Organizational Readiness for Artificial Intelligence: Evidence from SME Adoption Patterns

examining, competitiveness, leadership, Correlation

This study examines how SMEs adopt Artificial Intelligence and the factors that enable, inhibit, and influence the adoption process's outcomes. Data collection utilized a mixed-method approach. The researcher surveyed 150 SMEs and subsequently interviewed 20 managers and owners. The selected SMEs operate in the manufacturing, retail, and service sectors. The quantitative results indicate that organizational leadership support, financial resources, and company size are strong predictors of AI adoption, while cost, skill shortages, and resistance to change remain significant barriers to adoption. Regression analyses reveal that leadership vision has the greatest impact on adoption. Correlation analyses between organizational readiness and the likelihood of adoption show positive relationships. The effects of adoption are further explained by employee resistance, the use of consultants, and government incentives. In summary, SMEs that implement AI experience notable benefits in efficiency, customer satisfaction, and decision-making. This study contributes by extending the Technology–Organization–Environment (TOE) and Diffusion of Innovations (DOI) models, highlighting the disproportionate influence of leadership support, and demonstrating that partnerships and agility can offset resource limitations. It provides practical advice to SME leaders, policymakers, and ecosystem stakeholders on encouraging adoption through financial incentives, training, and networks. Ultimately, this research provides a unique perspective on how SMEs can leverage AI to enhance their competitiveness and resilience by examining the enablers and barriers.