AI-Driven Business Model Transformation and Risk Management for Micro-Firms: A Qualitative Synthesis Study

Abstract

Micro-firms, defined as businesses employing fewer than ten employees, constitute the backbone of the global economy. Nevertheless, they frequently encounter severe resource constraints and elevated operational risks. This study conducts a qualitative synthesis to examine how Artificial Intelligence (AI), particularly Generative Artificial Intelligence (GenAI), facilitates business model transformation and enhances risk management within this segment. Drawing upon 44 academic sources, the study adopts the Technology Organization–Environment (TOE) framework to identify the key drivers and barriers influencing AI adoption. The findings indicate that AI functions as a capability-leveling mechanism, enabling micro-firms to perform sophisticated activities such as predictive analytics and process automation at relatively low cost. The study further demonstrates AI’s role in the early detection of financial distress signals and the reduction of human error. However, challenges related to data quality and implementation costs remain significant obstacles. The paper proposes a human–AI symbiotic risk management framework and highlights the need for targeted policy interventions aimed at narrowing the digital divide faced by vulnerable enterprises.