Integrating AI-Driven Advanced Knowledge Management Systems to Mitigate Civil Servants' Risk - Andi Agus Salim; Hasbu Naim Syaddad; Luki Ishwara (Pt. Anagata Sembagi Education, 2026)
The rapid digitalization of public administration has positioned artificial intelligence (AI) as a strategic lever for strengthening institutional knowledge and reducing operational risk among civil servants. Yet public organizations continue to struggle with fragmented knowledge repositories, tacit knowledge loss due to workforce turnover, inconsistent decision-making, and exposure to compliance, legal, and reputational risks arising from manual and siloed information practices. This study examines how an AI-Driven Advanced Knowledge Management System (AI-AKMS) can be integrated into civil service institutions to mitigate such risks. Using a systematic literature review of twenty-five peer-reviewed sources published between 2021 and 2026, the study synthesizes evidence on AI-enabled knowledge capture, retrieval-augmented generation, predictive risk analytics, and generative AI governance in public administration. The novelty of this study lies in proposing an integrated conceptual framework that links AI-based knowledge management functions directly to specific civil-service risk categories, namely compliance risk, decision risk, knowledge-continuity risk, and reputational risk, an integration rarely addressed jointly in prior literature. Findings indicate that AI-AKMS adoption improves knowledge retrieval accuracy, accelerates policy compliance checking, and strengthens organizational resilience, provided that governance, data quality, and human oversight mechanisms are institutionalized. The study concludes with practical implications for public-sector digital transformation strategy and identifies avenues for future empirical validation.