A Conceptual Hybrid AI-Cloud Model for Government Information Systems: A Structured Literature Review

Abstract

This study develops a comprehensive Hybrid AI-Cloud conceptual model to enhance government information systems through digital transformation. Using a systematic literature review (PRISMA protocol) of 51 publications (2020-2025) from Scopus, IEEE Xplore, and ScienceDirect, we identify four critical components: a hybrid architecture combining private and public clouds achieves 97.46% prediction accuracy but faces interoperability challenges in Indonesia where 85% of agencies use disparate systems; layered security with Hyperledger Fabric blockchain reduces data breaches by 72%, though 65% of Indonesian institutions lack CSIRT teams; user-centric designs score 76.88 on SUS scales yet encounter 71% civil servant resistance to AI automation; and organizational adoption strategies based on UTAUT frameworks are hindered by only 12% of civil servants having digital certifications. The research reveals Indonesia's significant gaps in system integration, cybersecurity preparedness, and digital literacy compared to global leaders like Estonia and Singapore. Successful implementation requires standardized cloud architectures with API gateways, mandatory cybersecurity audits, comprehensive digital training programs, and phased adoption roadmaps with change management components. While offering a holistic framework for digital government transformation, the study acknowledges limitations including literature bias toward developed nations and the need for local empirical validation through pilot projects, suggesting future research should incorporate ethical AI governance considerations alongside technical implementations.

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