
A physical inventory systematically records and checks all media in a university library to identify damaged, missing, or obsolete items and to fulfill documentation requirements. Trainees Bruno Kaufmann and Max Schäfer explored how this process could be reimagined. Since manual inventory takes a lot of time and is prone to errors, they developed an AI-supported workflow that captures shelf images via smartphone, automatically compares holdings and statistically evaluates results. In doing so, they combined image recognition, prompting, and data analysis into an innovative practical concept. Their presentation to colleagues impressively demonstrated how AI can make library routines more efficient, precise, and forward-looking.





