AI Readiness, Information Governance, and Autonomous Workflow Optimization in Public Sector and Public-serving Institutions

Abstract

This study examines the relationship between artificial intelligence (AI) readiness, information governance maturity, and autonomous workflow optimisation in public-sector and public-serving institutions. As governments increasingly adopt AI and intelligent automation for service delivery, administrative operations, and decision support, the study proposes an integrated governance framework for assessing institutional preparedness. A mixed analytical design was applied to a cross-national dataset of 40 countries (n = 40), combining composite index construction, entropy-weighted TOPSIS analysis, machine learning classification, natural language policy analysis, and regression modelling. AI readiness served as the enabling condition, information governance maturity as the institutional governance mechanism, and autonomous workflow optimisation as the performance outcome. The Governance Readiness Framework, operationalised through an entropy-weighted TOPSIS index, produced readiness rankings with a maximum closeness coefficient of 0.994 and was independently validated through unsupervised clustering, achieving a Silhouette score of 0.6655. Across the sample, AI readiness averaged 58.05 (SD = 18.96), while governance maturity averaged 3.26. The results revealed substantial disparities across income groups, with higher-income countries demonstrating stronger digital infrastructure, interoperability, cybersecurity capacity, and governance maturity. The findings indicate a strong association between readiness indicators and workflow automation outcomes. The study contributes a practical framework for evaluating AI governance readiness while emphasising accountability, transparency, human oversight, and the need for broader longitudinal validation.