Rethinking Data Stewardship Roles in Times of Automation and AI

Over the past decade, research data management (RDM) has become a cornerstone of responsible research practices in universities and knowledge institutions. Libraries have played a pivotal role in this evolution by creating data stewardship roles in their organisation and leveraging their expertise in metadata and information literacy to support researchers in managing, sharing, and preserving research data. At KU Leuven Libraries, this commitment has resulted in a robust portfolio of services: reviewing data management plans, delivering FAIR data training, providing metadata and documentation guidance, and supporting data publication. These services are complemented by first- and second-line assistance and a comprehensive RDM website that serves as a central resource for researchers.

However, the context in which we operate is changing rapidly. Generative artificial intelligence and automation are transforming library workflows, offering opportunities to streamline processes and reduce repetitive tasks. At the same time, researchers themselves have become more experienced and self-reliant in managing their data. These developments challenge us to rethink traditional data stewardship roles and adapt our services to remain relevant, impactful, and future-proof.

Our strategy for this transition is guided by two key priorities:

1. Empowering researcher self-reliance

We aim to reduce the time our staff spends on routine, basic support by providing researchers with accessible, automated resources and modular training materials. This includes refining our online guidance, integrating AI-driven tools for common queries, and creating self-service options for tasks such as data management planning and FAIR compliance checks. By fostering independence, we free up capacity to deliver more advanced, bespoke guidance tailored to complex RDM challenges.

2. Developing advanced expertise in our support team

As researchers’ RDM knowledge grows, their needs become more complex and nuanced. To meet these evolving challenges, our staff must continuously expand their competencies. We are implementing targeted development tracks that focus on building expertise in areas most relevant to emerging researcher needs. These include more technical skills to support research software management, data anonymization, data workflows and data retrieval. At the same time, we are exploring how to balance deep specialization with broader generalist skills to ensure flexibility in workforce organization and planning. This dual approach of combining advanced specialization with adaptable support will enable KU Leuven Libraries to deliver discipline-specific, high-value services that address the diverse requirements of our research community.

In this poster, we present our plan of action for rethinking traditional data stewardship roles and transitioning toward a model that combines personalized services with continuous skill development. By aligning our expertise with emerging technologies and researcher expectations, KU Leuven Libraries aims to remain a trusted partner in advancing open, FAIR, and reproducible science.