AI Readiness in Organisations: A Systematic Literature Review and the TOP-L Framework Development

Abstract

The growing importance of artificial intelligence (AI), particularly Generative AI (GenAI), is opening up significant potential for corporate knowledge management (KM) and knowledge-intensive work. To remain competitive, organisations must effectively implement these technologies. AI readiness, understood as preparedness and capacity to successfully implement and use AI in a value-creating way (Ali & Khan, 2025; Alsheibani et al., 2018), has therefore become a critical concept. However, the underlying factors remain contested, and existing research is fragmented, with a strong focus on technical and environmental aspects, while human factors and organisational learning (OL) are underrepresented. In addition, practical assessment tools are still limited, particularly for small and medium-sized enterprises (SMEs). To address this gap, this paper presents a systematic literature review of AI readiness. A total of 34 frameworks and assessment instruments were analysed to identify key factors and evaluate existing approaches. Building on the socio-technical TOP framework (Kretschmer & Orth, 2025), the Technology-Organisation-People-Learning (TOP-L) framework is developed, integrating OL as a dynamic capability for continuous adaptation. The study identifies 372 AI readiness factors and synthesises them into the TOP-L framework consisting of four dimensions and 20 factor clusters, thereby laying the foundation for a practical assessment approach, particularly suited for SMEs.