Organisational Enablers of Artificial Intelligence Adoption in Public Institutions: A Systematic Literature Review

Keywords: AI adoption, artificial intelligence, organisational changes, organisational enablers, public institution, systematic literature review


Purpose: The purpose of the presented study was to develop a set of recommendations for decision-makers (policymakers and public managers) and public employees to enhance the effectiveness and efficiency of organisational elements in the adoption of artificial intelligence (AI) in public institutions.
Design/methodology/approach: Utilising a systematic literature review following the PRISMA protocol, the study examines the organisational enablers of AI adoption in public institutions. Comprehensive search queries in the Scopus database identified relevant literature focusing on the intersection of AI technologies and various organisational elements. The analysis was facilitated by NVivo 12, enabling a structured examination of key organisational facets for people, culture, structure, processes, and technology within public institutions.
Findings: Previous studies on AI adoption in public institutions identified numerous enablers of AI adoption associated with organisational elements like people/employees, structure, culture, technology, and processes. Several surveys and case studies stress the importance of concentrating on the introduction or transformation of these organisational elements prior to or concurrently with the adoption of AI.
Academic contribution to the field: By applying a systematic literature review protocol, the study represents the first holistic and systematic review of specific organisational elements that can serve as enablers of AI adoption in public institutions.
Research limitations/implications: This systematic literature review was subject to several limitations. Firstly, the division of AI literature between natural and social sciences, with the former focusing on technical aspects and the latter on broader organisational themes, may have resulted in an incomplete depiction of the intersection of AI and organisational change. Secondly, despite the broad search queries, inherent limitations of keyword-based searches may have excluded some relevant studies. Thirdly, considering the rapid evolution of AI technology, our review may not fully encapsulate the very latest developments in the field as it covers literature published until May 2023. Finally, the interpretation and coding of literature, despite the use of NVivo 12, involved subjective elements that could affect the study’s outcomes.
Practical implications: Drawing from experiences in the private sector, public institutions are increasingly adopting AI technologies across various subsectors such as public finance (taxation), research, healthcare, law enforcement, defence, education. This requires a transformation in both hard (structure, processes etc.) and soft aspects (people, organisational culture etc.). Therefore, the enablers identified in the study can serve as guidelines for decision-makers and implementers of AI at all levels of public institutions.
Social implications: If adopted effectively and efficiently and used professionally and ethically, the use of AI in public institutions can bring many benefits to society, such as transparency, justice, cost and time efficiency, high quality services, and improved collaboration between different stakeholders in society.
Originality/significance/value: Our study makes a distinct contribution by shifting the focus from technological barriers to organisational enablers of AI adoption in public institutions. It bridges a critical gap in the literature by integrating both technical and social science perspectives, providing valuable insights for theory and practice in the fields of organisation and management.

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How to Cite
Tomaževič, N., Murko, E., & Aristovnik, A. (2024). Organisational Enablers of Artificial Intelligence Adoption in Public Institutions: A Systematic Literature Review. Central European Public Administration Review, 22(1), 109-138.