METHODOLOGY FOR ECONOMIC ASSESSMENT OF AI IMPLEMENTATION IN PUBLIC ADMINISTRATION
Аннотация
Rapid digital transformations in public administration have made evident the need for extensive methodologies for the evaluation of economic impacts of artificial intelligence (AI) technologies. The goal of this research is to develop a comprehensive framework that would integrate traditional cost-benefit analysis and qualitative indicators for the assessment of tangible fiscal benefits as well as intangible socio-administrative impacts, especially focused on adapting this methodology to the context of Kazakhstan. It thus fills a significant gap in the literature where most conventional evaluation methods overlook factors such as citizen satisfaction, service quality, and policy effectiveness. The research, bringing together thus well-established economic theories with recent advancements in digital transformation, proposes some new conceptual indicators such as Adjusted Net Present Value (ANPV), Adjusted Benefit-Cost Ratio (ABCR), and Adjusted Return on Investment (AROI), as well as additional measures including the Digital Readiness Index (DRI) and the Government Policy Implementation Factor (GPIF). The proposed methodology is based on systematic literature review and document analysis of international case studies and policy reports, which serve as secondary data sources to inform the conceptual model. Introduction of qualitative measures along with financial metrics significantly strengthen the evaluation of AI implementations as learned from cases analyzed from Denmark. On the other hand, the current practices in Kazakhstan entirely rely on traditional methods for that and underestimates the far-reaching benefits of digital transformation. The findings confirmed the assumption that the integrated approach does yield a more robust context-sensitive evaluation. It offers an applicable model to the field which may guide informed policy decisions and effective AI investment optimization in public administration.
Автор
G. S. Smagulova
Kh. N. Sansyzbayeva
L. Zh. Ashirbekova
R. D. Doszhan
DOI
10.48081/FLQM9632
Ключевые слова
Artificial Intelligence
digital transformation
digital government
ANPV
ABCR
GPIF
Год
2025
Номер
Выпуск 3