Towards a Simplified AI Adoption Framework: Success Factors for the Implementation of Artificial Intelligence Information Systems

Adopting AI within organizations promises various benefits, including enhanced productivity, cost reduction, process automation, and the innovation of services and business models. However, many organizations, particularly small and medium-sized enterprises (SMEs) with limited resources, face challenges in successfully implementing AI, such as lack of expertise or insufficient data quality. Therefore, this study aims to address these challenges by developing a comprehensive framework for identifying and synthesizing success factors for AI adoption. Utilizing a design science research (DSR) approach, the study combines insights from a systematic literature review and expert interviews across different industries. The resulting outcome represents a simplified AI adoption framework designed to support AI adoption by aggregating success factors across several clusters: Strategy and Planning, AI Expertise and Support, Data Considerations, Infrastructure and Resources, Market and Competition, Ethical and Legal, and Implementation and Integration. Each cluster encompasses interrelated factors prioritized based on their frequency in the literature and validation through experts. The framework could serve as a starting point for organizations, particularly for SMEs, to navigate AI adoption effectively. It emphasizes the importance of relevant success factors such as defining an AI strategy, establishing data structures, fostering an innovative corporate culture, and ethical considerations. Organizations can mitigate risks and enhance their AI integration efforts by addressing these factors. The study contributes to the literature by offering a practice-based artifact aggregating existing success factors, providing a valuable entry point for organizations embarking on AI adoption projects.