A mixed-methods analysis of artificial intelligence adoption and perceived impacts in Czech municipal administration

Purpose While national strategies promote artificial intelligence (AI) for public sector digital transformation, there is a lack of granular evidence on how AI is being adopted at the local government level, particularly in non-capital regions of Central and Eastern Europe. This study aims to bridge this strategy–implementation gap by empirically investigating the adoption patterns and perceived impacts of AI in Czech municipal administration. Design/methodology/approach The study uses a mixed-methods sequential explanatory design. A stratified sample of 17 Czech municipalities, categorized by size (small, medium, large) and excluding the capital city of Prague, responded to a questionnaire combining quantitative Likert-scale questions and qualitative open-ended questions. Quantitative data were analyzed using descriptive statistics, while qualitative data underwent thematic analysis. Findings The results reveal a size-dependent digital divide. Large municipalities usually leverage long-standing smart city data infrastructures for AI applications, while smaller municipalities cautiously explore simpler, text-based AI. The primary perceived benefit across all municipalities is increased internal efficiency and reduced employee workload. The most significant barriers are a lack of financial and human resources, compounded by legislative uncertainty and fears of data misuse, which hinder adoption in smaller municipalities. Originality/value The study contributes a dual-track model of AI adoption for local government and provides empirical evidence from the Czech context. The findings show a disconnect between national AI ambitions and local-level capacity, highlighting the need for targeted support, clear legal frameworks and capacity-building initiatives to ensure equitable and effective digital transformation.