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Purpose This study aims to develop a decision-support framework that assists small- and medium-sized enterprises (SMEs) in navigating the challenges and opportunities presented by the integration of artificial intelligence (AI). The framework is designed to help SME leaders prioritize initiatives that enable them to gain and maintain a sustainable competitive advantage in an increasingly AI-driven business environment. Design/methodology/approach A constructivist research approach is used, facilitating collaborative knowledge exchange among a panel of experts. The study incorporates cognitive mapping, the nominal group technique (NGT), interpretive structural modeling (ISM), the Warshall algorithm and Matrice d’Impacts Croises Multiplication Appliqué a un Classement (MICMAC) analysis. These methods are used to identify critical factors and explore interrelationships that can empower SMEs in the AI context. Findings The decision-support framework developed is dynamic and iterative, allowing for continuous refinement as new insights emerge. It systematically prioritizes key initiatives that can enhance the ability of SMEs to effectively adopt and leverage AI technologies. These initiatives are organized into six key clusters: (1) Human Resources; (2) Innovation and Technological Infrastructure; (3) Organizational Culture; (4) Operational Efficiency; (5) Security and Privacy; and (6) Strategic Leadership. Originality/value The framework provides a structured approach for SMEs to address the key challenges associated with AI adoption, such as the need for significant financial resources, concerns about data privacy and security and the lack of technical expertise. By following this framework, SMEs can better equip themselves to integrate AI technologies and sustain a competitive edge in the marketplace. It also offers a novel decision-support framework for SMEs, with an emphasis on empowering leaders through a better understanding of AI and its potential to transform business practices. The dynamic, expert-driven methodology makes the framework highly adaptable to the changing AI landscape.
Machado et al. (Thu,) studied this question.