Developing an AI Readiness Framework for Digital Transformation in Emerging Economies
DOI:
https://doi.org/10.30750/ijarst.120104Keywords:
AI readiness, digital transformation, emerging economies, readiness assessment, complementarity, binding constraintsAbstract
Organisations in emerging economies face distinctive constraints when adopting artificial intelligence as part of digital transformation: infrastructure is uneven, data are fragmented, skills are scarce and governance frameworks are nascent. Readiness assessments imported from advanced economies average dimension scores into an index and assume that strengths offset weaknesses, which misrepresents conditions in which a single missing prerequisite can block transformation. This paper develops an AI readiness framework with six dimensions, namely infrastructure, data, skills, governance, finance and leadership, in which infrastructure is a floor condition, the dimensions are complements, and readiness is reported through a balance-sensitive composite and the binding dimension. The framework is examined in a simulation of 800 organisations whose transformation success depends on balanced readiness and on the infrastructure floor. Organisations below the infrastructure floor succeed 12% of the time against 49% above it; among organisations with above-median average readiness, those with no dimension below 0.3 succeed 94% of the time against 49% for those with a weak dimension; and the weakest-dimension and geometric-mean composites predict success better (AUC 0.81) than the additive mean (0.80). The paper translates the framework into an assessment procedure and a sequencing logic for organisations and policymakers, emphasising infrastructure and data as prerequisites and the identification of binding constraints over aggregate scores.
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