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