The rapid expansion of artificial intelligence (AI) and digital transformation has made understanding the conditions for its effective adoption in small and medium-sized enterprises (SME) operations an increasingly important and timely research priority. This study examines the economic, technological, social, and regulatory imperatives shaping the adoption of artificial intelligence in the operational activities of SMEs. The research focuses on Eastern Europe, Caucasus and Central Asia (EECCA) countries and selected former socialist bloc economies, using a combination of qualitative analysis and quantitative modeling. To strengthen the empirical analysis, a multi-year country-level dataset (2015–2024) is employed, allowing for a parsimonious regression approach. Two model specifications are estimated to examine the association between macro-level innovation indicators and technological readiness conditions. The results indicate that research and development expenditure represents the most robust and consistent factor associated with technological readiness, while entrepreneurial activity plays a complementary role. The findings suggest that national innovation environments shape the preconditions for AI adoption in SME operational and supply chain activities. The results are interpreted as exploratory evidence of macro-level patterns rather than predictive relationships. The study contributes to operations and supply chain management research by linking macro-level innovation conditions to the early stages of operational capability formation.