This article synthesizes insights from a 2024 workshop convened by the Academic Data Science Alliance (now the Alliance for Data Science and AI) and the Michigan Institute for Data and AI in Society, bringing together leaders from university-wide data science and AI organizations. It examines how these organizations are adapting to rapidly evolving mandates, particularly the integration of AI, amid resource constraints, unclear institutional roles, and increasing expectations for impact. Drawing on real-world examples, the paper outlines common organizational models, funding strategies, and approaches to strategic alignment, while highlighting challenges such as unfunded mandates and sustainability. The authors propose actionable frameworks for governance, funding diversification, and impact measurement, emphasizing the importance of institutional alignment, coordination across campus units, and metrics that capture long-term, transformative impact beyond traditional research outputs.
Aikat et al. (Tue,) studied this question.