Background. In recent years, the volume and complexity of data handled by Database Management Systems (DBMS) have surged, necessitating greater efforts and resources for efficient administration. In response, numerous automation tools for DBMS administration have emerged, particularly with the progression of AI and machine learning technologies. However, despite these advancements, the industry-wide adoption of such tools remains limited. Aims. This qualitative research aims to delve into the practices of DBMS users, identifying their difficulties around DBMS administration. By doing so, we intend to uncover key challenges and prospects for DBMS administration automation, thereby promoting its development and adoption. Method. This paper presents the findings of a qualitative study we conducted in an industrial setting to explore this particular issue. The study involved conducting in-depth interviews with 11 DBMS experts, and we analyzed the data to derive a set of implications. Results. We argue that our study offers two important contributions: firstly, it provides valuable insights into the challenges and opportunities of DBMS administration automation through interviewees' perceptions, routines, and experiences. Secondly, it presents a set of findings that can be derived to useful implications and promote DBMS administration automation.

Conclusions. This paper presents an empirical study conducted in an industrial context that examines the challenges and opportunities of DBMS administration automation within a particular company. Although the study's findings may not apply to all companies, we believe the results provide a valuable body of knowledge with implications that can be useful for future research endeavors.