Artificial Intelligence in Wartime Ukraine: Business Adoption, Investment Intentions, and Labour-Market Expectations

This study examines the factors that determine whether firms adopt AI and are ready to invest, and explores labour-market and employment-related expectations in wartime Ukraine. Methodology. The study is based on original survey data collected in a frontline region of Ukraine during wartime. The sample comprises 300 respondents from various socio-economic backgrounds. The empirical analysis uses descriptive statistics, nonparametric tests and ordinal and binary logistic regression models. The study also presents an original composite AI-readiness index (ranging from 0 to 100) which assesses preparedness for AI adoption by integrating organisational, behavioural and investment-related dimensions. All statistical analyses were conducted using R statistical software. Results. The findings suggest that organisational readiness plays a key role in determining current AI adoption and firms’ willingness to invest in AI. Expected efficiency gains from AI greatly increase investment intentions, while prior AI adoption has a cumulative effect on further investment readiness. Furthermore, adaptive capacity is positively associated with more favourable perceptions of AI in wartime. The fear of losing one's job to AI is significantly higher among employees engaged in routine-intensive tasks, but this effect is significantly reduced by higher education, which confirms the moderating role of human capital. Perceived AI-driven efficiency gains vary considerably across social groups, with citizens being more optimistic than business representatives and workers. The proposed AI-readiness index reveals significant differences in AI readiness among firms of different sizes, sectors, and geographic scopes. Practical implications. The results are relevant to business decision-making and economic policy in wartime. The findings show that the adoption of artificial intelligence in business is primarily determined by organisational capacity, institutional support and adaptive capability rather than technological accessibility alone. For firms, this highlights the importance of taking a systemic approach to the use of artificial intelligence in strategic planning. In frontline regions, human capital policy—including digital education, reskilling and regional co-operation between enterprises and educational institutions—is particularly relevant, as it can support economic adaptation and mitigate employment-related risks. Value/Originality. This article makes a contribution to the body of research on the adoption of artificial intelligence and employment-related perceptions under wartime conditions. Unlike previous empirical studies, which were conducted in contexts of institutional stability and predictable market environments, this study examines decision-making related to artificial intelligence in a border region of Ukraine that is close to active hostilities. Here, economic expectations, investment behaviour and perceptions of employment risks are all shaped by ongoing security threats. The study's originality lies in its integration of firm-level determinants of artificial intelligence (AI) adoption with individual-level economic expectations and employment-related responses. By incorporating organisational readiness, investment intentions and labour-market perceptions into a single analytical framework, the article broadens the scope of existing AI adoption approaches by considering organisational, economic and socio-behavioural factors in a wartime context.