is missing. We know what needs to change, but we don't yet know how companies prioritize these shifts or which components are the true 'deal-breakers' for AI integration. 

Goal of the Thesis

The aim of this thesis to evaluate and adapt the aforementioned framework through expert workshops and interviews, as well as to prioritize the components based on their importance. Furthermore, you will develop guidelines showing how companies can analyze the relevant components within the framework to evaluate them and derive recommendations for action. These guidelines will be based on existing scientific methods.  

Requirements

• Field of Study: Information Systems, Industrial Engineering (and Management), Business Administration with a Technical Focus, or Similar 
• AI expertise: Basic understanding of AI concepts and roles, such as data scientists.  
• Methodology: Experience conducting qualitative research, such as interviews and workshops, and content analysis, or literature reviews. 
• Language skills: Proficient English skills are required, as the basic literature is in English. 
• Soft skills: Strong communication skills for professional interaction with industry experts.