AI in IT and Engineering (KI-TE)

Project description

Software development and programming are considered a key application domain as well as a frontrunner in the adoption of artificial intelligence (AI). The increasing use of AI-based tools is currently transforming both the technical and social dimensions of this field of work. AI-supported tools for code generation, analysis, documentation, and debugging promise, in some cases, substantial efficiency gains and new forms of automation. However, these developments also fundamentally challenge established work practices, skill profiles, and professional identities. While many studies emphasize productivity gains associated with the use of AI in software development, others point to contradictory effects and new forms of workload that are also reflected in practice. Alongside efficiency improvements, new forms of effort emerge, for example, through interacting with AI systems, verifying outputs, or adapting coordination processes. However, it remains largely unclear what AI is actually used for in software development and programming, which tools are actually adopted in practice and to what extent, how work practices, processes, and task profiles are changing, and what implications this has for productivity, work quality, and professional identities. 

Against this background, the research project AI in IT and Engineering (KI-TE) investigates the transformation of work in software development and related engineering domains. The project focuses on how AI technologies are actually used in practice and how their application affects concrete work practices, processes, and work contexts, such as collaboration, knowledge sharing, or the embedding of development work within organizational settings. Particular attention is paid to the changing division of labor between humans and AI, as well as the emerging tensions between automation and control, efficiency and quality, and support and substitution. 

Methodologically, the project applies a mixed-methods design: qualitative interviews are combined with a quantitative online survey conducted in Germany, Austria, and Switzerland among employees in IT and engineering who spend at least 20 percent of their working time on programming or software development. Through a differentiated analysis, the project systematically captures the diffusion, use, and perceived, as well as actual, effects of AI-based tools. The aim is to generate a nuanced empirical understanding of AI use that reveals both concrete usage patterns and transformations in work processes, task profiles, and requirements. 

With its empirical findings, the KI-TE project contributes to a deeper understanding of the transformation of knowledge work through and with AI. The results provide not only academic insights but also practical guidance for companies, employees, and decision-makers seeking to shape the use of AI in IT and engineering in a reflective and responsible manner.

Project team