AI-Based Policy Model in the Governance of National Strategic Projects

Background: National Strategic Projects (PSN) play a vital role in Indonesia's development, yet their governance policy frequently encounters significant challenges concerning planning effectiveness, monitoring limitations, and difficulties in comprehensive impact evaluation, thereby hindering the optimal achievement of development objectives. With technological advancements, Artificial Intelligence (AI) emerges as a potential solution to address the complex challenges in the policy of these large-scale projects. Objective: Therefore, this research aims to analyze the potential application of AI in enhancing PSN governance policy during the planning, monitoring, and evaluation phases, and to formulate a conceptual model of AI-based policy for PSN management in Indonesia. Methods: This study employs a descriptive qualitative approach, relying entirely on credible secondary data (systematic literature review and document analysis from scientific journals, government reports, regulations, and other reliable sources). Data were analyzed using content analysis and thematic synthesis to map PSN governance challenges, identify potential AI-based solutions, and develop the conceptual model. Results: The analysis indicates that AI holds significant potential to enhance planning accuracy (feasibility analysis, cost/time estimation, resource optimization), monitoring effectiveness (real-time tracking, anomaly detection including potential fraud), holistic impact evaluation (economic, social, environmental), and the extraction of lessons learned within PSN governance policy. A conceptual "AI-Enhanced PSN Governance Policy" model is proposed, integrating data input layers, an integrated AI platform (planning, monitoring, evaluation modules), AI-driven governance processes, output & feedback layers, and ethical & security governance layers. While this policy model offers substantial benefits such as enhanced efficiency, effectiveness, transparency, accountability, and proactive risk management, its implementation confronts significant hurdles that must be addressed. Conclusion: These include technical aspects (data quality and integration, infrastructure), organizational and human resources factors (readiness, expertise, resistance), governance and regulation (legal framework, ethical standards), and socio-political and ethical implications (algorithmic bias, privacy, fairness, accountability) that require careful management.