This thesis explores how digital transformation unfolds in practice, focusing on both strategy and operations through the use of Big Data and Artificial Intelligence (AI). Using a mixed-methods approach, the study integrates quantitative insights from a survey of 63 professionals across European SMEs with qualitative evidence from six international case studies and four expert interviews conducted in Greece. The analysis reveals that both Big Data and AI play a significant role in enhancing organisa-tional readiness for digital transformation. Regression results show that these technologies jointly explain 35.4% of the variance in readiness, with AI demonstrating a stronger predictive influence (β = 0.323, p = 0.001) compared to Big Data (β = 0.177, p = 0.021). Survey data further indicates that 66.7% of organisations have adopted Big Data tools, and 52.4% report notable productivity gains from AI. Case studies, including those of DBS Bank, Netflix, and Amazon, illustrate measurable performance improvements such as profit growth, customer engagement, and operational efficiency. Complementary interviews with Greek firms confirm the practical benefits of AI-driven automation and analytics, while also highlighting key chal- lenges such as budget constraints, cultural resistance, and legacy infrastructure. The findings contribute to both theory and practice by validating socio-technical transformation models and offering actionable guidance for managers and policymakers. This includes strategic recommendations around phased implementation, data governance, AI upskilling, and infrastructure readiness. The thesis underscores that successful digital transformation is not solely dependent on technology adoption, but also on organisational culture, leadership alignment, and continuous learning.
Αναστάσιος Π. Παντζαρτζής (Wed,) studied this question.