Management control meets AI: From data-driven literature review to research gap

Abstract

The integration of artificial intelligence (AI) into academic research constitutes a high?impact instrument for managing scientific knowledge. Building on these capabilities, this paper presents a data?driven literature review that explores the intersection of management control systems (MCS) and AI, maps key thematic clusters, and identifies research gaps. Drawing on curated corpus of peer-reviewed articles published between 2010 and 2025, we identify five major thematic clusters and assess the extent to which each addresses transparency and explainability, core concerns in implementing AI within MCS contexts. Our findings reveal that only two clusters explicitly engage with explainable AI (XAI), revealing a significant research gap. This study offers a twofold contribution: it provides a systematic mapping of current research on AI-enabled control systems and proposes a research agenda that emphasizes the need for a more integrated and transparent approach to explainability in AI-driven decision-making contexts. The study further demonstrates the capacity of data?driven techniques to steer future inquiry, while simultaneously underscoring the indispensable role of critical reading and human judgment in the application of AI methods to scholarly research.