Effective strategies for AI-driven digital transformation in SMEs

Artificial intelligence (AI) presents both significant opportunities and complex challenges for small and medium-sized enterprises (SMEs). While large corporations often lead in AI adoption, SMEs face distinct hurdles related to resource scarcity, identifying appropriate use cases, managing employee resistance, and developing necessary skills within a rapidly evolving technological landscape. This master's thesis investigates effective strategies for AI-driven digital transformation in SMEs, with a specific focus on change management, cultural adaptation, and employee reskilling. The primary aim is to understand best practices and develop insights that can inform a practical framework for SMEs. The research employs a qualitative methodology, utilizing semi-structured interviews with 14 managers, business owners, and AI practitioners from SMEs who have experience with AI implementation. Thematic analysis of the interview data revealed five key themes: (1) the current AI adoption landscape, characterized by a focus on generative AI tools for efficiency, often driven by individual initiative, alongside difficulties in defining impactful use cases; (2) implementation challenges, particularly the unreliability of AI outputs, data readiness issues, and ROI uncertainty; (3) the human and cultural dimension, highlighting varied employee reactions in different company cultures; (4) informal approaches to building AI capabilities and skills, with a preference for practical, use-case-based learning; and (5) strategic considerations, including a pace of adoption often slower than market hype, AI viewed primarily as a tool for humans, and the need for critical AI literacy. The insights gathered are intended to provide a foundation for actionable recommendations for SMEs seeking to manage AI-driven transformations more effectively.