Beyond Automation: A Conceptual Framework for Human-Generative AI Collaboration in Operational Decision-Making for MSMEs

Abstract

The development of Generative Artificial Intelligence (Generative AI) offers opportunities for micro, small, and medium-sized enterprises (MSMEs) to enhance their information-processing capabilities, generate alternative solutions, and support operational decision-making. However, the use of AI does not automatically guarantee better decision quality, as the generated recommendations must be adapted to the specific business context, resource constraints, and actual operational conditions. This article aims to develop a conceptual framework explaining how collaboration between humans and Generative AI can influence the quality of operational decision-making in MSMEs. The study employs a conceptual approach based on a synthesis of the literature, integrating perspectives from bounded rationality, human–AI collaboration, sociotechnical systems, and trust and automation bias. The proposed framework encompasses four key elements: Generative AI capabilities, human contextual judgment, decision-control mechanisms, and the quality of operational decisions. Furthermore, the article identifies data quality, AI literacy, task uncertainty, and resource constraints as contextual conditions that may influence the relationships among these elements. The conceptual contribution of this article lies in positioning decision-control mechanisms as the link between AI recommendations, human deliberation, and operational actions. The article presents seven theoretical propositions that can serve as a foundation for future empirical research. This framework is expected to enrich the field of operations management and provide guidance for MSMEs on how to utilize Generative AI in a critical, contextual, and responsible manner.