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This thesis examines the impact of automation on workforce transformation through a comparative analysis of small and medium-sized enterprises (SMEs) and large enterprises. While often portrayed as a threat to jobs, automation also creates opportunities for role redefinition, upskilling, and new employment. The study explores how organizations of different sizes integrate automation and how these processes shape employees’ roles, skills, and perceptions.
The research follows a qualitative comparative case study design within an interpretivist paradigm. Data were gathered through semi-structured interviews with professionals from SMEs and large firms and analyzed using Braun and Clarke’s (2006) thematic analysis with open and semantic coding. This bottom-up approach revealed themes of organizational culture, training, perceptions of risk and opportunity, and the pace of technological change.
Findings show clear contrasts between contexts. SMEs adopt AI in ad-hoc and employee-driven ways, limited by resources but marked by agility. Large enterprises pursue structured yet slower implementation, hindered by bureaucracy and cultural resistance. Across both, automation is seen not just as cost reduction but as a catalyst for role transformation, analytical work, and strategic opportunities when supported by training.
The study contributes theoretically by demonstrating that organizational size and culture mediate automation’s impact, and practically by offering guidance for managers and policymakers seeking to balance efficiency with workforce development.
