Barriers to Institutional AI Integration: A Systematic Approach Using FUCOM and Segmentation Analysis

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Büyüksaatçı Kiriş S., Akıf B.

13th International Symposium on Intelligent Manufacturing and Service Systems (IMSS'25), Düzce, Türkiye, 25 - 27 Eylül 2025, ss.278-287, (Tam Metin Bildiri)

Özet

Artificial Intelligence (AI) technologies promise revolutionary transformations in many areas,

from decision-making processes to service delivery within the public sector. However, despite this

potential, the effective utilization of AI at institutional and societal levels in the public sector faces

significant barriers. Multi-layered challenges such as deficiencies in technological infrastructure,

concerns about data security and privacy, ethical uncertainties, employee resistance, inadequate legal

and administrative frameworks, and limited organizational learning capacity constitute major barriers

to the widespread adoption of AI applications. This study analyses the primary barriers to AI

utilization in public institutions through a holistic and systematic perspective. It identifies and

prioritizes these barriers using the Full Consistency Method (FUCOM), a Multi-Criteria Decision-

Making (MCDM) approach based on expert judgment. After weighting the barriers, decision-makers

within public sector organizations are clustered using segmentation analysis based on their

professional profiles and contextual characteristics, thereby revealing resistance patterns and varying

levels of institutional readiness in more detail. The findings indicate that, beyond technical and

infrastructural limitations, cognitive, ethical, and institutional factors also contribute to delays in AI

integration. Moreover, the study demonstrates that decision-makers’ and managers’ perceptions of

AI significantly influence both the severity and nature of these barriers. Drawing on both the literature

and empirical data, the study proposes strategic recommendations to overcome these barriers and

discusses the governance structures necessary for the sustainable dissemination of AI applications. In

addition, by combining methodological rigor with practical insight, this study offers evidence-based

recommendations to address the challenges hindering the systematic deployment of AI and highlights

the governance approaches essential for overcoming future adaptation constraints.