Validating an AHP-based decision-support framework for AI adoption in public administration

Abstract

Purpose – This study validates a multi-criteria decision-support framework for assessing organisational readiness for Artificial Intelligence (AI) adoption in public administration, with a specific focus on municipal administration. Validation ensures that the results are conceptually sound, credible, reliable, and robust. Research methodology – The framework integrates the Technology-Organisation-Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) approaches into a hierarchical readiness structure, operationalised through the Analytical Hierarchy Process (AHP). Expert judgments were aggregated using pairwise comparisons to derive criteria’ weights and support group decision-making. Validation combined expert assessment of conceptual coherence, completeness, clarity, interpretability, scale suitability, and practical utility with robust- ness testing across different Multi-Criteria Decision-Making methods (MCDM). Findings – Voluntariness of use, behavioural intention to use AI, social influence, innovation and readiness for change, skills and expertise, data, and leadership represent central AI-readiness conditions. Expert validation supports the relevance and usability of the proposed hierarchy for AI-readiness assessment. Rankings across alternative MCDM methods showed strong convergence, indicating stable assessment outcomes despite differences in aggregation logic. Research limitations – The framework was validated using three empirical organisational cases in public administration, along with five synthetically constructed readiness profiles for methodological testing, which may limit the generalisability of the findings. Practical implications – The framework helps administrations diagnose AI-readiness gaps, benchmark organisational capabilities, and prioritise improvement measures related to AI adoption. Originality/Value – The study contributes to a validated AI-readiness assessment framework that integrates structured expert judgment with multi-method robustness testing in the context of public administration.