(SMEs) must embrace artificial intelligence (AI) to remain competitive in an AI-driven business landscape. However, challenges such as limited resources, insufficient AI expertise, and inadequate data infrastructures often impede successful AI adoption. This paper proposes that AI organisational readiness is a critical determinant of effective AI implementation in SMEs. Through a comprehensive literature review and analysis of existing frameworks, this study develops a novel conceptual model integrating five interconnected dimensions: Strategic Readiness, Organisational Readiness, Technological Readiness, Capability Readiness, and Ecosystem Readiness. Each dimension is tailored to address SMEs’ specific needs and constraints, emphasising foundational digital capabilities as a prerequisite for AI readiness. The model includes a flexible assessment methodology and implementation framework adaptable to diverse SME contexts based on size, AI maturity, and industry. A comparative analysis highlights SMEs’ agility in AI adoption compared to larger organisations’ scale-driven approaches, tempered by unique challenges like ethical governance and data quality. By offering a holistic approach to assessing and enhancing AI readiness, this paper contributes to both theory and practice in AI adoption for SMEs. The proposed model provides researchers with a robust framework for studying AI readiness in SMEs and equips practitioners with practical guidance to maximise AI-driven outcomes. This paper presents a conceptual framework, acknowledging that empirical validation is beyond its scope but critical for future research to ensure practical applicability.
Building similarity graph...
Analyzing shared references across papers
Loading...
Sherin Ashraf Kalleparambil
Mohamed Akoum
Abu Dhabi University
Journal of Information & Knowledge Management
Abu Dhabi University
Building similarity graph...
Analyzing shared references across papers
Loading...
Kalleparambil et al. (Thu,) studied this question.
synapsesocial.com/papers/68af5701ad7bf08b1eadd643 — DOI: https://doi.org/10.1142/s0219649225500765