Artificial intelligence for risk management: A research agenda for public-private comparison

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Abstract

The paper analyses the role of artificial intelligence (AI) in supporting enterprise risk management and improving operational efficiency through a systematic review of 85 peer-reviewed articles published between 2014 and 2024. The aim is to develop a research agenda by mapping the current scientific literature on the contribution of AI to risk identification, prediction and mitigation, with a focus on its impact on organisational performance. This study provides a novel cross-sectoral perspective by systematically comparing public and private sector applications, an aspect that remains underexplored in the existing literature. The findings suggest that AI is increasingly positioned as a key enabler for predictive analytics and data-driven decision-making. However, the literature remains fragmented. The most significant gaps include a lack of empirical studies on implementation failures, insufficient integration of sustainability goals, and limited benchmarking between public and private sector applications. In particular, the public sector appears to be less dynamic in adopting AI than the private sector due to regulatory and structural constraints. Furthermore, the focus of the literature is more on citizen service delivery than on risk management, contributing to a misalignment between the two domains. By synthesising dispersed evidence and highlighting sector-specific differences, this article contributes an original framework for understanding how AI can support risk governance across organisational contexts. This article contributes a structured synthesis of existing knowledge and proposes future research directions to guide an effective and responsible integration of AI across organisational domains.

Keywords: Artificial Intelligence, Enterprise Risk Management, Operational Efficiency, Public Sector, Private Sector, Research Agenda

Authors’ individual contribution: Conceptualization — A.M.G., A.K., and U.C.; Methodology — A.M.G.; Software — A.M.G.; Validation — A.M.G. and U.C.; Formal Analysis — A.M.G., A.K., and U.C.; Investigation — A.M.G.; Resources — A.M.G., A.K., and U.C.; Data Curation — A.M.G., A.K., and U.C.; Writing — Original Draft — A.M.G. and U.C.; Writing — Review & Editing — A.M.G., A.K., and U.C.; Supervision — A.K. and U.C.; Project Administration — A.M.G., A.K., and U.C.

Declaration of conflicting interests: The Authors declare that there is no conflict of interest.

JEL Classification: D81, H83, M15

Received: 29.06.2025
Revised: 13.02.2026; 25.02.2026
Accepted: 16.03.2026
Published online: 18.03.2026

How to cite this paper: Gallo, A. M., Kostyuk, A., & Comite, U. (2026). Artificial intelligence for risk management: A research agenda for public-private comparison. Corporate Ownership & Control, 23(1), 95–101. https://doi.org/10.22495/cocv23i1art9